From 37d7bd6d82c173e5194bf6d2d427149298b2c282 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 11:10:56 -0500 Subject: [PATCH 001/274] Create time_series.py --- plantcv/plantcv/time_series/time_series.py | 184 +++++++++++++++++++++ 1 file changed, 184 insertions(+) create mode 100644 plantcv/plantcv/time_series/time_series.py diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py new file mode 100644 index 000000000..9dcc81c3d --- /dev/null +++ b/plantcv/plantcv/time_series/time_series.py @@ -0,0 +1,184 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 9 09:53:34 2020 + +Functions used in time series linking after getting leaf instance segmentation result (from maskRCNN) + +@author: hudanyunsheng +""" + +import os +import sys +import random +import math +import numpy as np +import skimage.io +import matplotlib +import matplotlib.pyplot as plt +import pickle as pkl +import re +from plantcv import plantcv as pcv +import copy +import link_utilities as funcs + + +def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']): + """ + Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) + Input: + imagedir: directory of original image used + segmentationdir: directory of leaf instance segmentation result + savedir: desired saving directory of linking result + time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment + link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 + class_names: used in bounding box visualization. by default there are background and leaf + Output: + There is no returned values as output, though the results will be saved in user defined "savedir" + 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time + 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment + 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. + 4. a folder called "visualization", which contains 3 subfolders: + 1) a folder call "visualization 1", which contains 1st set of visualization + In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. + result name: {}_{}_{}_{}.png + Naming rules for file names: + 1st digit: time of 1st emergence of the leaf + 2nd digit: leaf index when it first emerges + 3rd digit: current time point + 4th digit: current leaf index + + 2) a folder called "visualization 2", which contains 2nd set of visualization + This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent + There are several subfolders, the number of subfolders depends on the number of "new leaves" in total + Every subfolder is a "new leaf", whose name is {}_{}, with 1st digit represents time of 1st emergence of the leaf and 2nd digit represents leaf index when it first emerges + Inside every folder, images of leaves with names "time_{}.png" are contained; the number inside the {} represents the time point + + 3) a folder called "visualization 3", which containes 3rd set of visualization + This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. + Naming format: YYYY-MM-DD-HH-MM_visual.png + """ + + # initialize Plant class + Plant = funcs.PlantData(imagedir, segmentationdir, savedir) + + Plant.getpath(Plant.imagedir) + Plant.Sorttime(time_cond) + + # load mrcnn inferencing results + Plant.load_results() + + Plant.getinitleaf() + Plant.getmaxleaf() + + Plant.gettotaltime() + Plant.getnumemergence() + + # plot the #leaves vs. time relationship + plt.plot(np.array(range(0,len(Plant.num_leaves))), Plant.num_leaves) + + if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): + file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') + if link_logic == 1: + file.write('mode: IOU\n') + else: + file.write('mode: IOP\n') + file.write('Directory of original images: {}\n'.format(Plant.imagedir)) + file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) + file.write('Image conditions: {}'.format(time_cond)) + file.close() + + # linking initialization + Plant.initialize_linking() + + # link + for t in range(0, Plant.total_time-1): + Plant.linking(t, mode=link_logic) + Plant.get_series() + + ####### visualization ####### + # load original images + if len(Plant.images) == 0: + Plant.load_images() #Plant.images + + # visualization method 1: show only one leaf per image + path_visual1 = os.path.join(Plant.visualdir, 'visualization1') + if not os.path.exists(path_visual1): + os.makedirs(path_visual1) + + # visualization method 2: show with an alpha channel + path_visual2 = os.path.join(Plant.visualdir, 'visualization2') + if not os.path.exists(path_visual2): + os.makedirs(path_visual2) + + # visualization method 3: show with bounding boxes + path_visual3 = os.path.join(Plant.visualdir, 'visualization3') + if not os.path.exists(path_visual3): + os.makedirs(path_visual3) + + count = 0 + if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): + colors = funcs._random_colors(20) + pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) + else: + colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) + + color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] + for key_t in Plant.link_series: + start_time = int(key_t.replace('t', '')) + leaves_t = Plant.link_series[key_t]['new_leaf'] + for leaf in leaves_t: + key_leaf = 'leaf{}'.format(leaf) + link_leaf = Plant.link_series[key_t][key_leaf] + start_idx = link_leaf[start_time] + for t in range(start_time, Plant.total_time): + img = Plant.images[t] + if link_leaf[t] >= 0: + color_all[t][link_leaf[t]] = colors[count] + + mask_t = Plant.masks[t][:,:,link_leaf[t]] + + ## 1. save the masked image, i.e. single leaves + mask = np.zeros(mask_t.shape, dtype=np.uint8) + mask[np.where(mask_t)] = 255 + leaf_t = pcv.apply_mask(img, mask, mask_color='black') +# pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}.png'.format(start_time, start_idx, t, link_leaf[t]))) +# pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}.pkl'.format(start_time, start_idx, t, link_leaf[t])), 'wb')) + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, link_leaf[t], t, Plant.time[t]))) + pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, link_leaf[t], t, Plant.time[t])), 'wb')) + + + ## 2. show with an alpha channel + # update the mask where there is an alpha channel + mask_ = np.ones(mask_t.shape)*0.5 + mask_[np.where(mask_t == True)] = 1 + masked_im = np.concatenate((img.astype(float)/255, np.expand_dims(mask_, axis=2)), axis=2) + save_dir_ = os.path.join(path_visual2, '{}_{}.png'.format(start_time, start_idx)) + if not os.path.exists(save_dir_): + os.makedirs(save_dir_) + fig2 = plt.figure(figsize=(5,5)) + ax2 = fig2.add_subplot(1,1,1) + ax2.imshow(masked_im) + ax2.axis('off') +# plt.savefig(os.path.join(save_dir_, 'time_{}.png'.format(t))) + plt.savefig(os.path.join(save_dir_, str(Plant.time[t]) + '.png')) + plt.close(fig2) + # pcv.print_image(masked_im, os.path.join(save_dir_, 'time_{}.png'.format(t))) + count += 1 + + ## 3. visualize with bounding boxes + for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.time): + funcs.display_instances(img, roi, mask, class_id, + class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, + colors = color) + + plt.savefig(os.path.join(path_visual3, '{}_visual.png'.format(t))) + plt.close('all') + + # save all information + pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) + + + + + From ff677ed8562b2062deb0f8fd5647fb2f64a83636 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 11:11:00 -0500 Subject: [PATCH 002/274] Create link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 368 ++++++++++++++++++ 1 file changed, 368 insertions(+) create mode 100644 plantcv/plantcv/time_series/link_utilities.py diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py new file mode 100644 index 000000000..9878a3353 --- /dev/null +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -0,0 +1,368 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 9 09:53:34 2020 + +Functions used in time series linking after getting leaf instance segmentation result + + +@author: hudanyunsheng +""" + +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import os +import sys +import random +import math +import skimage.io +import pickle as pkl +import re +from skimage.measure import find_contours +from matplotlib import patches, lines +from matplotlib.patches import Polygon +from plantcv import plantcv as pcv +import datetime +import copy +import colorsys + +def _random_colors(N, bright=True): + """ + Generate random colors. + To get visually distinct colors, generate them in HSV space then + convert to RGB. + """ + brightness = 1.0 if bright else 0.7 + hsv = [(i / N, 1, brightness) for i in range(N)] + colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv)) + random.shuffle(colors) + return colors + +def _apply_mask(image, mask, color, alpha=0.5): + """Apply the given mask to the image. + """ + for c in range(3): + image[:, :, c] = np.where(mask == 1, + image[:, :, c] * + (1 - alpha) + alpha * color[c] * 255, + image[:, :, c]) + return image + +def display_instances(image, boxes, masks, class_ids, class_names, + scores=None, title="", + figsize=(16, 16), ax=None, + show_mask=True, show_bbox=True, + colors=None, captions=None): + """ + boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates. + masks: [height, width, num_instances] + class_ids: [num_instances] + class_names: list of class names of the dataset + scores: (optional) confidence scores for each box + title: (optional) Figure title + show_mask, show_bbox: To show masks and bounding boxes or not + figsize: (optional) the size of the image + colors: (optional) An array or colors to use with each object + captions: (optional) A list of strings to use as captions for each object + """ + # Number of instances + N = boxes.shape[0] + if not N: + print("\n*** No instances to display *** \n") + else: + assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0] + + # If no axis is passed, create one and automatically call show() + auto_show = False + if not ax: + _, ax = plt.subplots(1, figsize=figsize) + auto_show = True + + # Generate random colors + colors = colors or _random_colors(N) + + # Show area outside image boundaries. + height, width = image.shape[:2] + ax.set_ylim(height + 10, -10) + ax.set_xlim(-10, width + 10) + ax.axis('off') + ax.set_title(title) + + masked_image = image.astype(np.uint32).copy() + for i in range(N): + color = colors[i] + + # Bounding box + if not np.any(boxes[i]): + # Skip this instance. Has no bbox. Likely lost in image cropping. + continue + y1, x1, y2, x2 = boxes[i] + if show_bbox: + p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, + alpha=0.7, linestyle="dashed", + edgecolor=color, facecolor='none') + ax.add_patch(p) + + # Label + if not captions: + class_id = class_ids[i] + score = scores[i] if scores is not None else None + label = class_names[class_id] + caption = "{} {:.3f}".format(label, score) if score else label + else: + caption = captions[i] + ax.text(x1, y1 + 8, caption, + color='w', size=11, backgroundcolor="none") + + # Mask + mask = masks[:, :, i] + if show_mask: + masked_image = _apply_mask(masked_image, mask, color) + + # Mask Polygon + # Pad to ensure proper polygons for masks that touch image edges. + padded_mask = np.zeros( + (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8) + padded_mask[1:-1, 1:-1] = mask + contours = find_contours(padded_mask, 0.5) + for verts in contours: + # Subtract the padding and flip (y, x) to (x, y) + verts = np.fliplr(verts) - 1 + p = Polygon(verts, facecolor="none", edgecolor=color) + ax.add_patch(p) + ax.imshow(masked_image.astype(np.uint8)) + if auto_show: + plt.show() + + +def _compute_overlaps_masks(masks1, masks2, mode=1): + """Computes IoU overlaps between two sets of masks. + masks1, masks2: [Height, Width, instances] + mode: 1 or 2, 1 represent for IOU, 2 represent for intersection over the area of the previous mask + """ + + # If either set of masks is empty return empty result + if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # flatten masks and compute their areas + masks1 = np.reshape(masks1 > .5, (-1, masks1.shape[-1])).astype(np.float32) + masks2 = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) + area1 = np.sum(masks1, axis=0) + area2 = np.sum(masks2, axis=0) + + # intersections and union + intersections = np.dot(masks1.T, masks2) + if mode == 1: + union = area1[:, None] + area2[None, :] - intersections + overlaps = intersections / union + elif mode == 2: + overlaps = intersections / area1[:, None] + return overlaps + +def get_ax(rows=1, cols=1, size=16): #??? + """Return a Matplotlib Axes array to be used in + all visualizations in the notebook. Provide a + central point to control graph sizes. + + Adjust the size attribute to control how big to render images + """ + fig, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows)) + fig.tight_layout() + return ax + +def _getmatchedIndex(mask, masks, mode): + """Get the index from masks that matches the mask""" + score = _compute_overlaps_masks(np.expand_dims(mask, axis=-1), masks, mode) + if max(score[0])>0.15: + return np.argmax(score[0]), score + else: + return -1, score + + +class PlantData(): + def __init__(self, imagedir, segmentationdir, savedir): + + # store a list of time of the images + self.time = [] + # total num of leaves that being detected + self.numleaf = 0 + + # store all the images + self.images = [] + + # store all the mask detection during [starttime, endtime] + self.masks = [] + + # store all the rois (for visualization with bounding boxes) + self.rois = [] + + # store all the class_ids (for visualization with bounding boxes) + self.class_ids = [] + + # store all the scores (for visualization with bounding boxes) + self.scores = [] + + # store the dataset directory, instance segmentation result directory, time-series linking result directory and visualization directory + self.imagedir = imagedir + self.segmentationdir = segmentationdir + self.savedir = os.path.join(savedir, str(datetime.datetime.now())) + if not os.path.exists(self.savedir): + os.makedirs(self.savedir) + self.visualdir = os.path.join(self.savedir, 'visualization') + + self.total_time = 0 + self.max_nleaf = 0 + self.init_nleaf = 0 + self.num_leaves = [] + self.num_emergence = 0 + + self.emerging_info = [] # list length: self.total_time + self.link_info = [] # list length: self.total_time-1, dictionaries inside + self.weights = [] # list length: self.total_time-1, dictionaries inside + self.available_leaves = [] # list length: self.total_time + self.link_series = dict() # only for those newly emerging leaves + + def getinitleaf(self): + self.init_nleaf = self.masks[0].shape[2] + + def getmaxleaf(self): + for i in range(0, len(self.masks)): + self.num_leaves.append(self.masks[i].shape[2]) + self.max_nleaf = np.max(self.num_leaves) + + def gettotaltime(self): + # also represent for the total number of images + self.total_time = len(self.time) + + def getnumemergence(self): + self.num_emergence = np.ones((self.total_time), dtype=int)*self.init_nleaf + self.num_emergence[1:] = np.diff(self.num_leaves) + + def getpath(self, path): + self.dir = path + + def addimage(self): + for t in self.time: + filename = os.path.join(self.dir, '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t)) + image = skimage.io.imread(filename) + self.images.append(image) + + def Sorttime(self, time_cond): + """ + you need to modify this function accordint to you format + return: loop through the dataset_dir, and add time in time order + """ + + filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] + time_temp = [] + for x in filenames: + temp = re.search('\d{4}-\d{2}-\d{2}-\d{2}-\d{2}', x) + if temp: + timepart = temp.group() + for cond in time_cond: + if timepart.endswith(cond): + time_temp.append(timepart) + continue + + index_temp = np.argsort(time_temp) + # forward + self.time = [time_temp[i] for i in index_temp] + # backward +# self.time_back = [time_temp[i] for i in reversed(index_temp)] + + + def load_images(self): + """ Load original images + """ + for t in self.time: + file_name = '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t) + junk = skimage.io.imread(os.path.join(self.imagedir, file_name)) + img = junk[0: 499, 0: 499, :] # make all images the same size + self.images.append(img) + + def load_results(self): + """ Instead of running instance segmentation, load instance segmentation results (masks) + """ + for t in self.time: + file_name = '{}.pkl'.format(t) + r = pkl.load(open(os.path.join(self.segmentationdir, file_name), 'rb')) + self.masks.append(r["masks"][0: 499, 0: 499, :]) # make all masks the same size + self.rois.append(r["rois"]) + self.class_ids.append(r['class_ids']) + self.scores.append(r['scores']) + + def initialize_linking(self): + self.emerging_info = [[] for i in range(0, self.total_time)] + self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time-1)] + self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i+1])) for i in range(0, self.total_time-1)] + self.available_leaves = [np.array(range(0, self.num_leaves[i])) for i in range(0, self.total_time)] + self.time_compare = [np.ones((self.num_leaves[i]), dtype=int)*i for i in range(0, self.total_time)] + + def linking(self, start_time, mode): + masks1 = copy.deepcopy(self.masks[start_time]) + masks2 = copy.deepcopy(self.masks[start_time+1]) + leaves1 = copy.deepcopy(self.available_leaves[start_time]) + leaves2 = copy.deepcopy(self.available_leaves[start_time+1]) + n1 = len(leaves1) + n2 = len(leaves2) + n = np.min((n1, n2)) + N = np.max((n1, n2)) + weight = dict() + weight['forward'] = -np.inf*np.ones((n1, n2)) +# weight['backward'] = -np.inf*np.zeros((n2, n1)) + link = dict() + link['forward'] = -np.ones((n1)) +# link['backward'] = -np.ones((n2)) + + for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward']): +# for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward', 'backward']): + for (idx, main_leaf) in enumerate(leaves): + mask = masks[:,:,main_leaf] + link[key][idx], weight[key][idx,:] = _getmatchedIndex(mask, masks_all, mode) + # check for duplicates (and hence non-unique links) + val, cou = np.unique(link[key], return_counts=True) + idx_temp = np.where(cou>1)[0] + + if len(idx_temp) > 0: # matching to same leaf exists + idx_vs = val[idx_temp] + for idx_v in idx_vs: + idxs = np.where(link[key] == idx_v)[0] + weights_comp = weight[key][:,int(idx_v)] + + idx_keep = np.argmax(weights_comp) + idx_drop = np.delete(idxs, np.where(idxs==idx_keep)) + weight_temp = copy.deepcopy(weight[key][idx_drop]) + weight_temp[0, int(idx_v)] = 0 + if np.max(weight_temp) > 0.2: + link[key][idx_drop] = np.argmax(weight_temp) + else: + link[key][idx_drop] = -1 + + self.link_info[start_time] = link['forward'] + + def get_series(self): + t = 0 + key_t = 't{}'.format(t) + self.link_series[key_t] = dict() + self.link_series[key_t]['new_leaf'] = self.available_leaves[0] + + for t in range(1, self.total_time): + new_leaves = [i for i in self.available_leaves[t] if i not in self.link_info[t-1]] + if new_leaves: + key_t = 't{}'.format(t) + self.link_series[key_t] = dict() + self.link_series[key_t]['new_leaf'] = np.array(new_leaves) + for key_t in self.link_series: + t0 = int(key_t.replace('t','')) + for leaf in self.link_series[key_t]['new_leaf']: + key_leaf = 'leaf{}'.format(leaf) + self.link_series[key_t][key_leaf] = -np.ones(self.total_time, dtype=int) + self.link_series[key_t][key_leaf][t0] = leaf + self.link_series[key_t][key_leaf][t0+1] = self.link_info[t0][leaf] + for t_ in range(t0+2, self.total_time): + idx = self.link_series[key_t][key_leaf][t_-1] + if idx < 0: + break + else: + self.link_series[key_t][key_leaf][t_] = self.link_info[t_-1][idx] \ No newline at end of file From b8ba51100b1c547165c678eefc7a7748479de88e Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 11:11:03 -0500 Subject: [PATCH 003/274] Create __init__.py --- plantcv/plantcv/time_series/__init__.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 plantcv/plantcv/time_series/__init__.py diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py new file mode 100644 index 000000000..3e468d909 --- /dev/null +++ b/plantcv/plantcv/time_series/__init__.py @@ -0,0 +1,13 @@ +from plantcv.plantcv.time_series.link_utilities import random_colors +from plantcv.plantcv.time_series.link_utilities import apply_mask +from plantcv.plantcv.time_series.link_utilities import display_instances +from plantcv.plantcv.time_series.link_utilities import compute_overlaps_masks +from plantcv.plantcv.time_series.link_utilities import get_ax +from plantcv.plantcv.time_series.link_utilities import getmatchedIndex +from plantcv.plantcv.time_series.link_utilities import PlantData +from plantcv.plantcv.time_series.time_series import time_series_linking + + +# add new functions to end of lists +__all__ = ["random_colors", "apply_mask", "display_instances", "compute_overlaps_masks", "get_ax", + "getmatchedIndex", "time_series_linking"] From ec78449ea076b4cb7a944a163348bdd7f38de729 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 11:11:06 -0500 Subject: [PATCH 004/274] Create time_series.md --- docs/time_series.md | 60 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 docs/time_series.md diff --git a/docs/time_series.md b/docs/time_series.md new file mode 100644 index 000000000..050feffdb --- /dev/null +++ b/docs/time_series.md @@ -0,0 +1,60 @@ +## time_series + +This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, recording the growth of the plant, where there should be minimum or none movement of plant or camera). + +To run this function, the instance segmentation for every image should be achieved beforehand. For a demo of instance segmentation, check here: +As a reminder, you will need a maskRCNN environemnt to be able to use this tool. For more information, check here: + +When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. (Another example is k-means) + +To understand the growth of every leaf instance of a plant, we need pull out the same leaf across the whole time period. + + +**plantcv.time_series.time_series_linking**(*imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']*) + +**returns** no returned values, all the results are saved in the user specified directory + +- **Parameters:** + - imagedir: directory of original image used + - segmentationdir: directory of leaf instance segmentation result + - savedir: desired saving directory of linking result + - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment + - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 + - class_names: used in bounding box visualization. by default there are background and leaf +- **Output:** + There is no returned values as output, though the results will be saved in user defined "savedir" + 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time + 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment + 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. + 4. a folder called "visualization", which contains 3 subfolders: + 1) a folder call "visualization 1", which contains 1st set of visualization + In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. + result name: {}_{}_{}_{}_{}.png + Naming rules for file names: + 1st digit: time of 1st emergence of the leaf + 2nd digit: leaf index when it first emerges + 3rd digit: current time point + 4th digit: current leaf index + 5th digit: date and time included in original file name, typically, it follows the format of YYYY-MM-DD-HH-MM. + + 2) a folder called "visualization 2", which contains 2nd set of visualization + This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent + There are several subfolders, the number of subfolders depends on the number of "new leaves" in total + Every subfolder is a "new leaf", whose name is {}_{}, with 1st digit represents time of 1st emergence of the leaf and 2nd digit represents leaf index when it first emerges + Inside every folder, images of leaves with names "{}.png" are contained; inside the {} the date-time of the original image is contained, typically, it follows the format of YYYY-MM-DD-HH-MM. + + 3) a folder called "visualization 3", which containes 3rd set of visualization + This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. + Naming format: {}_visual.png; inside the {} the date-time of the original image is contained, typically, it follows the format of YYYY-MM-DD-HH-MM. + +```python +from plantcv import plantcv as pcv +# Below are examples of input variables, always adjust base on your own application. + +path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol/maskrcnn_test' +path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/detection/modified' +path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/test' +time_cond = ['08-05', '11-05', '17-05', '21-05'] +​pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=1, class_names=['BG', 'Leaf']) +``` +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) From b8247e8a628f2f551095110a8722a6cdc0e5e5db Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 12:03:52 -0500 Subject: [PATCH 005/274] Update time_series.md --- docs/time_series.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/time_series.md b/docs/time_series.md index 050feffdb..ec6098e85 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -17,7 +17,7 @@ To understand the growth of every leaf instance of a plant, we need pull out the - **Parameters:** - imagedir: directory of original image used - segmentationdir: directory of leaf instance segmentation result - - savedir: desired saving directory of linking result + - savedir: desired saving directory of linking result. Note that this function will generate a folder under your specified saving directory, with the date and time you run this programme - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 - class_names: used in bounding box visualization. by default there are background and leaf From 456c95cc177caf80c0ebc83814a6aac7d0ac979f Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 12:03:54 -0500 Subject: [PATCH 006/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 41 +++++++++++++------ 1 file changed, 28 insertions(+), 13 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 9878a3353..bb96839f0 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -190,6 +190,9 @@ def __init__(self, imagedir, segmentationdir, savedir): # store all the images self.images = [] + + # minimum dimension of image. To link time series, all images must have the same dimension, to make it easier, we make the image square + self.min_dim = 0 # store all the mask detection during [starttime, endtime] self.masks = [] @@ -207,8 +210,7 @@ def __init__(self, imagedir, segmentationdir, savedir): self.imagedir = imagedir self.segmentationdir = segmentationdir self.savedir = os.path.join(savedir, str(datetime.datetime.now())) - if not os.path.exists(self.savedir): - os.makedirs(self.savedir) + os.makedirs(self.savedir) self.visualdir = os.path.join(self.savedir, 'visualization') self.total_time = 0 @@ -242,16 +244,16 @@ def getnumemergence(self): def getpath(self, path): self.dir = path - def addimage(self): - for t in self.time: - filename = os.path.join(self.dir, '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t)) - image = skimage.io.imread(filename) - self.images.append(image) + # def addimage(self): + # for t in self.time: + # filename = os.path.join(self.dir, '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t)) + # image = skimage.io.imread(filename) + # self.images.append(image) def Sorttime(self, time_cond): """ - you need to modify this function accordint to you format - return: loop through the dataset_dir, and add time in time order + This function is designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM + Return: loop through the dataset_dir, and add time in time order """ filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] @@ -274,11 +276,24 @@ def Sorttime(self, time_cond): def load_images(self): """ Load original images + Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM """ + filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] + temp_imgs = [] + sz = [] for t in self.time: - file_name = '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t) - junk = skimage.io.imread(os.path.join(self.imagedir, file_name)) - img = junk[0: 499, 0: 499, :] # make all images the same size + for f in filenames: + temp = re.search(t, f) + if temp: + junk = skimage.io.imread(os.path.join(self.imagedir, f)) + temp_imgs.append(junk) + sz.append(np.min(junk.shape[0:2])) + # img = junk[0: 499, 0: 499, :] # make all images the same size + # self.images.append(img) + filenames.remove(f) + self.min_dim = np.min(sz) + for junk in temp_imgs: + img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size self.images.append(img) def load_results(self): @@ -287,7 +302,7 @@ def load_results(self): for t in self.time: file_name = '{}.pkl'.format(t) r = pkl.load(open(os.path.join(self.segmentationdir, file_name), 'rb')) - self.masks.append(r["masks"][0: 499, 0: 499, :]) # make all masks the same size + self.masks.append(r["masks"][0: self.min_dim, 0: self.min_dim, :]) # make all masks the same size self.rois.append(r["rois"]) self.class_ids.append(r['class_ids']) self.scores.append(r['scores']) From 7fb02847a24abe004b091f985919a05c85ab0386 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 12:03:58 -0500 Subject: [PATCH 007/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 9dcc81c3d..ad45dd963 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -65,6 +65,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi Plant.getpath(Plant.imagedir) Plant.Sorttime(time_cond) + Plant.load_images() + # load mrcnn inferencing results Plant.load_results() From e3a181128f62fb74384c1a0add02078497a0b72d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 14:42:16 -0500 Subject: [PATCH 008/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index ad45dd963..275fb7121 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -34,7 +34,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 class_names: used in bounding box visualization. by default there are background and leaf Output: - There is no returned values as output, though the results will be saved in user defined "savedir" + There is only one return value as output, which is the directory where the results saved + The results will be saved under the user defined "savedir", in a subfolder whose name is date&time when the program runs 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. @@ -58,7 +59,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. Naming format: YYYY-MM-DD-HH-MM_visual.png """ - + # initialize Plant class Plant = funcs.PlantData(imagedir, segmentationdir, savedir) @@ -180,6 +181,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi # save all information pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) + return Plant.savedir + From f5278b7e1ea5a5e2123dc3d67e34ef21ab0c7ad4 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 14:42:45 -0500 Subject: [PATCH 009/274] add test data --- .../2019-10-22-08-05.pkl | Bin 0 -> 1248143 bytes .../2019-10-22-11-05.pkl | Bin 0 -> 1248143 bytes .../2019-10-22-17-05.pkl | Bin 0 -> 1255648 bytes .../2019-10-22-21-05.pkl | Bin 0 -> 1503671 bytes .../2019-10-23-08-05.pkl | Bin 0 -> 1500667 bytes .../2019-10-23-11-05.pkl | Bin 0 -> 1506673 bytes .../2019-10-23-17-05.pkl | Bin 0 -> 1506673 bytes .../2019-10-23-21-05.pkl | Bin 0 -> 1750684 bytes .../2019-10-24-08-05.pkl | Bin 0 -> 1754191 bytes 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z81YOU{SK=E?9OHT4dzEK(5(2#+39r03tFFjm@i7wy!W%*TIjN)PfhphkdyqGTaX!- zJzlO+>L4K^^O|iHuWiR&?q!EtjKJkSoZfiia@1=m&^B>M}>^sH3I%bJMh6bgvpFd}d zfB%8#(pPIl0}%IHW8z`Etl-lCmv-VNM&yxc(x#k*c6Zvu)v>kjHV-2ssUy;)N9F)b z(G$=V8rQ*<`+=K1!ZiGyyQ-jOeXRrdlb&SfJGDxp(cpLFLmd zGe*dgq{&H$Oc^RdM|_;BriUov+LOm!|*FpWddC;iz`<6@_+c3G^D%gK1RvaD4u z;1|fsz}KaVF;7J&d#D0W#if#V9-pjE<*&y9$RFBd*W38e8T`^7cl9J4!zBn-+pRRZ zf}q$KKmNn%pGR!r3}E+~<9^Yh!H#QfP1fVPzKy@NL%ZiH$jR)b8>$GafT{rPG}0VeOa_1bLs%M-JklZr;o?4%ufeh{2Hl6Nhiz2!<|ZT8qp-DAOH$~|E&F+7GQ2;i|V>Ne?W z^rq^FT-Id-E8H2O1*T8L&CzOzd6-mR`kgP%h6Oagzk|nZ#q5vr%#G1M9LgpL-?K2l zRf!6)9BH)YKSBL#-(V*(Aj>;o5?|LDQs_erl6!?NX%lXdh_)V;`*bzvwJtG#?A)Z< zX;$$IV`8A{(J4Q_X{6ACMRx@`*vw-1{Ee|B=gR+_zRcnZ1N57h5ww}KfAr+hD#VWE&EMBeerQ-=)pv&r$pg;rrL;NeYsY2oU~8IAB*m(5BZI_~^6Uec_P>hoOjHD#d>|krgcetv39>=i_ny KH=M!0rT+)3&PBZd literal 0 HcmV?d00001 From 3b2f69ba6eab6de26e14c0dd209aa98e09e212a5 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 14:42:48 -0500 Subject: [PATCH 010/274] Update tests.py --- tests/tests.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/tests/tests.py b/tests/tests.py index 93fa0679a..b0c031ea4 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -659,6 +659,9 @@ def test_plantcv_parallel_process_results_invalid_json(): TEST_INPUT_THERMAL_CSV = "FLIR2600.csv" PIXEL_VALUES = "pixel_inspector_rgb_values.txt" +TIME_SERIES_TEST_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "seires_data") +TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im") +TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "instance_segmentation") # ########################## # Tests for the main package @@ -4249,6 +4252,7 @@ def test_plantcv_hyperspectral_analyze_index_outside_range_warning(): pcv.hyperspectral.analyze_index(index_array=index_array, mask=mask_img, min_bin=.5, max_bin=.55) out = f.getvalue() # assert os.listdir(cache_dir) is 0 + assert out[0:10] == 'WARNING!!!' @@ -5533,6 +5537,14 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) +def test_plantcv_time_series_time_series(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + time_cond = ['08-05', '11-05', '17-05', '21-05'] + savedir = pcv.time_seires.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf']) + assert len(os.listdir(savedir)) > 0 # ############################## # Tests for the utils subpackage From b4419d5a3bda31683fbf80f209d8c84eaab489c7 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 14:52:37 -0500 Subject: [PATCH 011/274] Update __init__.py --- plantcv/plantcv/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py index 7dce68137..0d883f70d 100644 --- a/plantcv/plantcv/__init__.py +++ b/plantcv/plantcv/__init__.py @@ -226,7 +226,7 @@ def __init__(self, array_data, max_wavelength, min_wavelength, max_value, min_va 'cluster_contour_splitimg', 'rotate', 'shift_img', 'output_mask', 'auto_crop', 'canny_edge_detect', 'background_subtraction', 'naive_bayes_classifier', 'acute', 'distance_transform', 'params', 'cluster_contour_mask', 'analyze_thermal_values', 'opening', - 'closing', 'within_frame', 'fill_holes', 'get_kernel', 'Spectral_data', 'crop', 'stdev_filter'] + 'closing', 'within_frame', 'fill_holes', 'get_kernel', 'Spectral_data', 'crop', 'stdev_filter', 'time_series'] from ._version import get_versions __version__ = get_versions()['version'] From 9408e5477ef39fd0bc76c4dc62440588aef2fa07 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 15:44:48 -0500 Subject: [PATCH 012/274] Update __init__.py --- plantcv/plantcv/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py index 0d883f70d..41f82a483 100644 --- a/plantcv/plantcv/__init__.py +++ b/plantcv/plantcv/__init__.py @@ -210,6 +210,7 @@ def __init__(self, array_data, max_wavelength, min_wavelength, max_value, min_va from plantcv.plantcv.get_kernel import get_kernel from plantcv.plantcv.crop import crop from plantcv.plantcv.stdev_filter import stdev_filter +from plantcv.plantcv import time_series # add new functions to end of lists From 3566ee347b2ec5ae66c4f1eaf3f486033d981b88 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 15:45:04 -0500 Subject: [PATCH 013/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index 3e468d909..8397f612f 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,13 +1,13 @@ -from plantcv.plantcv.time_series.link_utilities import random_colors -from plantcv.plantcv.time_series.link_utilities import apply_mask +from plantcv.plantcv.time_series.link_utilities import _random_colors +from plantcv.plantcv.time_series.link_utilities import _apply_mask from plantcv.plantcv.time_series.link_utilities import display_instances -from plantcv.plantcv.time_series.link_utilities import compute_overlaps_masks +from plantcv.plantcv.time_series.link_utilities import _compute_overlaps_masks from plantcv.plantcv.time_series.link_utilities import get_ax -from plantcv.plantcv.time_series.link_utilities import getmatchedIndex +from plantcv.plantcv.time_series.link_utilities import _getmatchedIndex from plantcv.plantcv.time_series.link_utilities import PlantData from plantcv.plantcv.time_series.time_series import time_series_linking # add new functions to end of lists -__all__ = ["random_colors", "apply_mask", "display_instances", "compute_overlaps_masks", "get_ax", - "getmatchedIndex", "time_series_linking"] +__all__ = ["_random_colors", "_apply_mask", "display_instances", "_compute_overlaps_masks", "get_ax", + "_getmatchedIndex", "PlantData", "time_series_linking"] From 6d222be6cd7f10367987afb0a9f99af8b1218a7c Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 15:45:10 -0500 Subject: [PATCH 014/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 275fb7121..427bf7df8 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -20,7 +20,7 @@ import re from plantcv import plantcv as pcv import copy -import link_utilities as funcs +from plantcv.plantcv.time_series import link_utilities as funcs def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']): From 25380ead928220bd317ca23cf544bad2d42383e4 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 15:45:14 -0500 Subject: [PATCH 015/274] Update tests.py --- tests/tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index b0c031ea4..760a155b2 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -5543,7 +5543,7 @@ def test_plantcv_time_series_time_series(): os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir time_cond = ['08-05', '11-05', '17-05', '21-05'] - savedir = pcv.time_seires.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf']) + savedir = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf']) assert len(os.listdir(savedir)) > 0 # ############################## From 8eb224f20b8b85ba13502e2e9b1859ba65784765 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 22 Jun 2020 09:35:28 -0500 Subject: [PATCH 016/274] Update time_series.py Instead of giving no returned values, give a returned value called Plant, which is a Plant class including all related time-series linking information Change the saving name format for output files --- plantcv/plantcv/time_series/time_series.py | 27 ++++++++-------------- 1 file changed, 9 insertions(+), 18 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 427bf7df8..7b0cac46e 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -21,6 +21,7 @@ from plantcv import plantcv as pcv import copy from plantcv.plantcv.time_series import link_utilities as funcs +# import link_utilities as funcs def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']): @@ -34,8 +35,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 class_names: used in bounding box visualization. by default there are background and leaf Output: - There is only one return value as output, which is the directory where the results saved - The results will be saved under the user defined "savedir", in a subfolder whose name is date&time when the program runs + There is no returned values as output, though the results will be saved in user defined "savedir" 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. @@ -59,7 +59,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. Naming format: YYYY-MM-DD-HH-MM_visual.png """ - + # initialize Plant class Plant = funcs.PlantData(imagedir, segmentationdir, savedir) @@ -145,10 +145,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi mask = np.zeros(mask_t.shape, dtype=np.uint8) mask[np.where(mask_t)] = 255 leaf_t = pcv.apply_mask(img, mask, mask_color='black') -# pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}.png'.format(start_time, start_idx, t, link_leaf[t]))) -# pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}.pkl'.format(start_time, start_idx, t, link_leaf[t])), 'wb')) - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, link_leaf[t], t, Plant.time[t]))) - pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, link_leaf[t], t, Plant.time[t])), 'wb')) + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, link_leaf[t], t, Plant.filename_pre[t]))) + pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, link_leaf[t], t, Plant.filename_pre[t])), 'wb')) ## 2. show with an alpha channel @@ -163,14 +161,12 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi ax2 = fig2.add_subplot(1,1,1) ax2.imshow(masked_im) ax2.axis('off') -# plt.savefig(os.path.join(save_dir_, 'time_{}.png'.format(t))) - plt.savefig(os.path.join(save_dir_, str(Plant.time[t]) + '.png')) + plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + '.png')) plt.close(fig2) - # pcv.print_image(masked_im, os.path.join(save_dir_, 'time_{}.png'.format(t))) count += 1 ## 3. visualize with bounding boxes - for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.time): + for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.filename_pre): funcs.display_instances(img, roi, mask, class_id, class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, colors = color) @@ -180,10 +176,5 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi # save all information pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) - - return Plant.savedir - - - - - + + return Plant From 8a7e30bec8ee1bc2bd3fdd5e02a883bf0ba8ecd4 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 22 Jun 2020 09:37:10 -0500 Subject: [PATCH 017/274] Update link_utilities.py Add to "filename_pre" to Plant (self) which stores the original image name prefixes (".jpg" not included) sorted by time. --- plantcv/plantcv/time_series/link_utilities.py | 51 ++++++++++++------- 1 file changed, 32 insertions(+), 19 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index bb96839f0..a4d89ee8c 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -185,6 +185,8 @@ def __init__(self, imagedir, segmentationdir, savedir): # store a list of time of the images self.time = [] + # store a list of original prefixes of the original images + self.filename_pre = [] # total num of leaves that being detected self.numleaf = 0 @@ -256,45 +258,56 @@ def Sorttime(self, time_cond): Return: loop through the dataset_dir, and add time in time order """ - filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] + filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] + filenames_ori = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] time_temp = [] - for x in filenames: - temp = re.search('\d{4}-\d{2}-\d{2}-\d{2}-\d{2}', x) + file_name = [] + for filename in filenames: + temp = re.search('\d{4}-\d{2}-\d{2}-\d{2}-\d{2}', filename) if temp: timepart = temp.group() for cond in time_cond: if timepart.endswith(cond): time_temp.append(timepart) + junk = [1 if re.search(timepart, f) is not None else 0 for f in filenames_ori] + file_name.append(filenames_ori[junk.index(1)].replace('.jpg', '')) continue index_temp = np.argsort(time_temp) # forward self.time = [time_temp[i] for i in index_temp] - # backward -# self.time_back = [time_temp[i] for i in reversed(index_temp)] - + self.filename_pre = [file_name[i] for i in index_temp] def load_images(self): """ Load original images Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM """ - filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] - temp_imgs = [] +# filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] +# temp_imgs = [] +# sz = [] +# for t in self.time: +# for f in filenames: +# temp = re.search(t, f) +# if temp: +# junk = skimage.io.imread(os.path.join(self.imagedir, f)) +# temp_imgs.append(junk) +# sz.append(np.min(junk.shape[0:2])) +# filenames.remove(f) +# self.min_dim = np.min(sz) +# for junk in temp_imgs: +# img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size +# self.images.append(img) + temp_imgs = [] sz = [] - for t in self.time: - for f in filenames: - temp = re.search(t, f) - if temp: - junk = skimage.io.imread(os.path.join(self.imagedir, f)) - temp_imgs.append(junk) - sz.append(np.min(junk.shape[0:2])) - # img = junk[0: 499, 0: 499, :] # make all images the same size - # self.images.append(img) - filenames.remove(f) + for pre in self.filename_pre: + filename = pre + '.jpg' + junk = skimage.io.imread(os.path.join(self.imagedir, filename)) + temp_imgs.append(junk) + sz.append(np.min(junk.shape[0:2])) self.min_dim = np.min(sz) for junk in temp_imgs: img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size - self.images.append(img) + self.images.append(img) def load_results(self): """ Instead of running instance segmentation, load instance segmentation results (masks) From 670fa0e938af7239b4bac71f35c08026b74860b9 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 22 Jun 2020 14:01:45 -0500 Subject: [PATCH 018/274] Update link_utilities.py Updated so that the folders won't contain space --- plantcv/plantcv/time_series/link_utilities.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index a4d89ee8c..b696f0767 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -211,7 +211,7 @@ def __init__(self, imagedir, segmentationdir, savedir): # store the dataset directory, instance segmentation result directory, time-series linking result directory and visualization directory self.imagedir = imagedir self.segmentationdir = segmentationdir - self.savedir = os.path.join(savedir, str(datetime.datetime.now())) + self.savedir = os.path.join(savedir, str(datetime.datetime.now()).replace(' ', '_')) os.makedirs(self.savedir) self.visualdir = os.path.join(self.savedir, 'visualization') From a659f38f234b0552edea965f72fcd43e9466fff5 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 23 Jun 2020 11:01:13 -0500 Subject: [PATCH 019/274] Update time_series.py Change the saving name for the result so it makes much sense. --- plantcv/plantcv/time_series/time_series.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 7b0cac46e..b9b815143 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -145,8 +145,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi mask = np.zeros(mask_t.shape, dtype=np.uint8) mask[np.where(mask_t)] = 255 leaf_t = pcv.apply_mask(img, mask, mask_color='black') - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, link_leaf[t], t, Plant.filename_pre[t]))) - pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, link_leaf[t], t, Plant.filename_pre[t])), 'wb')) + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) + pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) ## 2. show with an alpha channel From 103a6bac633af78a8cd8ae3018fba284d191d00b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 24 Jun 2020 09:41:13 -0500 Subject: [PATCH 020/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 250 +++++++++++---------- 1 file changed, 131 insertions(+), 119 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index b9b815143..61d2a7c84 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -24,7 +24,7 @@ # import link_utilities as funcs -def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']): +def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link'): """ Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) Input: @@ -35,19 +35,20 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 class_names: used in bounding box visualization. by default there are background and leaf Output: - There is no returned values as output, though the results will be saved in user defined "savedir" + An instance from "Plant" class would be returned, all the results will be saved in a folder with name of date and time when the function runs inside the user defined "savedir". Sample folder name: 2020-06-23-15-42 (a format of YYYY-MM-DD-HH-mm). 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. 4. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. - result name: {}_{}_{}_{}.png + result name: {}_{}_{}_{}_{}.png Naming rules for file names: 1st digit: time of 1st emergence of the leaf 2nd digit: leaf index when it first emerges 3rd digit: current time point 4th digit: current leaf index + 5th digit: original image name 2) a folder called "visualization 2", which contains 2nd set of visualization This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent @@ -57,124 +58,135 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi 3) a folder called "visualization 3", which containes 3rd set of visualization This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. - Naming format: YYYY-MM-DD-HH-MM_visual.png + Naming format: {}_visual.png + The original image name is inside the {}. """ - - # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir) - - Plant.getpath(Plant.imagedir) - Plant.Sorttime(time_cond) - - Plant.load_images() - - # load mrcnn inferencing results - Plant.load_results() - - Plant.getinitleaf() - Plant.getmaxleaf() - - Plant.gettotaltime() - Plant.getnumemergence() - - # plot the #leaves vs. time relationship - plt.plot(np.array(range(0,len(Plant.num_leaves))), Plant.num_leaves) - - if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): - file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') - if link_logic == 1: - file.write('mode: IOU\n') + if mode == 'link': + # initialize Plant class + Plant = funcs.PlantData(imagedir, segmentationdir, savedir) + + Plant.getpath(Plant.imagedir) + Plant.Sorttime(time_cond) + + Plant.load_images() + + # load mrcnn inferencing results + Plant.load_results() + + Plant.getinitleaf() + Plant.getmaxleaf() + + Plant.gettotaltime() + Plant.getnumemergence() + + # plot the #leaves vs. time relationship + plt.plot(np.array(range(0,len(Plant.num_leaves))), Plant.num_leaves) + + if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): + file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') + if link_logic == 1: + file.write('mode: IOU\n') + else: + file.write('mode: IOP\n') + file.write('Directory of original images: {}\n'.format(Plant.imagedir)) + file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) + file.write('Image conditions: {}'.format(time_cond)) + file.close() + + # linking initialization + Plant.initialize_linking() + + # link + for t in range(0, Plant.total_time-1): + Plant.linking(t, mode=link_logic) + Plant.get_series() + + ####### visualization ####### + # load original images + if len(Plant.images) == 0: + Plant.load_images() #Plant.images + + # visualization method 1: show only one leaf per image + path_visual1 = os.path.join(Plant.visualdir, 'visualization1') + if not os.path.exists(path_visual1): + os.makedirs(path_visual1) + + # visualization method 2: show with an alpha channel + path_visual2 = os.path.join(Plant.visualdir, 'visualization2') + if not os.path.exists(path_visual2): + os.makedirs(path_visual2) + + # visualization method 3: show with bounding boxes + path_visual3 = os.path.join(Plant.visualdir, 'visualization3') + if not os.path.exists(path_visual3): + os.makedirs(path_visual3) + + count = 0 + if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): + colors = funcs._random_colors(20) + pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) else: - file.write('mode: IOP\n') - file.write('Directory of original images: {}\n'.format(Plant.imagedir)) - file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) - file.write('Image conditions: {}'.format(time_cond)) - file.close() - - # linking initialization - Plant.initialize_linking() - - # link - for t in range(0, Plant.total_time-1): - Plant.linking(t, mode=link_logic) - Plant.get_series() - - ####### visualization ####### - # load original images - if len(Plant.images) == 0: - Plant.load_images() #Plant.images - - # visualization method 1: show only one leaf per image - path_visual1 = os.path.join(Plant.visualdir, 'visualization1') - if not os.path.exists(path_visual1): - os.makedirs(path_visual1) - - # visualization method 2: show with an alpha channel - path_visual2 = os.path.join(Plant.visualdir, 'visualization2') - if not os.path.exists(path_visual2): - os.makedirs(path_visual2) - - # visualization method 3: show with bounding boxes - path_visual3 = os.path.join(Plant.visualdir, 'visualization3') - if not os.path.exists(path_visual3): - os.makedirs(path_visual3) - - count = 0 - if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): - colors = funcs._random_colors(20) - pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) + colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) + + color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] + for key_t in Plant.link_series: + # + ids = Plant.link_series[key_t]['unique_id'] + + start_time = int(key_t.replace('t', '')) + leaves_t = Plant.link_series[key_t]['new_leaf'] + # for leaf in leaves_t: + for (unique_id, leaf) in zip(ids, leaves_t): + key_leaf = 'leaf{}'.format(leaf) + link_leaf = Plant.link_series[key_t][key_leaf] + start_idx = link_leaf[start_time] + for t in range(start_time, Plant.total_time): + img = Plant.images[t] + if link_leaf[t] >= 0: + color_all[t][link_leaf[t]] = colors[count] + + mask_t = Plant.masks[t][:,:,link_leaf[t]] + + ## 1. save the masked image, i.e. single leaves + mask = np.zeros(mask_t.shape, dtype=np.uint8) + mask[np.where(mask_t)] = 255 + leaf_t = pcv.apply_mask(img, mask, mask_color='black') + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) + pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) + + + ## 2. show with an alpha channel + # update the mask where there is an alpha channel + mask_ = np.ones(mask_t.shape)*0.5 + mask_[np.where(mask_t == True)] = 1 + masked_im = np.concatenate((img.astype(float)/255, np.expand_dims(mask_, axis=2)), axis=2) + save_dir_ = os.path.join(path_visual2, '{}_{}_{}.png'.format(unique_id, start_time, start_idx)) + if not os.path.exists(save_dir_): + os.makedirs(save_dir_) + fig2 = plt.figure(figsize=(5,5)) + ax2 = fig2.add_subplot(1,1,1) + ax2.imshow(masked_im) + ax2.axis('off') + plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + '.png')) + plt.close(fig2) + count += 1 + + ## 3. visualize with bounding boxes + for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.filename_pre): + funcs.display_instances(img, roi, mask, class_id, + class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, + colors = color) + + plt.savefig(os.path.join(path_visual3, '{}_visual.png'.format(t))) + plt.close('all') + + # save all information + pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) + else: - colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) - - color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] - for key_t in Plant.link_series: - start_time = int(key_t.replace('t', '')) - leaves_t = Plant.link_series[key_t]['new_leaf'] - for leaf in leaves_t: - key_leaf = 'leaf{}'.format(leaf) - link_leaf = Plant.link_series[key_t][key_leaf] - start_idx = link_leaf[start_time] - for t in range(start_time, Plant.total_time): - img = Plant.images[t] - if link_leaf[t] >= 0: - color_all[t][link_leaf[t]] = colors[count] - - mask_t = Plant.masks[t][:,:,link_leaf[t]] - - ## 1. save the masked image, i.e. single leaves - mask = np.zeros(mask_t.shape, dtype=np.uint8) - mask[np.where(mask_t)] = 255 - leaf_t = pcv.apply_mask(img, mask, mask_color='black') - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) - pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) - - - ## 2. show with an alpha channel - # update the mask where there is an alpha channel - mask_ = np.ones(mask_t.shape)*0.5 - mask_[np.where(mask_t == True)] = 1 - masked_im = np.concatenate((img.astype(float)/255, np.expand_dims(mask_, axis=2)), axis=2) - save_dir_ = os.path.join(path_visual2, '{}_{}.png'.format(start_time, start_idx)) - if not os.path.exists(save_dir_): - os.makedirs(save_dir_) - fig2 = plt.figure(figsize=(5,5)) - ax2 = fig2.add_subplot(1,1,1) - ax2.imshow(masked_im) - ax2.axis('off') - plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + '.png')) - plt.close(fig2) - count += 1 - - ## 3. visualize with bounding boxes - for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.filename_pre): - funcs.display_instances(img, roi, mask, class_id, - class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, - colors = color) - - plt.savefig(os.path.join(path_visual3, '{}_visual.png'.format(t))) - plt.close('all') - - # save all information - pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) + # initialize Plant class + Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode='load') + # Load from existed PlantData instance + Plant = pkl.load( open(os.path.join(path_save, 'saved_plant.pkl'), 'rb')) return Plant From ad0ee69cf1f8c9183e6c68c0a43e9c9d041d5e55 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 24 Jun 2020 09:41:16 -0500 Subject: [PATCH 021/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 70 ++++++++++--------- 1 file changed, 38 insertions(+), 32 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index b696f0767..5b04d0bfa 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -181,7 +181,7 @@ def _getmatchedIndex(mask, masks, mode): class PlantData(): - def __init__(self, imagedir, segmentationdir, savedir): + def __init__(self, imagedir, segmentationdir, savedir, mode='link'): # store a list of time of the images self.time = [] @@ -209,17 +209,23 @@ def __init__(self, imagedir, segmentationdir, savedir): self.scores = [] # store the dataset directory, instance segmentation result directory, time-series linking result directory and visualization directory - self.imagedir = imagedir + self.imagedir = imagedir self.segmentationdir = segmentationdir - self.savedir = os.path.join(savedir, str(datetime.datetime.now()).replace(' ', '_')) - os.makedirs(self.savedir) - self.visualdir = os.path.join(self.savedir, 'visualization') + junk = datetime.datetime.now() + if mode == 'link': + subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), str(junk.hour).zfill(2), str(junk.minute).zfill(2)) + self.savedir = os.path.join(savedir, subfolder) + os.makedirs(self.savedir) + else: + self.savedir = savedir + + self.visualdir = os.path.join(self.savedir, 'visualization') - self.total_time = 0 - self.max_nleaf = 0 - self.init_nleaf = 0 - self.num_leaves = [] - self.num_emergence = 0 + self.total_time = 0 + self.max_nleaf = 0 + self.init_nleaf = 0 + self.num_leaves = [] + self.num_emergence = 0 self.emerging_info = [] # list length: self.total_time self.link_info = [] # list length: self.total_time-1, dictionaries inside @@ -245,12 +251,6 @@ def getnumemergence(self): def getpath(self, path): self.dir = path - - # def addimage(self): - # for t in self.time: - # filename = os.path.join(self.dir, '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t)) - # image = skimage.io.imread(filename) - # self.images.append(image) def Sorttime(self, time_cond): """ @@ -282,21 +282,6 @@ def load_images(self): """ Load original images Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM """ -# filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] -# temp_imgs = [] -# sz = [] -# for t in self.time: -# for f in filenames: -# temp = re.search(t, f) -# if temp: -# junk = skimage.io.imread(os.path.join(self.imagedir, f)) -# temp_imgs.append(junk) -# sz.append(np.min(junk.shape[0:2])) -# filenames.remove(f) -# self.min_dim = np.min(sz) -# for junk in temp_imgs: -# img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size -# self.images.append(img) temp_imgs = [] sz = [] for pre in self.filename_pre: @@ -370,10 +355,14 @@ def linking(self, start_time, mode): self.link_info[start_time] = link['forward'] def get_series(self): + ## define new leaves and their unique identifiers at time points with new leaves emerging t = 0 key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = self.available_leaves[0] + # + self.link_series[key_t]['unique_id'] = self.link_series[key_t]['new_leaf'] + unique_id = len(self.link_series[key_t]['new_leaf']) for t in range(1, self.total_time): new_leaves = [i for i in self.available_leaves[t] if i not in self.link_info[t-1]] @@ -381,6 +370,22 @@ def get_series(self): key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = np.array(new_leaves) +# if len(new_leaves) > 1: +# id_temp = [] +# for new_leav in new_leaves: +# id_temp.append(unique_id) +# unique_id = unique_id + 1 +# self.link_series[key_t]['unique_id' = np.array(id_temp) +# else: +# self.link_series[key_t]['unique_id'] = np.array(unique_id) +# unique_id = unique_id + 1 + id_temp = [] + for new_leaf in new_leaves: + id_temp.append(unique_id) + unique_id = unique_id + 1 + self.link_series[key_t]['unique_id'] = np.array(id_temp) + + ## for time points with new leaves emerging, get the linking information for every new leaf for key_t in self.link_series: t0 = int(key_t.replace('t','')) for leaf in self.link_series[key_t]['new_leaf']: @@ -393,4 +398,5 @@ def get_series(self): if idx < 0: break else: - self.link_series[key_t][key_leaf][t_] = self.link_info[t_-1][idx] \ No newline at end of file + self.link_series[key_t][key_leaf][t_] = self.link_info[t_-1][idx] + From 24b8d5447185b5eb36773280764c67233bec9623 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 24 Jun 2020 09:59:13 -0500 Subject: [PATCH 022/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 61d2a7c84..8e90c75ed 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -33,7 +33,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi savedir: desired saving directory of linking result time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 - class_names: used in bounding box visualization. by default there are background and leaf + class_names: used in bounding box visualization. By default there are background and leaf + mode: either 'link' or 'load'. When creating a new linking, use in mode 'link', when trying to load from saved file, use in mode 'load'. By default the mode is 'link'. Output: An instance from "Plant" class would be returned, all the results will be saved in a folder with name of date and time when the function runs inside the user defined "savedir". Sample folder name: 2020-06-23-15-42 (a format of YYYY-MM-DD-HH-mm). 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time From 6fc93082169e8cc4dd202259ed2650216395c9e8 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 24 Jun 2020 10:37:06 -0500 Subject: [PATCH 023/274] Update tests.py --- tests/tests.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 760a155b2..8f3ada35b 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -5543,8 +5543,8 @@ def test_plantcv_time_series_time_series(): os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir time_cond = ['08-05', '11-05', '17-05', '21-05'] - savedir = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf']) - assert len(os.listdir(savedir)) > 0 + Plant_ = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link') + assert len(os.listdir(Plant_.savedir)) > 0 # ############################## # Tests for the utils subpackage From 7c64bb9720e06eded5955059d62d8d607e890818 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 25 Jun 2020 09:18:05 -0500 Subject: [PATCH 024/274] Update plantcv-workflow.py Add new metadata into current vocabulary: device, location, uidleaf --- plantcv-workflow.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/plantcv-workflow.py b/plantcv-workflow.py index be0522a7e..9d44a7866 100755 --- a/plantcv-workflow.py +++ b/plantcv-workflow.py @@ -99,11 +99,29 @@ def options(): "datatype": "", "value": "none" }, + # Device identifier + "device": { + "label": "devide identifier", + "datatype": "", + "value": "none" + }, + # Location + "location":{ + "label": "location", + "datatype": "", + "value": "none" + }, # Other "other": { "label": "other identifier", "datatype": "", "value": "none" + }, + # Unique Leaf Identifier + "uidleaf":{ + "label": "unique leaf identifier", + "datatype": "", + "value": None } } parser = argparse.ArgumentParser(description='Parallel imaging processing with PlantCV.') From 5b1d4d34050d44fcf9182a501c6111c666d47eb4 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 25 Jun 2020 09:18:15 -0500 Subject: [PATCH 025/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 43 ++++++++++++---------- 1 file changed, 24 insertions(+), 19 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 8e90c75ed..05061bf90 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -43,23 +43,28 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi 4. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. - result name: {}_{}_{}_{}_{}.png - Naming rules for file names: - 1st digit: time of 1st emergence of the leaf - 2nd digit: leaf index when it first emerges - 3rd digit: current time point - 4th digit: current leaf index - 5th digit: original image name + result name: {}_{}-{}-{}-{}_{}.png + Naming convention for file names: + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + 4rd digit: current time point + 5th digit: current leaf index + 6th digit: original image name 2) a folder called "visualization 2", which contains 2nd set of visualization This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent There are several subfolders, the number of subfolders depends on the number of "new leaves" in total - Every subfolder is a "new leaf", whose name is {}_{}, with 1st digit represents time of 1st emergence of the leaf and 2nd digit represents leaf index when it first emerges - Inside every folder, images of leaves with names "time_{}.png" are contained; the number inside the {} represents the time point + Every subfolder is a "new leaf", whose name is {}_{}-{} + Naming convention for folder names: + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + Inside every folder, images of leaves with names same as original image names are contained. 3) a folder called "visualization 3", which containes 3rd set of visualization This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. - Naming format: {}_visual.png + Naming format: {}-visual.png The original image name is inside the {}. """ if mode == 'link': @@ -136,7 +141,6 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi start_time = int(key_t.replace('t', '')) leaves_t = Plant.link_series[key_t]['new_leaf'] - # for leaf in leaves_t: for (unique_id, leaf) in zip(ids, leaves_t): key_leaf = 'leaf{}'.format(leaf) link_leaf = Plant.link_series[key_t][key_leaf] @@ -148,20 +152,20 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi mask_t = Plant.masks[t][:,:,link_leaf[t]] - ## 1. save the masked image, i.e. single leaves + ## 1. save the masked image, i.e. single leaves mask = np.zeros(mask_t.shape, dtype=np.uint8) mask[np.where(mask_t)] = 255 leaf_t = pcv.apply_mask(img, mask, mask_color='black') - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) - pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) +# pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) - ## 2. show with an alpha channel - # update the mask where there is an alpha channel + ## 2. show result with an alpha channel + # update the mask where there is an alpha channel (alpha=0.5) mask_ = np.ones(mask_t.shape)*0.5 mask_[np.where(mask_t == True)] = 1 masked_im = np.concatenate((img.astype(float)/255, np.expand_dims(mask_, axis=2)), axis=2) - save_dir_ = os.path.join(path_visual2, '{}_{}_{}.png'.format(unique_id, start_time, start_idx)) + save_dir_ = os.path.join(path_visual2, '{}_{}-{}'.format(unique_id, start_time, start_idx)) if not os.path.exists(save_dir_): os.makedirs(save_dir_) fig2 = plt.figure(figsize=(5,5)) @@ -178,7 +182,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, colors = color) - plt.savefig(os.path.join(path_visual3, '{}_visual.png'.format(t))) + plt.savefig(os.path.join(path_visual3, '{}-visual.png'.format(t))) plt.close('all') # save all information @@ -188,6 +192,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi # initialize Plant class Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode='load') # Load from existed PlantData instance - Plant = pkl.load( open(os.path.join(path_save, 'saved_plant.pkl'), 'rb')) + Plant = pkl.load( open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'rb')) return Plant + \ No newline at end of file From 24062cb735b9c5854ad41d40313e96d053602f35 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 25 Jun 2020 09:47:06 -0500 Subject: [PATCH 026/274] Update time_series.md --- docs/time_series.md | 50 +++++++++++++++++++++++++++++---------------- 1 file changed, 32 insertions(+), 18 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index ec6098e85..47dae9621 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -1,9 +1,8 @@ ## time_series -This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, recording the growth of the plant, where there should be minimum or none movement of plant or camera). +This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, recording the growth of the plant, where ideally there should be no or at least minimum movement of plant or camera). To run this function, the instance segmentation for every image should be achieved beforehand. For a demo of instance segmentation, check here: -As a reminder, you will need a maskRCNN environemnt to be able to use this tool. For more information, check here: When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. (Another example is k-means) @@ -18,34 +17,43 @@ To understand the growth of every leaf instance of a plant, we need pull out the - imagedir: directory of original image used - segmentationdir: directory of leaf instance segmentation result - savedir: desired saving directory of linking result. Note that this function will generate a folder under your specified saving directory, with the date and time you run this programme - - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment + - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 - - class_names: used in bounding box visualization. by default there are background and leaf + - class_names: used in bounding box visualization. by default there are background and leaf, i.e. class_names = ['BG', 'leaf'] + - mode: can be either 'link' or 'load', use 'link' when trying to get linked time-series from instance segmentation; use 'load' when trying to load from saved linking - **Output:** - There is no returned values as output, though the results will be saved in user defined "savedir" - 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time - 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment + An instance of Plant object is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir". + + Note: under the user specified saving directory, a new folder whose name is date-time when the function runs (format: YYYY-MM-DD-HH-mm) will be created to save results. + + 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time. + 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment. 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. 4. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. - result name: {}_{}_{}_{}_{}.png - Naming rules for file names: - 1st digit: time of 1st emergence of the leaf - 2nd digit: leaf index when it first emerges - 3rd digit: current time point - 4th digit: current leaf index - 5th digit: date and time included in original file name, typically, it follows the format of YYYY-MM-DD-HH-MM. + result name: {}_{}-{}-{}-{}_{}.png + Naming convention for file names: + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + 4rd digit: current time point + 5th digit: current leaf index + 6th digit: original image name 2) a folder called "visualization 2", which contains 2nd set of visualization This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent There are several subfolders, the number of subfolders depends on the number of "new leaves" in total - Every subfolder is a "new leaf", whose name is {}_{}, with 1st digit represents time of 1st emergence of the leaf and 2nd digit represents leaf index when it first emerges - Inside every folder, images of leaves with names "{}.png" are contained; inside the {} the date-time of the original image is contained, typically, it follows the format of YYYY-MM-DD-HH-MM. + Every subfolder is a "new leaf", whose name is {}_{}-{}. Naming convention for folder names: + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + Inside every folder, images of leaves with names same as original image names are contained. 3) a folder called "visualization 3", which containes 3rd set of visualization This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. - Naming format: {}_visual.png; inside the {} the date-time of the original image is contained, typically, it follows the format of YYYY-MM-DD-HH-MM. + Naming format: {}-visual.png + The original image name is inside the {}. ```python from plantcv import plantcv as pcv @@ -55,6 +63,12 @@ path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtC path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/detection/modified' path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/test' time_cond = ['08-05', '11-05', '17-05', '21-05'] -​pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=1, class_names=['BG', 'Leaf']) +​linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link') + +# Print the saving directory on the screen so that you can go check the result: +linked_Plant.savedir + +# Print linking information on the screen for a quick check: +linked_Plant.link_series ``` **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) From d5f25002ebc1122b35108a769774f018be95cd37 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 10:43:24 -0500 Subject: [PATCH 027/274] Update plantcv-workflow.py by adding new metadata into the original vocabulary --- plantcv-workflow.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/plantcv-workflow.py b/plantcv-workflow.py index 9d44a7866..52a107f7c 100755 --- a/plantcv-workflow.py +++ b/plantcv-workflow.py @@ -100,8 +100,8 @@ def options(): "value": "none" }, # Device identifier - "device": { - "label": "devide identifier", + "ip": { + "label": "ip address of device", "datatype": "", "value": "none" }, @@ -118,7 +118,7 @@ def options(): "value": "none" }, # Unique Leaf Identifier - "uidleaf":{ + "uid_leaf":{ "label": "unique leaf identifier", "datatype": "", "value": None From 9da0e963e6d50bcd894d13a392b61a86bbc5e275 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 10:45:42 -0500 Subject: [PATCH 028/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 220 ++++++++++-------- 1 file changed, 122 insertions(+), 98 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 5b04d0bfa..d80f24b31 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -27,6 +27,7 @@ import copy import colorsys + def _random_colors(N, bright=True): """ Generate random colors. @@ -39,6 +40,7 @@ def _random_colors(N, bright=True): random.shuffle(colors) return colors + def _apply_mask(image, mask, color, alpha=0.5): """Apply the given mask to the image. """ @@ -49,6 +51,7 @@ def _apply_mask(image, mask, color, alpha=0.5): image[:, :, c]) return image + def display_instances(image, boxes, masks, class_ids, class_names, scores=None, title="", figsize=(16, 16), ax=None, @@ -100,8 +103,8 @@ def display_instances(image, boxes, masks, class_ids, class_names, y1, x1, y2, x2 = boxes[i] if show_bbox: p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, - alpha=0.7, linestyle="dashed", - edgecolor=color, facecolor='none') + alpha=0.7, linestyle="dashed", + edgecolor=color, facecolor='none') ax.add_patch(p) # Label @@ -141,7 +144,7 @@ def _compute_overlaps_masks(masks1, masks2, mode=1): masks1, masks2: [Height, Width, instances] mode: 1 or 2, 1 represent for IOU, 2 represent for intersection over the area of the previous mask """ - + # If either set of masks is empty return empty result if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: return np.zeros((masks1.shape[-1], masks2.shape[-1])) @@ -159,107 +162,112 @@ def _compute_overlaps_masks(masks1, masks2, mode=1): elif mode == 2: overlaps = intersections / area1[:, None] return overlaps - -def get_ax(rows=1, cols=1, size=16): #??? + + +def get_ax(rows=1, cols=1, size=16): # ??? """Return a Matplotlib Axes array to be used in all visualizations in the notebook. Provide a central point to control graph sizes. - + Adjust the size attribute to control how big to render images """ - fig, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows)) + fig, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows)) fig.tight_layout() return ax + def _getmatchedIndex(mask, masks, mode): """Get the index from masks that matches the mask""" score = _compute_overlaps_masks(np.expand_dims(mask, axis=-1), masks, mode) - if max(score[0])>0.15: + if max(score[0]) > 0.15: return np.argmax(score[0]), score else: - return -1, score + return -1, score + - class PlantData(): - def __init__(self, imagedir, segmentationdir, savedir, mode='link'): - + def __init__(self, imagedir, segmentationdir, savedir, mode='link', suffix='.jpg'): + + self.suffix = suffix + # store a list of time of the images self.time = [] # store a list of original prefixes of the original images self.filename_pre = [] - # total num of leaves that being detected - self.numleaf = 0 + # total num of leaves that being detected + self.numleaf = 0 # store all the images self.images = [] # minimum dimension of image. To link time series, all images must have the same dimension, to make it easier, we make the image square self.min_dim = 0 - + # store all the mask detection during [starttime, endtime] self.masks = [] - + # store all the rois (for visualization with bounding boxes) self.rois = [] - + # store all the class_ids (for visualization with bounding boxes) self.class_ids = [] - + # store all the scores (for visualization with bounding boxes) self.scores = [] - + # store the dataset directory, instance segmentation result directory, time-series linking result directory and visualization directory - self.imagedir = imagedir + self.imagedir = imagedir self.segmentationdir = segmentationdir - junk = datetime.datetime.now() + junk = datetime.datetime.now() if mode == 'link': - subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), str(junk.hour).zfill(2), str(junk.minute).zfill(2)) - self.savedir = os.path.join(savedir, subfolder) + subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), + str(junk.hour).zfill(2), str(junk.minute).zfill(2)) + self.savedir = os.path.join(savedir, subfolder) os.makedirs(self.savedir) else: self.savedir = savedir - self.visualdir = os.path.join(self.savedir, 'visualization') - - self.total_time = 0 - self.max_nleaf = 0 - self.init_nleaf = 0 - self.num_leaves = [] - self.num_emergence = 0 - - self.emerging_info = [] # list length: self.total_time - self.link_info = [] # list length: self.total_time-1, dictionaries inside - self.weights = [] # list length: self.total_time-1, dictionaries inside - self.available_leaves = [] # list length: self.total_time - self.link_series = dict() # only for those newly emerging leaves - + self.visualdir = os.path.join(self.savedir, 'visualization') + + self.total_time = 0 + self.max_nleaf = 0 + self.init_nleaf = 0 + self.num_leaves = [] + self.num_emergence = 0 + + self.emerging_info = [] # list length: self.total_time + self.link_info = [] # list length: self.total_time-1, dictionaries inside + self.weights = [] # list length: self.total_time-1, dictionaries inside + self.available_leaves = [] # list length: self.total_time + self.link_series = dict() # only for those newly emerging leaves + def getinitleaf(self): self.init_nleaf = self.masks[0].shape[2] - + def getmaxleaf(self): for i in range(0, len(self.masks)): self.num_leaves.append(self.masks[i].shape[2]) self.max_nleaf = np.max(self.num_leaves) - + def gettotaltime(self): # also represent for the total number of images self.total_time = len(self.time) - + def getnumemergence(self): - self.num_emergence = np.ones((self.total_time), dtype=int)*self.init_nleaf + self.num_emergence = np.ones((self.total_time), dtype=int) * self.init_nleaf self.num_emergence[1:] = np.diff(self.num_leaves) - + def getpath(self, path): - self.dir = path + self.dir = path def Sorttime(self, time_cond): """ This function is designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM - Return: loop through the dataset_dir, and add time in time order + Return: loop through the dataset_dir, and add time in time order """ - - filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] - filenames_ori = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] + + filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] + filenames_ori = [f for f in os.listdir(self.imagedir) if f.endswith(self.suffix)] time_temp = [] file_name = [] for filename in filenames: @@ -276,14 +284,29 @@ def Sorttime(self, time_cond): index_temp = np.argsort(time_temp) # forward self.time = [time_temp[i] for i in index_temp] - self.filename_pre = [file_name[i] for i in index_temp] + self.filename_pre = [file_name[i] for i in index_temp] def load_images(self): """ Load original images Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM """ - temp_imgs = [] - sz = [] + # filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] + # temp_imgs = [] + # sz = [] + # for t in self.time: + # for f in filenames: + # temp = re.search(t, f) + # if temp: + # junk = skimage.io.imread(os.path.join(self.imagedir, f)) + # temp_imgs.append(junk) + # sz.append(np.min(junk.shape[0:2])) + # filenames.remove(f) + # self.min_dim = np.min(sz) + # for junk in temp_imgs: + # img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size + # self.images.append(img) + temp_imgs = [] + sz = [] for pre in self.filename_pre: filename = pre + '.jpg' junk = skimage.io.imread(os.path.join(self.imagedir, filename)) @@ -291,8 +314,8 @@ def load_images(self): sz.append(np.min(junk.shape[0:2])) self.min_dim = np.min(sz) for junk in temp_imgs: - img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size - self.images.append(img) + img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size + self.images.append(img) def load_results(self): """ Instead of running instance segmentation, load instance segmentation results (masks) @@ -300,60 +323,61 @@ def load_results(self): for t in self.time: file_name = '{}.pkl'.format(t) r = pkl.load(open(os.path.join(self.segmentationdir, file_name), 'rb')) - self.masks.append(r["masks"][0: self.min_dim, 0: self.min_dim, :]) # make all masks the same size + self.masks.append(r["masks"][0: self.min_dim, 0: self.min_dim, :]) # make all masks the same size self.rois.append(r["rois"]) self.class_ids.append(r['class_ids']) self.scores.append(r['scores']) - + def initialize_linking(self): - self.emerging_info = [[] for i in range(0, self.total_time)] - self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time-1)] - self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i+1])) for i in range(0, self.total_time-1)] + self.emerging_info = [[] for i in range(0, self.total_time)] + self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time - 1)] + self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i + 1])) for i in + range(0, self.total_time - 1)] self.available_leaves = [np.array(range(0, self.num_leaves[i])) for i in range(0, self.total_time)] - self.time_compare = [np.ones((self.num_leaves[i]), dtype=int)*i for i in range(0, self.total_time)] - + self.time_compare = [np.ones((self.num_leaves[i]), dtype=int) * i for i in range(0, self.total_time)] + def linking(self, start_time, mode): - masks1 = copy.deepcopy(self.masks[start_time]) - masks2 = copy.deepcopy(self.masks[start_time+1]) + masks1 = copy.deepcopy(self.masks[start_time]) + masks2 = copy.deepcopy(self.masks[start_time + 1]) leaves1 = copy.deepcopy(self.available_leaves[start_time]) - leaves2 = copy.deepcopy(self.available_leaves[start_time+1]) + leaves2 = copy.deepcopy(self.available_leaves[start_time + 1]) n1 = len(leaves1) n2 = len(leaves2) n = np.min((n1, n2)) N = np.max((n1, n2)) weight = dict() - weight['forward'] = -np.inf*np.ones((n1, n2)) -# weight['backward'] = -np.inf*np.zeros((n2, n1)) + weight['forward'] = -np.inf * np.ones((n1, n2)) + # weight['backward'] = -np.inf*np.zeros((n2, n1)) link = dict() link['forward'] = -np.ones((n1)) -# link['backward'] = -np.ones((n2)) + # link['backward'] = -np.ones((n2)) for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward']): -# for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward', 'backward']): + # for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward', 'backward']): for (idx, main_leaf) in enumerate(leaves): - mask = masks[:,:,main_leaf] - link[key][idx], weight[key][idx,:] = _getmatchedIndex(mask, masks_all, mode) + mask = masks[:, :, main_leaf] + link[key][idx], weight[key][idx, :] = _getmatchedIndex(mask, masks_all, mode) # check for duplicates (and hence non-unique links) val, cou = np.unique(link[key], return_counts=True) - idx_temp = np.where(cou>1)[0] + idx_temp = np.where(cou > 1)[0] - if len(idx_temp) > 0: # matching to same leaf exists + if len(idx_temp) > 0: # matching to same leaf exists idx_vs = val[idx_temp] for idx_v in idx_vs: - idxs = np.where(link[key] == idx_v)[0] - weights_comp = weight[key][:,int(idx_v)] + idxs = np.where(link[key] == idx_v)[0] + weights_comp = weight[key][:, int(idx_v)] - idx_keep = np.argmax(weights_comp) - idx_drop = np.delete(idxs, np.where(idxs==idx_keep)) + idx_keep = np.argmax(weights_comp) + idx_drop = np.delete(idxs, np.where(idxs == idx_keep)) weight_temp = copy.deepcopy(weight[key][idx_drop]) weight_temp[0, int(idx_v)] = 0 if np.max(weight_temp) > 0.2: link[key][idx_drop] = np.argmax(weight_temp) else: link[key][idx_drop] = -1 - + self.link_info[start_time] = link['forward'] - + def get_series(self): ## define new leaves and their unique identifiers at time points with new leaves emerging t = 0 @@ -363,40 +387,40 @@ def get_series(self): # self.link_series[key_t]['unique_id'] = self.link_series[key_t]['new_leaf'] unique_id = len(self.link_series[key_t]['new_leaf']) - + for t in range(1, self.total_time): - new_leaves = [i for i in self.available_leaves[t] if i not in self.link_info[t-1]] + # for t in range(1, self.total_time-1): + new_leaves = [i for i in self.available_leaves[t] if i not in self.link_info[t - 1]] if new_leaves: key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = np.array(new_leaves) -# if len(new_leaves) > 1: -# id_temp = [] -# for new_leav in new_leaves: -# id_temp.append(unique_id) -# unique_id = unique_id + 1 -# self.link_series[key_t]['unique_id' = np.array(id_temp) -# else: -# self.link_series[key_t]['unique_id'] = np.array(unique_id) -# unique_id = unique_id + 1 + # if len(new_leaves) > 1: + # id_temp = [] + # for new_leav in new_leaves: + # id_temp.append(unique_id) + # unique_id = unique_id + 1 + # self.link_series[key_t]['unique_id' = np.array(id_temp) + # else: + # self.link_series[key_t]['unique_id'] = np.array(unique_id) + # unique_id = unique_id + 1 id_temp = [] for new_leaf in new_leaves: id_temp.append(unique_id) unique_id = unique_id + 1 self.link_series[key_t]['unique_id'] = np.array(id_temp) - - ## for time points with new leaves emerging, get the linking information for every new leaf + ## for time points with new leaves emerging, get the linking information for every new leaf for key_t in self.link_series: - t0 = int(key_t.replace('t','')) + t0 = int(key_t.replace('t', '')) for leaf in self.link_series[key_t]['new_leaf']: key_leaf = 'leaf{}'.format(leaf) self.link_series[key_t][key_leaf] = -np.ones(self.total_time, dtype=int) self.link_series[key_t][key_leaf][t0] = leaf - self.link_series[key_t][key_leaf][t0+1] = self.link_info[t0][leaf] - for t_ in range(t0+2, self.total_time): - idx = self.link_series[key_t][key_leaf][t_-1] - if idx < 0: - break - else: - self.link_series[key_t][key_leaf][t_] = self.link_info[t_-1][idx] - + if t0 < self.total_time - 1: + self.link_series[key_t][key_leaf][t0 + 1] = self.link_info[t0][leaf] + for t_ in range(t0 + 2, self.total_time): + idx = self.link_series[key_t][key_leaf][t_ - 1] + if idx < 0: + break + else: + self.link_series[key_t][key_leaf][t_] = self.link_info[t_ - 1][idx] \ No newline at end of file From 92f89d51a4673de8d185e195a6dc0fb97a8f97f6 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 10:45:48 -0500 Subject: [PATCH 029/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 73 ++++++++++++---------- 1 file changed, 39 insertions(+), 34 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 05061bf90..98504138f 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -23,8 +23,8 @@ from plantcv.plantcv.time_series import link_utilities as funcs # import link_utilities as funcs - -def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link'): +def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], + mode='link', suffix='.jpg'): """ Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) Input: @@ -42,9 +42,9 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. 4. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization - In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. + In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. result name: {}_{}-{}-{}-{}_{}.png - Naming convention for file names: + Naming convention for file names: 1st digit: unique identifier of the leaf 2nd digit: time of first emergence of the leaf 3rd digut: leaf index when it first emerges @@ -62,14 +62,14 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi 3rd digut: leaf index when it first emerges Inside every folder, images of leaves with names same as original image names are contained. - 3) a folder called "visualization 3", which containes 3rd set of visualization - This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. + 3) a folder called "visualization 3", which containes 3rd set of visualization + This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. Naming format: {}-visual.png The original image name is inside the {}. """ if mode == 'link': # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir) + Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode, suffix) Plant.getpath(Plant.imagedir) Plant.Sorttime(time_cond) @@ -86,7 +86,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi Plant.getnumemergence() # plot the #leaves vs. time relationship - plt.plot(np.array(range(0,len(Plant.num_leaves))), Plant.num_leaves) + plt.plot(np.array(range(0, len(Plant.num_leaves))), Plant.num_leaves) if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') @@ -103,14 +103,14 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi Plant.initialize_linking() # link - for t in range(0, Plant.total_time-1): + for t in range(0, Plant.total_time - 1): Plant.linking(t, mode=link_logic) Plant.get_series() ####### visualization ####### # load original images if len(Plant.images) == 0: - Plant.load_images() #Plant.images + Plant.load_images() # Plant.images # visualization method 1: show only one leaf per image path_visual1 = os.path.join(Plant.visualdir, 'visualization1') @@ -129,70 +129,75 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi count = 0 if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): - colors = funcs._random_colors(20) + colors = funcs._random_colors(40) pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) else: colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] for key_t in Plant.link_series: - # + # ids = Plant.link_series[key_t]['unique_id'] start_time = int(key_t.replace('t', '')) + # if start_time == 40: + # pdb.set_trace() leaves_t = Plant.link_series[key_t]['new_leaf'] for (unique_id, leaf) in zip(ids, leaves_t): - key_leaf = 'leaf{}'.format(leaf) + key_leaf = 'leaf{}'.format(leaf) link_leaf = Plant.link_series[key_t][key_leaf] start_idx = link_leaf[start_time] for t in range(start_time, Plant.total_time): img = Plant.images[t] if link_leaf[t] >= 0: color_all[t][link_leaf[t]] = colors[count] + mask_t = Plant.masks[t][:, :, link_leaf[t]] - mask_t = Plant.masks[t][:,:,link_leaf[t]] - - ## 1. save the masked image, i.e. single leaves - mask = np.zeros(mask_t.shape, dtype=np.uint8) + ## 1. save the masked image, i.e. single leaves + mask = np.zeros(mask_t.shape, dtype=np.uint8) mask[np.where(mask_t)] = 255 leaf_t = pcv.apply_mask(img, mask, mask_color='black') - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) -# pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) + pcv.print_image(leaf_t, os.path.join(path_visual1, + '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, + start_idx, t, link_leaf[t], + Plant.filename_pre[t]))) + # pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) - - ## 2. show result with an alpha channel + ## 2. show result with an alpha channel # update the mask where there is an alpha channel (alpha=0.5) - mask_ = np.ones(mask_t.shape)*0.5 + mask_ = np.ones(mask_t.shape) * 0.5 mask_[np.where(mask_t == True)] = 1 - masked_im = np.concatenate((img.astype(float)/255, np.expand_dims(mask_, axis=2)), axis=2) + masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_, axis=2)), axis=2) save_dir_ = os.path.join(path_visual2, '{}_{}-{}'.format(unique_id, start_time, start_idx)) if not os.path.exists(save_dir_): os.makedirs(save_dir_) - fig2 = plt.figure(figsize=(5,5)) - ax2 = fig2.add_subplot(1,1,1) - ax2.imshow(masked_im) + fig2 = plt.figure(figsize=(5, 5)) + ax2 = fig2.add_subplot(1, 1, 1) + ax2.imshow(masked_im) ax2.axis('off') plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + '.png')) plt.close(fig2) - count += 1 + count += 1 ## 3. visualize with bounding boxes - for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.filename_pre): - funcs.display_instances(img, roi, mask, class_id, - class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, - colors = color) + for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, + Plant.scores, color_all, Plant.filename_pre): + funcs.display_instances(img, roi, mask, class_id, + class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, + show_mask=True, + colors=color) plt.savefig(os.path.join(path_visual3, '{}-visual.png'.format(t))) plt.close('all') # save all information pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) - + else: # initialize Plant class Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode='load') # Load from existed PlantData instance - Plant = pkl.load( open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'rb')) - + Plant = pkl.load(open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'rb')) + return Plant \ No newline at end of file From 008e4dc5fe6b5682ee2fbc83cc197deb9c343d9c Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 10:46:10 -0500 Subject: [PATCH 030/274] Update time_series.md Updated with embeded videos included. --- docs/time_series.md | 116 +++++++++++++++++++++++++++++--------------- 1 file changed, 78 insertions(+), 38 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index 47dae9621..7e12f4454 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -1,17 +1,16 @@ ## time_series -This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, recording the growth of the plant, where ideally there should be no or at least minimum movement of plant or camera). +This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, e.g. images taken every several hours, recording the growth of the plant, where ideally there should be no or at least minimum movement for plant or camera). -To run this function, the instance segmentation for every image should be achieved beforehand. For a demo of instance segmentation, check here: +To run this function, the instance segmentation for every image should be achieved beforehand. For more information and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). -When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. (Another example is k-means) +When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. -To understand the growth of every leaf instance of a plant, we need pull out the same leaf across the whole time period. +To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always have the same instance label across the whole time period. +**plantcv.time_series.time_series_linking**(*imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'*) -**plantcv.time_series.time_series_linking**(*imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']*) - -**returns** no returned values, all the results are saved in the user specified directory +**returns** An instance that is a Plant object - **Parameters:** - imagedir: directory of original image used @@ -21,39 +20,56 @@ To understand the growth of every leaf instance of a plant, we need pull out the - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 - class_names: used in bounding box visualization. by default there are background and leaf, i.e. class_names = ['BG', 'leaf'] - mode: can be either 'link' or 'load', use 'link' when trying to get linked time-series from instance segmentation; use 'load' when trying to load from saved linking + - suffix: the suffix of original images, make sure all images having the same suffix, e.g. suffix='.jpg' or suffix='-img8.jpg'. Make sure all the images desired having the same suffix pattern. - **Output:** An instance of Plant object is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir". - Note: under the user specified saving directory, a new folder whose name is date-time when the function runs (format: YYYY-MM-DD-HH-mm) will be created to save results. +Note: under the user specified saving directory, a new folder named after the date and time (format: YYYY-MM-DD-HH-mm) the function runs will be created to save results. + +1. colors.pkl: the colors (represented by arrays) used in bounding box visualization. With this predefined color set, same color will represent for the leaf instance with same instance label all the time, i.e. same color represents leaf with the same label. Without this predefined list of color, the assignment of color will be random, i.e. the color provides no information to leaf indices. +2. details.txt: the logic of linking as well as time condition will be saved in this text file, so that would be easier for users to check these parameters for the specific expreiment. +3. saved_plant.pkl: a "Plant" object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. +4. a folder called "visualization", which contains 3 subfolders: + + 1) a folder call "visualization 1", which contains 1st set of visualization + In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. + Naming convention for the saving names of the result: + + {}_{}-{}-{}-{}_{}.png + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + 4rd digit: current time point + 5th digit: current leaf index + 6th digit: original image name + This set of visualization is designed for shape analysis by easily applying plantcv workflow. + + 2) a folder called "visualization 2", which contains 2nd set of visualization + This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent + There are several subfolders, the number of subfolders depends on the number of "new leaves" in total + Every subfolder is a "new leaf". Naming convention for folder names: - 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time. - 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment. - 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. - 4. a folder called "visualization", which contains 3 subfolders: - 1) a folder call "visualization 1", which contains 1st set of visualization - In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. - result name: {}_{}-{}-{}-{}_{}.png - Naming convention for file names: - 1st digit: unique identifier of the leaf - 2nd digit: time of first emergence of the leaf - 3rd digut: leaf index when it first emerges - 4rd digit: current time point - 5th digit: current leaf index - 6th digit: original image name - - 2) a folder called "visualization 2", which contains 2nd set of visualization - This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent - There are several subfolders, the number of subfolders depends on the number of "new leaves" in total - Every subfolder is a "new leaf", whose name is {}_{}-{}. Naming convention for folder names: - 1st digit: unique identifier of the leaf - 2nd digit: time of first emergence of the leaf - 3rd digut: leaf index when it first emerges - Inside every folder, images of leaves with names same as original image names are contained. - - 3) a folder called "visualization 3", which containes 3rd set of visualization - This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. - Naming format: {}-visual.png - The original image name is inside the {}. + Folder name: {}_{}-{} + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + Inside every folder, images of leaves with names same as original image names are contained. + This set of visualization is designed for making videos to track the growth of every single leaf. Images inside a folder are supposed to represent for the save leaf. So simply using all images inside one folder and sort them by name in an ascending manner would create a video showing how this leaf grows. + + 3) a folder called "visualization 3", which containes 3rd set of visualization + This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. + Naming convention: + + {}-visual.png + The original image name is inside the {}. + This set of visualization is designed for making time-lapse videos of segmentation shown with bounding boxes. With the instance labels re-assigned such that one label represents one leaf across the whole time, we would observe that every leaf is represented by a specific color across the whold time period. + +Before running the time_series_linking function, it is always a good practice to check the quality of instance segmentation before run time_series_linking. One way to check the instance segmentation is to make a time-lapse video with your instance segmentation result shown in bounding boxes. To make time-lapse videos, you will need video editing softwares, e.g. imovie. An example is shown below. Notice that in almost all cases, you would observe that the colors for the same leaf change during the time. That is due to the random assignment of labels. You can later compare this time lapse video to that generate after all labels re-assigned by running time_series_linking function. + +Instead of making a video, another way to check the quality of instance segmentation is by checking the segmented image one by one. + + + ```python from plantcv import plantcv as pcv @@ -63,12 +79,36 @@ path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtC path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/detection/modified' path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/test' time_cond = ['08-05', '11-05', '17-05', '21-05'] -​linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link') +link_logic = 1 +class_names = ['BG', 'Leaf'] +mode = 'link' +suffix = '.jpg' +​linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=link_logic, class_names=class_names, mode=mode, suffix=suffix) -# Print the saving directory on the screen so that you can go check the result: +# Print the saving directory so that you can go check the result: linked_Plant.savedir +# Print visualization directory: +linked_Plant.visualdir + # Print linking information on the screen for a quick check: linked_Plant.link_series ``` + +1. Analysing shapes: +Create a workflow and use the PlantCV analyze_object function to traverse all images inside the 1st visualization folder. + +2. Making time-lapse videos to visualize the growth of every leaf: +To make videos, you will need video editing softwares, e.g. imovie.\ +Direct yourself to the visualization folder, and go into 'visualization2'. Every folder represents for a leaf. Include all images for one leaf in a video project, sort them by name in an ascending order, and generate a time-lapse video. +Here is an example of time-lapse of one leaf: + + +

growth of plant focusing on single leaf from Hudanyun Sheng on Vimeo.

+ +3. Making a time-lapse video to visualize the growth of the plant by showing the segmentation in bounding boxes, with the same leaf always shown with the same color. Direct yourself to "visualization 3" folder and include all images to a video project, sort them by name in an ascending order, and generate a time-lapse video. +An example of time-lapse video with visualization shown in bounding boxes is shown below. When compare to the video made of instance segmentation, result, you will observe that now every leaf is represented by the same color all the time, which means the label re-assignemnt is successful. + + + **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) From bd7e3866a20417e24dfddf729439b6d9642990e0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 11:11:10 -0500 Subject: [PATCH 031/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index d80f24b31..5d1cf3099 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -290,21 +290,6 @@ def load_images(self): """ Load original images Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM """ - # filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] - # temp_imgs = [] - # sz = [] - # for t in self.time: - # for f in filenames: - # temp = re.search(t, f) - # if temp: - # junk = skimage.io.imread(os.path.join(self.imagedir, f)) - # temp_imgs.append(junk) - # sz.append(np.min(junk.shape[0:2])) - # filenames.remove(f) - # self.min_dim = np.min(sz) - # for junk in temp_imgs: - # img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size - # self.images.append(img) temp_imgs = [] sz = [] for pre in self.filename_pre: From 16b0719d1c16511bcc0124f67dd05878b142c35d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 18:05:28 -0500 Subject: [PATCH 032/274] Update time_series.py Update by adding a pattern_datetime input --- plantcv/plantcv/time_series/time_series.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 98504138f..2ebfc2ac2 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -23,7 +23,7 @@ from plantcv.plantcv.time_series import link_utilities as funcs # import link_utilities as funcs -def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], +def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'): """ Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) @@ -69,7 +69,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi """ if mode == 'link': # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode, suffix) + Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, mode, suffix) Plant.getpath(Plant.imagedir) Plant.Sorttime(time_cond) @@ -140,8 +140,6 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi ids = Plant.link_series[key_t]['unique_id'] start_time = int(key_t.replace('t', '')) - # if start_time == 40: - # pdb.set_trace() leaves_t = Plant.link_series[key_t]['new_leaf'] for (unique_id, leaf) in zip(ids, leaves_t): key_leaf = 'leaf{}'.format(leaf) @@ -161,7 +159,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) - # pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) + # pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) ## 2. show result with an alpha channel # update the mask where there is an alpha channel (alpha=0.5) From 722d5aa2187d916d1ed6f55147ea99f14282731f Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 18:05:43 -0500 Subject: [PATCH 033/274] Update link_utilities.py Update by adding a pattern_datetime part --- plantcv/plantcv/time_series/link_utilities.py | 20 +++++++------------ 1 file changed, 7 insertions(+), 13 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 5d1cf3099..73ea8a129 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -186,10 +186,12 @@ def _getmatchedIndex(mask, masks, mode): class PlantData(): - def __init__(self, imagedir, segmentationdir, savedir, mode='link', suffix='.jpg'): + def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, mode='link', suffix='.jpg'): self.suffix = suffix + self.pattern_datetime = pattern_datetime + # store a list of time of the images self.time = [] # store a list of original prefixes of the original images @@ -262,7 +264,7 @@ def getpath(self, path): def Sorttime(self, time_cond): """ - This function is designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM + This function is designed for files with file names which contain a "date-time" part, with an user-defined pattern, e.g. YYYY-MM-DD-hh-mm Return: loop through the dataset_dir, and add time in time order """ @@ -271,7 +273,7 @@ def Sorttime(self, time_cond): time_temp = [] file_name = [] for filename in filenames: - temp = re.search('\d{4}-\d{2}-\d{2}-\d{2}-\d{2}', filename) + temp = re.search(self.pattern_datetime, filename) if temp: timepart = temp.group() for cond in time_cond: @@ -288,8 +290,9 @@ def Sorttime(self, time_cond): def load_images(self): """ Load original images - Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM + This function is also designed for files with file names which contain a "date-time" part, with a user defined pattern """ + temp_imgs = [] sz = [] for pre in self.filename_pre: @@ -380,15 +383,6 @@ def get_series(self): key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = np.array(new_leaves) - # if len(new_leaves) > 1: - # id_temp = [] - # for new_leav in new_leaves: - # id_temp.append(unique_id) - # unique_id = unique_id + 1 - # self.link_series[key_t]['unique_id' = np.array(id_temp) - # else: - # self.link_series[key_t]['unique_id'] = np.array(unique_id) - # unique_id = unique_id + 1 id_temp = [] for new_leaf in new_leaves: id_temp.append(unique_id) From d1588f44622bf16610e8b3f14d18735679ee612b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 2 Jul 2020 10:40:35 -0500 Subject: [PATCH 034/274] Update tests.py --- tests/tests.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 8f3ada35b..0d26d2f9f 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -5542,8 +5542,13 @@ def test_plantcv_time_series_time_series(): cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir - time_cond = ['08-05', '11-05', '17-05', '21-05'] - Plant_ = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link') + time_cond = ['08-05', '11-05', '17-05', '21-05'] + link_logic = 1 + class_names = ['BG', 'Leaf'] + pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm + mode = 'link' + suffix = '.jpg' + Plant_ = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, pattern_datetime, time_cond, link_logic, class_names, mode, suffix) assert len(os.listdir(Plant_.savedir)) > 0 # ############################## From e4597de0a6bec29834c531711e023d12eff6d32f Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 2 Jul 2020 10:40:40 -0500 Subject: [PATCH 035/274] Update mkdocs.yml --- mkdocs.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mkdocs.yml b/mkdocs.yml index 6e7fc691d..f31df5a7d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -32,6 +32,7 @@ nav: - 'Morphology Workflow': morphology_tutorial.md - 'Thermal Workflow': thermal_tutorial.md - 'Hyperspectral Workflow': hyperspectral_tutorial.md + - 'Instance Segmentation': instance_segmentation_tutorial.md - 'PlantCV Namespace': - 'PlantCV': - 'Analyze color': analyze_color.md @@ -144,6 +145,7 @@ nav: - 'Saturation Threshold': saturation_threshold.md - 'Triangle Auto Threshold': triangle_threshold.md - 'Texture Threshold': texture_threshold.md + - 'Time Series': time_series.md - 'Transformation Methods': - 'Correct Color': transform_correct_color.md - 'Nonuniform 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zS>xNSavxdNLul62m!-a4-7;8$4~<&(j5Dl|bC17_A^OmB6}}8i{`eD$%&VL{cqJ z`AV$$D$mH3Z&~pEz1>UqVu@SjNJdsLz((j8r=L_%2{Zt=yx3JcV{B8?q;i7@h%2Q> zjpA1>8b=?4L^3|B_|*z!6ua^%9978bLffY-FC2*@}FWM1TxIw*>vF;$$*C~F`>p>ndElwVp8%IlN? z(UcE<+A_se0x~M)3PJjbH+>93zl0{d_#S0o^3k;lYX<1JN8Y`Xt7C(pRb_>DWqrmj z2q~Q~fOil+6g{i$qTpX%Yn&xlJeZ)nD6}$;Ou*aa5Q8A0KWPZ9lxuz>m9|0WKsI+z zTNgDw-0m%M%iQ*p|WdN0S z7=W}`gO2A)L<$Q;0=*?Su5*ecf01eo+E#)VFRfpfHQg`epn}VwzhJ7zq*V(dBYs>4IF^&vFWhTD+T(}1J%SoA7?SJJOJYFGq( zaKOGJTq&r&%23~x>)a&Z`S=lMxc=+$6v9m ttMKxuVPAv({~m%1&{^7 Date: Mon, 13 Jul 2020 10:27:13 -0500 Subject: [PATCH 037/274] Create time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 138 ++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 plantcv/plantcv/visualize/time_lapse_video.py diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py new file mode 100644 index 000000000..19cc71aa0 --- /dev/null +++ b/plantcv/plantcv/visualize/time_lapse_video.py @@ -0,0 +1,138 @@ +# Color mask(s) in any color + +import os +import cv2 +import numpy as np +import copy +from plantcv.plantcv import params +from plantcv.plantcv import fatal_error +import warnings + + +def _resize_img(img, size): + """Resize the image to the given new size + If the given size is larger than the original image size, zero-pad the image. + If the given size is smaller than the origina image size, crop the image (right & bottom). + """ + + # original image size + r_ori, c_ori = np.shape(img)[0], np.shape(img)[1] + r, c = size[0], size[1] + + if len(img.shape) > 2: + b_ori = np.shape(img)[2] + junk = copy.deepcopy(img) + else: + b_ori = 1 + junk = np.expand_dims(img, axis=2) + + # deal with rows + img1 = copy.deepcopy(junk) + if r < r_ori: + img1 = junk[0:r, :, :] + elif r > r_ori: + img1 = np.zeros_like(junk, shape=[r, c_ori, b_ori]) + img1[0:r_ori, :, :] = junk + + # update + r_ori, c_ori = np.shape(img1)[0], np.shape(img1)[1] + img2 = copy.deepcopy(img1) + + # deal with columns + if c < c_ori: + img2 = img1[:, 0:c, :] + elif c > c_ori: + img2 = np.zeros_like(img1, shape=[r_ori, c, b_ori]) + img2[:, 0:c_ori, :] = img1 + + if len(img.shape) == 2: + output_img = np.squeeze(img2, axis=2) + else: + output_img = img2 + return output_img + + +def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=None, fps=29.97, + name_video='time_lapse_video', path_video=None, display='on'): + """Generate time-lapse video given a folder of images + Inputs: + img_directory: the directory of folder of images to make the time-lapse video + list_img: desired list of images in img_directory to create the video. If None is passed, all images would be included by default. + suffix_img: the suffix of input images. e.g. suffix='.jpg' or suffix='.png' or suffix='img10.jpg'. Make sure all images have the same suffix + note: If neigher list_img nor suffix_img is provided, all images in the directory will be included + name_video: the prefix of output video name + size_frame: the desired size of every frame. + In a video, every frame should have the same size. + The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. + If the frame size is given, if an images is larger than the given size, the image would be cropped automatically; if an image is smaller than the given size, the image would be zero-padded automatically + If the frame size is not given, the largest size of all images would be used as the frame size. + The assumption is that If some videos are smaller, + fps: (frames per second) frame rate + Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 + path_video: the desired saving path of output video. If not given, the video would be saved the the same directory of the images. + display: indicator of whether to display current status (by displaying saving directory and saving name) while running this function + No return value + """ + ## Get the list of image files and sort them alphabetically by their names + # make sure there is images inside the provided directory + list_img_1 = [img for img in os.listdir(img_directory) if img.endswith('.jpg')] + list_img_2 = [img for img in os.listdir(img_directory) if img.endswith('.png')] + if len(list_img_1) == 0 and len(list_img_2) == 0: + fatal_error("There is no file in the provided folder that is an image, please check the provided directory!") + elif len(list_img_1) > 0: + temp_list = list_img_1 + else: + temp_list = list_img_2 + + # if the list of images is provided, stick to it + if list_img is not None: + if not set(list_img) <= set(temp_list): + # print( + # "Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") + warnings.warn("Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") + remove_list = [f for f in list_img if f not in (temp_list)] + for f in remove_list: + list_img.remove(f) + # if a wrong list is proveded + if len(list_img) == 0: + fatal_error("The provided list of files is not contained in the given directory") + + # if the list of images is not provided, check if suffix information is available + elif suffix_img is not None: + list_img = [f for f in temp_list if f.endswith(suffix_img)] + if len(list_img) == 0: + fatal_error("There is no file that matches the provided suffix, please check again!") + # both are not provided + else: + list_img = temp_list + list_img.sort() + + imgs = [] + max_r = 0 + max_c = 0 + for file in list_img: + img = cv2.imread(os.path.join(img_directory, file)) + max_r = max(max_r, img.shape[0]) + max_c = max(max_c, img.shape[1]) + imgs.append(img) + + # If the frame size is not provided, use the largest size of the images as the frame size + if size_frame is None: + size_frame = (max_r, max_c) + + # If the video saving directory is not provided, save it in the same directory of the images + if path_video is None: + path_video = img_directory + + # Full video save name + save_name = os.path.join(path_video, name_video + '.mp4') + + fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case + out = cv2.VideoWriter(save_name, fourcc, fps, size_frame) + for img in imgs: + out.write(_resize_img(img, size_frame)) + + out.release() + cv2.destroyAllWindows() + if display is 'on': + print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) From 925f75c2739a7b028ada3910748710a7074d3834 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 13 Jul 2020 10:28:05 -0500 Subject: [PATCH 038/274] Update tests.py for the newly added time_lapse_video function into visualize subpackage --- tests/tests.py | 67 +++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 66 insertions(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index 0d26d2f9f..90304960b 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -556,7 +556,7 @@ def test_plantcv_parallel_process_results_invalid_json(): # #################################################################################################################### # ########################################### PLANTCV MAIN PACKAGE ################################################### -matplotlib.use('Template', warn=False) +# matplotlib.use('Template', warn=False) TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data") HYPERSPECTRAL_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "hyperspectral_data") @@ -5537,6 +5537,71 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) +# case1: only the correct directory of images is provided +def test_plantcv_visualize_time_lapse_video_case1(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case1') + os.mkdir(cache_dir) + + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case1.mp4')) + +# case2: the correct directory of images as well as the correct suffix of images is provided +def test_plantcv_visualize_time_lapse_video_case2(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case2') + os.mkdir(cache_dir) + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.jpg', fps=29.97, name_video='time_lapse_video_case2', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case2.mp4')) + +# case 3: the correct directory of images as well as the correct list of files are provided +def test_plantcv_visualize_time_lapse_video_case3(): + # Test cache directoy + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case3') + os.mkdir(cache_dir) + list_img = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case3', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case3.mp4')) + +# case 4: the correct directory of images as well as a list of files are provided, however the list is incorrect (contains correct part, but also contains incorrect part) +def test_plantcv_visualize_time_lapse_video_case4(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case4') + os.mkdir(cache_dir) + list_img = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] + list_img.append('junk.jpg') + with pytest.warns(UserWarning): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case4', path_video=cache_dir, display='off') + # warnings.warn("my warning", UserWarning) + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case4.mp4')) + +def test_plantcv_visualize_time_lapse_video_bad_dir(): + # Test cachae directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_dir') + os.mkdir(cache_dir) + with pytest.raises(RuntimeError): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_DIR, fps=29.97, path_video=cache_dir, display='off') + +def test_plantcv_visualize_time_lapse_video_bad_suffix(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_suffix') + os.mkdir(cache_dir) + with pytest.raises(RuntimeError): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.png', fps=29.97, path_video=None, display='on') + +# the correct directory of images as well as a list of files are provided, however the list is incorrect (none of the files contained is correct) +def test_plantcv_visualize_time_lapse_video_bad_list(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_list') + os.mkdir(cache_dir) + list_img_ = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] + list_img = [img.replace('.jpg', '_.jpg') for img in list_img_] + with pytest.raises(RuntimeError): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, path_video=cache_dir, display='off') + +# ############################## +# Tests for the time_series subpackage +# ############################## def test_plantcv_time_series_time_series(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") From 1757dafc97fdb225142af1b164200118a8edaffe Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 13 Jul 2020 10:28:25 -0500 Subject: [PATCH 039/274] Update __init__.py for the newly added time_lapse_video into visualize subpackage --- plantcv/plantcv/visualize/__init__.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/plantcv/plantcv/visualize/__init__.py b/plantcv/plantcv/visualize/__init__.py index 3674caeaf..05e4b0fda 100644 --- a/plantcv/plantcv/visualize/__init__.py +++ b/plantcv/plantcv/visualize/__init__.py @@ -4,5 +4,7 @@ from plantcv.plantcv.visualize.clustered_contours import clustered_contours from plantcv.plantcv.visualize.colorspaces import colorspaces from plantcv.plantcv.visualize.auto_threshold_methods import auto_threshold_methods +from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video +from plantcv.plantcv.visualize.time_lapse_video import _resize_img -__all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods"] +__all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] From 9c87ec638894bf89f203fe1dd56e736347420711 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 13 Jul 2020 10:28:33 -0500 Subject: [PATCH 040/274] Update mkdocs.yml for the newly added time_lapse_video into visualize subpackage --- mkdocs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/mkdocs.yml b/mkdocs.yml index f31df5a7d..9b8914ad0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -157,6 +157,7 @@ nav: - 'Colorspaces': visualize_colorspace.md - 'Histogram': visualize_histogram.md - 'Pseudocolor': visualize_pseudocolor.md + - 'Time Lapse Video': visualize_time_lapse_video.md - 'Watershed Segmentation': watershed.md - 'White balance': white_balance.md - 'Within Frame': within_frame.md From 1a93d051ff50249a87df8d12c06f2c2a48b1d720 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 13 Jul 2020 10:28:43 -0500 Subject: [PATCH 041/274] Create visualize_time_lapse_video.md --- docs/visualize_time_lapse_video.md | 75 ++++++++++++++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 docs/visualize_time_lapse_video.md diff --git a/docs/visualize_time_lapse_video.md b/docs/visualize_time_lapse_video.md new file mode 100644 index 000000000..afacbfc74 --- /dev/null +++ b/docs/visualize_time_lapse_video.md @@ -0,0 +1,75 @@ +## Automatically Generate Time-Lapse Videos + +This function automatically generates and saves time-lapse videos given the folder of images. + +**plantcv.visualize.time_lapse_video**(*path_img, list_img, suffix_img, size_frame=None, fps=29.97, name_video='time_lapse_video', + path_video=path_img, display='on'*) + +**returns** There is no returned value for this function. The generated video would be saved in the user specified directory. + +- **Parameters:** + - img_directory - Directory of images desired to be made into a video + - list_img - desired list of images in img_directory to create the video. If None is passed, all images would be included by default. + - suffix_img - The suffix of all input images. e.g. suffix='.jpg' or suffix='.png' or suffix='img10.jpg'. Make sure all images have the same suffix + + Note: If neigher list_img nor suffix_img is provided, all images in the directory will be included + + - size_frame - The desired size of every frame. + + Note: In a video, every frame should have the same size. + The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. + With a given frame size, if an images is larger than that, the image would be cropped automatically; if an image is smaller than that, the image would be zero-padded automatically + If the frame size is not given, the largest size of all images would be used as the frame size. + + - fps: (frames per second) frame rate. By default fps=29.97. + Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 + - name_video - The desired name of output video name. By default the name would be 'time_lapse_video' + - path_video - The desired saving path of the video file. By default, the video will be saved at the same directory of the images. + - display - Indicator of whether to display current status (by displaying saving directory and saving name) while running this function. By default the display is turned on. + +- **Context:** + - Used to generate time-lapse video given a bunch of images. + +- **Example Use:** + - Below + - [Use in Time Series Documentation](time_series.md) + +**Folder of images** + +As an example, you can used the sample saved inside PlantCV to test. You will need to know the directory of your PlantCV package. +You can find the test data here: "./plantcv/tests/seires_data/raw_im". We are to use all images inside the folder "raw_im" to generate a time-lapse video. +```python +from plantcv import plantcv as pcv +# Note you will have to change this part on your own +img_directory = './plantcv/tests/seires_data/raw_im' + +list_img = [img for img in os.listdir(dir_img) if img.endswith('.jpg')] + +# In this specific case, every image has a suffix of ".jpg" +suffix_img = '.jpg' + +# In this case I know the sizes for all of the images is from (499, 499) to (501, 501), and here I decide I want the frame size to be (500, 500). +size_frame = (500, 500) + +fps = 29.97 + +name_video = 'eg_time_lapse' + +# In this specific case the video will be saved in the save directory of the images +path_video = path_img + +display = 'on' + +pcv.visualize.time_lapse_video(img_directory=img_directory, list_img=list_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) +pcv.visualize.time_lapse_video(img_directory=img_directory, suffix_img=suffix_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) + + +``` + +**Video generated** + +The generated video is saved automatically in the user-specified directory (path_video). The generated video should look similar to the one below: + + + +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/visualize/time_lapse_video.py) From a0facf34875a9c245c6d0732cef38c45ad6175b7 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 17 Jul 2020 12:48:31 -0500 Subject: [PATCH 042/274] Update time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 155 ++++++++++-------- 1 file changed, 89 insertions(+), 66 deletions(-) diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py index 19cc71aa0..76239733c 100644 --- a/plantcv/plantcv/visualize/time_lapse_video.py +++ b/plantcv/plantcv/visualize/time_lapse_video.py @@ -1,9 +1,16 @@ -# Color mask(s) in any color +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon. July 06 08:00:34 2020 +Functions used to generate a time-lapse-video with provided directory of images +@author: hudanyunsheng +""" import os import cv2 import numpy as np import copy +import mimetypes from plantcv.plantcv import params from plantcv.plantcv import fatal_error import warnings @@ -58,81 +65,97 @@ def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=N Inputs: img_directory: the directory of folder of images to make the time-lapse video list_img: desired list of images in img_directory to create the video. If None is passed, all images would be included by default. - suffix_img: the suffix of input images. e.g. suffix='.jpg' or suffix='.png' or suffix='img10.jpg'. Make sure all images have the same suffix - note: If neigher list_img nor suffix_img is provided, all images in the directory will be included name_video: the prefix of output video name size_frame: the desired size of every frame. In a video, every frame should have the same size. The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. If the frame size is given, if an images is larger than the given size, the image would be cropped automatically; if an image is smaller than the given size, the image would be zero-padded automatically If the frame size is not given, the largest size of all images would be used as the frame size. - The assumption is that If some videos are smaller, fps: (frames per second) frame rate Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 path_video: the desired saving path of output video. If not given, the video would be saved the the same directory of the images. display: indicator of whether to display current status (by displaying saving directory and saving name) while running this function - No return value + Outputs: + list_img: the list of images used to generate the video + size_frame: the frame size of the video """ - ## Get the list of image files and sort them alphabetically by their names - # make sure there is images inside the provided directory - list_img_1 = [img for img in os.listdir(img_directory) if img.endswith('.jpg')] - list_img_2 = [img for img in os.listdir(img_directory) if img.endswith('.png')] - if len(list_img_1) == 0 and len(list_img_2) == 0: + ## Get the list of image files in the given directory and sort them alphabetically by their names + temp_list = [] + for f in os.listdir(img_directory): + type_f = mimetypes.guess_type(f)[0] + if type_f: + if type_f.startswith('image'): + temp_list.append(f) + if len(temp_list) <= 0: fatal_error("There is no file in the provided folder that is an image, please check the provided directory!") - elif len(list_img_1) > 0: - temp_list = list_img_1 else: - temp_list = list_img_2 - - # if the list of images is provided, stick to it - if list_img is not None: - if not set(list_img) <= set(temp_list): - # print( - # "Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") - warnings.warn("Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") - remove_list = [f for f in list_img if f not in (temp_list)] - for f in remove_list: - list_img.remove(f) - # if a wrong list is proveded - if len(list_img) == 0: - fatal_error("The provided list of files is not contained in the given directory") - - # if the list of images is not provided, check if suffix information is available - elif suffix_img is not None: - list_img = [f for f in temp_list if f.endswith(suffix_img)] - if len(list_img) == 0: - fatal_error("There is no file that matches the provided suffix, please check again!") - # both are not provided - else: - list_img = temp_list - list_img.sort() - - imgs = [] - max_r = 0 - max_c = 0 - for file in list_img: - img = cv2.imread(os.path.join(img_directory, file)) - max_r = max(max_r, img.shape[0]) - max_c = max(max_c, img.shape[1]) - imgs.append(img) - - # If the frame size is not provided, use the largest size of the images as the frame size - if size_frame is None: - size_frame = (max_r, max_c) - - # If the video saving directory is not provided, save it in the same directory of the images - if path_video is None: - path_video = img_directory - - # Full video save name - save_name = os.path.join(path_video, name_video + '.mp4') - - fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case - out = cv2.VideoWriter(save_name, fourcc, fps, size_frame) - for img in imgs: - out.write(_resize_img(img, size_frame)) - - out.release() - cv2.destroyAllWindows() - if display is 'on': - print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) + temp_list.sort() + # get an "extension free" list of images + temp_list_no_ext = [os.path.splitext(f)[0] if os.path.splitext(f)[1] else f for f in temp_list] + + ## if the list of images is provided, stick to it, but automatically handle the extension issue + if list_img is not None: + list_img.sort() + # make the list of images extension free + list_img_no_ext = [os.path.splitext(f)[0] if os.path.splitext(f)[1] else f for f in list_img] + list_img = [] + for (f_, f) in zip(temp_list_no_ext, temp_list): + try: + list_img_no_ext.index(f_) + list_img.append(f) + except: + continue + + if len(list_img) == 0: + fatal_error("The provided list of files is not contained in the given directory") + elif len(list_img) < len(list_img_no_ext): + warnings.warn( + "Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") + + # if the list of images is not provided, check if suffix information is available + elif suffix_img is not None: + list_img = [f for f in temp_list if f.endswith(suffix_img)] + if len(list_img) == 0: + fatal_error("There is no file that matches the provided suffix in the provided directory, please check again!") + + # neither the list of image nor the suffix is provided + else: + list_img = temp_list + + imgs = [] + list_r = [] + list_c = [] + for file in list_img: + img = cv2.imread(os.path.join(img_directory, file)) + list_r.append(img.shape[0]) + list_c.append(img.shape[1]) + imgs.append(img) + max_c = np.max(list_c) + max_r = np.max(list_r) + + # If the frame size is not provided, use the largest size of the images as the frame size + if size_frame is None: + size_frame = (max_c, max_r) + + if not (len(np.unique(list_r)) == 1 and len(np.unique(list_c)) == 1): + warnings.warn("The sizes of images are not the same, an image resizing (cropping or zero-padding) will be done to make all images the same size ({}x{}) before creating the video! If you assume the images should have the same size, please check the images used to generate this video!".format( + size_frame[0], size_frame[1])) + + # If the video saving directory is not provided, save it in the same directory of the images + if path_video is None: + path_video = img_directory + + # Full video save name + save_name = os.path.join(path_video, name_video + '.mp4') + + fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case + out = cv2.VideoWriter(save_name, fourcc, fps, size_frame) + for img in imgs: + out.write(_resize_img(img, (max_r, max_c))) + + out.release() + cv2.destroyAllWindows() + if display is 'on': + print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) + + return list_img, size_frame \ No newline at end of file From 597171d3da7a87efd7e8eeedf4f6066586c671e2 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 17 Jul 2020 12:49:03 -0500 Subject: [PATCH 043/274] Update tests.py Update tests for visualize/time_lapse_video --- tests/tests.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 90304960b..6b787e5c4 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -556,7 +556,7 @@ def test_plantcv_parallel_process_results_invalid_json(): # #################################################################################################################### # ########################################### PLANTCV MAIN PACKAGE ################################################### -# matplotlib.use('Template', warn=False) +matplotlib.use('Template', warn=False) TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data") HYPERSPECTRAL_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "hyperspectral_data") @@ -5542,17 +5542,18 @@ def test_plantcv_visualize_time_lapse_video_case1(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case1') os.mkdir(cache_dir) - - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case1.mp4')) + with pytest.warns(UserWarning): + _, _ = pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case1.mp4')) # case2: the correct directory of images as well as the correct suffix of images is provided def test_plantcv_visualize_time_lapse_video_case2(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case2') os.mkdir(cache_dir) - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.jpg', fps=29.97, name_video='time_lapse_video_case2', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case2.mp4')) + with pytest.warns(UserWarning): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.jpg', fps=29.97, name_video='time_lapse_video_case2', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case2.mp4')) # case 3: the correct directory of images as well as the correct list of files are provided def test_plantcv_visualize_time_lapse_video_case3(): @@ -5560,8 +5561,9 @@ def test_plantcv_visualize_time_lapse_video_case3(): cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case3') os.mkdir(cache_dir) list_img = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case3', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case3.mp4')) + with pytest.warns(UserWarning): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case3', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case3.mp4')) # case 4: the correct directory of images as well as a list of files are provided, however the list is incorrect (contains correct part, but also contains incorrect part) def test_plantcv_visualize_time_lapse_video_case4(): @@ -5572,7 +5574,6 @@ def test_plantcv_visualize_time_lapse_video_case4(): list_img.append('junk.jpg') with pytest.warns(UserWarning): pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case4', path_video=cache_dir, display='off') - # warnings.warn("my warning", UserWarning) assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case4.mp4')) def test_plantcv_visualize_time_lapse_video_bad_dir(): From 4b2f1c5276bede677820658af4b0a86302ed6757 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 17 Jul 2020 13:57:30 -0500 Subject: [PATCH 044/274] Update visualize_time_lapse_video.md --- docs/visualize_time_lapse_video.md | 38 ++++++++++++++++++++---------- 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/docs/visualize_time_lapse_video.md b/docs/visualize_time_lapse_video.md index afacbfc74..b2592aac4 100644 --- a/docs/visualize_time_lapse_video.md +++ b/docs/visualize_time_lapse_video.md @@ -5,27 +5,41 @@ This function automatically generates and saves time-lapse videos given the fold **plantcv.visualize.time_lapse_video**(*path_img, list_img, suffix_img, size_frame=None, fps=29.97, name_video='time_lapse_video', path_video=path_img, display='on'*) -**returns** There is no returned value for this function. The generated video would be saved in the user specified directory. +**returns** list of images, frame size + +(The generated video would be saved in the user specified directory.) - **Parameters:** - img_directory - Directory of images desired to be made into a video - list_img - desired list of images in img_directory to create the video. If None is passed, all images would be included by default. - suffix_img - The suffix of all input images. e.g. suffix='.jpg' or suffix='.png' or suffix='img10.jpg'. Make sure all images have the same suffix - Note: If neigher list_img nor suffix_img is provided, all images in the directory will be included + If neigher list_img nor suffix_img is provided, all images in the directory will be included. - size_frame - The desired size of every frame. - Note: In a video, every frame should have the same size. - The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. - With a given frame size, if an images is larger than that, the image would be cropped automatically; if an image is smaller than that, the image would be zero-padded automatically - If the frame size is not given, the largest size of all images would be used as the frame size. + To generate a video, the image used in every frame shold have the same size, known as frame size. + In most cases where the images have the same size, the image size is frame size, e.g. ```size_frame=(640, 480)```. + In some cases where not all images have the same size, image resizing would be done automaticaly + to be able to generate the video, with a warning message generated. + + If no value is passed to this parameter, the largest size of all images would be used as the frame size. + + The resizing is done by cropping for those larger then desired sizes, zero-padding for those smaller than desired sizes. - - fps: (frames per second) frame rate. By default fps=29.97. - Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 + Note: The most commonly used definition of frame size/image size is different from most commonly + used definition of matrix size (e.g. numpy arrays). The frame size/image size is defined as (width, height), + while the matrix size is defined as (num_rows, num_columns). + If you are trying to get the frame size/image size from the size of matrix (array), remember of change the + order of first and second dimension of the matrix, i.e. frame size = (num_columns, num_rows). + + - fps: (frames per second) frame rate. By default fps=29.97. Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 + - name_video - The desired name of output video name. By default the name would be 'time_lapse_video' - - path_video - The desired saving path of the video file. By default, the video will be saved at the same directory of the images. - - display - Indicator of whether to display current status (by displaying saving directory and saving name) while running this function. By default the display is turned on. + - path_video - The desired saving path of the video file. By default, the video will be saved at the same directory + of the images. + - display - Indicator of whether to display current status (by displaying saving directory and saving name) while + running this function. By default the display is turned on. - **Context:** - Used to generate time-lapse video given a bunch of images. @@ -60,8 +74,8 @@ path_video = path_img display = 'on' -pcv.visualize.time_lapse_video(img_directory=img_directory, list_img=list_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) -pcv.visualize.time_lapse_video(img_directory=img_directory, suffix_img=suffix_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) +list_img, frame_size = pcv.visualize.time_lapse_video(img_directory=img_directory, list_img=list_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) +list_img, frame_size = pcv.visualize.time_lapse_video(img_directory=img_directory, suffix_img=suffix_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) ``` From 6c5221aa1f4b2043449dea40869bce88649f3812 Mon Sep 17 00:00:00 2001 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z0lu;4)-@O7B&K6Jz?<8d(igczPR03ODBh<;zB~p#`{Kr&#@bJ^IyE|}cC#_+EMBqa z+YHf*WrF48{aD+F3?DoaPD|G0bD!}vc74SgWmY8oOJ|7l*2bE>y)0i)SS>tZx#Kba zf`m=2^iX`?*|pIT)R%LqF%ExH;^ND)_ci=>Do8YoyUs+_y_9_@2+2qqLh{;v^Wn48 zVEyzu&|kdLkyr^mrqvPAp(AyIWO5>WW_wi^pLC3{jPL6?ovVGQ)hCyWn`$;L;w;_v z55^eL7aVM+KPgn`HW+0dbZHjT_qIlu>x#;iH^Rmf4?}b>St(D4P~0PLTU(d0C>Z$IT1_i_ zq%s)f-Sa2!$R{!aS8WB~0I;D|UJ$x=Pg>4S3st8+OkT98n0H$LMTVvf>orvUU;R3tz-0i;uBXBh@?m4yMNw++k8qc!Gg{fLxxS4IT!XOr= zIXE9Us_g8dDy{vtsHn`yrh;?HKuasg|4+(XoJLF4)z@lC*=Z<4d%NU{bNDfF*_$_P zhFftnSBV^_wmJzKz7M~-u@ZjI9YHb?{5C#cRJC*-&44omJ->En`VJ>$h;_`~WqdAC z2s7JC-RTsR@;L78&g#2&C56Viuk&%+l@A%Gm!EtCL1j!w7ORi%-}$PmvusV$b=mGk z5_F0z*B3ii{X0H$@8vg4DcXz%^bxog3Pmy?>&^mYT1|QLR zz46hUV9+{eM){=}&rAwS)?SELl71+>CrnZd83Pn98zg0SzXCC;VUigLXLqg{&`nLPVOVXHi){mdm8QCn!D Date: Thu, 23 Jul 2020 08:13:04 -0500 Subject: [PATCH 046/274] Update time_series.md --- docs/time_series.md | 112 ++++++++++++++++++++++++++++++++------------ 1 file changed, 83 insertions(+), 29 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index 7e12f4454..7f8011b44 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -1,12 +1,12 @@ ## time_series -This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, e.g. images taken every several hours, recording the growth of the plant, where ideally there should be no or at least minimum movement for plant or camera). +This function is designed to link segmented objects over time. Images should be taken across a time (e.g. every several hours) and ideally there should be minimal movment of plant/camera. -To run this function, the instance segmentation for every image should be achieved beforehand. For more information and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). +To run this function, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. -To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always have the same instance label across the whole time period. +To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. **plantcv.time_series.time_series_linking**(*imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'*) @@ -16,7 +16,8 @@ To understand the growth of every leaf instance of a plant, we need to re-assign - imagedir: directory of original image used - segmentationdir: directory of leaf instance segmentation result - savedir: desired saving directory of linking result. Note that this function will generate a folder under your specified saving directory, with the date and time you run this programme - - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment + - pattern_datetime: the pattern of date and time part in original file names, dafault value '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' which represents YYYY-MM-DD-hh-mm. + - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment. Make sure the format of date matches the pattern-datetime. - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 - class_names: used in bounding box visualization. by default there are background and leaf, i.e. class_names = ['BG', 'leaf'] - mode: can be either 'link' or 'load', use 'link' when trying to get linked time-series from instance segmentation; use 'load' when trying to load from saved linking @@ -27,7 +28,7 @@ To understand the growth of every leaf instance of a plant, we need to re-assign Note: under the user specified saving directory, a new folder named after the date and time (format: YYYY-MM-DD-HH-mm) the function runs will be created to save results. 1. colors.pkl: the colors (represented by arrays) used in bounding box visualization. With this predefined color set, same color will represent for the leaf instance with same instance label all the time, i.e. same color represents leaf with the same label. Without this predefined list of color, the assignment of color will be random, i.e. the color provides no information to leaf indices. -2. details.txt: the logic of linking as well as time condition will be saved in this text file, so that would be easier for users to check these parameters for the specific expreiment. +2. details.txt: the logic of linking as well as time condition will be saved in this text file, so that would be easier for users to check these parameters for the specific experiment. 3. saved_plant.pkl: a "Plant" object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. 4. a folder called "visualization", which contains 3 subfolders: @@ -75,40 +76,93 @@ Instead of making a video, another way to check the quality of instance segmenta from plantcv import plantcv as pcv # Below are examples of input variables, always adjust base on your own application. -path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol/maskrcnn_test' -path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/detection/modified' -path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/test' -time_cond = ['08-05', '11-05', '17-05', '21-05'] -link_logic = 1 -class_names = ['BG', 'Leaf'] -mode = 'link' -suffix = '.jpg' -​linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=link_logic, class_names=class_names, mode=mode, suffix=suffix) +## Specify the directory of original image +path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol_512' -# Print the saving directory so that you can go check the result: -linked_Plant.savedir +## Specify the directory of instance segmentation result +path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index7/2020-06-25-10-35/subsampled/2020-06-30-10-51/' -# Print visualization directory: -linked_Plant.visualdir +## Specify the desired directory to save results +path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index7/2020-06-25-10-35/time_series_linking' -# Print linking information on the screen for a quick check: -linked_Plant.link_series +## Specify the date-time pattern of original image names +pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm + +## Specify the desired time point to include in to the analysis +time_cond = ['08-05', '11-05', '17-05', '21-05'] + +link_logic = 1 +class_names = ['BG', 'Leaf'] +mode = 'link' + +## Specify the suffix for all images you want to include, note that all the images shown have the same suffix as you specified +## Normally it would be something like '.png' or '.jpg', in this specific case, the image contained in the folder have name like +## 'xxx-img{}.jpg', the number inside the {} represents the index of plant. So in this case, to include images for the same plant, +## you will have to specify the index of plant. +suffix = '-img7.jpg' +​linked_Plant = pcv.time_series.time_series_linking(path_img, path_segmentation, path_save, pattern_datetime, + time_cond, link_logic, class_names, mode, suffix) ``` +When it finished, you can go ahead and check the saved result. If you are not sure where the results are saved, you can type: +```linked_Plant.savedir``` +in a cell to print out the saving directory. -1. Analysing shapes: -Create a workflow and use the PlantCV analyze_object function to traverse all images inside the 1st visualization folder. +You can also type ```linked_Plant.link_series``` in a cell to print linking information on the screen for a quick check. -2. Making time-lapse videos to visualize the growth of every leaf: -To make videos, you will need video editing softwares, e.g. imovie.\ -Direct yourself to the visualization folder, and go into 'visualization2'. Every folder represents for a leaf. Include all images for one leaf in a video project, sort them by name in an ascending order, and generate a time-lapse video. -Here is an example of time-lapse of one leaf: +Now that we have the linking results saved, we can have some analysis based on them. There are 3 suggested analysis described as below: - -

growth of plant focusing on single leaf from Hudanyun Sheng on Vimeo.

+1. Analysing shapes: + Create a workflow and use the PlantCV analyze_object function to traverse all images inside the 1st visualization folder. + +2. Making time-lapse videos to visualize the growth of every leaf. +You have two options of making time-lapse videos: using video editing software (e.g. iMovie) or using the PlantCV tool. + - Generate time-lapse video using video editing software: + + Direct yourself to the visualization folder, and go into 'visualization2'. Every folder represents for a leaf. Include all images for one leaf in a video project, sort them by name in an ascending order, and generate a time-lapse video. + Here is an example of time-lapse of one leaf: + + +

growth of plant focusing on single leaf from Hudanyun Sheng on Vimeo.

+ + - There is also a PlantCV tool that is able to automatically generate and save time-lapse videos. Check here for the usage. The example code is provided at the end of this documentation. + + 3. Making a time-lapse video to visualize the growth of the plant by showing the segmentation in bounding boxes, with the same leaf always shown with the same color. Direct yourself to "visualization 3" folder and include all images to a video project, sort them by name in an ascending order, and generate a time-lapse video. An example of time-lapse video with visualization shown in bounding boxes is shown below. When compare to the video made of instance segmentation, result, you will observe that now every leaf is represented by the same color all the time, which means the label re-assignemnt is successful. - + +```python +## Videos generated using the 2nd set of visualization +# The directory of the 2nd set of visualization +from plantcv import plantcv as pcv +path_visual2 = os.path.join(Plant.visualdir, 'visualization2') + +# Getting subfolders of the 2nd set of visualization +sub_folders = [x[0] for x in os.walk(path_visual2)][1:] + +# You are also to change this to your desired saving directory of the video. By default it will be saved in the same directory of the 2nd set of visualization. +path_video = path_visual2 + +print('\n Saving videos for 2nd set of visualization.') +for sub_f in sub_folders: + name_video = os.path.split(sub_f)[1] # name of the videos are set to be same as the name of subfolders (i.e. the "identifier" of leaves) + pcv.visualize.time_lapse_video(img_directory=sub_f, suffix_img=Plant.ext, name_video=name_video, path_video=path_video, display='off') +print('\nfinished') + +## Videos generated using the 3rd set of visualization +# The directory of the 3rd set of visualization +path_visual3 = os.path.join(Plant.visualdir, 'visualization3') +print('\n Saving videos for 3rd set of visualization.') + +# You are free to change the name of the video to your desired ones +name_video_3 = 'visualization_w_bounding_box' + +# You are free to change this to your desired saving directory of the video. By default it will be save in the same directory of visualization. +path_video_3 = Plant.visualdir +pcv.visualize.time_lapse_video(img_directory=path_visual3, suffix_img=Plant.ext, name_video=name_video_3, path_video=path_video_3, display='on') +``` **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) + + From 68e14045468b620f5b89331425ed9ea5b9fde3c7 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 23 Jul 2020 08:13:32 -0500 Subject: [PATCH 047/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 24 ++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 2ebfc2ac2..3536f7d86 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -23,8 +23,8 @@ from plantcv.plantcv.time_series import link_utilities as funcs # import link_utilities as funcs -def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, link_logic=1, class_names=['BG', 'Leaf'], - mode='link', suffix='.jpg'): +def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, link_logic=1, + class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'): """ Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) Input: @@ -73,7 +73,6 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti Plant.getpath(Plant.imagedir) Plant.Sorttime(time_cond) - Plant.load_images() # load mrcnn inferencing results @@ -99,6 +98,9 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti file.write('Image conditions: {}'.format(time_cond)) file.close() + print('start\n') + print('...') + # linking initialization Plant.initialize_linking() @@ -158,7 +160,13 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], - Plant.filename_pre[t]))) + Plant.filename_pre[t], + Plant.ext))) + pcv.print_image(leaf_t, os.path.join(path_visual1, + '{}_{}-{}-{}-{}_{}{}'.format(unique_id, start_time, + start_idx, t, link_leaf[t], + Plant.filename_pre[t], + Plant.ext))) # pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) ## 2. show result with an alpha channel @@ -173,7 +181,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti ax2 = fig2.add_subplot(1, 1, 1) ax2.imshow(masked_im) ax2.axis('off') - plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + '.png')) + plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + Plant.ext)) plt.close(fig2) count += 1 @@ -184,8 +192,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) - - plt.savefig(os.path.join(path_visual3, '{}-visual.png'.format(t))) + plt.savefig(os.path.join(path_visual3, '{}-visual{}'.format(t, Plant.ext))) plt.close('all') # save all information @@ -197,5 +204,6 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti # Load from existed PlantData instance Plant = pkl.load(open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'rb')) + print('\nFinished!\nThe results are saved here:\n{}'.format(Plant.savedir)) + return Plant - \ No newline at end of file From fabb7bfab21fc180fd626ba4ed8f2329248c2dcb Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 23 Jul 2020 08:13:36 -0500 Subject: [PATCH 048/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 36 +++++++++++++++++-- 1 file changed, 33 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 73ea8a129..52bc0b6b3 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -225,7 +225,8 @@ def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, mode='l subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), str(junk.hour).zfill(2), str(junk.minute).zfill(2)) self.savedir = os.path.join(savedir, subfolder) - os.makedirs(self.savedir) + if not os.path.exists(self.savedir): + os.makedirs(self.savedir) else: self.savedir = savedir @@ -272,6 +273,11 @@ def Sorttime(self, time_cond): filenames_ori = [f for f in os.listdir(self.imagedir) if f.endswith(self.suffix)] time_temp = [] file_name = [] + ext1, ext2 = os.path.splitext(self.suffix) + if ext1.startswith('.'): + ext = ext1 + elif ext2.startswith('.'): + ext = ext2 for filename in filenames: temp = re.search(self.pattern_datetime, filename) if temp: @@ -280,7 +286,8 @@ def Sorttime(self, time_cond): if timepart.endswith(cond): time_temp.append(timepart) junk = [1 if re.search(timepart, f) is not None else 0 for f in filenames_ori] - file_name.append(filenames_ori[junk.index(1)].replace('.jpg', '')) + # file_name.append(filenames_ori[junk.index(1)].replace('.jpg', '')) + file_name.append(filenames_ori[junk.index(1)].replace(ext, '')) continue index_temp = np.argsort(time_temp) @@ -292,7 +299,21 @@ def load_images(self): """ Load original images This function is also designed for files with file names which contain a "date-time" part, with a user defined pattern """ - + # filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] + # temp_imgs = [] + # sz = [] + # for t in self.time: + # for f in filenames: + # temp = re.search(t, f) + # if temp: + # junk = skimage.io.imread(os.path.join(self.imagedir, f)) + # temp_imgs.append(junk) + # sz.append(np.min(junk.shape[0:2])) + # filenames.remove(f) + # self.min_dim = np.min(sz) + # for junk in temp_imgs: + # img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size + # self.images.append(img) temp_imgs = [] sz = [] for pre in self.filename_pre: @@ -383,6 +404,15 @@ def get_series(self): key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = np.array(new_leaves) + # if len(new_leaves) > 1: + # id_temp = [] + # for new_leav in new_leaves: + # id_temp.append(unique_id) + # unique_id = unique_id + 1 + # self.link_series[key_t]['unique_id' = np.array(id_temp) + # else: + # self.link_series[key_t]['unique_id'] = np.array(unique_id) + # unique_id = unique_id + 1 id_temp = [] for new_leaf in new_leaves: id_temp.append(unique_id) From 8f81bac090ae58637e39de6e733244379e9ffd2c Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 23 Jul 2020 10:04:06 -0500 Subject: [PATCH 049/274] Create instance_segmentation_tutorial.md --- docs/instance_segmentation_tutorial.md | 129 +++++++++++++++++++++++++ 1 file changed, 129 insertions(+) create mode 100644 docs/instance_segmentation_tutorial.md diff --git a/docs/instance_segmentation_tutorial.md b/docs/instance_segmentation_tutorial.md new file mode 100644 index 000000000..5e80983b1 --- /dev/null +++ b/docs/instance_segmentation_tutorial.md @@ -0,0 +1,129 @@ +## Tutorial: Instance Segmentation using maskRCNN + +Instance segmentation is identifying each object instance for every known object within an image. Instance segmentation assigns a label to each pixel of the image. It can be used for tasks such as counting the number of objects. [reference: https://towardsdatascience.com/computer-vision-instance-segmentation-with-mask-r-cnn-7983502fcad1] + +Instance segmentation requires: + +1. Object detection of all objects in an image. Here the goal is to classify individual objects and localize each object instance using a bounding box. +2. Segmenting each instance. Here the goal is to classify each pixel into a fixed set of categories without differentiating object instances. + +Taking an image of plant as an example. As shown in the image below, the 1st image is an RGB image of an arabidopsis, the one in the middle is image segmentation result, and the 3rd image is the result of instance segmentation. +Now it is easy for us to tell that the goal for image segmentation is to have pixel level labels indicating "plant" or "not plant" for every pixel, and the output for image segmentation is a binary mask indicating where the plant is in the image. At this point we have no information regarding number of leaves in this image. +While for instance segmentation, as shown in the 3rd image, we can see that the goal is to segment out every leaf (hence, there is a label for every leaf, e.g. leaf 1, leaf 2, etc.) instance. In this specific example, 5 binary masks would be generated, every one represents for one leaf. Hence we are also able to tell that there are 5 leaves present in this image. + +![Screenshot](img/tutorial_images/instance_segmentation/original.jpg) +![Screenshot](img/tutorial_images/instance_segmentation/threshold.jpg) +![Screenshot](img/tutorial_images/instance_segmentation/instance_seg.jpg) + + +There are plenty of methods for instance segmentation, instance segmentation using maskRCNN is shown here as an example. + +For detailed information regrading maskRCNN, please check here: +https://github.com/matterport/Mask_RCNN + +Follow the installation steps, and it is highly recommended to create a conda environment for mask_rcnn. + +- Create a conda environment with tensorflow 1.13.1 and keras 2.1.0. + - Open a terminal window, type: +``` + conda create -n mrcnn tensorflow=1.13.1 + conda activate mrcnn + pip install keras==2.1.0 + conda install plantcv # install plantcv tools for this environment +``` +This would create a tensorflow environment (with tensorflow 1.13.1 and keras 2.1.0, those are required by the MaskRCNN package we are to install) with a name of mrcnn. You are free to change the name "mrcnn" based on you own preference. + +- Install MaskRCNN + - Clone [this](https://github.com/matterport/Mask_RCNN) github repository to your desired location. (It is suggested to put the same directory as you put your plantcv folder) + - Open a terminal, follow the instructions below: + +``` + cd Mask_RCNN # direct yourself to the folder of Mask_RCNN + pip install -r requirements.txt # install dependencies + python3 setup.py install # run setup +``` + +- (Option) Install pycocotools. + - Clone [this](https://github.com/cocodataset/cocoapi) github repository, and put to your desired destiny location. + - Open a terminal window, type: +``` + pip install pycocotools +``` + +With conda environment mrcnn activated (```conda activate mrcnn```), you are ready to get instance level segmentation with Mask_RCNN using a pre-trained model. You can find the download the pre-trained model here: +/home/nfahlgren/projects/mrcnn/mask_rcnn_leaves_0060.h5 + +It is recommended that you put this pre-trained model in the same folder of your project. + +Following this notebook for step-by-step implementation of instance segmentation. + +```python +# import packages +import os +import inferencing_utilities as funcs +``` + +The following block is where you want to change based on your own application: +```python +## suffix of original image files. Make sure that all files have the same suffix format +suffix = 'crop-img17.jpg' + +## pattern for the date-tima part in your data. Make sure that the date-time part in all filenames follow the same pattern +pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' + +## directory of original images +imagedir = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol' + +## desired saving directory for results +savedir = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/plant17/segmentation' +if not os.path.exists(savedir): + os.makedirs(savedir) + +## class names. Since a pre-trained model is used here, and the model is trained with 2 classes: either "Background" or "Leaf", there is really nothing to change here +class_names = ['BG', 'Leaf'] +``` + +Some detailed regarding parameters "suffix": + +For the next several blocks, there is really nothing for you to change. +```python +## Root directory of the project +rootdir = os.path.abspath("./") + +## initialize the instance segmentation +instance_seg = funcs.instance_seg_inferencing(imagedir, savedir, rootdir, pattern_datetime, suffix, class_names) + +## get configuration for instance segmentation +instance_seg.get_configure() + +## load the pre-trained model +instance_seg.load_model() + +## pre-define colors for visualization used later +instance_seg.define_colors() + +## get the list of all files +instance_seg.get_file_list() + +## option (print the file list) +instance_seg.list_f +``` + +For the next block, a randomly selected example is used to show the instance segmentation result +```python +## show one randomly selected image as an example +instance_seg.inferencing_random_sample() +``` + +If you run the following block, it will loop over all files in the file list you defined. Note it might take some time for the process to finish. +```python +## get the result of all images +instance_seg.inferencing_all() +``` + +If you would like to check the results inside the folder, you can print out the directory for results saving: +```python +instance_seg.segmentation_dir +``` + + From 9155c1add1c9e8737ee87ae59e4bd33fc4dabc0b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 24 Jul 2020 14:54:09 -0500 Subject: [PATCH 050/274] Update time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py index 76239733c..d2808679a 100644 --- a/plantcv/plantcv/visualize/time_lapse_video.py +++ b/plantcv/plantcv/visualize/time_lapse_video.py @@ -155,7 +155,7 @@ def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=N out.release() cv2.destroyAllWindows() - if display is 'on': + if display == 'on': print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) return list_img, size_frame \ No newline at end of file From 411939b42cc7d08e6d5ff1e5de0d704d5abeb50b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 24 Jul 2020 15:00:04 -0500 Subject: [PATCH 051/274] Update time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py index d2808679a..ac3139f1d 100644 --- a/plantcv/plantcv/visualize/time_lapse_video.py +++ b/plantcv/plantcv/visualize/time_lapse_video.py @@ -158,4 +158,4 @@ def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=N if display == 'on': print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) - return list_img, size_frame \ No newline at end of file + return list_img, size_frame From c7464005aaa76b3e357423230f2b3441a495b0f3 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 24 Jul 2020 17:03:09 -0500 Subject: [PATCH 052/274] Update __init__.py --- plantcv/plantcv/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py index 8dc4e0860..2f533cbd8 100644 --- a/plantcv/plantcv/__init__.py +++ b/plantcv/plantcv/__init__.py @@ -245,7 +245,7 @@ def __init__(self, array_data, max_wavelength, min_wavelength, max_value, min_va 'cluster_contour_splitimg', 'rotate', 'shift_img', 'output_mask', 'auto_crop', 'canny_edge_detect', 'background_subtraction', 'naive_bayes_classifier', 'acute', 'distance_transform', 'params', 'cluster_contour_mask', 'analyze_thermal_values', 'opening', - 'closing', 'within_frame', 'fill_holes', 'get_kernel', 'Spectral_data', 'crop', 'stdev_filter', 'time_series'] + 'closing', 'within_frame', 'fill_holes', 'get_kernel', 'Spectral_data', 'crop', 'stdev_filter', 'time_series', 'cluster_contour_mask','analyze_thermal_values', 'opening', 'closing','within_frame', 'fill_holes', 'get_kernel', 'Spectral_data', 'crop', 'stdev_filter', 'spatial_clustering'] From 26cd6bc1c0a985f722b640c0f8658e2a42553ed0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Sat, 25 Jul 2020 14:41:40 -0500 Subject: [PATCH 053/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 52bc0b6b3..5b8ea8500 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -189,6 +189,7 @@ class PlantData(): def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, mode='link', suffix='.jpg'): self.suffix = suffix + self.ext = None self.pattern_datetime = pattern_datetime @@ -275,9 +276,9 @@ def Sorttime(self, time_cond): file_name = [] ext1, ext2 = os.path.splitext(self.suffix) if ext1.startswith('.'): - ext = ext1 + self.ext = ext1 elif ext2.startswith('.'): - ext = ext2 + self.ext = ext2 for filename in filenames: temp = re.search(self.pattern_datetime, filename) if temp: @@ -287,7 +288,7 @@ def Sorttime(self, time_cond): time_temp.append(timepart) junk = [1 if re.search(timepart, f) is not None else 0 for f in filenames_ori] # file_name.append(filenames_ori[junk.index(1)].replace('.jpg', '')) - file_name.append(filenames_ori[junk.index(1)].replace(ext, '')) + file_name.append(filenames_ori[junk.index(1)].replace(self.ext, '')) continue index_temp = np.argsort(time_temp) @@ -432,4 +433,4 @@ def get_series(self): if idx < 0: break else: - self.link_series[key_t][key_leaf][t_] = self.link_info[t_ - 1][idx] \ No newline at end of file + self.link_series[key_t][key_leaf][t_] = self.link_info[t_ - 1][idx] From a876db8ea645fae653ccebb597bff75ab7a9a0eb Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 25 Aug 2020 17:22:07 -0500 Subject: [PATCH 054/274] Update link_utilities.py Update linking logic --- plantcv/plantcv/time_series/link_utilities.py | 168 ++++++------------ 1 file changed, 57 insertions(+), 111 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 5b8ea8500..f0e5ff8c0 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -1,10 +1,10 @@ +#### link utilities #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jun 9 09:53:34 2020 -Functions used in time series linking after getting leaf instance segmentation result - +Functions used in time series linking after getting leaf instances segmented @author: hudanyunsheng """ @@ -26,6 +26,8 @@ import datetime import copy import colorsys +from plantcv.plantcv import fatal_error +from scipy.optimize import linear_sum_assignment def _random_colors(N, bright=True): @@ -138,31 +140,29 @@ def display_instances(image, boxes, masks, class_ids, class_names, if auto_show: plt.show() - -def _compute_overlaps_masks(masks1, masks2, mode=1): +def _compute_overlaps_masks(masks1, masks2): """Computes IoU overlaps between two sets of masks. masks1, masks2: [Height, Width, instances] mode: 1 or 2, 1 represent for IOU, 2 represent for intersection over the area of the previous mask """ - + mode = 1 # If either set of masks is empty return empty result if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: return np.zeros((masks1.shape[-1], masks2.shape[-1])) - # flatten masks and compute their areas - masks1 = np.reshape(masks1 > .5, (-1, masks1.shape[-1])).astype(np.float32) - masks2 = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) - area1 = np.sum(masks1, axis=0) - area2 = np.sum(masks2, axis=0) - - # intersections and union - intersections = np.dot(masks1.T, masks2) - if mode == 1: - union = area1[:, None] + area2[None, :] - intersections - overlaps = intersections / union - elif mode == 2: - overlaps = intersections / area1[:, None] - return overlaps - + n1 = masks1.shape[2] + n2 = masks2.shape[2] + intersections = np.zeros((n1,n2)) + unions = np.zeros((n1,n2)) + for idx_m in range(0, n1): + maski = np.expand_dims(masks1[:,:,idx_m], axis=2) + masks_ = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) + maski_ = np.reshape(maski > .5, (-1, maski.shape[-1])).astype(np.float32) + intersection = np.dot(masks_.T, maski_).squeeze() + intersections[idx_m,:] = intersection + union = np.sum(masks_,0) + np.sum(maski_) - intersection + unions[idx_m,:] = union + IOUs = np.divide(intersections,unions) + return IOUs def get_ax(rows=1, cols=1, size=16): # ??? """Return a Matplotlib Axes array to be used in @@ -175,18 +175,8 @@ def get_ax(rows=1, cols=1, size=16): # ??? fig.tight_layout() return ax - -def _getmatchedIndex(mask, masks, mode): - """Get the index from masks that matches the mask""" - score = _compute_overlaps_masks(np.expand_dims(mask, axis=-1), masks, mode) - if max(score[0]) > 0.15: - return np.argmax(score[0]), score - else: - return -1, score - - class PlantData(): - def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, mode='link', suffix='.jpg'): + def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, suffix='.jpg'): self.suffix = suffix self.ext = None @@ -222,14 +212,12 @@ def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, mode='l self.imagedir = imagedir self.segmentationdir = segmentationdir junk = datetime.datetime.now() - if mode == 'link': - subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), - str(junk.hour).zfill(2), str(junk.minute).zfill(2)) - self.savedir = os.path.join(savedir, subfolder) - if not os.path.exists(self.savedir): - os.makedirs(self.savedir) - else: - self.savedir = savedir + + subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), + str(junk.hour).zfill(2), str(junk.minute).zfill(2)) + self.savedir = os.path.join(savedir, subfolder) + if not os.path.exists(self.savedir): + os.makedirs(self.savedir) self.visualdir = os.path.join(self.savedir, 'visualization') @@ -287,7 +275,6 @@ def Sorttime(self, time_cond): if timepart.endswith(cond): time_temp.append(timepart) junk = [1 if re.search(timepart, f) is not None else 0 for f in filenames_ori] - # file_name.append(filenames_ori[junk.index(1)].replace('.jpg', '')) file_name.append(filenames_ori[junk.index(1)].replace(self.ext, '')) continue @@ -300,21 +287,6 @@ def load_images(self): """ Load original images This function is also designed for files with file names which contain a "date-time" part, with a user defined pattern """ - # filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] - # temp_imgs = [] - # sz = [] - # for t in self.time: - # for f in filenames: - # temp = re.search(t, f) - # if temp: - # junk = skimage.io.imread(os.path.join(self.imagedir, f)) - # temp_imgs.append(junk) - # sz.append(np.min(junk.shape[0:2])) - # filenames.remove(f) - # self.min_dim = np.min(sz) - # for junk in temp_imgs: - # img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size - # self.images.append(img) temp_imgs = [] sz = [] for pre in self.filename_pre: @@ -339,54 +311,35 @@ def load_results(self): self.scores.append(r['scores']) def initialize_linking(self): - self.emerging_info = [[] for i in range(0, self.total_time)] - self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time - 1)] - self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i + 1])) for i in - range(0, self.total_time - 1)] + self.emerging_info = [[] for i in range(0, self.total_time)] + self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time - 1)] + self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i + 1])) for i in range(0, self.total_time - 1)] self.available_leaves = [np.array(range(0, self.num_leaves[i])) for i in range(0, self.total_time)] - self.time_compare = [np.ones((self.num_leaves[i]), dtype=int) * i for i in range(0, self.total_time)] - - def linking(self, start_time, mode): - masks1 = copy.deepcopy(self.masks[start_time]) - masks2 = copy.deepcopy(self.masks[start_time + 1]) - leaves1 = copy.deepcopy(self.available_leaves[start_time]) - leaves2 = copy.deepcopy(self.available_leaves[start_time + 1]) + self.time_compare = [np.ones((self.num_leaves[i]), dtype=int) * i for i in range(0, self.total_time)] + + def linking(self, t0): + threshold = 0.2 + masks0 = copy.deepcopy(self.masks[t0]) + masks1 = copy.deepcopy(self.masks[t0 + 1]) + leaves0 = copy.deepcopy(self.available_leaves[t0]) + leaves1 = copy.deepcopy(self.available_leaves[t0 + 1]) + n0 = len(leaves0) n1 = len(leaves1) - n2 = len(leaves2) - n = np.min((n1, n2)) - N = np.max((n1, n2)) - weight = dict() - weight['forward'] = -np.inf * np.ones((n1, n2)) - # weight['backward'] = -np.inf*np.zeros((n2, n1)) - link = dict() - link['forward'] = -np.ones((n1)) - # link['backward'] = -np.ones((n2)) - - for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward']): - # for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward', 'backward']): - for (idx, main_leaf) in enumerate(leaves): - mask = masks[:, :, main_leaf] - link[key][idx], weight[key][idx, :] = _getmatchedIndex(mask, masks_all, mode) - # check for duplicates (and hence non-unique links) - val, cou = np.unique(link[key], return_counts=True) - idx_temp = np.where(cou > 1)[0] - - if len(idx_temp) > 0: # matching to same leaf exists - idx_vs = val[idx_temp] - for idx_v in idx_vs: - idxs = np.where(link[key] == idx_v)[0] - weights_comp = weight[key][:, int(idx_v)] - - idx_keep = np.argmax(weights_comp) - idx_drop = np.delete(idxs, np.where(idxs == idx_keep)) - weight_temp = copy.deepcopy(weight[key][idx_drop]) - weight_temp[0, int(idx_v)] = 0 - if np.max(weight_temp) > 0.2: - link[key][idx_drop] = np.argmax(weight_temp) - else: - link[key][idx_drop] = -1 - - self.link_info[start_time] = link['forward'] + n = np.min((n0, n1)) + N = np.max((n0, n1)) + weight = -np.inf * np.ones((n0, n1)) + link = -np.ones((n0)) + weight = _compute_overlaps_masks(masks0, masks1) + idx_col = np.where(np.max(weight, axis=0) < threshold)[0] # find those volumns with maximum value < 0.15 + avail_col = [x for x in range(0,n1) if x not in idx_col] + weight_ = copy.deepcopy(weight) + + weight_ = np.delete(weight_, idx_col, 1) + row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) + for (r, c) in zip(row_ind, col_ind): + if weight_[r, c] >= threshold: + link[r] = avail_col[c] + self.link_info[t0] = link def get_series(self): ## define new leaves and their unique identifiers at time points with new leaves emerging @@ -405,15 +358,7 @@ def get_series(self): key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = np.array(new_leaves) - # if len(new_leaves) > 1: - # id_temp = [] - # for new_leav in new_leaves: - # id_temp.append(unique_id) - # unique_id = unique_id + 1 - # self.link_series[key_t]['unique_id' = np.array(id_temp) - # else: - # self.link_series[key_t]['unique_id'] = np.array(unique_id) - # unique_id = unique_id + 1 + id_temp = [] for new_leaf in new_leaves: id_temp.append(unique_id) @@ -421,7 +366,7 @@ def get_series(self): self.link_series[key_t]['unique_id'] = np.array(id_temp) ## for time points with new leaves emerging, get the linking information for every new leaf for key_t in self.link_series: - t0 = int(key_t.replace('t', '')) + t0 = int(key_t.replace('t', '')) for leaf in self.link_series[key_t]['new_leaf']: key_leaf = 'leaf{}'.format(leaf) self.link_series[key_t][key_leaf] = -np.ones(self.total_time, dtype=int) @@ -434,3 +379,4 @@ def get_series(self): break else: self.link_series[key_t][key_leaf][t_] = self.link_info[t_ - 1][idx] + \ No newline at end of file From 5a1da3c51c491ead21d7f97fffe3e9e06dea9c8b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 25 Aug 2020 17:22:41 -0500 Subject: [PATCH 055/274] Update time_series.py Update time_seried by adding new feature: generate a csv file which includes the linking information --- plantcv/plantcv/time_series/time_series.py | 284 ++++++++++----------- 1 file changed, 141 insertions(+), 143 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 3536f7d86..8c4869f1c 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -1,9 +1,10 @@ +#### link utilities #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jun 9 09:53:34 2020 -Functions used in time series linking after getting leaf instance segmentation result (from maskRCNN) +Time series linking after getting leaf instances segmented @author: hudanyunsheng """ @@ -20,27 +21,29 @@ import re from plantcv import plantcv as pcv import copy -from plantcv.plantcv.time_series import link_utilities as funcs -# import link_utilities as funcs +import csv +import link_utilities as funcs -def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, link_logic=1, - class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'): +def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, colors=None, class_names=['BG', 'Leaf'], suffix='.jpg'): + """ Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) Input: imagedir: directory of original image used segmentationdir: directory of leaf instance segmentation result savedir: desired saving directory of linking result - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 + pattern_datetime: the regular expression of date-time part in file names. e.g. if the date-time part in file names is: YYYY-MM-DD-hh-mm, then pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' + time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment, this should be consistent with the pattern_datetime + colors: a list of tuples where every tuple represent for one color (RGB values). By default it can be None. class_names: used in bounding box visualization. By default there are background and leaf - mode: either 'link' or 'load'. When creating a new linking, use in mode 'link', when trying to load from saved file, use in mode 'load'. By default the mode is 'link'. + suffix: the ending part of file names that every image have in common. By default it is '.jpg'. Output: An instance from "Plant" class would be returned, all the results will be saved in a folder with name of date and time when the function runs inside the user defined "savedir". Sample folder name: 2020-06-23-15-42 (a format of YYYY-MM-DD-HH-mm). 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment - 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. - 4. a folder called "visualization", which contains 3 subfolders: + 3. saved_plant.pkl: a Plant object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. + 4. linking_info.csv: a csv file includes the linking information will be saved. + 5. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. result name: {}_{}-{}-{}-{}_{}.png @@ -67,143 +70,138 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti Naming format: {}-visual.png The original image name is inside the {}. """ - if mode == 'link': # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, mode, suffix) - - Plant.getpath(Plant.imagedir) - Plant.Sorttime(time_cond) - Plant.load_images() - - # load mrcnn inferencing results - Plant.load_results() - - Plant.getinitleaf() - Plant.getmaxleaf() - - Plant.gettotaltime() - Plant.getnumemergence() - - # plot the #leaves vs. time relationship - plt.plot(np.array(range(0, len(Plant.num_leaves))), Plant.num_leaves) - - if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): - file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') - if link_logic == 1: - file.write('mode: IOU\n') - else: - file.write('mode: IOP\n') - file.write('Directory of original images: {}\n'.format(Plant.imagedir)) - file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) - file.write('Image conditions: {}'.format(time_cond)) - file.close() - - print('start\n') - print('...') - - # linking initialization - Plant.initialize_linking() - - # link - for t in range(0, Plant.total_time - 1): - Plant.linking(t, mode=link_logic) - Plant.get_series() - - ####### visualization ####### - # load original images - if len(Plant.images) == 0: - Plant.load_images() # Plant.images - - # visualization method 1: show only one leaf per image - path_visual1 = os.path.join(Plant.visualdir, 'visualization1') - if not os.path.exists(path_visual1): - os.makedirs(path_visual1) - - # visualization method 2: show with an alpha channel - path_visual2 = os.path.join(Plant.visualdir, 'visualization2') - if not os.path.exists(path_visual2): - os.makedirs(path_visual2) - - # visualization method 3: show with bounding boxes - path_visual3 = os.path.join(Plant.visualdir, 'visualization3') - if not os.path.exists(path_visual3): - os.makedirs(path_visual3) - - count = 0 + Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) +# Plant = PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) + + Plant.getpath(Plant.imagedir) + Plant.Sorttime(time_cond) + Plant.load_images() + + # load mrcnn inferencing results + Plant.load_results() + + Plant.getinitleaf() + Plant.getmaxleaf() + + Plant.gettotaltime() + Plant.getnumemergence() + + # plot the #leaves vs. time relationship + plt.plot(np.array(range(0, len(Plant.num_leaves))), Plant.num_leaves) + + if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): + file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') + file.write('mode: IOU\n') + file.write('Directory of original images: {}\n'.format(Plant.imagedir)) + file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) + file.write('Image conditions: {}'.format(time_cond)) + file.close() + + print('start\n') + print('...') + + # linking initialization + Plant.initialize_linking() + + # link + for t in range(0, Plant.total_time - 1): +# Plant.linking(t, mode=link_logic) + Plant.linking(t) + Plant.get_series() + + ####### visualization ####### + # load original images + if len(Plant.images) == 0: + Plant.load_images() # Plant.images + + # visualization method 1: show only one leaf per image + path_visual1 = os.path.join(Plant.visualdir, 'visualization1') + if not os.path.exists(path_visual1): + os.makedirs(path_visual1) + + # visualization method 2: show with an alpha channel + path_visual2 = os.path.join(Plant.visualdir, 'visualization2') + if not os.path.exists(path_visual2): + os.makedirs(path_visual2) + + # visualization method 3: show with bounding boxes + path_visual3 = os.path.join(Plant.visualdir, 'visualization3') + if not os.path.exists(path_visual3): + os.makedirs(path_visual3) + + count = 0 + if colors is None: if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): colors = funcs._random_colors(40) +# colors = _random_colors(40) pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) else: colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) - color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] - for key_t in Plant.link_series: - # - ids = Plant.link_series[key_t]['unique_id'] - - start_time = int(key_t.replace('t', '')) - leaves_t = Plant.link_series[key_t]['new_leaf'] - for (unique_id, leaf) in zip(ids, leaves_t): - key_leaf = 'leaf{}'.format(leaf) - link_leaf = Plant.link_series[key_t][key_leaf] - start_idx = link_leaf[start_time] - for t in range(start_time, Plant.total_time): - img = Plant.images[t] - if link_leaf[t] >= 0: - color_all[t][link_leaf[t]] = colors[count] - mask_t = Plant.masks[t][:, :, link_leaf[t]] - - ## 1. save the masked image, i.e. single leaves - mask = np.zeros(mask_t.shape, dtype=np.uint8) - mask[np.where(mask_t)] = 255 - leaf_t = pcv.apply_mask(img, mask, mask_color='black') - pcv.print_image(leaf_t, os.path.join(path_visual1, - '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, - start_idx, t, link_leaf[t], - Plant.filename_pre[t], - Plant.ext))) - pcv.print_image(leaf_t, os.path.join(path_visual1, - '{}_{}-{}-{}-{}_{}{}'.format(unique_id, start_time, - start_idx, t, link_leaf[t], - Plant.filename_pre[t], - Plant.ext))) - # pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) - - ## 2. show result with an alpha channel - # update the mask where there is an alpha channel (alpha=0.5) - mask_ = np.ones(mask_t.shape) * 0.5 - mask_[np.where(mask_t == True)] = 1 - masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_, axis=2)), axis=2) - save_dir_ = os.path.join(path_visual2, '{}_{}-{}'.format(unique_id, start_time, start_idx)) - if not os.path.exists(save_dir_): - os.makedirs(save_dir_) - fig2 = plt.figure(figsize=(5, 5)) - ax2 = fig2.add_subplot(1, 1, 1) - ax2.imshow(masked_im) - ax2.axis('off') - plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + Plant.ext)) - plt.close(fig2) - - count += 1 - ## 3. visualize with bounding boxes - for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, - Plant.scores, color_all, Plant.filename_pre): - funcs.display_instances(img, roi, mask, class_id, - class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, - show_mask=True, - colors=color) - plt.savefig(os.path.join(path_visual3, '{}-visual{}'.format(t, Plant.ext))) - plt.close('all') - - # save all information - pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) - - else: - # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode='load') - # Load from existed PlantData instance - Plant = pkl.load(open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'rb')) - - print('\nFinished!\nThe results are saved here:\n{}'.format(Plant.savedir)) - - return Plant + color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] + + csvfile = open(os.path.join(Plant.savedir, 'linking_info.csv'), 'w', newline='') + writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) + writer_junk.writerow(['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) + for key_t in Plant.link_series: + # + ids = Plant.link_series[key_t]['unique_id'] + + start_time = int(key_t.replace('t', '')) + leaves_t = Plant.link_series[key_t]['new_leaf'] + for (unique_id, leaf) in zip(ids, leaves_t): + key_leaf = 'leaf{}'.format(leaf) + link_leaf = Plant.link_series[key_t][key_leaf] + start_idx = link_leaf[start_time] + for t in range(start_time, Plant.total_time): + img = Plant.images[t] + if link_leaf[t] >= 0: + color_all[t][link_leaf[t]] = colors[count] + mask_t = Plant.masks[t][:, :, link_leaf[t]] + + ## 1. save the masked image, i.e. single leaves + mask = np.zeros(mask_t.shape, dtype=np.uint8) + mask[np.where(mask_t)] = 255 + leaf_t = pcv.apply_mask(img, mask, mask_color='black') + + writer_junk.writerow([unique_id, start_time, Plant.filename_pre[start_time], t, Plant.filename_pre[t], link_leaf[t]]) + + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, + start_idx, t, link_leaf[t], + Plant.filename_pre[t]))) + pcv.print_image(leaf_t, os.path.join(path_visual1, + '{}_{}-{}-{}-{}_{}{}'.format(unique_id, start_time, + start_idx, t, link_leaf[t], + Plant.filename_pre[t], + Plant.ext))) + + + ## 2. show result with an alpha channel + # update the mask where there is an alpha channel (alpha=0.5) + mask_ = np.ones(mask_t.shape) * 0.5 + mask_[np.where(mask_t == True)] = 1 + masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_, axis=2)), axis=2) + save_dir_ = os.path.join(path_visual2, '{}_{}-{}'.format(unique_id, start_time, start_idx)) + if not os.path.exists(save_dir_): + os.makedirs(save_dir_) + fig2 = plt.figure(figsize=(5, 5)) + ax2 = fig2.add_subplot(1, 1, 1) + ax2.imshow(masked_im) + ax2.axis('off') + plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + Plant.ext)) + plt.close(fig2) + + count += 1 + ## 3. visualize with bounding boxes + for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, + Plant.scores, color_all, Plant.filename_pre): + funcs.display_instances(img, roi, mask, class_id, class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) +# display_instances(img, roi, mask, class_id, class_names, score, ax=get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) + plt.savefig(os.path.join(path_visual3, '{}-visual{}'.format(t, Plant.ext))) + plt.close('all') + # save all information + pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) + print('\nFinished!\nThe results are saved here:\n{}'.format(Plant.savedir)) + + return Plant \ No newline at end of file From d860954cad182c1885dd6d7e8421a2d7f7663970 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 25 Aug 2020 17:22:55 -0500 Subject: [PATCH 056/274] Update tests.py Update tests for time_series linking --- tests/tests.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 35152f65a..5afc10500 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -5682,9 +5682,8 @@ def test_plantcv_time_series_time_series(): link_logic = 1 class_names = ['BG', 'Leaf'] pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm - mode = 'link' suffix = '.jpg' - Plant_ = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, pattern_datetime, time_cond, link_logic, class_names, mode, suffix) + Plant_ = pcv.time_series.time_series_linking(imagedir=TIME_SERIES_TEST_RAW, segmentationdir=TIME_SERIES_TEST_INSTANCE_SEG, savedir=cache_dir, pattern_datetime=pattern_datetime, time_cond=time_cond, colors=None, class_names=class_names, suffix=suffix) assert len(os.listdir(Plant_.savedir)) > 0 # ############################## From 22b9a161fb809d5f287f0647aabe8e368435ee18 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 25 Aug 2020 17:23:09 -0500 Subject: [PATCH 057/274] Update time_series.md Update doc for time_series --- docs/time_series.md | 28 ++++++++++++---------------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index 7f8011b44..0a3cb208b 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -18,9 +18,8 @@ To understand the growth of every leaf instance of a plant, we need to re-assign - savedir: desired saving directory of linking result. Note that this function will generate a folder under your specified saving directory, with the date and time you run this programme - pattern_datetime: the pattern of date and time part in original file names, dafault value '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' which represents YYYY-MM-DD-hh-mm. - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment. Make sure the format of date matches the pattern-datetime. - - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 + - colors: a list of tuples represent for pre-defined colors. By default it is set to None and a list of colors will be generated and saved. - class_names: used in bounding box visualization. by default there are background and leaf, i.e. class_names = ['BG', 'leaf'] - - mode: can be either 'link' or 'load', use 'link' when trying to get linked time-series from instance segmentation; use 'load' when trying to load from saved linking - suffix: the suffix of original images, make sure all images having the same suffix, e.g. suffix='.jpg' or suffix='-img8.jpg'. Make sure all the images desired having the same suffix pattern. - **Output:** An instance of Plant object is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir". @@ -30,7 +29,8 @@ Note: under the user specified saving directory, a new folder named after the da 1. colors.pkl: the colors (represented by arrays) used in bounding box visualization. With this predefined color set, same color will represent for the leaf instance with same instance label all the time, i.e. same color represents leaf with the same label. Without this predefined list of color, the assignment of color will be random, i.e. the color provides no information to leaf indices. 2. details.txt: the logic of linking as well as time condition will be saved in this text file, so that would be easier for users to check these parameters for the specific experiment. 3. saved_plant.pkl: a "Plant" object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. -4. a folder called "visualization", which contains 3 subfolders: +4. linking_info.csv: a csv file includes the linking information will be saved. +5. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. @@ -80,28 +80,24 @@ from plantcv import plantcv as pcv path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol_512' ## Specify the directory of instance segmentation result -path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index7/2020-06-25-10-35/subsampled/2020-06-30-10-51/' +path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/segmentation/updated' ## Specify the desired directory to save results -path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index7/2020-06-25-10-35/time_series_linking' +path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' ## Specify the date-time pattern of original image names pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm ## Specify the desired time point to include in to the analysis -time_cond = ['08-05', '11-05', '17-05', '21-05'] +time_cond = ['08-05', '11-05', '17-05', '21-05'] # exp5 & exp6 -link_logic = 1 class_names = ['BG', 'Leaf'] -mode = 'link' - -## Specify the suffix for all images you want to include, note that all the images shown have the same suffix as you specified -## Normally it would be something like '.png' or '.jpg', in this specific case, the image contained in the folder have name like -## 'xxx-img{}.jpg', the number inside the {} represents the index of plant. So in this case, to include images for the same plant, -## you will have to specify the index of plant. -suffix = '-img7.jpg' -​linked_Plant = pcv.time_series.time_series_linking(path_img, path_segmentation, path_save, pattern_datetime, - time_cond, link_logic, class_names, mode, suffix) + +suffix = '-img{}.jpg'.format(idx_plant) + +linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, + pattern_datetime=pattern_datetime, time_cond=time_cond, class_names=['BG', 'Leaf'], suffix=suffix) + ``` When it finished, you can go ahead and check the saved result. If you are not sure where the results are saved, you can type: ```linked_Plant.savedir``` From 651afd8312ef222d6167dc0afdfd0f1e2c0fa7b5 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 26 Aug 2020 07:24:13 -0500 Subject: [PATCH 058/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index 8397f612f..c5c7050a4 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -3,11 +3,10 @@ from plantcv.plantcv.time_series.link_utilities import display_instances from plantcv.plantcv.time_series.link_utilities import _compute_overlaps_masks from plantcv.plantcv.time_series.link_utilities import get_ax -from plantcv.plantcv.time_series.link_utilities import _getmatchedIndex from plantcv.plantcv.time_series.link_utilities import PlantData from plantcv.plantcv.time_series.time_series import time_series_linking # add new functions to end of lists __all__ = ["_random_colors", "_apply_mask", "display_instances", "_compute_overlaps_masks", "get_ax", - "_getmatchedIndex", "PlantData", "time_series_linking"] + "PlantData", "time_series_linking"] From baba94fe936f81b09147ab041ce4a6e2da5f96e8 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 26 Aug 2020 07:24:18 -0500 Subject: [PATCH 059/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 8c4869f1c..d8a60b148 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -22,7 +22,8 @@ from plantcv import plantcv as pcv import copy import csv -import link_utilities as funcs +# import link_utilities as funcs +# import pcv.time_series.link_utilities as funcs def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, colors=None, class_names=['BG', 'Leaf'], suffix='.jpg'): @@ -71,8 +72,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti The original image name is inside the {}. """ # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) -# Plant = PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) + # Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) + Plant = pcv.time_series.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) Plant.getpath(Plant.imagedir) Plant.Sorttime(time_cond) @@ -133,8 +134,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti count = 0 if colors is None: if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): - colors = funcs._random_colors(40) -# colors = _random_colors(40) + # colors = funcs._random_colors(40) + colors = pcv.time_series._random_colors(40) pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) else: colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) @@ -196,8 +197,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti ## 3. visualize with bounding boxes for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.filename_pre): - funcs.display_instances(img, roi, mask, class_id, class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) -# display_instances(img, roi, mask, class_id, class_names, score, ax=get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) + # funcs.display_instances(img, roi, mask, class_id, class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) + pcv.time_series.display_instances(img, roi, mask, class_id, class_names, score, ax=pcv.time_series.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) plt.savefig(os.path.join(path_visual3, '{}-visual{}'.format(t, Plant.ext))) plt.close('all') # save all information From 641a910b83847a6beecbc3fbd48bdf88fa9b63c4 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 16:18:42 -0500 Subject: [PATCH 060/274] Delete link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 382 ------------------ 1 file changed, 382 deletions(-) delete mode 100644 plantcv/plantcv/time_series/link_utilities.py diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py deleted file mode 100644 index f0e5ff8c0..000000000 --- a/plantcv/plantcv/time_series/link_utilities.py +++ /dev/null @@ -1,382 +0,0 @@ -#### link utilities -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Jun 9 09:53:34 2020 - -Functions used in time series linking after getting leaf instances segmented - -@author: hudanyunsheng -""" - -import numpy as np -import matplotlib -import matplotlib.pyplot as plt -import os -import sys -import random -import math -import skimage.io -import pickle as pkl -import re -from skimage.measure import find_contours -from matplotlib import patches, lines -from matplotlib.patches import Polygon -from plantcv import plantcv as pcv -import datetime -import copy -import colorsys -from plantcv.plantcv import fatal_error -from scipy.optimize import linear_sum_assignment - - -def _random_colors(N, bright=True): - """ - Generate random colors. - To get visually distinct colors, generate them in HSV space then - convert to RGB. - """ - brightness = 1.0 if bright else 0.7 - hsv = [(i / N, 1, brightness) for i in range(N)] - colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv)) - random.shuffle(colors) - return colors - - -def _apply_mask(image, mask, color, alpha=0.5): - """Apply the given mask to the image. - """ - for c in range(3): - image[:, :, c] = np.where(mask == 1, - image[:, :, c] * - (1 - alpha) + alpha * color[c] * 255, - image[:, :, c]) - return image - - -def display_instances(image, boxes, masks, class_ids, class_names, - scores=None, title="", - figsize=(16, 16), ax=None, - show_mask=True, show_bbox=True, - colors=None, captions=None): - """ - boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates. - masks: [height, width, num_instances] - class_ids: [num_instances] - class_names: list of class names of the dataset - scores: (optional) confidence scores for each box - title: (optional) Figure title - show_mask, show_bbox: To show masks and bounding boxes or not - figsize: (optional) the size of the image - colors: (optional) An array or colors to use with each object - captions: (optional) A list of strings to use as captions for each object - """ - # Number of instances - N = boxes.shape[0] - if not N: - print("\n*** No instances to display *** \n") - else: - assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0] - - # If no axis is passed, create one and automatically call show() - auto_show = False - if not ax: - _, ax = plt.subplots(1, figsize=figsize) - auto_show = True - - # Generate random colors - colors = colors or _random_colors(N) - - # Show area outside image boundaries. - height, width = image.shape[:2] - ax.set_ylim(height + 10, -10) - ax.set_xlim(-10, width + 10) - ax.axis('off') - ax.set_title(title) - - masked_image = image.astype(np.uint32).copy() - for i in range(N): - color = colors[i] - - # Bounding box - if not np.any(boxes[i]): - # Skip this instance. Has no bbox. Likely lost in image cropping. - continue - y1, x1, y2, x2 = boxes[i] - if show_bbox: - p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, - alpha=0.7, linestyle="dashed", - edgecolor=color, facecolor='none') - ax.add_patch(p) - - # Label - if not captions: - class_id = class_ids[i] - score = scores[i] if scores is not None else None - label = class_names[class_id] - caption = "{} {:.3f}".format(label, score) if score else label - else: - caption = captions[i] - ax.text(x1, y1 + 8, caption, - color='w', size=11, backgroundcolor="none") - - # Mask - mask = masks[:, :, i] - if show_mask: - masked_image = _apply_mask(masked_image, mask, color) - - # Mask Polygon - # Pad to ensure proper polygons for masks that touch image edges. - padded_mask = np.zeros( - (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8) - padded_mask[1:-1, 1:-1] = mask - contours = find_contours(padded_mask, 0.5) - for verts in contours: - # Subtract the padding and flip (y, x) to (x, y) - verts = np.fliplr(verts) - 1 - p = Polygon(verts, facecolor="none", edgecolor=color) - ax.add_patch(p) - ax.imshow(masked_image.astype(np.uint8)) - if auto_show: - plt.show() - -def _compute_overlaps_masks(masks1, masks2): - """Computes IoU overlaps between two sets of masks. - masks1, masks2: [Height, Width, instances] - mode: 1 or 2, 1 represent for IOU, 2 represent for intersection over the area of the previous mask - """ - mode = 1 - # If either set of masks is empty return empty result - if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: - return np.zeros((masks1.shape[-1], masks2.shape[-1])) - n1 = masks1.shape[2] - n2 = masks2.shape[2] - intersections = np.zeros((n1,n2)) - unions = np.zeros((n1,n2)) - for idx_m in range(0, n1): - maski = np.expand_dims(masks1[:,:,idx_m], axis=2) - masks_ = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) - maski_ = np.reshape(maski > .5, (-1, maski.shape[-1])).astype(np.float32) - intersection = np.dot(masks_.T, maski_).squeeze() - intersections[idx_m,:] = intersection - union = np.sum(masks_,0) + np.sum(maski_) - intersection - unions[idx_m,:] = union - IOUs = np.divide(intersections,unions) - return IOUs - -def get_ax(rows=1, cols=1, size=16): # ??? - """Return a Matplotlib Axes array to be used in - all visualizations in the notebook. Provide a - central point to control graph sizes. - - Adjust the size attribute to control how big to render images - """ - fig, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows)) - fig.tight_layout() - return ax - -class PlantData(): - def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, suffix='.jpg'): - - self.suffix = suffix - self.ext = None - - self.pattern_datetime = pattern_datetime - - # store a list of time of the images - self.time = [] - # store a list of original prefixes of the original images - self.filename_pre = [] - # total num of leaves that being detected - self.numleaf = 0 - - # store all the images - self.images = [] - - # minimum dimension of image. To link time series, all images must have the same dimension, to make it easier, we make the image square - self.min_dim = 0 - - # store all the mask detection during [starttime, endtime] - self.masks = [] - - # store all the rois (for visualization with bounding boxes) - self.rois = [] - - # store all the class_ids (for visualization with bounding boxes) - self.class_ids = [] - - # store all the scores (for visualization with bounding boxes) - self.scores = [] - - # store the dataset directory, instance segmentation result directory, time-series linking result directory and visualization directory - self.imagedir = imagedir - self.segmentationdir = segmentationdir - junk = datetime.datetime.now() - - subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), - str(junk.hour).zfill(2), str(junk.minute).zfill(2)) - self.savedir = os.path.join(savedir, subfolder) - if not os.path.exists(self.savedir): - os.makedirs(self.savedir) - - self.visualdir = os.path.join(self.savedir, 'visualization') - - self.total_time = 0 - self.max_nleaf = 0 - self.init_nleaf = 0 - self.num_leaves = [] - self.num_emergence = 0 - - self.emerging_info = [] # list length: self.total_time - self.link_info = [] # list length: self.total_time-1, dictionaries inside - self.weights = [] # list length: self.total_time-1, dictionaries inside - self.available_leaves = [] # list length: self.total_time - self.link_series = dict() # only for those newly emerging leaves - - def getinitleaf(self): - self.init_nleaf = self.masks[0].shape[2] - - def getmaxleaf(self): - for i in range(0, len(self.masks)): - self.num_leaves.append(self.masks[i].shape[2]) - self.max_nleaf = np.max(self.num_leaves) - - def gettotaltime(self): - # also represent for the total number of images - self.total_time = len(self.time) - - def getnumemergence(self): - self.num_emergence = np.ones((self.total_time), dtype=int) * self.init_nleaf - self.num_emergence[1:] = np.diff(self.num_leaves) - - def getpath(self, path): - self.dir = path - - def Sorttime(self, time_cond): - """ - This function is designed for files with file names which contain a "date-time" part, with an user-defined pattern, e.g. YYYY-MM-DD-hh-mm - Return: loop through the dataset_dir, and add time in time order - """ - - filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] - filenames_ori = [f for f in os.listdir(self.imagedir) if f.endswith(self.suffix)] - time_temp = [] - file_name = [] - ext1, ext2 = os.path.splitext(self.suffix) - if ext1.startswith('.'): - self.ext = ext1 - elif ext2.startswith('.'): - self.ext = ext2 - for filename in filenames: - temp = re.search(self.pattern_datetime, filename) - if temp: - timepart = temp.group() - for cond in time_cond: - if timepart.endswith(cond): - time_temp.append(timepart) - junk = [1 if re.search(timepart, f) is not None else 0 for f in filenames_ori] - file_name.append(filenames_ori[junk.index(1)].replace(self.ext, '')) - continue - - index_temp = np.argsort(time_temp) - # forward - self.time = [time_temp[i] for i in index_temp] - self.filename_pre = [file_name[i] for i in index_temp] - - def load_images(self): - """ Load original images - This function is also designed for files with file names which contain a "date-time" part, with a user defined pattern - """ - temp_imgs = [] - sz = [] - for pre in self.filename_pre: - filename = pre + '.jpg' - junk = skimage.io.imread(os.path.join(self.imagedir, filename)) - temp_imgs.append(junk) - sz.append(np.min(junk.shape[0:2])) - self.min_dim = np.min(sz) - for junk in temp_imgs: - img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size - self.images.append(img) - - def load_results(self): - """ Instead of running instance segmentation, load instance segmentation results (masks) - """ - for t in self.time: - file_name = '{}.pkl'.format(t) - r = pkl.load(open(os.path.join(self.segmentationdir, file_name), 'rb')) - self.masks.append(r["masks"][0: self.min_dim, 0: self.min_dim, :]) # make all masks the same size - self.rois.append(r["rois"]) - self.class_ids.append(r['class_ids']) - self.scores.append(r['scores']) - - def initialize_linking(self): - self.emerging_info = [[] for i in range(0, self.total_time)] - self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time - 1)] - self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i + 1])) for i in range(0, self.total_time - 1)] - self.available_leaves = [np.array(range(0, self.num_leaves[i])) for i in range(0, self.total_time)] - self.time_compare = [np.ones((self.num_leaves[i]), dtype=int) * i for i in range(0, self.total_time)] - - def linking(self, t0): - threshold = 0.2 - masks0 = copy.deepcopy(self.masks[t0]) - masks1 = copy.deepcopy(self.masks[t0 + 1]) - leaves0 = copy.deepcopy(self.available_leaves[t0]) - leaves1 = copy.deepcopy(self.available_leaves[t0 + 1]) - n0 = len(leaves0) - n1 = len(leaves1) - n = np.min((n0, n1)) - N = np.max((n0, n1)) - weight = -np.inf * np.ones((n0, n1)) - link = -np.ones((n0)) - weight = _compute_overlaps_masks(masks0, masks1) - idx_col = np.where(np.max(weight, axis=0) < threshold)[0] # find those volumns with maximum value < 0.15 - avail_col = [x for x in range(0,n1) if x not in idx_col] - weight_ = copy.deepcopy(weight) - - weight_ = np.delete(weight_, idx_col, 1) - row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) - for (r, c) in zip(row_ind, col_ind): - if weight_[r, c] >= threshold: - link[r] = avail_col[c] - self.link_info[t0] = link - - def get_series(self): - ## define new leaves and their unique identifiers at time points with new leaves emerging - t = 0 - key_t = 't{}'.format(t) - self.link_series[key_t] = dict() - self.link_series[key_t]['new_leaf'] = self.available_leaves[0] - # - self.link_series[key_t]['unique_id'] = self.link_series[key_t]['new_leaf'] - unique_id = len(self.link_series[key_t]['new_leaf']) - - for t in range(1, self.total_time): - # for t in range(1, self.total_time-1): - new_leaves = [i for i in self.available_leaves[t] if i not in self.link_info[t - 1]] - if new_leaves: - key_t = 't{}'.format(t) - self.link_series[key_t] = dict() - self.link_series[key_t]['new_leaf'] = np.array(new_leaves) - - id_temp = [] - for new_leaf in new_leaves: - id_temp.append(unique_id) - unique_id = unique_id + 1 - self.link_series[key_t]['unique_id'] = np.array(id_temp) - ## for time points with new leaves emerging, get the linking information for every new leaf - for key_t in self.link_series: - t0 = int(key_t.replace('t', '')) - for leaf in self.link_series[key_t]['new_leaf']: - key_leaf = 'leaf{}'.format(leaf) - self.link_series[key_t][key_leaf] = -np.ones(self.total_time, dtype=int) - self.link_series[key_t][key_leaf][t0] = leaf - if t0 < self.total_time - 1: - self.link_series[key_t][key_leaf][t0 + 1] = self.link_info[t0][leaf] - for t_ in range(t0 + 2, self.total_time): - idx = self.link_series[key_t][key_leaf][t_ - 1] - if idx < 0: - break - else: - self.link_series[key_t][key_leaf][t_] = self.link_info[t_ - 1][idx] - \ No newline at end of file From c096251981db04fe2bc88f182a98a48198d16160 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 16:18:52 -0500 Subject: [PATCH 061/274] Create time_series_linking.py --- .../time_series/time_series_linking.py | 544 ++++++++++++++++++ 1 file changed, 544 insertions(+) create mode 100644 plantcv/plantcv/time_series/time_series_linking.py diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py new file mode 100644 index 000000000..d27896afa --- /dev/null +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -0,0 +1,544 @@ +#### link utilities +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 9 09:53:34 2020 + +Functions used in time series linking after getting leaf instances segmented + +@author: hudanyunsheng +""" + +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import os +import sys +import random +import math +import skimage.io +import pickle as pkl +import re +from skimage.measure import find_contours +from matplotlib import patches, lines +from matplotlib.patches import Polygon +from plantcv import plantcv as pcv +import datetime +import copy +import colorsys +from plantcv.plantcv import fatal_error +from scipy.optimize import linear_sum_assignment +import csv + +def _random_colors(num, bright=True): + """ + Generate desired number of random colors. + To get visually distinct colors, generate them in HSV space then + convert to RGB. + Inputs: + num: number of colors to be generated + bright: True or False, if true, the brightness would be 1.0; if False, the brightness would be 0.7. By default it would be True (brightness 0.7) + Output: generated colors (a tuple) + """ + brightness = 1.0 if bright else 0.7 + hsv = [(i / num, 1, brightness) for i in range(num)] + colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv)) + random.shuffle(colors) + return colors + + +def _apply_mask(image, mask, color, alpha=0.5): + """Apply the given mask to the image with a color and alpha channel assigned. + Inputs: + image: original RGB image + mask: a binary mask of same size to the image + color: a list of 3 values + alpha: alpha value indicating transparency, by default 0.5 + """ + for c in range(3): + image[:, :, c] = np.where(mask == 1, + image[:, :, c] * + (1 - alpha) + alpha * color[c] * 255, + image[:, :, c]) + return image + + +def _get_ax(rows=1, cols=1, size=16): # ??? + """Return a Matplotlib Axes array to be used in + all visualizations in the notebook. Provide a + central point to control graph sizes. + + Adjust the size attribute to control how big to render images + """ + fig, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows)) + fig.tight_layout() + return ax + + +def _display_instances(image, masks, figsize=(16, 16), title="", ax=None, colors=None): + """ + This function is inspired by the function in mrcnn + masks: [height, width, num_instances] + inst_ids: [num_instances] + title: (optional) Figure title + show_mask, show_bbox: To show masks and bounding boxes or not + figsize: (optional) the size of the image + colors: (optional) An array or colors to use with each object + captions: (optional) A list of strings to use as captions for each object + """ + # If no axis is passed, create one and automatically call show() + # auto_show = False + if not ax: + _, ax = plt.subplots(1, figsize=figsize) + # auto_show = True + + num_insts = masks.shape[2] + # Generate random colors + colors = colors or _random_colors(num_insts) + + # Show area outside image boundaries. + height, width = image.shape[:2] + ax.set_ylim(height + 10, -10) + ax.set_xlim(-10, width + 10) + ax.axis('off') + ax.set_title(title) + + masked_image = image.astype(np.uint32).copy() + for i in range(num_insts): + color = colors[i] + + # Mask + mask = masks[:, :, i] + masked_image = _apply_mask(masked_image, mask, color) + + # Mask Polygon + # Pad to ensure proper polygons for masks that touch image edges. + padded_mask = np.zeros( + (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8) + padded_mask[1:-1, 1:-1] = mask + contours = find_contours(padded_mask, 0.5) + for verts in contours: + # Subtract the padding and flip (y, x) to (x, y) + verts = np.fliplr(verts) - 1 + p = Polygon(verts, facecolor="none", edgecolor=color) + ax.add_patch(p) + ax.imshow(masked_image.astype(np.uint8)) + # if auto_show: + # plt.show() + + +def _compute_overlaps_masks(masks1, masks2): + """Computes IoU and IoS overlaps between two sets of masks. + The masks should be of the same size + masks1, masks2: [Height, Width, instances] + """ + # If either set of masks is empty return empty result + if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + return np.zeros((masks1.shape[-1], masks2.shape[-1])) + n1 = masks1.shape[2] + n2 = masks2.shape[2] + intersections = np.zeros((n1, n2)) + unions = np.zeros((n1, n2)) + ioss = np.zeros((n1, n2)) + for idx_m in range(0, n1): + maski = np.expand_dims(masks1[:, :, idx_m], axis=2) + masks_ = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) + maski_ = np.reshape(maski > .5, (-1, maski.shape[-1])).astype(np.float32) + intersection = np.dot(masks_.T, maski_).squeeze() + intersections[idx_m, :] = intersection + union = np.sum(masks_, 0) + np.sum(maski_) - intersection + unions[idx_m, :] = union + ioss[idx_m, :] = intersection / maski_.sum() + ious = np.divide(intersections, unions) + return ious, ioss + + +class InstanceTimeSeriesLinking(object): + """A class that links segmented instances throughout time + Assumption: the timepoints are all sorted, the images and masks are also sorted by timepoints (chronologically) + """ + + def __init__(self, images, masks, timepoints, thres, logic='IOS', name_sub='instance'): + # a list of images which are ndarrays + self.images = images + # a list of masks which are ndarrays (of the same length of images) + self.masks = masks + # a list of timepoints (of the same length of images) + self.timepoints = timepoints + self.total_time = len(self.timepoints) + # number of instances: a list in which every element represent for number of instances in corresponding image + self.n_insts = [] + for i in range(0, len(self.masks)): + self.n_insts.append(self.masks[i].shape[2]) + + # initialization for linking + self.thres = thres + self.link_info = [-np.ones((self.n_insts[i]), dtype=int) for i in range(0, self.total_time - 1)] + self.unlinked_insts = [np.array(range(0, self.n_insts[i])) for i in range(0, self.total_time)] + self.link_series = dict() # only for those newly emerging leaves + self.weights = [] + self.logic = logic.upper() + self.name_sub = name_sub + self.key_id = '{}_ids'.format(name_sub) + + def save_linked_series(self, savedir, savename): + """save lining information into a .csv file and a .pkl file with the same prefix of filename + Inputs: savedir and savename + """ + l0 = ['', ''] + [x for (idx, x) in enumerate(self.timepoints)] + l1 = ['unique_id', 'current_id'] + ['t{}'.format(idx) for (idx, x) in enumerate(self.timepoints)] + csvfile = open(os.path.join(savedir, savename + '.csv'), 'w', newline='') + writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) + writer_junk.writerow(l0) + writer_junk.writerow(l1) + for (t0, item) in self.link_series.items(): + for uid, cid in zip(item['unique_id'], item[self.key_id]): + new_line = ['{}'.format(uid), '{}'.format(cid)] + item['{}{}'.format(self.name_sub, cid)].tolist() + writer_junk.writerow(new_line) + csvfile.close() + pkl.dump(self, open(os.path.join(savedir, savename + '.pkl'), 'wb')) + + def linking(self, t0): + """ + Link instance segments of t0 to those of t1 (t0+1) by calculating the overlaps of their masks + :param t0: starting time point + :return: None, but self.link_info would be updated + """ + masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy( + self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays + n0, n1 = masks0.shape[2], masks1.shape[2] + weight = -np.inf * np.ones((n0, n1)) + link = -np.ones(n0) + ious, ioss = _compute_overlaps_masks(masks0, masks1) + if self.logic == 'IOU': + weight = ious + elif self.logic == 'IOS': + weight = ioss + self.weights.append(weight) + idx_col = np.where(np.max(weight, axis=0) < self.thres)[ + 0] # find those columns with maximum value < threshold (self.thres) + avail_col = [x for x in range(0, n1) if x not in idx_col] + weight_ = copy.deepcopy(weight) + weight_ = np.delete(weight_, idx_col, 1) + + row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) + for (r, c) in zip(row_ind, col_ind): + if weight_[r, c] >= self.thres: + link[r] = avail_col[c] + self.link_info[t0] = link + + def get_series(self): + """ + Using the self.link_info to create a dictionary save in self.link_series + which is a dictionary contains information for every timepoint with new instances not seen before, + and the linking information for every instance + :return: + """ + # define new leaves and their unique identifiers at time points with new leaves emerging + t = 0 + key_t = 't{}'.format(t) + self.link_series[key_t] = dict() + self.link_series[key_t][self.key_id] = self.unlinked_insts[0] + # + self.link_series[key_t]['unique_id'] = self.link_series[key_t][self.key_id] + unique_id = len(self.link_series[key_t][self.key_id]) + + for t in range(1, self.total_time): + new_insts = [i for i in self.unlinked_insts[t] if i not in self.link_info[t - 1]] + if new_insts: + key_t = 't{}'.format(t) + self.link_series[key_t] = dict() + self.link_series[key_t][self.key_id] = np.array(new_insts) + + id_temp = [] + for new_inst in new_insts: + id_temp.append(unique_id) + unique_id = unique_id + 1 + self.link_series[key_t]['unique_id'] = np.array(id_temp) + ## for time points with new leaves emerging, get the linking information for every new instance + for key_t in self.link_series: + t0 = int(key_t.replace('t', '')) + for (leaf_unique, inst) in zip(self.link_series[key_t]['unique_id'], self.link_series[key_t][self.key_id]): + key_inst = '{}{}'.format(self.name_sub, inst) + self.link_series[key_t][key_inst] = -np.ones(self.total_time, dtype=int) + self.link_series[key_t][key_inst][t0] = inst + if t0 < self.total_time - 1: + self.link_series[key_t][key_inst][t0 + 1] = self.link_info[t0][inst] + for t_ in range(t0 + 2, self.total_time): + idx = self.link_series[key_t][key_inst][t_ - 1] + if idx < 0: + break + else: + self.link_series[key_t][key_inst][t_] = self.link_info[t_ - 1][idx] + + def update_series(self): + """ + Update the linking by comparing pairs of timepoint which are t and t+2 + :return: + """ + link_series = copy.deepcopy(self.link_series) + for t_, content in link_series.items(): + t = int(t_.replace('t', '')) + for inst_id in content[self.key_id]: + temp = np.where(content['{}{}'.format(self.name_sub, inst_id)] < 0)[0] + if len(temp) > 0: + t0 = temp[0] - 1 + t2 = t0 + 2 + if t2 <= self.total_time - 1: + t2_ = 't{}'.format(t2) + if t2_ in self.link_series: + t0_ = 't{}'.format(t0) + # print('\nt0: {}, t2: {}'.format(t0_, t2_)) + idx0 = content['{}{}'.format(self.name_sub, inst_id)][t0] + mask0 = np.expand_dims(self.masks[t0][:, :, idx0], axis=2) + idxs2 = self.link_series[t2_][self.key_id] + masks2 = self.masks[t2][:, :, idxs2] + n2 = masks2.shape[2] + ious, ioss = _compute_overlaps_masks(mask0, masks2) + if self.logic == 'IOU': + weight = ious + elif self.logic == 'IOS': + weight = ioss + idx_col = np.where(np.max(weight, axis=0) < self.thres)[ + 0] # find those columns with maximum value < thres + # avail_col = [x for x in range(0, n2) if x not in idx_col] + weight_ = copy.deepcopy(weight) + weight_ = np.delete(weight_, idx_col, 1) + row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) + for (r, c) in zip(row_ind, col_ind): + if weight_[r, c] >= self.thres: + inst0 = idx0 + inst2 = idxs2[c] + + # find the time point + idx_l2 = np.where(self.link_series[t2_][self.key_id] == inst2)[0][0] + # add information to t_ + self.link_series[t_]['{}{}'.format(self.name_sub, leaf_id)][t0 + 1:] = \ + self.link_series[t2_]['{}{}'.format(self.name_sub, inst2)][t0 + 1:] + # delete from t2_ + self.link_series[t2_][self.key_id] = np.delete(self.link_series[t2_][self.key_id], + idx_l2) + self.link_series[t2_]['unique_id'] = np.delete(self.link_series[t2_]['unique_id'], + idx_l2) + del self.link_series[t2_]['{}{}'.format(self.name_sub, inst2)] + # if nothing left for t2_, delete it entirely + if len(self.link_series[t2_]['unique_id']) == 0: + del self.link_series[t2_] + # update unique ids + uid = 0 + for t in self.link_series: + num = len(self.link_series[t][self.key_id]) + self.link_series[t]['unique_id'] = np.arange(uid, uid + num) + uid = uid + num + + def visualize(self, visualdir, csvdir, csvname='linking_info', colors=None, color_all=None): + """ + Create 3 sets of visualization + visualization set 1: one leaf per image (visualdir['1']) + visualization set 2: show with an alpha channel (visualdir['2']) + visualization method 3: show with bounding boxes (visualdir['3']) + Also save a csv file called linking_info.csv (or other user defined name) that includes the linking information + """ + + # create subfolders inside the provided directory of visualization + visualdirs = dict() + for i in range(1, 4): + idx = str(i) + visualdirs[idx] = os.path.join(visualdir, 'visualization{}'.format(idx)) + if not os.path.exists(visualdirs[idx]): + os.makedirs(visualdirs[idx]) + + if colors is None: + colors = _random_colors(40) + if color_all is None: + color_all = [[tuple() for i in range(0, num)] for num in self.n_insts] + + count = 0 + csvfile = open(os.path.join(csvdir, csvname + '.csv'), 'w', newline='') + writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) + writer_junk.writerow( + ['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) + for key_t in self.link_series: + ids = self.link_series[key_t]['unique_id'] + start_time = int(key_t.replace('t', '')) + insts_t = self.link_series[key_t][self.key_id] + for (unique_id, inst) in zip(ids, insts_t): + key_inst = '{}{}'.format(self.name_sub, inst) + link_inst = self.link_series[key_t][key_inst] + start_idx = link_inst[start_time] + for t in range(0, self.total_time): + img_t = self.images[t] + # initialize mask used in 1st visualization + mask_1 = np.zeros(img_t.shape[0:2], dtype=np.uint8) + # initialize mask used in 2nd visualization (with an alpha channel, ahpha=0.5) + mask_2 = np.ones(img_t.shape[0:2]) * 0.5 + title_vis2 = '{} (t{})'.format(self.timepoints[t], t) + if link_inst[t] >= 0: + title_vis2 = title_vis2 + ' leaf {}'.format(link_inst[t]) + mask_t = self.masks[t][:, :, link_inst[t]] + color_all[t][link_inst[t]] = colors[count] + + ## 1. save the masked image, i.e. single leaves + mask_1[np.where(mask_t)] = 255 + leaf_t = pcv.apply_mask(img_t, mask_1, mask_color='black') + writer_junk.writerow( + [unique_id, start_time, self.timepoints[start_time], t, self.timepoints[t], link_inst[t]]) + + pcv.print_image(leaf_t, os.path.join(visualdirs['1'], + '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, + start_idx, t, link_inst[t], + self.timepoints[t]))) + mask_2[np.where(mask_t == True)] = 1 + masked_im = np.concatenate((img_t.astype(float) / 255, np.expand_dims(mask_2, axis=2)), axis=2) + # for 2nd set of visualization, a folder for one unique instance + save_dir_ = os.path.join(visualdirs['2'], '{}_{}-{}'.format(unique_id, start_time, start_idx)) + if not os.path.exists(save_dir_): + os.makedirs(save_dir_) + fig2 = plt.figure(figsize=(5, 5)) + ax2 = fig2.add_subplot(1, 1, 1) + ax2.imshow(masked_im) + ax2.axis('off') + ax2.set_title(title_vis2, fontsize=16) + plt.savefig(os.path.join(save_dir_, str(self.timepoints[t]) + '.png')) + plt.close(fig2) + + count += 1 + csvfile.close() + ## 3. visualize with bounding boxes + for (img, masks, color, t) in zip(self.images, self.masks, color_all, self.timepoints): + _display_instances(img, masks, figsize=(16, 16), title="", ax=_get_ax(rows=1, cols=1, size=16), + colors=color) + plt.savefig(os.path.join(visualdirs['3'], '{}.png'.format(t))) + plt.close('all') + + def __call__(self, savedir, visualdir_, visualdir, savename_, savename, colors=None, color_all=None): + for t0 in range(0, self.total_time - 1): + self.linking(t0) + self.get_series() + self.visualize(visualdir_, savedir, 'link_info_old', colors, color_all) + self.save_linked_series(savedir, savename_) + + self.update_series() + self.visualize(visualdir, savedir, 'link_info', colors, color_all) + self.save_linked_series(savedir, savename) + +class InstanceTSLinkingWrapper(object): + """A class that is a wrapper for InstanceTimeSeriesLinking + Assumptions: + 1) The segmentation are saved in a different folder from original images + 2) The segmentation result and the original images have corresponding image names, which include "date-time" part with a specific pattern + the date-time pattern for names of original images and names of segmentation results are the same + 3) For every image, there is a corresponding segmentation result + Otherwise, please get your data prepared and use the "InstanceTimeSeriesLinking" class directly + """ + def __init__(self, dir_save, savename): + self.dir_save = dir_save + self.savename = savename + self.savename_ = '{}_old'.format(savename) + self.dir_img = None + self.dir_seg = None + self.time_cond = None + self.suffix = None + self.ext = None + self.suffix_seg = None + self.timepoints = [] + self.imgfiles = [] + self.segfiles = [] + self.dir_visual = None + self.dir_visual_ = None + + def set_save_dirs(self): + junk = datetime.datetime.now() + subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), + str(junk.hour).zfill(2), str(junk.minute).zfill(2), str(junk.second).zfill(2)) + self.dir_save = os.path.join(self.dir_save, subfolder) + if not os.path.exists(self.dir_save): + os.makedirs(self.dir_save) + self.dir_visual = os.path.join(self.dir_save, 'visualization') + if not os.path.exists(self.dir_visual): + os.makedirs(self.dir_visual) + self.dir_visual_ = os.path.join(self.dir_save, 'visualization_old') + if not os.path.exists(self.dir_visual_): + os.makedirs(self.dir_visual_) + + def sort_time(self, dir_img, dir_seg, pattern_dt, time_cond, suffix='.jpg', suffix_seg='.pkl'): + """ + This function is designed for files with file names which contain a "date-time" part, with an user-defined pattern + sort time based on names of original images + :param dir_img: directory of original images + :param dir_seg: directory of segmentation results + :param pattern_dt: the common pattern of date-time part in file names, e.g. YYYY-MM-DD-hh-mm + :param time_cond: the date time of data intended to be included in the analysis + :param suffix: the common suffix of all original image files + :param suffix_seg: the common suffix of all segmentation result files + :return: sorted and trimed list of images as well as segmentation files + """ + self.dir_img = dir_img + self.dir_seg = dir_seg + self.time_cond = time_cond + self.suffix = suffix + self.suffix_seg = suffix_seg + + # file name extension of original images + ext1, ext2 = os.path.splitext(self.suffix) + if ext1.startswith('.'): + self.ext = ext1 + elif ext2.startswith('.'): + self.ext = ext2 + + imgfs_all = [f for f in os.listdir(self.dir_img) if f.endswith(self.suffix)] + segfs_all = [f for f in os.listdir(self.dir_seg) if f.endswith(self.suffix_seg)] + imgfs_all.sort() + segfs_all.sort() + for (fi, fs) in zip(imgfs_all, segfs_all): + tempi = re.search(pattern_dt, fi) + temps = re.search(pattern_dt, fs) + if tempi and temps: + timepart = tempi.group() #timeparts = temps.group() the assumption is that original image and the results have the same dt_pattern + for cond in self.time_cond: + if timepart.endswith(cond): + self.timepoints.append(timepart) + continue + index_temp = np.argsort(self.timepoints) + self.timepoints = [self.timepoints[i] for i in index_temp] + self.imgfiles = [imgfs_all[i] for i in index_temp] + self.segfiles = [segfs_all[i] for i in index_temp] + + def load_images(self): + """ + Get the list of images (nd-arrays) ready + :return: + """ + temp_imgs = [] + sz = [] + imgs = [] + for f in self.imgfiles: + junk = skimage.io.imread(os.path.join(self.dir_img, f)) + temp_imgs.append(junk) + sz.append(np.min(junk.shape[0:2])) + min_dim = np.min(sz) + for junk in temp_imgs: + img = junk[0: min_dim, 0: min_dim, :] # make all images the same size + imgs.append(img) + return imgs, min_dim + + def load_segs(self, min_dim): + """ + Get the list of segmentation masks (nd-arrays) ready + :param min_dim: minimum dimension of images (get from original images) (Assumption: square images) + :return: + """ + segs = [] + for f in self.segfiles: + r = pkl.load(open(os.path.join(self.dir_seg, f), 'rb')) + segs.append(r["masks"][0:min_dim, 0:min_dim, :]) # make all masks the same size + return segs + + def __call__(self, dir_img, dir_seg, pattern_dt, time_cond, thres, logic, name_sub, suffix, suffix_seg,colors=None,color_all=None): + self.set_save_dirs() + self.sort_time(dir_img, dir_seg, pattern_dt, time_cond, suffix, suffix_seg) + imgs,min_dim = self.load_images() + segs = self.load_segs(min_dim) + # create an instance of InstanceTimeSeriesLinkingClass + inst_ts_linking = InstanceTimeSeriesLinking(imgs, segs, self.timepoints, thres, logic, name_sub) + inst_ts_linking(self.dir_save, self.dir_visual_, self.dir_visual, self.savename_, self.savename, colors, color_all) From 4e87aa421157c8065bcbaa4ebcb85a331f4c9200 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 16:19:15 -0500 Subject: [PATCH 062/274] Update tests.py --- tests/tests.py | 26 ++++++++++++++++++++------ 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 5afc10500..3f8e763bb 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -5673,18 +5673,32 @@ def test_plantcv_visualize_time_lapse_video_bad_list(): # ############################## # Tests for the time_series subpackage # ############################## + def test_plantcv_time_series_time_series(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir - time_cond = ['08-05', '11-05', '17-05', '21-05'] - link_logic = 1 - class_names = ['BG', 'Leaf'] + dir_img = TIME_SERIES_TEST_RAW + dir_seg = TIME_SERIES_TEST_INSTANCE_SEG + dir_save = cache_dir + dir_visual_ = os.path.join(dir_save,'visual_old') + os.mkdir(dir_visual_) + dir_visual = os.path.join(dir_save,'visual') + os.mkdir(dir_visual) pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm - suffix = '.jpg' - Plant_ = pcv.time_series.time_series_linking(imagedir=TIME_SERIES_TEST_RAW, segmentationdir=TIME_SERIES_TEST_INSTANCE_SEG, savedir=cache_dir, pattern_datetime=pattern_datetime, time_cond=time_cond, colors=None, class_names=class_names, suffix=suffix) - assert len(os.listdir(Plant_.savedir)) > 0 + time_cond = ['08-05', '11-05', '17-05', '21-05'] + suffix = '.jpg' + suffix_seg = '.pkl' + thres = 0.2 + logic = 'IOS' + name_sub = 'leaf' + name_series = 'linked_series' + inst_ts_linking = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) + inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, thres, logic, name_sub, suffix, + suffix_seg) + assert len(os.listdir(inst_ts_linking.dir_save)) > 0 + # ############################## # Tests for the utils subpackage From 7953402942b1ad487ec799add48371387a5abdb6 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 16:19:17 -0500 Subject: [PATCH 063/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index c5c7050a4..b93fec640 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,12 +1,12 @@ -from plantcv.plantcv.time_series.link_utilities import _random_colors -from plantcv.plantcv.time_series.link_utilities import _apply_mask -from plantcv.plantcv.time_series.link_utilities import display_instances -from plantcv.plantcv.time_series.link_utilities import _compute_overlaps_masks -from plantcv.plantcv.time_series.link_utilities import get_ax -from plantcv.plantcv.time_series.link_utilities import PlantData -from plantcv.plantcv.time_series.time_series import time_series_linking +from plantcv.plantcv.time_series.time_series_linking import _random_colors +from plantcv.plantcv.time_series.time_series_linking import _apply_mask +from plantcv.plantcv.time_series.time_series_linking import _get_ax +from plantcv.plantcv.time_series.time_series_linking import _display_instances +from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks +from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking +from plantcv.plantcv.time_series.time_series_linking import InstanceTSLinkingWrapper # add new functions to end of lists -__all__ = ["_random_colors", "_apply_mask", "display_instances", "_compute_overlaps_masks", "get_ax", - "PlantData", "time_series_linking"] +__all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", + "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] From 3ae098c714dc7c11c5fd79f1ecf4e0e9fe5bda80 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 18:39:30 -0500 Subject: [PATCH 064/274] Update time_series.md Update docs for time_series --- docs/time_series.md | 153 +++++++++++++++++++++++++++++--------------- 1 file changed, 101 insertions(+), 52 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index 0a3cb208b..e31e58e26 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -1,36 +1,46 @@ ## time_series -This function is designed to link segmented objects over time. Images should be taken across a time (e.g. every several hours) and ideally there should be minimal movment of plant/camera. +This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every several hours) and ideally there should be minimal movment of plant/camera. -To run this function, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). +To use this class for generating time-series linking, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. -**plantcv.time_series.time_series_linking**(*imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'*) +There are two way of using this class: either using it directly or using the wrapper. Examples of both are provided below. -**returns** An instance that is a Plant object +**\# initialize an instance of InstanceTimeSeriesLinking class:** + +**inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking**(*images, masks, timepoints, logic, thres, name_sub*) +**inst_ts_linking**(*savedir, visualdir_, visualdir, savename_, savename*) + +**returns** No returned value, the inst_ts_linking is an instance which belongs to InstanceTimeSeriesLinking class. + +- **Parameters for initialization:** + - images: a list of images. Every element of this list is an array represents one image + - masks: a list of instance segmentation masks. Every element of this list is an array represents instance segmentation masks correspond to one image. + - timepoints: a list of timepoints. The lengths for images, masks and timepoint should be the same and the elements are correspond to each other + - logic: the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) + If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". "IOS" is recommended. + - thres: threshold used in the linking logic as mentioned above. For "IOS" it is recommended to start with a threshod of 0.2. + - name_sub: name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". + +Once you get the class object initialized, it is callable, which means it is a callable function itself so that you can get the linking result by running the 2nd line of code presented above. +- **Parameters to call functions:** + - savedir: desired saving directory of linking result + - visualdir_: desired saving directory of visualization (before update) + - visualdir: desired saving directory of visualization (final) + - savename_: desired saving name of results (before update) + - savename: desired saving name of results (final) -- **Parameters:** - - imagedir: directory of original image used - - segmentationdir: directory of leaf instance segmentation result - - savedir: desired saving directory of linking result. Note that this function will generate a folder under your specified saving directory, with the date and time you run this programme - - pattern_datetime: the pattern of date and time part in original file names, dafault value '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' which represents YYYY-MM-DD-hh-mm. - - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment. Make sure the format of date matches the pattern-datetime. - - colors: a list of tuples represent for pre-defined colors. By default it is set to None and a list of colors will be generated and saved. - - class_names: used in bounding box visualization. by default there are background and leaf, i.e. class_names = ['BG', 'leaf'] - - suffix: the suffix of original images, make sure all images having the same suffix, e.g. suffix='.jpg' or suffix='-img8.jpg'. Make sure all the images desired having the same suffix pattern. - **Output:** - An instance of Plant object is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir". - -Note: under the user specified saving directory, a new folder named after the date and time (format: YYYY-MM-DD-HH-mm) the function runs will be created to save results. - -1. colors.pkl: the colors (represented by arrays) used in bounding box visualization. With this predefined color set, same color will represent for the leaf instance with same instance label all the time, i.e. same color represents leaf with the same label. Without this predefined list of color, the assignment of color will be random, i.e. the color provides no information to leaf indices. -2. details.txt: the logic of linking as well as time condition will be saved in this text file, so that would be easier for users to check these parameters for the specific experiment. -3. saved_plant.pkl: a "Plant" object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. -4. linking_info.csv: a csv file includes the linking information will be saved. -5. a folder called "visualization", which contains 3 subfolders: + An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir": +You will get two sets of results: those end with "_old" are results before updating; others are final results. +1. {}.pkl (or {}_old.pkl): an "InstanceTimeSeriesLinking" class instance, with all the information included: time points, original images, instance segmentation masks, etc. The filename is specified by user. +2. {}.csv (or {}_old.csv): a csv file includes the linking series information (every row in the table is a unique instance throughtout time). +3. link_info.csv (or {}_old.csv): a csv file includes the linking information. +4. a folder called "visualization" ("visualization_old"), which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. @@ -64,46 +74,24 @@ Note: under the user specified saving directory, a new folder named after the da {}-visual.png The original image name is inside the {}. This set of visualization is designed for making time-lapse videos of segmentation shown with bounding boxes. With the instance labels re-assigned such that one label represents one leaf across the whole time, we would observe that every leaf is represented by a specific color across the whold time period. - -Before running the time_series_linking function, it is always a good practice to check the quality of instance segmentation before run time_series_linking. One way to check the instance segmentation is to make a time-lapse video with your instance segmentation result shown in bounding boxes. To make time-lapse videos, you will need video editing softwares, e.g. imovie. An example is shown below. Notice that in almost all cases, you would observe that the colors for the same leaf change during the time. That is due to the random assignment of labels. You can later compare this time lapse video to that generate after all labels re-assigned by running time_series_linking function. + +It is always a good practice to check the quality of instance segmentation before running time_series_linking. One way to check the instance segmentation is to make a time-lapse video with your instance segmentation result shown in bounding boxes. To make time-lapse videos, you will need video editing softwares, e.g. imovie. An example is shown below. Notice that in almost all cases, you would observe that the colors for the same leaf change during the time. That is due to the random assignment of labels. You can later compare this time lapse video to that generate after all labels re-assigned by running time_series_linking function. Instead of making a video, another way to check the quality of instance segmentation is by checking the segmented image one by one. - ```python from plantcv import plantcv as pcv # Below are examples of input variables, always adjust base on your own application. - -## Specify the directory of original image -path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol_512' - -## Specify the directory of instance segmentation result -path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/segmentation/updated' - -## Specify the desired directory to save results -path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' - -## Specify the date-time pattern of original image names -pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm - -## Specify the desired time point to include in to the analysis -time_cond = ['08-05', '11-05', '17-05', '21-05'] # exp5 & exp6 - -class_names = ['BG', 'Leaf'] - -suffix = '-img{}.jpg'.format(idx_plant) - -linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, - pattern_datetime=pattern_datetime, time_cond=time_cond, class_names=['BG', 'Leaf'], suffix=suffix) - +inst_ts_linking = InstanceTimeSeriesLinking(images, masks, timepoints, logic, thres, name_sub) +inst_ts_linking(save_dir, visualdir_, visualdir, savename_, savename, csvname_, csvname) ``` When it finished, you can go ahead and check the saved result. If you are not sure where the results are saved, you can type: -```linked_Plant.savedir``` +```inst_ts_linking.savedir``` in a cell to print out the saving directory. -You can also type ```linked_Plant.link_series``` in a cell to print linking information on the screen for a quick check. +You can also type ```inst_ts_linking.link_series``` in a cell to print linking information on the screen for a quick check. Now that we have the linking results saved, we can have some analysis based on them. There are 3 suggested analysis described as below: @@ -122,8 +110,6 @@ You have two options of making time-lapse videos: using video editing software ( - There is also a PlantCV tool that is able to automatically generate and save time-lapse videos. Check here for the usage. The example code is provided at the end of this documentation. - - 3. Making a time-lapse video to visualize the growth of the plant by showing the segmentation in bounding boxes, with the same leaf always shown with the same color. Direct yourself to "visualization 3" folder and include all images to a video project, sort them by name in an ascending order, and generate a time-lapse video. An example of time-lapse video with visualization shown in bounding boxes is shown below. When compare to the video made of instance segmentation, result, you will observe that now every leaf is represented by the same color all the time, which means the label re-assignemnt is successful. @@ -159,6 +145,69 @@ path_video_3 = Plant.visualdir pcv.visualize.time_lapse_video(img_directory=path_visual3, suffix_img=Plant.ext, name_video=name_video_3, path_video=path_video_3, display='on') ``` +You might have noticed that to use the method described above, you will have to get data prepared, i.e. the images should be in a list and the masks should be in a list of same lenghth. +Both of them should be sorted chronologically. You can absolutely do this, and it is not hard to do, but alternatively, you are also welcome to use another class, which is a wrapper, by which +you would only need to provide directories, and conditions for time points. +InstanceTSLinkingWrapper(object): + +**inst_ts_link_wrapper = plantcv.time_series.InstanceTSLinkingWrapper**(*dir_save, savename*) +**inst_ts_link = inst_ts_link_wrapper**(*dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, suffix, suffix_seg*) +**returns** An instance which belongs to InstanceTimeSeriesLinking class. + +- **Parameters for initialization:** + - dir_save: the desired directory to save the results + Note: under the user specified saving directory, a new folder named after the date and time (format: YYYY-MM-DD-HH-mm) the function runs will be created to save results. + - savename: the desired name to save the result + +Once you get the class object initialized, it is callable, which means it is a callable function itself so that you can get the linking result by running the 2nd line of code presented above. +- **Parameters to call functions:** + - dir_img: directory of original images + - dir_seg: directory of segmentation results + - pattern_dt: the pattern of date and time part in original file names, dafault value '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' which represents YYYY-MM-DD-hh-mm. + - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment. Make sure the format of date matches the pattern-datetime. + - logic: the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) + If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". "IOS" is recommended. + - thres: threshold used in the linking logic as mentioned above. For "IOS" it is recommended to start with a threshod of 0.2. + - name_sub: name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". + - suffix: the suffix of original images, make sure all images having the same suffix, e.g. suffix='.jpg' or suffix='-img8.jpg'. Make sure all the images desired having the same suffix pattern. By default ".jpg" + - suffix_seg: the suffix of segmentation results, make sure all segmentation results having the same suffix. By default '.pkl' + +```python +from plantcv import plantcv as pcv +# Below are examples of input variables, always adjust base on your own application. +## Specify the desired directory to save results +dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' + +## Specify the desired name to save the result (prefix) +savename = 'linked_series' + +## Initialize and instance of class InstanceTSLinkingWrapper +inst_ts_linking_wrap = InstanceTSLinkingWrapper(dir_save=path_save, savename=name_series) + +## Specify the directory of original image +dir_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol_512' +## Specify the directory of instance segmentation result +dir_seg = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/segmentation/updated' +## Specify the date-time pattern of original image names +pattern_dt = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm +## Specify the desired time point to include in to the analysis +time_cond = ['08-05', '11-05', '17-05', '21-05'] +## Specify the linking logic and threshold +logic = 'IOS' +thres = 0.2 +## Specify the main subject +name_sub = 'leaf' +## Specify the common suffix of interested original images +suffix = '-img12.jpg' +## Specify the common suffix of interested segmentation result +suffix_seg = '.pkl' + +inst_ts_linking = pcv.time_series.inst_ts_linking_wrap(dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, suffix, suffix_seg) +``` +All the analysis for the results are same to what described above. + + + **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) From 051dbfd1d7b5f67ca11e9ce83cdaf8f0e6f08070 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 18:39:38 -0500 Subject: [PATCH 065/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index d27896afa..b3a802b99 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -158,7 +158,7 @@ class InstanceTimeSeriesLinking(object): Assumption: the timepoints are all sorted, the images and masks are also sorted by timepoints (chronologically) """ - def __init__(self, images, masks, timepoints, thres, logic='IOS', name_sub='instance'): + def __init__(self, images, masks, timepoints, logic='IOS', thres=0.2, name_sub='instance'): # a list of images which are ndarrays self.images = images # a list of masks which are ndarrays (of the same length of images) @@ -534,11 +534,12 @@ def load_segs(self, min_dim): segs.append(r["masks"][0:min_dim, 0:min_dim, :]) # make all masks the same size return segs - def __call__(self, dir_img, dir_seg, pattern_dt, time_cond, thres, logic, name_sub, suffix, suffix_seg,colors=None,color_all=None): + def __call__(self, dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, suffix, suffix_seg,colors=None,color_all=None): self.set_save_dirs() self.sort_time(dir_img, dir_seg, pattern_dt, time_cond, suffix, suffix_seg) imgs,min_dim = self.load_images() segs = self.load_segs(min_dim) # create an instance of InstanceTimeSeriesLinkingClass - inst_ts_linking = InstanceTimeSeriesLinking(imgs, segs, self.timepoints, thres, logic, name_sub) + inst_ts_linking = InstanceTimeSeriesLinking(imgs, segs, self.timepoints, logic, thres, name_sub) inst_ts_linking(self.dir_save, self.dir_visual_, self.dir_visual, self.savename_, self.savename, colors, color_all) + return inst_ts_linking From 22211a4944a08db7cf4bed8e1ad006b96f1afd30 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 18:39:41 -0500 Subject: [PATCH 066/274] Update tests.py --- tests/tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index 3f8e763bb..390205fd2 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -5695,7 +5695,7 @@ def test_plantcv_time_series_time_series(): name_sub = 'leaf' name_series = 'linked_series' inst_ts_linking = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) - inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, thres, logic, name_sub, suffix, + inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, logic, thres, name_sub, suffix, suffix_seg) assert len(os.listdir(inst_ts_linking.dir_save)) > 0 From 8b1015d8687c1f239f4524092d562d68b343da94 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 22:20:11 -0500 Subject: [PATCH 067/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index b3a802b99..26ee0a97b 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -313,7 +313,7 @@ def update_series(self): # find the time point idx_l2 = np.where(self.link_series[t2_][self.key_id] == inst2)[0][0] # add information to t_ - self.link_series[t_]['{}{}'.format(self.name_sub, leaf_id)][t0 + 1:] = \ + self.link_series[t_]['{}{}'.format(self.name_sub, inst_id)][t0 + 1:] = \ self.link_series[t2_]['{}{}'.format(self.name_sub, inst2)][t0 + 1:] # delete from t2_ self.link_series[t2_][self.key_id] = np.delete(self.link_series[t2_][self.key_id], From 0b44ead991be9092e447cb721f16a7a38de5b99d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 25 Sep 2020 08:21:31 -0500 Subject: [PATCH 068/274] Delete time_series.py --- plantcv/plantcv/time_series/time_series.py | 208 --------------------- 1 file changed, 208 deletions(-) delete mode 100644 plantcv/plantcv/time_series/time_series.py diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py deleted file mode 100644 index d8a60b148..000000000 --- a/plantcv/plantcv/time_series/time_series.py +++ /dev/null @@ -1,208 +0,0 @@ -#### link utilities -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Jun 9 09:53:34 2020 - -Time series linking after getting leaf instances segmented - -@author: hudanyunsheng -""" - -import os -import sys -import random -import math -import numpy as np -import skimage.io -import matplotlib -import matplotlib.pyplot as plt -import pickle as pkl -import re -from plantcv import plantcv as pcv -import copy -import csv -# import link_utilities as funcs -# import pcv.time_series.link_utilities as funcs - -def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, colors=None, class_names=['BG', 'Leaf'], suffix='.jpg'): - - """ - Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) - Input: - imagedir: directory of original image used - segmentationdir: directory of leaf instance segmentation result - savedir: desired saving directory of linking result - pattern_datetime: the regular expression of date-time part in file names. e.g. if the date-time part in file names is: YYYY-MM-DD-hh-mm, then pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment, this should be consistent with the pattern_datetime - colors: a list of tuples where every tuple represent for one color (RGB values). By default it can be None. - class_names: used in bounding box visualization. By default there are background and leaf - suffix: the ending part of file names that every image have in common. By default it is '.jpg'. - Output: - An instance from "Plant" class would be returned, all the results will be saved in a folder with name of date and time when the function runs inside the user defined "savedir". Sample folder name: 2020-06-23-15-42 (a format of YYYY-MM-DD-HH-mm). - 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time - 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment - 3. saved_plant.pkl: a Plant object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. - 4. linking_info.csv: a csv file includes the linking information will be saved. - 5. a folder called "visualization", which contains 3 subfolders: - 1) a folder call "visualization 1", which contains 1st set of visualization - In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. - result name: {}_{}-{}-{}-{}_{}.png - Naming convention for file names: - 1st digit: unique identifier of the leaf - 2nd digit: time of first emergence of the leaf - 3rd digut: leaf index when it first emerges - 4rd digit: current time point - 5th digit: current leaf index - 6th digit: original image name - - 2) a folder called "visualization 2", which contains 2nd set of visualization - This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent - There are several subfolders, the number of subfolders depends on the number of "new leaves" in total - Every subfolder is a "new leaf", whose name is {}_{}-{} - Naming convention for folder names: - 1st digit: unique identifier of the leaf - 2nd digit: time of first emergence of the leaf - 3rd digut: leaf index when it first emerges - Inside every folder, images of leaves with names same as original image names are contained. - - 3) a folder called "visualization 3", which containes 3rd set of visualization - This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. - Naming format: {}-visual.png - The original image name is inside the {}. - """ - # initialize Plant class - # Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) - Plant = pcv.time_series.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) - - Plant.getpath(Plant.imagedir) - Plant.Sorttime(time_cond) - Plant.load_images() - - # load mrcnn inferencing results - Plant.load_results() - - Plant.getinitleaf() - Plant.getmaxleaf() - - Plant.gettotaltime() - Plant.getnumemergence() - - # plot the #leaves vs. time relationship - plt.plot(np.array(range(0, len(Plant.num_leaves))), Plant.num_leaves) - - if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): - file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') - file.write('mode: IOU\n') - file.write('Directory of original images: {}\n'.format(Plant.imagedir)) - file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) - file.write('Image conditions: {}'.format(time_cond)) - file.close() - - print('start\n') - print('...') - - # linking initialization - Plant.initialize_linking() - - # link - for t in range(0, Plant.total_time - 1): -# Plant.linking(t, mode=link_logic) - Plant.linking(t) - Plant.get_series() - - ####### visualization ####### - # load original images - if len(Plant.images) == 0: - Plant.load_images() # Plant.images - - # visualization method 1: show only one leaf per image - path_visual1 = os.path.join(Plant.visualdir, 'visualization1') - if not os.path.exists(path_visual1): - os.makedirs(path_visual1) - - # visualization method 2: show with an alpha channel - path_visual2 = os.path.join(Plant.visualdir, 'visualization2') - if not os.path.exists(path_visual2): - os.makedirs(path_visual2) - - # visualization method 3: show with bounding boxes - path_visual3 = os.path.join(Plant.visualdir, 'visualization3') - if not os.path.exists(path_visual3): - os.makedirs(path_visual3) - - count = 0 - if colors is None: - if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): - # colors = funcs._random_colors(40) - colors = pcv.time_series._random_colors(40) - pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) - else: - colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) - - color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] - - csvfile = open(os.path.join(Plant.savedir, 'linking_info.csv'), 'w', newline='') - writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) - writer_junk.writerow(['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) - for key_t in Plant.link_series: - # - ids = Plant.link_series[key_t]['unique_id'] - - start_time = int(key_t.replace('t', '')) - leaves_t = Plant.link_series[key_t]['new_leaf'] - for (unique_id, leaf) in zip(ids, leaves_t): - key_leaf = 'leaf{}'.format(leaf) - link_leaf = Plant.link_series[key_t][key_leaf] - start_idx = link_leaf[start_time] - for t in range(start_time, Plant.total_time): - img = Plant.images[t] - if link_leaf[t] >= 0: - color_all[t][link_leaf[t]] = colors[count] - mask_t = Plant.masks[t][:, :, link_leaf[t]] - - ## 1. save the masked image, i.e. single leaves - mask = np.zeros(mask_t.shape, dtype=np.uint8) - mask[np.where(mask_t)] = 255 - leaf_t = pcv.apply_mask(img, mask, mask_color='black') - - writer_junk.writerow([unique_id, start_time, Plant.filename_pre[start_time], t, Plant.filename_pre[t], link_leaf[t]]) - - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, - start_idx, t, link_leaf[t], - Plant.filename_pre[t]))) - pcv.print_image(leaf_t, os.path.join(path_visual1, - '{}_{}-{}-{}-{}_{}{}'.format(unique_id, start_time, - start_idx, t, link_leaf[t], - Plant.filename_pre[t], - Plant.ext))) - - - ## 2. show result with an alpha channel - # update the mask where there is an alpha channel (alpha=0.5) - mask_ = np.ones(mask_t.shape) * 0.5 - mask_[np.where(mask_t == True)] = 1 - masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_, axis=2)), axis=2) - save_dir_ = os.path.join(path_visual2, '{}_{}-{}'.format(unique_id, start_time, start_idx)) - if not os.path.exists(save_dir_): - os.makedirs(save_dir_) - fig2 = plt.figure(figsize=(5, 5)) - ax2 = fig2.add_subplot(1, 1, 1) - ax2.imshow(masked_im) - ax2.axis('off') - plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + Plant.ext)) - plt.close(fig2) - - count += 1 - ## 3. visualize with bounding boxes - for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, - Plant.scores, color_all, Plant.filename_pre): - # funcs.display_instances(img, roi, mask, class_id, class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) - pcv.time_series.display_instances(img, roi, mask, class_id, class_names, score, ax=pcv.time_series.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) - plt.savefig(os.path.join(path_visual3, '{}-visual{}'.format(t, Plant.ext))) - plt.close('all') - # save all information - pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) - print('\nFinished!\nThe results are saved here:\n{}'.format(Plant.savedir)) - - return Plant \ No newline at end of file From b40c6eb0651d2c4022b2f6a759d6d09e85130a52 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 25 Sep 2020 12:42:23 -0500 Subject: [PATCH 069/274] Update time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 45 +++++++++++-------- 1 file changed, 26 insertions(+), 19 deletions(-) diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py index ac3139f1d..759f450c5 100644 --- a/plantcv/plantcv/visualize/time_lapse_video.py +++ b/plantcv/plantcv/visualize/time_lapse_video.py @@ -61,24 +61,27 @@ def _resize_img(img, size): def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=None, fps=29.97, name_video='time_lapse_video', path_video=None, display='on'): - """Generate time-lapse video given a folder of images - Inputs: - img_directory: the directory of folder of images to make the time-lapse video - list_img: desired list of images in img_directory to create the video. If None is passed, all images would be included by default. - name_video: the prefix of output video name - size_frame: the desired size of every frame. + """ + Generate time-lapse video given a folder of images + :param img_directory: the directory of folder of images to make the time-lapse video. + :param list_img: desired list of images in img_directory to create the video. If None is passed, all images would be included by default. + :param suffix_img: + :param size_frame: the desired size of every frame. In a video, every frame should have the same size. The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. If the frame size is given, if an images is larger than the given size, the image would be cropped automatically; if an image is smaller than the given size, the image would be zero-padded automatically If the frame size is not given, the largest size of all images would be used as the frame size. - fps: (frames per second) frame rate + :param fps: (frames per second) frame rate Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 - path_video: the desired saving path of output video. If not given, the video would be saved the the same directory of the images. - display: indicator of whether to display current status (by displaying saving directory and saving name) while running this function - Outputs: - list_img: the list of images used to generate the video - size_frame: the frame size of the video - """ + :param name_video: + :param path_video: the desired saving path of output video. If not given, the video would be saved the the same directory of the images. + :param display: indicator of whether to display current status (by displaying saving directory and saving name) while running this function + :param kw: possible name and value: compress, and a number indicating what fraction compared to the original image. e.g. 0.5 represent compress the image to 1/2 of its original size + :return: + list_img: the list of images used to generate the video + size_frame: the frame size of the video + """ + ## Get the list of image files in the given directory and sort them alphabetically by their names temp_list = [] for f in os.listdir(img_directory): @@ -130,12 +133,10 @@ def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=N list_r.append(img.shape[0]) list_c.append(img.shape[1]) imgs.append(img) - max_c = np.max(list_c) - max_r = np.max(list_r) + max_c, max_r = np.max(list_c), np.max(list_r) # If the frame size is not provided, use the largest size of the images as the frame size - if size_frame is None: - size_frame = (max_c, max_r) + size_frame = size_frame or (max_c, max_r) if not (len(np.unique(list_r)) == 1 and len(np.unique(list_c)) == 1): warnings.warn("The sizes of images are not the same, an image resizing (cropping or zero-padding) will be done to make all images the same size ({}x{}) before creating the video! If you assume the images should have the same size, please check the images used to generate this video!".format( @@ -149,10 +150,16 @@ def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=N save_name = os.path.join(path_video, name_video + '.mp4') fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case + # if 'compress' in kw: + # ratio = kw['compress'] + # else: + # ratio = 1 + # size_frame_ = tuple(int(x*ratio) for x in size_frame) out = cv2.VideoWriter(save_name, fourcc, fps, size_frame) - for img in imgs: - out.write(_resize_img(img, (max_r, max_c))) + for img in imgs: + # out.write(cv2.resize(_resize_img(img, (size_frame[-1],size_frame[0])), (size_frame_))) + out.write(_resize_img(img, (size_frame[-1], size_frame[0]))) out.release() cv2.destroyAllWindows() if display == 'on': From ce985c5023d578c85a26fcd56578b0e656d96e35 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 25 Sep 2020 12:42:30 -0500 Subject: [PATCH 070/274] Update tests.py --- tests/tests.py | 38 +++++++++++++++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index efecd6489..78411691e 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -5966,7 +5966,6 @@ def test_plantcv_visualize_colorspaces(): vis_img = pcv.visualize.colorspaces(rgb_img=img) assert np.shape(vis_img)[1] > (np.shape(img)[1]) and np.shape(vis_img_small)[1] > (np.shape(img)[1]) - def test_plantcv_visualize_colorspaces_bad_input(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_plot_hist") @@ -5977,6 +5976,34 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) +def test_plantcv_visualize_time_lapse_video_resize_img(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + resized_img = pcv.visualize._resize_img(img, (10,10)) + assert np.shape(resized_img) == (10,10,3) + +def test_plantcv_visualize_time_lapse_video_resize_img_large(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + resized_img = pcv.visualize._resize_img(img, (1000,1000)) + assert np.shape(resized_img) == (1000,1000,3) + +def test_plantcv_visualize_time_lapse_video_resize_img_gray(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) + resized_img = pcv.visualize._resize_img(img, (10,10)) + assert np.shape(resized_img) == (10,10) + +def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) + resized_img = pcv.visualize._resize_img(img, (1000,1000)) + assert np.shape(resized_img) == (1000,1000) + # case1: only the correct directory of images is provided def test_plantcv_visualize_time_lapse_video_case1(): # Test cache directory @@ -5986,6 +6013,15 @@ def test_plantcv_visualize_time_lapse_video_case1(): _, _ = pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', path_video=cache_dir, display='off') assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case1.mp4')) +# case1.5: no path_video provided and display "on" +def test_plantcv_visualize_time_lapse_video_case1(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case1') + os.mkdir(cache_dir) + with pytest.warns(UserWarning): + _, _ = pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', display='on') + assert os.path.exists(os.path.join(TIME_SERIES_TEST_RAW, 'time_lapse_video_case1.mp4')) + # case2: the correct directory of images as well as the correct suffix of images is provided def test_plantcv_visualize_time_lapse_video_case2(): # Test cache directory From 9d92d34ffe0f619b07d0d33c35787bda1a966d84 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 08:47:06 -0500 Subject: [PATCH 071/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index b93fec640..ca0a009fb 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,8 +1,8 @@ -from plantcv.plantcv.time_series.time_series_linking import _random_colors -from plantcv.plantcv.time_series.time_series_linking import _apply_mask -from plantcv.plantcv.time_series.time_series_linking import _get_ax -from plantcv.plantcv.time_series.time_series_linking import _display_instances -from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks +# from plantcv.plantcv.time_series.time_series_linking import _random_colors +# from plantcv.plantcv.time_series.time_series_linking import _apply_mask +# from plantcv.plantcv.time_series.time_series_linking import _get_ax +# from plantcv.plantcv.time_series.time_series_linking import _display_instances +# from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking from plantcv.plantcv.time_series.time_series_linking import InstanceTSLinkingWrapper From c3e769db96e7b13801dac3e969b3c6e955faaf8b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 10:05:26 -0500 Subject: [PATCH 072/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index ca0a009fb..a187a3f02 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -8,5 +8,7 @@ # add new functions to end of lists -__all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", - "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] +# __all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", +# "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] + +__all__ = ["InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] From 106201c8fea649edc9bd87194c1e8cad7883868a Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 10:05:28 -0500 Subject: [PATCH 073/274] Update __init__.py --- plantcv/plantcv/visualize/__init__.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/visualize/__init__.py b/plantcv/plantcv/visualize/__init__.py index 05e4b0fda..1f430745d 100644 --- a/plantcv/plantcv/visualize/__init__.py +++ b/plantcv/plantcv/visualize/__init__.py @@ -5,6 +5,7 @@ from plantcv.plantcv.visualize.colorspaces import colorspaces from plantcv.plantcv.visualize.auto_threshold_methods import auto_threshold_methods from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video -from plantcv.plantcv.visualize.time_lapse_video import _resize_img +# from plantcv.plantcv.visualize.time_lapse_video import _resize_img -__all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] +# __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] +__all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video"] \ No newline at end of file From 8f454b8d381d8a3861e074316f95972066e44e35 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 10:39:45 -0500 Subject: [PATCH 074/274] Update tests.py --- tests/tests.py | 54 +++++++++++++++++++++++++------------------------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 78411691e..bd237c296 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -5976,33 +5976,33 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) -def test_plantcv_visualize_time_lapse_video_resize_img(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - resized_img = pcv.visualize._resize_img(img, (10,10)) - assert np.shape(resized_img) == (10,10,3) - -def test_plantcv_visualize_time_lapse_video_resize_img_large(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - resized_img = pcv.visualize._resize_img(img, (1000,1000)) - assert np.shape(resized_img) == (1000,1000,3) - -def test_plantcv_visualize_time_lapse_video_resize_img_gray(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) - resized_img = pcv.visualize._resize_img(img, (10,10)) - assert np.shape(resized_img) == (10,10) - -def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) - resized_img = pcv.visualize._resize_img(img, (1000,1000)) - assert np.shape(resized_img) == (1000,1000) +# def test_plantcv_visualize_time_lapse_video_resize_img(): +# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') +# os.mkdir(cache_dir) +# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) +# resized_img = pcv.visualize._resize_img(img, (10,10)) +# assert np.shape(resized_img) == (10,10,3) +# +# def test_plantcv_visualize_time_lapse_video_resize_img_large(): +# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') +# os.mkdir(cache_dir) +# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) +# resized_img = pcv.visualize._resize_img(img, (1000,1000)) +# assert np.shape(resized_img) == (1000,1000,3) +# +# def test_plantcv_visualize_time_lapse_video_resize_img_gray(): +# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') +# os.mkdir(cache_dir) +# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) +# resized_img = pcv.visualize._resize_img(img, (10,10)) +# assert np.shape(resized_img) == (10,10) +# +# def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): +# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') +# os.mkdir(cache_dir) +# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) +# resized_img = pcv.visualize._resize_img(img, (1000,1000)) +# assert np.shape(resized_img) == (1000,1000) # case1: only the correct directory of images is provided def test_plantcv_visualize_time_lapse_video_case1(): From f57109796170491398c19ce5b7b2fd5fe2d459d2 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 15:27:36 -0500 Subject: [PATCH 075/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index a187a3f02..b93fec640 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,14 +1,12 @@ -# from plantcv.plantcv.time_series.time_series_linking import _random_colors -# from plantcv.plantcv.time_series.time_series_linking import _apply_mask -# from plantcv.plantcv.time_series.time_series_linking import _get_ax -# from plantcv.plantcv.time_series.time_series_linking import _display_instances -# from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks +from plantcv.plantcv.time_series.time_series_linking import _random_colors +from plantcv.plantcv.time_series.time_series_linking import _apply_mask +from plantcv.plantcv.time_series.time_series_linking import _get_ax +from plantcv.plantcv.time_series.time_series_linking import _display_instances +from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking from plantcv.plantcv.time_series.time_series_linking import InstanceTSLinkingWrapper # add new functions to end of lists -# __all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", -# "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] - -__all__ = ["InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] +__all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", + "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] From a62cf9e80f2bd6d5f2235b2e43e3a54630c49160 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 15:27:38 -0500 Subject: [PATCH 076/274] Update __init__.py --- plantcv/plantcv/visualize/__init__.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/visualize/__init__.py b/plantcv/plantcv/visualize/__init__.py index 1f430745d..348cee3f5 100644 --- a/plantcv/plantcv/visualize/__init__.py +++ b/plantcv/plantcv/visualize/__init__.py @@ -5,7 +5,6 @@ from plantcv.plantcv.visualize.colorspaces import colorspaces from plantcv.plantcv.visualize.auto_threshold_methods import auto_threshold_methods from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video -# from plantcv.plantcv.visualize.time_lapse_video import _resize_img +from plantcv.plantcv.visualize.time_lapse_video import _resize_img -# __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] -__all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video"] \ No newline at end of file +__all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] \ No newline at end of file From 8f7251a59b3950713d5464e3ed7f8f5e7c0d9677 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 15:27:54 -0500 Subject: [PATCH 077/274] Update tests.py --- tests/tests.py | 54 +++++++++++++++++++++++++------------------------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index bd237c296..78411691e 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -5976,33 +5976,33 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) -# def test_plantcv_visualize_time_lapse_video_resize_img(): -# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') -# os.mkdir(cache_dir) -# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) -# resized_img = pcv.visualize._resize_img(img, (10,10)) -# assert np.shape(resized_img) == (10,10,3) -# -# def test_plantcv_visualize_time_lapse_video_resize_img_large(): -# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') -# os.mkdir(cache_dir) -# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) -# resized_img = pcv.visualize._resize_img(img, (1000,1000)) -# assert np.shape(resized_img) == (1000,1000,3) -# -# def test_plantcv_visualize_time_lapse_video_resize_img_gray(): -# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') -# os.mkdir(cache_dir) -# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) -# resized_img = pcv.visualize._resize_img(img, (10,10)) -# assert np.shape(resized_img) == (10,10) -# -# def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): -# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') -# os.mkdir(cache_dir) -# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) -# resized_img = pcv.visualize._resize_img(img, (1000,1000)) -# assert np.shape(resized_img) == (1000,1000) +def test_plantcv_visualize_time_lapse_video_resize_img(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + resized_img = pcv.visualize._resize_img(img, (10,10)) + assert np.shape(resized_img) == (10,10,3) + +def test_plantcv_visualize_time_lapse_video_resize_img_large(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + resized_img = pcv.visualize._resize_img(img, (1000,1000)) + assert np.shape(resized_img) == (1000,1000,3) + +def test_plantcv_visualize_time_lapse_video_resize_img_gray(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) + resized_img = pcv.visualize._resize_img(img, (10,10)) + assert np.shape(resized_img) == (10,10) + +def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) + resized_img = pcv.visualize._resize_img(img, (1000,1000)) + assert np.shape(resized_img) == (1000,1000) # case1: only the correct directory of images is provided def test_plantcv_visualize_time_lapse_video_case1(): From 7b3fd0d6ae1bb31d19a46a990c40861b7c5be484 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 09:20:21 -0500 Subject: [PATCH 078/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 26ee0a97b..40a966d48 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -132,9 +132,9 @@ def _compute_overlaps_masks(masks1, masks2): The masks should be of the same size masks1, masks2: [Height, Width, instances] """ - # If either set of masks is empty return empty result - if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: - return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # # If either set of masks is empty return empty result + # if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + # return np.zeros((masks1.shape[-1], masks2.shape[-1])) n1 = masks1.shape[2] n2 = masks2.shape[2] intersections = np.zeros((n1, n2)) From e49194d5b1ff12a097054c64678723eab522fd95 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 09:20:24 -0500 Subject: [PATCH 079/274] Update tests.py --- tests/tests.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index 78411691e..eb89ada73 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -18,6 +18,7 @@ import matplotlib import dask from dask.distributed import Client +import pickle as pkl PARALLEL_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "parallel_data") TEST_TMPDIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", ".cache") @@ -6080,6 +6081,13 @@ def test_plantcv_visualize_time_lapse_video_bad_list(): # Tests for the time_series subpackage # ############################## +def test_plantcv_time_series_display_instances(): + # Test cache directory + loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_INSTANCE_SEG, "img_segment_.pkl"), 'rb')) + img = loaded['img'] + masks = loaded['segment']['masks'] + pcv.time_series._display_instances(img, masks, figsize=(16, 16), title="", ax=None, colors=None) + def test_plantcv_time_series_time_series(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") @@ -6103,7 +6111,10 @@ def test_plantcv_time_series_time_series(): inst_ts_linking = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, logic, thres, name_sub, suffix, suffix_seg) - assert len(os.listdir(inst_ts_linking.dir_save)) > 0 + inst_ts_linking2 = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) + inst_ts_linking2(dir_img, dir_seg, pattern_datetime, time_cond, 'IOU', thres, name_sub, suffix, + suffix_seg) + assert (len(os.listdir(inst_ts_linking.dir_save)) > 0) and (len(os.listdir(inst_ts_linking2.dir_save)) > 0) # ############################## From 24fcf5ebc9778d719f2d5832ea6afc4c047f70c0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 12:44:26 -0500 Subject: [PATCH 080/274] Create img_segment_.pkl --- .../instance_segmentation/img_segment_.pkl | Bin 0 -> 2637508 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/seires_data/instance_segmentation/img_segment_.pkl diff --git a/tests/seires_data/instance_segmentation/img_segment_.pkl b/tests/seires_data/instance_segmentation/img_segment_.pkl new file mode 100644 index 0000000000000000000000000000000000000000..3ee6e9f1b6c3b9ee16cadd153b3cc1e72e5fc85b GIT binary patch literal 2637508 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"seires_data") -TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im") -TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "instance_segmentation") +TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im_2") +TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "inst_seg_2") # ########################## # Tests for the main package From 69711a239bca2b002eb0b8ddedc03c30ae8cf753 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 14:49:58 -0500 Subject: [PATCH 083/274] Update tests.py --- tests/tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index 2f723e4ac..1208d4658 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6112,7 +6112,7 @@ def test_plantcv_time_series_time_series(): inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, logic, thres, name_sub, suffix, suffix_seg) inst_ts_linking2 = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) - inst_ts_linking2(dir_img, dir_seg, pattern_datetime, time_cond, 'IOU', thres, name_sub, suffix, + inst_ts_linking2(dir_img, dir_seg, pattern_datetime, time_cond, 'IOU', thres, name_sub, '12.jpg', suffix_seg) assert (len(os.listdir(inst_ts_linking.dir_save)) > 0) and (len(os.listdir(inst_ts_linking2.dir_save)) > 0) From b88b88488c6beb67dbf5811f7650db1585775885 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 15:30:39 -0500 Subject: [PATCH 084/274] update test data --- .../2019-10-21-21-05.pkl | Bin .../seires_data/inst_seg/2019-10-22-08-05.pkl | Bin 1248143 -> 788463 bytes .../seires_data/inst_seg/2019-10-22-11-05.pkl | Bin 1248143 -> 1059359 bytes .../2019-10-22-14-05.pkl | Bin .../seires_data/inst_seg/2019-10-22-17-05.pkl | Bin 1255648 -> 1053195 bytes .../seires_data/inst_seg/2019-10-22-21-05.pkl | Bin 1503671 -> 1057299 bytes .../seires_data/inst_seg/2019-10-23-08-05.pkl | Bin 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z81YOU{SK=E?9OHT4dzEK(5(2#+39r03tFFjm@i7wy!W%*TIjN)PfhphkdyqGTaX!- zJzlO+>L4K^^O|iHuWiR&?q!EtjKJkSoZfiia@1=m&^B>M}>^sH3I%bJMh6bgvpFd}d zfB%8#(pPIl0}%IHW8z`Etl-lCmv-VNM&yxc(x#k*c6Zvu)v>kjHV-2ssUy;)N9F)b z(G$=V8rQ*<`+=K1!ZiGyyQ-jOeXRrdlb&SfJGDxp(cpLFLmd zGe*dgq{&H$Oc^RdM|_;BriUov+LOm!|*FpWddC;iz`<6@_+c3G^D%gK1RvaD4u z;1|fsz}KaVF;7J&d#D0W#if#V9-pjE<*&y9$RFBd*W38e8T`^7cl9J4!zBn-+pRRZ zf}q$KKmNn%pGR!r3}E+~<9^Yh!H#QfP1fVPzKy@NL%ZiH$jR)b8>$GafT{rPG}0VeOa_1bLs%M-JklZr;o?4%ufeh{2Hl6Nhiz2!<|ZT8qp-DAOH$~|E&F+7GQ2;i|V>Ne?W z^rq^FT-Id-E8H2O1*T8L&CzOzd6-mR`kgP%h6Oagzk|nZ#q5vr%#G1M9LgpL-?K2l zRf!6)9BH)YKSBL#-(V*(Aj>;o5?|LDQs_erl6!?NX%lXdh_)V;`*bzvwJtG#?A)Z< zX;$$IV`8A{(J4Q_X{6ACMRx@`*vw-1{Ee|B=gR+_zRcnZ1N57h5ww}KfAr+hD#VWE&EMBeerQ-=)pv&r$pg;rrL;NeYsY2oU~8IAB*m(5BZI_~^6Uec_P>hoOjHD#d>|krgcetv39>=i_ny KH=M!0rT+)3&PBZd From cc0af79fb0dc68e60104c05b695c71f5dca6a112 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 15:30:44 -0500 Subject: [PATCH 085/274] Update tests.py --- tests/tests.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 1208d4658..e0f415d0e 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -942,8 +942,8 @@ def test_plantcv_parallel_process_results_invalid_json(): PIXEL_VALUES = "pixel_inspector_rgb_values.txt" TIME_SERIES_TEST_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "seires_data") -TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im_2") -TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "inst_seg_2") +TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im") +TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "inst_seg") # ########################## # Tests for the main package From 1b0c284e0c517dcc205059caaf64410a37e5d71b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 19 Oct 2020 12:46:39 -0500 Subject: [PATCH 086/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 184 +++++++++++------- 1 file changed, 113 insertions(+), 71 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 40a966d48..fcd0fbd77 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -132,9 +132,9 @@ def _compute_overlaps_masks(masks1, masks2): The masks should be of the same size masks1, masks2: [Height, Width, instances] """ - # # If either set of masks is empty return empty result - # if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: - # return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # If either set of masks is empty return empty result + if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + return np.zeros((masks1.shape[-1], masks2.shape[-1])) n1 = masks1.shape[2] n2 = masks2.shape[2] intersections = np.zeros((n1, n2)) @@ -152,34 +152,31 @@ def _compute_overlaps_masks(masks1, masks2): ious = np.divide(intersections, unions) return ious, ioss - class InstanceTimeSeriesLinking(object): """A class that links segmented instances throughout time Assumption: the timepoints are all sorted, the images and masks are also sorted by timepoints (chronologically) """ - def __init__(self, images, masks, timepoints, logic='IOS', thres=0.2, name_sub='instance'): + def __init__(self): # a list of images which are ndarrays - self.images = images + self.images = None # a list of masks which are ndarrays (of the same length of images) - self.masks = masks + self.masks = None # a list of timepoints (of the same length of images) - self.timepoints = timepoints - self.total_time = len(self.timepoints) + self.timepoints = None + self.total_time = None # number of instances: a list in which every element represent for number of instances in corresponding image - self.n_insts = [] - for i in range(0, len(self.masks)): - self.n_insts.append(self.masks[i].shape[2]) + self.n_insts = None # initialization for linking - self.thres = thres - self.link_info = [-np.ones((self.n_insts[i]), dtype=int) for i in range(0, self.total_time - 1)] - self.unlinked_insts = [np.array(range(0, self.n_insts[i])) for i in range(0, self.total_time)] - self.link_series = dict() # only for those newly emerging leaves - self.weights = [] - self.logic = logic.upper() - self.name_sub = name_sub - self.key_id = '{}_ids'.format(name_sub) + self.thres = None + self.link_info = None + self.unlinked_insts = None + self.link_series = None + self.weights = None + self.logic = None + self.name_sub = None + self.key_id = None def save_linked_series(self, savedir, savename): """save lining information into a .csv file and a .pkl file with the same prefix of filename @@ -196,7 +193,19 @@ def save_linked_series(self, savedir, savename): new_line = ['{}'.format(uid), '{}'.format(cid)] + item['{}{}'.format(self.name_sub, cid)].tolist() writer_junk.writerow(new_line) csvfile.close() - pkl.dump(self, open(os.path.join(savedir, savename + '.pkl'), 'wb')) + # pkl.dump(self, open(os.path.join(savedir, savename + '.pkl'), 'wb')) + pkl.dump(vars(self), open(os.path.join(savedir, savename+".pkl"), 'wb')) + + def import_linked_series(self, savedir, savename): + """import a linked time-series from previously saved file + + :param savedir: saving directory + :param savename: saving name + :return: + """ + linked = pkl.load(open(os.path.join(savedir, savename+'.pkl'),"rb")) + for key, value in linked.items(): + setattr(self, key, value) def linking(self, t0): """ @@ -204,8 +213,7 @@ def linking(self, t0): :param t0: starting time point :return: None, but self.link_info would be updated """ - masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy( - self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays + masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays n0, n1 = masks0.shape[2], masks1.shape[2] weight = -np.inf * np.ones((n0, n1)) link = -np.ones(n0) @@ -385,7 +393,7 @@ def visualize(self, visualdir, csvdir, csvname='linking_info', colors=None, colo [unique_id, start_time, self.timepoints[start_time], t, self.timepoints[t], link_inst[t]]) pcv.print_image(leaf_t, os.path.join(visualdirs['1'], - '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, + '{}_{}-{}-{}-{}_{}.jpg'.format(unique_id, start_time, start_idx, t, link_inst[t], self.timepoints[t]))) mask_2[np.where(mask_t == True)] = 1 @@ -399,7 +407,7 @@ def visualize(self, visualdir, csvdir, csvname='linking_info', colors=None, colo ax2.imshow(masked_im) ax2.axis('off') ax2.set_title(title_vis2, fontsize=16) - plt.savefig(os.path.join(save_dir_, str(self.timepoints[t]) + '.png')) + plt.savefig(os.path.join(save_dir_, str(self.timepoints[t]) + '.jpg')) plt.close(fig2) count += 1 @@ -408,10 +416,32 @@ def visualize(self, visualdir, csvdir, csvname='linking_info', colors=None, colo for (img, masks, color, t) in zip(self.images, self.masks, color_all, self.timepoints): _display_instances(img, masks, figsize=(16, 16), title="", ax=_get_ax(rows=1, cols=1, size=16), colors=color) - plt.savefig(os.path.join(visualdirs['3'], '{}.png'.format(t))) + plt.savefig(os.path.join(visualdirs['3'], '{}.jpg'.format(t))) plt.close('all') - def __call__(self, savedir, visualdir_, visualdir, savename_, savename, colors=None, color_all=None): + def __call__(self, images, masks, timepoints, savedir, visualdir_, visualdir, savename_, savename, logic="IOS", thres=0.2, name_sub="instance",colors=None, color_all=None): + # a list of images which are ndarrays + self.images = images + # a list of masks which are ndarrays (of the same length of images) + self.masks = masks + # a list of timepoints (of the same length of images) + self.timepoints = timepoints + self.total_time = len(self.timepoints) + # number of instances: a list in which every element represent for number of instances in corresponding image + self.n_insts = [] + for i in range(0, len(self.masks)): + self.n_insts.append(self.masks[i].shape[2]) + + # initialization for linking + self.thres = thres + self.link_info = [-np.ones((self.n_insts[i]), dtype=int) for i in range(0, self.total_time - 1)] + self.unlinked_insts = [np.array(range(0, self.n_insts[i])) for i in range(0, self.total_time)] + self.link_series = dict() # only for those newly emerging leaves + self.weights = [] + self.logic = logic.upper() + self.name_sub = name_sub + self.key_id = '{}_ids'.format(name_sub) + for t0 in range(0, self.total_time - 1): self.linking(t0) self.get_series() @@ -431,26 +461,29 @@ class InstanceTSLinkingWrapper(object): 3) For every image, there is a corresponding segmentation result Otherwise, please get your data prepared and use the "InstanceTimeSeriesLinking" class directly """ - def __init__(self, dir_save, savename): - self.dir_save = dir_save - self.savename = savename - self.savename_ = '{}_old'.format(savename) + + def __init__(self): + self.dir_save = None + self.savename = None + self.savename_ = None self.dir_img = None self.dir_seg = None self.time_cond = None - self.suffix = None - self.ext = None + self.suffix = None + self.ext = None self.suffix_seg = None - self.timepoints = [] - self.imgfiles = [] - self.segfiles = [] + self.timepoints = None + self.imgfiles = None + self.segfiles = None self.dir_visual = None self.dir_visual_ = None + self.pattern_dt = None def set_save_dirs(self): junk = datetime.datetime.now() subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), - str(junk.hour).zfill(2), str(junk.minute).zfill(2), str(junk.second).zfill(2)) + str(junk.hour).zfill(2), str(junk.minute).zfill(2), + str(junk.second).zfill(2)) self.dir_save = os.path.join(self.dir_save, subfolder) if not os.path.exists(self.dir_save): os.makedirs(self.dir_save) @@ -461,23 +494,11 @@ def set_save_dirs(self): if not os.path.exists(self.dir_visual_): os.makedirs(self.dir_visual_) - def sort_time(self, dir_img, dir_seg, pattern_dt, time_cond, suffix='.jpg', suffix_seg='.pkl'): + def sort_time(self): """ This function is designed for files with file names which contain a "date-time" part, with an user-defined pattern sort time based on names of original images - :param dir_img: directory of original images - :param dir_seg: directory of segmentation results - :param pattern_dt: the common pattern of date-time part in file names, e.g. YYYY-MM-DD-hh-mm - :param time_cond: the date time of data intended to be included in the analysis - :param suffix: the common suffix of all original image files - :param suffix_seg: the common suffix of all segmentation result files - :return: sorted and trimed list of images as well as segmentation files """ - self.dir_img = dir_img - self.dir_seg = dir_seg - self.time_cond = time_cond - self.suffix = suffix - self.suffix_seg = suffix_seg # file name extension of original images ext1, ext2 = os.path.splitext(self.suffix) @@ -491,55 +512,76 @@ def sort_time(self, dir_img, dir_seg, pattern_dt, time_cond, suffix='.jpg', suff imgfs_all.sort() segfs_all.sort() for (fi, fs) in zip(imgfs_all, segfs_all): - tempi = re.search(pattern_dt, fi) - temps = re.search(pattern_dt, fs) + tempi = re.search(self.pattern_dt, fi) + temps = re.search(self.pattern_dt, fs) if tempi and temps: - timepart = tempi.group() #timeparts = temps.group() the assumption is that original image and the results have the same dt_pattern + timepart = tempi.group() # timeparts = temps.group() the assumption is that original image and the results have the same dt_pattern for cond in self.time_cond: if timepart.endswith(cond): self.timepoints.append(timepart) continue index_temp = np.argsort(self.timepoints) - self.timepoints = [self.timepoints[i] for i in index_temp] + self.timepoints = [self.timepoints[i] for i in index_temp] self.imgfiles = [imgfs_all[i] for i in index_temp] self.segfiles = [segfs_all[i] for i in index_temp] def load_images(self): """ - Get the list of images (nd-arrays) ready + Load images and put them into a list :return: + max_dim: common maximum dimension of all images """ temp_imgs = [] sz = [] - imgs = [] for f in self.imgfiles: junk = skimage.io.imread(os.path.join(self.dir_img, f)) temp_imgs.append(junk) sz.append(np.min(junk.shape[0:2])) - min_dim = np.min(sz) + max_dim = np.min(sz) for junk in temp_imgs: img = junk[0: min_dim, 0: min_dim, :] # make all images the same size - imgs.append(img) - return imgs, min_dim + self.imgs.append(img) + return max_dim - def load_segs(self, min_dim): + def load_segs(self, max_dim): """ Get the list of segmentation masks (nd-arrays) ready - :param min_dim: minimum dimension of images (get from original images) (Assumption: square images) + :param max_dim: common maximum dimension of all images (get from original images) (Assumption: square images) :return: """ - segs = [] + # segs = [] for f in self.segfiles: r = pkl.load(open(os.path.join(self.dir_seg, f), 'rb')) - segs.append(r["masks"][0:min_dim, 0:min_dim, :]) # make all masks the same size - return segs + self.segs.append(r["masks"][0:max_dim, 0:max_dim, :]) # make all masks the same size + # return segs - def __call__(self, dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, suffix, suffix_seg,colors=None,color_all=None): + def __call__(self, dir_save, savename, dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, + suffix=".jpg", suffix_seg=".pkl", colors=None, color_all=None): + self.dir_save = dir_save + self.savename = savename + self.savename_ = '{}_old'.format(savename) self.set_save_dirs() - self.sort_time(dir_img, dir_seg, pattern_dt, time_cond, suffix, suffix_seg) - imgs,min_dim = self.load_images() - segs = self.load_segs(min_dim) + self.dir_img = dir_img + self.dir_seg = dir_seg + self.pattern_dt = pattern_dt + self.time_cond = time_cond + self.logic = logic + self.thres = thres + self.name_sub = name_sub + self.suffix = suffix + self.suffix_seg = suffix_seg + self.imgs = [] + self.segs = [] + self.timepoints = [] + + self.sort_time() + min_dim = self.load_images() + self.load_segs(min_dim) # create an instance of InstanceTimeSeriesLinkingClass - inst_ts_linking = InstanceTimeSeriesLinking(imgs, segs, self.timepoints, logic, thres, name_sub) - inst_ts_linking(self.dir_save, self.dir_visual_, self.dir_visual, self.savename_, self.savename, colors, color_all) - return inst_ts_linking + + inst_ts_linking = InstanceTimeSeriesLinking() + inst_ts_linking(images=self.imgs, masks=self.segs, timepoints=self.timepoints, savedir=self.dir_save, + visualdir_=self.dir_visual_, visualdir=self.dir_visual, savename_=self.savename_, + savename=self.savename, logic=self.logic, thres=self.thres, name_sub=self.name_sub) + + return inst_ts_linking \ No newline at end of file From 706747b3cd6797be2f9718003afa9202460242ca Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 19 Oct 2020 12:46:49 -0500 Subject: [PATCH 087/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index fcd0fbd77..6f0b6a4c9 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -223,8 +223,7 @@ def linking(self, t0): elif self.logic == 'IOS': weight = ioss self.weights.append(weight) - idx_col = np.where(np.max(weight, axis=0) < self.thres)[ - 0] # find those columns with maximum value < threshold (self.thres) + idx_col = np.where(np.max(weight, axis=0) < self.thres)[0] # find those columns with maximum value < threshold (self.thres) avail_col = [x for x in range(0, n1) if x not in idx_col] weight_ = copy.deepcopy(weight) weight_ = np.delete(weight_, idx_col, 1) @@ -307,8 +306,7 @@ def update_series(self): weight = ious elif self.logic == 'IOS': weight = ioss - idx_col = np.where(np.max(weight, axis=0) < self.thres)[ - 0] # find those columns with maximum value < thres + idx_col = np.where(np.max(weight, axis=0) < self.thres)[0] # find those columns with maximum value < thres # avail_col = [x for x in range(0, n2) if x not in idx_col] weight_ = copy.deepcopy(weight) weight_ = np.delete(weight_, idx_col, 1) From 78a243bf95940a55ab191730bd685861d81850f3 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 17 Dec 2020 14:21:54 -0600 Subject: [PATCH 088/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 852 ++++++++---------- 1 file changed, 384 insertions(+), 468 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 6f0b6a4c9..0962ee0ed 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -1,4 +1,3 @@ -#### link utilities # !/usr/bin/env python3 # -*- coding: utf-8 -*- """ @@ -29,112 +28,32 @@ from plantcv.plantcv import fatal_error from scipy.optimize import linear_sum_assignment import csv - -def _random_colors(num, bright=True): - """ - Generate desired number of random colors. - To get visually distinct colors, generate them in HSV space then - convert to RGB. - Inputs: - num: number of colors to be generated - bright: True or False, if true, the brightness would be 1.0; if False, the brightness would be 0.7. By default it would be True (brightness 0.7) - Output: generated colors (a tuple) - """ - brightness = 1.0 if bright else 0.7 - hsv = [(i / num, 1, brightness) for i in range(num)] - colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv)) - random.shuffle(colors) - return colors - - -def _apply_mask(image, mask, color, alpha=0.5): - """Apply the given mask to the image with a color and alpha channel assigned. - Inputs: - image: original RGB image - mask: a binary mask of same size to the image - color: a list of 3 values - alpha: alpha value indicating transparency, by default 0.5 - """ - for c in range(3): - image[:, :, c] = np.where(mask == 1, - image[:, :, c] * - (1 - alpha) + alpha * color[c] * 255, - image[:, :, c]) - return image - - -def _get_ax(rows=1, cols=1, size=16): # ??? - """Return a Matplotlib Axes array to be used in - all visualizations in the notebook. Provide a - central point to control graph sizes. - - Adjust the size attribute to control how big to render images - """ - fig, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows)) - fig.tight_layout() - return ax - - -def _display_instances(image, masks, figsize=(16, 16), title="", ax=None, colors=None): - """ - This function is inspired by the function in mrcnn - masks: [height, width, num_instances] - inst_ids: [num_instances] - title: (optional) Figure title - show_mask, show_bbox: To show masks and bounding boxes or not - figsize: (optional) the size of the image - colors: (optional) An array or colors to use with each object - captions: (optional) A list of strings to use as captions for each object - """ - # If no axis is passed, create one and automatically call show() - # auto_show = False - if not ax: - _, ax = plt.subplots(1, figsize=figsize) - # auto_show = True - - num_insts = masks.shape[2] - # Generate random colors - colors = colors or _random_colors(num_insts) - - # Show area outside image boundaries. - height, width = image.shape[:2] - ax.set_ylim(height + 10, -10) - ax.set_xlim(-10, width + 10) - ax.axis('off') - ax.set_title(title) - - masked_image = image.astype(np.uint32).copy() - for i in range(num_insts): - color = colors[i] - - # Mask - mask = masks[:, :, i] - masked_image = _apply_mask(masked_image, mask, color) - - # Mask Polygon - # Pad to ensure proper polygons for masks that touch image edges. - padded_mask = np.zeros( - (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8) - padded_mask[1:-1, 1:-1] = mask - contours = find_contours(padded_mask, 0.5) - for verts in contours: - # Subtract the padding and flip (y, x) to (x, y) - verts = np.fliplr(verts) - 1 - p = Polygon(verts, facecolor="none", edgecolor=color) - ax.add_patch(p) - ax.imshow(masked_image.astype(np.uint8)) - # if auto_show: - # plt.show() - +# from scipy.stats import wasserstein_distance +# sys.path.append('/shares/mgehan_share/hsheng/projects/test_plantcv/visualize_display_instances') +# import visualize_display_instances def _compute_overlaps_masks(masks1, masks2): - """Computes IoU and IoS overlaps between two sets of masks. - The masks should be of the same size - masks1, masks2: [Height, Width, instances] + """Compute overlaps of two sets of binary masks. + The overlaps are represented by IoU (intersection over union) and IoS (intersection over self-area). + + :param masks1: (numpy.ndarray of shape: [Height, Width, n1]) , where n1 is the number of instances + :param masks2: (numpy.ndarray of shape: [Height, Width, n2]) , where n2 is the number of instances + :return: + n1: the number of instances in 1st set of binary masks + n2: the number of instances in 2nd set of binary masks + ious: (numpy.ndarray of shape: [n1, n2]) inversection over union between any pairs of instances in masks1 and masks2 + ioss: (numpy.ndarray of shape: [n1, n2]) inversection over self-area (areas of instances in 1st set of masks) between any pairs of instances in masks1 and masks2 + unions: (numpy.ndarray of shape: [n1, n2]) unions between any pairs of instances in masks1 and masks2 """ + # If either set of masks is empty return empty result if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # If either set of masks contains only one mask, expand the 2nd dimension + if len(masks1.shape) == 2: + masks1 = np.expand_dims(masks1, 2) + if len(masks2.shape) == 2: + masks2 = np.expand_dims(masks2, 2) n1 = masks1.shape[2] n2 = masks2.shape[2] intersections = np.zeros((n1, n2)) @@ -150,7 +69,166 @@ def _compute_overlaps_masks(masks1, masks2): unions[idx_m, :] = union ioss[idx_m, :] = intersection / maski_.sum() ious = np.divide(intersections, unions) - return ious, ioss + return n1, n2, ious, ioss, unions + + +def _get_link(masks1, masks2, logic, thres): + """ + Get link information between two sets of binary instance segmentation masks + :param masks1: (numpy.ndarray of shape: [Height, Width, n1]), where n1 is the number of instances + :param masks2: (numpy.ndarray of shape: [Height, Width, n1]), where n1 is the number of instances + :param logic: linking logic to use. (currently) either "IOU" or "IOS" + :param thres: threshold to link two masks (two binary segmentation masks can only be linked if the calculated overlap between is greater than the threshold) + :return: + weight (numpy.ndarray of shape: [n1,n2]): weight matrix (indicated by IoU or IoS) calculated based on two sets of masks + link (numpy.1darray of length n1): link[i] = j means that the i-th mask in masks1 should be linked to j-th mask in masks2 + row_ind, col_ind: selected row indices and column indices based on the weight matrix to finalize the "link" + """ + + n1, n2, ious, ioss, _ = _compute_overlaps_masks(masks1, masks2) + if logic.upper() == 'IOU': + weight = ious + elif logic.upper() == 'IOS': + weight = ioss + else: + fatal_error("Currently only IOU and IOS as supported linking logic!") + weight_ = copy.deepcopy(weight) + link = -np.ones(n1, dtype=np.int64) + + idx_col = np.where(np.max(weight_, axis=0) < thres)[ + 0] # find those columns with maximum value < threshold (self.thres) + avail_col = [x for x in range(0, n2) if x not in idx_col] + + weight_ = np.delete(weight_, idx_col, 1) + + row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) + for (r, c) in zip(row_ind, col_ind): + if weight_[r, c] >= thres: + link[r] = avail_col[c] + return weight, link, row_ind, col_ind + + +def _get_emergence(uids): + """ + get emergence as well as emerge times + :param uids: unique ids at every time point + (list of length T, where T is the total time) + every sub-list uids[t] (for t in range of (0,T)) is also a list of length nt, where nt is the number of instances at t + :return: + emergence: new segments (leaves) compared to previous time-point + (list of length T, where T is the total time) + every sub-list emergence[t] if also a list. If there is no new segments at t compared to (t-1), the emergence[t] is empty; otherwise, emergence[t] is a list of unique ids appear at t but not at (t-1). + emerge_times: (list) time points with emergence + """ + + emergence = [[] for i in uids] + emergence[0] = list(uids[0]) + # uid_lst = [] + # uid_lst += list(uids[0]) + for (t, temp) in enumerate(uids): + if t >= 1: + # new = [x for x in temp if x not in uid_lst] + new = [x for x in temp if x not in uids[t - 1]] + emergence[t] = new + # uid_lst += new + emerge_times = [i for i in range(len(emergence)) if len(emergence[i]) > 0] + return emergence, emerge_times + + +def _get_ti(num_insts, uids, link_info): + """ + getting ti (tracking information) + :param num_insts: (list) number of instances at every time-point t + :param uids: unique ids at every time point + :param link_info: + :return: + ti: tracking information (np.ndarray of shape (T,max_uid)) + ti[t,k]=j represents for: at time t the k-th leaf has a local index of j + if ti[t,k]=-1, the k-th leaf does not appear at time t + number of non-negative elements every row: number of instances + t_appear: (list, length: N) the appear time of every unique id + t_disappear: (list, length: N) the disappear time of every unique id + """ + + emergence, emerge_times = _get_emergence(uids) + T = len(num_insts) + max_uid = max([max(temp) for temp in uids]) + N = max_uid + 1 + ti = -np.ones((T, N), dtype=np.int64) + for t in range(T): + if t == 0: + ti[t, 0:num_insts[t]] = uids[0] + else: + link = link_info[t - 1] + prev = ti[t - 1] + cids = list(np.arange(0, num_insts[t])) + + for (uid, pid) in enumerate(prev): + # pid: previous local id + if pid >= 0: + cid = link[pid] + ti[t, uid] = cid + if cid >= 0: + cids.remove(cid) + + if t in emerge_times: + new_ids = emergence[t] + for (cid, new_id) in zip(cids, new_ids): + ti[t, new_id] = cid + + t_appear = np.zeros(N, dtype=np.int64) + t_disappear = -np.ones(N, dtype=np.int64) + for (t, uids_t) in enumerate(emergence): + if uids_t: + for uid in uids_t: + t_appear[uid] = t + for uid in range(0, N): + t = 0 + while t < T: + if (ti[t][uid] == -1) and (t > t_appear[uid]): + t_disappear[uid] = t + break + else: + t += 1 + return ti, t_appear, t_disappear + + +# def _visualize1(img, mask, savename=None): +# """ +# Apply mask to original image +# """ +# mask_1 = np.zeros(img.shape[0:2], dtype=np.uint8) +# mask_1[np.where(mask)] = 255 +# masked_im = pcv.apply_mask(img, mask_1, mask_color='black') +# if savename is not None: +# pcv.print_image(masked_im, savename) +# return masked_im + +# def _visualize2(img, fig_title, mask=None, savename=None): +# """ +# Apply mask with an alpha channel to the original image +# Can be replaced with "overlay two images in the future" +# img +# mask +# """ +# mask_2 = np.ones(img.shape[0:2]) * 0.5 +# if mask is not None: +# mask_2[np.where(mask == True)] = 1 +# masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_2, axis=2)), axis=2) +# +# fig = plt.figure(figsize=(5, 5)) +# ax = fig.add_subplot(1, 1, 1) +# ax.imshow(masked_im) +# ax.axis('off') +# ax.set_title(fig_title, fontsize=16) +# if savename is not None: +# plt.savefig(savename) +# return masked_im + +# def _visualize3(img, masks, colors, figsize=(16, 16), ax=None, captions=None, savename=None, title="", show_bbox=True): +# visualize_display_instances.display_instances(img, masks.astype(np.uint8), figsize=figsize, title=title, ax=ax, colors=colors, captions=captions, show_bbox=True) +# if savename is not None: +# plt.savefig(savename) class InstanceTimeSeriesLinking(object): """A class that links segmented instances throughout time @@ -164,37 +242,42 @@ def __init__(self): self.masks = None # a list of timepoints (of the same length of images) self.timepoints = None - self.total_time = None + self.T = None # number of instances: a list in which every element represent for number of instances in corresponding image self.n_insts = None # initialization for linking self.thres = None self.link_info = None - self.unlinked_insts = None - self.link_series = None + self.uids = None + self.max_uid = None + self.emergence = None + self.emerge_times = None + self.ti = None + self.t_appear = None + self.t_disappear = None self.weights = None - self.logic = None + self.logic = None self.name_sub = None self.key_id = None + # update releated + self.updated = 0 + self.delta_t = None + self.disp_uids = None + self.ids_update = None + + self.ti_ = None + self.t_appear_ = None + self.t_disappear_ = None + def save_linked_series(self, savedir, savename): - """save lining information into a .csv file and a .pkl file with the same prefix of filename + """save linking information into a .pkl file with the same prefix of filename Inputs: savedir and savename """ - l0 = ['', ''] + [x for (idx, x) in enumerate(self.timepoints)] - l1 = ['unique_id', 'current_id'] + ['t{}'.format(idx) for (idx, x) in enumerate(self.timepoints)] - csvfile = open(os.path.join(savedir, savename + '.csv'), 'w', newline='') - writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) - writer_junk.writerow(l0) - writer_junk.writerow(l1) - for (t0, item) in self.link_series.items(): - for uid, cid in zip(item['unique_id'], item[self.key_id]): - new_line = ['{}'.format(uid), '{}'.format(cid)] + item['{}{}'.format(self.name_sub, cid)].tolist() - writer_junk.writerow(new_line) - csvfile.close() + # pkl.dump(self, open(os.path.join(savedir, savename + '.pkl'), 'wb')) - pkl.dump(vars(self), open(os.path.join(savedir, savename+".pkl"), 'wb')) + pkl.dump(vars(self), open(os.path.join(savedir, savename + ".pkl"), 'wb')) def import_linked_series(self, savedir, savename): """import a linked time-series from previously saved file @@ -203,383 +286,216 @@ def import_linked_series(self, savedir, savename): :param savename: saving name :return: """ - linked = pkl.load(open(os.path.join(savedir, savename+'.pkl'),"rb")) + linked = pkl.load(open(os.path.join(savedir, savename + '.pkl'), "rb")) for key, value in linked.items(): setattr(self, key, value) + # def save_to_csv(self, savedir, csvname1="link_series.csv", csvname2="link_series"): + # """ save linking information into 2 .csv files + # 1. link_series.csv + # 2. link_info.csv + # """ + # # csvname1 = "link_series.csv" + # # csvname2 = "link_info.csv" + # l0 = ['', ''] + [x for (idx, x) in enumerate(self.timepoints)] + # l1 = ['unique_id', 'current_id'] + ['t{}'.format(idx) for (idx, x) in enumerate(self.timepoints)] + # csvfile1 = open(os.path.join(savedir, csvname1), 'w', newline='') + # writer1 = csv.writer(csvfile1, delimiter=',', quoting=csv.QUOTE_MINIMAL) + # writer1.writerow(l0) + # writer1.writerow(l1) + # csvfile2 = open(os.path.join(savedir, csvname2), 'w', newline='') + # writer_junk2 = csv.writer(csvfile2, delimiter=',', quoting=csv.QUOTE_MINIMAL) + # writer_junk2.writerow(['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) + # for (t0_, item) in self.link_series.items(): + # t0 = int(t0_.replace('t', '')) + # for uid, cid in zip(item['uids'], item['ids']): + # link_t0_i = item['inst{}'.format(cid)] + # new_line = ['{}'.format(uid), '{}'.format(cid)] + link_t0_i.tolist() + # writer1.writerow(new_line) + # for t in range(t0, self.T): + # writer_junk2.writerow([uid, t0, self.timepoints[t0], t, self.timepoints[t], link_t0_i[t]]) + # csvfile1.close() + # csvfile2.close() + def linking(self, t0): """ Link instance segments of t0 to those of t1 (t0+1) by calculating the overlaps of their masks :param t0: starting time point :return: None, but self.link_info would be updated """ - masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays - n0, n1 = masks0.shape[2], masks1.shape[2] - weight = -np.inf * np.ones((n0, n1)) - link = -np.ones(n0) - ious, ioss = _compute_overlaps_masks(masks0, masks1) - if self.logic == 'IOU': - weight = ious - elif self.logic == 'IOS': - weight = ioss + masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy( + self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays + weight, self.link_info[t0], _, _ = _get_link(masks0, masks1, self.logic, self.thres) self.weights.append(weight) - idx_col = np.where(np.max(weight, axis=0) < self.thres)[0] # find those columns with maximum value < threshold (self.thres) - avail_col = [x for x in range(0, n1) if x not in idx_col] - weight_ = copy.deepcopy(weight) - weight_ = np.delete(weight_, idx_col, 1) - - row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) - for (r, c) in zip(row_ind, col_ind): - if weight_[r, c] >= self.thres: - link[r] = avail_col[c] - self.link_info[t0] = link - - def get_series(self): - """ - Using the self.link_info to create a dictionary save in self.link_series - which is a dictionary contains information for every timepoint with new instances not seen before, - and the linking information for every instance - :return: - """ - # define new leaves and their unique identifiers at time points with new leaves emerging - t = 0 - key_t = 't{}'.format(t) - self.link_series[key_t] = dict() - self.link_series[key_t][self.key_id] = self.unlinked_insts[0] - # - self.link_series[key_t]['unique_id'] = self.link_series[key_t][self.key_id] - unique_id = len(self.link_series[key_t][self.key_id]) - - for t in range(1, self.total_time): - new_insts = [i for i in self.unlinked_insts[t] if i not in self.link_info[t - 1]] - if new_insts: - key_t = 't{}'.format(t) - self.link_series[key_t] = dict() - self.link_series[key_t][self.key_id] = np.array(new_insts) - - id_temp = [] - for new_inst in new_insts: - id_temp.append(unique_id) - unique_id = unique_id + 1 - self.link_series[key_t]['unique_id'] = np.array(id_temp) - ## for time points with new leaves emerging, get the linking information for every new instance - for key_t in self.link_series: - t0 = int(key_t.replace('t', '')) - for (leaf_unique, inst) in zip(self.link_series[key_t]['unique_id'], self.link_series[key_t][self.key_id]): - key_inst = '{}{}'.format(self.name_sub, inst) - self.link_series[key_t][key_inst] = -np.ones(self.total_time, dtype=int) - self.link_series[key_t][key_inst][t0] = inst - if t0 < self.total_time - 1: - self.link_series[key_t][key_inst][t0 + 1] = self.link_info[t0][inst] - for t_ in range(t0 + 2, self.total_time): - idx = self.link_series[key_t][key_inst][t_ - 1] - if idx < 0: - break - else: - self.link_series[key_t][key_inst][t_] = self.link_info[t_ - 1][idx] - def update_series(self): - """ - Update the linking by comparing pairs of timepoint which are t and t+2 - :return: - """ - link_series = copy.deepcopy(self.link_series) - for t_, content in link_series.items(): - t = int(t_.replace('t', '')) - for inst_id in content[self.key_id]: - temp = np.where(content['{}{}'.format(self.name_sub, inst_id)] < 0)[0] - if len(temp) > 0: - t0 = temp[0] - 1 - t2 = t0 + 2 - if t2 <= self.total_time - 1: - t2_ = 't{}'.format(t2) - if t2_ in self.link_series: - t0_ = 't{}'.format(t0) - # print('\nt0: {}, t2: {}'.format(t0_, t2_)) - idx0 = content['{}{}'.format(self.name_sub, inst_id)][t0] - mask0 = np.expand_dims(self.masks[t0][:, :, idx0], axis=2) - idxs2 = self.link_series[t2_][self.key_id] - masks2 = self.masks[t2][:, :, idxs2] - n2 = masks2.shape[2] - ious, ioss = _compute_overlaps_masks(mask0, masks2) - if self.logic == 'IOU': - weight = ious - elif self.logic == 'IOS': - weight = ioss - idx_col = np.where(np.max(weight, axis=0) < self.thres)[0] # find those columns with maximum value < thres - # avail_col = [x for x in range(0, n2) if x not in idx_col] - weight_ = copy.deepcopy(weight) - weight_ = np.delete(weight_, idx_col, 1) - row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) - for (r, c) in zip(row_ind, col_ind): - if weight_[r, c] >= self.thres: - inst0 = idx0 - inst2 = idxs2[c] - - # find the time point - idx_l2 = np.where(self.link_series[t2_][self.key_id] == inst2)[0][0] - # add information to t_ - self.link_series[t_]['{}{}'.format(self.name_sub, inst_id)][t0 + 1:] = \ - self.link_series[t2_]['{}{}'.format(self.name_sub, inst2)][t0 + 1:] - # delete from t2_ - self.link_series[t2_][self.key_id] = np.delete(self.link_series[t2_][self.key_id], - idx_l2) - self.link_series[t2_]['unique_id'] = np.delete(self.link_series[t2_]['unique_id'], - idx_l2) - del self.link_series[t2_]['{}{}'.format(self.name_sub, inst2)] - # if nothing left for t2_, delete it entirely - if len(self.link_series[t2_]['unique_id']) == 0: - del self.link_series[t2_] - # update unique ids - uid = 0 - for t in self.link_series: - num = len(self.link_series[t][self.key_id]) - self.link_series[t]['unique_id'] = np.arange(uid, uid + num) - uid = uid + num - - def visualize(self, visualdir, csvdir, csvname='linking_info', colors=None, color_all=None): + def get_uid(self): """ - Create 3 sets of visualization - visualization set 1: one leaf per image (visualdir['1']) - visualization set 2: show with an alpha channel (visualdir['2']) - visualization method 3: show with bounding boxes (visualdir['3']) - Also save a csv file called linking_info.csv (or other user defined name) that includes the linking information + get unique ids for every timepoint based on link_info + uids: a list + arrays inside the list + every array has a length of num_insts for that corresponding time """ - - # create subfolders inside the provided directory of visualization - visualdirs = dict() - for i in range(1, 4): - idx = str(i) - visualdirs[idx] = os.path.join(visualdir, 'visualization{}'.format(idx)) - if not os.path.exists(visualdirs[idx]): - os.makedirs(visualdirs[idx]) - - if colors is None: - colors = _random_colors(40) - if color_all is None: - color_all = [[tuple() for i in range(0, num)] for num in self.n_insts] - - count = 0 - csvfile = open(os.path.join(csvdir, csvname + '.csv'), 'w', newline='') - writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) - writer_junk.writerow( - ['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) - for key_t in self.link_series: - ids = self.link_series[key_t]['unique_id'] - start_time = int(key_t.replace('t', '')) - insts_t = self.link_series[key_t][self.key_id] - for (unique_id, inst) in zip(ids, insts_t): - key_inst = '{}{}'.format(self.name_sub, inst) - link_inst = self.link_series[key_t][key_inst] - start_idx = link_inst[start_time] - for t in range(0, self.total_time): - img_t = self.images[t] - # initialize mask used in 1st visualization - mask_1 = np.zeros(img_t.shape[0:2], dtype=np.uint8) - # initialize mask used in 2nd visualization (with an alpha channel, ahpha=0.5) - mask_2 = np.ones(img_t.shape[0:2]) * 0.5 - title_vis2 = '{} (t{})'.format(self.timepoints[t], t) - if link_inst[t] >= 0: - title_vis2 = title_vis2 + ' leaf {}'.format(link_inst[t]) - mask_t = self.masks[t][:, :, link_inst[t]] - color_all[t][link_inst[t]] = colors[count] - - ## 1. save the masked image, i.e. single leaves - mask_1[np.where(mask_t)] = 255 - leaf_t = pcv.apply_mask(img_t, mask_1, mask_color='black') - writer_junk.writerow( - [unique_id, start_time, self.timepoints[start_time], t, self.timepoints[t], link_inst[t]]) - - pcv.print_image(leaf_t, os.path.join(visualdirs['1'], - '{}_{}-{}-{}-{}_{}.jpg'.format(unique_id, start_time, - start_idx, t, link_inst[t], - self.timepoints[t]))) - mask_2[np.where(mask_t == True)] = 1 - masked_im = np.concatenate((img_t.astype(float) / 255, np.expand_dims(mask_2, axis=2)), axis=2) - # for 2nd set of visualization, a folder for one unique instance - save_dir_ = os.path.join(visualdirs['2'], '{}_{}-{}'.format(unique_id, start_time, start_idx)) - if not os.path.exists(save_dir_): - os.makedirs(save_dir_) - fig2 = plt.figure(figsize=(5, 5)) - ax2 = fig2.add_subplot(1, 1, 1) - ax2.imshow(masked_im) - ax2.axis('off') - ax2.set_title(title_vis2, fontsize=16) - plt.savefig(os.path.join(save_dir_, str(self.timepoints[t]) + '.jpg')) - plt.close(fig2) - - count += 1 - csvfile.close() - ## 3. visualize with bounding boxes - for (img, masks, color, t) in zip(self.images, self.masks, color_all, self.timepoints): - _display_instances(img, masks, figsize=(16, 16), title="", ax=_get_ax(rows=1, cols=1, size=16), - colors=color) - plt.savefig(os.path.join(visualdirs['3'], '{}.jpg'.format(t))) - plt.close('all') - - def __call__(self, images, masks, timepoints, savedir, visualdir_, visualdir, savename_, savename, logic="IOS", thres=0.2, name_sub="instance",colors=None, color_all=None): + self.uids = [-np.ones(n, dtype=np.int64) for n in self.n_insts] + self.uids[0] = np.arange(len(self.link_info[0]), dtype=np.int64) + self.max_uid = max(self.uids[0]) + for (t, link_t) in enumerate(self.link_info): + for (cidt, cidt_) in enumerate(link_t): + if cidt_ >= 0: + self.uids[t + 1][cidt_] = self.uids[t][cidt] + if -1 in self.uids[t + 1]: # -1 means there is not predecessor from a former timepoint -> assign new uids + temp = np.where(self.uids[t + 1] == -1)[0] + for tid in temp: + self.max_uid += 1 + self.uids[t + 1][tid] = self.max_uid + + def update_ti(self, delta_t=2): + self.delta_t = delta_t if delta_t else self.delta_t + + """ make copies for tc, t_appear, t_disappear """ + + ti_ = copy.deepcopy(self.ti) + t_appear_ = copy.deepcopy(self.t_appear) + t_disappear_ = copy.deepcopy(self.t_disappear) + + # find disappeared uids + disp_uids = np.where((t_disappear_ > -1) & (t_disappear_ < (self.T - 2)))[0] + + if len(disp_uids) == 0: + print("\nno updates!") + else: + # use an array to record how to update tc + ids_update = -np.ones(len(disp_uids), dtype=np.int64) + for (uid_i, uid) in enumerate(disp_uids): + if not (self.ti[:, uid] == -1).all(): + disp_time = t_disappear_[uid] + t0 = disp_time - 1 # time last appear + t_ = t0 + 2 # start with t_ = t0+2 + + while (t_ <= t0 + self.delta_t) and (t_ <= self.T - 1): + uids_t = np.where(t_appear_ == t_)[0] # find if there is new emergence at t_ (t0+delta) + if len(uids_t) > 0: + cidt0 = self.ti[t0, uid] + maskt0 = self.masks[t0][:, :, cidt0] # there is only one mask + cidst_ = self.ti[t_, uids_t] # there can be more than one potential indices at t_ + maskst_ = self.masks[t_][:, :, cidst_] + weight, _, row_ind, col_ind = _get_link(maskt0, maskst_, self.logic, self.thres) + if len(row_ind) > 0: + for (r, c) in zip(row_ind, col_ind): + if weight[r, c] >= self.thres: + # cidt0 = cidt0 + # cidt_ = cidst_[c] + """find corresponding uid """ + uidt_ = uids_t[c] + """ recore how to update tc """ + ids_update[uid_i] = uidt_ + t_ += 1 + else: + t_ += 1 + else: + t_ += 1 + to_del = [idx for idx in ids_update if idx != -1] + if len(to_del) == 0: + print("\nno updates!") + else: + print("updates!!") + # there will be updates, so save previous result in self.ti_, self.t_appear_, and self.t_disappear_, respectively + self.ti_, self.t_appear_, self.t_disappear_ = ti_, t_appear_, t_disappear_ + for (idx, id_up) in enumerate(ids_update): + if id_up != -1: + id_old = disp_uids[idx] + t_ = t_appear_[id_up] + self.ti[t_:, id_old] = self.ti[t_:, id_up] + self.ti = np.delete(self.ti, to_del, 1) + self.t_appear = np.delete(self.t_appear, to_del) + self.t_disappear = np.delete(self.t_disappear, to_del) + + self.ids_update = ids_update + self.disp_uids = disp_uids + self.updated = 1 + self.delta_t = delta_t + self.max_uid = self.max_uid - len(np.where(ids_update > -1)[0]) + + # def visualize(self, visualdir, colors=None, color_all=None): + # """ + # Create 3 sets of visualization + # visualization set 1: one leaf per image (visualdir['1']) + # visualization set 2: show with an alpha channel (visualdir['2']) + # visualization method 3: show with bounding boxes (visualdir['3']) + # Also save a csv file called linking_info.csv (or other user defined name) that includes the linking information + # """ + # + # # create subfolders inside the provided directory of visualization + # visualdirs = dict() + # for i in range(1, 4): + # idx = str(i) + # visualdirs[idx] = os.path.join(visualdir, 'visualization{}'.format(idx)) + # if not os.path.exists(visualdirs[idx]): + # os.makedirs(visualdirs[idx]) + # + # if colors is None: + # colors = visualize_display_instances._random_colors(self.max_uid) # worest case: all leaves are unique + # if color_all is None: + # color_all = [[tuple() for i in range(0, num)] for num in self.n_insts] + # + # for (t,ti_t) in enumerate(self.ti): + # uids_t = [x for x in ti_t if x >= 0] + # for (idx,uid) in enumerate(uids_t): + # color_all[t][idx] = colors[uid] + # + # for (img, masks, colors_t, t) in zip(self.images, self.masks, color_all, self.timepoints): + # savename3 = os.path.join(visualdirs['3'], '{}.jpg'.format(t)) + # _visualize3(img, masks, colors_t, savename=savename3) + # plt.close("all") + + def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", + colors=None, color_all=None, update=False, max_delta_t=2, savedir_=None, savename_=None): # a list of images which are ndarrays self.images = images # a list of masks which are ndarrays (of the same length of images) - self.masks = masks + self.masks = masks # a list of timepoints (of the same length of images) self.timepoints = timepoints - self.total_time = len(self.timepoints) + self.T = len(self.timepoints) # number of instances: a list in which every element represent for number of instances in corresponding image self.n_insts = [] for i in range(0, len(self.masks)): self.n_insts.append(self.masks[i].shape[2]) # initialization for linking - self.thres = thres - self.link_info = [-np.ones((self.n_insts[i]), dtype=int) for i in range(0, self.total_time - 1)] - self.unlinked_insts = [np.array(range(0, self.n_insts[i])) for i in range(0, self.total_time)] - self.link_series = dict() # only for those newly emerging leaves - self.weights = [] - self.logic = logic.upper() + self.thres = thres + self.link_info = [-np.ones((self.n_insts[i]), dtype=np.int64) for i in range(0, self.T - 1)] + + self.weights = [] + self.logic = logic.upper() self.name_sub = name_sub - self.key_id = '{}_ids'.format(name_sub) + self.key_id = '{}_ids'.format(name_sub) - for t0 in range(0, self.total_time - 1): + for t0 in range(0, self.T - 1): self.linking(t0) - self.get_series() - self.visualize(visualdir_, savedir, 'link_info_old', colors, color_all) - self.save_linked_series(savedir, savename_) - - self.update_series() - self.visualize(visualdir, savedir, 'link_info', colors, color_all) + self.get_uid() + self.emergence, self.emerge_times = _get_emergence(self.uids) + self.ti, self.t_appear, self.t_disappear = _get_ti(self.n_insts, self.uids, self.link_info) + # visualdir = os.path.join(savedir, "visualization") + # if not os.path.exists(visualdir): + # os.makedirs(visualdir) + # self.visualize(visualdir) self.save_linked_series(savedir, savename) -class InstanceTSLinkingWrapper(object): - """A class that is a wrapper for InstanceTimeSeriesLinking - Assumptions: - 1) The segmentation are saved in a different folder from original images - 2) The segmentation result and the original images have corresponding image names, which include "date-time" part with a specific pattern - the date-time pattern for names of original images and names of segmentation results are the same - 3) For every image, there is a corresponding segmentation result - Otherwise, please get your data prepared and use the "InstanceTimeSeriesLinking" class directly - """ + if update: + self.update_ti(max_delta_t) + if self.updated == 1: + if savedir_ is not None and savedir_ != savedir: + visualdir_ = os.path.join(savedir, "visualization") + else: + savedir_ = savedir + visualdir_ = os.path.join(savedir, "updated_visualization") + if not os.path.exists(visualdir_): + os.makedirs(visualdir_) - def __init__(self): - self.dir_save = None - self.savename = None - self.savename_ = None - self.dir_img = None - self.dir_seg = None - self.time_cond = None - self.suffix = None - self.ext = None - self.suffix_seg = None - self.timepoints = None - self.imgfiles = None - self.segfiles = None - self.dir_visual = None - self.dir_visual_ = None - self.pattern_dt = None - - def set_save_dirs(self): - junk = datetime.datetime.now() - subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), - str(junk.hour).zfill(2), str(junk.minute).zfill(2), - str(junk.second).zfill(2)) - self.dir_save = os.path.join(self.dir_save, subfolder) - if not os.path.exists(self.dir_save): - os.makedirs(self.dir_save) - self.dir_visual = os.path.join(self.dir_save, 'visualization') - if not os.path.exists(self.dir_visual): - os.makedirs(self.dir_visual) - self.dir_visual_ = os.path.join(self.dir_save, 'visualization_old') - if not os.path.exists(self.dir_visual_): - os.makedirs(self.dir_visual_) - - def sort_time(self): - """ - This function is designed for files with file names which contain a "date-time" part, with an user-defined pattern - sort time based on names of original images - """ + if savename_ is None: + savename_ = "{}_{}".format(savename, max_delta_t) - # file name extension of original images - ext1, ext2 = os.path.splitext(self.suffix) - if ext1.startswith('.'): - self.ext = ext1 - elif ext2.startswith('.'): - self.ext = ext2 - - imgfs_all = [f for f in os.listdir(self.dir_img) if f.endswith(self.suffix)] - segfs_all = [f for f in os.listdir(self.dir_seg) if f.endswith(self.suffix_seg)] - imgfs_all.sort() - segfs_all.sort() - for (fi, fs) in zip(imgfs_all, segfs_all): - tempi = re.search(self.pattern_dt, fi) - temps = re.search(self.pattern_dt, fs) - if tempi and temps: - timepart = tempi.group() # timeparts = temps.group() the assumption is that original image and the results have the same dt_pattern - for cond in self.time_cond: - if timepart.endswith(cond): - self.timepoints.append(timepart) - continue - index_temp = np.argsort(self.timepoints) - self.timepoints = [self.timepoints[i] for i in index_temp] - self.imgfiles = [imgfs_all[i] for i in index_temp] - self.segfiles = [segfs_all[i] for i in index_temp] - - def load_images(self): - """ - Load images and put them into a list - :return: - max_dim: common maximum dimension of all images - """ - temp_imgs = [] - sz = [] - for f in self.imgfiles: - junk = skimage.io.imread(os.path.join(self.dir_img, f)) - temp_imgs.append(junk) - sz.append(np.min(junk.shape[0:2])) - max_dim = np.min(sz) - for junk in temp_imgs: - img = junk[0: min_dim, 0: min_dim, :] # make all images the same size - self.imgs.append(img) - return max_dim - - def load_segs(self, max_dim): - """ - Get the list of segmentation masks (nd-arrays) ready - :param max_dim: common maximum dimension of all images (get from original images) (Assumption: square images) - :return: - """ - # segs = [] - for f in self.segfiles: - r = pkl.load(open(os.path.join(self.dir_seg, f), 'rb')) - self.segs.append(r["masks"][0:max_dim, 0:max_dim, :]) # make all masks the same size - # return segs - - def __call__(self, dir_save, savename, dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, - suffix=".jpg", suffix_seg=".pkl", colors=None, color_all=None): - self.dir_save = dir_save - self.savename = savename - self.savename_ = '{}_old'.format(savename) - self.set_save_dirs() - self.dir_img = dir_img - self.dir_seg = dir_seg - self.pattern_dt = pattern_dt - self.time_cond = time_cond - self.logic = logic - self.thres = thres - self.name_sub = name_sub - self.suffix = suffix - self.suffix_seg = suffix_seg - self.imgs = [] - self.segs = [] - self.timepoints = [] - - self.sort_time() - min_dim = self.load_images() - self.load_segs(min_dim) - # create an instance of InstanceTimeSeriesLinkingClass - - inst_ts_linking = InstanceTimeSeriesLinking() - inst_ts_linking(images=self.imgs, masks=self.segs, timepoints=self.timepoints, savedir=self.dir_save, - visualdir_=self.dir_visual_, visualdir=self.dir_visual, savename_=self.savename_, - savename=self.savename, logic=self.logic, thres=self.thres, name_sub=self.name_sub) - - return inst_ts_linking \ No newline at end of file + # self.visualize(visualdir_) + self.save_linked_series(savedir_, savename_) + + # self.save_to_csv(savedir, "link_series_old.csv", "link_info_old.csv") From af1221f74bc3b91ae472b6a41f9e777874a6c872 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 17 Dec 2020 14:22:07 -0600 Subject: [PATCH 089/274] upload new test data --- ...s-165-003-020_2019-10-22-14-05_crop-img12.jpg | Bin 0 -> 6429 bytes ...s-165-003-020_2019-10-22-21-05_crop-img12.jpg | Bin 0 -> 7020 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg create mode 100644 tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_crop-img12.jpg diff --git a/tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg 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090/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index b93fec640..1bb96b3ff 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,12 +1,8 @@ -from plantcv.plantcv.time_series.time_series_linking import _random_colors -from plantcv.plantcv.time_series.time_series_linking import _apply_mask -from plantcv.plantcv.time_series.time_series_linking import _get_ax -from plantcv.plantcv.time_series.time_series_linking import _display_instances +from plantcv.plantcv.time_series.time_series_linking import _get_link +from plantcv.plantcv.time_series.time_series_linking import _get_emergence +from plantcv.plantcv.time_series.time_series_linking import _get_ti from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking -from plantcv.plantcv.time_series.time_series_linking import InstanceTSLinkingWrapper - # add new functions to end of lists -__all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", - "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] +__all__ = ["_get_link", "_get_emergence", "_get_ti", "_compute_overlaps_masks", "InstanceTimeSeriesLinking"] From 983a417043058cf5ba058f4dae08107fc2684d8c Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 17 Dec 2020 14:22:14 -0600 Subject: [PATCH 091/274] Update tests.py --- tests/tests.py | 88 +++++++++++++++++++++++++++++++++++--------------- 1 file changed, 62 insertions(+), 26 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index d75dfb425..e7975b4e7 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -19,6 +19,9 @@ import dask from dask.distributed import Client import pickle as pkl +import glob +import re +import skimage.io PARALLEL_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "parallel_data") TEST_TMPDIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", ".cache") @@ -6127,41 +6130,74 @@ def test_plantcv_visualize_time_lapse_video_bad_list(): # Tests for the time_series subpackage # ############################## -def test_plantcv_time_series_display_instances(): - # Test cache directory - loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_INSTANCE_SEG, "img_segment_.pkl"), 'rb')) - img = loaded['img'] - masks = loaded['segment']['masks'] - pcv.time_series._display_instances(img, masks, figsize=(16, 16), title="", ax=None, colors=None) +# def test_plantcv_time_series_display_instances(): +# # Test cache directory +# loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_INSTANCE_SEG, "img_segment_.pkl"), 'rb')) +# img = loaded['img'] +# masks = loaded['segment']['masks'] +# pcv.time_series._display_instances(img, masks, figsize=(16, 16), title="", ax=None, colors=None) def test_plantcv_time_series_time_series(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir - dir_img = TIME_SERIES_TEST_RAW - dir_seg = TIME_SERIES_TEST_INSTANCE_SEG - dir_save = cache_dir - dir_visual_ = os.path.join(dir_save,'visual_old') - os.mkdir(dir_visual_) - dir_visual = os.path.join(dir_save,'visual') - os.mkdir(dir_visual) - pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm - time_cond = ['08-05', '11-05', '17-05', '21-05'] - suffix = '.jpg' - suffix_seg = '.pkl' - thres = 0.2 + + path_img = TIME_SERIES_TEST_RAW + path_segmentation = TIME_SERIES_TEST_INSTANCE_SEG + path_save = cache_dir + savename = "link_series" + pattern_datetime = "\d{4}-\d{2}-\d{2}-\d{2}-\d{2}" # YYYY-MM-DD-hh + ext = "_crop-img12.jpg" + ext_seg = ".pkl" + + list_seg = glob.glob(os.path.join(path_segmentation, "2*{}".format(ext_seg))) + timepoints = [] + for f_seg in list_seg: + tp_temp = re.search(pattern_datetime, f_seg).group() + timepoints.append(tp_temp) + timepoints.sort() + # Load original images + images = [] + temp_imgs = [] + sz = [] + + for tp in timepoints: + filename_ = "*_{}{}".format(tp, ext) + filename = glob.glob(os.path.join(path_img, filename_))[0] + + junk_ = skimage.io.imread(filename) + junk = junk_ + if len(junk_.shape) == 2: + junk = cv2.cvtColor(junk_, cv2.COLOR_GRAY2BGR) + temp_imgs.append(junk) + sz.append(np.min(junk.shape[0:2])) + min_dim = np.min(sz) + for junk in temp_imgs: + img = junk[0: min_dim, 0:min_dim, :] # make all images the same size + images.append(img) + # load instance segmentation results (masks) + masks = [] + for tp in timepoints: + print(tp) + print(ext_seg) + filename_ = "*{}{}".format(tp, ext_seg) + print(filename_) + filename = glob.glob(os.path.join(path_segmentation, filename_))[0] + print("file name is {}".format(filename)) + r = pkl.load(open(filename, 'rb')) + masks.append(r['masks'][0: min_dim, 0:min_dim, :]) # make all masks the same size + logic = 'IOS' + thres = 0.1 + + # create an instance of Instance_timeseries_linking name_sub = 'leaf' - name_series = 'linked_series' - inst_ts_linking = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) - inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, logic, thres, name_sub, suffix, - suffix_seg) - inst_ts_linking2 = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) - inst_ts_linking2(dir_img, dir_seg, pattern_datetime, time_cond, 'IOU', thres, name_sub, '12.jpg', - suffix_seg) - assert (len(os.listdir(inst_ts_linking.dir_save)) > 0) and (len(os.listdir(inst_ts_linking2.dir_save)) > 0) + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + inst_ts_linking(images=images, masks=masks, timepoints=timepoints, savedir=path_save, savename=savename, + logic=logic, thres=thres, name_sub=name_sub, update=False) + assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From 21c74348aeaaa3cd5f9527bd5224b8b6d5001d1f Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 18 Dec 2020 17:10:20 -0600 Subject: [PATCH 092/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 37 ++++++++++--------- 1 file changed, 20 insertions(+), 17 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 0962ee0ed..1c6f3b898 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -447,8 +447,11 @@ def update_ti(self, delta_t=2): # _visualize3(img, masks, colors_t, savename=savename3) # plt.close("all") - def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", - colors=None, color_all=None, update=False, max_delta_t=2, savedir_=None, savename_=None): + # def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", + # colors=None, color_all=None, update=False, max_delta_t=2, savedir_=None, savename_=None): + # def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", + # update=False, max_delta_t=2, savedir_=None, savename_=None): + def __call__(self, images, masks, timepoints, logic="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): # a list of images which are ndarrays self.images = images # a list of masks which are ndarrays (of the same length of images) @@ -479,23 +482,23 @@ def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", th # if not os.path.exists(visualdir): # os.makedirs(visualdir) # self.visualize(visualdir) - self.save_linked_series(savedir, savename) + # self.save_linked_series(savedir, savename) if update: self.update_ti(max_delta_t) - if self.updated == 1: - if savedir_ is not None and savedir_ != savedir: - visualdir_ = os.path.join(savedir, "visualization") - else: - savedir_ = savedir - visualdir_ = os.path.join(savedir, "updated_visualization") - if not os.path.exists(visualdir_): - os.makedirs(visualdir_) - - if savename_ is None: - savename_ = "{}_{}".format(savename, max_delta_t) - - # self.visualize(visualdir_) - self.save_linked_series(savedir_, savename_) + # if self.updated == 1: + # if savedir_ is not None and savedir_ != savedir: + # visualdir_ = os.path.join(savedir, "visualization") + # else: + # savedir_ = savedir + # visualdir_ = os.path.join(savedir, "updated_visualization") + # if not os.path.exists(visualdir_): + # os.makedirs(visualdir_) + # + # if savename_ is None: + # savename_ = "{}_{}".format(savename, max_delta_t) + # + # # self.visualize(visualdir_) + # self.save_linked_series(savedir_, savename_) # self.save_to_csv(savedir, "link_series_old.csv", "link_info_old.csv") From 98c91fee320be07e2fac862f2ac801d4a9edb471 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 18 Dec 2020 17:10:23 -0600 Subject: [PATCH 093/274] Update tests.py --- tests/tests.py | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index e7975b4e7..8151a2d95 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6179,12 +6179,8 @@ def test_plantcv_time_series_time_series(): # load instance segmentation results (masks) masks = [] for tp in timepoints: - print(tp) - print(ext_seg) filename_ = "*{}{}".format(tp, ext_seg) - print(filename_) filename = glob.glob(os.path.join(path_segmentation, filename_))[0] - print("file name is {}".format(filename)) r = pkl.load(open(filename, 'rb')) masks.append(r['masks'][0: min_dim, 0:min_dim, :]) # make all masks the same size @@ -6194,10 +6190,9 @@ def test_plantcv_time_series_time_series(): # create an instance of Instance_timeseries_linking name_sub = 'leaf' inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() - inst_ts_linking(images=images, masks=masks, timepoints=timepoints, savedir=path_save, savename=savename, - logic=logic, thres=thres, name_sub=name_sub, update=False) - - assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 + inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) + assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From 507f0671182d7d5bc2800ead4d222d8159e02ae4 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 18 Dec 2020 17:10:26 -0600 Subject: [PATCH 094/274] Update time_series.md --- docs/time_series.md | 86 ++++++++++++++++++++++++++++++++------------- 1 file changed, 62 insertions(+), 24 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index e31e58e26..f868b7876 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -1,6 +1,6 @@ ## time_series -This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every several hours) and ideally there should be minimal movment of plant/camera. +This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every 4 hours for several days), and ideally there should be minimal or no movment of plant and camera. To use this class for generating time-series linking, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). @@ -8,34 +8,73 @@ When using instance segmentation algorithms like maskRCNN, the assignment for th To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. -There are two way of using this class: either using it directly or using the wrapper. Examples of both are provided below. +For details of using this class, see examples below. -**\# initialize an instance of InstanceTimeSeriesLinking class:** +*initialize an instance of InstanceTimeSeriesLinking class:* -**inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking**(*images, masks, timepoints, logic, thres, name_sub*) -**inst_ts_linking**(*savedir, visualdir_, visualdir, savename_, savename*) +**inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking()** -**returns** No returned value, the inst_ts_linking is an instance which belongs to InstanceTimeSeriesLinking class. +*run the callable object* -- **Parameters for initialization:** +**inst_ts_linking**(*images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance",update=False, max_delta_t=2*) +**returns** No returned value, the inst_ts_linking is an instance object which belongs to InstanceTimeSeriesLinking class. + +- **Parameters** - images: a list of images. Every element of this list is an array represents one image - masks: a list of instance segmentation masks. Every element of this list is an array represents instance segmentation masks correspond to one image. - timepoints: a list of timepoints. The lengths for images, masks and timepoint should be the same and the elements are correspond to each other - - logic: the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) - If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". "IOS" is recommended. - - thres: threshold used in the linking logic as mentioned above. For "IOS" it is recommended to start with a threshod of 0.2. - - name_sub: name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". - -Once you get the class object initialized, it is callable, which means it is a callable function itself so that you can get the linking result by running the 2nd line of code presented above. -- **Parameters to call functions:** - - savedir: desired saving directory of linking result - - visualdir_: desired saving directory of visualization (before update) - - visualdir: desired saving directory of visualization (final) - - savename_: desired saving name of results (before update) - - savename: desired saving name of results (final) + - logic (optional): the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) + If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". Default value is "IOS". + - thres (optional): threshold used in the linking logic as mentioned above. Different threshold is needed when using different linking logic. Default value is 0.2. + - name_sub (optional): name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". + - update (optional): whether or not updating the time-series linking result. By default (not updating) the time-series linking would only examine the neighboring pairs of time points (time interval = 1). By updating the time-series linking we will be examining a larger interval of time. + - max_delta_t (optional): only needed when update=True. By default max_delta_t=2. - **Output:** - An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir": + An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. + +```python +from plantcv import plantcv as pcv +# Below are examples of input variables, always adjust base on your own application. +logic = 'IOS' +thres = 0.1 +name_sub = 'leaf' +## Initialize an instance of class InstanceTimeSeriesLinking +inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() +inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub) +``` +Make sure all the images and masks are already sorted based on timepoints. + +To save the result, simply specify the directory of saving as well as the name (prefix) +```python +## Specify the desired directory to save results +dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' + +## Specify the desired name to save the result (prefix) +savename = 'linked_series' + +inst_ts_linking.save_linked_series(savedir=dir_save, savename=savename) +``` +To import a previously saved result: +```python +dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' +savename = 'linked_series' +inst_ts_linking = time_series_linking.InstanceTimeSeriesLinking() +inst_ts_linking.import_linked_series(savedir=dir_save, savename=savename) +``` + +The option to update the time-series linking result is also available: +```python +max_delta_t = 3 +inst_ts_linking.update_ti(delta_t=max_delta_t) +``` + +Alternatively, the time-series linking can be done with ```update=True``` +```python +inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=True, max_delta_t=3) +``` + + **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) From fb2fa8a1a7cf521318415c196110bd775cc18cc2 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 08:36:45 -0600 Subject: [PATCH 095/274] Update tests.py --- tests/tests.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/tests/tests.py b/tests/tests.py index 8151a2d95..70e32caa7 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6184,6 +6184,13 @@ def test_plantcv_time_series_time_series(): r = pkl.load(open(filename, 'rb')) masks.append(r['masks'][0: min_dim, 0:min_dim, :]) # make all masks the same size + # test for "_compute_overlap_masks" + masks1 = masks[0] + masks2 = masks[1] + n1, n2, _, _, _ = pcv.time_series._compute_overlaps_masks(masks1[:, :, 0], masks2) + n1_, n2_, _, _, _ = pcv.time_series._compute_overlaps_masks(masks1, masks2[:, :, 0]) + assert n1 == 1 and n2 == masks2.shape[2] and n1_ == masks1.shape[2] and n2_ == 1 + logic = 'IOS' thres = 0.1 @@ -6194,6 +6201,19 @@ def test_plantcv_time_series_time_series(): assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 +# def test_plantcv_time_series_compute_overlap_masks(): +# path_segmentation = TIME_SERIES_TEST_INSTANCE_SEG +# ext_seg = ".pkl" +# list_seg = glob.glob(os.path.join(path_segmentation, "2*{}".format(ext_seg))) +# loaded1 = pkl.load(open(list_seg[0], 'rb')) +# loaded2 = pkl.load(open(list_seg[1], 'rb')) +# masks1 = loaded1['masks'] +# masks2 = loaded2['masks'] +# n1, n2, _, _, _ = _compute_overlaps_masks(masks1[:,:,0], masks2) +# assert n1 == 1 and n2 == masks2.shape[2] +# n1_, n2_, _, _, _ = _compute_overlaps_masks(masks1, masks2[:, :, 0]) +# assert n1_ == masks1.shape[2] and n2_ == 1 + def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_visualize_overlay_two_imgs") From b360382e1c0429331ae8af2b3090e62d94bc1b53 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 11:07:33 -0600 Subject: [PATCH 096/274] Create evaluation.py --- plantcv/plantcv/time_series/evaluation.py | 157 ++++++++++++++++++++++ 1 file changed, 157 insertions(+) create mode 100644 plantcv/plantcv/time_series/evaluation.py diff --git a/plantcv/plantcv/time_series/evaluation.py b/plantcv/plantcv/time_series/evaluation.py new file mode 100644 index 000000000..8b3aa3f95 --- /dev/null +++ b/plantcv/plantcv/time_series/evaluation.py @@ -0,0 +1,157 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Dec 7 2020 + +Functions used to evaluate time-series linking result + +@author: hudanyunsheng + +Notations and Definitions: +T: total time +N: total number of (unique) leaves in ground truth +N_: total number of (unique) leaves in tracking result +li (link info): a list of length (T-1) + every element of li, i.e. li[t] represents how n_t link to n_{t+1} + length of li[t]: n_t + li[t][i]=j: the i-th segment at time t is the j-th segment at time t+1 +li_gt (link info in ground truth): same definition as li, represents the same information in ground truth +ti (tracking info): a matrix (2d-array) of size (T,N) + tk-th element of ti: ti[t,k]: local index of leaf k at time t if k leaf k appears at time t, -1 others. + Total number of non-negative elements in a row t: # of leaves at time t + Every column k: local indices for leaf k at different timepoints(ts). +ti_gt (tracking info in ground truth): same definition as ti, represents the same information in ground truth +""" + +import numpy as np +from scipy.optimize import linear_sum_assignment +import copy +from plantcv.plantcv import fatal_error + + +def evaluate_link(li, li_gt): + """ + Evaluate the link_info by comparing the result to the ground truth + :param li: link info + :param li_gt: link info in ground truth + :return: + score: final score for linking, defined as total # of correct matches / total # of matches + num_insts: number of instances at every time point except the last one + num_matched: number of matched instances at every time point except the last one + """ + if not len(li) == len(li_gt): + fatal_error('Linking information not same length!!') + else: + max_t = len(li_gt) + if not sum([len(x) == len(y) for (x, y) in zip(li_gt, li)]) == max_t: + fatal_error('Different number of instances!!') + num_insts = [len(x) for x in li_gt] + # num_matched = [(sum(x == y)) for (x,y) in zip(li_gt, li)] + num_matched = [(len(x == y)) for (x, y) in zip(li_gt, li)] + score = sum(num_matched) / sum(num_insts) + return score, num_insts, num_matched + + +def mismatch_rate(ti, ti_gt): + """ + Calculate rates related to two types of mis-match: unmatched and fake-new + :param ti: tracking info + :param ti_gt: tracking info in ground truth + :return: + r_unmatched: 0 if N_ >= N + r_fake_new: 0 if N_ <= N + """ + r_unmatched, r_fake_new = 0, 0 + N, N_ = ti_gt.shape[1], ti.shape[1] + if N_ < N: + r_unmatched = (N - N_) / N + elif N_ > N: + r_fake_new = (N_ - N) / N + return r_unmatched, r_fake_new + + +def confusion(ti, ti_gt): + """ + Generate a confusion matrix based on tracking info and ground truth of tracking info + :param ti: tracking info + :param ti_gt: tracking info in ground truth + :return: + confu: confusion matrix of size (N,N_) + match: (list of length N) "diagonal" elements of confu. (If N_N, there will still be N elements in "match") + rate: (list of length N) match/existence_times + score: averate rate of all leaves + """ + N, N_ = ti_gt.shape[1], ti.shape[1] + # existance times for every leaf (unique id in ground truth) + life_gt = [len(np.where(ti_gt[:, i] > -1)[0]) for i in range(N)] + life = [len(np.where(ti[:, i] > -1)[0]) for i in range(N_)] + confu = np.zeros((N, N_), dtype=np.int64) + for t, (ti_t, ti_gt_t) in enumerate(zip(ti, ti_gt)): + temp = np.zeros((N, N_), dtype=np.int64) + for (uid_gt, cid_gt) in enumerate(ti_gt_t): + if cid_gt > -1: + uid_t = np.where(ti_t == cid_gt)[0][0] + temp[uid_gt, uid_t] = 1 + confu = confu + temp + + # if N_ Date: Mon, 21 Dec 2020 11:07:36 -0600 Subject: [PATCH 097/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index 1bb96b3ff..9509934f1 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -3,6 +3,10 @@ from plantcv.plantcv.time_series.time_series_linking import _get_ti from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking +from plantcv.plantcv.time_series.evaluation import evaluate_link +from plantcv.plantcv.time_series.evaluation import mismatch_rate +from plantcv.plantcv.time_series.evaluation import confusion +from plantcv.plantcv.time_series.evaluation import get_scores # add new functions to end of lists -__all__ = ["_get_link", "_get_emergence", "_get_ti", "_compute_overlaps_masks", "InstanceTimeSeriesLinking"] +__all__ = ["_get_link", "_get_emergence", "_get_ti", "_compute_overlaps_masks", "InstanceTimeSeriesLinking", "evaluate_link", "mismatch_rate", "confusion", "get_scores"] From 7fc0f1dc1f7f424a056db2c5135dee0c62f48fa8 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 11:07:42 -0600 Subject: [PATCH 098/274] Update tests.py --- tests/tests.py | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 4bbb90618..5fda06274 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -947,7 +947,7 @@ def test_plantcv_parallel_process_results_invalid_json(): TEST_INPUT_THERMAL_CSV = "FLIR2600.csv" PIXEL_VALUES = "pixel_inspector_rgb_values.txt" -TIME_SERIES_TEST_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "seires_data") +TIME_SERIES_TEST_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "time_seires_data") TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im") TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "inst_seg") @@ -6221,18 +6221,19 @@ def test_plantcv_time_series_time_series(): assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 -# def test_plantcv_time_series_compute_overlap_masks(): -# path_segmentation = TIME_SERIES_TEST_INSTANCE_SEG -# ext_seg = ".pkl" -# list_seg = glob.glob(os.path.join(path_segmentation, "2*{}".format(ext_seg))) -# loaded1 = pkl.load(open(list_seg[0], 'rb')) -# loaded2 = pkl.load(open(list_seg[1], 'rb')) -# masks1 = loaded1['masks'] -# masks2 = loaded2['masks'] -# n1, n2, _, _, _ = _compute_overlaps_masks(masks1[:,:,0], masks2) -# assert n1 == 1 and n2 == masks2.shape[2] -# n1_, n2_, _, _, _ = _compute_overlaps_masks(masks1, masks2[:, :, 0]) -# assert n1_ == masks1.shape[2] and n2_ == 1 +def test_plantcv_time_series_evaluation(): + loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"result.pkl"),'rb')) + loaded_gt = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"gt.pkl"),'rb')) + li = loaded['li'] + ti = loaded['ti'] + li_gt = loaded_gt['li_gt'] + ti_gt = loaded_gt['ti_gt'] + + with pytest.raises(RuntimeError): + pcv.time_series.get_scores(li[0:-1], ti[0:-1,:], li_gt, ti_gt) + + scores = pcv.time_series.get_scores(li, ti, li_gt, ti_gt) + assert len(li) == ti.shape[0]-1 and len(li_gt) == ti_gt.shape[0]-1 and ti.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From 3385a5ad74dc9c6ff49bf1b172710e0afc6d6643 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 11:08:14 -0600 Subject: [PATCH 099/274] upload testdata --- .../plantcv/time_series/time_series_linking.py | 4 ++-- tests/time_seires_data/gt.pkl | Bin 0 -> 16699 bytes .../inst_seg/2019-10-21-21-05.pkl | Bin .../inst_seg/2019-10-22-08-05.pkl | Bin .../inst_seg/2019-10-22-11-05.pkl | Bin .../inst_seg/2019-10-22-14-05.pkl | Bin .../inst_seg/2019-10-22-17-05.pkl | Bin .../inst_seg/2019-10-22-21-05.pkl | Bin .../inst_seg/img_segment_.pkl | Bin ...-165-003-020_2019-10-21-21-05_crop-img12.jpg | Bin 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time_seires_data}/inst_seg/2019-10-22-21-05.pkl (100%) rename tests/{seires_data => time_seires_data}/inst_seg/img_segment_.pkl (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-21-21-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-08-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-11-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_mask-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-17-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_mask-img12.jpg (100%) create mode 100644 tests/time_seires_data/result.pkl diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 1c6f3b898..92826f637 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -1,11 +1,11 @@ # !/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Created on Tue Jun 9 09:53:34 2020 +Created on Tue Jun 9 2020 Functions used in time series linking after getting leaf instances segmented -@author: hudanyunsheng +@author: hudanyun sheng """ import numpy as np diff --git a/tests/time_seires_data/gt.pkl b/tests/time_seires_data/gt.pkl new file mode 100644 index 0000000000000000000000000000000000000000..18e5daa1a36dc6a2d58704355d60be750da2984e GIT binary patch literal 16699 zcmeI2cYIV;7RM7p4FnV$RzMLWYQ&C;Qi6&^A|4fEK_DTLD1@BDi4qkBWPqT%y2Y}q 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tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-08-05_crop-img12.jpg rename to tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-08-05_crop-img12.jpg diff --git a/tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-11-05_crop-img12.jpg b/tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-11-05_crop-img12.jpg similarity index 100% rename from tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-11-05_crop-img12.jpg rename to tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-11-05_crop-img12.jpg diff --git a/tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg b/tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg similarity index 100% rename from tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg rename to tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg diff --git a/tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_mask-img12.jpg b/tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_mask-img12.jpg similarity index 100% rename from tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_mask-img12.jpg rename to tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_mask-img12.jpg diff --git a/tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-17-05_crop-img12.jpg b/tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-17-05_crop-img12.jpg similarity index 100% rename from tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-17-05_crop-img12.jpg rename to tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-17-05_crop-img12.jpg diff --git a/tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_crop-img12.jpg b/tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_crop-img12.jpg similarity index 100% rename from tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_crop-img12.jpg rename to tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_crop-img12.jpg diff --git a/tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_mask-img12.jpg b/tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_mask-img12.jpg similarity index 100% rename from tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_mask-img12.jpg rename to tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_mask-img12.jpg diff --git a/tests/time_seires_data/result.pkl b/tests/time_seires_data/result.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b398db592585ee6a01b9758934a87ed1709d3986 GIT binary patch literal 17717 zcmeI2cYIV;7RM7p4FoKx*s;eJv7@3Qu^`ci$A$%gglM7=atQ>#=t@t}L_uLPkF_3xlW+tELn?KIU%z5+seeb#VzIih*>l)T_UGt(b zb#-+sdboZuk7@4g?O)k9(9*d|x>{EDuh5=lBFhFEmq=IVs@~Nq{hiv#4TYl`dzTlg zJhrVa$KzV-J9zvCoG_9{(dZ{>ZW#=fSp zAMyS{jxQfoYA;ahqw)DwS)T^PQ!9cGPS)&2xws2F-KJ((c-LGz;WG#4pf z95pX7%}a?d3!00|)4W`HNz`0wn#+hgf@WuVn#+~DqUIH*c_neTX->#-k7>?MI!EJu zuC7wPI%=;l?Uls6L3>p??LK9W+G5(Pi8W~Vr_;Vh`P!(x#-$8t5(0)NW?H4M) zC~Cjhv|mE}(x81;I_o&Po0Z=ZJ>F^_ZzFzt@OVdMJl?7NuITY@^LP*O zdxOXOD&z5fJa&M`Mn^W*!UE?vKKma?_@X zzF)qs{Eg`OP4oN~@wd%$QjWi4p7YasPI9mBDt|9}ec!x(K>Winz8_U&d_Jz|`D5ju zM9-g^=g)|L9z1_h9nW7X|0;U^+B|*ExH{!pCUO$oV^_IRX`SrA4w-%q*w>aJ>mH!bv|7o62 z5kDQq`>*Pb_utC@iQfM;@Bb11Ka6)%e!P|LvDmjwm3l;XmN9q&b7?>(8H-pzka1S1 z#95=b`!XKNDiaVR#Y8g$#Ux~sM#RD-$Yd*2bIr>XIH)usMw+Q+3YuxiB-;oBSxx2%p6pQB9pWr8dXzH4zmKNx~sje{If)6!BORK#7K99L4fW^WRjy0 zL(h)3LQQ&h44hPsMT}Il%^Xz6A(I@B7^qILLQSaVz)9sq#7K3LnS<(NWRkgvMm04j zr&s}0ebt`HDZyZ>pE2gaS)~;*Ql4rspgawkJK-q~*vK%o`c3ELWD6fFC%9V(bvfE%l*@H}S6=I;g+6p5=xdP5A zD-k1Qufc$F6*5U5VxVLzY)28Fh%1wxoZk<7Z?q+0? zTMz@?tyZWh-EDAG$su-h2s}9${(Zc7y ztyZs7Lo1iszl<;I-_RXM_iEDh=l34cxt9L!jIZ=w4RvJJzoEL4{I#r)$$Pf>JF@<5 z-i_pcm)k%8-=E};$=;FiCF@u5(OLhBJCf{q*1u%^Dn9zT-ao%TZmj4dp z--DgQeVxZSy?;J#Kd#*DI!QU*&$*o2dA*)CKi~O$zRT@nwfi?(`20?B(mvLze;Hq) z(|#WGc^dBg;_Gj;^w0knz{fXS&emU{1EXbreLj7j{XeGs_2N8I^Xq*6ABbDx%=m_0 zf2H=%&tFOV=cSXnA}B6jq*JOjAl0gwOy literal 0 HcmV?d00001 From 3d04ba292f5a826cdce3266d65bc26053b4418d9 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 16:55:18 -0600 Subject: [PATCH 100/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 92826f637..3ae64ad99 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -46,9 +46,9 @@ def _compute_overlaps_masks(masks1, masks2): unions: (numpy.ndarray of shape: [n1, n2]) unions between any pairs of instances in masks1 and masks2 """ - # If either set of masks is empty return empty result - if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: - return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # If either set of masks is empty return an empty result + # if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + # return np.zeros((masks1.shape[-1], masks2.shape[-1])) # If either set of masks contains only one mask, expand the 2nd dimension if len(masks1.shape) == 2: masks1 = np.expand_dims(masks1, 2) From b29b5818439bc30fd6b907a7840267ec7214a65d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 16:55:22 -0600 Subject: [PATCH 101/274] Update tests.py --- tests/tests.py | 47 +++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 41 insertions(+), 6 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 5fda06274..1a92a39a4 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6219,21 +6219,56 @@ def test_plantcv_time_series_time_series(): inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + inst_ts_linking.update_ti(delta_t=2) + assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + + inst_ts_linking.save_linked_series(path_save,'linked_series') + assert (len(os.listdir(path_save)) > 0) + + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + inst_ts_linking.import_linked_series(path_save, 'linked_series') + assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + + logic = 'IOU' + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) + assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + + logic = 'emd' + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + with pytest.raises(RuntimeError): + inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 def test_plantcv_time_series_evaluation(): - loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"result.pkl"),'rb')) + loaded17 = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"result_N_17.pkl"),'rb')) + loaded18 = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR, "result_N_18.pkl"), 'rb')) + loaded20 = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR, "result_N_20.pkl"), 'rb')) loaded_gt = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"gt.pkl"),'rb')) - li = loaded['li'] - ti = loaded['ti'] + li17 = loaded17['li'] + ti17 = loaded17['ti'] + li18 = loaded18['li'] + ti18 = loaded18['ti'] + li20 = loaded20['li'] + ti20 = loaded20['ti'] li_gt = loaded_gt['li_gt'] ti_gt = loaded_gt['ti_gt'] with pytest.raises(RuntimeError): - pcv.time_series.get_scores(li[0:-1], ti[0:-1,:], li_gt, ti_gt) + pcv.time_series.get_scores(li18[0:-1], ti18[0:-1,:], li_gt, ti_gt) + + with pytest.raises(RuntimeError): + pcv.time_series.get_scores(li18[1:], ti18[1:,:], li_gt, ti_gt) + + scores = pcv.time_series.get_scores(li18, ti18, li_gt, ti_gt) + assert len(li18) == ti18.shape[0]-1 and len(li_gt) == ti_gt.shape[0]-1 and ti18.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] + + scores = pcv.time_series.get_scores(li20, ti20, li_gt, ti_gt) + assert len(li20) == ti20.shape[0]-1 and len(li_gt) == ti_gt.shape[0]-1 and ti20.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] + + scores = pcv.time_series.get_scores(li17, ti17, li_gt, ti_gt) + assert len(li17) == ti17.shape[0] - 1 and len(li_gt) == ti_gt.shape[0] - 1 and ti17.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] - scores = pcv.time_series.get_scores(li, ti, li_gt, ti_gt) - assert len(li) == ti.shape[0]-1 and len(li_gt) == ti_gt.shape[0]-1 and ti.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From 74ec5e6c99b0ebf2af5ea3a27863fc0268844c11 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 16:55:31 -0600 Subject: [PATCH 102/274] load new test data --- tests/time_seires_data/result_N_17.pkl | Bin 0 -> 16181 bytes tests/time_seires_data/result_N_18.pkl | Bin 0 -> 16693 bytes .../{result.pkl => result_N_20.pkl} | Bin 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/time_seires_data/result_N_17.pkl create mode 100644 tests/time_seires_data/result_N_18.pkl rename tests/time_seires_data/{result.pkl => result_N_20.pkl} (100%) diff --git a/tests/time_seires_data/result_N_17.pkl b/tests/time_seires_data/result_N_17.pkl new file mode 100644 index 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z+x0^Eed?BVjytP8u5rFWE7Cs0>MydEf50fPYgg_3yg+hQ48!}AMo{W9qs2LOBIuHd%iCF z;&b;lRbGmGjAc&un-^*~HyUrq^Ex+7dHh*A?8&U5=V$yLSaPLwMWKJjHjk2HiPNR( z8&W?lx%fZ7G4i|K>xvKGg#(6%jQiESQAh?-2UzrdPi~WGq$a zBDUj#A`SYO*ij^$?!n}xQW1ur(IkRubmic1L!2I8`H5uV=~3*6A>-&CxGAbl?CL9g*$2zdM$^v)#gOsBKDsC{G`)B-A;L}?Ib)cf#PHMD X61#e06!ul7A&S Date: Tue, 22 Dec 2020 08:43:22 -0600 Subject: [PATCH 104/274] Update tests.py --- tests/tests.py | 43 ++++++++++++++++++++++++++++++++----------- 1 file changed, 32 insertions(+), 11 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 1a92a39a4..660b40119 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6219,27 +6219,41 @@ def test_plantcv_time_series_time_series(): inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + + # test with update_ti = True + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=True) + assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + + # test on update_ti inst_ts_linking.update_ti(delta_t=2) assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) - inst_ts_linking.save_linked_series(path_save,'linked_series') + # test on save_linked_series + inst_ts_linking.save_linked_series(path_save,savename) assert (len(os.listdir(path_save)) > 0) - inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() - inst_ts_linking.import_linked_series(path_save, 'linked_series') - assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) - + # test on using IOU as linking logic logic = 'IOU' inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + # test on using logic that is currently unavailable logic = 'emd' inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() with pytest.raises(RuntimeError): inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 + # test on import_linked_series and then for the case where there is no potential updates + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + inst_ts_linking.import_linked_series(TIME_SERIES_TEST_DIR, 'link_series_no_updates') + assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + inst_ts_linking.update_ti() + assert inst_ts_linking.updated == 0 + + def test_plantcv_time_series_evaluation(): loaded17 = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"result_N_17.pkl"),'rb')) loaded18 = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR, "result_N_18.pkl"), 'rb')) @@ -6254,12 +6268,6 @@ def test_plantcv_time_series_evaluation(): li_gt = loaded_gt['li_gt'] ti_gt = loaded_gt['ti_gt'] - with pytest.raises(RuntimeError): - pcv.time_series.get_scores(li18[0:-1], ti18[0:-1,:], li_gt, ti_gt) - - with pytest.raises(RuntimeError): - pcv.time_series.get_scores(li18[1:], ti18[1:,:], li_gt, ti_gt) - scores = pcv.time_series.get_scores(li18, ti18, li_gt, ti_gt) assert len(li18) == ti18.shape[0]-1 and len(li_gt) == ti_gt.shape[0]-1 and ti18.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] @@ -6269,6 +6277,19 @@ def test_plantcv_time_series_evaluation(): scores = pcv.time_series.get_scores(li17, ti17, li_gt, ti_gt) assert len(li17) == ti17.shape[0] - 1 and len(li_gt) == ti_gt.shape[0] - 1 and ti17.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] + # 1st fatal_error in evaluate_link + with pytest.raises(RuntimeError): + pcv.time_series.evaluate_link(li18[0:-1], li_gt) + + with pytest.raises(RuntimeError): + pcv.time_series.evaluate_link(li18[1:], li_gt) + + # 2nd fatal_error in evaluate_link + li18_ = li18 + li18_[0] = np.delete(li18[0],2) + with pytest.raises(RuntimeError): + pcv.time_series.evaluate_link(li18_, li_gt) + def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From dc4b6afcc49aa60e274f8ac3ed8d2f912566046d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 22 Dec 2020 13:34:28 -0600 Subject: [PATCH 105/274] Delete time_series.md --- docs/time_series.md | 251 -------------------------------------------- 1 file changed, 251 deletions(-) delete mode 100644 docs/time_series.md diff --git a/docs/time_series.md b/docs/time_series.md deleted file mode 100644 index f868b7876..000000000 --- a/docs/time_series.md +++ /dev/null @@ -1,251 +0,0 @@ -## time_series - -This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every 4 hours for several days), and ideally there should be minimal or no movment of plant and camera. - -To use this class for generating time-series linking, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). - -When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. - -To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. - -For details of using this class, see examples below. - -*initialize an instance of InstanceTimeSeriesLinking class:* - -**inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking()** - -*run the callable object* - -**inst_ts_linking**(*images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance",update=False, max_delta_t=2*) -**returns** No returned value, the inst_ts_linking is an instance object which belongs to InstanceTimeSeriesLinking class. - -- **Parameters** - - images: a list of images. Every element of this list is an array represents one image - - masks: a list of instance segmentation masks. Every element of this list is an array represents instance segmentation masks correspond to one image. - - timepoints: a list of timepoints. The lengths for images, masks and timepoint should be the same and the elements are correspond to each other - - logic (optional): the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) - If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". Default value is "IOS". - - thres (optional): threshold used in the linking logic as mentioned above. Different threshold is needed when using different linking logic. Default value is 0.2. - - name_sub (optional): name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". - - update (optional): whether or not updating the time-series linking result. By default (not updating) the time-series linking would only examine the neighboring pairs of time points (time interval = 1). By updating the time-series linking we will be examining a larger interval of time. - - max_delta_t (optional): only needed when update=True. By default max_delta_t=2. - -- **Output:** - An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. - -```python -from plantcv import plantcv as pcv -# Below are examples of input variables, always adjust base on your own application. -logic = 'IOS' -thres = 0.1 -name_sub = 'leaf' -## Initialize an instance of class InstanceTimeSeriesLinking -inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() -inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub) -``` -Make sure all the images and masks are already sorted based on timepoints. - -To save the result, simply specify the directory of saving as well as the name (prefix) -```python -## Specify the desired directory to save results -dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' - -## Specify the desired name to save the result (prefix) -savename = 'linked_series' - -inst_ts_linking.save_linked_series(savedir=dir_save, savename=savename) -``` -To import a previously saved result: -```python -dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' -savename = 'linked_series' -inst_ts_linking = time_series_linking.InstanceTimeSeriesLinking() -inst_ts_linking.import_linked_series(savedir=dir_save, savename=savename) -``` - -The option to update the time-series linking result is also available: -```python -max_delta_t = 3 -inst_ts_linking.update_ti(delta_t=max_delta_t) -``` - -Alternatively, the time-series linking can be done with ```update=True``` -```python -inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=True, max_delta_t=3) -``` - - - - -**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) - - From 8327869e96d10d228387bd9ef841901bd0bad7dd Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 22 Dec 2020 13:34:45 -0600 Subject: [PATCH 106/274] Create time_series_evaluation.md --- docs/time_series_evaluation.md | 62 ++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 docs/time_series_evaluation.md diff --git a/docs/time_series_evaluation.md b/docs/time_series_evaluation.md new file mode 100644 index 000000000..272591f5e --- /dev/null +++ b/docs/time_series_evaluation.md @@ -0,0 +1,62 @@ +## Evaluation of time-series linking result + +This set of functions are designed to evaluate the time-series leaf tracking result by comparing to the ground-truth. + +To start, get familiar with some notations and definitions: + +**T**: total time + +**N**: total number of unique leaves in ground truth + +**nt**: number of leaves at time t + +**li**: link info, a list of length (T-1) +- t-th element of li, i.e. lit represents how leaves at t (nt leaves) link to leaves at t+1 (nt+1 leaves) +- length of lit: nt +- lit,i=j: the i-th leaf at time t is the j-th leaf at time t+1 + +**ti**: tracking info, a matrix (2d-array) of size (T,N) +- tk-th element of ti (tit,k): (local) index of leaf k at time t if leaf k appears at time t, -1 others. +- Total number of non-negative elements in a t-th row: # of leaves at time t +- Every column k: (local) indices for the leaf k at different timepoints(ts). + +**N'**: total number of (unique) leaves in leaf tracking result + +- *note: N=N' not necessary holds.* + +**li'**: link info in tracking result + +**ti'**: tracking info in tracking result + +The performance of leaf tracking can be evaluated by 4 different scores: +1. linking score +``` +linking score = # correct matches / # total matches +``` + +2. unmatched leaf rate +``` +if N' < N, unmatched leaf rate = (N-N')/N +otherwise, unmatched leaf rate = 0 +``` + +3. fake new leaf rate +``` +if N < N' <= 2N, fake new leaf rate = (N'-N)/N +if N'> 2N, fake new leaf rate = 1 +otherwise, fake new leaf rate = 0 +``` +4. tracking score
+To define the tracking score, first define the confusion matrix **C** based on ti and ti'. + - size: N×N' + - ij-th element C (Ci,j): how many times for leaf i becomes leaf j when tracking. + - row sum (*sumi*): total existence of leaf i in ground truth (i = 1,2,...,N) + - column sum (*sumj*): total existence of leaf j in tracking result (j = 1,2,...,N') + - tracking score for leaf i (*tracking-scorei*): + tracking-scorei=Ci,i/total existence of leaf i in ground truth +``` +tracking score = summation tracking-score (for i = 1,...,N)/N +``` + +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/evaluation.py) + From b5f4106043c6d1c67cb71a319dec257ef085ed02 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 22 Dec 2020 13:34:49 -0600 Subject: [PATCH 107/274] Create time_series_InstanceTimeSeriesLinking..md --- .../time_series_InstanceTimeSeriesLinking..md | 251 ++++++++++++++++++ 1 file changed, 251 insertions(+) create mode 100644 docs/time_series_InstanceTimeSeriesLinking..md diff --git a/docs/time_series_InstanceTimeSeriesLinking..md b/docs/time_series_InstanceTimeSeriesLinking..md new file mode 100644 index 000000000..f868b7876 --- /dev/null +++ b/docs/time_series_InstanceTimeSeriesLinking..md @@ -0,0 +1,251 @@ +## time_series + +This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every 4 hours for several days), and ideally there should be minimal or no movment of plant and camera. + +To use this class for generating time-series linking, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). + +When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. + +To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. + +For details of using this class, see examples below. + +*initialize an instance of InstanceTimeSeriesLinking class:* + +**inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking()** + +*run the callable object* + +**inst_ts_linking**(*images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance",update=False, max_delta_t=2*) +**returns** No returned value, the inst_ts_linking is an instance object which belongs to InstanceTimeSeriesLinking class. + +- **Parameters** + - images: a list of images. Every element of this list is an array represents one image + - masks: a list of instance segmentation masks. Every element of this list is an array represents instance segmentation masks correspond to one image. + - timepoints: a list of timepoints. The lengths for images, masks and timepoint should be the same and the elements are correspond to each other + - logic (optional): the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) + If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". Default value is "IOS". + - thres (optional): threshold used in the linking logic as mentioned above. Different threshold is needed when using different linking logic. Default value is 0.2. + - name_sub (optional): name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". + - update (optional): whether or not updating the time-series linking result. By default (not updating) the time-series linking would only examine the neighboring pairs of time points (time interval = 1). By updating the time-series linking we will be examining a larger interval of time. + - max_delta_t (optional): only needed when update=True. By default max_delta_t=2. + +- **Output:** + An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. + +```python +from plantcv import plantcv as pcv +# Below are examples of input variables, always adjust base on your own application. +logic = 'IOS' +thres = 0.1 +name_sub = 'leaf' +## Initialize an instance of class InstanceTimeSeriesLinking +inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() +inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub) +``` +Make sure all the images and masks are already sorted based on timepoints. + +To save the result, simply specify the directory of saving as well as the name (prefix) +```python +## Specify the desired directory to save results +dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' + +## Specify the desired name to save the result (prefix) +savename = 'linked_series' + +inst_ts_linking.save_linked_series(savedir=dir_save, savename=savename) +``` +To import a previously saved result: +```python +dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' +savename = 'linked_series' +inst_ts_linking = time_series_linking.InstanceTimeSeriesLinking() +inst_ts_linking.import_linked_series(savedir=dir_save, savename=savename) +``` + +The option to update the time-series linking result is also available: +```python +max_delta_t = 3 +inst_ts_linking.update_ti(delta_t=max_delta_t) +``` + +Alternatively, the time-series linking can be done with ```update=True``` +```python +inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=True, max_delta_t=3) +``` + + + + +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) + + From 34e90dac994d6b0ec4cd946d768b9bfd293a1f9c Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 6 Jan 2021 20:02:04 -0600 Subject: [PATCH 108/274] Update evaluation.py --- plantcv/plantcv/time_series/evaluation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/evaluation.py b/plantcv/plantcv/time_series/evaluation.py index 8b3aa3f95..8258dca94 100644 --- a/plantcv/plantcv/time_series/evaluation.py +++ b/plantcv/plantcv/time_series/evaluation.py @@ -47,7 +47,7 @@ def evaluate_link(li, li_gt): fatal_error('Different number of instances!!') num_insts = [len(x) for x in li_gt] # num_matched = [(sum(x == y)) for (x,y) in zip(li_gt, li)] - num_matched = [(len(x == y)) for (x, y) in zip(li_gt, li)] + num_matched = [(sum(x == y)) for (x, y) in zip(li_gt, li)] score = sum(num_matched) / sum(num_insts) return score, num_insts, num_matched From 0d463d6da74796b4c16a1651642905d528be69a9 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 6 Jan 2021 20:02:07 -0600 Subject: [PATCH 109/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 3ae64ad99..00386dfce 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -76,7 +76,7 @@ def _get_link(masks1, masks2, logic, thres): """ Get link information between two sets of binary instance segmentation masks :param masks1: (numpy.ndarray of shape: [Height, Width, n1]), where n1 is the number of instances - :param masks2: (numpy.ndarray of shape: [Height, Width, n1]), where n1 is the number of instances + :param masks2: (numpy.ndarray of shape: [Height, Width, n2]), where n2 is the number of instances :param logic: linking logic to use. (currently) either "IOU" or "IOS" :param thres: threshold to link two masks (two binary segmentation masks can only be linked if the calculated overlap between is greater than the threshold) :return: @@ -192,6 +192,23 @@ def _get_ti(num_insts, uids, link_info): t += 1 return ti, t_appear, t_disappear +def _get_li_from_ti(ti): + """ + + :param ti: + :return: + """ + li = [] + for t in range(len(ti)-1): + ti_0 = ti[t,:] + ti_1 = ti[t+1,:] + l0 = [x for x in ti_0 if x >= 0] + l1 = [x for (x, y) in zip(ti_1, ti_0) if y >= 0] + link_t = -np.ones(len(l0), dtype=np.int64) + for (idx, x) in enumerate(l0): + link_t[x] = l1[idx] + li.append(link_t) + return li # def _visualize1(img, mask, savename=None): # """ From 0c67d1f683172c9ffad59d1d93ea4303d98db08a Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 16:39:06 -0500 Subject: [PATCH 110/274] Delete time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 168 ------------------ 1 file changed, 168 deletions(-) delete mode 100644 plantcv/plantcv/visualize/time_lapse_video.py diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py deleted file mode 100644 index 759f450c5..000000000 --- a/plantcv/plantcv/visualize/time_lapse_video.py +++ /dev/null @@ -1,168 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Mon. July 06 08:00:34 2020 -Functions used to generate a time-lapse-video with provided directory of images -@author: hudanyunsheng -""" - -import os -import cv2 -import numpy as np -import copy -import mimetypes -from plantcv.plantcv import params -from plantcv.plantcv import fatal_error -import warnings - - -def _resize_img(img, size): - """Resize the image to the given new size - If the given size is larger than the original image size, zero-pad the image. - If the given size is smaller than the origina image size, crop the image (right & bottom). - """ - - # original image size - r_ori, c_ori = np.shape(img)[0], np.shape(img)[1] - r, c = size[0], size[1] - - if len(img.shape) > 2: - b_ori = np.shape(img)[2] - junk = copy.deepcopy(img) - else: - b_ori = 1 - junk = np.expand_dims(img, axis=2) - - # deal with rows - img1 = copy.deepcopy(junk) - if r < r_ori: - img1 = junk[0:r, :, :] - elif r > r_ori: - img1 = np.zeros_like(junk, shape=[r, c_ori, b_ori]) - img1[0:r_ori, :, :] = junk - - # update - r_ori, c_ori = np.shape(img1)[0], np.shape(img1)[1] - img2 = copy.deepcopy(img1) - - # deal with columns - if c < c_ori: - img2 = img1[:, 0:c, :] - elif c > c_ori: - img2 = np.zeros_like(img1, shape=[r_ori, c, b_ori]) - img2[:, 0:c_ori, :] = img1 - - if len(img.shape) == 2: - output_img = np.squeeze(img2, axis=2) - else: - output_img = img2 - return output_img - - -def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=None, fps=29.97, - name_video='time_lapse_video', path_video=None, display='on'): - """ - Generate time-lapse video given a folder of images - :param img_directory: the directory of folder of images to make the time-lapse video. - :param list_img: desired list of images in img_directory to create the video. If None is passed, all images would be included by default. - :param suffix_img: - :param size_frame: the desired size of every frame. - In a video, every frame should have the same size. - The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. - If the frame size is given, if an images is larger than the given size, the image would be cropped automatically; if an image is smaller than the given size, the image would be zero-padded automatically - If the frame size is not given, the largest size of all images would be used as the frame size. - :param fps: (frames per second) frame rate - Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 - :param name_video: - :param path_video: the desired saving path of output video. If not given, the video would be saved the the same directory of the images. - :param display: indicator of whether to display current status (by displaying saving directory and saving name) while running this function - :param kw: possible name and value: compress, and a number indicating what fraction compared to the original image. e.g. 0.5 represent compress the image to 1/2 of its original size - :return: - list_img: the list of images used to generate the video - size_frame: the frame size of the video - """ - - ## Get the list of image files in the given directory and sort them alphabetically by their names - temp_list = [] - for f in os.listdir(img_directory): - type_f = mimetypes.guess_type(f)[0] - if type_f: - if type_f.startswith('image'): - temp_list.append(f) - if len(temp_list) <= 0: - fatal_error("There is no file in the provided folder that is an image, please check the provided directory!") - else: - temp_list.sort() - # get an "extension free" list of images - temp_list_no_ext = [os.path.splitext(f)[0] if os.path.splitext(f)[1] else f for f in temp_list] - - ## if the list of images is provided, stick to it, but automatically handle the extension issue - if list_img is not None: - list_img.sort() - # make the list of images extension free - list_img_no_ext = [os.path.splitext(f)[0] if os.path.splitext(f)[1] else f for f in list_img] - list_img = [] - for (f_, f) in zip(temp_list_no_ext, temp_list): - try: - list_img_no_ext.index(f_) - list_img.append(f) - except: - continue - - if len(list_img) == 0: - fatal_error("The provided list of files is not contained in the given directory") - elif len(list_img) < len(list_img_no_ext): - warnings.warn( - "Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") - - # if the list of images is not provided, check if suffix information is available - elif suffix_img is not None: - list_img = [f for f in temp_list if f.endswith(suffix_img)] - if len(list_img) == 0: - fatal_error("There is no file that matches the provided suffix in the provided directory, please check again!") - - # neither the list of image nor the suffix is provided - else: - list_img = temp_list - - imgs = [] - list_r = [] - list_c = [] - for file in list_img: - img = cv2.imread(os.path.join(img_directory, file)) - list_r.append(img.shape[0]) - list_c.append(img.shape[1]) - imgs.append(img) - max_c, max_r = np.max(list_c), np.max(list_r) - - # If the frame size is not provided, use the largest size of the images as the frame size - size_frame = size_frame or (max_c, max_r) - - if not (len(np.unique(list_r)) == 1 and len(np.unique(list_c)) == 1): - warnings.warn("The sizes of images are not the same, an image resizing (cropping or zero-padding) will be done to make all images the same size ({}x{}) before creating the video! If you assume the images should have the same size, please check the images used to generate this video!".format( - size_frame[0], size_frame[1])) - - # If the video saving directory is not provided, save it in the same directory of the images - if path_video is None: - path_video = img_directory - - # Full video save name - save_name = os.path.join(path_video, name_video + '.mp4') - - fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case - # if 'compress' in kw: - # ratio = kw['compress'] - # else: - # ratio = 1 - # size_frame_ = tuple(int(x*ratio) for x in size_frame) - out = cv2.VideoWriter(save_name, fourcc, fps, size_frame) - - for img in imgs: - # out.write(cv2.resize(_resize_img(img, (size_frame[-1],size_frame[0])), (size_frame_))) - out.write(_resize_img(img, (size_frame[-1], size_frame[0]))) - out.release() - cv2.destroyAllWindows() - if display == 'on': - print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) - - return list_img, size_frame From a20e17c61e4c4c6282f6676b0e6bb218ec59619d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 16:39:09 -0500 Subject: [PATCH 111/274] Update __init__.py --- plantcv/plantcv/visualize/__init__.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/plantcv/plantcv/visualize/__init__.py b/plantcv/plantcv/visualize/__init__.py index bbdae389d..77b8cbddb 100644 --- a/plantcv/plantcv/visualize/__init__.py +++ b/plantcv/plantcv/visualize/__init__.py @@ -4,8 +4,6 @@ from plantcv.plantcv.visualize.clustered_contours import clustered_contours from plantcv.plantcv.visualize.colorspaces import colorspaces from plantcv.plantcv.visualize.auto_threshold_methods import auto_threshold_methods -from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video -from plantcv.plantcv.visualize.time_lapse_video import _resize_img from plantcv.plantcv.visualize.overlay_two_imgs import overlay_two_imgs __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", From 3b389efc951c11353695dc1e04912b43360a5043 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 16:39:23 -0500 Subject: [PATCH 112/274] Update evaluation.py --- plantcv/plantcv/time_series/evaluation.py | 28 +++++++++++------------ 1 file changed, 13 insertions(+), 15 deletions(-) diff --git a/plantcv/plantcv/time_series/evaluation.py b/plantcv/plantcv/time_series/evaluation.py index 8258dca94..5c8d4efe7 100644 --- a/plantcv/plantcv/time_series/evaluation.py +++ b/plantcv/plantcv/time_series/evaluation.py @@ -43,12 +43,12 @@ def evaluate_link(li, li_gt): fatal_error('Linking information not same length!!') else: max_t = len(li_gt) - if not sum([len(x) == len(y) for (x, y) in zip(li_gt, li)]) == max_t: + if not sum([len(x)==len(y) for (x,y) in zip(li_gt, li)]) == max_t: fatal_error('Different number of instances!!') - num_insts = [len(x) for x in li_gt] + num_insts = [len(x) for x in li_gt] # num_matched = [(sum(x == y)) for (x,y) in zip(li_gt, li)] num_matched = [(sum(x == y)) for (x, y) in zip(li_gt, li)] - score = sum(num_matched) / sum(num_insts) + score = sum(num_matched)/sum(num_insts) return score, num_insts, num_matched @@ -64,9 +64,9 @@ def mismatch_rate(ti, ti_gt): r_unmatched, r_fake_new = 0, 0 N, N_ = ti_gt.shape[1], ti.shape[1] if N_ < N: - r_unmatched = (N - N_) / N + r_unmatched = (N-N_)/N elif N_ > N: - r_fake_new = (N_ - N) / N + r_fake_new = (N_-N)/N return r_unmatched, r_fake_new @@ -103,21 +103,19 @@ def confusion(ti, ti_gt): life = life + [0 for n in range(delta_N)] # delta matrix of lift and life_gt - delta_life = -np.ones((N, max(N_, N)), dtype=int) - for i in range(0, N): - delta_life[i, :] = abs(np.int64(life_gt[i]) - life) + # delta_life = -np.ones((N,max(N_,N)),dtype=int) + # for i in range(0,N): + # delta_life[i,:] = abs(np.int64(life_gt[i])-life) + # linear assignment based on delta_life: find most probable corresponding based on life - row_i, col_i = linear_sum_assignment(delta_life) + # row_i, col_i = linear_sum_assignment(delta_life) + + # linear assignment based on confusion matrix: find those with largest values in confusion matrix + row_i, col_i = linear_sum_assignment(confu_, maximize=True) match = [confu_[i, j] for (i, j) in zip(row_i, col_i)] # "diagonal" rate = [match_t / time_i for (match_t, time_i) in zip(match, life_gt)] - # row_ind, col_ind = linear_sum_assignment(confu, maximize=True) - - # match = [confu[i,j] for (i,j) in zip(row_ind, col_ind)] # "diagonal" - # # rate = [match[i]/existance_times[i] for i in col_ind] - # rate = [match_t/time_i for (match_t,time_i) in zip(match, existance_times)] - # # always make the same length of num_gt # if N_ < N: # for i in range(N_-N): From 0db77b0d4815d897c011b63e91b52c0f48f425da Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 16:39:25 -0500 Subject: [PATCH 113/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 130 ++++++++---------- 1 file changed, 54 insertions(+), 76 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 00386dfce..7d14ea468 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -12,6 +12,7 @@ import matplotlib import matplotlib.pyplot as plt import os +import os.path as osp import sys import random import math @@ -142,7 +143,7 @@ def _get_ti(num_insts, uids, link_info): :param uids: unique ids at every time point :param link_info: :return: - ti: tracking information (np.ndarray of shape (T,max_uid)) + ti: tracking information (np.ndarray of shape (T,N)) ti[t,k]=j represents for: at time t the k-th leaf has a local index of j if ti[t,k]=-1, the k-th leaf does not appear at time t number of non-negative elements every row: number of instances @@ -152,8 +153,9 @@ def _get_ti(num_insts, uids, link_info): emergence, emerge_times = _get_emergence(uids) T = len(num_insts) - max_uid = max([max(temp) for temp in uids]) - N = max_uid + 1 + # max_uid = max([max(temp) for temp in uids]) + # N = max_uid + 1 + N = len(np.unique(np.concatenate( uids, axis=0 ))) ti = -np.ones((T, N), dtype=np.int64) for t in range(T): if t == 0: @@ -220,7 +222,7 @@ def _get_li_from_ti(ti): # if savename is not None: # pcv.print_image(masked_im, savename) # return masked_im - +# # def _visualize2(img, fig_title, mask=None, savename=None): # """ # Apply mask with an alpha channel to the original image @@ -241,11 +243,12 @@ def _get_li_from_ti(ti): # if savename is not None: # plt.savefig(savename) # return masked_im - +# # def _visualize3(img, masks, colors, figsize=(16, 16), ax=None, captions=None, savename=None, title="", show_bbox=True): # visualize_display_instances.display_instances(img, masks.astype(np.uint8), figsize=figsize, title=title, ax=ax, colors=colors, captions=captions, show_bbox=True) # if savename is not None: -# plt.savefig(savename) +# # plt.savefig(savename) +# plt.savefig(savename, bbox_inches="tight",pad_inches=0) class InstanceTimeSeriesLinking(object): """A class that links segmented instances throughout time @@ -267,7 +270,8 @@ def __init__(self): self.thres = None self.link_info = None self.uids = None - self.max_uid = None + # self.max_uid = None + self.N = None self.emergence = None self.emerge_times = None self.ti = None @@ -292,9 +296,7 @@ def save_linked_series(self, savedir, savename): """save linking information into a .pkl file with the same prefix of filename Inputs: savedir and savename """ - - # pkl.dump(self, open(os.path.join(savedir, savename + '.pkl'), 'wb')) - pkl.dump(vars(self), open(os.path.join(savedir, savename + ".pkl"), 'wb')) + pkl.dump(vars(self), open(osp.join(savedir, savename + ".pkl"), 'wb')) def import_linked_series(self, savedir, savename): """import a linked time-series from previously saved file @@ -303,37 +305,10 @@ def import_linked_series(self, savedir, savename): :param savename: saving name :return: """ - linked = pkl.load(open(os.path.join(savedir, savename + '.pkl'), "rb")) + linked = pkl.load(open(osp.join(savedir, savename + '.pkl'), "rb")) for key, value in linked.items(): setattr(self, key, value) - # def save_to_csv(self, savedir, csvname1="link_series.csv", csvname2="link_series"): - # """ save linking information into 2 .csv files - # 1. link_series.csv - # 2. link_info.csv - # """ - # # csvname1 = "link_series.csv" - # # csvname2 = "link_info.csv" - # l0 = ['', ''] + [x for (idx, x) in enumerate(self.timepoints)] - # l1 = ['unique_id', 'current_id'] + ['t{}'.format(idx) for (idx, x) in enumerate(self.timepoints)] - # csvfile1 = open(os.path.join(savedir, csvname1), 'w', newline='') - # writer1 = csv.writer(csvfile1, delimiter=',', quoting=csv.QUOTE_MINIMAL) - # writer1.writerow(l0) - # writer1.writerow(l1) - # csvfile2 = open(os.path.join(savedir, csvname2), 'w', newline='') - # writer_junk2 = csv.writer(csvfile2, delimiter=',', quoting=csv.QUOTE_MINIMAL) - # writer_junk2.writerow(['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) - # for (t0_, item) in self.link_series.items(): - # t0 = int(t0_.replace('t', '')) - # for uid, cid in zip(item['uids'], item['ids']): - # link_t0_i = item['inst{}'.format(cid)] - # new_line = ['{}'.format(uid), '{}'.format(cid)] + link_t0_i.tolist() - # writer1.writerow(new_line) - # for t in range(t0, self.T): - # writer_junk2.writerow([uid, t0, self.timepoints[t0], t, self.timepoints[t], link_t0_i[t]]) - # csvfile1.close() - # csvfile2.close() - def linking(self, t0): """ Link instance segments of t0 to those of t1 (t0+1) by calculating the overlaps of their masks @@ -354,16 +329,22 @@ def get_uid(self): """ self.uids = [-np.ones(n, dtype=np.int64) for n in self.n_insts] self.uids[0] = np.arange(len(self.link_info[0]), dtype=np.int64) - self.max_uid = max(self.uids[0]) + # self.max_uid = max(self.uids[0]) + max_uid = max(self.uids[0]) + self.N = len(self.uids[0]) for (t, link_t) in enumerate(self.link_info): for (cidt, cidt_) in enumerate(link_t): if cidt_ >= 0: self.uids[t + 1][cidt_] = self.uids[t][cidt] - if -1 in self.uids[t + 1]: # -1 means there is not predecessor from a former timepoint -> assign new uids + if -1 in self.uids[t + 1]: # -1 means there is no predecessor from a former timepoint -> potential to assign new uids temp = np.where(self.uids[t + 1] == -1)[0] for tid in temp: - self.max_uid += 1 - self.uids[t + 1][tid] = self.max_uid + # self.max_uid += 1 + # self.uids[t + 1][tid] = self.max_uid + max_uid += 1 + self.uids[t + 1][tid] = max_uid + self.N += 1 + def update_ti(self, delta_t=2): self.delta_t = delta_t if delta_t else self.delta_t @@ -417,9 +398,9 @@ def update_ti(self, delta_t=2): print("updates!!") # there will be updates, so save previous result in self.ti_, self.t_appear_, and self.t_disappear_, respectively self.ti_, self.t_appear_, self.t_disappear_ = ti_, t_appear_, t_disappear_ - for (idx, id_up) in enumerate(ids_update): + for (idx, id_up) in enumerate(reversed(ids_update)): if id_up != -1: - id_old = disp_uids[idx] + id_old = disp_uids[len(ids_update)-1-idx] t_ = t_appear_[id_up] self.ti[t_:, id_old] = self.ti[t_:, id_up] self.ti = np.delete(self.ti, to_del, 1) @@ -430,7 +411,9 @@ def update_ti(self, delta_t=2): self.disp_uids = disp_uids self.updated = 1 self.delta_t = delta_t - self.max_uid = self.max_uid - len(np.where(ids_update > -1)[0]) + # self.max_uid = self.max_uid - len(np.where(ids_update > -1)[0]) +1 + # self.max_uid = self.ti.shape[1]-1 + self.N = self.ti.shape[1] # def visualize(self, visualdir, colors=None, color_all=None): # """ @@ -445,29 +428,44 @@ def update_ti(self, delta_t=2): # visualdirs = dict() # for i in range(1, 4): # idx = str(i) - # visualdirs[idx] = os.path.join(visualdir, 'visualization{}'.format(idx)) - # if not os.path.exists(visualdirs[idx]): + # visualdirs[idx] = osp.join(visualdir, 'visualization{}'.format(idx)) + # if not osp.exists(visualdirs[idx]): # os.makedirs(visualdirs[idx]) # # if colors is None: - # colors = visualize_display_instances._random_colors(self.max_uid) # worest case: all leaves are unique + # # colors = visualize_display_instances._random_colors(self.max_uid + 1) # worest case: all leaves are unique + # colors = visualize_display_instances._random_colors(self.N) # worest case: all leaves are unique # if color_all is None: # color_all = [[tuple() for i in range(0, num)] for num in self.n_insts] # - # for (t,ti_t) in enumerate(self.ti): + # for (t, ti_t) in enumerate(self.ti): # uids_t = [x for x in ti_t if x >= 0] - # for (idx,uid) in enumerate(uids_t): + # for (idx, uid) in enumerate(uids_t): # color_all[t][idx] = colors[uid] # - # for (img, masks, colors_t, t) in zip(self.images, self.masks, color_all, self.timepoints): - # savename3 = os.path.join(visualdirs['3'], '{}.jpg'.format(t)) + # for id_t, (img, masks, colors_t, t) in enumerate(zip(self.images, self.masks, color_all, self.timepoints)): + # # find exists unique ids at t + # uids = np.where(self.ti[id_t] >= 0)[0] + # for uid in uids: + # cid = self.ti[id_t][uid] + # mask = masks[:, :, cid] + # savedir_uid1 = osp.join(visualdirs['1'], "{}_{}".format(self.name_sub, uid)) + # savedir_uid2 = osp.join(visualdirs['2'], "{}_{}".format(self.name_sub, uid)) + # savename1 = osp.join(savedir_uid1, '{}_{}.jpg'.format(t, cid)) + # title2 = '{}_{}'.format(t, cid) + # savename2 = os.path.join(savedir_uid2, '{}.jpg'.format(title2)) + # if not osp.exists(savedir_uid1): + # os.makedirs(savedir_uid1) + # if not osp.exists(savedir_uid2): + # os.makedirs(savedir_uid2) + # _visualize1(img, mask, savename=savename1) + # plt.close('all') + # _visualize2(img, fig_title=title2, mask=mask, savename=savename2) + # plt.close('all') + # savename3 = osp.join(visualdirs['3'], '{}.jpg'.format(t)) # _visualize3(img, masks, colors_t, savename=savename3) # plt.close("all") - # def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", - # colors=None, color_all=None, update=False, max_delta_t=2, savedir_=None, savename_=None): - # def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", - # update=False, max_delta_t=2, savedir_=None, savename_=None): def __call__(self, images, masks, timepoints, logic="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): # a list of images which are ndarrays self.images = images @@ -495,27 +493,7 @@ def __call__(self, images, masks, timepoints, logic="IOS", thres=0.2, name_sub=" self.get_uid() self.emergence, self.emerge_times = _get_emergence(self.uids) self.ti, self.t_appear, self.t_disappear = _get_ti(self.n_insts, self.uids, self.link_info) - # visualdir = os.path.join(savedir, "visualization") - # if not os.path.exists(visualdir): - # os.makedirs(visualdir) - # self.visualize(visualdir) # self.save_linked_series(savedir, savename) if update: self.update_ti(max_delta_t) - # if self.updated == 1: - # if savedir_ is not None and savedir_ != savedir: - # visualdir_ = os.path.join(savedir, "visualization") - # else: - # savedir_ = savedir - # visualdir_ = os.path.join(savedir, "updated_visualization") - # if not os.path.exists(visualdir_): - # os.makedirs(visualdir_) - # - # if savename_ is None: - # savename_ = "{}_{}".format(savename, max_delta_t) - # - # # self.visualize(visualdir_) - # self.save_linked_series(savedir_, savename_) - - # self.save_to_csv(savedir, "link_series_old.csv", "link_info_old.csv") From 7e9dd8db7f5af7d984b5c11647429b0e1462c373 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 17:11:02 -0500 Subject: [PATCH 114/274] Delete visualize_time_lapse_video.md --- docs/visualize_time_lapse_video.md | 89 ------------------------------ 1 file changed, 89 deletions(-) delete mode 100644 docs/visualize_time_lapse_video.md diff --git a/docs/visualize_time_lapse_video.md b/docs/visualize_time_lapse_video.md deleted file mode 100644 index b2592aac4..000000000 --- a/docs/visualize_time_lapse_video.md +++ /dev/null @@ -1,89 +0,0 @@ -## Automatically Generate Time-Lapse Videos - -This function automatically generates and saves time-lapse videos given the folder of images. - -**plantcv.visualize.time_lapse_video**(*path_img, list_img, suffix_img, size_frame=None, fps=29.97, name_video='time_lapse_video', - path_video=path_img, display='on'*) - -**returns** list of images, frame size - -(The generated video would be saved in the user specified directory.) - -- **Parameters:** - - img_directory - Directory of images desired to be made into a video - - list_img - desired list of images in img_directory to create the video. If None is passed, all images would be included by default. - - suffix_img - The suffix of all input images. e.g. suffix='.jpg' or suffix='.png' or suffix='img10.jpg'. Make sure all images have the same suffix - - If neigher list_img nor suffix_img is provided, all images in the directory will be included. - - - size_frame - The desired size of every frame. - - To generate a video, the image used in every frame shold have the same size, known as frame size. - In most cases where the images have the same size, the image size is frame size, e.g. ```size_frame=(640, 480)```. - In some cases where not all images have the same size, image resizing would be done automaticaly - to be able to generate the video, with a warning message generated. - - If no value is passed to this parameter, the largest size of all images would be used as the frame size. - - The resizing is done by cropping for those larger then desired sizes, zero-padding for those smaller than desired sizes. - - Note: The most commonly used definition of frame size/image size is different from most commonly - used definition of matrix size (e.g. numpy arrays). The frame size/image size is defined as (width, height), - while the matrix size is defined as (num_rows, num_columns). - If you are trying to get the frame size/image size from the size of matrix (array), remember of change the - order of first and second dimension of the matrix, i.e. frame size = (num_columns, num_rows). - - - fps: (frames per second) frame rate. By default fps=29.97. Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 - - - name_video - The desired name of output video name. By default the name would be 'time_lapse_video' - - path_video - The desired saving path of the video file. By default, the video will be saved at the same directory - of the images. - - display - Indicator of whether to display current status (by displaying saving directory and saving name) while - running this function. By default the display is turned on. - -- **Context:** - - Used to generate time-lapse video given a bunch of images. - -- **Example Use:** - - Below - - [Use in Time Series Documentation](time_series.md) - -**Folder of images** - -As an example, you can used the sample saved inside PlantCV to test. You will need to know the directory of your PlantCV package. -You can find the test data here: "./plantcv/tests/seires_data/raw_im". We are to use all images inside the folder "raw_im" to generate a time-lapse video. -```python -from plantcv import plantcv as pcv -# Note you will have to change this part on your own -img_directory = './plantcv/tests/seires_data/raw_im' - -list_img = [img for img in os.listdir(dir_img) if img.endswith('.jpg')] - -# In this specific case, every image has a suffix of ".jpg" -suffix_img = '.jpg' - -# In this case I know the sizes for all of the images is from (499, 499) to (501, 501), and here I decide I want the frame size to be (500, 500). -size_frame = (500, 500) - -fps = 29.97 - -name_video = 'eg_time_lapse' - -# In this specific case the video will be saved in the save directory of the images -path_video = path_img - -display = 'on' - -list_img, frame_size = pcv.visualize.time_lapse_video(img_directory=img_directory, list_img=list_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) -list_img, frame_size = pcv.visualize.time_lapse_video(img_directory=img_directory, suffix_img=suffix_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) - - -``` - -**Video generated** - -The generated video is saved automatically in the user-specified directory (path_video). The generated video should look similar to the one below: - - - -**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/visualize/time_lapse_video.py) From 5622dbbc4970f7649a9f304215d432e0bb9208af Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 17:34:40 -0500 Subject: [PATCH 115/274] remove time_lapse_video related tests --- tests/tests.py | 99 -------------------------------------------------- 1 file changed, 99 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index c84ba5f86..4084f4b43 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6178,105 +6178,6 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) -def test_plantcv_visualize_time_lapse_video_resize_img(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - resized_img = pcv.visualize._resize_img(img, (10,10)) - assert np.shape(resized_img) == (10,10,3) - -def test_plantcv_visualize_time_lapse_video_resize_img_large(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - resized_img = pcv.visualize._resize_img(img, (1000,1000)) - assert np.shape(resized_img) == (1000,1000,3) - -def test_plantcv_visualize_time_lapse_video_resize_img_gray(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) - resized_img = pcv.visualize._resize_img(img, (10,10)) - assert np.shape(resized_img) == (10,10) - -def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) - resized_img = pcv.visualize._resize_img(img, (1000,1000)) - assert np.shape(resized_img) == (1000,1000) - -# case1: only the correct directory of images is provided -def test_plantcv_visualize_time_lapse_video_case1(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case1') - os.mkdir(cache_dir) - with pytest.warns(UserWarning): - _, _ = pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case1.mp4')) - -# case1.5: no path_video provided and display "on" -def test_plantcv_visualize_time_lapse_video_case1(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case1') - os.mkdir(cache_dir) - with pytest.warns(UserWarning): - _, _ = pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', display='on') - assert os.path.exists(os.path.join(TIME_SERIES_TEST_RAW, 'time_lapse_video_case1.mp4')) - -# case2: the correct directory of images as well as the correct suffix of images is provided -def test_plantcv_visualize_time_lapse_video_case2(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case2') - os.mkdir(cache_dir) - with pytest.warns(UserWarning): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.jpg', fps=29.97, name_video='time_lapse_video_case2', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case2.mp4')) - -# case 3: the correct directory of images as well as the correct list of files are provided -def test_plantcv_visualize_time_lapse_video_case3(): - # Test cache directoy - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case3') - os.mkdir(cache_dir) - list_img = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] - with pytest.warns(UserWarning): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case3', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case3.mp4')) - -# case 4: the correct directory of images as well as a list of files are provided, however the list is incorrect (contains correct part, but also contains incorrect part) -def test_plantcv_visualize_time_lapse_video_case4(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case4') - os.mkdir(cache_dir) - list_img = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] - list_img.append('junk.jpg') - with pytest.warns(UserWarning): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case4', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case4.mp4')) - -def test_plantcv_visualize_time_lapse_video_bad_dir(): - # Test cachae directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_dir') - os.mkdir(cache_dir) - with pytest.raises(RuntimeError): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_DIR, fps=29.97, path_video=cache_dir, display='off') - -def test_plantcv_visualize_time_lapse_video_bad_suffix(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_suffix') - os.mkdir(cache_dir) - with pytest.raises(RuntimeError): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.png', fps=29.97, path_video=None, display='on') - -# the correct directory of images as well as a list of files are provided, however the list is incorrect (none of the files contained is correct) -def test_plantcv_visualize_time_lapse_video_bad_list(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_list') - os.mkdir(cache_dir) - list_img_ = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] - list_img = [img.replace('.jpg', '_.jpg') for img in list_img_] - with pytest.raises(RuntimeError): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, path_video=cache_dir, display='off') # ############################## # Tests for the time_series subpackage From 0adc5e11c6172f822de44fdc6f9cad082ce23327 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 29 Apr 2021 17:43:41 -0500 Subject: [PATCH 116/274] Update evaluation.py --- plantcv/plantcv/time_series/evaluation.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/plantcv/plantcv/time_series/evaluation.py b/plantcv/plantcv/time_series/evaluation.py index 5c8d4efe7..45e94e188 100644 --- a/plantcv/plantcv/time_series/evaluation.py +++ b/plantcv/plantcv/time_series/evaluation.py @@ -1,12 +1,5 @@ -# !/usr/bin/env python3 -# -*- coding: utf-8 -*- +# Functions used to evaulate time-series linking result """ -Created on Mon Dec 7 2020 - -Functions used to evaluate time-series linking result - -@author: hudanyunsheng - Notations and Definitions: T: total time N: total number of (unique) leaves in ground truth From 3054ff21e265c1bd67127beaeb35987d2e8daec0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 29 Apr 2021 17:43:44 -0500 Subject: [PATCH 117/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 122 +++++++++++------- 1 file changed, 73 insertions(+), 49 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 7d14ea468..118744208 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -1,12 +1,4 @@ -# !/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Jun 9 2020 - -Functions used in time series linking after getting leaf instances segmented - -@author: hudanyun sheng -""" +# Link time-series import numpy as np import matplotlib @@ -35,16 +27,23 @@ def _compute_overlaps_masks(masks1, masks2): """Compute overlaps of two sets of binary masks. - The overlaps are represented by IoU (intersection over union) and IoS (intersection over self-area). - + The overlaps are represented by IoU (intersection over union) and IoS (intersection over self-area of the 1st mask). + Inputs: + masks1 = Binary masks data correspond to the 1st image + masks1 = Binary masks data correspond to the 2nd image + Outputs: + n1 = the number of instances in 1st set of binary masks + n2 = the number of instances in 2nd set of binary masks + ious = inversection over union between any pairs of instances in masks1 and masks2 + ioss = inversection over self-area (areas of instances in 1st set of masks) between any pairs of instances in masks1 and masks2 + unions = unions between any pairs of instances in masks1 and masks2 :param masks1: (numpy.ndarray of shape: [Height, Width, n1]) , where n1 is the number of instances :param masks2: (numpy.ndarray of shape: [Height, Width, n2]) , where n2 is the number of instances - :return: - n1: the number of instances in 1st set of binary masks - n2: the number of instances in 2nd set of binary masks - ious: (numpy.ndarray of shape: [n1, n2]) inversection over union between any pairs of instances in masks1 and masks2 - ioss: (numpy.ndarray of shape: [n1, n2]) inversection over self-area (areas of instances in 1st set of masks) between any pairs of instances in masks1 and masks2 - unions: (numpy.ndarray of shape: [n1, n2]) unions between any pairs of instances in masks1 and masks2 + :return: n1: int + :return: n2: int + :return: ious: numpy.ndarray of shape: [n1, n2] + :return: ioss: numpy.ndarray of shape: [n1, n2] + :return: unions: numpy.ndarray of shape: [n1, n2] """ # If either set of masks is empty return an empty result @@ -74,16 +73,27 @@ def _compute_overlaps_masks(masks1, masks2): def _get_link(masks1, masks2, logic, thres): - """ - Get link information between two sets of binary instance segmentation masks - :param masks1: (numpy.ndarray of shape: [Height, Width, n1]), where n1 is the number of instances - :param masks2: (numpy.ndarray of shape: [Height, Width, n2]), where n2 is the number of instances - :param logic: linking logic to use. (currently) either "IOU" or "IOS" - :param thres: threshold to link two masks (two binary segmentation masks can only be linked if the calculated overlap between is greater than the threshold) - :return: - weight (numpy.ndarray of shape: [n1,n2]): weight matrix (indicated by IoU or IoS) calculated based on two sets of masks - link (numpy.1darray of length n1): link[i] = j means that the i-th mask in masks1 should be linked to j-th mask in masks2 - row_ind, col_ind: selected row indices and column indices based on the weight matrix to finalize the "link" + """Get link information between two sets of binary instance segmentation masks + Inputs: + masks1 = Binary masks data correspond to the 1st image + masks2 = Binary masks data correspond to the 2nd image + logic = linking logic to use. (currently) either "IOU" or "IOS" + thres = threshold to link two masks (two binary segmentation masks can only be linked if the calculated overlap + between is greater than the threshold) + Outputs: + weight = weight matrix (indicated by IoU or IoS) calculated based on two sets of masks + link = linking information (numpy.1darray of length n1)). link[i] = j means that the i-th mask in masks1 should be linked to j-th mask in masks2 + row_ind = selected row indices based on the weight matrix to finalize the "link" + col_ind = selected column indices based on the weight matrix to finalize the "link" + + :param masks1: numpy.ndarray + :param masks2: numpy.ndarray + :param logic: str + :param thres: float + :return weight: numpy.ndarray + :return link: numpy.1darray of length n1 + :return row_ind: int + :return col_ind: int """ n1, n2, ious, ioss, _ = _compute_overlaps_masks(masks1, masks2) @@ -110,16 +120,19 @@ def _get_link(masks1, masks2, logic, thres): def _get_emergence(uids): - """ - get emergence as well as emerge times - :param uids: unique ids at every time point - (list of length T, where T is the total time) - every sub-list uids[t] (for t in range of (0,T)) is also a list of length nt, where nt is the number of instances at t - :return: - emergence: new segments (leaves) compared to previous time-point - (list of length T, where T is the total time) - every sub-list emergence[t] if also a list. If there is no new segments at t compared to (t-1), the emergence[t] is empty; otherwise, emergence[t] is a list of unique ids appear at t but not at (t-1). - emerge_times: (list) time points with emergence + """ Get the emerging information as well as emerging times + Inputs: + uids = unique indices present at every time point. + (list of length T, where T is the total time + every sub-list uids[t] (t is in range of (0,T)) is also a list of length nt, + where nt is the number of instances at t) + Outputs: + emergence = new unique indices that don't exist in the previous time-point + emerge_times = time points with new indices appear + + :param uids: list + :return emergence: list + :return emerge_times: list """ emergence = [[] for i in uids] @@ -137,18 +150,25 @@ def _get_emergence(uids): def _get_ti(num_insts, uids, link_info): - """ - getting ti (tracking information) - :param num_insts: (list) number of instances at every time-point t - :param uids: unique ids at every time point - :param link_info: - :return: - ti: tracking information (np.ndarray of shape (T,N)) + """ Get tracking information matrix (ti) + Inputs: + num_insts = number of instances at every time-point t + uids = unique ids at every time point + link_info = + Outputs: + ti (tracking information) = (numpy.ndarray of shape (T,N)) ti[t,k]=j represents for: at time t the k-th leaf has a local index of j if ti[t,k]=-1, the k-th leaf does not appear at time t number of non-negative elements every row: number of instances - t_appear: (list, length: N) the appear time of every unique id - t_disappear: (list, length: N) the disappear time of every unique id + t_appear = (list, length: N) the appear time of every unique id + t_disappear = (list, length: N) the disappear time of every unique id + + :param num_insts: list + :param uids: list + :param link_info: + :return ti: numpy.ndarray + :return t_appear: list + :return t_disappear: list """ emergence, emerge_times = _get_emergence(uids) @@ -195,11 +215,15 @@ def _get_ti(num_insts, uids, link_info): return ti, t_appear, t_disappear def _get_li_from_ti(ti): + """ Get linking information from tracking information + Inputs: + ti = tracking information + Outputs: + li = linking information + :param ti: numpy.ndarray + :return li: list """ - :param ti: - :return: - """ li = [] for t in range(len(ti)-1): ti_0 = ti[t,:] From bec00a3d814e92aa3449ac9c9d05b537446c2f15 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 11:10:56 -0500 Subject: [PATCH 118/274] Create time_series.py --- plantcv/plantcv/time_series/time_series.py | 184 +++++++++++++++++++++ 1 file changed, 184 insertions(+) create mode 100644 plantcv/plantcv/time_series/time_series.py diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py new file mode 100644 index 000000000..9dcc81c3d --- /dev/null +++ b/plantcv/plantcv/time_series/time_series.py @@ -0,0 +1,184 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 9 09:53:34 2020 + +Functions used in time series linking after getting leaf instance segmentation result (from maskRCNN) + +@author: hudanyunsheng +""" + +import os +import sys +import random +import math +import numpy as np +import skimage.io +import matplotlib +import matplotlib.pyplot as plt +import pickle as pkl +import re +from plantcv import plantcv as pcv +import copy +import link_utilities as funcs + + +def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']): + """ + Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) + Input: + imagedir: directory of original image used + segmentationdir: directory of leaf instance segmentation result + savedir: desired saving directory of linking result + time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment + link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 + class_names: used in bounding box visualization. by default there are background and leaf + Output: + There is no returned values as output, though the results will be saved in user defined "savedir" + 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time + 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment + 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. + 4. a folder called "visualization", which contains 3 subfolders: + 1) a folder call "visualization 1", which contains 1st set of visualization + In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. + result name: {}_{}_{}_{}.png + Naming rules for file names: + 1st digit: time of 1st emergence of the leaf + 2nd digit: leaf index when it first emerges + 3rd digit: current time point + 4th digit: current leaf index + + 2) a folder called "visualization 2", which contains 2nd set of visualization + This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent + There are several subfolders, the number of subfolders depends on the number of "new leaves" in total + Every subfolder is a "new leaf", whose name is {}_{}, with 1st digit represents time of 1st emergence of the leaf and 2nd digit represents leaf index when it first emerges + Inside every folder, images of leaves with names "time_{}.png" are contained; the number inside the {} represents the time point + + 3) a folder called "visualization 3", which containes 3rd set of visualization + This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. + Naming format: YYYY-MM-DD-HH-MM_visual.png + """ + + # initialize Plant class + Plant = funcs.PlantData(imagedir, segmentationdir, savedir) + + Plant.getpath(Plant.imagedir) + Plant.Sorttime(time_cond) + + # load mrcnn inferencing results + Plant.load_results() + + Plant.getinitleaf() + Plant.getmaxleaf() + + Plant.gettotaltime() + Plant.getnumemergence() + + # plot the #leaves vs. time relationship + plt.plot(np.array(range(0,len(Plant.num_leaves))), Plant.num_leaves) + + if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): + file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') + if link_logic == 1: + file.write('mode: IOU\n') + else: + file.write('mode: IOP\n') + file.write('Directory of original images: {}\n'.format(Plant.imagedir)) + file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) + file.write('Image conditions: {}'.format(time_cond)) + file.close() + + # linking initialization + Plant.initialize_linking() + + # link + for t in range(0, Plant.total_time-1): + Plant.linking(t, mode=link_logic) + Plant.get_series() + + ####### visualization ####### + # load original images + if len(Plant.images) == 0: + Plant.load_images() #Plant.images + + # visualization method 1: show only one leaf per image + path_visual1 = os.path.join(Plant.visualdir, 'visualization1') + if not os.path.exists(path_visual1): + os.makedirs(path_visual1) + + # visualization method 2: show with an alpha channel + path_visual2 = os.path.join(Plant.visualdir, 'visualization2') + if not os.path.exists(path_visual2): + os.makedirs(path_visual2) + + # visualization method 3: show with bounding boxes + path_visual3 = os.path.join(Plant.visualdir, 'visualization3') + if not os.path.exists(path_visual3): + os.makedirs(path_visual3) + + count = 0 + if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): + colors = funcs._random_colors(20) + pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) + else: + colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) + + color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] + for key_t in Plant.link_series: + start_time = int(key_t.replace('t', '')) + leaves_t = Plant.link_series[key_t]['new_leaf'] + for leaf in leaves_t: + key_leaf = 'leaf{}'.format(leaf) + link_leaf = Plant.link_series[key_t][key_leaf] + start_idx = link_leaf[start_time] + for t in range(start_time, Plant.total_time): + img = Plant.images[t] + if link_leaf[t] >= 0: + color_all[t][link_leaf[t]] = colors[count] + + mask_t = Plant.masks[t][:,:,link_leaf[t]] + + ## 1. save the masked image, i.e. single leaves + mask = np.zeros(mask_t.shape, dtype=np.uint8) + mask[np.where(mask_t)] = 255 + leaf_t = pcv.apply_mask(img, mask, mask_color='black') +# pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}.png'.format(start_time, start_idx, t, link_leaf[t]))) +# pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}.pkl'.format(start_time, start_idx, t, link_leaf[t])), 'wb')) + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, link_leaf[t], t, Plant.time[t]))) + pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, link_leaf[t], t, Plant.time[t])), 'wb')) + + + ## 2. show with an alpha channel + # update the mask where there is an alpha channel + mask_ = np.ones(mask_t.shape)*0.5 + mask_[np.where(mask_t == True)] = 1 + masked_im = np.concatenate((img.astype(float)/255, np.expand_dims(mask_, axis=2)), axis=2) + save_dir_ = os.path.join(path_visual2, '{}_{}.png'.format(start_time, start_idx)) + if not os.path.exists(save_dir_): + os.makedirs(save_dir_) + fig2 = plt.figure(figsize=(5,5)) + ax2 = fig2.add_subplot(1,1,1) + ax2.imshow(masked_im) + ax2.axis('off') +# plt.savefig(os.path.join(save_dir_, 'time_{}.png'.format(t))) + plt.savefig(os.path.join(save_dir_, str(Plant.time[t]) + '.png')) + plt.close(fig2) + # pcv.print_image(masked_im, os.path.join(save_dir_, 'time_{}.png'.format(t))) + count += 1 + + ## 3. visualize with bounding boxes + for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.time): + funcs.display_instances(img, roi, mask, class_id, + class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, + colors = color) + + plt.savefig(os.path.join(path_visual3, '{}_visual.png'.format(t))) + plt.close('all') + + # save all information + pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) + + + + + From 4448f4548ecd29d527c9ae5e382cce69d6b7bf74 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 11:11:00 -0500 Subject: [PATCH 119/274] Create link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 368 ++++++++++++++++++ 1 file changed, 368 insertions(+) create mode 100644 plantcv/plantcv/time_series/link_utilities.py diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py new file mode 100644 index 000000000..9878a3353 --- /dev/null +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -0,0 +1,368 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 9 09:53:34 2020 + +Functions used in time series linking after getting leaf instance segmentation result + + +@author: hudanyunsheng +""" + +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import os +import sys +import random +import math +import skimage.io +import pickle as pkl +import re +from skimage.measure import find_contours +from matplotlib import patches, lines +from matplotlib.patches import Polygon +from plantcv import plantcv as pcv +import datetime +import copy +import colorsys + +def _random_colors(N, bright=True): + """ + Generate random colors. + To get visually distinct colors, generate them in HSV space then + convert to RGB. + """ + brightness = 1.0 if bright else 0.7 + hsv = [(i / N, 1, brightness) for i in range(N)] + colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv)) + random.shuffle(colors) + return colors + +def _apply_mask(image, mask, color, alpha=0.5): + """Apply the given mask to the image. + """ + for c in range(3): + image[:, :, c] = np.where(mask == 1, + image[:, :, c] * + (1 - alpha) + alpha * color[c] * 255, + image[:, :, c]) + return image + +def display_instances(image, boxes, masks, class_ids, class_names, + scores=None, title="", + figsize=(16, 16), ax=None, + show_mask=True, show_bbox=True, + colors=None, captions=None): + """ + boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates. + masks: [height, width, num_instances] + class_ids: [num_instances] + class_names: list of class names of the dataset + scores: (optional) confidence scores for each box + title: (optional) Figure title + show_mask, show_bbox: To show masks and bounding boxes or not + figsize: (optional) the size of the image + colors: (optional) An array or colors to use with each object + captions: (optional) A list of strings to use as captions for each object + """ + # Number of instances + N = boxes.shape[0] + if not N: + print("\n*** No instances to display *** \n") + else: + assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0] + + # If no axis is passed, create one and automatically call show() + auto_show = False + if not ax: + _, ax = plt.subplots(1, figsize=figsize) + auto_show = True + + # Generate random colors + colors = colors or _random_colors(N) + + # Show area outside image boundaries. + height, width = image.shape[:2] + ax.set_ylim(height + 10, -10) + ax.set_xlim(-10, width + 10) + ax.axis('off') + ax.set_title(title) + + masked_image = image.astype(np.uint32).copy() + for i in range(N): + color = colors[i] + + # Bounding box + if not np.any(boxes[i]): + # Skip this instance. Has no bbox. Likely lost in image cropping. + continue + y1, x1, y2, x2 = boxes[i] + if show_bbox: + p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, + alpha=0.7, linestyle="dashed", + edgecolor=color, facecolor='none') + ax.add_patch(p) + + # Label + if not captions: + class_id = class_ids[i] + score = scores[i] if scores is not None else None + label = class_names[class_id] + caption = "{} {:.3f}".format(label, score) if score else label + else: + caption = captions[i] + ax.text(x1, y1 + 8, caption, + color='w', size=11, backgroundcolor="none") + + # Mask + mask = masks[:, :, i] + if show_mask: + masked_image = _apply_mask(masked_image, mask, color) + + # Mask Polygon + # Pad to ensure proper polygons for masks that touch image edges. + padded_mask = np.zeros( + (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8) + padded_mask[1:-1, 1:-1] = mask + contours = find_contours(padded_mask, 0.5) + for verts in contours: + # Subtract the padding and flip (y, x) to (x, y) + verts = np.fliplr(verts) - 1 + p = Polygon(verts, facecolor="none", edgecolor=color) + ax.add_patch(p) + ax.imshow(masked_image.astype(np.uint8)) + if auto_show: + plt.show() + + +def _compute_overlaps_masks(masks1, masks2, mode=1): + """Computes IoU overlaps between two sets of masks. + masks1, masks2: [Height, Width, instances] + mode: 1 or 2, 1 represent for IOU, 2 represent for intersection over the area of the previous mask + """ + + # If either set of masks is empty return empty result + if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # flatten masks and compute their areas + masks1 = np.reshape(masks1 > .5, (-1, masks1.shape[-1])).astype(np.float32) + masks2 = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) + area1 = np.sum(masks1, axis=0) + area2 = np.sum(masks2, axis=0) + + # intersections and union + intersections = np.dot(masks1.T, masks2) + if mode == 1: + union = area1[:, None] + area2[None, :] - intersections + overlaps = intersections / union + elif mode == 2: + overlaps = intersections / area1[:, None] + return overlaps + +def get_ax(rows=1, cols=1, size=16): #??? + """Return a Matplotlib Axes array to be used in + all visualizations in the notebook. Provide a + central point to control graph sizes. + + Adjust the size attribute to control how big to render images + """ + fig, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows)) + fig.tight_layout() + return ax + +def _getmatchedIndex(mask, masks, mode): + """Get the index from masks that matches the mask""" + score = _compute_overlaps_masks(np.expand_dims(mask, axis=-1), masks, mode) + if max(score[0])>0.15: + return np.argmax(score[0]), score + else: + return -1, score + + +class PlantData(): + def __init__(self, imagedir, segmentationdir, savedir): + + # store a list of time of the images + self.time = [] + # total num of leaves that being detected + self.numleaf = 0 + + # store all the images + self.images = [] + + # store all the mask detection during [starttime, endtime] + self.masks = [] + + # store all the rois (for visualization with bounding boxes) + self.rois = [] + + # store all the class_ids (for visualization with bounding boxes) + self.class_ids = [] + + # store all the scores (for visualization with bounding boxes) + self.scores = [] + + # store the dataset directory, instance segmentation result directory, time-series linking result directory and visualization directory + self.imagedir = imagedir + self.segmentationdir = segmentationdir + self.savedir = os.path.join(savedir, str(datetime.datetime.now())) + if not os.path.exists(self.savedir): + os.makedirs(self.savedir) + self.visualdir = os.path.join(self.savedir, 'visualization') + + self.total_time = 0 + self.max_nleaf = 0 + self.init_nleaf = 0 + self.num_leaves = [] + self.num_emergence = 0 + + self.emerging_info = [] # list length: self.total_time + self.link_info = [] # list length: self.total_time-1, dictionaries inside + self.weights = [] # list length: self.total_time-1, dictionaries inside + self.available_leaves = [] # list length: self.total_time + self.link_series = dict() # only for those newly emerging leaves + + def getinitleaf(self): + self.init_nleaf = self.masks[0].shape[2] + + def getmaxleaf(self): + for i in range(0, len(self.masks)): + self.num_leaves.append(self.masks[i].shape[2]) + self.max_nleaf = np.max(self.num_leaves) + + def gettotaltime(self): + # also represent for the total number of images + self.total_time = len(self.time) + + def getnumemergence(self): + self.num_emergence = np.ones((self.total_time), dtype=int)*self.init_nleaf + self.num_emergence[1:] = np.diff(self.num_leaves) + + def getpath(self, path): + self.dir = path + + def addimage(self): + for t in self.time: + filename = os.path.join(self.dir, '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t)) + image = skimage.io.imread(filename) + self.images.append(image) + + def Sorttime(self, time_cond): + """ + you need to modify this function accordint to you format + return: loop through the dataset_dir, and add time in time order + """ + + filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] + time_temp = [] + for x in filenames: + temp = re.search('\d{4}-\d{2}-\d{2}-\d{2}-\d{2}', x) + if temp: + timepart = temp.group() + for cond in time_cond: + if timepart.endswith(cond): + time_temp.append(timepart) + continue + + index_temp = np.argsort(time_temp) + # forward + self.time = [time_temp[i] for i in index_temp] + # backward +# self.time_back = [time_temp[i] for i in reversed(index_temp)] + + + def load_images(self): + """ Load original images + """ + for t in self.time: + file_name = '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t) + junk = skimage.io.imread(os.path.join(self.imagedir, file_name)) + img = junk[0: 499, 0: 499, :] # make all images the same size + self.images.append(img) + + def load_results(self): + """ Instead of running instance segmentation, load instance segmentation results (masks) + """ + for t in self.time: + file_name = '{}.pkl'.format(t) + r = pkl.load(open(os.path.join(self.segmentationdir, file_name), 'rb')) + self.masks.append(r["masks"][0: 499, 0: 499, :]) # make all masks the same size + self.rois.append(r["rois"]) + self.class_ids.append(r['class_ids']) + self.scores.append(r['scores']) + + def initialize_linking(self): + self.emerging_info = [[] for i in range(0, self.total_time)] + self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time-1)] + self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i+1])) for i in range(0, self.total_time-1)] + self.available_leaves = [np.array(range(0, self.num_leaves[i])) for i in range(0, self.total_time)] + self.time_compare = [np.ones((self.num_leaves[i]), dtype=int)*i for i in range(0, self.total_time)] + + def linking(self, start_time, mode): + masks1 = copy.deepcopy(self.masks[start_time]) + masks2 = copy.deepcopy(self.masks[start_time+1]) + leaves1 = copy.deepcopy(self.available_leaves[start_time]) + leaves2 = copy.deepcopy(self.available_leaves[start_time+1]) + n1 = len(leaves1) + n2 = len(leaves2) + n = np.min((n1, n2)) + N = np.max((n1, n2)) + weight = dict() + weight['forward'] = -np.inf*np.ones((n1, n2)) +# weight['backward'] = -np.inf*np.zeros((n2, n1)) + link = dict() + link['forward'] = -np.ones((n1)) +# link['backward'] = -np.ones((n2)) + + for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward']): +# for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward', 'backward']): + for (idx, main_leaf) in enumerate(leaves): + mask = masks[:,:,main_leaf] + link[key][idx], weight[key][idx,:] = _getmatchedIndex(mask, masks_all, mode) + # check for duplicates (and hence non-unique links) + val, cou = np.unique(link[key], return_counts=True) + idx_temp = np.where(cou>1)[0] + + if len(idx_temp) > 0: # matching to same leaf exists + idx_vs = val[idx_temp] + for idx_v in idx_vs: + idxs = np.where(link[key] == idx_v)[0] + weights_comp = weight[key][:,int(idx_v)] + + idx_keep = np.argmax(weights_comp) + idx_drop = np.delete(idxs, np.where(idxs==idx_keep)) + weight_temp = copy.deepcopy(weight[key][idx_drop]) + weight_temp[0, int(idx_v)] = 0 + if np.max(weight_temp) > 0.2: + link[key][idx_drop] = np.argmax(weight_temp) + else: + link[key][idx_drop] = -1 + + self.link_info[start_time] = link['forward'] + + def get_series(self): + t = 0 + key_t = 't{}'.format(t) + self.link_series[key_t] = dict() + self.link_series[key_t]['new_leaf'] = self.available_leaves[0] + + for t in range(1, self.total_time): + new_leaves = [i for i in self.available_leaves[t] if i not in self.link_info[t-1]] + if new_leaves: + key_t = 't{}'.format(t) + self.link_series[key_t] = dict() + self.link_series[key_t]['new_leaf'] = np.array(new_leaves) + for key_t in self.link_series: + t0 = int(key_t.replace('t','')) + for leaf in self.link_series[key_t]['new_leaf']: + key_leaf = 'leaf{}'.format(leaf) + self.link_series[key_t][key_leaf] = -np.ones(self.total_time, dtype=int) + self.link_series[key_t][key_leaf][t0] = leaf + self.link_series[key_t][key_leaf][t0+1] = self.link_info[t0][leaf] + for t_ in range(t0+2, self.total_time): + idx = self.link_series[key_t][key_leaf][t_-1] + if idx < 0: + break + else: + self.link_series[key_t][key_leaf][t_] = self.link_info[t_-1][idx] \ No newline at end of file From 6a061e4e167ff69873f98270706f25fc95c9051b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 11:11:03 -0500 Subject: [PATCH 120/274] Create __init__.py --- plantcv/plantcv/time_series/__init__.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 plantcv/plantcv/time_series/__init__.py diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py new file mode 100644 index 000000000..3e468d909 --- /dev/null +++ b/plantcv/plantcv/time_series/__init__.py @@ -0,0 +1,13 @@ +from plantcv.plantcv.time_series.link_utilities import random_colors +from plantcv.plantcv.time_series.link_utilities import apply_mask +from plantcv.plantcv.time_series.link_utilities import display_instances +from plantcv.plantcv.time_series.link_utilities import compute_overlaps_masks +from plantcv.plantcv.time_series.link_utilities import get_ax +from plantcv.plantcv.time_series.link_utilities import getmatchedIndex +from plantcv.plantcv.time_series.link_utilities import PlantData +from plantcv.plantcv.time_series.time_series import time_series_linking + + +# add new functions to end of lists +__all__ = ["random_colors", "apply_mask", "display_instances", "compute_overlaps_masks", "get_ax", + "getmatchedIndex", "time_series_linking"] From b9450e0d25cdade8a9699eb0f6ebd1dc054bf144 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 11:11:06 -0500 Subject: [PATCH 121/274] Create time_series.md --- docs/time_series.md | 60 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 docs/time_series.md diff --git a/docs/time_series.md b/docs/time_series.md new file mode 100644 index 000000000..050feffdb --- /dev/null +++ b/docs/time_series.md @@ -0,0 +1,60 @@ +## time_series + +This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, recording the growth of the plant, where there should be minimum or none movement of plant or camera). + +To run this function, the instance segmentation for every image should be achieved beforehand. For a demo of instance segmentation, check here: +As a reminder, you will need a maskRCNN environemnt to be able to use this tool. For more information, check here: + +When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. (Another example is k-means) + +To understand the growth of every leaf instance of a plant, we need pull out the same leaf across the whole time period. + + +**plantcv.time_series.time_series_linking**(*imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']*) + +**returns** no returned values, all the results are saved in the user specified directory + +- **Parameters:** + - imagedir: directory of original image used + - segmentationdir: directory of leaf instance segmentation result + - savedir: desired saving directory of linking result + - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment + - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 + - class_names: used in bounding box visualization. by default there are background and leaf +- **Output:** + There is no returned values as output, though the results will be saved in user defined "savedir" + 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time + 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment + 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. + 4. a folder called "visualization", which contains 3 subfolders: + 1) a folder call "visualization 1", which contains 1st set of visualization + In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. + result name: {}_{}_{}_{}_{}.png + Naming rules for file names: + 1st digit: time of 1st emergence of the leaf + 2nd digit: leaf index when it first emerges + 3rd digit: current time point + 4th digit: current leaf index + 5th digit: date and time included in original file name, typically, it follows the format of YYYY-MM-DD-HH-MM. + + 2) a folder called "visualization 2", which contains 2nd set of visualization + This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent + There are several subfolders, the number of subfolders depends on the number of "new leaves" in total + Every subfolder is a "new leaf", whose name is {}_{}, with 1st digit represents time of 1st emergence of the leaf and 2nd digit represents leaf index when it first emerges + Inside every folder, images of leaves with names "{}.png" are contained; inside the {} the date-time of the original image is contained, typically, it follows the format of YYYY-MM-DD-HH-MM. + + 3) a folder called "visualization 3", which containes 3rd set of visualization + This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. + Naming format: {}_visual.png; inside the {} the date-time of the original image is contained, typically, it follows the format of YYYY-MM-DD-HH-MM. + +```python +from plantcv import plantcv as pcv +# Below are examples of input variables, always adjust base on your own application. + +path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol/maskrcnn_test' +path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/detection/modified' +path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/test' +time_cond = ['08-05', '11-05', '17-05', '21-05'] +​pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=1, class_names=['BG', 'Leaf']) +``` +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) From a796ed3b40e84e143ce052d0ae0f6533331c7875 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 12:03:52 -0500 Subject: [PATCH 122/274] Update time_series.md --- docs/time_series.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/time_series.md b/docs/time_series.md index 050feffdb..ec6098e85 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -17,7 +17,7 @@ To understand the growth of every leaf instance of a plant, we need pull out the - **Parameters:** - imagedir: directory of original image used - segmentationdir: directory of leaf instance segmentation result - - savedir: desired saving directory of linking result + - savedir: desired saving directory of linking result. Note that this function will generate a folder under your specified saving directory, with the date and time you run this programme - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 - class_names: used in bounding box visualization. by default there are background and leaf From 5ee139e3e385bca720e092c770f58e22db7f901f Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 12:03:54 -0500 Subject: [PATCH 123/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 41 +++++++++++++------ 1 file changed, 28 insertions(+), 13 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 9878a3353..bb96839f0 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -190,6 +190,9 @@ def __init__(self, imagedir, segmentationdir, savedir): # store all the images self.images = [] + + # minimum dimension of image. To link time series, all images must have the same dimension, to make it easier, we make the image square + self.min_dim = 0 # store all the mask detection during [starttime, endtime] self.masks = [] @@ -207,8 +210,7 @@ def __init__(self, imagedir, segmentationdir, savedir): self.imagedir = imagedir self.segmentationdir = segmentationdir self.savedir = os.path.join(savedir, str(datetime.datetime.now())) - if not os.path.exists(self.savedir): - os.makedirs(self.savedir) + os.makedirs(self.savedir) self.visualdir = os.path.join(self.savedir, 'visualization') self.total_time = 0 @@ -242,16 +244,16 @@ def getnumemergence(self): def getpath(self, path): self.dir = path - def addimage(self): - for t in self.time: - filename = os.path.join(self.dir, '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t)) - image = skimage.io.imread(filename) - self.images.append(image) + # def addimage(self): + # for t in self.time: + # filename = os.path.join(self.dir, '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t)) + # image = skimage.io.imread(filename) + # self.images.append(image) def Sorttime(self, time_cond): """ - you need to modify this function accordint to you format - return: loop through the dataset_dir, and add time in time order + This function is designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM + Return: loop through the dataset_dir, and add time in time order """ filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] @@ -274,11 +276,24 @@ def Sorttime(self, time_cond): def load_images(self): """ Load original images + Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM """ + filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] + temp_imgs = [] + sz = [] for t in self.time: - file_name = '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t) - junk = skimage.io.imread(os.path.join(self.imagedir, file_name)) - img = junk[0: 499, 0: 499, :] # make all images the same size + for f in filenames: + temp = re.search(t, f) + if temp: + junk = skimage.io.imread(os.path.join(self.imagedir, f)) + temp_imgs.append(junk) + sz.append(np.min(junk.shape[0:2])) + # img = junk[0: 499, 0: 499, :] # make all images the same size + # self.images.append(img) + filenames.remove(f) + self.min_dim = np.min(sz) + for junk in temp_imgs: + img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size self.images.append(img) def load_results(self): @@ -287,7 +302,7 @@ def load_results(self): for t in self.time: file_name = '{}.pkl'.format(t) r = pkl.load(open(os.path.join(self.segmentationdir, file_name), 'rb')) - self.masks.append(r["masks"][0: 499, 0: 499, :]) # make all masks the same size + self.masks.append(r["masks"][0: self.min_dim, 0: self.min_dim, :]) # make all masks the same size self.rois.append(r["rois"]) self.class_ids.append(r['class_ids']) self.scores.append(r['scores']) From 023655956e48d216c3b20584edbc5ef87ba941a9 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 12:03:58 -0500 Subject: [PATCH 124/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 9dcc81c3d..ad45dd963 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -65,6 +65,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi Plant.getpath(Plant.imagedir) Plant.Sorttime(time_cond) + Plant.load_images() + # load mrcnn inferencing results Plant.load_results() From 5913ff79ceadae9bc6045e542d5ee2d10d63d4a4 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 14:42:16 -0500 Subject: [PATCH 125/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index ad45dd963..275fb7121 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -34,7 +34,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 class_names: used in bounding box visualization. by default there are background and leaf Output: - There is no returned values as output, though the results will be saved in user defined "savedir" + There is only one return value as output, which is the directory where the results saved + The results will be saved under the user defined "savedir", in a subfolder whose name is date&time when the program runs 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. @@ -58,7 +59,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. Naming format: YYYY-MM-DD-HH-MM_visual.png """ - + # initialize Plant class Plant = funcs.PlantData(imagedir, segmentationdir, savedir) @@ -180,6 +181,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi # save all information pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) + return Plant.savedir + From 795f697dae579922062c821cee3b38926592a876 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 14:42:45 -0500 Subject: [PATCH 126/274] add test data --- .../2019-10-22-08-05.pkl | Bin 0 -> 1248143 bytes .../2019-10-22-11-05.pkl | Bin 0 -> 1248143 bytes .../2019-10-22-17-05.pkl | Bin 0 -> 1255648 bytes .../2019-10-22-21-05.pkl | Bin 0 -> 1503671 bytes .../2019-10-23-08-05.pkl | Bin 0 -> 1500667 bytes .../2019-10-23-11-05.pkl | Bin 0 -> 1506673 bytes .../2019-10-23-17-05.pkl | Bin 0 -> 1506673 bytes .../2019-10-23-21-05.pkl | Bin 0 -> 1750684 bytes .../2019-10-24-08-05.pkl | Bin 0 -> 1754191 bytes 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z81YOU{SK=E?9OHT4dzEK(5(2#+39r03tFFjm@i7wy!W%*TIjN)PfhphkdyqGTaX!- zJzlO+>L4K^^O|iHuWiR&?q!EtjKJkSoZfiia@1=m&^B>M}>^sH3I%bJMh6bgvpFd}d zfB%8#(pPIl0}%IHW8z`Etl-lCmv-VNM&yxc(x#k*c6Zvu)v>kjHV-2ssUy;)N9F)b z(G$=V8rQ*<`+=K1!ZiGyyQ-jOeXRrdlb&SfJGDxp(cpLFLmd zGe*dgq{&H$Oc^RdM|_;BriUov+LOm!|*FpWddC;iz`<6@_+c3G^D%gK1RvaD4u z;1|fsz}KaVF;7J&d#D0W#if#V9-pjE<*&y9$RFBd*W38e8T`^7cl9J4!zBn-+pRRZ zf}q$KKmNn%pGR!r3}E+~<9^Yh!H#QfP1fVPzKy@NL%ZiH$jR)b8>$GafT{rPG}0VeOa_1bLs%M-JklZr;o?4%ufeh{2Hl6Nhiz2!<|ZT8qp-DAOH$~|E&F+7GQ2;i|V>Ne?W z^rq^FT-Id-E8H2O1*T8L&CzOzd6-mR`kgP%h6Oagzk|nZ#q5vr%#G1M9LgpL-?K2l zRf!6)9BH)YKSBL#-(V*(Aj>;o5?|LDQs_erl6!?NX%lXdh_)V;`*bzvwJtG#?A)Z< zX;$$IV`8A{(J4Q_X{6ACMRx@`*vw-1{Ee|B=gR+_zRcnZ1N57h5ww}KfAr+hD#VWE&EMBeerQ-=)pv&r$pg;rrL;NeYsY2oU~8IAB*m(5BZI_~^6Uec_P>hoOjHD#d>|krgcetv39>=i_ny KH=M!0rT+)3&PBZd literal 0 HcmV?d00001 From 0e61cee067106d34e3bcd30b24240442b16e8522 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 14:42:48 -0500 Subject: [PATCH 127/274] Update tests.py --- tests/tests.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/tests/tests.py b/tests/tests.py index d341fbdb0..071f06f95 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -995,6 +995,10 @@ def test_plantcv_parallel_process_results_invalid_json(): TEST_INPUT_INSTANCE_IMG = "visualize_inst_seg_img.png" TEST_INPUT_INSTANCE_MASK = "visualize_inst_seg_mask.pkl" +TIME_SERIES_TEST_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "seires_data") +TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im") +TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "instance_segmentation") + # ########################## # Tests for the main package # ########################## @@ -4850,6 +4854,7 @@ def test_plantcv_hyperspectral_analyze_index_outside_range_warning(): pcv.hyperspectral.analyze_index(index_array=index_array, mask=mask_img, min_bin=.5, max_bin=.55, label="i") out = f.getvalue() # assert os.listdir(cache_dir) is 0 + assert out[0:10] == 'WARNING!!!' @@ -6310,6 +6315,14 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) +def test_plantcv_time_series_time_series(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + time_cond = ['08-05', '11-05', '17-05', '21-05'] + savedir = pcv.time_seires.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf']) + assert len(os.listdir(savedir)) > 0 def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From 58ac75df12b3bcae1d7e8e038d975735ab339e5c Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 14:52:37 -0500 Subject: [PATCH 128/274] Update __init__.py --- plantcv/plantcv/__init__.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py index 5eb8bcd24..964f9e2b0 100644 --- a/plantcv/plantcv/__init__.py +++ b/plantcv/plantcv/__init__.py @@ -107,3 +107,8 @@ 'cluster_contour_mask', 'analyze_thermal_values', 'opening', 'closing', 'within_frame', 'fill_holes', 'get_kernel', 'crop', 'stdev_filter', 'spatial_clustering', 'photosynthesis'] + 'closing', 'within_frame', 'fill_holes', 'get_kernel', 'Spectral_data', 'crop', 'stdev_filter', 'time_series'] + +from ._version import get_versions +__version__ = get_versions()['version'] +del get_versions From 064f72c4c0003e3083faf8ac502da6541b89c899 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 15:44:48 -0500 Subject: [PATCH 129/274] Update __init__.py --- plantcv/plantcv/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py index 964f9e2b0..cd30f1bff 100644 --- a/plantcv/plantcv/__init__.py +++ b/plantcv/plantcv/__init__.py @@ -85,6 +85,7 @@ from plantcv.plantcv.stdev_filter import stdev_filter from plantcv.plantcv.spatial_clustering import spatial_clustering from plantcv.plantcv import photosynthesis +from plantcv.plantcv import time_series # add new functions to end of lists # Auto versioning From f0eca880f2bdc9fa3aae79d03742b80843dfe0e0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 15:45:04 -0500 Subject: [PATCH 130/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index 3e468d909..8397f612f 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,13 +1,13 @@ -from plantcv.plantcv.time_series.link_utilities import random_colors -from plantcv.plantcv.time_series.link_utilities import apply_mask +from plantcv.plantcv.time_series.link_utilities import _random_colors +from plantcv.plantcv.time_series.link_utilities import _apply_mask from plantcv.plantcv.time_series.link_utilities import display_instances -from plantcv.plantcv.time_series.link_utilities import compute_overlaps_masks +from plantcv.plantcv.time_series.link_utilities import _compute_overlaps_masks from plantcv.plantcv.time_series.link_utilities import get_ax -from plantcv.plantcv.time_series.link_utilities import getmatchedIndex +from plantcv.plantcv.time_series.link_utilities import _getmatchedIndex from plantcv.plantcv.time_series.link_utilities import PlantData from plantcv.plantcv.time_series.time_series import time_series_linking # add new functions to end of lists -__all__ = ["random_colors", "apply_mask", "display_instances", "compute_overlaps_masks", "get_ax", - "getmatchedIndex", "time_series_linking"] +__all__ = ["_random_colors", "_apply_mask", "display_instances", "_compute_overlaps_masks", "get_ax", + "_getmatchedIndex", "PlantData", "time_series_linking"] From 0052f613b94a262204842c8a0b642fe348981ddb Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 15:45:10 -0500 Subject: [PATCH 131/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 275fb7121..427bf7df8 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -20,7 +20,7 @@ import re from plantcv import plantcv as pcv import copy -import link_utilities as funcs +from plantcv.plantcv.time_series import link_utilities as funcs def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']): From e067507fecb3afe17d42d1cbbdac0e6844004189 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 17 Jun 2020 15:45:14 -0500 Subject: [PATCH 132/274] Update tests.py --- tests/tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index 071f06f95..87edb7f98 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6321,7 +6321,7 @@ def test_plantcv_time_series_time_series(): os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir time_cond = ['08-05', '11-05', '17-05', '21-05'] - savedir = pcv.time_seires.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf']) + savedir = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf']) assert len(os.listdir(savedir)) > 0 def test_plantcv_visualize_overlay_two_imgs(): From d968e6ca30a17e85e5205ee3dbd7856a8d2c301f Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 22 Jun 2020 09:35:28 -0500 Subject: [PATCH 133/274] Update time_series.py Instead of giving no returned values, give a returned value called Plant, which is a Plant class including all related time-series linking information Change the saving name format for output files --- plantcv/plantcv/time_series/time_series.py | 27 ++++++++-------------- 1 file changed, 9 insertions(+), 18 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 427bf7df8..7b0cac46e 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -21,6 +21,7 @@ from plantcv import plantcv as pcv import copy from plantcv.plantcv.time_series import link_utilities as funcs +# import link_utilities as funcs def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']): @@ -34,8 +35,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 class_names: used in bounding box visualization. by default there are background and leaf Output: - There is only one return value as output, which is the directory where the results saved - The results will be saved under the user defined "savedir", in a subfolder whose name is date&time when the program runs + There is no returned values as output, though the results will be saved in user defined "savedir" 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. @@ -59,7 +59,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. Naming format: YYYY-MM-DD-HH-MM_visual.png """ - + # initialize Plant class Plant = funcs.PlantData(imagedir, segmentationdir, savedir) @@ -145,10 +145,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi mask = np.zeros(mask_t.shape, dtype=np.uint8) mask[np.where(mask_t)] = 255 leaf_t = pcv.apply_mask(img, mask, mask_color='black') -# pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}.png'.format(start_time, start_idx, t, link_leaf[t]))) -# pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}.pkl'.format(start_time, start_idx, t, link_leaf[t])), 'wb')) - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, link_leaf[t], t, Plant.time[t]))) - pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, link_leaf[t], t, Plant.time[t])), 'wb')) + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, link_leaf[t], t, Plant.filename_pre[t]))) + pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, link_leaf[t], t, Plant.filename_pre[t])), 'wb')) ## 2. show with an alpha channel @@ -163,14 +161,12 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi ax2 = fig2.add_subplot(1,1,1) ax2.imshow(masked_im) ax2.axis('off') -# plt.savefig(os.path.join(save_dir_, 'time_{}.png'.format(t))) - plt.savefig(os.path.join(save_dir_, str(Plant.time[t]) + '.png')) + plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + '.png')) plt.close(fig2) - # pcv.print_image(masked_im, os.path.join(save_dir_, 'time_{}.png'.format(t))) count += 1 ## 3. visualize with bounding boxes - for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.time): + for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.filename_pre): funcs.display_instances(img, roi, mask, class_id, class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, colors = color) @@ -180,10 +176,5 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi # save all information pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) - - return Plant.savedir - - - - - + + return Plant From 03e60a3b510f5035e3119d5f4ed053d722e8411a Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 22 Jun 2020 09:37:10 -0500 Subject: [PATCH 134/274] Update link_utilities.py Add to "filename_pre" to Plant (self) which stores the original image name prefixes (".jpg" not included) sorted by time. --- plantcv/plantcv/time_series/link_utilities.py | 51 ++++++++++++------- 1 file changed, 32 insertions(+), 19 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index bb96839f0..a4d89ee8c 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -185,6 +185,8 @@ def __init__(self, imagedir, segmentationdir, savedir): # store a list of time of the images self.time = [] + # store a list of original prefixes of the original images + self.filename_pre = [] # total num of leaves that being detected self.numleaf = 0 @@ -256,45 +258,56 @@ def Sorttime(self, time_cond): Return: loop through the dataset_dir, and add time in time order """ - filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] + filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] + filenames_ori = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] time_temp = [] - for x in filenames: - temp = re.search('\d{4}-\d{2}-\d{2}-\d{2}-\d{2}', x) + file_name = [] + for filename in filenames: + temp = re.search('\d{4}-\d{2}-\d{2}-\d{2}-\d{2}', filename) if temp: timepart = temp.group() for cond in time_cond: if timepart.endswith(cond): time_temp.append(timepart) + junk = [1 if re.search(timepart, f) is not None else 0 for f in filenames_ori] + file_name.append(filenames_ori[junk.index(1)].replace('.jpg', '')) continue index_temp = np.argsort(time_temp) # forward self.time = [time_temp[i] for i in index_temp] - # backward -# self.time_back = [time_temp[i] for i in reversed(index_temp)] - + self.filename_pre = [file_name[i] for i in index_temp] def load_images(self): """ Load original images Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM """ - filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] - temp_imgs = [] +# filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] +# temp_imgs = [] +# sz = [] +# for t in self.time: +# for f in filenames: +# temp = re.search(t, f) +# if temp: +# junk = skimage.io.imread(os.path.join(self.imagedir, f)) +# temp_imgs.append(junk) +# sz.append(np.min(junk.shape[0:2])) +# filenames.remove(f) +# self.min_dim = np.min(sz) +# for junk in temp_imgs: +# img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size +# self.images.append(img) + temp_imgs = [] sz = [] - for t in self.time: - for f in filenames: - temp = re.search(t, f) - if temp: - junk = skimage.io.imread(os.path.join(self.imagedir, f)) - temp_imgs.append(junk) - sz.append(np.min(junk.shape[0:2])) - # img = junk[0: 499, 0: 499, :] # make all images the same size - # self.images.append(img) - filenames.remove(f) + for pre in self.filename_pre: + filename = pre + '.jpg' + junk = skimage.io.imread(os.path.join(self.imagedir, filename)) + temp_imgs.append(junk) + sz.append(np.min(junk.shape[0:2])) self.min_dim = np.min(sz) for junk in temp_imgs: img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size - self.images.append(img) + self.images.append(img) def load_results(self): """ Instead of running instance segmentation, load instance segmentation results (masks) From 47449458ef1c18cb0779dacae0d849afe2b1da86 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 22 Jun 2020 14:01:45 -0500 Subject: [PATCH 135/274] Update link_utilities.py Updated so that the folders won't contain space --- plantcv/plantcv/time_series/link_utilities.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index a4d89ee8c..b696f0767 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -211,7 +211,7 @@ def __init__(self, imagedir, segmentationdir, savedir): # store the dataset directory, instance segmentation result directory, time-series linking result directory and visualization directory self.imagedir = imagedir self.segmentationdir = segmentationdir - self.savedir = os.path.join(savedir, str(datetime.datetime.now())) + self.savedir = os.path.join(savedir, str(datetime.datetime.now()).replace(' ', '_')) os.makedirs(self.savedir) self.visualdir = os.path.join(self.savedir, 'visualization') From d12685165d05918e32a13ac0f19177b6b53eefbd Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 23 Jun 2020 11:01:13 -0500 Subject: [PATCH 136/274] Update time_series.py Change the saving name for the result so it makes much sense. --- plantcv/plantcv/time_series/time_series.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 7b0cac46e..b9b815143 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -145,8 +145,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi mask = np.zeros(mask_t.shape, dtype=np.uint8) mask[np.where(mask_t)] = 255 leaf_t = pcv.apply_mask(img, mask, mask_color='black') - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, link_leaf[t], t, Plant.filename_pre[t]))) - pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, link_leaf[t], t, Plant.filename_pre[t])), 'wb')) + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) + pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) ## 2. show with an alpha channel From c17a5d32d76830d54640b6a25e84f3efd9d1ae60 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 24 Jun 2020 09:41:13 -0500 Subject: [PATCH 137/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 250 +++++++++++---------- 1 file changed, 131 insertions(+), 119 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index b9b815143..61d2a7c84 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -24,7 +24,7 @@ # import link_utilities as funcs -def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']): +def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link'): """ Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) Input: @@ -35,19 +35,20 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 class_names: used in bounding box visualization. by default there are background and leaf Output: - There is no returned values as output, though the results will be saved in user defined "savedir" + An instance from "Plant" class would be returned, all the results will be saved in a folder with name of date and time when the function runs inside the user defined "savedir". Sample folder name: 2020-06-23-15-42 (a format of YYYY-MM-DD-HH-mm). 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. 4. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. - result name: {}_{}_{}_{}.png + result name: {}_{}_{}_{}_{}.png Naming rules for file names: 1st digit: time of 1st emergence of the leaf 2nd digit: leaf index when it first emerges 3rd digit: current time point 4th digit: current leaf index + 5th digit: original image name 2) a folder called "visualization 2", which contains 2nd set of visualization This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent @@ -57,124 +58,135 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi 3) a folder called "visualization 3", which containes 3rd set of visualization This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. - Naming format: YYYY-MM-DD-HH-MM_visual.png + Naming format: {}_visual.png + The original image name is inside the {}. """ - - # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir) - - Plant.getpath(Plant.imagedir) - Plant.Sorttime(time_cond) - - Plant.load_images() - - # load mrcnn inferencing results - Plant.load_results() - - Plant.getinitleaf() - Plant.getmaxleaf() - - Plant.gettotaltime() - Plant.getnumemergence() - - # plot the #leaves vs. time relationship - plt.plot(np.array(range(0,len(Plant.num_leaves))), Plant.num_leaves) - - if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): - file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') - if link_logic == 1: - file.write('mode: IOU\n') + if mode == 'link': + # initialize Plant class + Plant = funcs.PlantData(imagedir, segmentationdir, savedir) + + Plant.getpath(Plant.imagedir) + Plant.Sorttime(time_cond) + + Plant.load_images() + + # load mrcnn inferencing results + Plant.load_results() + + Plant.getinitleaf() + Plant.getmaxleaf() + + Plant.gettotaltime() + Plant.getnumemergence() + + # plot the #leaves vs. time relationship + plt.plot(np.array(range(0,len(Plant.num_leaves))), Plant.num_leaves) + + if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): + file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') + if link_logic == 1: + file.write('mode: IOU\n') + else: + file.write('mode: IOP\n') + file.write('Directory of original images: {}\n'.format(Plant.imagedir)) + file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) + file.write('Image conditions: {}'.format(time_cond)) + file.close() + + # linking initialization + Plant.initialize_linking() + + # link + for t in range(0, Plant.total_time-1): + Plant.linking(t, mode=link_logic) + Plant.get_series() + + ####### visualization ####### + # load original images + if len(Plant.images) == 0: + Plant.load_images() #Plant.images + + # visualization method 1: show only one leaf per image + path_visual1 = os.path.join(Plant.visualdir, 'visualization1') + if not os.path.exists(path_visual1): + os.makedirs(path_visual1) + + # visualization method 2: show with an alpha channel + path_visual2 = os.path.join(Plant.visualdir, 'visualization2') + if not os.path.exists(path_visual2): + os.makedirs(path_visual2) + + # visualization method 3: show with bounding boxes + path_visual3 = os.path.join(Plant.visualdir, 'visualization3') + if not os.path.exists(path_visual3): + os.makedirs(path_visual3) + + count = 0 + if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): + colors = funcs._random_colors(20) + pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) else: - file.write('mode: IOP\n') - file.write('Directory of original images: {}\n'.format(Plant.imagedir)) - file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) - file.write('Image conditions: {}'.format(time_cond)) - file.close() - - # linking initialization - Plant.initialize_linking() - - # link - for t in range(0, Plant.total_time-1): - Plant.linking(t, mode=link_logic) - Plant.get_series() - - ####### visualization ####### - # load original images - if len(Plant.images) == 0: - Plant.load_images() #Plant.images - - # visualization method 1: show only one leaf per image - path_visual1 = os.path.join(Plant.visualdir, 'visualization1') - if not os.path.exists(path_visual1): - os.makedirs(path_visual1) - - # visualization method 2: show with an alpha channel - path_visual2 = os.path.join(Plant.visualdir, 'visualization2') - if not os.path.exists(path_visual2): - os.makedirs(path_visual2) - - # visualization method 3: show with bounding boxes - path_visual3 = os.path.join(Plant.visualdir, 'visualization3') - if not os.path.exists(path_visual3): - os.makedirs(path_visual3) - - count = 0 - if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): - colors = funcs._random_colors(20) - pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) + colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) + + color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] + for key_t in Plant.link_series: + # + ids = Plant.link_series[key_t]['unique_id'] + + start_time = int(key_t.replace('t', '')) + leaves_t = Plant.link_series[key_t]['new_leaf'] + # for leaf in leaves_t: + for (unique_id, leaf) in zip(ids, leaves_t): + key_leaf = 'leaf{}'.format(leaf) + link_leaf = Plant.link_series[key_t][key_leaf] + start_idx = link_leaf[start_time] + for t in range(start_time, Plant.total_time): + img = Plant.images[t] + if link_leaf[t] >= 0: + color_all[t][link_leaf[t]] = colors[count] + + mask_t = Plant.masks[t][:,:,link_leaf[t]] + + ## 1. save the masked image, i.e. single leaves + mask = np.zeros(mask_t.shape, dtype=np.uint8) + mask[np.where(mask_t)] = 255 + leaf_t = pcv.apply_mask(img, mask, mask_color='black') + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) + pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) + + + ## 2. show with an alpha channel + # update the mask where there is an alpha channel + mask_ = np.ones(mask_t.shape)*0.5 + mask_[np.where(mask_t == True)] = 1 + masked_im = np.concatenate((img.astype(float)/255, np.expand_dims(mask_, axis=2)), axis=2) + save_dir_ = os.path.join(path_visual2, '{}_{}_{}.png'.format(unique_id, start_time, start_idx)) + if not os.path.exists(save_dir_): + os.makedirs(save_dir_) + fig2 = plt.figure(figsize=(5,5)) + ax2 = fig2.add_subplot(1,1,1) + ax2.imshow(masked_im) + ax2.axis('off') + plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + '.png')) + plt.close(fig2) + count += 1 + + ## 3. visualize with bounding boxes + for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.filename_pre): + funcs.display_instances(img, roi, mask, class_id, + class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, + colors = color) + + plt.savefig(os.path.join(path_visual3, '{}_visual.png'.format(t))) + plt.close('all') + + # save all information + pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) + else: - colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) - - color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] - for key_t in Plant.link_series: - start_time = int(key_t.replace('t', '')) - leaves_t = Plant.link_series[key_t]['new_leaf'] - for leaf in leaves_t: - key_leaf = 'leaf{}'.format(leaf) - link_leaf = Plant.link_series[key_t][key_leaf] - start_idx = link_leaf[start_time] - for t in range(start_time, Plant.total_time): - img = Plant.images[t] - if link_leaf[t] >= 0: - color_all[t][link_leaf[t]] = colors[count] - - mask_t = Plant.masks[t][:,:,link_leaf[t]] - - ## 1. save the masked image, i.e. single leaves - mask = np.zeros(mask_t.shape, dtype=np.uint8) - mask[np.where(mask_t)] = 255 - leaf_t = pcv.apply_mask(img, mask, mask_color='black') - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}.png'.format(start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) - pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}.pkl'.format(start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) - - - ## 2. show with an alpha channel - # update the mask where there is an alpha channel - mask_ = np.ones(mask_t.shape)*0.5 - mask_[np.where(mask_t == True)] = 1 - masked_im = np.concatenate((img.astype(float)/255, np.expand_dims(mask_, axis=2)), axis=2) - save_dir_ = os.path.join(path_visual2, '{}_{}.png'.format(start_time, start_idx)) - if not os.path.exists(save_dir_): - os.makedirs(save_dir_) - fig2 = plt.figure(figsize=(5,5)) - ax2 = fig2.add_subplot(1,1,1) - ax2.imshow(masked_im) - ax2.axis('off') - plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + '.png')) - plt.close(fig2) - count += 1 - - ## 3. visualize with bounding boxes - for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.filename_pre): - funcs.display_instances(img, roi, mask, class_id, - class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, - colors = color) - - plt.savefig(os.path.join(path_visual3, '{}_visual.png'.format(t))) - plt.close('all') - - # save all information - pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) + # initialize Plant class + Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode='load') + # Load from existed PlantData instance + Plant = pkl.load( open(os.path.join(path_save, 'saved_plant.pkl'), 'rb')) return Plant From 9e9cbb942b29c159118d0ec9ded0bf7a6093d5d4 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 24 Jun 2020 09:41:16 -0500 Subject: [PATCH 138/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 70 ++++++++++--------- 1 file changed, 38 insertions(+), 32 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index b696f0767..5b04d0bfa 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -181,7 +181,7 @@ def _getmatchedIndex(mask, masks, mode): class PlantData(): - def __init__(self, imagedir, segmentationdir, savedir): + def __init__(self, imagedir, segmentationdir, savedir, mode='link'): # store a list of time of the images self.time = [] @@ -209,17 +209,23 @@ def __init__(self, imagedir, segmentationdir, savedir): self.scores = [] # store the dataset directory, instance segmentation result directory, time-series linking result directory and visualization directory - self.imagedir = imagedir + self.imagedir = imagedir self.segmentationdir = segmentationdir - self.savedir = os.path.join(savedir, str(datetime.datetime.now()).replace(' ', '_')) - os.makedirs(self.savedir) - self.visualdir = os.path.join(self.savedir, 'visualization') + junk = datetime.datetime.now() + if mode == 'link': + subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), str(junk.hour).zfill(2), str(junk.minute).zfill(2)) + self.savedir = os.path.join(savedir, subfolder) + os.makedirs(self.savedir) + else: + self.savedir = savedir + + self.visualdir = os.path.join(self.savedir, 'visualization') - self.total_time = 0 - self.max_nleaf = 0 - self.init_nleaf = 0 - self.num_leaves = [] - self.num_emergence = 0 + self.total_time = 0 + self.max_nleaf = 0 + self.init_nleaf = 0 + self.num_leaves = [] + self.num_emergence = 0 self.emerging_info = [] # list length: self.total_time self.link_info = [] # list length: self.total_time-1, dictionaries inside @@ -245,12 +251,6 @@ def getnumemergence(self): def getpath(self, path): self.dir = path - - # def addimage(self): - # for t in self.time: - # filename = os.path.join(self.dir, '10.9.1.241_pos-165-003-020_{}_crop-img11.jpg'.format(t)) - # image = skimage.io.imread(filename) - # self.images.append(image) def Sorttime(self, time_cond): """ @@ -282,21 +282,6 @@ def load_images(self): """ Load original images Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM """ -# filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] -# temp_imgs = [] -# sz = [] -# for t in self.time: -# for f in filenames: -# temp = re.search(t, f) -# if temp: -# junk = skimage.io.imread(os.path.join(self.imagedir, f)) -# temp_imgs.append(junk) -# sz.append(np.min(junk.shape[0:2])) -# filenames.remove(f) -# self.min_dim = np.min(sz) -# for junk in temp_imgs: -# img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size -# self.images.append(img) temp_imgs = [] sz = [] for pre in self.filename_pre: @@ -370,10 +355,14 @@ def linking(self, start_time, mode): self.link_info[start_time] = link['forward'] def get_series(self): + ## define new leaves and their unique identifiers at time points with new leaves emerging t = 0 key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = self.available_leaves[0] + # + self.link_series[key_t]['unique_id'] = self.link_series[key_t]['new_leaf'] + unique_id = len(self.link_series[key_t]['new_leaf']) for t in range(1, self.total_time): new_leaves = [i for i in self.available_leaves[t] if i not in self.link_info[t-1]] @@ -381,6 +370,22 @@ def get_series(self): key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = np.array(new_leaves) +# if len(new_leaves) > 1: +# id_temp = [] +# for new_leav in new_leaves: +# id_temp.append(unique_id) +# unique_id = unique_id + 1 +# self.link_series[key_t]['unique_id' = np.array(id_temp) +# else: +# self.link_series[key_t]['unique_id'] = np.array(unique_id) +# unique_id = unique_id + 1 + id_temp = [] + for new_leaf in new_leaves: + id_temp.append(unique_id) + unique_id = unique_id + 1 + self.link_series[key_t]['unique_id'] = np.array(id_temp) + + ## for time points with new leaves emerging, get the linking information for every new leaf for key_t in self.link_series: t0 = int(key_t.replace('t','')) for leaf in self.link_series[key_t]['new_leaf']: @@ -393,4 +398,5 @@ def get_series(self): if idx < 0: break else: - self.link_series[key_t][key_leaf][t_] = self.link_info[t_-1][idx] \ No newline at end of file + self.link_series[key_t][key_leaf][t_] = self.link_info[t_-1][idx] + From 2c0fbab4beee97faba11081740ee9dda432f9400 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 24 Jun 2020 09:59:13 -0500 Subject: [PATCH 139/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 61d2a7c84..8e90c75ed 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -33,7 +33,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi savedir: desired saving directory of linking result time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 - class_names: used in bounding box visualization. by default there are background and leaf + class_names: used in bounding box visualization. By default there are background and leaf + mode: either 'link' or 'load'. When creating a new linking, use in mode 'link', when trying to load from saved file, use in mode 'load'. By default the mode is 'link'. Output: An instance from "Plant" class would be returned, all the results will be saved in a folder with name of date and time when the function runs inside the user defined "savedir". Sample folder name: 2020-06-23-15-42 (a format of YYYY-MM-DD-HH-mm). 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time From 504a731a2de70a45bbb5b1b324a03817c8f9f9fe Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 24 Jun 2020 10:37:06 -0500 Subject: [PATCH 140/274] Update tests.py --- tests/tests.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 87edb7f98..a69aac952 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6321,8 +6321,8 @@ def test_plantcv_time_series_time_series(): os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir time_cond = ['08-05', '11-05', '17-05', '21-05'] - savedir = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf']) - assert len(os.listdir(savedir)) > 0 + Plant_ = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link') + assert len(os.listdir(Plant_.savedir)) > 0 def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From 9090abdde05d3356a8172a45f8ac138e3ce5b345 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 25 Jun 2020 09:18:05 -0500 Subject: [PATCH 141/274] Update plantcv-workflow.py Add new metadata into current vocabulary: device, location, uidleaf --- plantcv-workflow.py | 97 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 97 insertions(+) diff --git a/plantcv-workflow.py b/plantcv-workflow.py index 64784886b..85372a1ba 100755 --- a/plantcv-workflow.py +++ b/plantcv-workflow.py @@ -26,6 +26,103 @@ def options(): ValueError: if a metadata field is not supported. """ + # These are metadata types that PlantCV deals with. + # Values are default values in the event the metadata is missing + valid_meta = { + # Camera settings + "camera": { + "label": "camera identifier", + "datatype": "", + "value": "none" + }, + "imgtype": { + "label": "image type", + "datatype": "", + "value": "none" + }, + "zoom": { + "label": "camera zoom setting", + "datatype": "", + "value": "none" + }, + "exposure": { + "label": "camera exposure setting", + "datatype": "", + "value": "none" + }, + "gain": { + "label": "camera gain setting", + "datatype": "", + "value": "none" + }, + "frame": { + "label": "image series frame identifier", + "datatype": "", + "value": "none" + }, + "lifter": { + "label": "imaging platform height setting", + "datatype": "", + "value": "none" + }, + # Date-Time + "timestamp": { + "label": "datetime of image", + "datatype": "", + "value": None + }, + # Sample attributes + "id": { + "label": "image identifier", + "datatype": "", + "value": "none" + }, + "plantbarcode": { + "label": "plant barcode identifier", + "datatype": "", + "value": "none" + }, + "treatment": { + "label": "treatment identifier", + "datatype": "", + "value": "none" + }, + "cartag": { + "label": "plant carrier identifier", + "datatype": "", + "value": "none" + }, + # Experiment attributes + "measurementlabel": { + "label": "experiment identifier", + "datatype": "", + "value": "none" + }, + # Device identifier + "device": { + "label": "devide identifier", + "datatype": "", + "value": "none" + }, + # Location + "location":{ + "label": "location", + "datatype": "", + "value": "none" + }, + # Other + "other": { + "label": "other identifier", + "datatype": "", + "value": "none" + }, + # Unique Leaf Identifier + "uidleaf":{ + "label": "unique leaf identifier", + "datatype": "", + "value": None + } + } parser = argparse.ArgumentParser(description='Parallel imaging processing with PlantCV.') config_grp = parser.add_argument_group('CONFIG') config_grp.add_argument("--config", required=False, From db0627dab67e71d63cbb7e77946dfb710679c645 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 25 Jun 2020 09:18:15 -0500 Subject: [PATCH 142/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 43 ++++++++++++---------- 1 file changed, 24 insertions(+), 19 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 8e90c75ed..05061bf90 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -43,23 +43,28 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi 4. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. - result name: {}_{}_{}_{}_{}.png - Naming rules for file names: - 1st digit: time of 1st emergence of the leaf - 2nd digit: leaf index when it first emerges - 3rd digit: current time point - 4th digit: current leaf index - 5th digit: original image name + result name: {}_{}-{}-{}-{}_{}.png + Naming convention for file names: + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + 4rd digit: current time point + 5th digit: current leaf index + 6th digit: original image name 2) a folder called "visualization 2", which contains 2nd set of visualization This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent There are several subfolders, the number of subfolders depends on the number of "new leaves" in total - Every subfolder is a "new leaf", whose name is {}_{}, with 1st digit represents time of 1st emergence of the leaf and 2nd digit represents leaf index when it first emerges - Inside every folder, images of leaves with names "time_{}.png" are contained; the number inside the {} represents the time point + Every subfolder is a "new leaf", whose name is {}_{}-{} + Naming convention for folder names: + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + Inside every folder, images of leaves with names same as original image names are contained. 3) a folder called "visualization 3", which containes 3rd set of visualization This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. - Naming format: {}_visual.png + Naming format: {}-visual.png The original image name is inside the {}. """ if mode == 'link': @@ -136,7 +141,6 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi start_time = int(key_t.replace('t', '')) leaves_t = Plant.link_series[key_t]['new_leaf'] - # for leaf in leaves_t: for (unique_id, leaf) in zip(ids, leaves_t): key_leaf = 'leaf{}'.format(leaf) link_leaf = Plant.link_series[key_t][key_leaf] @@ -148,20 +152,20 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi mask_t = Plant.masks[t][:,:,link_leaf[t]] - ## 1. save the masked image, i.e. single leaves + ## 1. save the masked image, i.e. single leaves mask = np.zeros(mask_t.shape, dtype=np.uint8) mask[np.where(mask_t)] = 255 leaf_t = pcv.apply_mask(img, mask, mask_color='black') - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) - pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) +# pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) - ## 2. show with an alpha channel - # update the mask where there is an alpha channel + ## 2. show result with an alpha channel + # update the mask where there is an alpha channel (alpha=0.5) mask_ = np.ones(mask_t.shape)*0.5 mask_[np.where(mask_t == True)] = 1 masked_im = np.concatenate((img.astype(float)/255, np.expand_dims(mask_, axis=2)), axis=2) - save_dir_ = os.path.join(path_visual2, '{}_{}_{}.png'.format(unique_id, start_time, start_idx)) + save_dir_ = os.path.join(path_visual2, '{}_{}-{}'.format(unique_id, start_time, start_idx)) if not os.path.exists(save_dir_): os.makedirs(save_dir_) fig2 = plt.figure(figsize=(5,5)) @@ -178,7 +182,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, colors = color) - plt.savefig(os.path.join(path_visual3, '{}_visual.png'.format(t))) + plt.savefig(os.path.join(path_visual3, '{}-visual.png'.format(t))) plt.close('all') # save all information @@ -188,6 +192,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi # initialize Plant class Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode='load') # Load from existed PlantData instance - Plant = pkl.load( open(os.path.join(path_save, 'saved_plant.pkl'), 'rb')) + Plant = pkl.load( open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'rb')) return Plant + \ No newline at end of file From 9635c7fd709732eb71566a86dc9b58e5bcd0101e Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 25 Jun 2020 09:47:06 -0500 Subject: [PATCH 143/274] Update time_series.md --- docs/time_series.md | 50 +++++++++++++++++++++++++++++---------------- 1 file changed, 32 insertions(+), 18 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index ec6098e85..47dae9621 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -1,9 +1,8 @@ ## time_series -This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, recording the growth of the plant, where there should be minimum or none movement of plant or camera). +This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, recording the growth of the plant, where ideally there should be no or at least minimum movement of plant or camera). To run this function, the instance segmentation for every image should be achieved beforehand. For a demo of instance segmentation, check here: -As a reminder, you will need a maskRCNN environemnt to be able to use this tool. For more information, check here: When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. (Another example is k-means) @@ -18,34 +17,43 @@ To understand the growth of every leaf instance of a plant, we need pull out the - imagedir: directory of original image used - segmentationdir: directory of leaf instance segmentation result - savedir: desired saving directory of linking result. Note that this function will generate a folder under your specified saving directory, with the date and time you run this programme - - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment + - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 - - class_names: used in bounding box visualization. by default there are background and leaf + - class_names: used in bounding box visualization. by default there are background and leaf, i.e. class_names = ['BG', 'leaf'] + - mode: can be either 'link' or 'load', use 'link' when trying to get linked time-series from instance segmentation; use 'load' when trying to load from saved linking - **Output:** - There is no returned values as output, though the results will be saved in user defined "savedir" - 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time - 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment + An instance of Plant object is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir". + + Note: under the user specified saving directory, a new folder whose name is date-time when the function runs (format: YYYY-MM-DD-HH-mm) will be created to save results. + + 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time. + 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment. 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. 4. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. - result name: {}_{}_{}_{}_{}.png - Naming rules for file names: - 1st digit: time of 1st emergence of the leaf - 2nd digit: leaf index when it first emerges - 3rd digit: current time point - 4th digit: current leaf index - 5th digit: date and time included in original file name, typically, it follows the format of YYYY-MM-DD-HH-MM. + result name: {}_{}-{}-{}-{}_{}.png + Naming convention for file names: + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + 4rd digit: current time point + 5th digit: current leaf index + 6th digit: original image name 2) a folder called "visualization 2", which contains 2nd set of visualization This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent There are several subfolders, the number of subfolders depends on the number of "new leaves" in total - Every subfolder is a "new leaf", whose name is {}_{}, with 1st digit represents time of 1st emergence of the leaf and 2nd digit represents leaf index when it first emerges - Inside every folder, images of leaves with names "{}.png" are contained; inside the {} the date-time of the original image is contained, typically, it follows the format of YYYY-MM-DD-HH-MM. + Every subfolder is a "new leaf", whose name is {}_{}-{}. Naming convention for folder names: + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + Inside every folder, images of leaves with names same as original image names are contained. 3) a folder called "visualization 3", which containes 3rd set of visualization This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. - Naming format: {}_visual.png; inside the {} the date-time of the original image is contained, typically, it follows the format of YYYY-MM-DD-HH-MM. + Naming format: {}-visual.png + The original image name is inside the {}. ```python from plantcv import plantcv as pcv @@ -55,6 +63,12 @@ path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtC path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/detection/modified' path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/test' time_cond = ['08-05', '11-05', '17-05', '21-05'] -​pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=1, class_names=['BG', 'Leaf']) +​linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link') + +# Print the saving directory on the screen so that you can go check the result: +linked_Plant.savedir + +# Print linking information on the screen for a quick check: +linked_Plant.link_series ``` **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) From f02f25b96b790a20738a792ffac3a8606f0b50a9 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 10:43:24 -0500 Subject: [PATCH 144/274] Update plantcv-workflow.py by adding new metadata into the original vocabulary --- plantcv-workflow.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/plantcv-workflow.py b/plantcv-workflow.py index 85372a1ba..613423e94 100755 --- a/plantcv-workflow.py +++ b/plantcv-workflow.py @@ -99,8 +99,8 @@ def options(): "value": "none" }, # Device identifier - "device": { - "label": "devide identifier", + "ip": { + "label": "ip address of device", "datatype": "", "value": "none" }, @@ -117,7 +117,7 @@ def options(): "value": "none" }, # Unique Leaf Identifier - "uidleaf":{ + "uid_leaf":{ "label": "unique leaf identifier", "datatype": "", "value": None From c7876d844a71ed3ddd1e1c43bb133a83df12fa94 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 10:45:42 -0500 Subject: [PATCH 145/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 220 ++++++++++-------- 1 file changed, 122 insertions(+), 98 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 5b04d0bfa..d80f24b31 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -27,6 +27,7 @@ import copy import colorsys + def _random_colors(N, bright=True): """ Generate random colors. @@ -39,6 +40,7 @@ def _random_colors(N, bright=True): random.shuffle(colors) return colors + def _apply_mask(image, mask, color, alpha=0.5): """Apply the given mask to the image. """ @@ -49,6 +51,7 @@ def _apply_mask(image, mask, color, alpha=0.5): image[:, :, c]) return image + def display_instances(image, boxes, masks, class_ids, class_names, scores=None, title="", figsize=(16, 16), ax=None, @@ -100,8 +103,8 @@ def display_instances(image, boxes, masks, class_ids, class_names, y1, x1, y2, x2 = boxes[i] if show_bbox: p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, - alpha=0.7, linestyle="dashed", - edgecolor=color, facecolor='none') + alpha=0.7, linestyle="dashed", + edgecolor=color, facecolor='none') ax.add_patch(p) # Label @@ -141,7 +144,7 @@ def _compute_overlaps_masks(masks1, masks2, mode=1): masks1, masks2: [Height, Width, instances] mode: 1 or 2, 1 represent for IOU, 2 represent for intersection over the area of the previous mask """ - + # If either set of masks is empty return empty result if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: return np.zeros((masks1.shape[-1], masks2.shape[-1])) @@ -159,107 +162,112 @@ def _compute_overlaps_masks(masks1, masks2, mode=1): elif mode == 2: overlaps = intersections / area1[:, None] return overlaps - -def get_ax(rows=1, cols=1, size=16): #??? + + +def get_ax(rows=1, cols=1, size=16): # ??? """Return a Matplotlib Axes array to be used in all visualizations in the notebook. Provide a central point to control graph sizes. - + Adjust the size attribute to control how big to render images """ - fig, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows)) + fig, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows)) fig.tight_layout() return ax + def _getmatchedIndex(mask, masks, mode): """Get the index from masks that matches the mask""" score = _compute_overlaps_masks(np.expand_dims(mask, axis=-1), masks, mode) - if max(score[0])>0.15: + if max(score[0]) > 0.15: return np.argmax(score[0]), score else: - return -1, score + return -1, score + - class PlantData(): - def __init__(self, imagedir, segmentationdir, savedir, mode='link'): - + def __init__(self, imagedir, segmentationdir, savedir, mode='link', suffix='.jpg'): + + self.suffix = suffix + # store a list of time of the images self.time = [] # store a list of original prefixes of the original images self.filename_pre = [] - # total num of leaves that being detected - self.numleaf = 0 + # total num of leaves that being detected + self.numleaf = 0 # store all the images self.images = [] # minimum dimension of image. To link time series, all images must have the same dimension, to make it easier, we make the image square self.min_dim = 0 - + # store all the mask detection during [starttime, endtime] self.masks = [] - + # store all the rois (for visualization with bounding boxes) self.rois = [] - + # store all the class_ids (for visualization with bounding boxes) self.class_ids = [] - + # store all the scores (for visualization with bounding boxes) self.scores = [] - + # store the dataset directory, instance segmentation result directory, time-series linking result directory and visualization directory - self.imagedir = imagedir + self.imagedir = imagedir self.segmentationdir = segmentationdir - junk = datetime.datetime.now() + junk = datetime.datetime.now() if mode == 'link': - subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), str(junk.hour).zfill(2), str(junk.minute).zfill(2)) - self.savedir = os.path.join(savedir, subfolder) + subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), + str(junk.hour).zfill(2), str(junk.minute).zfill(2)) + self.savedir = os.path.join(savedir, subfolder) os.makedirs(self.savedir) else: self.savedir = savedir - self.visualdir = os.path.join(self.savedir, 'visualization') - - self.total_time = 0 - self.max_nleaf = 0 - self.init_nleaf = 0 - self.num_leaves = [] - self.num_emergence = 0 - - self.emerging_info = [] # list length: self.total_time - self.link_info = [] # list length: self.total_time-1, dictionaries inside - self.weights = [] # list length: self.total_time-1, dictionaries inside - self.available_leaves = [] # list length: self.total_time - self.link_series = dict() # only for those newly emerging leaves - + self.visualdir = os.path.join(self.savedir, 'visualization') + + self.total_time = 0 + self.max_nleaf = 0 + self.init_nleaf = 0 + self.num_leaves = [] + self.num_emergence = 0 + + self.emerging_info = [] # list length: self.total_time + self.link_info = [] # list length: self.total_time-1, dictionaries inside + self.weights = [] # list length: self.total_time-1, dictionaries inside + self.available_leaves = [] # list length: self.total_time + self.link_series = dict() # only for those newly emerging leaves + def getinitleaf(self): self.init_nleaf = self.masks[0].shape[2] - + def getmaxleaf(self): for i in range(0, len(self.masks)): self.num_leaves.append(self.masks[i].shape[2]) self.max_nleaf = np.max(self.num_leaves) - + def gettotaltime(self): # also represent for the total number of images self.total_time = len(self.time) - + def getnumemergence(self): - self.num_emergence = np.ones((self.total_time), dtype=int)*self.init_nleaf + self.num_emergence = np.ones((self.total_time), dtype=int) * self.init_nleaf self.num_emergence[1:] = np.diff(self.num_leaves) - + def getpath(self, path): - self.dir = path + self.dir = path def Sorttime(self, time_cond): """ This function is designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM - Return: loop through the dataset_dir, and add time in time order + Return: loop through the dataset_dir, and add time in time order """ - - filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] - filenames_ori = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] + + filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] + filenames_ori = [f for f in os.listdir(self.imagedir) if f.endswith(self.suffix)] time_temp = [] file_name = [] for filename in filenames: @@ -276,14 +284,29 @@ def Sorttime(self, time_cond): index_temp = np.argsort(time_temp) # forward self.time = [time_temp[i] for i in index_temp] - self.filename_pre = [file_name[i] for i in index_temp] + self.filename_pre = [file_name[i] for i in index_temp] def load_images(self): """ Load original images Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM """ - temp_imgs = [] - sz = [] + # filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] + # temp_imgs = [] + # sz = [] + # for t in self.time: + # for f in filenames: + # temp = re.search(t, f) + # if temp: + # junk = skimage.io.imread(os.path.join(self.imagedir, f)) + # temp_imgs.append(junk) + # sz.append(np.min(junk.shape[0:2])) + # filenames.remove(f) + # self.min_dim = np.min(sz) + # for junk in temp_imgs: + # img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size + # self.images.append(img) + temp_imgs = [] + sz = [] for pre in self.filename_pre: filename = pre + '.jpg' junk = skimage.io.imread(os.path.join(self.imagedir, filename)) @@ -291,8 +314,8 @@ def load_images(self): sz.append(np.min(junk.shape[0:2])) self.min_dim = np.min(sz) for junk in temp_imgs: - img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size - self.images.append(img) + img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size + self.images.append(img) def load_results(self): """ Instead of running instance segmentation, load instance segmentation results (masks) @@ -300,60 +323,61 @@ def load_results(self): for t in self.time: file_name = '{}.pkl'.format(t) r = pkl.load(open(os.path.join(self.segmentationdir, file_name), 'rb')) - self.masks.append(r["masks"][0: self.min_dim, 0: self.min_dim, :]) # make all masks the same size + self.masks.append(r["masks"][0: self.min_dim, 0: self.min_dim, :]) # make all masks the same size self.rois.append(r["rois"]) self.class_ids.append(r['class_ids']) self.scores.append(r['scores']) - + def initialize_linking(self): - self.emerging_info = [[] for i in range(0, self.total_time)] - self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time-1)] - self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i+1])) for i in range(0, self.total_time-1)] + self.emerging_info = [[] for i in range(0, self.total_time)] + self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time - 1)] + self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i + 1])) for i in + range(0, self.total_time - 1)] self.available_leaves = [np.array(range(0, self.num_leaves[i])) for i in range(0, self.total_time)] - self.time_compare = [np.ones((self.num_leaves[i]), dtype=int)*i for i in range(0, self.total_time)] - + self.time_compare = [np.ones((self.num_leaves[i]), dtype=int) * i for i in range(0, self.total_time)] + def linking(self, start_time, mode): - masks1 = copy.deepcopy(self.masks[start_time]) - masks2 = copy.deepcopy(self.masks[start_time+1]) + masks1 = copy.deepcopy(self.masks[start_time]) + masks2 = copy.deepcopy(self.masks[start_time + 1]) leaves1 = copy.deepcopy(self.available_leaves[start_time]) - leaves2 = copy.deepcopy(self.available_leaves[start_time+1]) + leaves2 = copy.deepcopy(self.available_leaves[start_time + 1]) n1 = len(leaves1) n2 = len(leaves2) n = np.min((n1, n2)) N = np.max((n1, n2)) weight = dict() - weight['forward'] = -np.inf*np.ones((n1, n2)) -# weight['backward'] = -np.inf*np.zeros((n2, n1)) + weight['forward'] = -np.inf * np.ones((n1, n2)) + # weight['backward'] = -np.inf*np.zeros((n2, n1)) link = dict() link['forward'] = -np.ones((n1)) -# link['backward'] = -np.ones((n2)) + # link['backward'] = -np.ones((n2)) for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward']): -# for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward', 'backward']): + # for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward', 'backward']): for (idx, main_leaf) in enumerate(leaves): - mask = masks[:,:,main_leaf] - link[key][idx], weight[key][idx,:] = _getmatchedIndex(mask, masks_all, mode) + mask = masks[:, :, main_leaf] + link[key][idx], weight[key][idx, :] = _getmatchedIndex(mask, masks_all, mode) # check for duplicates (and hence non-unique links) val, cou = np.unique(link[key], return_counts=True) - idx_temp = np.where(cou>1)[0] + idx_temp = np.where(cou > 1)[0] - if len(idx_temp) > 0: # matching to same leaf exists + if len(idx_temp) > 0: # matching to same leaf exists idx_vs = val[idx_temp] for idx_v in idx_vs: - idxs = np.where(link[key] == idx_v)[0] - weights_comp = weight[key][:,int(idx_v)] + idxs = np.where(link[key] == idx_v)[0] + weights_comp = weight[key][:, int(idx_v)] - idx_keep = np.argmax(weights_comp) - idx_drop = np.delete(idxs, np.where(idxs==idx_keep)) + idx_keep = np.argmax(weights_comp) + idx_drop = np.delete(idxs, np.where(idxs == idx_keep)) weight_temp = copy.deepcopy(weight[key][idx_drop]) weight_temp[0, int(idx_v)] = 0 if np.max(weight_temp) > 0.2: link[key][idx_drop] = np.argmax(weight_temp) else: link[key][idx_drop] = -1 - + self.link_info[start_time] = link['forward'] - + def get_series(self): ## define new leaves and their unique identifiers at time points with new leaves emerging t = 0 @@ -363,40 +387,40 @@ def get_series(self): # self.link_series[key_t]['unique_id'] = self.link_series[key_t]['new_leaf'] unique_id = len(self.link_series[key_t]['new_leaf']) - + for t in range(1, self.total_time): - new_leaves = [i for i in self.available_leaves[t] if i not in self.link_info[t-1]] + # for t in range(1, self.total_time-1): + new_leaves = [i for i in self.available_leaves[t] if i not in self.link_info[t - 1]] if new_leaves: key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = np.array(new_leaves) -# if len(new_leaves) > 1: -# id_temp = [] -# for new_leav in new_leaves: -# id_temp.append(unique_id) -# unique_id = unique_id + 1 -# self.link_series[key_t]['unique_id' = np.array(id_temp) -# else: -# self.link_series[key_t]['unique_id'] = np.array(unique_id) -# unique_id = unique_id + 1 + # if len(new_leaves) > 1: + # id_temp = [] + # for new_leav in new_leaves: + # id_temp.append(unique_id) + # unique_id = unique_id + 1 + # self.link_series[key_t]['unique_id' = np.array(id_temp) + # else: + # self.link_series[key_t]['unique_id'] = np.array(unique_id) + # unique_id = unique_id + 1 id_temp = [] for new_leaf in new_leaves: id_temp.append(unique_id) unique_id = unique_id + 1 self.link_series[key_t]['unique_id'] = np.array(id_temp) - - ## for time points with new leaves emerging, get the linking information for every new leaf + ## for time points with new leaves emerging, get the linking information for every new leaf for key_t in self.link_series: - t0 = int(key_t.replace('t','')) + t0 = int(key_t.replace('t', '')) for leaf in self.link_series[key_t]['new_leaf']: key_leaf = 'leaf{}'.format(leaf) self.link_series[key_t][key_leaf] = -np.ones(self.total_time, dtype=int) self.link_series[key_t][key_leaf][t0] = leaf - self.link_series[key_t][key_leaf][t0+1] = self.link_info[t0][leaf] - for t_ in range(t0+2, self.total_time): - idx = self.link_series[key_t][key_leaf][t_-1] - if idx < 0: - break - else: - self.link_series[key_t][key_leaf][t_] = self.link_info[t_-1][idx] - + if t0 < self.total_time - 1: + self.link_series[key_t][key_leaf][t0 + 1] = self.link_info[t0][leaf] + for t_ in range(t0 + 2, self.total_time): + idx = self.link_series[key_t][key_leaf][t_ - 1] + if idx < 0: + break + else: + self.link_series[key_t][key_leaf][t_] = self.link_info[t_ - 1][idx] \ No newline at end of file From cb458b8a1d93ef8b699bac152f836d30185f4b76 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 10:45:48 -0500 Subject: [PATCH 146/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 73 ++++++++++++---------- 1 file changed, 39 insertions(+), 34 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 05061bf90..98504138f 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -23,8 +23,8 @@ from plantcv.plantcv.time_series import link_utilities as funcs # import link_utilities as funcs - -def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link'): +def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], + mode='link', suffix='.jpg'): """ Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) Input: @@ -42,9 +42,9 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. 4. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization - In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. + In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. result name: {}_{}-{}-{}-{}_{}.png - Naming convention for file names: + Naming convention for file names: 1st digit: unique identifier of the leaf 2nd digit: time of first emergence of the leaf 3rd digut: leaf index when it first emerges @@ -62,14 +62,14 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi 3rd digut: leaf index when it first emerges Inside every folder, images of leaves with names same as original image names are contained. - 3) a folder called "visualization 3", which containes 3rd set of visualization - This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. + 3) a folder called "visualization 3", which containes 3rd set of visualization + This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. Naming format: {}-visual.png The original image name is inside the {}. """ if mode == 'link': # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir) + Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode, suffix) Plant.getpath(Plant.imagedir) Plant.Sorttime(time_cond) @@ -86,7 +86,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi Plant.getnumemergence() # plot the #leaves vs. time relationship - plt.plot(np.array(range(0,len(Plant.num_leaves))), Plant.num_leaves) + plt.plot(np.array(range(0, len(Plant.num_leaves))), Plant.num_leaves) if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') @@ -103,14 +103,14 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi Plant.initialize_linking() # link - for t in range(0, Plant.total_time-1): + for t in range(0, Plant.total_time - 1): Plant.linking(t, mode=link_logic) Plant.get_series() ####### visualization ####### # load original images if len(Plant.images) == 0: - Plant.load_images() #Plant.images + Plant.load_images() # Plant.images # visualization method 1: show only one leaf per image path_visual1 = os.path.join(Plant.visualdir, 'visualization1') @@ -129,70 +129,75 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi count = 0 if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): - colors = funcs._random_colors(20) + colors = funcs._random_colors(40) pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) else: colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] for key_t in Plant.link_series: - # + # ids = Plant.link_series[key_t]['unique_id'] start_time = int(key_t.replace('t', '')) + # if start_time == 40: + # pdb.set_trace() leaves_t = Plant.link_series[key_t]['new_leaf'] for (unique_id, leaf) in zip(ids, leaves_t): - key_leaf = 'leaf{}'.format(leaf) + key_leaf = 'leaf{}'.format(leaf) link_leaf = Plant.link_series[key_t][key_leaf] start_idx = link_leaf[start_time] for t in range(start_time, Plant.total_time): img = Plant.images[t] if link_leaf[t] >= 0: color_all[t][link_leaf[t]] = colors[count] + mask_t = Plant.masks[t][:, :, link_leaf[t]] - mask_t = Plant.masks[t][:,:,link_leaf[t]] - - ## 1. save the masked image, i.e. single leaves - mask = np.zeros(mask_t.shape, dtype=np.uint8) + ## 1. save the masked image, i.e. single leaves + mask = np.zeros(mask_t.shape, dtype=np.uint8) mask[np.where(mask_t)] = 255 leaf_t = pcv.apply_mask(img, mask, mask_color='black') - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) -# pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) + pcv.print_image(leaf_t, os.path.join(path_visual1, + '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, + start_idx, t, link_leaf[t], + Plant.filename_pre[t]))) + # pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) - - ## 2. show result with an alpha channel + ## 2. show result with an alpha channel # update the mask where there is an alpha channel (alpha=0.5) - mask_ = np.ones(mask_t.shape)*0.5 + mask_ = np.ones(mask_t.shape) * 0.5 mask_[np.where(mask_t == True)] = 1 - masked_im = np.concatenate((img.astype(float)/255, np.expand_dims(mask_, axis=2)), axis=2) + masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_, axis=2)), axis=2) save_dir_ = os.path.join(path_visual2, '{}_{}-{}'.format(unique_id, start_time, start_idx)) if not os.path.exists(save_dir_): os.makedirs(save_dir_) - fig2 = plt.figure(figsize=(5,5)) - ax2 = fig2.add_subplot(1,1,1) - ax2.imshow(masked_im) + fig2 = plt.figure(figsize=(5, 5)) + ax2 = fig2.add_subplot(1, 1, 1) + ax2.imshow(masked_im) ax2.axis('off') plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + '.png')) plt.close(fig2) - count += 1 + count += 1 ## 3. visualize with bounding boxes - for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.filename_pre): - funcs.display_instances(img, roi, mask, class_id, - class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16),show_bbox=True, show_mask=True, - colors = color) + for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, + Plant.scores, color_all, Plant.filename_pre): + funcs.display_instances(img, roi, mask, class_id, + class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, + show_mask=True, + colors=color) plt.savefig(os.path.join(path_visual3, '{}-visual.png'.format(t))) plt.close('all') # save all information pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) - + else: # initialize Plant class Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode='load') # Load from existed PlantData instance - Plant = pkl.load( open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'rb')) - + Plant = pkl.load(open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'rb')) + return Plant \ No newline at end of file From 708b76308e25e1a0842c9c2eb28fcae8f9bef513 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 10:46:10 -0500 Subject: [PATCH 147/274] Update time_series.md Updated with embeded videos included. --- docs/time_series.md | 116 +++++++++++++++++++++++++++++--------------- 1 file changed, 78 insertions(+), 38 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index 47dae9621..7e12f4454 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -1,17 +1,16 @@ ## time_series -This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, recording the growth of the plant, where ideally there should be no or at least minimum movement of plant or camera). +This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, e.g. images taken every several hours, recording the growth of the plant, where ideally there should be no or at least minimum movement for plant or camera). -To run this function, the instance segmentation for every image should be achieved beforehand. For a demo of instance segmentation, check here: +To run this function, the instance segmentation for every image should be achieved beforehand. For more information and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). -When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. (Another example is k-means) +When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. -To understand the growth of every leaf instance of a plant, we need pull out the same leaf across the whole time period. +To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always have the same instance label across the whole time period. +**plantcv.time_series.time_series_linking**(*imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'*) -**plantcv.time_series.time_series_linking**(*imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf']*) - -**returns** no returned values, all the results are saved in the user specified directory +**returns** An instance that is a Plant object - **Parameters:** - imagedir: directory of original image used @@ -21,39 +20,56 @@ To understand the growth of every leaf instance of a plant, we need pull out the - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 - class_names: used in bounding box visualization. by default there are background and leaf, i.e. class_names = ['BG', 'leaf'] - mode: can be either 'link' or 'load', use 'link' when trying to get linked time-series from instance segmentation; use 'load' when trying to load from saved linking + - suffix: the suffix of original images, make sure all images having the same suffix, e.g. suffix='.jpg' or suffix='-img8.jpg'. Make sure all the images desired having the same suffix pattern. - **Output:** An instance of Plant object is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir". - Note: under the user specified saving directory, a new folder whose name is date-time when the function runs (format: YYYY-MM-DD-HH-mm) will be created to save results. +Note: under the user specified saving directory, a new folder named after the date and time (format: YYYY-MM-DD-HH-mm) the function runs will be created to save results. + +1. colors.pkl: the colors (represented by arrays) used in bounding box visualization. With this predefined color set, same color will represent for the leaf instance with same instance label all the time, i.e. same color represents leaf with the same label. Without this predefined list of color, the assignment of color will be random, i.e. the color provides no information to leaf indices. +2. details.txt: the logic of linking as well as time condition will be saved in this text file, so that would be easier for users to check these parameters for the specific expreiment. +3. saved_plant.pkl: a "Plant" object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. +4. a folder called "visualization", which contains 3 subfolders: + + 1) a folder call "visualization 1", which contains 1st set of visualization + In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. + Naming convention for the saving names of the result: + + {}_{}-{}-{}-{}_{}.png + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + 4rd digit: current time point + 5th digit: current leaf index + 6th digit: original image name + This set of visualization is designed for shape analysis by easily applying plantcv workflow. + + 2) a folder called "visualization 2", which contains 2nd set of visualization + This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent + There are several subfolders, the number of subfolders depends on the number of "new leaves" in total + Every subfolder is a "new leaf". Naming convention for folder names: - 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time. - 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment. - 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. - 4. a folder called "visualization", which contains 3 subfolders: - 1) a folder call "visualization 1", which contains 1st set of visualization - In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. - result name: {}_{}-{}-{}-{}_{}.png - Naming convention for file names: - 1st digit: unique identifier of the leaf - 2nd digit: time of first emergence of the leaf - 3rd digut: leaf index when it first emerges - 4rd digit: current time point - 5th digit: current leaf index - 6th digit: original image name - - 2) a folder called "visualization 2", which contains 2nd set of visualization - This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent - There are several subfolders, the number of subfolders depends on the number of "new leaves" in total - Every subfolder is a "new leaf", whose name is {}_{}-{}. Naming convention for folder names: - 1st digit: unique identifier of the leaf - 2nd digit: time of first emergence of the leaf - 3rd digut: leaf index when it first emerges - Inside every folder, images of leaves with names same as original image names are contained. - - 3) a folder called "visualization 3", which containes 3rd set of visualization - This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. - Naming format: {}-visual.png - The original image name is inside the {}. + Folder name: {}_{}-{} + 1st digit: unique identifier of the leaf + 2nd digit: time of first emergence of the leaf + 3rd digut: leaf index when it first emerges + Inside every folder, images of leaves with names same as original image names are contained. + This set of visualization is designed for making videos to track the growth of every single leaf. Images inside a folder are supposed to represent for the save leaf. So simply using all images inside one folder and sort them by name in an ascending manner would create a video showing how this leaf grows. + + 3) a folder called "visualization 3", which containes 3rd set of visualization + This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. + Naming convention: + + {}-visual.png + The original image name is inside the {}. + This set of visualization is designed for making time-lapse videos of segmentation shown with bounding boxes. With the instance labels re-assigned such that one label represents one leaf across the whole time, we would observe that every leaf is represented by a specific color across the whold time period. + +Before running the time_series_linking function, it is always a good practice to check the quality of instance segmentation before run time_series_linking. One way to check the instance segmentation is to make a time-lapse video with your instance segmentation result shown in bounding boxes. To make time-lapse videos, you will need video editing softwares, e.g. imovie. An example is shown below. Notice that in almost all cases, you would observe that the colors for the same leaf change during the time. That is due to the random assignment of labels. You can later compare this time lapse video to that generate after all labels re-assigned by running time_series_linking function. + +Instead of making a video, another way to check the quality of instance segmentation is by checking the segmented image one by one. + + + ```python from plantcv import plantcv as pcv @@ -63,12 +79,36 @@ path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtC path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/detection/modified' path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/test' time_cond = ['08-05', '11-05', '17-05', '21-05'] -​linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link') +link_logic = 1 +class_names = ['BG', 'Leaf'] +mode = 'link' +suffix = '.jpg' +​linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=link_logic, class_names=class_names, mode=mode, suffix=suffix) -# Print the saving directory on the screen so that you can go check the result: +# Print the saving directory so that you can go check the result: linked_Plant.savedir +# Print visualization directory: +linked_Plant.visualdir + # Print linking information on the screen for a quick check: linked_Plant.link_series ``` + +1. Analysing shapes: +Create a workflow and use the PlantCV analyze_object function to traverse all images inside the 1st visualization folder. + +2. Making time-lapse videos to visualize the growth of every leaf: +To make videos, you will need video editing softwares, e.g. imovie.\ +Direct yourself to the visualization folder, and go into 'visualization2'. Every folder represents for a leaf. Include all images for one leaf in a video project, sort them by name in an ascending order, and generate a time-lapse video. +Here is an example of time-lapse of one leaf: + + +

growth of plant focusing on single leaf from Hudanyun Sheng on Vimeo.

+ +3. Making a time-lapse video to visualize the growth of the plant by showing the segmentation in bounding boxes, with the same leaf always shown with the same color. Direct yourself to "visualization 3" folder and include all images to a video project, sort them by name in an ascending order, and generate a time-lapse video. +An example of time-lapse video with visualization shown in bounding boxes is shown below. When compare to the video made of instance segmentation, result, you will observe that now every leaf is represented by the same color all the time, which means the label re-assignemnt is successful. + + + **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) From 34e54ed9fee11d57820d511bb9cdd0d7945caaae Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 11:11:10 -0500 Subject: [PATCH 148/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index d80f24b31..5d1cf3099 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -290,21 +290,6 @@ def load_images(self): """ Load original images Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM """ - # filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] - # temp_imgs = [] - # sz = [] - # for t in self.time: - # for f in filenames: - # temp = re.search(t, f) - # if temp: - # junk = skimage.io.imread(os.path.join(self.imagedir, f)) - # temp_imgs.append(junk) - # sz.append(np.min(junk.shape[0:2])) - # filenames.remove(f) - # self.min_dim = np.min(sz) - # for junk in temp_imgs: - # img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size - # self.images.append(img) temp_imgs = [] sz = [] for pre in self.filename_pre: From fc75b1306acc634239dd7f5f0e47d94431ec92d7 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 18:05:28 -0500 Subject: [PATCH 149/274] Update time_series.py Update by adding a pattern_datetime input --- plantcv/plantcv/time_series/time_series.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 98504138f..2ebfc2ac2 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -23,7 +23,7 @@ from plantcv.plantcv.time_series import link_utilities as funcs # import link_utilities as funcs -def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], +def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'): """ Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) @@ -69,7 +69,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi """ if mode == 'link': # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode, suffix) + Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, mode, suffix) Plant.getpath(Plant.imagedir) Plant.Sorttime(time_cond) @@ -140,8 +140,6 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi ids = Plant.link_series[key_t]['unique_id'] start_time = int(key_t.replace('t', '')) - # if start_time == 40: - # pdb.set_trace() leaves_t = Plant.link_series[key_t]['new_leaf'] for (unique_id, leaf) in zip(ids, leaves_t): key_leaf = 'leaf{}'.format(leaf) @@ -161,7 +159,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, time_cond, link_logi '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t]))) - # pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) + # pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) ## 2. show result with an alpha channel # update the mask where there is an alpha channel (alpha=0.5) From b1539ef6222f544f60eb429c9998503a5b9d159d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 1 Jul 2020 18:05:43 -0500 Subject: [PATCH 150/274] Update link_utilities.py Update by adding a pattern_datetime part --- plantcv/plantcv/time_series/link_utilities.py | 20 +++++++------------ 1 file changed, 7 insertions(+), 13 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 5d1cf3099..73ea8a129 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -186,10 +186,12 @@ def _getmatchedIndex(mask, masks, mode): class PlantData(): - def __init__(self, imagedir, segmentationdir, savedir, mode='link', suffix='.jpg'): + def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, mode='link', suffix='.jpg'): self.suffix = suffix + self.pattern_datetime = pattern_datetime + # store a list of time of the images self.time = [] # store a list of original prefixes of the original images @@ -262,7 +264,7 @@ def getpath(self, path): def Sorttime(self, time_cond): """ - This function is designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM + This function is designed for files with file names which contain a "date-time" part, with an user-defined pattern, e.g. YYYY-MM-DD-hh-mm Return: loop through the dataset_dir, and add time in time order """ @@ -271,7 +273,7 @@ def Sorttime(self, time_cond): time_temp = [] file_name = [] for filename in filenames: - temp = re.search('\d{4}-\d{2}-\d{2}-\d{2}-\d{2}', filename) + temp = re.search(self.pattern_datetime, filename) if temp: timepart = temp.group() for cond in time_cond: @@ -288,8 +290,9 @@ def Sorttime(self, time_cond): def load_images(self): """ Load original images - Again this function is also designed for files with file names which contain a "date-time" part, with an format of YYYY-MM-DD-HH-MM + This function is also designed for files with file names which contain a "date-time" part, with a user defined pattern """ + temp_imgs = [] sz = [] for pre in self.filename_pre: @@ -380,15 +383,6 @@ def get_series(self): key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = np.array(new_leaves) - # if len(new_leaves) > 1: - # id_temp = [] - # for new_leav in new_leaves: - # id_temp.append(unique_id) - # unique_id = unique_id + 1 - # self.link_series[key_t]['unique_id' = np.array(id_temp) - # else: - # self.link_series[key_t]['unique_id'] = np.array(unique_id) - # unique_id = unique_id + 1 id_temp = [] for new_leaf in new_leaves: id_temp.append(unique_id) From 6bdf4c52daa2cfe7a6cad329dadd3254244a73a1 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 2 Jul 2020 10:40:35 -0500 Subject: [PATCH 151/274] Update tests.py --- tests/tests.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index a69aac952..d21136cc6 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6320,8 +6320,13 @@ def test_plantcv_time_series_time_series(): cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir - time_cond = ['08-05', '11-05', '17-05', '21-05'] - Plant_ = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link') + time_cond = ['08-05', '11-05', '17-05', '21-05'] + link_logic = 1 + class_names = ['BG', 'Leaf'] + pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm + mode = 'link' + suffix = '.jpg' + Plant_ = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, pattern_datetime, time_cond, link_logic, class_names, mode, suffix) assert len(os.listdir(Plant_.savedir)) > 0 def test_plantcv_visualize_overlay_two_imgs(): From 0d3e2a99a332a25c333e24c1b39333564c5602c0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 2 Jul 2020 10:40:40 -0500 Subject: [PATCH 152/274] Update mkdocs.yml --- mkdocs.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mkdocs.yml b/mkdocs.yml index 4bcedc069..92f5e1c29 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -32,6 +32,7 @@ nav: - 'Morphology Workflow': morphology_tutorial.md - 'Thermal Workflow': thermal_tutorial.md - 'Hyperspectral Workflow': hyperspectral_tutorial.md + - 'Instance Segmentation': instance_segmentation_tutorial.md - 'PlantCV Namespace': - 'PlantCV': - 'Analyze Color': analyze_color.md @@ -150,6 +151,7 @@ nav: - 'Saturation Threshold': saturation_threshold.md - 'Triangle Auto Threshold': triangle_threshold.md - 'Texture Threshold': texture_threshold.md + - 'Time Series': time_series.md - 'Transformation Methods': - 'Auto-Detect Color Card': find_color_card.md - 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zS>xNSavxdNLul62m!-a4-7;8$4~<&(j5Dl|bC17_A^OmB6}}8i{`eD$%&VL{cqJ z`AV$$D$mH3Z&~pEz1>UqVu@SjNJdsLz((j8r=L_%2{Zt=yx3JcV{B8?q;i7@h%2Q> zjpA1>8b=?4L^3|B_|*z!6ua^%9978bLffY-FC2*@}FWM1TxIw*>vF;$$*C~F`>p>ndElwVp8%IlN? z(UcE<+A_se0x~M)3PJjbH+>93zl0{d_#S0o^3k;lYX<1JN8Y`Xt7C(pRb_>DWqrmj z2q~Q~fOil+6g{i$qTpX%Yn&xlJeZ)nD6}$;Ou*aa5Q8A0KWPZ9lxuz>m9|0WKsI+z zTNgDw-0m%M%iQ*p|WdN0S z7=W}`gO2A)L<$Q;0=*?Su5*ecf01eo+E#)VFRfpfHQg`epn}VwzhJ7zq*V(dBYs>4IF^&vFWhTD+T(}1J%SoA7?SJJOJYFGq( zaKOGJTq&r&%23~x>)a&Z`S=lMxc=+$6v9m ttMKxuVPAv({~m%1&{^7 Date: Mon, 13 Jul 2020 10:27:13 -0500 Subject: [PATCH 154/274] Create time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 138 ++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 plantcv/plantcv/visualize/time_lapse_video.py diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py new file mode 100644 index 000000000..19cc71aa0 --- /dev/null +++ b/plantcv/plantcv/visualize/time_lapse_video.py @@ -0,0 +1,138 @@ +# Color mask(s) in any color + +import os +import cv2 +import numpy as np +import copy +from plantcv.plantcv import params +from plantcv.plantcv import fatal_error +import warnings + + +def _resize_img(img, size): + """Resize the image to the given new size + If the given size is larger than the original image size, zero-pad the image. + If the given size is smaller than the origina image size, crop the image (right & bottom). + """ + + # original image size + r_ori, c_ori = np.shape(img)[0], np.shape(img)[1] + r, c = size[0], size[1] + + if len(img.shape) > 2: + b_ori = np.shape(img)[2] + junk = copy.deepcopy(img) + else: + b_ori = 1 + junk = np.expand_dims(img, axis=2) + + # deal with rows + img1 = copy.deepcopy(junk) + if r < r_ori: + img1 = junk[0:r, :, :] + elif r > r_ori: + img1 = np.zeros_like(junk, shape=[r, c_ori, b_ori]) + img1[0:r_ori, :, :] = junk + + # update + r_ori, c_ori = np.shape(img1)[0], np.shape(img1)[1] + img2 = copy.deepcopy(img1) + + # deal with columns + if c < c_ori: + img2 = img1[:, 0:c, :] + elif c > c_ori: + img2 = np.zeros_like(img1, shape=[r_ori, c, b_ori]) + img2[:, 0:c_ori, :] = img1 + + if len(img.shape) == 2: + output_img = np.squeeze(img2, axis=2) + else: + output_img = img2 + return output_img + + +def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=None, fps=29.97, + name_video='time_lapse_video', path_video=None, display='on'): + """Generate time-lapse video given a folder of images + Inputs: + img_directory: the directory of folder of images to make the time-lapse video + list_img: desired list of images in img_directory to create the video. If None is passed, all images would be included by default. + suffix_img: the suffix of input images. e.g. suffix='.jpg' or suffix='.png' or suffix='img10.jpg'. Make sure all images have the same suffix + note: If neigher list_img nor suffix_img is provided, all images in the directory will be included + name_video: the prefix of output video name + size_frame: the desired size of every frame. + In a video, every frame should have the same size. + The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. + If the frame size is given, if an images is larger than the given size, the image would be cropped automatically; if an image is smaller than the given size, the image would be zero-padded automatically + If the frame size is not given, the largest size of all images would be used as the frame size. + The assumption is that If some videos are smaller, + fps: (frames per second) frame rate + Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 + path_video: the desired saving path of output video. If not given, the video would be saved the the same directory of the images. + display: indicator of whether to display current status (by displaying saving directory and saving name) while running this function + No return value + """ + ## Get the list of image files and sort them alphabetically by their names + # make sure there is images inside the provided directory + list_img_1 = [img for img in os.listdir(img_directory) if img.endswith('.jpg')] + list_img_2 = [img for img in os.listdir(img_directory) if img.endswith('.png')] + if len(list_img_1) == 0 and len(list_img_2) == 0: + fatal_error("There is no file in the provided folder that is an image, please check the provided directory!") + elif len(list_img_1) > 0: + temp_list = list_img_1 + else: + temp_list = list_img_2 + + # if the list of images is provided, stick to it + if list_img is not None: + if not set(list_img) <= set(temp_list): + # print( + # "Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") + warnings.warn("Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") + remove_list = [f for f in list_img if f not in (temp_list)] + for f in remove_list: + list_img.remove(f) + # if a wrong list is proveded + if len(list_img) == 0: + fatal_error("The provided list of files is not contained in the given directory") + + # if the list of images is not provided, check if suffix information is available + elif suffix_img is not None: + list_img = [f for f in temp_list if f.endswith(suffix_img)] + if len(list_img) == 0: + fatal_error("There is no file that matches the provided suffix, please check again!") + # both are not provided + else: + list_img = temp_list + list_img.sort() + + imgs = [] + max_r = 0 + max_c = 0 + for file in list_img: + img = cv2.imread(os.path.join(img_directory, file)) + max_r = max(max_r, img.shape[0]) + max_c = max(max_c, img.shape[1]) + imgs.append(img) + + # If the frame size is not provided, use the largest size of the images as the frame size + if size_frame is None: + size_frame = (max_r, max_c) + + # If the video saving directory is not provided, save it in the same directory of the images + if path_video is None: + path_video = img_directory + + # Full video save name + save_name = os.path.join(path_video, name_video + '.mp4') + + fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case + out = cv2.VideoWriter(save_name, fourcc, fps, size_frame) + for img in imgs: + out.write(_resize_img(img, size_frame)) + + out.release() + cv2.destroyAllWindows() + if display is 'on': + print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) From 128ce496ffc91daab233d712b8fc9f8c74316c0d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 13 Jul 2020 10:28:05 -0500 Subject: [PATCH 155/274] Update tests.py for the newly added time_lapse_video function into visualize subpackage --- tests/tests.py | 66 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) diff --git a/tests/tests.py b/tests/tests.py index d21136cc6..ca5c872ec 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -883,6 +883,7 @@ def test_plantcv_parallel_process_results_invalid_json(): # #################################################################################################################### # ########################################### PLANTCV MAIN PACKAGE ################################################### matplotlib.use('Template') +# matplotlib.use('Template', warn=False) TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data") HYPERSPECTRAL_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "hyperspectral_data") @@ -6315,6 +6316,71 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) +# case1: only the correct directory of images is provided +def test_plantcv_visualize_time_lapse_video_case1(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case1') + os.mkdir(cache_dir) + + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case1.mp4')) + +# case2: the correct directory of images as well as the correct suffix of images is provided +def test_plantcv_visualize_time_lapse_video_case2(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case2') + os.mkdir(cache_dir) + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.jpg', fps=29.97, name_video='time_lapse_video_case2', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case2.mp4')) + +# case 3: the correct directory of images as well as the correct list of files are provided +def test_plantcv_visualize_time_lapse_video_case3(): + # Test cache directoy + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case3') + os.mkdir(cache_dir) + list_img = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case3', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case3.mp4')) + +# case 4: the correct directory of images as well as a list of files are provided, however the list is incorrect (contains correct part, but also contains incorrect part) +def test_plantcv_visualize_time_lapse_video_case4(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case4') + os.mkdir(cache_dir) + list_img = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] + list_img.append('junk.jpg') + with pytest.warns(UserWarning): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case4', path_video=cache_dir, display='off') + # warnings.warn("my warning", UserWarning) + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case4.mp4')) + +def test_plantcv_visualize_time_lapse_video_bad_dir(): + # Test cachae directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_dir') + os.mkdir(cache_dir) + with pytest.raises(RuntimeError): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_DIR, fps=29.97, path_video=cache_dir, display='off') + +def test_plantcv_visualize_time_lapse_video_bad_suffix(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_suffix') + os.mkdir(cache_dir) + with pytest.raises(RuntimeError): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.png', fps=29.97, path_video=None, display='on') + +# the correct directory of images as well as a list of files are provided, however the list is incorrect (none of the files contained is correct) +def test_plantcv_visualize_time_lapse_video_bad_list(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_list') + os.mkdir(cache_dir) + list_img_ = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] + list_img = [img.replace('.jpg', '_.jpg') for img in list_img_] + with pytest.raises(RuntimeError): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, path_video=cache_dir, display='off') + +# ############################## +# Tests for the time_series subpackage +# ############################## def test_plantcv_time_series_time_series(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") From fdc69103064662a5b0d9da85265ad9e1f707c568 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 13 Jul 2020 10:28:25 -0500 Subject: [PATCH 156/274] Update __init__.py for the newly added time_lapse_video into visualize subpackage --- plantcv/plantcv/visualize/__init__.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/plantcv/plantcv/visualize/__init__.py b/plantcv/plantcv/visualize/__init__.py index e0f0e2b1a..b5fd7da87 100644 --- a/plantcv/plantcv/visualize/__init__.py +++ b/plantcv/plantcv/visualize/__init__.py @@ -9,3 +9,9 @@ __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "overlay_two_imgs","display_instances"] + +from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video +from plantcv.plantcv.visualize.time_lapse_video import _resize_img + +__all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] + From 13a186c0f868a04e9059b7c62fa29949b44acfac Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 13 Jul 2020 10:28:33 -0500 Subject: [PATCH 157/274] Update mkdocs.yml for the newly added time_lapse_video into visualize subpackage --- mkdocs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/mkdocs.yml b/mkdocs.yml index 92f5e1c29..78905b78b 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -175,6 +175,7 @@ nav: - 'Histogram': visualize_histogram.md - 'Overlay Images': visualize_ovelay_two_imgs.md - 'Pseudocolor': visualize_pseudocolor.md + - 'Time Lapse Video': visualize_time_lapse_video.md - 'Watershed Segmentation': watershed.md - 'White balance': white_balance.md - 'Within Frame': within_frame.md From e8499cc304257d02fc5eb7d796bf6f745cc4c962 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 13 Jul 2020 10:28:43 -0500 Subject: [PATCH 158/274] Create visualize_time_lapse_video.md --- docs/visualize_time_lapse_video.md | 75 ++++++++++++++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 docs/visualize_time_lapse_video.md diff --git a/docs/visualize_time_lapse_video.md b/docs/visualize_time_lapse_video.md new file mode 100644 index 000000000..afacbfc74 --- /dev/null +++ b/docs/visualize_time_lapse_video.md @@ -0,0 +1,75 @@ +## Automatically Generate Time-Lapse Videos + +This function automatically generates and saves time-lapse videos given the folder of images. + +**plantcv.visualize.time_lapse_video**(*path_img, list_img, suffix_img, size_frame=None, fps=29.97, name_video='time_lapse_video', + path_video=path_img, display='on'*) + +**returns** There is no returned value for this function. The generated video would be saved in the user specified directory. + +- **Parameters:** + - img_directory - Directory of images desired to be made into a video + - list_img - desired list of images in img_directory to create the video. If None is passed, all images would be included by default. + - suffix_img - The suffix of all input images. e.g. suffix='.jpg' or suffix='.png' or suffix='img10.jpg'. Make sure all images have the same suffix + + Note: If neigher list_img nor suffix_img is provided, all images in the directory will be included + + - size_frame - The desired size of every frame. + + Note: In a video, every frame should have the same size. + The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. + With a given frame size, if an images is larger than that, the image would be cropped automatically; if an image is smaller than that, the image would be zero-padded automatically + If the frame size is not given, the largest size of all images would be used as the frame size. + + - fps: (frames per second) frame rate. By default fps=29.97. + Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 + - name_video - The desired name of output video name. By default the name would be 'time_lapse_video' + - path_video - The desired saving path of the video file. By default, the video will be saved at the same directory of the images. + - display - Indicator of whether to display current status (by displaying saving directory and saving name) while running this function. By default the display is turned on. + +- **Context:** + - Used to generate time-lapse video given a bunch of images. + +- **Example Use:** + - Below + - [Use in Time Series Documentation](time_series.md) + +**Folder of images** + +As an example, you can used the sample saved inside PlantCV to test. You will need to know the directory of your PlantCV package. +You can find the test data here: "./plantcv/tests/seires_data/raw_im". We are to use all images inside the folder "raw_im" to generate a time-lapse video. +```python +from plantcv import plantcv as pcv +# Note you will have to change this part on your own +img_directory = './plantcv/tests/seires_data/raw_im' + +list_img = [img for img in os.listdir(dir_img) if img.endswith('.jpg')] + +# In this specific case, every image has a suffix of ".jpg" +suffix_img = '.jpg' + +# In this case I know the sizes for all of the images is from (499, 499) to (501, 501), and here I decide I want the frame size to be (500, 500). +size_frame = (500, 500) + +fps = 29.97 + +name_video = 'eg_time_lapse' + +# In this specific case the video will be saved in the save directory of the images +path_video = path_img + +display = 'on' + +pcv.visualize.time_lapse_video(img_directory=img_directory, list_img=list_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) +pcv.visualize.time_lapse_video(img_directory=img_directory, suffix_img=suffix_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) + + +``` + +**Video generated** + +The generated video is saved automatically in the user-specified directory (path_video). The generated video should look similar to the one below: + + + +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/visualize/time_lapse_video.py) From 40918ce99d7d9a577c46b361fd9cb96f99b4b21a Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 17 Jul 2020 12:48:31 -0500 Subject: [PATCH 159/274] Update time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 155 ++++++++++-------- 1 file changed, 89 insertions(+), 66 deletions(-) diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py index 19cc71aa0..76239733c 100644 --- a/plantcv/plantcv/visualize/time_lapse_video.py +++ b/plantcv/plantcv/visualize/time_lapse_video.py @@ -1,9 +1,16 @@ -# Color mask(s) in any color +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon. July 06 08:00:34 2020 +Functions used to generate a time-lapse-video with provided directory of images +@author: hudanyunsheng +""" import os import cv2 import numpy as np import copy +import mimetypes from plantcv.plantcv import params from plantcv.plantcv import fatal_error import warnings @@ -58,81 +65,97 @@ def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=N Inputs: img_directory: the directory of folder of images to make the time-lapse video list_img: desired list of images in img_directory to create the video. If None is passed, all images would be included by default. - suffix_img: the suffix of input images. e.g. suffix='.jpg' or suffix='.png' or suffix='img10.jpg'. Make sure all images have the same suffix - note: If neigher list_img nor suffix_img is provided, all images in the directory will be included name_video: the prefix of output video name size_frame: the desired size of every frame. In a video, every frame should have the same size. The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. If the frame size is given, if an images is larger than the given size, the image would be cropped automatically; if an image is smaller than the given size, the image would be zero-padded automatically If the frame size is not given, the largest size of all images would be used as the frame size. - The assumption is that If some videos are smaller, fps: (frames per second) frame rate Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 path_video: the desired saving path of output video. If not given, the video would be saved the the same directory of the images. display: indicator of whether to display current status (by displaying saving directory and saving name) while running this function - No return value + Outputs: + list_img: the list of images used to generate the video + size_frame: the frame size of the video """ - ## Get the list of image files and sort them alphabetically by their names - # make sure there is images inside the provided directory - list_img_1 = [img for img in os.listdir(img_directory) if img.endswith('.jpg')] - list_img_2 = [img for img in os.listdir(img_directory) if img.endswith('.png')] - if len(list_img_1) == 0 and len(list_img_2) == 0: + ## Get the list of image files in the given directory and sort them alphabetically by their names + temp_list = [] + for f in os.listdir(img_directory): + type_f = mimetypes.guess_type(f)[0] + if type_f: + if type_f.startswith('image'): + temp_list.append(f) + if len(temp_list) <= 0: fatal_error("There is no file in the provided folder that is an image, please check the provided directory!") - elif len(list_img_1) > 0: - temp_list = list_img_1 else: - temp_list = list_img_2 - - # if the list of images is provided, stick to it - if list_img is not None: - if not set(list_img) <= set(temp_list): - # print( - # "Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") - warnings.warn("Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") - remove_list = [f for f in list_img if f not in (temp_list)] - for f in remove_list: - list_img.remove(f) - # if a wrong list is proveded - if len(list_img) == 0: - fatal_error("The provided list of files is not contained in the given directory") - - # if the list of images is not provided, check if suffix information is available - elif suffix_img is not None: - list_img = [f for f in temp_list if f.endswith(suffix_img)] - if len(list_img) == 0: - fatal_error("There is no file that matches the provided suffix, please check again!") - # both are not provided - else: - list_img = temp_list - list_img.sort() - - imgs = [] - max_r = 0 - max_c = 0 - for file in list_img: - img = cv2.imread(os.path.join(img_directory, file)) - max_r = max(max_r, img.shape[0]) - max_c = max(max_c, img.shape[1]) - imgs.append(img) - - # If the frame size is not provided, use the largest size of the images as the frame size - if size_frame is None: - size_frame = (max_r, max_c) - - # If the video saving directory is not provided, save it in the same directory of the images - if path_video is None: - path_video = img_directory - - # Full video save name - save_name = os.path.join(path_video, name_video + '.mp4') - - fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case - out = cv2.VideoWriter(save_name, fourcc, fps, size_frame) - for img in imgs: - out.write(_resize_img(img, size_frame)) - - out.release() - cv2.destroyAllWindows() - if display is 'on': - print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) + temp_list.sort() + # get an "extension free" list of images + temp_list_no_ext = [os.path.splitext(f)[0] if os.path.splitext(f)[1] else f for f in temp_list] + + ## if the list of images is provided, stick to it, but automatically handle the extension issue + if list_img is not None: + list_img.sort() + # make the list of images extension free + list_img_no_ext = [os.path.splitext(f)[0] if os.path.splitext(f)[1] else f for f in list_img] + list_img = [] + for (f_, f) in zip(temp_list_no_ext, temp_list): + try: + list_img_no_ext.index(f_) + list_img.append(f) + except: + continue + + if len(list_img) == 0: + fatal_error("The provided list of files is not contained in the given directory") + elif len(list_img) < len(list_img_no_ext): + warnings.warn( + "Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") + + # if the list of images is not provided, check if suffix information is available + elif suffix_img is not None: + list_img = [f for f in temp_list if f.endswith(suffix_img)] + if len(list_img) == 0: + fatal_error("There is no file that matches the provided suffix in the provided directory, please check again!") + + # neither the list of image nor the suffix is provided + else: + list_img = temp_list + + imgs = [] + list_r = [] + list_c = [] + for file in list_img: + img = cv2.imread(os.path.join(img_directory, file)) + list_r.append(img.shape[0]) + list_c.append(img.shape[1]) + imgs.append(img) + max_c = np.max(list_c) + max_r = np.max(list_r) + + # If the frame size is not provided, use the largest size of the images as the frame size + if size_frame is None: + size_frame = (max_c, max_r) + + if not (len(np.unique(list_r)) == 1 and len(np.unique(list_c)) == 1): + warnings.warn("The sizes of images are not the same, an image resizing (cropping or zero-padding) will be done to make all images the same size ({}x{}) before creating the video! If you assume the images should have the same size, please check the images used to generate this video!".format( + size_frame[0], size_frame[1])) + + # If the video saving directory is not provided, save it in the same directory of the images + if path_video is None: + path_video = img_directory + + # Full video save name + save_name = os.path.join(path_video, name_video + '.mp4') + + fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case + out = cv2.VideoWriter(save_name, fourcc, fps, size_frame) + for img in imgs: + out.write(_resize_img(img, (max_r, max_c))) + + out.release() + cv2.destroyAllWindows() + if display is 'on': + print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) + + return list_img, size_frame \ No newline at end of file From 07b94a9bb6a435ed2e2880a1c115fcb113c36421 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 17 Jul 2020 12:49:03 -0500 Subject: [PATCH 160/274] Update tests.py Update tests for visualize/time_lapse_video --- tests/tests.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index ca5c872ec..87291c2ea 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -883,7 +883,6 @@ def test_plantcv_parallel_process_results_invalid_json(): # #################################################################################################################### # ########################################### PLANTCV MAIN PACKAGE ################################################### matplotlib.use('Template') -# matplotlib.use('Template', warn=False) TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data") HYPERSPECTRAL_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "hyperspectral_data") @@ -6321,17 +6320,18 @@ def test_plantcv_visualize_time_lapse_video_case1(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case1') os.mkdir(cache_dir) - - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case1.mp4')) + with pytest.warns(UserWarning): + _, _ = pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case1.mp4')) # case2: the correct directory of images as well as the correct suffix of images is provided def test_plantcv_visualize_time_lapse_video_case2(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case2') os.mkdir(cache_dir) - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.jpg', fps=29.97, name_video='time_lapse_video_case2', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case2.mp4')) + with pytest.warns(UserWarning): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.jpg', fps=29.97, name_video='time_lapse_video_case2', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case2.mp4')) # case 3: the correct directory of images as well as the correct list of files are provided def test_plantcv_visualize_time_lapse_video_case3(): @@ -6339,8 +6339,9 @@ def test_plantcv_visualize_time_lapse_video_case3(): cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case3') os.mkdir(cache_dir) list_img = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case3', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case3.mp4')) + with pytest.warns(UserWarning): + pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case3', path_video=cache_dir, display='off') + assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case3.mp4')) # case 4: the correct directory of images as well as a list of files are provided, however the list is incorrect (contains correct part, but also contains incorrect part) def test_plantcv_visualize_time_lapse_video_case4(): @@ -6351,7 +6352,6 @@ def test_plantcv_visualize_time_lapse_video_case4(): list_img.append('junk.jpg') with pytest.warns(UserWarning): pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case4', path_video=cache_dir, display='off') - # warnings.warn("my warning", UserWarning) assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case4.mp4')) def test_plantcv_visualize_time_lapse_video_bad_dir(): From 7f31288c232ac6891b8d842bfb701264ded1087d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 17 Jul 2020 13:57:30 -0500 Subject: [PATCH 161/274] Update visualize_time_lapse_video.md --- docs/visualize_time_lapse_video.md | 38 ++++++++++++++++++++---------- 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/docs/visualize_time_lapse_video.md b/docs/visualize_time_lapse_video.md index afacbfc74..b2592aac4 100644 --- a/docs/visualize_time_lapse_video.md +++ b/docs/visualize_time_lapse_video.md @@ -5,27 +5,41 @@ This function automatically generates and saves time-lapse videos given the fold **plantcv.visualize.time_lapse_video**(*path_img, list_img, suffix_img, size_frame=None, fps=29.97, name_video='time_lapse_video', path_video=path_img, display='on'*) -**returns** There is no returned value for this function. The generated video would be saved in the user specified directory. +**returns** list of images, frame size + +(The generated video would be saved in the user specified directory.) - **Parameters:** - img_directory - Directory of images desired to be made into a video - list_img - desired list of images in img_directory to create the video. If None is passed, all images would be included by default. - suffix_img - The suffix of all input images. e.g. suffix='.jpg' or suffix='.png' or suffix='img10.jpg'. Make sure all images have the same suffix - Note: If neigher list_img nor suffix_img is provided, all images in the directory will be included + If neigher list_img nor suffix_img is provided, all images in the directory will be included. - size_frame - The desired size of every frame. - Note: In a video, every frame should have the same size. - The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. - With a given frame size, if an images is larger than that, the image would be cropped automatically; if an image is smaller than that, the image would be zero-padded automatically - If the frame size is not given, the largest size of all images would be used as the frame size. + To generate a video, the image used in every frame shold have the same size, known as frame size. + In most cases where the images have the same size, the image size is frame size, e.g. ```size_frame=(640, 480)```. + In some cases where not all images have the same size, image resizing would be done automaticaly + to be able to generate the video, with a warning message generated. + + If no value is passed to this parameter, the largest size of all images would be used as the frame size. + + The resizing is done by cropping for those larger then desired sizes, zero-padding for those smaller than desired sizes. - - fps: (frames per second) frame rate. By default fps=29.97. - Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 + Note: The most commonly used definition of frame size/image size is different from most commonly + used definition of matrix size (e.g. numpy arrays). The frame size/image size is defined as (width, height), + while the matrix size is defined as (num_rows, num_columns). + If you are trying to get the frame size/image size from the size of matrix (array), remember of change the + order of first and second dimension of the matrix, i.e. frame size = (num_columns, num_rows). + + - fps: (frames per second) frame rate. By default fps=29.97. Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 + - name_video - The desired name of output video name. By default the name would be 'time_lapse_video' - - path_video - The desired saving path of the video file. By default, the video will be saved at the same directory of the images. - - display - Indicator of whether to display current status (by displaying saving directory and saving name) while running this function. By default the display is turned on. + - path_video - The desired saving path of the video file. By default, the video will be saved at the same directory + of the images. + - display - Indicator of whether to display current status (by displaying saving directory and saving name) while + running this function. By default the display is turned on. - **Context:** - Used to generate time-lapse video given a bunch of images. @@ -60,8 +74,8 @@ path_video = path_img display = 'on' -pcv.visualize.time_lapse_video(img_directory=img_directory, list_img=list_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) -pcv.visualize.time_lapse_video(img_directory=img_directory, suffix_img=suffix_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) +list_img, frame_size = pcv.visualize.time_lapse_video(img_directory=img_directory, list_img=list_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) +list_img, frame_size = pcv.visualize.time_lapse_video(img_directory=img_directory, suffix_img=suffix_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) ``` From 6e844e01699eedd3798e20e341d633efa2109c26 Mon Sep 17 00:00:00 2001 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z0lu;4)-@O7B&K6Jz?<8d(igczPR03ODBh<;zB~p#`{Kr&#@bJ^IyE|}cC#_+EMBqa z+YHf*WrF48{aD+F3?DoaPD|G0bD!}vc74SgWmY8oOJ|7l*2bE>y)0i)SS>tZx#Kba zf`m=2^iX`?*|pIT)R%LqF%ExH;^ND)_ci=>Do8YoyUs+_y_9_@2+2qqLh{;v^Wn48 zVEyzu&|kdLkyr^mrqvPAp(AyIWO5>WW_wi^pLC3{jPL6?ovVGQ)hCyWn`$;L;w;_v z55^eL7aVM+KPgn`HW+0dbZHjT_qIlu>x#;iH^Rmf4?}b>St(D4P~0PLTU(d0C>Z$IT1_i_ zq%s)f-Sa2!$R{!aS8WB~0I;D|UJ$x=Pg>4S3st8+OkT98n0H$LMTVvf>orvUU;R3tz-0i;uBXBh@?m4yMNw++k8qc!Gg{fLxxS4IT!XOr= zIXE9Us_g8dDy{vtsHn`yrh;?HKuasg|4+(XoJLF4)z@lC*=Z<4d%NU{bNDfF*_$_P zhFftnSBV^_wmJzKz7M~-u@ZjI9YHb?{5C#cRJC*-&44omJ->En`VJ>$h;_`~WqdAC z2s7JC-RTsR@;L78&g#2&C56Viuk&%+l@A%Gm!EtCL1j!w7ORi%-}$PmvusV$b=mGk z5_F0z*B3ii{X0H$@8vg4DcXz%^bxog3Pmy?>&^mYT1|QLR zz46hUV9+{eM){=}&rAwS)?SELl71+>CrnZd83Pn98zg0SzXCC;VUigLXLqg{&`nLPVOVXHi){mdm8QCn!D Date: Thu, 23 Jul 2020 08:13:04 -0500 Subject: [PATCH 163/274] Update time_series.md --- docs/time_series.md | 112 ++++++++++++++++++++++++++++++++------------ 1 file changed, 83 insertions(+), 29 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index 7e12f4454..7f8011b44 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -1,12 +1,12 @@ ## time_series -This function is designed to get leaf instance growth information for a plant given a series of images (images taken across a time period, e.g. images taken every several hours, recording the growth of the plant, where ideally there should be no or at least minimum movement for plant or camera). +This function is designed to link segmented objects over time. Images should be taken across a time (e.g. every several hours) and ideally there should be minimal movment of plant/camera. -To run this function, the instance segmentation for every image should be achieved beforehand. For more information and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). +To run this function, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. -To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always have the same instance label across the whole time period. +To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. **plantcv.time_series.time_series_linking**(*imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'*) @@ -16,7 +16,8 @@ To understand the growth of every leaf instance of a plant, we need to re-assign - imagedir: directory of original image used - segmentationdir: directory of leaf instance segmentation result - savedir: desired saving directory of linking result. Note that this function will generate a folder under your specified saving directory, with the date and time you run this programme - - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment + - pattern_datetime: the pattern of date and time part in original file names, dafault value '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' which represents YYYY-MM-DD-hh-mm. + - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment. Make sure the format of date matches the pattern-datetime. - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 - class_names: used in bounding box visualization. by default there are background and leaf, i.e. class_names = ['BG', 'leaf'] - mode: can be either 'link' or 'load', use 'link' when trying to get linked time-series from instance segmentation; use 'load' when trying to load from saved linking @@ -27,7 +28,7 @@ To understand the growth of every leaf instance of a plant, we need to re-assign Note: under the user specified saving directory, a new folder named after the date and time (format: YYYY-MM-DD-HH-mm) the function runs will be created to save results. 1. colors.pkl: the colors (represented by arrays) used in bounding box visualization. With this predefined color set, same color will represent for the leaf instance with same instance label all the time, i.e. same color represents leaf with the same label. Without this predefined list of color, the assignment of color will be random, i.e. the color provides no information to leaf indices. -2. details.txt: the logic of linking as well as time condition will be saved in this text file, so that would be easier for users to check these parameters for the specific expreiment. +2. details.txt: the logic of linking as well as time condition will be saved in this text file, so that would be easier for users to check these parameters for the specific experiment. 3. saved_plant.pkl: a "Plant" object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. 4. a folder called "visualization", which contains 3 subfolders: @@ -75,40 +76,93 @@ Instead of making a video, another way to check the quality of instance segmenta from plantcv import plantcv as pcv # Below are examples of input variables, always adjust base on your own application. -path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol/maskrcnn_test' -path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/detection/modified' -path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/exp3/test' -time_cond = ['08-05', '11-05', '17-05', '21-05'] -link_logic = 1 -class_names = ['BG', 'Leaf'] -mode = 'link' -suffix = '.jpg' -​linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, time_cond=time_cond, link_logic=link_logic, class_names=class_names, mode=mode, suffix=suffix) +## Specify the directory of original image +path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol_512' -# Print the saving directory so that you can go check the result: -linked_Plant.savedir +## Specify the directory of instance segmentation result +path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index7/2020-06-25-10-35/subsampled/2020-06-30-10-51/' -# Print visualization directory: -linked_Plant.visualdir +## Specify the desired directory to save results +path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index7/2020-06-25-10-35/time_series_linking' -# Print linking information on the screen for a quick check: -linked_Plant.link_series +## Specify the date-time pattern of original image names +pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm + +## Specify the desired time point to include in to the analysis +time_cond = ['08-05', '11-05', '17-05', '21-05'] + +link_logic = 1 +class_names = ['BG', 'Leaf'] +mode = 'link' + +## Specify the suffix for all images you want to include, note that all the images shown have the same suffix as you specified +## Normally it would be something like '.png' or '.jpg', in this specific case, the image contained in the folder have name like +## 'xxx-img{}.jpg', the number inside the {} represents the index of plant. So in this case, to include images for the same plant, +## you will have to specify the index of plant. +suffix = '-img7.jpg' +​linked_Plant = pcv.time_series.time_series_linking(path_img, path_segmentation, path_save, pattern_datetime, + time_cond, link_logic, class_names, mode, suffix) ``` +When it finished, you can go ahead and check the saved result. If you are not sure where the results are saved, you can type: +```linked_Plant.savedir``` +in a cell to print out the saving directory. -1. Analysing shapes: -Create a workflow and use the PlantCV analyze_object function to traverse all images inside the 1st visualization folder. +You can also type ```linked_Plant.link_series``` in a cell to print linking information on the screen for a quick check. -2. Making time-lapse videos to visualize the growth of every leaf: -To make videos, you will need video editing softwares, e.g. imovie.\ -Direct yourself to the visualization folder, and go into 'visualization2'. Every folder represents for a leaf. Include all images for one leaf in a video project, sort them by name in an ascending order, and generate a time-lapse video. -Here is an example of time-lapse of one leaf: +Now that we have the linking results saved, we can have some analysis based on them. There are 3 suggested analysis described as below: - -

growth of plant focusing on single leaf from Hudanyun Sheng on Vimeo.

+1. Analysing shapes: + Create a workflow and use the PlantCV analyze_object function to traverse all images inside the 1st visualization folder. + +2. Making time-lapse videos to visualize the growth of every leaf. +You have two options of making time-lapse videos: using video editing software (e.g. iMovie) or using the PlantCV tool. + - Generate time-lapse video using video editing software: + + Direct yourself to the visualization folder, and go into 'visualization2'. Every folder represents for a leaf. Include all images for one leaf in a video project, sort them by name in an ascending order, and generate a time-lapse video. + Here is an example of time-lapse of one leaf: + + +

growth of plant focusing on single leaf from Hudanyun Sheng on Vimeo.

+ + - There is also a PlantCV tool that is able to automatically generate and save time-lapse videos. Check here for the usage. The example code is provided at the end of this documentation. + + 3. Making a time-lapse video to visualize the growth of the plant by showing the segmentation in bounding boxes, with the same leaf always shown with the same color. Direct yourself to "visualization 3" folder and include all images to a video project, sort them by name in an ascending order, and generate a time-lapse video. An example of time-lapse video with visualization shown in bounding boxes is shown below. When compare to the video made of instance segmentation, result, you will observe that now every leaf is represented by the same color all the time, which means the label re-assignemnt is successful. - + +```python +## Videos generated using the 2nd set of visualization +# The directory of the 2nd set of visualization +from plantcv import plantcv as pcv +path_visual2 = os.path.join(Plant.visualdir, 'visualization2') + +# Getting subfolders of the 2nd set of visualization +sub_folders = [x[0] for x in os.walk(path_visual2)][1:] + +# You are also to change this to your desired saving directory of the video. By default it will be saved in the same directory of the 2nd set of visualization. +path_video = path_visual2 + +print('\n Saving videos for 2nd set of visualization.') +for sub_f in sub_folders: + name_video = os.path.split(sub_f)[1] # name of the videos are set to be same as the name of subfolders (i.e. the "identifier" of leaves) + pcv.visualize.time_lapse_video(img_directory=sub_f, suffix_img=Plant.ext, name_video=name_video, path_video=path_video, display='off') +print('\nfinished') + +## Videos generated using the 3rd set of visualization +# The directory of the 3rd set of visualization +path_visual3 = os.path.join(Plant.visualdir, 'visualization3') +print('\n Saving videos for 3rd set of visualization.') + +# You are free to change the name of the video to your desired ones +name_video_3 = 'visualization_w_bounding_box' + +# You are free to change this to your desired saving directory of the video. By default it will be save in the same directory of visualization. +path_video_3 = Plant.visualdir +pcv.visualize.time_lapse_video(img_directory=path_visual3, suffix_img=Plant.ext, name_video=name_video_3, path_video=path_video_3, display='on') +``` **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) + + From c15ce4dfddc3e5298944bfd001681dd29c871867 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 23 Jul 2020 08:13:32 -0500 Subject: [PATCH 164/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 24 ++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 2ebfc2ac2..3536f7d86 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -23,8 +23,8 @@ from plantcv.plantcv.time_series import link_utilities as funcs # import link_utilities as funcs -def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, link_logic=1, class_names=['BG', 'Leaf'], - mode='link', suffix='.jpg'): +def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, link_logic=1, + class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'): """ Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) Input: @@ -73,7 +73,6 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti Plant.getpath(Plant.imagedir) Plant.Sorttime(time_cond) - Plant.load_images() # load mrcnn inferencing results @@ -99,6 +98,9 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti file.write('Image conditions: {}'.format(time_cond)) file.close() + print('start\n') + print('...') + # linking initialization Plant.initialize_linking() @@ -158,7 +160,13 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, start_idx, t, link_leaf[t], - Plant.filename_pre[t]))) + Plant.filename_pre[t], + Plant.ext))) + pcv.print_image(leaf_t, os.path.join(path_visual1, + '{}_{}-{}-{}-{}_{}{}'.format(unique_id, start_time, + start_idx, t, link_leaf[t], + Plant.filename_pre[t], + Plant.ext))) # pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) ## 2. show result with an alpha channel @@ -173,7 +181,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti ax2 = fig2.add_subplot(1, 1, 1) ax2.imshow(masked_im) ax2.axis('off') - plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + '.png')) + plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + Plant.ext)) plt.close(fig2) count += 1 @@ -184,8 +192,7 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) - - plt.savefig(os.path.join(path_visual3, '{}-visual.png'.format(t))) + plt.savefig(os.path.join(path_visual3, '{}-visual{}'.format(t, Plant.ext))) plt.close('all') # save all information @@ -197,5 +204,6 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti # Load from existed PlantData instance Plant = pkl.load(open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'rb')) + print('\nFinished!\nThe results are saved here:\n{}'.format(Plant.savedir)) + return Plant - \ No newline at end of file From f6cdbbd3e51324738c1ab16e3fce8808fa6f6b93 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 23 Jul 2020 08:13:36 -0500 Subject: [PATCH 165/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 36 +++++++++++++++++-- 1 file changed, 33 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 73ea8a129..52bc0b6b3 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -225,7 +225,8 @@ def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, mode='l subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), str(junk.hour).zfill(2), str(junk.minute).zfill(2)) self.savedir = os.path.join(savedir, subfolder) - os.makedirs(self.savedir) + if not os.path.exists(self.savedir): + os.makedirs(self.savedir) else: self.savedir = savedir @@ -272,6 +273,11 @@ def Sorttime(self, time_cond): filenames_ori = [f for f in os.listdir(self.imagedir) if f.endswith(self.suffix)] time_temp = [] file_name = [] + ext1, ext2 = os.path.splitext(self.suffix) + if ext1.startswith('.'): + ext = ext1 + elif ext2.startswith('.'): + ext = ext2 for filename in filenames: temp = re.search(self.pattern_datetime, filename) if temp: @@ -280,7 +286,8 @@ def Sorttime(self, time_cond): if timepart.endswith(cond): time_temp.append(timepart) junk = [1 if re.search(timepart, f) is not None else 0 for f in filenames_ori] - file_name.append(filenames_ori[junk.index(1)].replace('.jpg', '')) + # file_name.append(filenames_ori[junk.index(1)].replace('.jpg', '')) + file_name.append(filenames_ori[junk.index(1)].replace(ext, '')) continue index_temp = np.argsort(time_temp) @@ -292,7 +299,21 @@ def load_images(self): """ Load original images This function is also designed for files with file names which contain a "date-time" part, with a user defined pattern """ - + # filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] + # temp_imgs = [] + # sz = [] + # for t in self.time: + # for f in filenames: + # temp = re.search(t, f) + # if temp: + # junk = skimage.io.imread(os.path.join(self.imagedir, f)) + # temp_imgs.append(junk) + # sz.append(np.min(junk.shape[0:2])) + # filenames.remove(f) + # self.min_dim = np.min(sz) + # for junk in temp_imgs: + # img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size + # self.images.append(img) temp_imgs = [] sz = [] for pre in self.filename_pre: @@ -383,6 +404,15 @@ def get_series(self): key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = np.array(new_leaves) + # if len(new_leaves) > 1: + # id_temp = [] + # for new_leav in new_leaves: + # id_temp.append(unique_id) + # unique_id = unique_id + 1 + # self.link_series[key_t]['unique_id' = np.array(id_temp) + # else: + # self.link_series[key_t]['unique_id'] = np.array(unique_id) + # unique_id = unique_id + 1 id_temp = [] for new_leaf in new_leaves: id_temp.append(unique_id) From 11e77468e555800086c38ee8510fc6eee9e418d4 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 23 Jul 2020 10:04:06 -0500 Subject: [PATCH 166/274] Create instance_segmentation_tutorial.md --- docs/instance_segmentation_tutorial.md | 129 +++++++++++++++++++++++++ 1 file changed, 129 insertions(+) create mode 100644 docs/instance_segmentation_tutorial.md diff --git a/docs/instance_segmentation_tutorial.md b/docs/instance_segmentation_tutorial.md new file mode 100644 index 000000000..5e80983b1 --- /dev/null +++ b/docs/instance_segmentation_tutorial.md @@ -0,0 +1,129 @@ +## Tutorial: Instance Segmentation using maskRCNN + +Instance segmentation is identifying each object instance for every known object within an image. Instance segmentation assigns a label to each pixel of the image. It can be used for tasks such as counting the number of objects. [reference: https://towardsdatascience.com/computer-vision-instance-segmentation-with-mask-r-cnn-7983502fcad1] + +Instance segmentation requires: + +1. Object detection of all objects in an image. Here the goal is to classify individual objects and localize each object instance using a bounding box. +2. Segmenting each instance. Here the goal is to classify each pixel into a fixed set of categories without differentiating object instances. + +Taking an image of plant as an example. As shown in the image below, the 1st image is an RGB image of an arabidopsis, the one in the middle is image segmentation result, and the 3rd image is the result of instance segmentation. +Now it is easy for us to tell that the goal for image segmentation is to have pixel level labels indicating "plant" or "not plant" for every pixel, and the output for image segmentation is a binary mask indicating where the plant is in the image. At this point we have no information regarding number of leaves in this image. +While for instance segmentation, as shown in the 3rd image, we can see that the goal is to segment out every leaf (hence, there is a label for every leaf, e.g. leaf 1, leaf 2, etc.) instance. In this specific example, 5 binary masks would be generated, every one represents for one leaf. Hence we are also able to tell that there are 5 leaves present in this image. + +![Screenshot](img/tutorial_images/instance_segmentation/original.jpg) +![Screenshot](img/tutorial_images/instance_segmentation/threshold.jpg) +![Screenshot](img/tutorial_images/instance_segmentation/instance_seg.jpg) + + +There are plenty of methods for instance segmentation, instance segmentation using maskRCNN is shown here as an example. + +For detailed information regrading maskRCNN, please check here: +https://github.com/matterport/Mask_RCNN + +Follow the installation steps, and it is highly recommended to create a conda environment for mask_rcnn. + +- Create a conda environment with tensorflow 1.13.1 and keras 2.1.0. + - Open a terminal window, type: +``` + conda create -n mrcnn tensorflow=1.13.1 + conda activate mrcnn + pip install keras==2.1.0 + conda install plantcv # install plantcv tools for this environment +``` +This would create a tensorflow environment (with tensorflow 1.13.1 and keras 2.1.0, those are required by the MaskRCNN package we are to install) with a name of mrcnn. You are free to change the name "mrcnn" based on you own preference. + +- Install MaskRCNN + - Clone [this](https://github.com/matterport/Mask_RCNN) github repository to your desired location. (It is suggested to put the same directory as you put your plantcv folder) + - Open a terminal, follow the instructions below: + +``` + cd Mask_RCNN # direct yourself to the folder of Mask_RCNN + pip install -r requirements.txt # install dependencies + python3 setup.py install # run setup +``` + +- (Option) Install pycocotools. + - Clone [this](https://github.com/cocodataset/cocoapi) github repository, and put to your desired destiny location. + - Open a terminal window, type: +``` + pip install pycocotools +``` + +With conda environment mrcnn activated (```conda activate mrcnn```), you are ready to get instance level segmentation with Mask_RCNN using a pre-trained model. You can find the download the pre-trained model here: +/home/nfahlgren/projects/mrcnn/mask_rcnn_leaves_0060.h5 + +It is recommended that you put this pre-trained model in the same folder of your project. + +Following this notebook for step-by-step implementation of instance segmentation. + +```python +# import packages +import os +import inferencing_utilities as funcs +``` + +The following block is where you want to change based on your own application: +```python +## suffix of original image files. Make sure that all files have the same suffix format +suffix = 'crop-img17.jpg' + +## pattern for the date-tima part in your data. Make sure that the date-time part in all filenames follow the same pattern +pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' + +## directory of original images +imagedir = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol' + +## desired saving directory for results +savedir = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol/plant17/segmentation' +if not os.path.exists(savedir): + os.makedirs(savedir) + +## class names. Since a pre-trained model is used here, and the model is trained with 2 classes: either "Background" or "Leaf", there is really nothing to change here +class_names = ['BG', 'Leaf'] +``` + +Some detailed regarding parameters "suffix": + +For the next several blocks, there is really nothing for you to change. +```python +## Root directory of the project +rootdir = os.path.abspath("./") + +## initialize the instance segmentation +instance_seg = funcs.instance_seg_inferencing(imagedir, savedir, rootdir, pattern_datetime, suffix, class_names) + +## get configuration for instance segmentation +instance_seg.get_configure() + +## load the pre-trained model +instance_seg.load_model() + +## pre-define colors for visualization used later +instance_seg.define_colors() + +## get the list of all files +instance_seg.get_file_list() + +## option (print the file list) +instance_seg.list_f +``` + +For the next block, a randomly selected example is used to show the instance segmentation result +```python +## show one randomly selected image as an example +instance_seg.inferencing_random_sample() +``` + +If you run the following block, it will loop over all files in the file list you defined. Note it might take some time for the process to finish. +```python +## get the result of all images +instance_seg.inferencing_all() +``` + +If you would like to check the results inside the folder, you can print out the directory for results saving: +```python +instance_seg.segmentation_dir +``` + + From fb102ab5dde12796dcbaf40a96789ff75e0c7392 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 24 Jul 2020 14:54:09 -0500 Subject: [PATCH 167/274] Update time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py index 76239733c..d2808679a 100644 --- a/plantcv/plantcv/visualize/time_lapse_video.py +++ b/plantcv/plantcv/visualize/time_lapse_video.py @@ -155,7 +155,7 @@ def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=N out.release() cv2.destroyAllWindows() - if display is 'on': + if display == 'on': print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) return list_img, size_frame \ No newline at end of file From 103d05633dd1dcc04843f7be6bae03ce1e7a9fff Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 24 Jul 2020 15:00:04 -0500 Subject: [PATCH 168/274] Update time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py index d2808679a..ac3139f1d 100644 --- a/plantcv/plantcv/visualize/time_lapse_video.py +++ b/plantcv/plantcv/visualize/time_lapse_video.py @@ -158,4 +158,4 @@ def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=N if display == 'on': print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) - return list_img, size_frame \ No newline at end of file + return list_img, size_frame From 5093b6d44e60a371a39e00a722ccaf3f4586e12d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 24 Jul 2020 17:03:09 -0500 Subject: [PATCH 169/274] Update __init__.py --- plantcv/plantcv/__init__.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py index cd30f1bff..9a990ca2b 100644 --- a/plantcv/plantcv/__init__.py +++ b/plantcv/plantcv/__init__.py @@ -106,9 +106,10 @@ 'cluster_contour_splitimg', 'rotate', 'shift_img', 'output_mask', 'auto_crop', 'canny_edge_detect', 'background_subtraction', 'naive_bayes_classifier', 'acute', 'distance_transform', 'params', 'cluster_contour_mask', 'analyze_thermal_values', 'opening', - 'closing', 'within_frame', 'fill_holes', 'get_kernel', 'crop', 'stdev_filter', - 'spatial_clustering', 'photosynthesis'] - 'closing', 'within_frame', 'fill_holes', 'get_kernel', 'Spectral_data', 'crop', 'stdev_filter', 'time_series'] + 'closing', 'within_frame', 'fill_holes', 'get_kernel', 'Spectral_data', 'crop', 'stdev_filter', 'time_series', + 'cluster_contour_mask','analyze_thermal_values', 'opening', + 'closing','within_frame', 'fill_holes', 'get_kernel', 'Spectral_data', 'crop', 'stdev_filter', + 'spatial_clustering'] from ._version import get_versions __version__ = get_versions()['version'] From 0c7ff4ed065ee2ef856d69776f8720838bcc5125 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Sat, 25 Jul 2020 14:41:40 -0500 Subject: [PATCH 170/274] Update link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 52bc0b6b3..5b8ea8500 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -189,6 +189,7 @@ class PlantData(): def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, mode='link', suffix='.jpg'): self.suffix = suffix + self.ext = None self.pattern_datetime = pattern_datetime @@ -275,9 +276,9 @@ def Sorttime(self, time_cond): file_name = [] ext1, ext2 = os.path.splitext(self.suffix) if ext1.startswith('.'): - ext = ext1 + self.ext = ext1 elif ext2.startswith('.'): - ext = ext2 + self.ext = ext2 for filename in filenames: temp = re.search(self.pattern_datetime, filename) if temp: @@ -287,7 +288,7 @@ def Sorttime(self, time_cond): time_temp.append(timepart) junk = [1 if re.search(timepart, f) is not None else 0 for f in filenames_ori] # file_name.append(filenames_ori[junk.index(1)].replace('.jpg', '')) - file_name.append(filenames_ori[junk.index(1)].replace(ext, '')) + file_name.append(filenames_ori[junk.index(1)].replace(self.ext, '')) continue index_temp = np.argsort(time_temp) @@ -432,4 +433,4 @@ def get_series(self): if idx < 0: break else: - self.link_series[key_t][key_leaf][t_] = self.link_info[t_ - 1][idx] \ No newline at end of file + self.link_series[key_t][key_leaf][t_] = self.link_info[t_ - 1][idx] From 2aeec019eba5910f3a0ab0c4582e764f9058d147 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 25 Aug 2020 17:22:07 -0500 Subject: [PATCH 171/274] Update link_utilities.py Update linking logic --- plantcv/plantcv/time_series/link_utilities.py | 168 ++++++------------ 1 file changed, 57 insertions(+), 111 deletions(-) diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py index 5b8ea8500..f0e5ff8c0 100644 --- a/plantcv/plantcv/time_series/link_utilities.py +++ b/plantcv/plantcv/time_series/link_utilities.py @@ -1,10 +1,10 @@ +#### link utilities #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jun 9 09:53:34 2020 -Functions used in time series linking after getting leaf instance segmentation result - +Functions used in time series linking after getting leaf instances segmented @author: hudanyunsheng """ @@ -26,6 +26,8 @@ import datetime import copy import colorsys +from plantcv.plantcv import fatal_error +from scipy.optimize import linear_sum_assignment def _random_colors(N, bright=True): @@ -138,31 +140,29 @@ def display_instances(image, boxes, masks, class_ids, class_names, if auto_show: plt.show() - -def _compute_overlaps_masks(masks1, masks2, mode=1): +def _compute_overlaps_masks(masks1, masks2): """Computes IoU overlaps between two sets of masks. masks1, masks2: [Height, Width, instances] mode: 1 or 2, 1 represent for IOU, 2 represent for intersection over the area of the previous mask """ - + mode = 1 # If either set of masks is empty return empty result if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: return np.zeros((masks1.shape[-1], masks2.shape[-1])) - # flatten masks and compute their areas - masks1 = np.reshape(masks1 > .5, (-1, masks1.shape[-1])).astype(np.float32) - masks2 = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) - area1 = np.sum(masks1, axis=0) - area2 = np.sum(masks2, axis=0) - - # intersections and union - intersections = np.dot(masks1.T, masks2) - if mode == 1: - union = area1[:, None] + area2[None, :] - intersections - overlaps = intersections / union - elif mode == 2: - overlaps = intersections / area1[:, None] - return overlaps - + n1 = masks1.shape[2] + n2 = masks2.shape[2] + intersections = np.zeros((n1,n2)) + unions = np.zeros((n1,n2)) + for idx_m in range(0, n1): + maski = np.expand_dims(masks1[:,:,idx_m], axis=2) + masks_ = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) + maski_ = np.reshape(maski > .5, (-1, maski.shape[-1])).astype(np.float32) + intersection = np.dot(masks_.T, maski_).squeeze() + intersections[idx_m,:] = intersection + union = np.sum(masks_,0) + np.sum(maski_) - intersection + unions[idx_m,:] = union + IOUs = np.divide(intersections,unions) + return IOUs def get_ax(rows=1, cols=1, size=16): # ??? """Return a Matplotlib Axes array to be used in @@ -175,18 +175,8 @@ def get_ax(rows=1, cols=1, size=16): # ??? fig.tight_layout() return ax - -def _getmatchedIndex(mask, masks, mode): - """Get the index from masks that matches the mask""" - score = _compute_overlaps_masks(np.expand_dims(mask, axis=-1), masks, mode) - if max(score[0]) > 0.15: - return np.argmax(score[0]), score - else: - return -1, score - - class PlantData(): - def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, mode='link', suffix='.jpg'): + def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, suffix='.jpg'): self.suffix = suffix self.ext = None @@ -222,14 +212,12 @@ def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, mode='l self.imagedir = imagedir self.segmentationdir = segmentationdir junk = datetime.datetime.now() - if mode == 'link': - subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), - str(junk.hour).zfill(2), str(junk.minute).zfill(2)) - self.savedir = os.path.join(savedir, subfolder) - if not os.path.exists(self.savedir): - os.makedirs(self.savedir) - else: - self.savedir = savedir + + subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), + str(junk.hour).zfill(2), str(junk.minute).zfill(2)) + self.savedir = os.path.join(savedir, subfolder) + if not os.path.exists(self.savedir): + os.makedirs(self.savedir) self.visualdir = os.path.join(self.savedir, 'visualization') @@ -287,7 +275,6 @@ def Sorttime(self, time_cond): if timepart.endswith(cond): time_temp.append(timepart) junk = [1 if re.search(timepart, f) is not None else 0 for f in filenames_ori] - # file_name.append(filenames_ori[junk.index(1)].replace('.jpg', '')) file_name.append(filenames_ori[junk.index(1)].replace(self.ext, '')) continue @@ -300,21 +287,6 @@ def load_images(self): """ Load original images This function is also designed for files with file names which contain a "date-time" part, with a user defined pattern """ - # filenames = [f for f in os.listdir(self.imagedir) if f.endswith('.jpg')] - # temp_imgs = [] - # sz = [] - # for t in self.time: - # for f in filenames: - # temp = re.search(t, f) - # if temp: - # junk = skimage.io.imread(os.path.join(self.imagedir, f)) - # temp_imgs.append(junk) - # sz.append(np.min(junk.shape[0:2])) - # filenames.remove(f) - # self.min_dim = np.min(sz) - # for junk in temp_imgs: - # img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size - # self.images.append(img) temp_imgs = [] sz = [] for pre in self.filename_pre: @@ -339,54 +311,35 @@ def load_results(self): self.scores.append(r['scores']) def initialize_linking(self): - self.emerging_info = [[] for i in range(0, self.total_time)] - self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time - 1)] - self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i + 1])) for i in - range(0, self.total_time - 1)] + self.emerging_info = [[] for i in range(0, self.total_time)] + self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time - 1)] + self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i + 1])) for i in range(0, self.total_time - 1)] self.available_leaves = [np.array(range(0, self.num_leaves[i])) for i in range(0, self.total_time)] - self.time_compare = [np.ones((self.num_leaves[i]), dtype=int) * i for i in range(0, self.total_time)] - - def linking(self, start_time, mode): - masks1 = copy.deepcopy(self.masks[start_time]) - masks2 = copy.deepcopy(self.masks[start_time + 1]) - leaves1 = copy.deepcopy(self.available_leaves[start_time]) - leaves2 = copy.deepcopy(self.available_leaves[start_time + 1]) + self.time_compare = [np.ones((self.num_leaves[i]), dtype=int) * i for i in range(0, self.total_time)] + + def linking(self, t0): + threshold = 0.2 + masks0 = copy.deepcopy(self.masks[t0]) + masks1 = copy.deepcopy(self.masks[t0 + 1]) + leaves0 = copy.deepcopy(self.available_leaves[t0]) + leaves1 = copy.deepcopy(self.available_leaves[t0 + 1]) + n0 = len(leaves0) n1 = len(leaves1) - n2 = len(leaves2) - n = np.min((n1, n2)) - N = np.max((n1, n2)) - weight = dict() - weight['forward'] = -np.inf * np.ones((n1, n2)) - # weight['backward'] = -np.inf*np.zeros((n2, n1)) - link = dict() - link['forward'] = -np.ones((n1)) - # link['backward'] = -np.ones((n2)) - - for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward']): - # for (masks, masks_all, leaves, key) in zip([masks1, masks2], [masks2, masks1], [leaves1, leaves2], ['forward', 'backward']): - for (idx, main_leaf) in enumerate(leaves): - mask = masks[:, :, main_leaf] - link[key][idx], weight[key][idx, :] = _getmatchedIndex(mask, masks_all, mode) - # check for duplicates (and hence non-unique links) - val, cou = np.unique(link[key], return_counts=True) - idx_temp = np.where(cou > 1)[0] - - if len(idx_temp) > 0: # matching to same leaf exists - idx_vs = val[idx_temp] - for idx_v in idx_vs: - idxs = np.where(link[key] == idx_v)[0] - weights_comp = weight[key][:, int(idx_v)] - - idx_keep = np.argmax(weights_comp) - idx_drop = np.delete(idxs, np.where(idxs == idx_keep)) - weight_temp = copy.deepcopy(weight[key][idx_drop]) - weight_temp[0, int(idx_v)] = 0 - if np.max(weight_temp) > 0.2: - link[key][idx_drop] = np.argmax(weight_temp) - else: - link[key][idx_drop] = -1 - - self.link_info[start_time] = link['forward'] + n = np.min((n0, n1)) + N = np.max((n0, n1)) + weight = -np.inf * np.ones((n0, n1)) + link = -np.ones((n0)) + weight = _compute_overlaps_masks(masks0, masks1) + idx_col = np.where(np.max(weight, axis=0) < threshold)[0] # find those volumns with maximum value < 0.15 + avail_col = [x for x in range(0,n1) if x not in idx_col] + weight_ = copy.deepcopy(weight) + + weight_ = np.delete(weight_, idx_col, 1) + row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) + for (r, c) in zip(row_ind, col_ind): + if weight_[r, c] >= threshold: + link[r] = avail_col[c] + self.link_info[t0] = link def get_series(self): ## define new leaves and their unique identifiers at time points with new leaves emerging @@ -405,15 +358,7 @@ def get_series(self): key_t = 't{}'.format(t) self.link_series[key_t] = dict() self.link_series[key_t]['new_leaf'] = np.array(new_leaves) - # if len(new_leaves) > 1: - # id_temp = [] - # for new_leav in new_leaves: - # id_temp.append(unique_id) - # unique_id = unique_id + 1 - # self.link_series[key_t]['unique_id' = np.array(id_temp) - # else: - # self.link_series[key_t]['unique_id'] = np.array(unique_id) - # unique_id = unique_id + 1 + id_temp = [] for new_leaf in new_leaves: id_temp.append(unique_id) @@ -421,7 +366,7 @@ def get_series(self): self.link_series[key_t]['unique_id'] = np.array(id_temp) ## for time points with new leaves emerging, get the linking information for every new leaf for key_t in self.link_series: - t0 = int(key_t.replace('t', '')) + t0 = int(key_t.replace('t', '')) for leaf in self.link_series[key_t]['new_leaf']: key_leaf = 'leaf{}'.format(leaf) self.link_series[key_t][key_leaf] = -np.ones(self.total_time, dtype=int) @@ -434,3 +379,4 @@ def get_series(self): break else: self.link_series[key_t][key_leaf][t_] = self.link_info[t_ - 1][idx] + \ No newline at end of file From fe3b45d6de97a4671f36ff0be360b31d7e29762d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 25 Aug 2020 17:22:41 -0500 Subject: [PATCH 172/274] Update time_series.py Update time_seried by adding new feature: generate a csv file which includes the linking information --- plantcv/plantcv/time_series/time_series.py | 284 ++++++++++----------- 1 file changed, 141 insertions(+), 143 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 3536f7d86..8c4869f1c 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -1,9 +1,10 @@ +#### link utilities #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jun 9 09:53:34 2020 -Functions used in time series linking after getting leaf instance segmentation result (from maskRCNN) +Time series linking after getting leaf instances segmented @author: hudanyunsheng """ @@ -20,27 +21,29 @@ import re from plantcv import plantcv as pcv import copy -from plantcv.plantcv.time_series import link_utilities as funcs -# import link_utilities as funcs +import csv +import link_utilities as funcs -def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, link_logic=1, - class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'): +def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, colors=None, class_names=['BG', 'Leaf'], suffix='.jpg'): + """ Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) Input: imagedir: directory of original image used segmentationdir: directory of leaf instance segmentation result savedir: desired saving directory of linking result - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 + pattern_datetime: the regular expression of date-time part in file names. e.g. if the date-time part in file names is: YYYY-MM-DD-hh-mm, then pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' + time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment, this should be consistent with the pattern_datetime + colors: a list of tuples where every tuple represent for one color (RGB values). By default it can be None. class_names: used in bounding box visualization. By default there are background and leaf - mode: either 'link' or 'load'. When creating a new linking, use in mode 'link', when trying to load from saved file, use in mode 'load'. By default the mode is 'link'. + suffix: the ending part of file names that every image have in common. By default it is '.jpg'. Output: An instance from "Plant" class would be returned, all the results will be saved in a folder with name of date and time when the function runs inside the user defined "savedir". Sample folder name: 2020-06-23-15-42 (a format of YYYY-MM-DD-HH-mm). 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment - 3. saved_plant.pkl: a "Plant" instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. - 4. a folder called "visualization", which contains 3 subfolders: + 3. saved_plant.pkl: a Plant object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. + 4. linking_info.csv: a csv file includes the linking information will be saved. + 5. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. result name: {}_{}-{}-{}-{}_{}.png @@ -67,143 +70,138 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti Naming format: {}-visual.png The original image name is inside the {}. """ - if mode == 'link': # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, mode, suffix) - - Plant.getpath(Plant.imagedir) - Plant.Sorttime(time_cond) - Plant.load_images() - - # load mrcnn inferencing results - Plant.load_results() - - Plant.getinitleaf() - Plant.getmaxleaf() - - Plant.gettotaltime() - Plant.getnumemergence() - - # plot the #leaves vs. time relationship - plt.plot(np.array(range(0, len(Plant.num_leaves))), Plant.num_leaves) - - if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): - file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') - if link_logic == 1: - file.write('mode: IOU\n') - else: - file.write('mode: IOP\n') - file.write('Directory of original images: {}\n'.format(Plant.imagedir)) - file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) - file.write('Image conditions: {}'.format(time_cond)) - file.close() - - print('start\n') - print('...') - - # linking initialization - Plant.initialize_linking() - - # link - for t in range(0, Plant.total_time - 1): - Plant.linking(t, mode=link_logic) - Plant.get_series() - - ####### visualization ####### - # load original images - if len(Plant.images) == 0: - Plant.load_images() # Plant.images - - # visualization method 1: show only one leaf per image - path_visual1 = os.path.join(Plant.visualdir, 'visualization1') - if not os.path.exists(path_visual1): - os.makedirs(path_visual1) - - # visualization method 2: show with an alpha channel - path_visual2 = os.path.join(Plant.visualdir, 'visualization2') - if not os.path.exists(path_visual2): - os.makedirs(path_visual2) - - # visualization method 3: show with bounding boxes - path_visual3 = os.path.join(Plant.visualdir, 'visualization3') - if not os.path.exists(path_visual3): - os.makedirs(path_visual3) - - count = 0 + Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) +# Plant = PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) + + Plant.getpath(Plant.imagedir) + Plant.Sorttime(time_cond) + Plant.load_images() + + # load mrcnn inferencing results + Plant.load_results() + + Plant.getinitleaf() + Plant.getmaxleaf() + + Plant.gettotaltime() + Plant.getnumemergence() + + # plot the #leaves vs. time relationship + plt.plot(np.array(range(0, len(Plant.num_leaves))), Plant.num_leaves) + + if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): + file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') + file.write('mode: IOU\n') + file.write('Directory of original images: {}\n'.format(Plant.imagedir)) + file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) + file.write('Image conditions: {}'.format(time_cond)) + file.close() + + print('start\n') + print('...') + + # linking initialization + Plant.initialize_linking() + + # link + for t in range(0, Plant.total_time - 1): +# Plant.linking(t, mode=link_logic) + Plant.linking(t) + Plant.get_series() + + ####### visualization ####### + # load original images + if len(Plant.images) == 0: + Plant.load_images() # Plant.images + + # visualization method 1: show only one leaf per image + path_visual1 = os.path.join(Plant.visualdir, 'visualization1') + if not os.path.exists(path_visual1): + os.makedirs(path_visual1) + + # visualization method 2: show with an alpha channel + path_visual2 = os.path.join(Plant.visualdir, 'visualization2') + if not os.path.exists(path_visual2): + os.makedirs(path_visual2) + + # visualization method 3: show with bounding boxes + path_visual3 = os.path.join(Plant.visualdir, 'visualization3') + if not os.path.exists(path_visual3): + os.makedirs(path_visual3) + + count = 0 + if colors is None: if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): colors = funcs._random_colors(40) +# colors = _random_colors(40) pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) else: colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) - color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] - for key_t in Plant.link_series: - # - ids = Plant.link_series[key_t]['unique_id'] - - start_time = int(key_t.replace('t', '')) - leaves_t = Plant.link_series[key_t]['new_leaf'] - for (unique_id, leaf) in zip(ids, leaves_t): - key_leaf = 'leaf{}'.format(leaf) - link_leaf = Plant.link_series[key_t][key_leaf] - start_idx = link_leaf[start_time] - for t in range(start_time, Plant.total_time): - img = Plant.images[t] - if link_leaf[t] >= 0: - color_all[t][link_leaf[t]] = colors[count] - mask_t = Plant.masks[t][:, :, link_leaf[t]] - - ## 1. save the masked image, i.e. single leaves - mask = np.zeros(mask_t.shape, dtype=np.uint8) - mask[np.where(mask_t)] = 255 - leaf_t = pcv.apply_mask(img, mask, mask_color='black') - pcv.print_image(leaf_t, os.path.join(path_visual1, - '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, - start_idx, t, link_leaf[t], - Plant.filename_pre[t], - Plant.ext))) - pcv.print_image(leaf_t, os.path.join(path_visual1, - '{}_{}-{}-{}-{}_{}{}'.format(unique_id, start_time, - start_idx, t, link_leaf[t], - Plant.filename_pre[t], - Plant.ext))) - # pkl.dump(leaf_t, open(os.path.join(path_visual1, '{}_{}_{}_{}_{}_{}.pkl'.format(unique_id, start_time, start_idx, t, link_leaf[t], Plant.filename_pre[t])), 'wb')) - - ## 2. show result with an alpha channel - # update the mask where there is an alpha channel (alpha=0.5) - mask_ = np.ones(mask_t.shape) * 0.5 - mask_[np.where(mask_t == True)] = 1 - masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_, axis=2)), axis=2) - save_dir_ = os.path.join(path_visual2, '{}_{}-{}'.format(unique_id, start_time, start_idx)) - if not os.path.exists(save_dir_): - os.makedirs(save_dir_) - fig2 = plt.figure(figsize=(5, 5)) - ax2 = fig2.add_subplot(1, 1, 1) - ax2.imshow(masked_im) - ax2.axis('off') - plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + Plant.ext)) - plt.close(fig2) - - count += 1 - ## 3. visualize with bounding boxes - for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, - Plant.scores, color_all, Plant.filename_pre): - funcs.display_instances(img, roi, mask, class_id, - class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, - show_mask=True, - colors=color) - plt.savefig(os.path.join(path_visual3, '{}-visual{}'.format(t, Plant.ext))) - plt.close('all') - - # save all information - pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) - - else: - # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir, mode='load') - # Load from existed PlantData instance - Plant = pkl.load(open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'rb')) - - print('\nFinished!\nThe results are saved here:\n{}'.format(Plant.savedir)) - - return Plant + color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] + + csvfile = open(os.path.join(Plant.savedir, 'linking_info.csv'), 'w', newline='') + writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) + writer_junk.writerow(['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) + for key_t in Plant.link_series: + # + ids = Plant.link_series[key_t]['unique_id'] + + start_time = int(key_t.replace('t', '')) + leaves_t = Plant.link_series[key_t]['new_leaf'] + for (unique_id, leaf) in zip(ids, leaves_t): + key_leaf = 'leaf{}'.format(leaf) + link_leaf = Plant.link_series[key_t][key_leaf] + start_idx = link_leaf[start_time] + for t in range(start_time, Plant.total_time): + img = Plant.images[t] + if link_leaf[t] >= 0: + color_all[t][link_leaf[t]] = colors[count] + mask_t = Plant.masks[t][:, :, link_leaf[t]] + + ## 1. save the masked image, i.e. single leaves + mask = np.zeros(mask_t.shape, dtype=np.uint8) + mask[np.where(mask_t)] = 255 + leaf_t = pcv.apply_mask(img, mask, mask_color='black') + + writer_junk.writerow([unique_id, start_time, Plant.filename_pre[start_time], t, Plant.filename_pre[t], link_leaf[t]]) + + pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, + start_idx, t, link_leaf[t], + Plant.filename_pre[t]))) + pcv.print_image(leaf_t, os.path.join(path_visual1, + '{}_{}-{}-{}-{}_{}{}'.format(unique_id, start_time, + start_idx, t, link_leaf[t], + Plant.filename_pre[t], + Plant.ext))) + + + ## 2. show result with an alpha channel + # update the mask where there is an alpha channel (alpha=0.5) + mask_ = np.ones(mask_t.shape) * 0.5 + mask_[np.where(mask_t == True)] = 1 + masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_, axis=2)), axis=2) + save_dir_ = os.path.join(path_visual2, '{}_{}-{}'.format(unique_id, start_time, start_idx)) + if not os.path.exists(save_dir_): + os.makedirs(save_dir_) + fig2 = plt.figure(figsize=(5, 5)) + ax2 = fig2.add_subplot(1, 1, 1) + ax2.imshow(masked_im) + ax2.axis('off') + plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + Plant.ext)) + plt.close(fig2) + + count += 1 + ## 3. visualize with bounding boxes + for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, + Plant.scores, color_all, Plant.filename_pre): + funcs.display_instances(img, roi, mask, class_id, class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) +# display_instances(img, roi, mask, class_id, class_names, score, ax=get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) + plt.savefig(os.path.join(path_visual3, '{}-visual{}'.format(t, Plant.ext))) + plt.close('all') + # save all information + pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) + print('\nFinished!\nThe results are saved here:\n{}'.format(Plant.savedir)) + + return Plant \ No newline at end of file From fd65fa7ab0172575e5a4f2b8abe18c30f12821c5 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 25 Aug 2020 17:22:55 -0500 Subject: [PATCH 173/274] Update tests.py Update tests for time_series linking --- tests/tests.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 87291c2ea..1732b4234 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6390,9 +6390,8 @@ def test_plantcv_time_series_time_series(): link_logic = 1 class_names = ['BG', 'Leaf'] pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm - mode = 'link' suffix = '.jpg' - Plant_ = pcv.time_series.time_series_linking(TIME_SERIES_TEST_RAW, TIME_SERIES_TEST_INSTANCE_SEG, cache_dir, pattern_datetime, time_cond, link_logic, class_names, mode, suffix) + Plant_ = pcv.time_series.time_series_linking(imagedir=TIME_SERIES_TEST_RAW, segmentationdir=TIME_SERIES_TEST_INSTANCE_SEG, savedir=cache_dir, pattern_datetime=pattern_datetime, time_cond=time_cond, colors=None, class_names=class_names, suffix=suffix) assert len(os.listdir(Plant_.savedir)) > 0 def test_plantcv_visualize_overlay_two_imgs(): From c2dafc107e0f1c905eb7d6191563cfd767f1e4a1 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 25 Aug 2020 17:23:09 -0500 Subject: [PATCH 174/274] Update time_series.md Update doc for time_series --- docs/time_series.md | 28 ++++++++++++---------------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index 7f8011b44..0a3cb208b 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -18,9 +18,8 @@ To understand the growth of every leaf instance of a plant, we need to re-assign - savedir: desired saving directory of linking result. Note that this function will generate a folder under your specified saving directory, with the date and time you run this programme - pattern_datetime: the pattern of date and time part in original file names, dafault value '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' which represents YYYY-MM-DD-hh-mm. - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment. Make sure the format of date matches the pattern-datetime. - - link_logic: 1: IoU (intersection over union), 2: Io1A (intersection over 1st area), default value: 1 + - colors: a list of tuples represent for pre-defined colors. By default it is set to None and a list of colors will be generated and saved. - class_names: used in bounding box visualization. by default there are background and leaf, i.e. class_names = ['BG', 'leaf'] - - mode: can be either 'link' or 'load', use 'link' when trying to get linked time-series from instance segmentation; use 'load' when trying to load from saved linking - suffix: the suffix of original images, make sure all images having the same suffix, e.g. suffix='.jpg' or suffix='-img8.jpg'. Make sure all the images desired having the same suffix pattern. - **Output:** An instance of Plant object is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir". @@ -30,7 +29,8 @@ Note: under the user specified saving directory, a new folder named after the da 1. colors.pkl: the colors (represented by arrays) used in bounding box visualization. With this predefined color set, same color will represent for the leaf instance with same instance label all the time, i.e. same color represents leaf with the same label. Without this predefined list of color, the assignment of color will be random, i.e. the color provides no information to leaf indices. 2. details.txt: the logic of linking as well as time condition will be saved in this text file, so that would be easier for users to check these parameters for the specific experiment. 3. saved_plant.pkl: a "Plant" object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. -4. a folder called "visualization", which contains 3 subfolders: +4. linking_info.csv: a csv file includes the linking information will be saved. +5. a folder called "visualization", which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. @@ -80,28 +80,24 @@ from plantcv import plantcv as pcv path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol_512' ## Specify the directory of instance segmentation result -path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index7/2020-06-25-10-35/subsampled/2020-06-30-10-51/' +path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/segmentation/updated' ## Specify the desired directory to save results -path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index7/2020-06-25-10-35/time_series_linking' +path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' ## Specify the date-time pattern of original image names pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm ## Specify the desired time point to include in to the analysis -time_cond = ['08-05', '11-05', '17-05', '21-05'] +time_cond = ['08-05', '11-05', '17-05', '21-05'] # exp5 & exp6 -link_logic = 1 class_names = ['BG', 'Leaf'] -mode = 'link' - -## Specify the suffix for all images you want to include, note that all the images shown have the same suffix as you specified -## Normally it would be something like '.png' or '.jpg', in this specific case, the image contained in the folder have name like -## 'xxx-img{}.jpg', the number inside the {} represents the index of plant. So in this case, to include images for the same plant, -## you will have to specify the index of plant. -suffix = '-img7.jpg' -​linked_Plant = pcv.time_series.time_series_linking(path_img, path_segmentation, path_save, pattern_datetime, - time_cond, link_logic, class_names, mode, suffix) + +suffix = '-img{}.jpg'.format(idx_plant) + +linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, + pattern_datetime=pattern_datetime, time_cond=time_cond, class_names=['BG', 'Leaf'], suffix=suffix) + ``` When it finished, you can go ahead and check the saved result. If you are not sure where the results are saved, you can type: ```linked_Plant.savedir``` From 72f59b73ff78737ff19f6f98a9912b6ee106406b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 26 Aug 2020 07:24:13 -0500 Subject: [PATCH 175/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index 8397f612f..c5c7050a4 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -3,11 +3,10 @@ from plantcv.plantcv.time_series.link_utilities import display_instances from plantcv.plantcv.time_series.link_utilities import _compute_overlaps_masks from plantcv.plantcv.time_series.link_utilities import get_ax -from plantcv.plantcv.time_series.link_utilities import _getmatchedIndex from plantcv.plantcv.time_series.link_utilities import PlantData from plantcv.plantcv.time_series.time_series import time_series_linking # add new functions to end of lists __all__ = ["_random_colors", "_apply_mask", "display_instances", "_compute_overlaps_masks", "get_ax", - "_getmatchedIndex", "PlantData", "time_series_linking"] + "PlantData", "time_series_linking"] From a9f178b0ea203a7b89234c135aead62b710673e7 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 26 Aug 2020 07:24:18 -0500 Subject: [PATCH 176/274] Update time_series.py --- plantcv/plantcv/time_series/time_series.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py index 8c4869f1c..d8a60b148 100644 --- a/plantcv/plantcv/time_series/time_series.py +++ b/plantcv/plantcv/time_series/time_series.py @@ -22,7 +22,8 @@ from plantcv import plantcv as pcv import copy import csv -import link_utilities as funcs +# import link_utilities as funcs +# import pcv.time_series.link_utilities as funcs def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, colors=None, class_names=['BG', 'Leaf'], suffix='.jpg'): @@ -71,8 +72,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti The original image name is inside the {}. """ # initialize Plant class - Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) -# Plant = PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) + # Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) + Plant = pcv.time_series.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) Plant.getpath(Plant.imagedir) Plant.Sorttime(time_cond) @@ -133,8 +134,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti count = 0 if colors is None: if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): - colors = funcs._random_colors(40) -# colors = _random_colors(40) + # colors = funcs._random_colors(40) + colors = pcv.time_series._random_colors(40) pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) else: colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) @@ -196,8 +197,8 @@ def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, ti ## 3. visualize with bounding boxes for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, Plant.scores, color_all, Plant.filename_pre): - funcs.display_instances(img, roi, mask, class_id, class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) -# display_instances(img, roi, mask, class_id, class_names, score, ax=get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) + # funcs.display_instances(img, roi, mask, class_id, class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) + pcv.time_series.display_instances(img, roi, mask, class_id, class_names, score, ax=pcv.time_series.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) plt.savefig(os.path.join(path_visual3, '{}-visual{}'.format(t, Plant.ext))) plt.close('all') # save all information From 801740581e68700cdfbd9c01ea7c6f9ced2f36d5 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 16:18:42 -0500 Subject: [PATCH 177/274] Delete link_utilities.py --- plantcv/plantcv/time_series/link_utilities.py | 382 ------------------ 1 file changed, 382 deletions(-) delete mode 100644 plantcv/plantcv/time_series/link_utilities.py diff --git a/plantcv/plantcv/time_series/link_utilities.py b/plantcv/plantcv/time_series/link_utilities.py deleted file mode 100644 index f0e5ff8c0..000000000 --- a/plantcv/plantcv/time_series/link_utilities.py +++ /dev/null @@ -1,382 +0,0 @@ -#### link utilities -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Jun 9 09:53:34 2020 - -Functions used in time series linking after getting leaf instances segmented - -@author: hudanyunsheng -""" - -import numpy as np -import matplotlib -import matplotlib.pyplot as plt -import os -import sys -import random -import math -import skimage.io -import pickle as pkl -import re -from skimage.measure import find_contours -from matplotlib import patches, lines -from matplotlib.patches import Polygon -from plantcv import plantcv as pcv -import datetime -import copy -import colorsys -from plantcv.plantcv import fatal_error -from scipy.optimize import linear_sum_assignment - - -def _random_colors(N, bright=True): - """ - Generate random colors. - To get visually distinct colors, generate them in HSV space then - convert to RGB. - """ - brightness = 1.0 if bright else 0.7 - hsv = [(i / N, 1, brightness) for i in range(N)] - colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv)) - random.shuffle(colors) - return colors - - -def _apply_mask(image, mask, color, alpha=0.5): - """Apply the given mask to the image. - """ - for c in range(3): - image[:, :, c] = np.where(mask == 1, - image[:, :, c] * - (1 - alpha) + alpha * color[c] * 255, - image[:, :, c]) - return image - - -def display_instances(image, boxes, masks, class_ids, class_names, - scores=None, title="", - figsize=(16, 16), ax=None, - show_mask=True, show_bbox=True, - colors=None, captions=None): - """ - boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates. - masks: [height, width, num_instances] - class_ids: [num_instances] - class_names: list of class names of the dataset - scores: (optional) confidence scores for each box - title: (optional) Figure title - show_mask, show_bbox: To show masks and bounding boxes or not - figsize: (optional) the size of the image - colors: (optional) An array or colors to use with each object - captions: (optional) A list of strings to use as captions for each object - """ - # Number of instances - N = boxes.shape[0] - if not N: - print("\n*** No instances to display *** \n") - else: - assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0] - - # If no axis is passed, create one and automatically call show() - auto_show = False - if not ax: - _, ax = plt.subplots(1, figsize=figsize) - auto_show = True - - # Generate random colors - colors = colors or _random_colors(N) - - # Show area outside image boundaries. - height, width = image.shape[:2] - ax.set_ylim(height + 10, -10) - ax.set_xlim(-10, width + 10) - ax.axis('off') - ax.set_title(title) - - masked_image = image.astype(np.uint32).copy() - for i in range(N): - color = colors[i] - - # Bounding box - if not np.any(boxes[i]): - # Skip this instance. Has no bbox. Likely lost in image cropping. - continue - y1, x1, y2, x2 = boxes[i] - if show_bbox: - p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, - alpha=0.7, linestyle="dashed", - edgecolor=color, facecolor='none') - ax.add_patch(p) - - # Label - if not captions: - class_id = class_ids[i] - score = scores[i] if scores is not None else None - label = class_names[class_id] - caption = "{} {:.3f}".format(label, score) if score else label - else: - caption = captions[i] - ax.text(x1, y1 + 8, caption, - color='w', size=11, backgroundcolor="none") - - # Mask - mask = masks[:, :, i] - if show_mask: - masked_image = _apply_mask(masked_image, mask, color) - - # Mask Polygon - # Pad to ensure proper polygons for masks that touch image edges. - padded_mask = np.zeros( - (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8) - padded_mask[1:-1, 1:-1] = mask - contours = find_contours(padded_mask, 0.5) - for verts in contours: - # Subtract the padding and flip (y, x) to (x, y) - verts = np.fliplr(verts) - 1 - p = Polygon(verts, facecolor="none", edgecolor=color) - ax.add_patch(p) - ax.imshow(masked_image.astype(np.uint8)) - if auto_show: - plt.show() - -def _compute_overlaps_masks(masks1, masks2): - """Computes IoU overlaps between two sets of masks. - masks1, masks2: [Height, Width, instances] - mode: 1 or 2, 1 represent for IOU, 2 represent for intersection over the area of the previous mask - """ - mode = 1 - # If either set of masks is empty return empty result - if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: - return np.zeros((masks1.shape[-1], masks2.shape[-1])) - n1 = masks1.shape[2] - n2 = masks2.shape[2] - intersections = np.zeros((n1,n2)) - unions = np.zeros((n1,n2)) - for idx_m in range(0, n1): - maski = np.expand_dims(masks1[:,:,idx_m], axis=2) - masks_ = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) - maski_ = np.reshape(maski > .5, (-1, maski.shape[-1])).astype(np.float32) - intersection = np.dot(masks_.T, maski_).squeeze() - intersections[idx_m,:] = intersection - union = np.sum(masks_,0) + np.sum(maski_) - intersection - unions[idx_m,:] = union - IOUs = np.divide(intersections,unions) - return IOUs - -def get_ax(rows=1, cols=1, size=16): # ??? - """Return a Matplotlib Axes array to be used in - all visualizations in the notebook. Provide a - central point to control graph sizes. - - Adjust the size attribute to control how big to render images - """ - fig, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows)) - fig.tight_layout() - return ax - -class PlantData(): - def __init__(self, imagedir, segmentationdir, savedir, pattern_datetime, suffix='.jpg'): - - self.suffix = suffix - self.ext = None - - self.pattern_datetime = pattern_datetime - - # store a list of time of the images - self.time = [] - # store a list of original prefixes of the original images - self.filename_pre = [] - # total num of leaves that being detected - self.numleaf = 0 - - # store all the images - self.images = [] - - # minimum dimension of image. To link time series, all images must have the same dimension, to make it easier, we make the image square - self.min_dim = 0 - - # store all the mask detection during [starttime, endtime] - self.masks = [] - - # store all the rois (for visualization with bounding boxes) - self.rois = [] - - # store all the class_ids (for visualization with bounding boxes) - self.class_ids = [] - - # store all the scores (for visualization with bounding boxes) - self.scores = [] - - # store the dataset directory, instance segmentation result directory, time-series linking result directory and visualization directory - self.imagedir = imagedir - self.segmentationdir = segmentationdir - junk = datetime.datetime.now() - - subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), - str(junk.hour).zfill(2), str(junk.minute).zfill(2)) - self.savedir = os.path.join(savedir, subfolder) - if not os.path.exists(self.savedir): - os.makedirs(self.savedir) - - self.visualdir = os.path.join(self.savedir, 'visualization') - - self.total_time = 0 - self.max_nleaf = 0 - self.init_nleaf = 0 - self.num_leaves = [] - self.num_emergence = 0 - - self.emerging_info = [] # list length: self.total_time - self.link_info = [] # list length: self.total_time-1, dictionaries inside - self.weights = [] # list length: self.total_time-1, dictionaries inside - self.available_leaves = [] # list length: self.total_time - self.link_series = dict() # only for those newly emerging leaves - - def getinitleaf(self): - self.init_nleaf = self.masks[0].shape[2] - - def getmaxleaf(self): - for i in range(0, len(self.masks)): - self.num_leaves.append(self.masks[i].shape[2]) - self.max_nleaf = np.max(self.num_leaves) - - def gettotaltime(self): - # also represent for the total number of images - self.total_time = len(self.time) - - def getnumemergence(self): - self.num_emergence = np.ones((self.total_time), dtype=int) * self.init_nleaf - self.num_emergence[1:] = np.diff(self.num_leaves) - - def getpath(self, path): - self.dir = path - - def Sorttime(self, time_cond): - """ - This function is designed for files with file names which contain a "date-time" part, with an user-defined pattern, e.g. YYYY-MM-DD-hh-mm - Return: loop through the dataset_dir, and add time in time order - """ - - filenames = [f for f in os.listdir(self.segmentationdir) if f.endswith('.pkl')] - filenames_ori = [f for f in os.listdir(self.imagedir) if f.endswith(self.suffix)] - time_temp = [] - file_name = [] - ext1, ext2 = os.path.splitext(self.suffix) - if ext1.startswith('.'): - self.ext = ext1 - elif ext2.startswith('.'): - self.ext = ext2 - for filename in filenames: - temp = re.search(self.pattern_datetime, filename) - if temp: - timepart = temp.group() - for cond in time_cond: - if timepart.endswith(cond): - time_temp.append(timepart) - junk = [1 if re.search(timepart, f) is not None else 0 for f in filenames_ori] - file_name.append(filenames_ori[junk.index(1)].replace(self.ext, '')) - continue - - index_temp = np.argsort(time_temp) - # forward - self.time = [time_temp[i] for i in index_temp] - self.filename_pre = [file_name[i] for i in index_temp] - - def load_images(self): - """ Load original images - This function is also designed for files with file names which contain a "date-time" part, with a user defined pattern - """ - temp_imgs = [] - sz = [] - for pre in self.filename_pre: - filename = pre + '.jpg' - junk = skimage.io.imread(os.path.join(self.imagedir, filename)) - temp_imgs.append(junk) - sz.append(np.min(junk.shape[0:2])) - self.min_dim = np.min(sz) - for junk in temp_imgs: - img = junk[0: self.min_dim, 0: self.min_dim, :] # make all images the same size - self.images.append(img) - - def load_results(self): - """ Instead of running instance segmentation, load instance segmentation results (masks) - """ - for t in self.time: - file_name = '{}.pkl'.format(t) - r = pkl.load(open(os.path.join(self.segmentationdir, file_name), 'rb')) - self.masks.append(r["masks"][0: self.min_dim, 0: self.min_dim, :]) # make all masks the same size - self.rois.append(r["rois"]) - self.class_ids.append(r['class_ids']) - self.scores.append(r['scores']) - - def initialize_linking(self): - self.emerging_info = [[] for i in range(0, self.total_time)] - self.link_info = [-np.ones((self.num_leaves[i]), dtype=int) for i in range(0, self.total_time - 1)] - self.weights = [-np.inf * np.ones((self.num_leaves[i], self.num_leaves[i + 1])) for i in range(0, self.total_time - 1)] - self.available_leaves = [np.array(range(0, self.num_leaves[i])) for i in range(0, self.total_time)] - self.time_compare = [np.ones((self.num_leaves[i]), dtype=int) * i for i in range(0, self.total_time)] - - def linking(self, t0): - threshold = 0.2 - masks0 = copy.deepcopy(self.masks[t0]) - masks1 = copy.deepcopy(self.masks[t0 + 1]) - leaves0 = copy.deepcopy(self.available_leaves[t0]) - leaves1 = copy.deepcopy(self.available_leaves[t0 + 1]) - n0 = len(leaves0) - n1 = len(leaves1) - n = np.min((n0, n1)) - N = np.max((n0, n1)) - weight = -np.inf * np.ones((n0, n1)) - link = -np.ones((n0)) - weight = _compute_overlaps_masks(masks0, masks1) - idx_col = np.where(np.max(weight, axis=0) < threshold)[0] # find those volumns with maximum value < 0.15 - avail_col = [x for x in range(0,n1) if x not in idx_col] - weight_ = copy.deepcopy(weight) - - weight_ = np.delete(weight_, idx_col, 1) - row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) - for (r, c) in zip(row_ind, col_ind): - if weight_[r, c] >= threshold: - link[r] = avail_col[c] - self.link_info[t0] = link - - def get_series(self): - ## define new leaves and their unique identifiers at time points with new leaves emerging - t = 0 - key_t = 't{}'.format(t) - self.link_series[key_t] = dict() - self.link_series[key_t]['new_leaf'] = self.available_leaves[0] - # - self.link_series[key_t]['unique_id'] = self.link_series[key_t]['new_leaf'] - unique_id = len(self.link_series[key_t]['new_leaf']) - - for t in range(1, self.total_time): - # for t in range(1, self.total_time-1): - new_leaves = [i for i in self.available_leaves[t] if i not in self.link_info[t - 1]] - if new_leaves: - key_t = 't{}'.format(t) - self.link_series[key_t] = dict() - self.link_series[key_t]['new_leaf'] = np.array(new_leaves) - - id_temp = [] - for new_leaf in new_leaves: - id_temp.append(unique_id) - unique_id = unique_id + 1 - self.link_series[key_t]['unique_id'] = np.array(id_temp) - ## for time points with new leaves emerging, get the linking information for every new leaf - for key_t in self.link_series: - t0 = int(key_t.replace('t', '')) - for leaf in self.link_series[key_t]['new_leaf']: - key_leaf = 'leaf{}'.format(leaf) - self.link_series[key_t][key_leaf] = -np.ones(self.total_time, dtype=int) - self.link_series[key_t][key_leaf][t0] = leaf - if t0 < self.total_time - 1: - self.link_series[key_t][key_leaf][t0 + 1] = self.link_info[t0][leaf] - for t_ in range(t0 + 2, self.total_time): - idx = self.link_series[key_t][key_leaf][t_ - 1] - if idx < 0: - break - else: - self.link_series[key_t][key_leaf][t_] = self.link_info[t_ - 1][idx] - \ No newline at end of file From 9b73cd5f9f465603df8cccaa2a49092554f6cf36 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 16:18:52 -0500 Subject: [PATCH 178/274] Create time_series_linking.py --- .../time_series/time_series_linking.py | 544 ++++++++++++++++++ 1 file changed, 544 insertions(+) create mode 100644 plantcv/plantcv/time_series/time_series_linking.py diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py new file mode 100644 index 000000000..d27896afa --- /dev/null +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -0,0 +1,544 @@ +#### link utilities +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 9 09:53:34 2020 + +Functions used in time series linking after getting leaf instances segmented + +@author: hudanyunsheng +""" + +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import os +import sys +import random +import math +import skimage.io +import pickle as pkl +import re +from skimage.measure import find_contours +from matplotlib import patches, lines +from matplotlib.patches import Polygon +from plantcv import plantcv as pcv +import datetime +import copy +import colorsys +from plantcv.plantcv import fatal_error +from scipy.optimize import linear_sum_assignment +import csv + +def _random_colors(num, bright=True): + """ + Generate desired number of random colors. + To get visually distinct colors, generate them in HSV space then + convert to RGB. + Inputs: + num: number of colors to be generated + bright: True or False, if true, the brightness would be 1.0; if False, the brightness would be 0.7. By default it would be True (brightness 0.7) + Output: generated colors (a tuple) + """ + brightness = 1.0 if bright else 0.7 + hsv = [(i / num, 1, brightness) for i in range(num)] + colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv)) + random.shuffle(colors) + return colors + + +def _apply_mask(image, mask, color, alpha=0.5): + """Apply the given mask to the image with a color and alpha channel assigned. + Inputs: + image: original RGB image + mask: a binary mask of same size to the image + color: a list of 3 values + alpha: alpha value indicating transparency, by default 0.5 + """ + for c in range(3): + image[:, :, c] = np.where(mask == 1, + image[:, :, c] * + (1 - alpha) + alpha * color[c] * 255, + image[:, :, c]) + return image + + +def _get_ax(rows=1, cols=1, size=16): # ??? + """Return a Matplotlib Axes array to be used in + all visualizations in the notebook. Provide a + central point to control graph sizes. + + Adjust the size attribute to control how big to render images + """ + fig, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows)) + fig.tight_layout() + return ax + + +def _display_instances(image, masks, figsize=(16, 16), title="", ax=None, colors=None): + """ + This function is inspired by the function in mrcnn + masks: [height, width, num_instances] + inst_ids: [num_instances] + title: (optional) Figure title + show_mask, show_bbox: To show masks and bounding boxes or not + figsize: (optional) the size of the image + colors: (optional) An array or colors to use with each object + captions: (optional) A list of strings to use as captions for each object + """ + # If no axis is passed, create one and automatically call show() + # auto_show = False + if not ax: + _, ax = plt.subplots(1, figsize=figsize) + # auto_show = True + + num_insts = masks.shape[2] + # Generate random colors + colors = colors or _random_colors(num_insts) + + # Show area outside image boundaries. + height, width = image.shape[:2] + ax.set_ylim(height + 10, -10) + ax.set_xlim(-10, width + 10) + ax.axis('off') + ax.set_title(title) + + masked_image = image.astype(np.uint32).copy() + for i in range(num_insts): + color = colors[i] + + # Mask + mask = masks[:, :, i] + masked_image = _apply_mask(masked_image, mask, color) + + # Mask Polygon + # Pad to ensure proper polygons for masks that touch image edges. + padded_mask = np.zeros( + (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8) + padded_mask[1:-1, 1:-1] = mask + contours = find_contours(padded_mask, 0.5) + for verts in contours: + # Subtract the padding and flip (y, x) to (x, y) + verts = np.fliplr(verts) - 1 + p = Polygon(verts, facecolor="none", edgecolor=color) + ax.add_patch(p) + ax.imshow(masked_image.astype(np.uint8)) + # if auto_show: + # plt.show() + + +def _compute_overlaps_masks(masks1, masks2): + """Computes IoU and IoS overlaps between two sets of masks. + The masks should be of the same size + masks1, masks2: [Height, Width, instances] + """ + # If either set of masks is empty return empty result + if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + return np.zeros((masks1.shape[-1], masks2.shape[-1])) + n1 = masks1.shape[2] + n2 = masks2.shape[2] + intersections = np.zeros((n1, n2)) + unions = np.zeros((n1, n2)) + ioss = np.zeros((n1, n2)) + for idx_m in range(0, n1): + maski = np.expand_dims(masks1[:, :, idx_m], axis=2) + masks_ = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) + maski_ = np.reshape(maski > .5, (-1, maski.shape[-1])).astype(np.float32) + intersection = np.dot(masks_.T, maski_).squeeze() + intersections[idx_m, :] = intersection + union = np.sum(masks_, 0) + np.sum(maski_) - intersection + unions[idx_m, :] = union + ioss[idx_m, :] = intersection / maski_.sum() + ious = np.divide(intersections, unions) + return ious, ioss + + +class InstanceTimeSeriesLinking(object): + """A class that links segmented instances throughout time + Assumption: the timepoints are all sorted, the images and masks are also sorted by timepoints (chronologically) + """ + + def __init__(self, images, masks, timepoints, thres, logic='IOS', name_sub='instance'): + # a list of images which are ndarrays + self.images = images + # a list of masks which are ndarrays (of the same length of images) + self.masks = masks + # a list of timepoints (of the same length of images) + self.timepoints = timepoints + self.total_time = len(self.timepoints) + # number of instances: a list in which every element represent for number of instances in corresponding image + self.n_insts = [] + for i in range(0, len(self.masks)): + self.n_insts.append(self.masks[i].shape[2]) + + # initialization for linking + self.thres = thres + self.link_info = [-np.ones((self.n_insts[i]), dtype=int) for i in range(0, self.total_time - 1)] + self.unlinked_insts = [np.array(range(0, self.n_insts[i])) for i in range(0, self.total_time)] + self.link_series = dict() # only for those newly emerging leaves + self.weights = [] + self.logic = logic.upper() + self.name_sub = name_sub + self.key_id = '{}_ids'.format(name_sub) + + def save_linked_series(self, savedir, savename): + """save lining information into a .csv file and a .pkl file with the same prefix of filename + Inputs: savedir and savename + """ + l0 = ['', ''] + [x for (idx, x) in enumerate(self.timepoints)] + l1 = ['unique_id', 'current_id'] + ['t{}'.format(idx) for (idx, x) in enumerate(self.timepoints)] + csvfile = open(os.path.join(savedir, savename + '.csv'), 'w', newline='') + writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) + writer_junk.writerow(l0) + writer_junk.writerow(l1) + for (t0, item) in self.link_series.items(): + for uid, cid in zip(item['unique_id'], item[self.key_id]): + new_line = ['{}'.format(uid), '{}'.format(cid)] + item['{}{}'.format(self.name_sub, cid)].tolist() + writer_junk.writerow(new_line) + csvfile.close() + pkl.dump(self, open(os.path.join(savedir, savename + '.pkl'), 'wb')) + + def linking(self, t0): + """ + Link instance segments of t0 to those of t1 (t0+1) by calculating the overlaps of their masks + :param t0: starting time point + :return: None, but self.link_info would be updated + """ + masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy( + self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays + n0, n1 = masks0.shape[2], masks1.shape[2] + weight = -np.inf * np.ones((n0, n1)) + link = -np.ones(n0) + ious, ioss = _compute_overlaps_masks(masks0, masks1) + if self.logic == 'IOU': + weight = ious + elif self.logic == 'IOS': + weight = ioss + self.weights.append(weight) + idx_col = np.where(np.max(weight, axis=0) < self.thres)[ + 0] # find those columns with maximum value < threshold (self.thres) + avail_col = [x for x in range(0, n1) if x not in idx_col] + weight_ = copy.deepcopy(weight) + weight_ = np.delete(weight_, idx_col, 1) + + row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) + for (r, c) in zip(row_ind, col_ind): + if weight_[r, c] >= self.thres: + link[r] = avail_col[c] + self.link_info[t0] = link + + def get_series(self): + """ + Using the self.link_info to create a dictionary save in self.link_series + which is a dictionary contains information for every timepoint with new instances not seen before, + and the linking information for every instance + :return: + """ + # define new leaves and their unique identifiers at time points with new leaves emerging + t = 0 + key_t = 't{}'.format(t) + self.link_series[key_t] = dict() + self.link_series[key_t][self.key_id] = self.unlinked_insts[0] + # + self.link_series[key_t]['unique_id'] = self.link_series[key_t][self.key_id] + unique_id = len(self.link_series[key_t][self.key_id]) + + for t in range(1, self.total_time): + new_insts = [i for i in self.unlinked_insts[t] if i not in self.link_info[t - 1]] + if new_insts: + key_t = 't{}'.format(t) + self.link_series[key_t] = dict() + self.link_series[key_t][self.key_id] = np.array(new_insts) + + id_temp = [] + for new_inst in new_insts: + id_temp.append(unique_id) + unique_id = unique_id + 1 + self.link_series[key_t]['unique_id'] = np.array(id_temp) + ## for time points with new leaves emerging, get the linking information for every new instance + for key_t in self.link_series: + t0 = int(key_t.replace('t', '')) + for (leaf_unique, inst) in zip(self.link_series[key_t]['unique_id'], self.link_series[key_t][self.key_id]): + key_inst = '{}{}'.format(self.name_sub, inst) + self.link_series[key_t][key_inst] = -np.ones(self.total_time, dtype=int) + self.link_series[key_t][key_inst][t0] = inst + if t0 < self.total_time - 1: + self.link_series[key_t][key_inst][t0 + 1] = self.link_info[t0][inst] + for t_ in range(t0 + 2, self.total_time): + idx = self.link_series[key_t][key_inst][t_ - 1] + if idx < 0: + break + else: + self.link_series[key_t][key_inst][t_] = self.link_info[t_ - 1][idx] + + def update_series(self): + """ + Update the linking by comparing pairs of timepoint which are t and t+2 + :return: + """ + link_series = copy.deepcopy(self.link_series) + for t_, content in link_series.items(): + t = int(t_.replace('t', '')) + for inst_id in content[self.key_id]: + temp = np.where(content['{}{}'.format(self.name_sub, inst_id)] < 0)[0] + if len(temp) > 0: + t0 = temp[0] - 1 + t2 = t0 + 2 + if t2 <= self.total_time - 1: + t2_ = 't{}'.format(t2) + if t2_ in self.link_series: + t0_ = 't{}'.format(t0) + # print('\nt0: {}, t2: {}'.format(t0_, t2_)) + idx0 = content['{}{}'.format(self.name_sub, inst_id)][t0] + mask0 = np.expand_dims(self.masks[t0][:, :, idx0], axis=2) + idxs2 = self.link_series[t2_][self.key_id] + masks2 = self.masks[t2][:, :, idxs2] + n2 = masks2.shape[2] + ious, ioss = _compute_overlaps_masks(mask0, masks2) + if self.logic == 'IOU': + weight = ious + elif self.logic == 'IOS': + weight = ioss + idx_col = np.where(np.max(weight, axis=0) < self.thres)[ + 0] # find those columns with maximum value < thres + # avail_col = [x for x in range(0, n2) if x not in idx_col] + weight_ = copy.deepcopy(weight) + weight_ = np.delete(weight_, idx_col, 1) + row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) + for (r, c) in zip(row_ind, col_ind): + if weight_[r, c] >= self.thres: + inst0 = idx0 + inst2 = idxs2[c] + + # find the time point + idx_l2 = np.where(self.link_series[t2_][self.key_id] == inst2)[0][0] + # add information to t_ + self.link_series[t_]['{}{}'.format(self.name_sub, leaf_id)][t0 + 1:] = \ + self.link_series[t2_]['{}{}'.format(self.name_sub, inst2)][t0 + 1:] + # delete from t2_ + self.link_series[t2_][self.key_id] = np.delete(self.link_series[t2_][self.key_id], + idx_l2) + self.link_series[t2_]['unique_id'] = np.delete(self.link_series[t2_]['unique_id'], + idx_l2) + del self.link_series[t2_]['{}{}'.format(self.name_sub, inst2)] + # if nothing left for t2_, delete it entirely + if len(self.link_series[t2_]['unique_id']) == 0: + del self.link_series[t2_] + # update unique ids + uid = 0 + for t in self.link_series: + num = len(self.link_series[t][self.key_id]) + self.link_series[t]['unique_id'] = np.arange(uid, uid + num) + uid = uid + num + + def visualize(self, visualdir, csvdir, csvname='linking_info', colors=None, color_all=None): + """ + Create 3 sets of visualization + visualization set 1: one leaf per image (visualdir['1']) + visualization set 2: show with an alpha channel (visualdir['2']) + visualization method 3: show with bounding boxes (visualdir['3']) + Also save a csv file called linking_info.csv (or other user defined name) that includes the linking information + """ + + # create subfolders inside the provided directory of visualization + visualdirs = dict() + for i in range(1, 4): + idx = str(i) + visualdirs[idx] = os.path.join(visualdir, 'visualization{}'.format(idx)) + if not os.path.exists(visualdirs[idx]): + os.makedirs(visualdirs[idx]) + + if colors is None: + colors = _random_colors(40) + if color_all is None: + color_all = [[tuple() for i in range(0, num)] for num in self.n_insts] + + count = 0 + csvfile = open(os.path.join(csvdir, csvname + '.csv'), 'w', newline='') + writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) + writer_junk.writerow( + ['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) + for key_t in self.link_series: + ids = self.link_series[key_t]['unique_id'] + start_time = int(key_t.replace('t', '')) + insts_t = self.link_series[key_t][self.key_id] + for (unique_id, inst) in zip(ids, insts_t): + key_inst = '{}{}'.format(self.name_sub, inst) + link_inst = self.link_series[key_t][key_inst] + start_idx = link_inst[start_time] + for t in range(0, self.total_time): + img_t = self.images[t] + # initialize mask used in 1st visualization + mask_1 = np.zeros(img_t.shape[0:2], dtype=np.uint8) + # initialize mask used in 2nd visualization (with an alpha channel, ahpha=0.5) + mask_2 = np.ones(img_t.shape[0:2]) * 0.5 + title_vis2 = '{} (t{})'.format(self.timepoints[t], t) + if link_inst[t] >= 0: + title_vis2 = title_vis2 + ' leaf {}'.format(link_inst[t]) + mask_t = self.masks[t][:, :, link_inst[t]] + color_all[t][link_inst[t]] = colors[count] + + ## 1. save the masked image, i.e. single leaves + mask_1[np.where(mask_t)] = 255 + leaf_t = pcv.apply_mask(img_t, mask_1, mask_color='black') + writer_junk.writerow( + [unique_id, start_time, self.timepoints[start_time], t, self.timepoints[t], link_inst[t]]) + + pcv.print_image(leaf_t, os.path.join(visualdirs['1'], + '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, + start_idx, t, link_inst[t], + self.timepoints[t]))) + mask_2[np.where(mask_t == True)] = 1 + masked_im = np.concatenate((img_t.astype(float) / 255, np.expand_dims(mask_2, axis=2)), axis=2) + # for 2nd set of visualization, a folder for one unique instance + save_dir_ = os.path.join(visualdirs['2'], '{}_{}-{}'.format(unique_id, start_time, start_idx)) + if not os.path.exists(save_dir_): + os.makedirs(save_dir_) + fig2 = plt.figure(figsize=(5, 5)) + ax2 = fig2.add_subplot(1, 1, 1) + ax2.imshow(masked_im) + ax2.axis('off') + ax2.set_title(title_vis2, fontsize=16) + plt.savefig(os.path.join(save_dir_, str(self.timepoints[t]) + '.png')) + plt.close(fig2) + + count += 1 + csvfile.close() + ## 3. visualize with bounding boxes + for (img, masks, color, t) in zip(self.images, self.masks, color_all, self.timepoints): + _display_instances(img, masks, figsize=(16, 16), title="", ax=_get_ax(rows=1, cols=1, size=16), + colors=color) + plt.savefig(os.path.join(visualdirs['3'], '{}.png'.format(t))) + plt.close('all') + + def __call__(self, savedir, visualdir_, visualdir, savename_, savename, colors=None, color_all=None): + for t0 in range(0, self.total_time - 1): + self.linking(t0) + self.get_series() + self.visualize(visualdir_, savedir, 'link_info_old', colors, color_all) + self.save_linked_series(savedir, savename_) + + self.update_series() + self.visualize(visualdir, savedir, 'link_info', colors, color_all) + self.save_linked_series(savedir, savename) + +class InstanceTSLinkingWrapper(object): + """A class that is a wrapper for InstanceTimeSeriesLinking + Assumptions: + 1) The segmentation are saved in a different folder from original images + 2) The segmentation result and the original images have corresponding image names, which include "date-time" part with a specific pattern + the date-time pattern for names of original images and names of segmentation results are the same + 3) For every image, there is a corresponding segmentation result + Otherwise, please get your data prepared and use the "InstanceTimeSeriesLinking" class directly + """ + def __init__(self, dir_save, savename): + self.dir_save = dir_save + self.savename = savename + self.savename_ = '{}_old'.format(savename) + self.dir_img = None + self.dir_seg = None + self.time_cond = None + self.suffix = None + self.ext = None + self.suffix_seg = None + self.timepoints = [] + self.imgfiles = [] + self.segfiles = [] + self.dir_visual = None + self.dir_visual_ = None + + def set_save_dirs(self): + junk = datetime.datetime.now() + subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), + str(junk.hour).zfill(2), str(junk.minute).zfill(2), str(junk.second).zfill(2)) + self.dir_save = os.path.join(self.dir_save, subfolder) + if not os.path.exists(self.dir_save): + os.makedirs(self.dir_save) + self.dir_visual = os.path.join(self.dir_save, 'visualization') + if not os.path.exists(self.dir_visual): + os.makedirs(self.dir_visual) + self.dir_visual_ = os.path.join(self.dir_save, 'visualization_old') + if not os.path.exists(self.dir_visual_): + os.makedirs(self.dir_visual_) + + def sort_time(self, dir_img, dir_seg, pattern_dt, time_cond, suffix='.jpg', suffix_seg='.pkl'): + """ + This function is designed for files with file names which contain a "date-time" part, with an user-defined pattern + sort time based on names of original images + :param dir_img: directory of original images + :param dir_seg: directory of segmentation results + :param pattern_dt: the common pattern of date-time part in file names, e.g. YYYY-MM-DD-hh-mm + :param time_cond: the date time of data intended to be included in the analysis + :param suffix: the common suffix of all original image files + :param suffix_seg: the common suffix of all segmentation result files + :return: sorted and trimed list of images as well as segmentation files + """ + self.dir_img = dir_img + self.dir_seg = dir_seg + self.time_cond = time_cond + self.suffix = suffix + self.suffix_seg = suffix_seg + + # file name extension of original images + ext1, ext2 = os.path.splitext(self.suffix) + if ext1.startswith('.'): + self.ext = ext1 + elif ext2.startswith('.'): + self.ext = ext2 + + imgfs_all = [f for f in os.listdir(self.dir_img) if f.endswith(self.suffix)] + segfs_all = [f for f in os.listdir(self.dir_seg) if f.endswith(self.suffix_seg)] + imgfs_all.sort() + segfs_all.sort() + for (fi, fs) in zip(imgfs_all, segfs_all): + tempi = re.search(pattern_dt, fi) + temps = re.search(pattern_dt, fs) + if tempi and temps: + timepart = tempi.group() #timeparts = temps.group() the assumption is that original image and the results have the same dt_pattern + for cond in self.time_cond: + if timepart.endswith(cond): + self.timepoints.append(timepart) + continue + index_temp = np.argsort(self.timepoints) + self.timepoints = [self.timepoints[i] for i in index_temp] + self.imgfiles = [imgfs_all[i] for i in index_temp] + self.segfiles = [segfs_all[i] for i in index_temp] + + def load_images(self): + """ + Get the list of images (nd-arrays) ready + :return: + """ + temp_imgs = [] + sz = [] + imgs = [] + for f in self.imgfiles: + junk = skimage.io.imread(os.path.join(self.dir_img, f)) + temp_imgs.append(junk) + sz.append(np.min(junk.shape[0:2])) + min_dim = np.min(sz) + for junk in temp_imgs: + img = junk[0: min_dim, 0: min_dim, :] # make all images the same size + imgs.append(img) + return imgs, min_dim + + def load_segs(self, min_dim): + """ + Get the list of segmentation masks (nd-arrays) ready + :param min_dim: minimum dimension of images (get from original images) (Assumption: square images) + :return: + """ + segs = [] + for f in self.segfiles: + r = pkl.load(open(os.path.join(self.dir_seg, f), 'rb')) + segs.append(r["masks"][0:min_dim, 0:min_dim, :]) # make all masks the same size + return segs + + def __call__(self, dir_img, dir_seg, pattern_dt, time_cond, thres, logic, name_sub, suffix, suffix_seg,colors=None,color_all=None): + self.set_save_dirs() + self.sort_time(dir_img, dir_seg, pattern_dt, time_cond, suffix, suffix_seg) + imgs,min_dim = self.load_images() + segs = self.load_segs(min_dim) + # create an instance of InstanceTimeSeriesLinkingClass + inst_ts_linking = InstanceTimeSeriesLinking(imgs, segs, self.timepoints, thres, logic, name_sub) + inst_ts_linking(self.dir_save, self.dir_visual_, self.dir_visual, self.savename_, self.savename, colors, color_all) From 8b8b98a5e7af6c4b82390d29899166baa4a32142 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 16:19:15 -0500 Subject: [PATCH 179/274] Update tests.py --- tests/tests.py | 26 ++++++++++++++++++++------ 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 1732b4234..d26facd91 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6381,18 +6381,32 @@ def test_plantcv_visualize_time_lapse_video_bad_list(): # ############################## # Tests for the time_series subpackage # ############################## + def test_plantcv_time_series_time_series(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir - time_cond = ['08-05', '11-05', '17-05', '21-05'] - link_logic = 1 - class_names = ['BG', 'Leaf'] + dir_img = TIME_SERIES_TEST_RAW + dir_seg = TIME_SERIES_TEST_INSTANCE_SEG + dir_save = cache_dir + dir_visual_ = os.path.join(dir_save,'visual_old') + os.mkdir(dir_visual_) + dir_visual = os.path.join(dir_save,'visual') + os.mkdir(dir_visual) pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm - suffix = '.jpg' - Plant_ = pcv.time_series.time_series_linking(imagedir=TIME_SERIES_TEST_RAW, segmentationdir=TIME_SERIES_TEST_INSTANCE_SEG, savedir=cache_dir, pattern_datetime=pattern_datetime, time_cond=time_cond, colors=None, class_names=class_names, suffix=suffix) - assert len(os.listdir(Plant_.savedir)) > 0 + time_cond = ['08-05', '11-05', '17-05', '21-05'] + suffix = '.jpg' + suffix_seg = '.pkl' + thres = 0.2 + logic = 'IOS' + name_sub = 'leaf' + name_series = 'linked_series' + inst_ts_linking = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) + inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, thres, logic, name_sub, suffix, + suffix_seg) + assert len(os.listdir(inst_ts_linking.dir_save)) > 0 + def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From 424c7d3070c99c25a1e94d7a28736e346a97707b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 16:19:17 -0500 Subject: [PATCH 180/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index c5c7050a4..b93fec640 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,12 +1,12 @@ -from plantcv.plantcv.time_series.link_utilities import _random_colors -from plantcv.plantcv.time_series.link_utilities import _apply_mask -from plantcv.plantcv.time_series.link_utilities import display_instances -from plantcv.plantcv.time_series.link_utilities import _compute_overlaps_masks -from plantcv.plantcv.time_series.link_utilities import get_ax -from plantcv.plantcv.time_series.link_utilities import PlantData -from plantcv.plantcv.time_series.time_series import time_series_linking +from plantcv.plantcv.time_series.time_series_linking import _random_colors +from plantcv.plantcv.time_series.time_series_linking import _apply_mask +from plantcv.plantcv.time_series.time_series_linking import _get_ax +from plantcv.plantcv.time_series.time_series_linking import _display_instances +from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks +from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking +from plantcv.plantcv.time_series.time_series_linking import InstanceTSLinkingWrapper # add new functions to end of lists -__all__ = ["_random_colors", "_apply_mask", "display_instances", "_compute_overlaps_masks", "get_ax", - "PlantData", "time_series_linking"] +__all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", + "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] From 77b0541e042b549f5b55061ea4d5885a7ed94cb6 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 18:39:30 -0500 Subject: [PATCH 181/274] Update time_series.md Update docs for time_series --- docs/time_series.md | 153 +++++++++++++++++++++++++++++--------------- 1 file changed, 101 insertions(+), 52 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index 0a3cb208b..e31e58e26 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -1,36 +1,46 @@ ## time_series -This function is designed to link segmented objects over time. Images should be taken across a time (e.g. every several hours) and ideally there should be minimal movment of plant/camera. +This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every several hours) and ideally there should be minimal movment of plant/camera. -To run this function, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). +To use this class for generating time-series linking, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. -**plantcv.time_series.time_series_linking**(*imagedir, segmentationdir, savedir, time_cond, link_logic=1, class_names=['BG', 'Leaf'], mode='link', suffix='.jpg'*) +There are two way of using this class: either using it directly or using the wrapper. Examples of both are provided below. -**returns** An instance that is a Plant object +**\# initialize an instance of InstanceTimeSeriesLinking class:** + +**inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking**(*images, masks, timepoints, logic, thres, name_sub*) +**inst_ts_linking**(*savedir, visualdir_, visualdir, savename_, savename*) + +**returns** No returned value, the inst_ts_linking is an instance which belongs to InstanceTimeSeriesLinking class. + +- **Parameters for initialization:** + - images: a list of images. Every element of this list is an array represents one image + - masks: a list of instance segmentation masks. Every element of this list is an array represents instance segmentation masks correspond to one image. + - timepoints: a list of timepoints. The lengths for images, masks and timepoint should be the same and the elements are correspond to each other + - logic: the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) + If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". "IOS" is recommended. + - thres: threshold used in the linking logic as mentioned above. For "IOS" it is recommended to start with a threshod of 0.2. + - name_sub: name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". + +Once you get the class object initialized, it is callable, which means it is a callable function itself so that you can get the linking result by running the 2nd line of code presented above. +- **Parameters to call functions:** + - savedir: desired saving directory of linking result + - visualdir_: desired saving directory of visualization (before update) + - visualdir: desired saving directory of visualization (final) + - savename_: desired saving name of results (before update) + - savename: desired saving name of results (final) -- **Parameters:** - - imagedir: directory of original image used - - segmentationdir: directory of leaf instance segmentation result - - savedir: desired saving directory of linking result. Note that this function will generate a folder under your specified saving directory, with the date and time you run this programme - - pattern_datetime: the pattern of date and time part in original file names, dafault value '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' which represents YYYY-MM-DD-hh-mm. - - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment. Make sure the format of date matches the pattern-datetime. - - colors: a list of tuples represent for pre-defined colors. By default it is set to None and a list of colors will be generated and saved. - - class_names: used in bounding box visualization. by default there are background and leaf, i.e. class_names = ['BG', 'leaf'] - - suffix: the suffix of original images, make sure all images having the same suffix, e.g. suffix='.jpg' or suffix='-img8.jpg'. Make sure all the images desired having the same suffix pattern. - **Output:** - An instance of Plant object is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir". - -Note: under the user specified saving directory, a new folder named after the date and time (format: YYYY-MM-DD-HH-mm) the function runs will be created to save results. - -1. colors.pkl: the colors (represented by arrays) used in bounding box visualization. With this predefined color set, same color will represent for the leaf instance with same instance label all the time, i.e. same color represents leaf with the same label. Without this predefined list of color, the assignment of color will be random, i.e. the color provides no information to leaf indices. -2. details.txt: the logic of linking as well as time condition will be saved in this text file, so that would be easier for users to check these parameters for the specific experiment. -3. saved_plant.pkl: a "Plant" object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. -4. linking_info.csv: a csv file includes the linking information will be saved. -5. a folder called "visualization", which contains 3 subfolders: + An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir": +You will get two sets of results: those end with "_old" are results before updating; others are final results. +1. {}.pkl (or {}_old.pkl): an "InstanceTimeSeriesLinking" class instance, with all the information included: time points, original images, instance segmentation masks, etc. The filename is specified by user. +2. {}.csv (or {}_old.csv): a csv file includes the linking series information (every row in the table is a unique instance throughtout time). +3. link_info.csv (or {}_old.csv): a csv file includes the linking information. +4. a folder called "visualization" ("visualization_old"), which contains 3 subfolders: 1) a folder call "visualization 1", which contains 1st set of visualization In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. @@ -64,46 +74,24 @@ Note: under the user specified saving directory, a new folder named after the da {}-visual.png The original image name is inside the {}. This set of visualization is designed for making time-lapse videos of segmentation shown with bounding boxes. With the instance labels re-assigned such that one label represents one leaf across the whole time, we would observe that every leaf is represented by a specific color across the whold time period. - -Before running the time_series_linking function, it is always a good practice to check the quality of instance segmentation before run time_series_linking. One way to check the instance segmentation is to make a time-lapse video with your instance segmentation result shown in bounding boxes. To make time-lapse videos, you will need video editing softwares, e.g. imovie. An example is shown below. Notice that in almost all cases, you would observe that the colors for the same leaf change during the time. That is due to the random assignment of labels. You can later compare this time lapse video to that generate after all labels re-assigned by running time_series_linking function. + +It is always a good practice to check the quality of instance segmentation before running time_series_linking. One way to check the instance segmentation is to make a time-lapse video with your instance segmentation result shown in bounding boxes. To make time-lapse videos, you will need video editing softwares, e.g. imovie. An example is shown below. Notice that in almost all cases, you would observe that the colors for the same leaf change during the time. That is due to the random assignment of labels. You can later compare this time lapse video to that generate after all labels re-assigned by running time_series_linking function. Instead of making a video, another way to check the quality of instance segmentation is by checking the segmented image one by one. - ```python from plantcv import plantcv as pcv # Below are examples of input variables, always adjust base on your own application. - -## Specify the directory of original image -path_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol_512' - -## Specify the directory of instance segmentation result -path_segmentation = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/segmentation/updated' - -## Specify the desired directory to save results -path_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' - -## Specify the date-time pattern of original image names -pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm - -## Specify the desired time point to include in to the analysis -time_cond = ['08-05', '11-05', '17-05', '21-05'] # exp5 & exp6 - -class_names = ['BG', 'Leaf'] - -suffix = '-img{}.jpg'.format(idx_plant) - -linked_Plant = pcv.time_series.time_series_linking(imagedir=path_img, segmentationdir=path_segmentation, savedir=path_save, - pattern_datetime=pattern_datetime, time_cond=time_cond, class_names=['BG', 'Leaf'], suffix=suffix) - +inst_ts_linking = InstanceTimeSeriesLinking(images, masks, timepoints, logic, thres, name_sub) +inst_ts_linking(save_dir, visualdir_, visualdir, savename_, savename, csvname_, csvname) ``` When it finished, you can go ahead and check the saved result. If you are not sure where the results are saved, you can type: -```linked_Plant.savedir``` +```inst_ts_linking.savedir``` in a cell to print out the saving directory. -You can also type ```linked_Plant.link_series``` in a cell to print linking information on the screen for a quick check. +You can also type ```inst_ts_linking.link_series``` in a cell to print linking information on the screen for a quick check. Now that we have the linking results saved, we can have some analysis based on them. There are 3 suggested analysis described as below: @@ -122,8 +110,6 @@ You have two options of making time-lapse videos: using video editing software ( - There is also a PlantCV tool that is able to automatically generate and save time-lapse videos. Check here for the usage. The example code is provided at the end of this documentation. - - 3. Making a time-lapse video to visualize the growth of the plant by showing the segmentation in bounding boxes, with the same leaf always shown with the same color. Direct yourself to "visualization 3" folder and include all images to a video project, sort them by name in an ascending order, and generate a time-lapse video. An example of time-lapse video with visualization shown in bounding boxes is shown below. When compare to the video made of instance segmentation, result, you will observe that now every leaf is represented by the same color all the time, which means the label re-assignemnt is successful. @@ -159,6 +145,69 @@ path_video_3 = Plant.visualdir pcv.visualize.time_lapse_video(img_directory=path_visual3, suffix_img=Plant.ext, name_video=name_video_3, path_video=path_video_3, display='on') ``` +You might have noticed that to use the method described above, you will have to get data prepared, i.e. the images should be in a list and the masks should be in a list of same lenghth. +Both of them should be sorted chronologically. You can absolutely do this, and it is not hard to do, but alternatively, you are also welcome to use another class, which is a wrapper, by which +you would only need to provide directories, and conditions for time points. +InstanceTSLinkingWrapper(object): + +**inst_ts_link_wrapper = plantcv.time_series.InstanceTSLinkingWrapper**(*dir_save, savename*) +**inst_ts_link = inst_ts_link_wrapper**(*dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, suffix, suffix_seg*) +**returns** An instance which belongs to InstanceTimeSeriesLinking class. + +- **Parameters for initialization:** + - dir_save: the desired directory to save the results + Note: under the user specified saving directory, a new folder named after the date and time (format: YYYY-MM-DD-HH-mm) the function runs will be created to save results. + - savename: the desired name to save the result + +Once you get the class object initialized, it is callable, which means it is a callable function itself so that you can get the linking result by running the 2nd line of code presented above. +- **Parameters to call functions:** + - dir_img: directory of original images + - dir_seg: directory of segmentation results + - pattern_dt: the pattern of date and time part in original file names, dafault value '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' which represents YYYY-MM-DD-hh-mm. + - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ['08-05', '15-05'] represents for including data collected at 8:05am and 3:05pm everyday in this experiment. Make sure the format of date matches the pattern-datetime. + - logic: the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) + If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". "IOS" is recommended. + - thres: threshold used in the linking logic as mentioned above. For "IOS" it is recommended to start with a threshod of 0.2. + - name_sub: name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". + - suffix: the suffix of original images, make sure all images having the same suffix, e.g. suffix='.jpg' or suffix='-img8.jpg'. Make sure all the images desired having the same suffix pattern. By default ".jpg" + - suffix_seg: the suffix of segmentation results, make sure all segmentation results having the same suffix. By default '.pkl' + +```python +from plantcv import plantcv as pcv +# Below are examples of input variables, always adjust base on your own application. +## Specify the desired directory to save results +dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' + +## Specify the desired name to save the result (prefix) +savename = 'linked_series' + +## Initialize and instance of class InstanceTSLinkingWrapper +inst_ts_linking_wrap = InstanceTSLinkingWrapper(dir_save=path_save, savename=name_series) + +## Specify the directory of original image +dir_img = '/shares/mgehan_share/acasto/auto_crop/output_10.1.9.214_wtCol_512' +## Specify the directory of instance segmentation result +dir_seg = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/segmentation/updated' +## Specify the date-time pattern of original image names +pattern_dt = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm +## Specify the desired time point to include in to the analysis +time_cond = ['08-05', '11-05', '17-05', '21-05'] +## Specify the linking logic and threshold +logic = 'IOS' +thres = 0.2 +## Specify the main subject +name_sub = 'leaf' +## Specify the common suffix of interested original images +suffix = '-img12.jpg' +## Specify the common suffix of interested segmentation result +suffix_seg = '.pkl' + +inst_ts_linking = pcv.time_series.inst_ts_linking_wrap(dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, suffix, suffix_seg) +``` +All the analysis for the results are same to what described above. + + + **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) From 5c77c2d79e969d66d41c60ecd5b3f886b5676f89 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 18:39:38 -0500 Subject: [PATCH 182/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index d27896afa..b3a802b99 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -158,7 +158,7 @@ class InstanceTimeSeriesLinking(object): Assumption: the timepoints are all sorted, the images and masks are also sorted by timepoints (chronologically) """ - def __init__(self, images, masks, timepoints, thres, logic='IOS', name_sub='instance'): + def __init__(self, images, masks, timepoints, logic='IOS', thres=0.2, name_sub='instance'): # a list of images which are ndarrays self.images = images # a list of masks which are ndarrays (of the same length of images) @@ -534,11 +534,12 @@ def load_segs(self, min_dim): segs.append(r["masks"][0:min_dim, 0:min_dim, :]) # make all masks the same size return segs - def __call__(self, dir_img, dir_seg, pattern_dt, time_cond, thres, logic, name_sub, suffix, suffix_seg,colors=None,color_all=None): + def __call__(self, dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, suffix, suffix_seg,colors=None,color_all=None): self.set_save_dirs() self.sort_time(dir_img, dir_seg, pattern_dt, time_cond, suffix, suffix_seg) imgs,min_dim = self.load_images() segs = self.load_segs(min_dim) # create an instance of InstanceTimeSeriesLinkingClass - inst_ts_linking = InstanceTimeSeriesLinking(imgs, segs, self.timepoints, thres, logic, name_sub) + inst_ts_linking = InstanceTimeSeriesLinking(imgs, segs, self.timepoints, logic, thres, name_sub) inst_ts_linking(self.dir_save, self.dir_visual_, self.dir_visual, self.savename_, self.savename, colors, color_all) + return inst_ts_linking From 888324bb69a39131ac696ec5d70ff4734c25b112 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 18:39:41 -0500 Subject: [PATCH 183/274] Update tests.py --- tests/tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index d26facd91..e7cc98051 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6403,7 +6403,7 @@ def test_plantcv_time_series_time_series(): name_sub = 'leaf' name_series = 'linked_series' inst_ts_linking = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) - inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, thres, logic, name_sub, suffix, + inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, logic, thres, name_sub, suffix, suffix_seg) assert len(os.listdir(inst_ts_linking.dir_save)) > 0 From c11ef1fa10322486b9659a21e2893fc43a3978b0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 23 Sep 2020 22:20:11 -0500 Subject: [PATCH 184/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index b3a802b99..26ee0a97b 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -313,7 +313,7 @@ def update_series(self): # find the time point idx_l2 = np.where(self.link_series[t2_][self.key_id] == inst2)[0][0] # add information to t_ - self.link_series[t_]['{}{}'.format(self.name_sub, leaf_id)][t0 + 1:] = \ + self.link_series[t_]['{}{}'.format(self.name_sub, inst_id)][t0 + 1:] = \ self.link_series[t2_]['{}{}'.format(self.name_sub, inst2)][t0 + 1:] # delete from t2_ self.link_series[t2_][self.key_id] = np.delete(self.link_series[t2_][self.key_id], From a16ecea2dd6372753ab232ae7377be5ca7df2323 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 25 Sep 2020 08:21:31 -0500 Subject: [PATCH 185/274] Delete time_series.py --- plantcv/plantcv/time_series/time_series.py | 208 --------------------- 1 file changed, 208 deletions(-) delete mode 100644 plantcv/plantcv/time_series/time_series.py diff --git a/plantcv/plantcv/time_series/time_series.py b/plantcv/plantcv/time_series/time_series.py deleted file mode 100644 index d8a60b148..000000000 --- a/plantcv/plantcv/time_series/time_series.py +++ /dev/null @@ -1,208 +0,0 @@ -#### link utilities -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Jun 9 09:53:34 2020 - -Time series linking after getting leaf instances segmented - -@author: hudanyunsheng -""" - -import os -import sys -import random -import math -import numpy as np -import skimage.io -import matplotlib -import matplotlib.pyplot as plt -import pickle as pkl -import re -from plantcv import plantcv as pcv -import copy -import csv -# import link_utilities as funcs -# import pcv.time_series.link_utilities as funcs - -def time_series_linking(imagedir, segmentationdir, savedir, pattern_datetime, time_cond, colors=None, class_names=['BG', 'Leaf'], suffix='.jpg'): - - """ - Function used to get leaf instance growth information after segment leaf instances (using maskrcnn or other methods) - Input: - imagedir: directory of original image used - segmentationdir: directory of leaf instance segmentation result - savedir: desired saving directory of linking result - pattern_datetime: the regular expression of date-time part in file names. e.g. if the date-time part in file names is: YYYY-MM-DD-hh-mm, then pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' - time_cond: condition of data used, indicated by list of times, e.g. time_cond = ["08-05", "15-05"] represents for including data collected at 8:05am and 3:05pm everyday in this experiment, this should be consistent with the pattern_datetime - colors: a list of tuples where every tuple represent for one color (RGB values). By default it can be None. - class_names: used in bounding box visualization. By default there are background and leaf - suffix: the ending part of file names that every image have in common. By default it is '.jpg'. - Output: - An instance from "Plant" class would be returned, all the results will be saved in a folder with name of date and time when the function runs inside the user defined "savedir". Sample folder name: 2020-06-23-15-42 (a format of YYYY-MM-DD-HH-mm). - 1. colors.pkl: the colors (indicated by arrays) used in bounding box visualization. Without this predefined list of color, the assignment of color will be random. With this predefined color set, same color will represent for the same leaf all the time - 2. details.txt: the logic of linking as well as time condition will be shown, so that would be easier for users to check these parameters for the specific expreiment - 3. saved_plant.pkl: a Plant object instance will be saved, with all the information included: time points, original images, instance segmentation masks, etc. - 4. linking_info.csv: a csv file includes the linking information will be saved. - 5. a folder called "visualization", which contains 3 subfolders: - 1) a folder call "visualization 1", which contains 1st set of visualization - In this set of visualization, the instance segmentation masks are applied to original images, so that there is only 1 leaf in every image. - result name: {}_{}-{}-{}-{}_{}.png - Naming convention for file names: - 1st digit: unique identifier of the leaf - 2nd digit: time of first emergence of the leaf - 3rd digut: leaf index when it first emerges - 4rd digit: current time point - 5th digit: current leaf index - 6th digit: original image name - - 2) a folder called "visualization 2", which contains 2nd set of visualization - This set of visualization show results with an alpha channel, such that we can see the main leaf in the original image, with other parts being half transparent - There are several subfolders, the number of subfolders depends on the number of "new leaves" in total - Every subfolder is a "new leaf", whose name is {}_{}-{} - Naming convention for folder names: - 1st digit: unique identifier of the leaf - 2nd digit: time of first emergence of the leaf - 3rd digut: leaf index when it first emerges - Inside every folder, images of leaves with names same as original image names are contained. - - 3) a folder called "visualization 3", which containes 3rd set of visualization - This set of visualization show results with bounding boxes. In every image, different leaves are show in bounding boxes with different colors. - Naming format: {}-visual.png - The original image name is inside the {}. - """ - # initialize Plant class - # Plant = funcs.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) - Plant = pcv.time_series.PlantData(imagedir, segmentationdir, savedir, pattern_datetime, suffix) - - Plant.getpath(Plant.imagedir) - Plant.Sorttime(time_cond) - Plant.load_images() - - # load mrcnn inferencing results - Plant.load_results() - - Plant.getinitleaf() - Plant.getmaxleaf() - - Plant.gettotaltime() - Plant.getnumemergence() - - # plot the #leaves vs. time relationship - plt.plot(np.array(range(0, len(Plant.num_leaves))), Plant.num_leaves) - - if not os.path.exists(os.path.join(Plant.savedir, 'details.txt')): - file = open(os.path.join(Plant.savedir, 'details.txt'), 'w') - file.write('mode: IOU\n') - file.write('Directory of original images: {}\n'.format(Plant.imagedir)) - file.write('Directory of instance segmentation: {}\n'.format(Plant.segmentationdir)) - file.write('Image conditions: {}'.format(time_cond)) - file.close() - - print('start\n') - print('...') - - # linking initialization - Plant.initialize_linking() - - # link - for t in range(0, Plant.total_time - 1): -# Plant.linking(t, mode=link_logic) - Plant.linking(t) - Plant.get_series() - - ####### visualization ####### - # load original images - if len(Plant.images) == 0: - Plant.load_images() # Plant.images - - # visualization method 1: show only one leaf per image - path_visual1 = os.path.join(Plant.visualdir, 'visualization1') - if not os.path.exists(path_visual1): - os.makedirs(path_visual1) - - # visualization method 2: show with an alpha channel - path_visual2 = os.path.join(Plant.visualdir, 'visualization2') - if not os.path.exists(path_visual2): - os.makedirs(path_visual2) - - # visualization method 3: show with bounding boxes - path_visual3 = os.path.join(Plant.visualdir, 'visualization3') - if not os.path.exists(path_visual3): - os.makedirs(path_visual3) - - count = 0 - if colors is None: - if not os.path.exists('{}/colors.pkl'.format(Plant.savedir)): - # colors = funcs._random_colors(40) - colors = pcv.time_series._random_colors(40) - pkl.dump(colors, open('{}/colors.pkl'.format(Plant.savedir), 'wb')) - else: - colors = pkl.load(open('{}/colors.pkl'.format(Plant.savedir), 'rb')) - - color_all = [[tuple() for i in range(0, num)] for num in Plant.num_leaves] - - csvfile = open(os.path.join(Plant.savedir, 'linking_info.csv'), 'w', newline='') - writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) - writer_junk.writerow(['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) - for key_t in Plant.link_series: - # - ids = Plant.link_series[key_t]['unique_id'] - - start_time = int(key_t.replace('t', '')) - leaves_t = Plant.link_series[key_t]['new_leaf'] - for (unique_id, leaf) in zip(ids, leaves_t): - key_leaf = 'leaf{}'.format(leaf) - link_leaf = Plant.link_series[key_t][key_leaf] - start_idx = link_leaf[start_time] - for t in range(start_time, Plant.total_time): - img = Plant.images[t] - if link_leaf[t] >= 0: - color_all[t][link_leaf[t]] = colors[count] - mask_t = Plant.masks[t][:, :, link_leaf[t]] - - ## 1. save the masked image, i.e. single leaves - mask = np.zeros(mask_t.shape, dtype=np.uint8) - mask[np.where(mask_t)] = 255 - leaf_t = pcv.apply_mask(img, mask, mask_color='black') - - writer_junk.writerow([unique_id, start_time, Plant.filename_pre[start_time], t, Plant.filename_pre[t], link_leaf[t]]) - - pcv.print_image(leaf_t, os.path.join(path_visual1, '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, - start_idx, t, link_leaf[t], - Plant.filename_pre[t]))) - pcv.print_image(leaf_t, os.path.join(path_visual1, - '{}_{}-{}-{}-{}_{}{}'.format(unique_id, start_time, - start_idx, t, link_leaf[t], - Plant.filename_pre[t], - Plant.ext))) - - - ## 2. show result with an alpha channel - # update the mask where there is an alpha channel (alpha=0.5) - mask_ = np.ones(mask_t.shape) * 0.5 - mask_[np.where(mask_t == True)] = 1 - masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_, axis=2)), axis=2) - save_dir_ = os.path.join(path_visual2, '{}_{}-{}'.format(unique_id, start_time, start_idx)) - if not os.path.exists(save_dir_): - os.makedirs(save_dir_) - fig2 = plt.figure(figsize=(5, 5)) - ax2 = fig2.add_subplot(1, 1, 1) - ax2.imshow(masked_im) - ax2.axis('off') - plt.savefig(os.path.join(save_dir_, str(Plant.filename_pre[t]) + Plant.ext)) - plt.close(fig2) - - count += 1 - ## 3. visualize with bounding boxes - for (img, mask, roi, class_id, score, color, t) in zip(Plant.images, Plant.masks, Plant.rois, Plant.class_ids, - Plant.scores, color_all, Plant.filename_pre): - # funcs.display_instances(img, roi, mask, class_id, class_names, score, ax=funcs.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) - pcv.time_series.display_instances(img, roi, mask, class_id, class_names, score, ax=pcv.time_series.get_ax(rows=1, cols=1, size=16), show_bbox=True, show_mask=True, colors=color) - plt.savefig(os.path.join(path_visual3, '{}-visual{}'.format(t, Plant.ext))) - plt.close('all') - # save all information - pkl.dump(Plant, open(os.path.join(Plant.savedir, 'saved_plant.pkl'), 'wb')) - print('\nFinished!\nThe results are saved here:\n{}'.format(Plant.savedir)) - - return Plant \ No newline at end of file From a8f5e6122281ee26bb9359029464d4e8a96b564a Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 25 Sep 2020 12:42:23 -0500 Subject: [PATCH 186/274] Update time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 45 +++++++++++-------- 1 file changed, 26 insertions(+), 19 deletions(-) diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py index ac3139f1d..759f450c5 100644 --- a/plantcv/plantcv/visualize/time_lapse_video.py +++ b/plantcv/plantcv/visualize/time_lapse_video.py @@ -61,24 +61,27 @@ def _resize_img(img, size): def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=None, fps=29.97, name_video='time_lapse_video', path_video=None, display='on'): - """Generate time-lapse video given a folder of images - Inputs: - img_directory: the directory of folder of images to make the time-lapse video - list_img: desired list of images in img_directory to create the video. If None is passed, all images would be included by default. - name_video: the prefix of output video name - size_frame: the desired size of every frame. + """ + Generate time-lapse video given a folder of images + :param img_directory: the directory of folder of images to make the time-lapse video. + :param list_img: desired list of images in img_directory to create the video. If None is passed, all images would be included by default. + :param suffix_img: + :param size_frame: the desired size of every frame. In a video, every frame should have the same size. The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. If the frame size is given, if an images is larger than the given size, the image would be cropped automatically; if an image is smaller than the given size, the image would be zero-padded automatically If the frame size is not given, the largest size of all images would be used as the frame size. - fps: (frames per second) frame rate + :param fps: (frames per second) frame rate Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 - path_video: the desired saving path of output video. If not given, the video would be saved the the same directory of the images. - display: indicator of whether to display current status (by displaying saving directory and saving name) while running this function - Outputs: - list_img: the list of images used to generate the video - size_frame: the frame size of the video - """ + :param name_video: + :param path_video: the desired saving path of output video. If not given, the video would be saved the the same directory of the images. + :param display: indicator of whether to display current status (by displaying saving directory and saving name) while running this function + :param kw: possible name and value: compress, and a number indicating what fraction compared to the original image. e.g. 0.5 represent compress the image to 1/2 of its original size + :return: + list_img: the list of images used to generate the video + size_frame: the frame size of the video + """ + ## Get the list of image files in the given directory and sort them alphabetically by their names temp_list = [] for f in os.listdir(img_directory): @@ -130,12 +133,10 @@ def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=N list_r.append(img.shape[0]) list_c.append(img.shape[1]) imgs.append(img) - max_c = np.max(list_c) - max_r = np.max(list_r) + max_c, max_r = np.max(list_c), np.max(list_r) # If the frame size is not provided, use the largest size of the images as the frame size - if size_frame is None: - size_frame = (max_c, max_r) + size_frame = size_frame or (max_c, max_r) if not (len(np.unique(list_r)) == 1 and len(np.unique(list_c)) == 1): warnings.warn("The sizes of images are not the same, an image resizing (cropping or zero-padding) will be done to make all images the same size ({}x{}) before creating the video! If you assume the images should have the same size, please check the images used to generate this video!".format( @@ -149,10 +150,16 @@ def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=N save_name = os.path.join(path_video, name_video + '.mp4') fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case + # if 'compress' in kw: + # ratio = kw['compress'] + # else: + # ratio = 1 + # size_frame_ = tuple(int(x*ratio) for x in size_frame) out = cv2.VideoWriter(save_name, fourcc, fps, size_frame) - for img in imgs: - out.write(_resize_img(img, (max_r, max_c))) + for img in imgs: + # out.write(cv2.resize(_resize_img(img, (size_frame[-1],size_frame[0])), (size_frame_))) + out.write(_resize_img(img, (size_frame[-1], size_frame[0]))) out.release() cv2.destroyAllWindows() if display == 'on': From 4e50631402fca730df1015ed02c18de1ccee402b Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 25 Sep 2020 12:42:30 -0500 Subject: [PATCH 187/274] Update tests.py --- tests/tests.py | 38 +++++++++++++++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index e7cc98051..390311459 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6304,7 +6304,6 @@ def test_plantcv_visualize_colorspaces(): vis_img = pcv.visualize.colorspaces(rgb_img=img) assert np.shape(vis_img)[1] > (np.shape(img)[1]) and np.shape(vis_img_small)[1] > (np.shape(img)[1]) - def test_plantcv_visualize_colorspaces_bad_input(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_plot_hist") @@ -6315,6 +6314,34 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) +def test_plantcv_visualize_time_lapse_video_resize_img(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + resized_img = pcv.visualize._resize_img(img, (10,10)) + assert np.shape(resized_img) == (10,10,3) + +def test_plantcv_visualize_time_lapse_video_resize_img_large(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + resized_img = pcv.visualize._resize_img(img, (1000,1000)) + assert np.shape(resized_img) == (1000,1000,3) + +def test_plantcv_visualize_time_lapse_video_resize_img_gray(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) + resized_img = pcv.visualize._resize_img(img, (10,10)) + assert np.shape(resized_img) == (10,10) + +def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) + resized_img = pcv.visualize._resize_img(img, (1000,1000)) + assert np.shape(resized_img) == (1000,1000) + # case1: only the correct directory of images is provided def test_plantcv_visualize_time_lapse_video_case1(): # Test cache directory @@ -6324,6 +6351,15 @@ def test_plantcv_visualize_time_lapse_video_case1(): _, _ = pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', path_video=cache_dir, display='off') assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case1.mp4')) +# case1.5: no path_video provided and display "on" +def test_plantcv_visualize_time_lapse_video_case1(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case1') + os.mkdir(cache_dir) + with pytest.warns(UserWarning): + _, _ = pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', display='on') + assert os.path.exists(os.path.join(TIME_SERIES_TEST_RAW, 'time_lapse_video_case1.mp4')) + # case2: the correct directory of images as well as the correct suffix of images is provided def test_plantcv_visualize_time_lapse_video_case2(): # Test cache directory From efc20967142d0e5353b7aaffac0032a9bc037a70 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 08:47:06 -0500 Subject: [PATCH 188/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index b93fec640..ca0a009fb 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,8 +1,8 @@ -from plantcv.plantcv.time_series.time_series_linking import _random_colors -from plantcv.plantcv.time_series.time_series_linking import _apply_mask -from plantcv.plantcv.time_series.time_series_linking import _get_ax -from plantcv.plantcv.time_series.time_series_linking import _display_instances -from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks +# from plantcv.plantcv.time_series.time_series_linking import _random_colors +# from plantcv.plantcv.time_series.time_series_linking import _apply_mask +# from plantcv.plantcv.time_series.time_series_linking import _get_ax +# from plantcv.plantcv.time_series.time_series_linking import _display_instances +# from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking from plantcv.plantcv.time_series.time_series_linking import InstanceTSLinkingWrapper From f3464b371f7fcc17281b36eb7d0b8b968ee98009 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 10:05:26 -0500 Subject: [PATCH 189/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index ca0a009fb..a187a3f02 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -8,5 +8,7 @@ # add new functions to end of lists -__all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", - "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] +# __all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", +# "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] + +__all__ = ["InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] From 4aee79b60051195eb7a9b48519032295d238dad9 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 10:05:28 -0500 Subject: [PATCH 190/274] Update __init__.py --- plantcv/plantcv/visualize/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/visualize/__init__.py b/plantcv/plantcv/visualize/__init__.py index b5fd7da87..ef3addb9f 100644 --- a/plantcv/plantcv/visualize/__init__.py +++ b/plantcv/plantcv/visualize/__init__.py @@ -11,7 +11,7 @@ "overlay_two_imgs","display_instances"] from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video -from plantcv.plantcv.visualize.time_lapse_video import _resize_img - -__all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] +# from plantcv.plantcv.visualize.time_lapse_video import _resize_img +# __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] +__all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video"] From a465fba1ac41c4897b1a857e3e342bb233c889d1 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 10:39:45 -0500 Subject: [PATCH 191/274] Update tests.py --- tests/tests.py | 54 +++++++++++++++++++++++++------------------------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 390311459..17fa274e9 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6314,33 +6314,33 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) -def test_plantcv_visualize_time_lapse_video_resize_img(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - resized_img = pcv.visualize._resize_img(img, (10,10)) - assert np.shape(resized_img) == (10,10,3) - -def test_plantcv_visualize_time_lapse_video_resize_img_large(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - resized_img = pcv.visualize._resize_img(img, (1000,1000)) - assert np.shape(resized_img) == (1000,1000,3) - -def test_plantcv_visualize_time_lapse_video_resize_img_gray(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) - resized_img = pcv.visualize._resize_img(img, (10,10)) - assert np.shape(resized_img) == (10,10) - -def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) - resized_img = pcv.visualize._resize_img(img, (1000,1000)) - assert np.shape(resized_img) == (1000,1000) +# def test_plantcv_visualize_time_lapse_video_resize_img(): +# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') +# os.mkdir(cache_dir) +# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) +# resized_img = pcv.visualize._resize_img(img, (10,10)) +# assert np.shape(resized_img) == (10,10,3) +# +# def test_plantcv_visualize_time_lapse_video_resize_img_large(): +# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') +# os.mkdir(cache_dir) +# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) +# resized_img = pcv.visualize._resize_img(img, (1000,1000)) +# assert np.shape(resized_img) == (1000,1000,3) +# +# def test_plantcv_visualize_time_lapse_video_resize_img_gray(): +# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') +# os.mkdir(cache_dir) +# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) +# resized_img = pcv.visualize._resize_img(img, (10,10)) +# assert np.shape(resized_img) == (10,10) +# +# def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): +# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') +# os.mkdir(cache_dir) +# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) +# resized_img = pcv.visualize._resize_img(img, (1000,1000)) +# assert np.shape(resized_img) == (1000,1000) # case1: only the correct directory of images is provided def test_plantcv_visualize_time_lapse_video_case1(): From c8faaf60ac3e339e3410cc05ac79398179f1d0b5 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 15:27:36 -0500 Subject: [PATCH 192/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index a187a3f02..b93fec640 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,14 +1,12 @@ -# from plantcv.plantcv.time_series.time_series_linking import _random_colors -# from plantcv.plantcv.time_series.time_series_linking import _apply_mask -# from plantcv.plantcv.time_series.time_series_linking import _get_ax -# from plantcv.plantcv.time_series.time_series_linking import _display_instances -# from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks +from plantcv.plantcv.time_series.time_series_linking import _random_colors +from plantcv.plantcv.time_series.time_series_linking import _apply_mask +from plantcv.plantcv.time_series.time_series_linking import _get_ax +from plantcv.plantcv.time_series.time_series_linking import _display_instances +from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking from plantcv.plantcv.time_series.time_series_linking import InstanceTSLinkingWrapper # add new functions to end of lists -# __all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", -# "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] - -__all__ = ["InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] +__all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", + "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] From 54fff81984a1c99f2c74ed59e1c2fb3eeae5d079 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 15:27:38 -0500 Subject: [PATCH 193/274] Update __init__.py --- plantcv/plantcv/visualize/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/visualize/__init__.py b/plantcv/plantcv/visualize/__init__.py index ef3addb9f..307f32563 100644 --- a/plantcv/plantcv/visualize/__init__.py +++ b/plantcv/plantcv/visualize/__init__.py @@ -11,7 +11,7 @@ "overlay_two_imgs","display_instances"] from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video -# from plantcv.plantcv.visualize.time_lapse_video import _resize_img +from plantcv.plantcv.visualize.time_lapse_video import _resize_img # __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video"] From 270ffeffe66380f602e93e4d1fe61bbd26195fff Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 28 Sep 2020 15:27:54 -0500 Subject: [PATCH 194/274] Update tests.py --- tests/tests.py | 54 +++++++++++++++++++++++++------------------------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 17fa274e9..390311459 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6314,33 +6314,33 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) -# def test_plantcv_visualize_time_lapse_video_resize_img(): -# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') -# os.mkdir(cache_dir) -# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) -# resized_img = pcv.visualize._resize_img(img, (10,10)) -# assert np.shape(resized_img) == (10,10,3) -# -# def test_plantcv_visualize_time_lapse_video_resize_img_large(): -# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') -# os.mkdir(cache_dir) -# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) -# resized_img = pcv.visualize._resize_img(img, (1000,1000)) -# assert np.shape(resized_img) == (1000,1000,3) -# -# def test_plantcv_visualize_time_lapse_video_resize_img_gray(): -# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') -# os.mkdir(cache_dir) -# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) -# resized_img = pcv.visualize._resize_img(img, (10,10)) -# assert np.shape(resized_img) == (10,10) -# -# def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): -# cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') -# os.mkdir(cache_dir) -# img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) -# resized_img = pcv.visualize._resize_img(img, (1000,1000)) -# assert np.shape(resized_img) == (1000,1000) +def test_plantcv_visualize_time_lapse_video_resize_img(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + resized_img = pcv.visualize._resize_img(img, (10,10)) + assert np.shape(resized_img) == (10,10,3) + +def test_plantcv_visualize_time_lapse_video_resize_img_large(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + resized_img = pcv.visualize._resize_img(img, (1000,1000)) + assert np.shape(resized_img) == (1000,1000,3) + +def test_plantcv_visualize_time_lapse_video_resize_img_gray(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) + resized_img = pcv.visualize._resize_img(img, (10,10)) + assert np.shape(resized_img) == (10,10) + +def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): + cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) + resized_img = pcv.visualize._resize_img(img, (1000,1000)) + assert np.shape(resized_img) == (1000,1000) # case1: only the correct directory of images is provided def test_plantcv_visualize_time_lapse_video_case1(): From b1cecbe8e6a4c9f55602220bfde61ed8ca717162 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 09:20:21 -0500 Subject: [PATCH 195/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 26ee0a97b..40a966d48 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -132,9 +132,9 @@ def _compute_overlaps_masks(masks1, masks2): The masks should be of the same size masks1, masks2: [Height, Width, instances] """ - # If either set of masks is empty return empty result - if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: - return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # # If either set of masks is empty return empty result + # if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + # return np.zeros((masks1.shape[-1], masks2.shape[-1])) n1 = masks1.shape[2] n2 = masks2.shape[2] intersections = np.zeros((n1, n2)) From 11fdd1b51a44e934622ba3c657568079b4710e42 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 09:20:24 -0500 Subject: [PATCH 196/274] Update tests.py --- tests/tests.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index 390311459..ed3520c14 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6418,6 +6418,13 @@ def test_plantcv_visualize_time_lapse_video_bad_list(): # Tests for the time_series subpackage # ############################## +def test_plantcv_time_series_display_instances(): + # Test cache directory + loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_INSTANCE_SEG, "img_segment_.pkl"), 'rb')) + img = loaded['img'] + masks = loaded['segment']['masks'] + pcv.time_series._display_instances(img, masks, figsize=(16, 16), title="", ax=None, colors=None) + def test_plantcv_time_series_time_series(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") @@ -6441,7 +6448,10 @@ def test_plantcv_time_series_time_series(): inst_ts_linking = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, logic, thres, name_sub, suffix, suffix_seg) - assert len(os.listdir(inst_ts_linking.dir_save)) > 0 + inst_ts_linking2 = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) + inst_ts_linking2(dir_img, dir_seg, pattern_datetime, time_cond, 'IOU', thres, name_sub, suffix, + suffix_seg) + assert (len(os.listdir(inst_ts_linking.dir_save)) > 0) and (len(os.listdir(inst_ts_linking2.dir_save)) > 0) def test_plantcv_visualize_overlay_two_imgs(): From 9f70f8f068fe11c11d17a3f1b606c114ff52d956 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 12:44:26 -0500 Subject: [PATCH 197/274] Create img_segment_.pkl --- .../instance_segmentation/img_segment_.pkl | Bin 0 -> 2637508 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/seires_data/instance_segmentation/img_segment_.pkl diff --git a/tests/seires_data/instance_segmentation/img_segment_.pkl 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"seires_data") -TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im") -TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "instance_segmentation") +TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im_2") +TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "inst_seg_2") # ########################## # Tests for the main package From bf74ed7467233a10e7dfae4d5debc97e00a0effd Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 14:49:58 -0500 Subject: [PATCH 200/274] Update tests.py --- tests/tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index e6e5b8153..93fab5d4b 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6449,7 +6449,7 @@ def test_plantcv_time_series_time_series(): inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, logic, thres, name_sub, suffix, suffix_seg) inst_ts_linking2 = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) - inst_ts_linking2(dir_img, dir_seg, pattern_datetime, time_cond, 'IOU', thres, name_sub, suffix, + inst_ts_linking2(dir_img, dir_seg, pattern_datetime, time_cond, 'IOU', thres, name_sub, '12.jpg', suffix_seg) assert (len(os.listdir(inst_ts_linking.dir_save)) > 0) and (len(os.listdir(inst_ts_linking2.dir_save)) > 0) From f9f81dcdb36651bd489ba2682ab7306e325b2a26 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 15:30:39 -0500 Subject: [PATCH 201/274] update test data --- .../2019-10-21-21-05.pkl | Bin .../seires_data/inst_seg/2019-10-22-08-05.pkl | Bin 1248143 -> 788463 bytes .../seires_data/inst_seg/2019-10-22-11-05.pkl | Bin 1248143 -> 1059359 bytes .../2019-10-22-14-05.pkl | Bin .../seires_data/inst_seg/2019-10-22-17-05.pkl | Bin 1255648 -> 1053195 bytes .../seires_data/inst_seg/2019-10-22-21-05.pkl | Bin 1503671 -> 1057299 bytes .../seires_data/inst_seg/2019-10-23-08-05.pkl | Bin 1500667 -> 0 bytes .../seires_data/inst_seg/2019-10-23-11-05.pkl | Bin 1506673 -> 0 bytes .../seires_data/inst_seg/2019-10-23-17-05.pkl | Bin 1506673 -> 0 bytes .../seires_data/inst_seg/2019-10-23-21-05.pkl | Bin 1750684 -> 0 bytes .../seires_data/inst_seg/2019-10-24-08-05.pkl | Bin 1754191 -> 0 bytes .../seires_data/inst_seg/2019-10-24-11-05.pkl | Bin 1750695 -> 0 bytes .../seires_data/inst_seg/2019-10-24-17-05.pkl | Bin 1754191 -> 0 bytes .../seires_data/inst_seg/2019-10-24-21-05.pkl | Bin 1750691 -> 0 bytes .../seires_data/inst_seg/2019-10-25-08-05.pkl | Bin 1497667 -> 0 bytes .../seires_data/inst_seg/2019-10-25-11-05.pkl | Bin 1750691 -> 0 bytes .../seires_data/inst_seg/2019-10-25-17-05.pkl | Bin 1747191 -> 0 bytes .../seires_data/inst_seg/2019-10-25-21-05.pkl | Bin 1743698 -> 0 bytes .../seires_data/inst_seg/2019-10-26-08-05.pkl | Bin 2000715 -> 0 bytes .../seires_data/inst_seg/2019-10-26-11-05.pkl | Bin 2004715 -> 0 bytes .../seires_data/inst_seg/2019-10-26-17-05.pkl | Bin 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z81YOU{SK=E?9OHT4dzEK(5(2#+39r03tFFjm@i7wy!W%*TIjN)PfhphkdyqGTaX!- zJzlO+>L4K^^O|iHuWiR&?q!EtjKJkSoZfiia@1=m&^B>M}>^sH3I%bJMh6bgvpFd}d zfB%8#(pPIl0}%IHW8z`Etl-lCmv-VNM&yxc(x#k*c6Zvu)v>kjHV-2ssUy;)N9F)b z(G$=V8rQ*<`+=K1!ZiGyyQ-jOeXRrdlb&SfJGDxp(cpLFLmd zGe*dgq{&H$Oc^RdM|_;BriUov+LOm!|*FpWddC;iz`<6@_+c3G^D%gK1RvaD4u z;1|fsz}KaVF;7J&d#D0W#if#V9-pjE<*&y9$RFBd*W38e8T`^7cl9J4!zBn-+pRRZ zf}q$KKmNn%pGR!r3}E+~<9^Yh!H#QfP1fVPzKy@NL%ZiH$jR)b8>$GafT{rPG}0VeOa_1bLs%M-JklZr;o?4%ufeh{2Hl6Nhiz2!<|ZT8qp-DAOH$~|E&F+7GQ2;i|V>Ne?W z^rq^FT-Id-E8H2O1*T8L&CzOzd6-mR`kgP%h6Oagzk|nZ#q5vr%#G1M9LgpL-?K2l zRf!6)9BH)YKSBL#-(V*(Aj>;o5?|LDQs_erl6!?NX%lXdh_)V;`*bzvwJtG#?A)Z< zX;$$IV`8A{(J4Q_X{6ACMRx@`*vw-1{Ee|B=gR+_zRcnZ1N57h5ww}KfAr+hD#VWE&EMBeerQ-=)pv&r$pg;rrL;NeYsY2oU~8IAB*m(5BZI_~^6Uec_P>hoOjHD#d>|krgcetv39>=i_ny KH=M!0rT+)3&PBZd From 99800bd22101d9650ad23bef67a8c09ad932acce Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 2 Oct 2020 15:30:44 -0500 Subject: [PATCH 202/274] Update tests.py --- tests/tests.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 93fab5d4b..35cfe2f42 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -996,8 +996,8 @@ def test_plantcv_parallel_process_results_invalid_json(): TEST_INPUT_INSTANCE_MASK = "visualize_inst_seg_mask.pkl" TIME_SERIES_TEST_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "seires_data") -TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im_2") -TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "inst_seg_2") +TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im") +TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "inst_seg") # ########################## # Tests for the main package From c548d81cf9a6e0e4bcc794f428f8aff5b67d1282 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 19 Oct 2020 12:46:39 -0500 Subject: [PATCH 203/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 184 +++++++++++------- 1 file changed, 113 insertions(+), 71 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 40a966d48..fcd0fbd77 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -132,9 +132,9 @@ def _compute_overlaps_masks(masks1, masks2): The masks should be of the same size masks1, masks2: [Height, Width, instances] """ - # # If either set of masks is empty return empty result - # if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: - # return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # If either set of masks is empty return empty result + if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + return np.zeros((masks1.shape[-1], masks2.shape[-1])) n1 = masks1.shape[2] n2 = masks2.shape[2] intersections = np.zeros((n1, n2)) @@ -152,34 +152,31 @@ def _compute_overlaps_masks(masks1, masks2): ious = np.divide(intersections, unions) return ious, ioss - class InstanceTimeSeriesLinking(object): """A class that links segmented instances throughout time Assumption: the timepoints are all sorted, the images and masks are also sorted by timepoints (chronologically) """ - def __init__(self, images, masks, timepoints, logic='IOS', thres=0.2, name_sub='instance'): + def __init__(self): # a list of images which are ndarrays - self.images = images + self.images = None # a list of masks which are ndarrays (of the same length of images) - self.masks = masks + self.masks = None # a list of timepoints (of the same length of images) - self.timepoints = timepoints - self.total_time = len(self.timepoints) + self.timepoints = None + self.total_time = None # number of instances: a list in which every element represent for number of instances in corresponding image - self.n_insts = [] - for i in range(0, len(self.masks)): - self.n_insts.append(self.masks[i].shape[2]) + self.n_insts = None # initialization for linking - self.thres = thres - self.link_info = [-np.ones((self.n_insts[i]), dtype=int) for i in range(0, self.total_time - 1)] - self.unlinked_insts = [np.array(range(0, self.n_insts[i])) for i in range(0, self.total_time)] - self.link_series = dict() # only for those newly emerging leaves - self.weights = [] - self.logic = logic.upper() - self.name_sub = name_sub - self.key_id = '{}_ids'.format(name_sub) + self.thres = None + self.link_info = None + self.unlinked_insts = None + self.link_series = None + self.weights = None + self.logic = None + self.name_sub = None + self.key_id = None def save_linked_series(self, savedir, savename): """save lining information into a .csv file and a .pkl file with the same prefix of filename @@ -196,7 +193,19 @@ def save_linked_series(self, savedir, savename): new_line = ['{}'.format(uid), '{}'.format(cid)] + item['{}{}'.format(self.name_sub, cid)].tolist() writer_junk.writerow(new_line) csvfile.close() - pkl.dump(self, open(os.path.join(savedir, savename + '.pkl'), 'wb')) + # pkl.dump(self, open(os.path.join(savedir, savename + '.pkl'), 'wb')) + pkl.dump(vars(self), open(os.path.join(savedir, savename+".pkl"), 'wb')) + + def import_linked_series(self, savedir, savename): + """import a linked time-series from previously saved file + + :param savedir: saving directory + :param savename: saving name + :return: + """ + linked = pkl.load(open(os.path.join(savedir, savename+'.pkl'),"rb")) + for key, value in linked.items(): + setattr(self, key, value) def linking(self, t0): """ @@ -204,8 +213,7 @@ def linking(self, t0): :param t0: starting time point :return: None, but self.link_info would be updated """ - masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy( - self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays + masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays n0, n1 = masks0.shape[2], masks1.shape[2] weight = -np.inf * np.ones((n0, n1)) link = -np.ones(n0) @@ -385,7 +393,7 @@ def visualize(self, visualdir, csvdir, csvname='linking_info', colors=None, colo [unique_id, start_time, self.timepoints[start_time], t, self.timepoints[t], link_inst[t]]) pcv.print_image(leaf_t, os.path.join(visualdirs['1'], - '{}_{}-{}-{}-{}_{}.png'.format(unique_id, start_time, + '{}_{}-{}-{}-{}_{}.jpg'.format(unique_id, start_time, start_idx, t, link_inst[t], self.timepoints[t]))) mask_2[np.where(mask_t == True)] = 1 @@ -399,7 +407,7 @@ def visualize(self, visualdir, csvdir, csvname='linking_info', colors=None, colo ax2.imshow(masked_im) ax2.axis('off') ax2.set_title(title_vis2, fontsize=16) - plt.savefig(os.path.join(save_dir_, str(self.timepoints[t]) + '.png')) + plt.savefig(os.path.join(save_dir_, str(self.timepoints[t]) + '.jpg')) plt.close(fig2) count += 1 @@ -408,10 +416,32 @@ def visualize(self, visualdir, csvdir, csvname='linking_info', colors=None, colo for (img, masks, color, t) in zip(self.images, self.masks, color_all, self.timepoints): _display_instances(img, masks, figsize=(16, 16), title="", ax=_get_ax(rows=1, cols=1, size=16), colors=color) - plt.savefig(os.path.join(visualdirs['3'], '{}.png'.format(t))) + plt.savefig(os.path.join(visualdirs['3'], '{}.jpg'.format(t))) plt.close('all') - def __call__(self, savedir, visualdir_, visualdir, savename_, savename, colors=None, color_all=None): + def __call__(self, images, masks, timepoints, savedir, visualdir_, visualdir, savename_, savename, logic="IOS", thres=0.2, name_sub="instance",colors=None, color_all=None): + # a list of images which are ndarrays + self.images = images + # a list of masks which are ndarrays (of the same length of images) + self.masks = masks + # a list of timepoints (of the same length of images) + self.timepoints = timepoints + self.total_time = len(self.timepoints) + # number of instances: a list in which every element represent for number of instances in corresponding image + self.n_insts = [] + for i in range(0, len(self.masks)): + self.n_insts.append(self.masks[i].shape[2]) + + # initialization for linking + self.thres = thres + self.link_info = [-np.ones((self.n_insts[i]), dtype=int) for i in range(0, self.total_time - 1)] + self.unlinked_insts = [np.array(range(0, self.n_insts[i])) for i in range(0, self.total_time)] + self.link_series = dict() # only for those newly emerging leaves + self.weights = [] + self.logic = logic.upper() + self.name_sub = name_sub + self.key_id = '{}_ids'.format(name_sub) + for t0 in range(0, self.total_time - 1): self.linking(t0) self.get_series() @@ -431,26 +461,29 @@ class InstanceTSLinkingWrapper(object): 3) For every image, there is a corresponding segmentation result Otherwise, please get your data prepared and use the "InstanceTimeSeriesLinking" class directly """ - def __init__(self, dir_save, savename): - self.dir_save = dir_save - self.savename = savename - self.savename_ = '{}_old'.format(savename) + + def __init__(self): + self.dir_save = None + self.savename = None + self.savename_ = None self.dir_img = None self.dir_seg = None self.time_cond = None - self.suffix = None - self.ext = None + self.suffix = None + self.ext = None self.suffix_seg = None - self.timepoints = [] - self.imgfiles = [] - self.segfiles = [] + self.timepoints = None + self.imgfiles = None + self.segfiles = None self.dir_visual = None self.dir_visual_ = None + self.pattern_dt = None def set_save_dirs(self): junk = datetime.datetime.now() subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), - str(junk.hour).zfill(2), str(junk.minute).zfill(2), str(junk.second).zfill(2)) + str(junk.hour).zfill(2), str(junk.minute).zfill(2), + str(junk.second).zfill(2)) self.dir_save = os.path.join(self.dir_save, subfolder) if not os.path.exists(self.dir_save): os.makedirs(self.dir_save) @@ -461,23 +494,11 @@ def set_save_dirs(self): if not os.path.exists(self.dir_visual_): os.makedirs(self.dir_visual_) - def sort_time(self, dir_img, dir_seg, pattern_dt, time_cond, suffix='.jpg', suffix_seg='.pkl'): + def sort_time(self): """ This function is designed for files with file names which contain a "date-time" part, with an user-defined pattern sort time based on names of original images - :param dir_img: directory of original images - :param dir_seg: directory of segmentation results - :param pattern_dt: the common pattern of date-time part in file names, e.g. YYYY-MM-DD-hh-mm - :param time_cond: the date time of data intended to be included in the analysis - :param suffix: the common suffix of all original image files - :param suffix_seg: the common suffix of all segmentation result files - :return: sorted and trimed list of images as well as segmentation files """ - self.dir_img = dir_img - self.dir_seg = dir_seg - self.time_cond = time_cond - self.suffix = suffix - self.suffix_seg = suffix_seg # file name extension of original images ext1, ext2 = os.path.splitext(self.suffix) @@ -491,55 +512,76 @@ def sort_time(self, dir_img, dir_seg, pattern_dt, time_cond, suffix='.jpg', suff imgfs_all.sort() segfs_all.sort() for (fi, fs) in zip(imgfs_all, segfs_all): - tempi = re.search(pattern_dt, fi) - temps = re.search(pattern_dt, fs) + tempi = re.search(self.pattern_dt, fi) + temps = re.search(self.pattern_dt, fs) if tempi and temps: - timepart = tempi.group() #timeparts = temps.group() the assumption is that original image and the results have the same dt_pattern + timepart = tempi.group() # timeparts = temps.group() the assumption is that original image and the results have the same dt_pattern for cond in self.time_cond: if timepart.endswith(cond): self.timepoints.append(timepart) continue index_temp = np.argsort(self.timepoints) - self.timepoints = [self.timepoints[i] for i in index_temp] + self.timepoints = [self.timepoints[i] for i in index_temp] self.imgfiles = [imgfs_all[i] for i in index_temp] self.segfiles = [segfs_all[i] for i in index_temp] def load_images(self): """ - Get the list of images (nd-arrays) ready + Load images and put them into a list :return: + max_dim: common maximum dimension of all images """ temp_imgs = [] sz = [] - imgs = [] for f in self.imgfiles: junk = skimage.io.imread(os.path.join(self.dir_img, f)) temp_imgs.append(junk) sz.append(np.min(junk.shape[0:2])) - min_dim = np.min(sz) + max_dim = np.min(sz) for junk in temp_imgs: img = junk[0: min_dim, 0: min_dim, :] # make all images the same size - imgs.append(img) - return imgs, min_dim + self.imgs.append(img) + return max_dim - def load_segs(self, min_dim): + def load_segs(self, max_dim): """ Get the list of segmentation masks (nd-arrays) ready - :param min_dim: minimum dimension of images (get from original images) (Assumption: square images) + :param max_dim: common maximum dimension of all images (get from original images) (Assumption: square images) :return: """ - segs = [] + # segs = [] for f in self.segfiles: r = pkl.load(open(os.path.join(self.dir_seg, f), 'rb')) - segs.append(r["masks"][0:min_dim, 0:min_dim, :]) # make all masks the same size - return segs + self.segs.append(r["masks"][0:max_dim, 0:max_dim, :]) # make all masks the same size + # return segs - def __call__(self, dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, suffix, suffix_seg,colors=None,color_all=None): + def __call__(self, dir_save, savename, dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, + suffix=".jpg", suffix_seg=".pkl", colors=None, color_all=None): + self.dir_save = dir_save + self.savename = savename + self.savename_ = '{}_old'.format(savename) self.set_save_dirs() - self.sort_time(dir_img, dir_seg, pattern_dt, time_cond, suffix, suffix_seg) - imgs,min_dim = self.load_images() - segs = self.load_segs(min_dim) + self.dir_img = dir_img + self.dir_seg = dir_seg + self.pattern_dt = pattern_dt + self.time_cond = time_cond + self.logic = logic + self.thres = thres + self.name_sub = name_sub + self.suffix = suffix + self.suffix_seg = suffix_seg + self.imgs = [] + self.segs = [] + self.timepoints = [] + + self.sort_time() + min_dim = self.load_images() + self.load_segs(min_dim) # create an instance of InstanceTimeSeriesLinkingClass - inst_ts_linking = InstanceTimeSeriesLinking(imgs, segs, self.timepoints, logic, thres, name_sub) - inst_ts_linking(self.dir_save, self.dir_visual_, self.dir_visual, self.savename_, self.savename, colors, color_all) - return inst_ts_linking + + inst_ts_linking = InstanceTimeSeriesLinking() + inst_ts_linking(images=self.imgs, masks=self.segs, timepoints=self.timepoints, savedir=self.dir_save, + visualdir_=self.dir_visual_, visualdir=self.dir_visual, savename_=self.savename_, + savename=self.savename, logic=self.logic, thres=self.thres, name_sub=self.name_sub) + + return inst_ts_linking \ No newline at end of file From f330b449d26e49499379a211580f880228e4e997 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 19 Oct 2020 12:46:49 -0500 Subject: [PATCH 204/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index fcd0fbd77..6f0b6a4c9 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -223,8 +223,7 @@ def linking(self, t0): elif self.logic == 'IOS': weight = ioss self.weights.append(weight) - idx_col = np.where(np.max(weight, axis=0) < self.thres)[ - 0] # find those columns with maximum value < threshold (self.thres) + idx_col = np.where(np.max(weight, axis=0) < self.thres)[0] # find those columns with maximum value < threshold (self.thres) avail_col = [x for x in range(0, n1) if x not in idx_col] weight_ = copy.deepcopy(weight) weight_ = np.delete(weight_, idx_col, 1) @@ -307,8 +306,7 @@ def update_series(self): weight = ious elif self.logic == 'IOS': weight = ioss - idx_col = np.where(np.max(weight, axis=0) < self.thres)[ - 0] # find those columns with maximum value < thres + idx_col = np.where(np.max(weight, axis=0) < self.thres)[0] # find those columns with maximum value < thres # avail_col = [x for x in range(0, n2) if x not in idx_col] weight_ = copy.deepcopy(weight) weight_ = np.delete(weight_, idx_col, 1) From 6ee81c4da24cf80f3665c40e7269e67b9067c4fe Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 17 Dec 2020 14:21:54 -0600 Subject: [PATCH 205/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 852 ++++++++---------- 1 file changed, 384 insertions(+), 468 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 6f0b6a4c9..0962ee0ed 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -1,4 +1,3 @@ -#### link utilities # !/usr/bin/env python3 # -*- coding: utf-8 -*- """ @@ -29,112 +28,32 @@ from plantcv.plantcv import fatal_error from scipy.optimize import linear_sum_assignment import csv - -def _random_colors(num, bright=True): - """ - Generate desired number of random colors. - To get visually distinct colors, generate them in HSV space then - convert to RGB. - Inputs: - num: number of colors to be generated - bright: True or False, if true, the brightness would be 1.0; if False, the brightness would be 0.7. By default it would be True (brightness 0.7) - Output: generated colors (a tuple) - """ - brightness = 1.0 if bright else 0.7 - hsv = [(i / num, 1, brightness) for i in range(num)] - colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv)) - random.shuffle(colors) - return colors - - -def _apply_mask(image, mask, color, alpha=0.5): - """Apply the given mask to the image with a color and alpha channel assigned. - Inputs: - image: original RGB image - mask: a binary mask of same size to the image - color: a list of 3 values - alpha: alpha value indicating transparency, by default 0.5 - """ - for c in range(3): - image[:, :, c] = np.where(mask == 1, - image[:, :, c] * - (1 - alpha) + alpha * color[c] * 255, - image[:, :, c]) - return image - - -def _get_ax(rows=1, cols=1, size=16): # ??? - """Return a Matplotlib Axes array to be used in - all visualizations in the notebook. Provide a - central point to control graph sizes. - - Adjust the size attribute to control how big to render images - """ - fig, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows)) - fig.tight_layout() - return ax - - -def _display_instances(image, masks, figsize=(16, 16), title="", ax=None, colors=None): - """ - This function is inspired by the function in mrcnn - masks: [height, width, num_instances] - inst_ids: [num_instances] - title: (optional) Figure title - show_mask, show_bbox: To show masks and bounding boxes or not - figsize: (optional) the size of the image - colors: (optional) An array or colors to use with each object - captions: (optional) A list of strings to use as captions for each object - """ - # If no axis is passed, create one and automatically call show() - # auto_show = False - if not ax: - _, ax = plt.subplots(1, figsize=figsize) - # auto_show = True - - num_insts = masks.shape[2] - # Generate random colors - colors = colors or _random_colors(num_insts) - - # Show area outside image boundaries. - height, width = image.shape[:2] - ax.set_ylim(height + 10, -10) - ax.set_xlim(-10, width + 10) - ax.axis('off') - ax.set_title(title) - - masked_image = image.astype(np.uint32).copy() - for i in range(num_insts): - color = colors[i] - - # Mask - mask = masks[:, :, i] - masked_image = _apply_mask(masked_image, mask, color) - - # Mask Polygon - # Pad to ensure proper polygons for masks that touch image edges. - padded_mask = np.zeros( - (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8) - padded_mask[1:-1, 1:-1] = mask - contours = find_contours(padded_mask, 0.5) - for verts in contours: - # Subtract the padding and flip (y, x) to (x, y) - verts = np.fliplr(verts) - 1 - p = Polygon(verts, facecolor="none", edgecolor=color) - ax.add_patch(p) - ax.imshow(masked_image.astype(np.uint8)) - # if auto_show: - # plt.show() - +# from scipy.stats import wasserstein_distance +# sys.path.append('/shares/mgehan_share/hsheng/projects/test_plantcv/visualize_display_instances') +# import visualize_display_instances def _compute_overlaps_masks(masks1, masks2): - """Computes IoU and IoS overlaps between two sets of masks. - The masks should be of the same size - masks1, masks2: [Height, Width, instances] + """Compute overlaps of two sets of binary masks. + The overlaps are represented by IoU (intersection over union) and IoS (intersection over self-area). + + :param masks1: (numpy.ndarray of shape: [Height, Width, n1]) , where n1 is the number of instances + :param masks2: (numpy.ndarray of shape: [Height, Width, n2]) , where n2 is the number of instances + :return: + n1: the number of instances in 1st set of binary masks + n2: the number of instances in 2nd set of binary masks + ious: (numpy.ndarray of shape: [n1, n2]) inversection over union between any pairs of instances in masks1 and masks2 + ioss: (numpy.ndarray of shape: [n1, n2]) inversection over self-area (areas of instances in 1st set of masks) between any pairs of instances in masks1 and masks2 + unions: (numpy.ndarray of shape: [n1, n2]) unions between any pairs of instances in masks1 and masks2 """ + # If either set of masks is empty return empty result if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # If either set of masks contains only one mask, expand the 2nd dimension + if len(masks1.shape) == 2: + masks1 = np.expand_dims(masks1, 2) + if len(masks2.shape) == 2: + masks2 = np.expand_dims(masks2, 2) n1 = masks1.shape[2] n2 = masks2.shape[2] intersections = np.zeros((n1, n2)) @@ -150,7 +69,166 @@ def _compute_overlaps_masks(masks1, masks2): unions[idx_m, :] = union ioss[idx_m, :] = intersection / maski_.sum() ious = np.divide(intersections, unions) - return ious, ioss + return n1, n2, ious, ioss, unions + + +def _get_link(masks1, masks2, logic, thres): + """ + Get link information between two sets of binary instance segmentation masks + :param masks1: (numpy.ndarray of shape: [Height, Width, n1]), where n1 is the number of instances + :param masks2: (numpy.ndarray of shape: [Height, Width, n1]), where n1 is the number of instances + :param logic: linking logic to use. (currently) either "IOU" or "IOS" + :param thres: threshold to link two masks (two binary segmentation masks can only be linked if the calculated overlap between is greater than the threshold) + :return: + weight (numpy.ndarray of shape: [n1,n2]): weight matrix (indicated by IoU or IoS) calculated based on two sets of masks + link (numpy.1darray of length n1): link[i] = j means that the i-th mask in masks1 should be linked to j-th mask in masks2 + row_ind, col_ind: selected row indices and column indices based on the weight matrix to finalize the "link" + """ + + n1, n2, ious, ioss, _ = _compute_overlaps_masks(masks1, masks2) + if logic.upper() == 'IOU': + weight = ious + elif logic.upper() == 'IOS': + weight = ioss + else: + fatal_error("Currently only IOU and IOS as supported linking logic!") + weight_ = copy.deepcopy(weight) + link = -np.ones(n1, dtype=np.int64) + + idx_col = np.where(np.max(weight_, axis=0) < thres)[ + 0] # find those columns with maximum value < threshold (self.thres) + avail_col = [x for x in range(0, n2) if x not in idx_col] + + weight_ = np.delete(weight_, idx_col, 1) + + row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) + for (r, c) in zip(row_ind, col_ind): + if weight_[r, c] >= thres: + link[r] = avail_col[c] + return weight, link, row_ind, col_ind + + +def _get_emergence(uids): + """ + get emergence as well as emerge times + :param uids: unique ids at every time point + (list of length T, where T is the total time) + every sub-list uids[t] (for t in range of (0,T)) is also a list of length nt, where nt is the number of instances at t + :return: + emergence: new segments (leaves) compared to previous time-point + (list of length T, where T is the total time) + every sub-list emergence[t] if also a list. If there is no new segments at t compared to (t-1), the emergence[t] is empty; otherwise, emergence[t] is a list of unique ids appear at t but not at (t-1). + emerge_times: (list) time points with emergence + """ + + emergence = [[] for i in uids] + emergence[0] = list(uids[0]) + # uid_lst = [] + # uid_lst += list(uids[0]) + for (t, temp) in enumerate(uids): + if t >= 1: + # new = [x for x in temp if x not in uid_lst] + new = [x for x in temp if x not in uids[t - 1]] + emergence[t] = new + # uid_lst += new + emerge_times = [i for i in range(len(emergence)) if len(emergence[i]) > 0] + return emergence, emerge_times + + +def _get_ti(num_insts, uids, link_info): + """ + getting ti (tracking information) + :param num_insts: (list) number of instances at every time-point t + :param uids: unique ids at every time point + :param link_info: + :return: + ti: tracking information (np.ndarray of shape (T,max_uid)) + ti[t,k]=j represents for: at time t the k-th leaf has a local index of j + if ti[t,k]=-1, the k-th leaf does not appear at time t + number of non-negative elements every row: number of instances + t_appear: (list, length: N) the appear time of every unique id + t_disappear: (list, length: N) the disappear time of every unique id + """ + + emergence, emerge_times = _get_emergence(uids) + T = len(num_insts) + max_uid = max([max(temp) for temp in uids]) + N = max_uid + 1 + ti = -np.ones((T, N), dtype=np.int64) + for t in range(T): + if t == 0: + ti[t, 0:num_insts[t]] = uids[0] + else: + link = link_info[t - 1] + prev = ti[t - 1] + cids = list(np.arange(0, num_insts[t])) + + for (uid, pid) in enumerate(prev): + # pid: previous local id + if pid >= 0: + cid = link[pid] + ti[t, uid] = cid + if cid >= 0: + cids.remove(cid) + + if t in emerge_times: + new_ids = emergence[t] + for (cid, new_id) in zip(cids, new_ids): + ti[t, new_id] = cid + + t_appear = np.zeros(N, dtype=np.int64) + t_disappear = -np.ones(N, dtype=np.int64) + for (t, uids_t) in enumerate(emergence): + if uids_t: + for uid in uids_t: + t_appear[uid] = t + for uid in range(0, N): + t = 0 + while t < T: + if (ti[t][uid] == -1) and (t > t_appear[uid]): + t_disappear[uid] = t + break + else: + t += 1 + return ti, t_appear, t_disappear + + +# def _visualize1(img, mask, savename=None): +# """ +# Apply mask to original image +# """ +# mask_1 = np.zeros(img.shape[0:2], dtype=np.uint8) +# mask_1[np.where(mask)] = 255 +# masked_im = pcv.apply_mask(img, mask_1, mask_color='black') +# if savename is not None: +# pcv.print_image(masked_im, savename) +# return masked_im + +# def _visualize2(img, fig_title, mask=None, savename=None): +# """ +# Apply mask with an alpha channel to the original image +# Can be replaced with "overlay two images in the future" +# img +# mask +# """ +# mask_2 = np.ones(img.shape[0:2]) * 0.5 +# if mask is not None: +# mask_2[np.where(mask == True)] = 1 +# masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_2, axis=2)), axis=2) +# +# fig = plt.figure(figsize=(5, 5)) +# ax = fig.add_subplot(1, 1, 1) +# ax.imshow(masked_im) +# ax.axis('off') +# ax.set_title(fig_title, fontsize=16) +# if savename is not None: +# plt.savefig(savename) +# return masked_im + +# def _visualize3(img, masks, colors, figsize=(16, 16), ax=None, captions=None, savename=None, title="", show_bbox=True): +# visualize_display_instances.display_instances(img, masks.astype(np.uint8), figsize=figsize, title=title, ax=ax, colors=colors, captions=captions, show_bbox=True) +# if savename is not None: +# plt.savefig(savename) class InstanceTimeSeriesLinking(object): """A class that links segmented instances throughout time @@ -164,37 +242,42 @@ def __init__(self): self.masks = None # a list of timepoints (of the same length of images) self.timepoints = None - self.total_time = None + self.T = None # number of instances: a list in which every element represent for number of instances in corresponding image self.n_insts = None # initialization for linking self.thres = None self.link_info = None - self.unlinked_insts = None - self.link_series = None + self.uids = None + self.max_uid = None + self.emergence = None + self.emerge_times = None + self.ti = None + self.t_appear = None + self.t_disappear = None self.weights = None - self.logic = None + self.logic = None self.name_sub = None self.key_id = None + # update releated + self.updated = 0 + self.delta_t = None + self.disp_uids = None + self.ids_update = None + + self.ti_ = None + self.t_appear_ = None + self.t_disappear_ = None + def save_linked_series(self, savedir, savename): - """save lining information into a .csv file and a .pkl file with the same prefix of filename + """save linking information into a .pkl file with the same prefix of filename Inputs: savedir and savename """ - l0 = ['', ''] + [x for (idx, x) in enumerate(self.timepoints)] - l1 = ['unique_id', 'current_id'] + ['t{}'.format(idx) for (idx, x) in enumerate(self.timepoints)] - csvfile = open(os.path.join(savedir, savename + '.csv'), 'w', newline='') - writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) - writer_junk.writerow(l0) - writer_junk.writerow(l1) - for (t0, item) in self.link_series.items(): - for uid, cid in zip(item['unique_id'], item[self.key_id]): - new_line = ['{}'.format(uid), '{}'.format(cid)] + item['{}{}'.format(self.name_sub, cid)].tolist() - writer_junk.writerow(new_line) - csvfile.close() + # pkl.dump(self, open(os.path.join(savedir, savename + '.pkl'), 'wb')) - pkl.dump(vars(self), open(os.path.join(savedir, savename+".pkl"), 'wb')) + pkl.dump(vars(self), open(os.path.join(savedir, savename + ".pkl"), 'wb')) def import_linked_series(self, savedir, savename): """import a linked time-series from previously saved file @@ -203,383 +286,216 @@ def import_linked_series(self, savedir, savename): :param savename: saving name :return: """ - linked = pkl.load(open(os.path.join(savedir, savename+'.pkl'),"rb")) + linked = pkl.load(open(os.path.join(savedir, savename + '.pkl'), "rb")) for key, value in linked.items(): setattr(self, key, value) + # def save_to_csv(self, savedir, csvname1="link_series.csv", csvname2="link_series"): + # """ save linking information into 2 .csv files + # 1. link_series.csv + # 2. link_info.csv + # """ + # # csvname1 = "link_series.csv" + # # csvname2 = "link_info.csv" + # l0 = ['', ''] + [x for (idx, x) in enumerate(self.timepoints)] + # l1 = ['unique_id', 'current_id'] + ['t{}'.format(idx) for (idx, x) in enumerate(self.timepoints)] + # csvfile1 = open(os.path.join(savedir, csvname1), 'w', newline='') + # writer1 = csv.writer(csvfile1, delimiter=',', quoting=csv.QUOTE_MINIMAL) + # writer1.writerow(l0) + # writer1.writerow(l1) + # csvfile2 = open(os.path.join(savedir, csvname2), 'w', newline='') + # writer_junk2 = csv.writer(csvfile2, delimiter=',', quoting=csv.QUOTE_MINIMAL) + # writer_junk2.writerow(['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) + # for (t0_, item) in self.link_series.items(): + # t0 = int(t0_.replace('t', '')) + # for uid, cid in zip(item['uids'], item['ids']): + # link_t0_i = item['inst{}'.format(cid)] + # new_line = ['{}'.format(uid), '{}'.format(cid)] + link_t0_i.tolist() + # writer1.writerow(new_line) + # for t in range(t0, self.T): + # writer_junk2.writerow([uid, t0, self.timepoints[t0], t, self.timepoints[t], link_t0_i[t]]) + # csvfile1.close() + # csvfile2.close() + def linking(self, t0): """ Link instance segments of t0 to those of t1 (t0+1) by calculating the overlaps of their masks :param t0: starting time point :return: None, but self.link_info would be updated """ - masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays - n0, n1 = masks0.shape[2], masks1.shape[2] - weight = -np.inf * np.ones((n0, n1)) - link = -np.ones(n0) - ious, ioss = _compute_overlaps_masks(masks0, masks1) - if self.logic == 'IOU': - weight = ious - elif self.logic == 'IOS': - weight = ioss + masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy( + self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays + weight, self.link_info[t0], _, _ = _get_link(masks0, masks1, self.logic, self.thres) self.weights.append(weight) - idx_col = np.where(np.max(weight, axis=0) < self.thres)[0] # find those columns with maximum value < threshold (self.thres) - avail_col = [x for x in range(0, n1) if x not in idx_col] - weight_ = copy.deepcopy(weight) - weight_ = np.delete(weight_, idx_col, 1) - - row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) - for (r, c) in zip(row_ind, col_ind): - if weight_[r, c] >= self.thres: - link[r] = avail_col[c] - self.link_info[t0] = link - - def get_series(self): - """ - Using the self.link_info to create a dictionary save in self.link_series - which is a dictionary contains information for every timepoint with new instances not seen before, - and the linking information for every instance - :return: - """ - # define new leaves and their unique identifiers at time points with new leaves emerging - t = 0 - key_t = 't{}'.format(t) - self.link_series[key_t] = dict() - self.link_series[key_t][self.key_id] = self.unlinked_insts[0] - # - self.link_series[key_t]['unique_id'] = self.link_series[key_t][self.key_id] - unique_id = len(self.link_series[key_t][self.key_id]) - - for t in range(1, self.total_time): - new_insts = [i for i in self.unlinked_insts[t] if i not in self.link_info[t - 1]] - if new_insts: - key_t = 't{}'.format(t) - self.link_series[key_t] = dict() - self.link_series[key_t][self.key_id] = np.array(new_insts) - - id_temp = [] - for new_inst in new_insts: - id_temp.append(unique_id) - unique_id = unique_id + 1 - self.link_series[key_t]['unique_id'] = np.array(id_temp) - ## for time points with new leaves emerging, get the linking information for every new instance - for key_t in self.link_series: - t0 = int(key_t.replace('t', '')) - for (leaf_unique, inst) in zip(self.link_series[key_t]['unique_id'], self.link_series[key_t][self.key_id]): - key_inst = '{}{}'.format(self.name_sub, inst) - self.link_series[key_t][key_inst] = -np.ones(self.total_time, dtype=int) - self.link_series[key_t][key_inst][t0] = inst - if t0 < self.total_time - 1: - self.link_series[key_t][key_inst][t0 + 1] = self.link_info[t0][inst] - for t_ in range(t0 + 2, self.total_time): - idx = self.link_series[key_t][key_inst][t_ - 1] - if idx < 0: - break - else: - self.link_series[key_t][key_inst][t_] = self.link_info[t_ - 1][idx] - def update_series(self): - """ - Update the linking by comparing pairs of timepoint which are t and t+2 - :return: - """ - link_series = copy.deepcopy(self.link_series) - for t_, content in link_series.items(): - t = int(t_.replace('t', '')) - for inst_id in content[self.key_id]: - temp = np.where(content['{}{}'.format(self.name_sub, inst_id)] < 0)[0] - if len(temp) > 0: - t0 = temp[0] - 1 - t2 = t0 + 2 - if t2 <= self.total_time - 1: - t2_ = 't{}'.format(t2) - if t2_ in self.link_series: - t0_ = 't{}'.format(t0) - # print('\nt0: {}, t2: {}'.format(t0_, t2_)) - idx0 = content['{}{}'.format(self.name_sub, inst_id)][t0] - mask0 = np.expand_dims(self.masks[t0][:, :, idx0], axis=2) - idxs2 = self.link_series[t2_][self.key_id] - masks2 = self.masks[t2][:, :, idxs2] - n2 = masks2.shape[2] - ious, ioss = _compute_overlaps_masks(mask0, masks2) - if self.logic == 'IOU': - weight = ious - elif self.logic == 'IOS': - weight = ioss - idx_col = np.where(np.max(weight, axis=0) < self.thres)[0] # find those columns with maximum value < thres - # avail_col = [x for x in range(0, n2) if x not in idx_col] - weight_ = copy.deepcopy(weight) - weight_ = np.delete(weight_, idx_col, 1) - row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) - for (r, c) in zip(row_ind, col_ind): - if weight_[r, c] >= self.thres: - inst0 = idx0 - inst2 = idxs2[c] - - # find the time point - idx_l2 = np.where(self.link_series[t2_][self.key_id] == inst2)[0][0] - # add information to t_ - self.link_series[t_]['{}{}'.format(self.name_sub, inst_id)][t0 + 1:] = \ - self.link_series[t2_]['{}{}'.format(self.name_sub, inst2)][t0 + 1:] - # delete from t2_ - self.link_series[t2_][self.key_id] = np.delete(self.link_series[t2_][self.key_id], - idx_l2) - self.link_series[t2_]['unique_id'] = np.delete(self.link_series[t2_]['unique_id'], - idx_l2) - del self.link_series[t2_]['{}{}'.format(self.name_sub, inst2)] - # if nothing left for t2_, delete it entirely - if len(self.link_series[t2_]['unique_id']) == 0: - del self.link_series[t2_] - # update unique ids - uid = 0 - for t in self.link_series: - num = len(self.link_series[t][self.key_id]) - self.link_series[t]['unique_id'] = np.arange(uid, uid + num) - uid = uid + num - - def visualize(self, visualdir, csvdir, csvname='linking_info', colors=None, color_all=None): + def get_uid(self): """ - Create 3 sets of visualization - visualization set 1: one leaf per image (visualdir['1']) - visualization set 2: show with an alpha channel (visualdir['2']) - visualization method 3: show with bounding boxes (visualdir['3']) - Also save a csv file called linking_info.csv (or other user defined name) that includes the linking information + get unique ids for every timepoint based on link_info + uids: a list + arrays inside the list + every array has a length of num_insts for that corresponding time """ - - # create subfolders inside the provided directory of visualization - visualdirs = dict() - for i in range(1, 4): - idx = str(i) - visualdirs[idx] = os.path.join(visualdir, 'visualization{}'.format(idx)) - if not os.path.exists(visualdirs[idx]): - os.makedirs(visualdirs[idx]) - - if colors is None: - colors = _random_colors(40) - if color_all is None: - color_all = [[tuple() for i in range(0, num)] for num in self.n_insts] - - count = 0 - csvfile = open(os.path.join(csvdir, csvname + '.csv'), 'w', newline='') - writer_junk = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL) - writer_junk.writerow( - ['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) - for key_t in self.link_series: - ids = self.link_series[key_t]['unique_id'] - start_time = int(key_t.replace('t', '')) - insts_t = self.link_series[key_t][self.key_id] - for (unique_id, inst) in zip(ids, insts_t): - key_inst = '{}{}'.format(self.name_sub, inst) - link_inst = self.link_series[key_t][key_inst] - start_idx = link_inst[start_time] - for t in range(0, self.total_time): - img_t = self.images[t] - # initialize mask used in 1st visualization - mask_1 = np.zeros(img_t.shape[0:2], dtype=np.uint8) - # initialize mask used in 2nd visualization (with an alpha channel, ahpha=0.5) - mask_2 = np.ones(img_t.shape[0:2]) * 0.5 - title_vis2 = '{} (t{})'.format(self.timepoints[t], t) - if link_inst[t] >= 0: - title_vis2 = title_vis2 + ' leaf {}'.format(link_inst[t]) - mask_t = self.masks[t][:, :, link_inst[t]] - color_all[t][link_inst[t]] = colors[count] - - ## 1. save the masked image, i.e. single leaves - mask_1[np.where(mask_t)] = 255 - leaf_t = pcv.apply_mask(img_t, mask_1, mask_color='black') - writer_junk.writerow( - [unique_id, start_time, self.timepoints[start_time], t, self.timepoints[t], link_inst[t]]) - - pcv.print_image(leaf_t, os.path.join(visualdirs['1'], - '{}_{}-{}-{}-{}_{}.jpg'.format(unique_id, start_time, - start_idx, t, link_inst[t], - self.timepoints[t]))) - mask_2[np.where(mask_t == True)] = 1 - masked_im = np.concatenate((img_t.astype(float) / 255, np.expand_dims(mask_2, axis=2)), axis=2) - # for 2nd set of visualization, a folder for one unique instance - save_dir_ = os.path.join(visualdirs['2'], '{}_{}-{}'.format(unique_id, start_time, start_idx)) - if not os.path.exists(save_dir_): - os.makedirs(save_dir_) - fig2 = plt.figure(figsize=(5, 5)) - ax2 = fig2.add_subplot(1, 1, 1) - ax2.imshow(masked_im) - ax2.axis('off') - ax2.set_title(title_vis2, fontsize=16) - plt.savefig(os.path.join(save_dir_, str(self.timepoints[t]) + '.jpg')) - plt.close(fig2) - - count += 1 - csvfile.close() - ## 3. visualize with bounding boxes - for (img, masks, color, t) in zip(self.images, self.masks, color_all, self.timepoints): - _display_instances(img, masks, figsize=(16, 16), title="", ax=_get_ax(rows=1, cols=1, size=16), - colors=color) - plt.savefig(os.path.join(visualdirs['3'], '{}.jpg'.format(t))) - plt.close('all') - - def __call__(self, images, masks, timepoints, savedir, visualdir_, visualdir, savename_, savename, logic="IOS", thres=0.2, name_sub="instance",colors=None, color_all=None): + self.uids = [-np.ones(n, dtype=np.int64) for n in self.n_insts] + self.uids[0] = np.arange(len(self.link_info[0]), dtype=np.int64) + self.max_uid = max(self.uids[0]) + for (t, link_t) in enumerate(self.link_info): + for (cidt, cidt_) in enumerate(link_t): + if cidt_ >= 0: + self.uids[t + 1][cidt_] = self.uids[t][cidt] + if -1 in self.uids[t + 1]: # -1 means there is not predecessor from a former timepoint -> assign new uids + temp = np.where(self.uids[t + 1] == -1)[0] + for tid in temp: + self.max_uid += 1 + self.uids[t + 1][tid] = self.max_uid + + def update_ti(self, delta_t=2): + self.delta_t = delta_t if delta_t else self.delta_t + + """ make copies for tc, t_appear, t_disappear """ + + ti_ = copy.deepcopy(self.ti) + t_appear_ = copy.deepcopy(self.t_appear) + t_disappear_ = copy.deepcopy(self.t_disappear) + + # find disappeared uids + disp_uids = np.where((t_disappear_ > -1) & (t_disappear_ < (self.T - 2)))[0] + + if len(disp_uids) == 0: + print("\nno updates!") + else: + # use an array to record how to update tc + ids_update = -np.ones(len(disp_uids), dtype=np.int64) + for (uid_i, uid) in enumerate(disp_uids): + if not (self.ti[:, uid] == -1).all(): + disp_time = t_disappear_[uid] + t0 = disp_time - 1 # time last appear + t_ = t0 + 2 # start with t_ = t0+2 + + while (t_ <= t0 + self.delta_t) and (t_ <= self.T - 1): + uids_t = np.where(t_appear_ == t_)[0] # find if there is new emergence at t_ (t0+delta) + if len(uids_t) > 0: + cidt0 = self.ti[t0, uid] + maskt0 = self.masks[t0][:, :, cidt0] # there is only one mask + cidst_ = self.ti[t_, uids_t] # there can be more than one potential indices at t_ + maskst_ = self.masks[t_][:, :, cidst_] + weight, _, row_ind, col_ind = _get_link(maskt0, maskst_, self.logic, self.thres) + if len(row_ind) > 0: + for (r, c) in zip(row_ind, col_ind): + if weight[r, c] >= self.thres: + # cidt0 = cidt0 + # cidt_ = cidst_[c] + """find corresponding uid """ + uidt_ = uids_t[c] + """ recore how to update tc """ + ids_update[uid_i] = uidt_ + t_ += 1 + else: + t_ += 1 + else: + t_ += 1 + to_del = [idx for idx in ids_update if idx != -1] + if len(to_del) == 0: + print("\nno updates!") + else: + print("updates!!") + # there will be updates, so save previous result in self.ti_, self.t_appear_, and self.t_disappear_, respectively + self.ti_, self.t_appear_, self.t_disappear_ = ti_, t_appear_, t_disappear_ + for (idx, id_up) in enumerate(ids_update): + if id_up != -1: + id_old = disp_uids[idx] + t_ = t_appear_[id_up] + self.ti[t_:, id_old] = self.ti[t_:, id_up] + self.ti = np.delete(self.ti, to_del, 1) + self.t_appear = np.delete(self.t_appear, to_del) + self.t_disappear = np.delete(self.t_disappear, to_del) + + self.ids_update = ids_update + self.disp_uids = disp_uids + self.updated = 1 + self.delta_t = delta_t + self.max_uid = self.max_uid - len(np.where(ids_update > -1)[0]) + + # def visualize(self, visualdir, colors=None, color_all=None): + # """ + # Create 3 sets of visualization + # visualization set 1: one leaf per image (visualdir['1']) + # visualization set 2: show with an alpha channel (visualdir['2']) + # visualization method 3: show with bounding boxes (visualdir['3']) + # Also save a csv file called linking_info.csv (or other user defined name) that includes the linking information + # """ + # + # # create subfolders inside the provided directory of visualization + # visualdirs = dict() + # for i in range(1, 4): + # idx = str(i) + # visualdirs[idx] = os.path.join(visualdir, 'visualization{}'.format(idx)) + # if not os.path.exists(visualdirs[idx]): + # os.makedirs(visualdirs[idx]) + # + # if colors is None: + # colors = visualize_display_instances._random_colors(self.max_uid) # worest case: all leaves are unique + # if color_all is None: + # color_all = [[tuple() for i in range(0, num)] for num in self.n_insts] + # + # for (t,ti_t) in enumerate(self.ti): + # uids_t = [x for x in ti_t if x >= 0] + # for (idx,uid) in enumerate(uids_t): + # color_all[t][idx] = colors[uid] + # + # for (img, masks, colors_t, t) in zip(self.images, self.masks, color_all, self.timepoints): + # savename3 = os.path.join(visualdirs['3'], '{}.jpg'.format(t)) + # _visualize3(img, masks, colors_t, savename=savename3) + # plt.close("all") + + def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", + colors=None, color_all=None, update=False, max_delta_t=2, savedir_=None, savename_=None): # a list of images which are ndarrays self.images = images # a list of masks which are ndarrays (of the same length of images) - self.masks = masks + self.masks = masks # a list of timepoints (of the same length of images) self.timepoints = timepoints - self.total_time = len(self.timepoints) + self.T = len(self.timepoints) # number of instances: a list in which every element represent for number of instances in corresponding image self.n_insts = [] for i in range(0, len(self.masks)): self.n_insts.append(self.masks[i].shape[2]) # initialization for linking - self.thres = thres - self.link_info = [-np.ones((self.n_insts[i]), dtype=int) for i in range(0, self.total_time - 1)] - self.unlinked_insts = [np.array(range(0, self.n_insts[i])) for i in range(0, self.total_time)] - self.link_series = dict() # only for those newly emerging leaves - self.weights = [] - self.logic = logic.upper() + self.thres = thres + self.link_info = [-np.ones((self.n_insts[i]), dtype=np.int64) for i in range(0, self.T - 1)] + + self.weights = [] + self.logic = logic.upper() self.name_sub = name_sub - self.key_id = '{}_ids'.format(name_sub) + self.key_id = '{}_ids'.format(name_sub) - for t0 in range(0, self.total_time - 1): + for t0 in range(0, self.T - 1): self.linking(t0) - self.get_series() - self.visualize(visualdir_, savedir, 'link_info_old', colors, color_all) - self.save_linked_series(savedir, savename_) - - self.update_series() - self.visualize(visualdir, savedir, 'link_info', colors, color_all) + self.get_uid() + self.emergence, self.emerge_times = _get_emergence(self.uids) + self.ti, self.t_appear, self.t_disappear = _get_ti(self.n_insts, self.uids, self.link_info) + # visualdir = os.path.join(savedir, "visualization") + # if not os.path.exists(visualdir): + # os.makedirs(visualdir) + # self.visualize(visualdir) self.save_linked_series(savedir, savename) -class InstanceTSLinkingWrapper(object): - """A class that is a wrapper for InstanceTimeSeriesLinking - Assumptions: - 1) The segmentation are saved in a different folder from original images - 2) The segmentation result and the original images have corresponding image names, which include "date-time" part with a specific pattern - the date-time pattern for names of original images and names of segmentation results are the same - 3) For every image, there is a corresponding segmentation result - Otherwise, please get your data prepared and use the "InstanceTimeSeriesLinking" class directly - """ + if update: + self.update_ti(max_delta_t) + if self.updated == 1: + if savedir_ is not None and savedir_ != savedir: + visualdir_ = os.path.join(savedir, "visualization") + else: + savedir_ = savedir + visualdir_ = os.path.join(savedir, "updated_visualization") + if not os.path.exists(visualdir_): + os.makedirs(visualdir_) - def __init__(self): - self.dir_save = None - self.savename = None - self.savename_ = None - self.dir_img = None - self.dir_seg = None - self.time_cond = None - self.suffix = None - self.ext = None - self.suffix_seg = None - self.timepoints = None - self.imgfiles = None - self.segfiles = None - self.dir_visual = None - self.dir_visual_ = None - self.pattern_dt = None - - def set_save_dirs(self): - junk = datetime.datetime.now() - subfolder = '{}-{}-{}-{}-{}'.format(junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), - str(junk.hour).zfill(2), str(junk.minute).zfill(2), - str(junk.second).zfill(2)) - self.dir_save = os.path.join(self.dir_save, subfolder) - if not os.path.exists(self.dir_save): - os.makedirs(self.dir_save) - self.dir_visual = os.path.join(self.dir_save, 'visualization') - if not os.path.exists(self.dir_visual): - os.makedirs(self.dir_visual) - self.dir_visual_ = os.path.join(self.dir_save, 'visualization_old') - if not os.path.exists(self.dir_visual_): - os.makedirs(self.dir_visual_) - - def sort_time(self): - """ - This function is designed for files with file names which contain a "date-time" part, with an user-defined pattern - sort time based on names of original images - """ + if savename_ is None: + savename_ = "{}_{}".format(savename, max_delta_t) - # file name extension of original images - ext1, ext2 = os.path.splitext(self.suffix) - if ext1.startswith('.'): - self.ext = ext1 - elif ext2.startswith('.'): - self.ext = ext2 - - imgfs_all = [f for f in os.listdir(self.dir_img) if f.endswith(self.suffix)] - segfs_all = [f for f in os.listdir(self.dir_seg) if f.endswith(self.suffix_seg)] - imgfs_all.sort() - segfs_all.sort() - for (fi, fs) in zip(imgfs_all, segfs_all): - tempi = re.search(self.pattern_dt, fi) - temps = re.search(self.pattern_dt, fs) - if tempi and temps: - timepart = tempi.group() # timeparts = temps.group() the assumption is that original image and the results have the same dt_pattern - for cond in self.time_cond: - if timepart.endswith(cond): - self.timepoints.append(timepart) - continue - index_temp = np.argsort(self.timepoints) - self.timepoints = [self.timepoints[i] for i in index_temp] - self.imgfiles = [imgfs_all[i] for i in index_temp] - self.segfiles = [segfs_all[i] for i in index_temp] - - def load_images(self): - """ - Load images and put them into a list - :return: - max_dim: common maximum dimension of all images - """ - temp_imgs = [] - sz = [] - for f in self.imgfiles: - junk = skimage.io.imread(os.path.join(self.dir_img, f)) - temp_imgs.append(junk) - sz.append(np.min(junk.shape[0:2])) - max_dim = np.min(sz) - for junk in temp_imgs: - img = junk[0: min_dim, 0: min_dim, :] # make all images the same size - self.imgs.append(img) - return max_dim - - def load_segs(self, max_dim): - """ - Get the list of segmentation masks (nd-arrays) ready - :param max_dim: common maximum dimension of all images (get from original images) (Assumption: square images) - :return: - """ - # segs = [] - for f in self.segfiles: - r = pkl.load(open(os.path.join(self.dir_seg, f), 'rb')) - self.segs.append(r["masks"][0:max_dim, 0:max_dim, :]) # make all masks the same size - # return segs - - def __call__(self, dir_save, savename, dir_img, dir_seg, pattern_dt, time_cond, logic, thres, name_sub, - suffix=".jpg", suffix_seg=".pkl", colors=None, color_all=None): - self.dir_save = dir_save - self.savename = savename - self.savename_ = '{}_old'.format(savename) - self.set_save_dirs() - self.dir_img = dir_img - self.dir_seg = dir_seg - self.pattern_dt = pattern_dt - self.time_cond = time_cond - self.logic = logic - self.thres = thres - self.name_sub = name_sub - self.suffix = suffix - self.suffix_seg = suffix_seg - self.imgs = [] - self.segs = [] - self.timepoints = [] - - self.sort_time() - min_dim = self.load_images() - self.load_segs(min_dim) - # create an instance of InstanceTimeSeriesLinkingClass - - inst_ts_linking = InstanceTimeSeriesLinking() - inst_ts_linking(images=self.imgs, masks=self.segs, timepoints=self.timepoints, savedir=self.dir_save, - visualdir_=self.dir_visual_, visualdir=self.dir_visual, savename_=self.savename_, - savename=self.savename, logic=self.logic, thres=self.thres, name_sub=self.name_sub) - - return inst_ts_linking \ No newline at end of file + # self.visualize(visualdir_) + self.save_linked_series(savedir_, savename_) + + # self.save_to_csv(savedir, "link_series_old.csv", "link_info_old.csv") From bc55fa2db4f092defa141df7eeb6e45cf2b375f0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 17 Dec 2020 14:22:07 -0600 Subject: [PATCH 206/274] upload new test data --- ...s-165-003-020_2019-10-22-14-05_crop-img12.jpg | Bin 0 -> 6429 bytes ...s-165-003-020_2019-10-22-21-05_crop-img12.jpg | Bin 0 -> 7020 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg create mode 100644 tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_crop-img12.jpg diff --git a/tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg 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207/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index b93fec640..1bb96b3ff 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,12 +1,8 @@ -from plantcv.plantcv.time_series.time_series_linking import _random_colors -from plantcv.plantcv.time_series.time_series_linking import _apply_mask -from plantcv.plantcv.time_series.time_series_linking import _get_ax -from plantcv.plantcv.time_series.time_series_linking import _display_instances +from plantcv.plantcv.time_series.time_series_linking import _get_link +from plantcv.plantcv.time_series.time_series_linking import _get_emergence +from plantcv.plantcv.time_series.time_series_linking import _get_ti from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking -from plantcv.plantcv.time_series.time_series_linking import InstanceTSLinkingWrapper - # add new functions to end of lists -__all__ = ["_random_colors", "_apply_mask", "_get_ax", "_display_instances", "_compute_overlaps_masks", - "InstanceTimeSeriesLinking", "InstanceTSLinkingWrapper"] +__all__ = ["_get_link", "_get_emergence", "_get_ti", "_compute_overlaps_masks", "InstanceTimeSeriesLinking"] From 221fedbc665eebd94e617cbab0f2bc02b03dbb33 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 17 Dec 2020 14:22:14 -0600 Subject: [PATCH 208/274] Update tests.py --- tests/tests.py | 88 +++++++++++++++++++++++++++++++++++--------------- 1 file changed, 62 insertions(+), 26 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 35cfe2f42..d5902432c 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -21,6 +21,9 @@ from dask.distributed import Client import pickle as pkl from skimage.util import img_as_ubyte +import glob +import re +import skimage.io PARALLEL_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "parallel_data") TEST_TMPDIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", ".cache") @@ -6418,41 +6421,74 @@ def test_plantcv_visualize_time_lapse_video_bad_list(): # Tests for the time_series subpackage # ############################## -def test_plantcv_time_series_display_instances(): - # Test cache directory - loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_INSTANCE_SEG, "img_segment_.pkl"), 'rb')) - img = loaded['img'] - masks = loaded['segment']['masks'] - pcv.time_series._display_instances(img, masks, figsize=(16, 16), title="", ax=None, colors=None) +# def test_plantcv_time_series_display_instances(): +# # Test cache directory +# loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_INSTANCE_SEG, "img_segment_.pkl"), 'rb')) +# img = loaded['img'] +# masks = loaded['segment']['masks'] +# pcv.time_series._display_instances(img, masks, figsize=(16, 16), title="", ax=None, colors=None) def test_plantcv_time_series_time_series(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir - dir_img = TIME_SERIES_TEST_RAW - dir_seg = TIME_SERIES_TEST_INSTANCE_SEG - dir_save = cache_dir - dir_visual_ = os.path.join(dir_save,'visual_old') - os.mkdir(dir_visual_) - dir_visual = os.path.join(dir_save,'visual') - os.mkdir(dir_visual) - pattern_datetime = '\d{4}-\d{2}-\d{2}-\d{2}-\d{2}' # YYYY-MM-DD-hh-mm - time_cond = ['08-05', '11-05', '17-05', '21-05'] - suffix = '.jpg' - suffix_seg = '.pkl' - thres = 0.2 + + path_img = TIME_SERIES_TEST_RAW + path_segmentation = TIME_SERIES_TEST_INSTANCE_SEG + path_save = cache_dir + savename = "link_series" + pattern_datetime = "\d{4}-\d{2}-\d{2}-\d{2}-\d{2}" # YYYY-MM-DD-hh + ext = "_crop-img12.jpg" + ext_seg = ".pkl" + + list_seg = glob.glob(os.path.join(path_segmentation, "2*{}".format(ext_seg))) + timepoints = [] + for f_seg in list_seg: + tp_temp = re.search(pattern_datetime, f_seg).group() + timepoints.append(tp_temp) + timepoints.sort() + # Load original images + images = [] + temp_imgs = [] + sz = [] + + for tp in timepoints: + filename_ = "*_{}{}".format(tp, ext) + filename = glob.glob(os.path.join(path_img, filename_))[0] + + junk_ = skimage.io.imread(filename) + junk = junk_ + if len(junk_.shape) == 2: + junk = cv2.cvtColor(junk_, cv2.COLOR_GRAY2BGR) + temp_imgs.append(junk) + sz.append(np.min(junk.shape[0:2])) + min_dim = np.min(sz) + for junk in temp_imgs: + img = junk[0: min_dim, 0:min_dim, :] # make all images the same size + images.append(img) + # load instance segmentation results (masks) + masks = [] + for tp in timepoints: + print(tp) + print(ext_seg) + filename_ = "*{}{}".format(tp, ext_seg) + print(filename_) + filename = glob.glob(os.path.join(path_segmentation, filename_))[0] + print("file name is {}".format(filename)) + r = pkl.load(open(filename, 'rb')) + masks.append(r['masks'][0: min_dim, 0:min_dim, :]) # make all masks the same size + logic = 'IOS' + thres = 0.1 + + # create an instance of Instance_timeseries_linking name_sub = 'leaf' - name_series = 'linked_series' - inst_ts_linking = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) - inst_ts_linking(dir_img, dir_seg, pattern_datetime, time_cond, logic, thres, name_sub, suffix, - suffix_seg) - inst_ts_linking2 = pcv.time_series.InstanceTSLinkingWrapper(dir_save=dir_save, savename=name_series) - inst_ts_linking2(dir_img, dir_seg, pattern_datetime, time_cond, 'IOU', thres, name_sub, '12.jpg', - suffix_seg) - assert (len(os.listdir(inst_ts_linking.dir_save)) > 0) and (len(os.listdir(inst_ts_linking2.dir_save)) > 0) + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + inst_ts_linking(images=images, masks=masks, timepoints=timepoints, savedir=path_save, savename=savename, + logic=logic, thres=thres, name_sub=name_sub, update=False) + assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From ff7223eda930e1d86d3a38144fd242698e542607 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 18 Dec 2020 17:10:20 -0600 Subject: [PATCH 209/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 37 ++++++++++--------- 1 file changed, 20 insertions(+), 17 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 0962ee0ed..1c6f3b898 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -447,8 +447,11 @@ def update_ti(self, delta_t=2): # _visualize3(img, masks, colors_t, savename=savename3) # plt.close("all") - def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", - colors=None, color_all=None, update=False, max_delta_t=2, savedir_=None, savename_=None): + # def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", + # colors=None, color_all=None, update=False, max_delta_t=2, savedir_=None, savename_=None): + # def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", + # update=False, max_delta_t=2, savedir_=None, savename_=None): + def __call__(self, images, masks, timepoints, logic="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): # a list of images which are ndarrays self.images = images # a list of masks which are ndarrays (of the same length of images) @@ -479,23 +482,23 @@ def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", th # if not os.path.exists(visualdir): # os.makedirs(visualdir) # self.visualize(visualdir) - self.save_linked_series(savedir, savename) + # self.save_linked_series(savedir, savename) if update: self.update_ti(max_delta_t) - if self.updated == 1: - if savedir_ is not None and savedir_ != savedir: - visualdir_ = os.path.join(savedir, "visualization") - else: - savedir_ = savedir - visualdir_ = os.path.join(savedir, "updated_visualization") - if not os.path.exists(visualdir_): - os.makedirs(visualdir_) - - if savename_ is None: - savename_ = "{}_{}".format(savename, max_delta_t) - - # self.visualize(visualdir_) - self.save_linked_series(savedir_, savename_) + # if self.updated == 1: + # if savedir_ is not None and savedir_ != savedir: + # visualdir_ = os.path.join(savedir, "visualization") + # else: + # savedir_ = savedir + # visualdir_ = os.path.join(savedir, "updated_visualization") + # if not os.path.exists(visualdir_): + # os.makedirs(visualdir_) + # + # if savename_ is None: + # savename_ = "{}_{}".format(savename, max_delta_t) + # + # # self.visualize(visualdir_) + # self.save_linked_series(savedir_, savename_) # self.save_to_csv(savedir, "link_series_old.csv", "link_info_old.csv") From 0762f1f4a77c95c0b379c904591e54cdf36234cc Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 18 Dec 2020 17:10:23 -0600 Subject: [PATCH 210/274] Update tests.py --- tests/tests.py | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index d5902432c..7532476cf 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6470,12 +6470,8 @@ def test_plantcv_time_series_time_series(): # load instance segmentation results (masks) masks = [] for tp in timepoints: - print(tp) - print(ext_seg) filename_ = "*{}{}".format(tp, ext_seg) - print(filename_) filename = glob.glob(os.path.join(path_segmentation, filename_))[0] - print("file name is {}".format(filename)) r = pkl.load(open(filename, 'rb')) masks.append(r['masks'][0: min_dim, 0:min_dim, :]) # make all masks the same size @@ -6485,10 +6481,9 @@ def test_plantcv_time_series_time_series(): # create an instance of Instance_timeseries_linking name_sub = 'leaf' inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() - inst_ts_linking(images=images, masks=masks, timepoints=timepoints, savedir=path_save, savename=savename, - logic=logic, thres=thres, name_sub=name_sub, update=False) - - assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 + inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) + assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From 09cd4be6ca85f18befdc0c0f272cce0f6f53360c Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 18 Dec 2020 17:10:26 -0600 Subject: [PATCH 211/274] Update time_series.md --- docs/time_series.md | 86 ++++++++++++++++++++++++++++++++------------- 1 file changed, 62 insertions(+), 24 deletions(-) diff --git a/docs/time_series.md b/docs/time_series.md index e31e58e26..f868b7876 100644 --- a/docs/time_series.md +++ b/docs/time_series.md @@ -1,6 +1,6 @@ ## time_series -This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every several hours) and ideally there should be minimal movment of plant/camera. +This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every 4 hours for several days), and ideally there should be minimal or no movment of plant and camera. To use this class for generating time-series linking, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). @@ -8,34 +8,73 @@ When using instance segmentation algorithms like maskRCNN, the assignment for th To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. -There are two way of using this class: either using it directly or using the wrapper. Examples of both are provided below. +For details of using this class, see examples below. -**\# initialize an instance of InstanceTimeSeriesLinking class:** +*initialize an instance of InstanceTimeSeriesLinking class:* -**inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking**(*images, masks, timepoints, logic, thres, name_sub*) -**inst_ts_linking**(*savedir, visualdir_, visualdir, savename_, savename*) +**inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking()** -**returns** No returned value, the inst_ts_linking is an instance which belongs to InstanceTimeSeriesLinking class. +*run the callable object* -- **Parameters for initialization:** +**inst_ts_linking**(*images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance",update=False, max_delta_t=2*) +**returns** No returned value, the inst_ts_linking is an instance object which belongs to InstanceTimeSeriesLinking class. + +- **Parameters** - images: a list of images. Every element of this list is an array represents one image - masks: a list of instance segmentation masks. Every element of this list is an array represents instance segmentation masks correspond to one image. - timepoints: a list of timepoints. The lengths for images, masks and timepoint should be the same and the elements are correspond to each other - - logic: the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) - If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". "IOS" is recommended. - - thres: threshold used in the linking logic as mentioned above. For "IOS" it is recommended to start with a threshod of 0.2. - - name_sub: name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". - -Once you get the class object initialized, it is callable, which means it is a callable function itself so that you can get the linking result by running the 2nd line of code presented above. -- **Parameters to call functions:** - - savedir: desired saving directory of linking result - - visualdir_: desired saving directory of visualization (before update) - - visualdir: desired saving directory of visualization (final) - - savename_: desired saving name of results (before update) - - savename: desired saving name of results (final) + - logic (optional): the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) + If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". Default value is "IOS". + - thres (optional): threshold used in the linking logic as mentioned above. Different threshold is needed when using different linking logic. Default value is 0.2. + - name_sub (optional): name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". + - update (optional): whether or not updating the time-series linking result. By default (not updating) the time-series linking would only examine the neighboring pairs of time points (time interval = 1). By updating the time-series linking we will be examining a larger interval of time. + - max_delta_t (optional): only needed when update=True. By default max_delta_t=2. - **Output:** - An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. Besides, all the results will be saved in user defined "savedir": + An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. + +```python +from plantcv import plantcv as pcv +# Below are examples of input variables, always adjust base on your own application. +logic = 'IOS' +thres = 0.1 +name_sub = 'leaf' +## Initialize an instance of class InstanceTimeSeriesLinking +inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() +inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub) +``` +Make sure all the images and masks are already sorted based on timepoints. + +To save the result, simply specify the directory of saving as well as the name (prefix) +```python +## Specify the desired directory to save results +dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' + +## Specify the desired name to save the result (prefix) +savename = 'linked_series' + +inst_ts_linking.save_linked_series(savedir=dir_save, savename=savename) +``` +To import a previously saved result: +```python +dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' +savename = 'linked_series' +inst_ts_linking = time_series_linking.InstanceTimeSeriesLinking() +inst_ts_linking.import_linked_series(savedir=dir_save, savename=savename) +``` + +The option to update the time-series linking result is also available: +```python +max_delta_t = 3 +inst_ts_linking.update_ti(delta_t=max_delta_t) +``` + +Alternatively, the time-series linking can be done with ```update=True``` +```python +inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=True, max_delta_t=3) +``` + + **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) From 19655c94c0324ef097662c6a82153713a6c763c3 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 08:36:45 -0600 Subject: [PATCH 212/274] Update tests.py --- tests/tests.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/tests/tests.py b/tests/tests.py index 7532476cf..a49c973f0 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6475,6 +6475,13 @@ def test_plantcv_time_series_time_series(): r = pkl.load(open(filename, 'rb')) masks.append(r['masks'][0: min_dim, 0:min_dim, :]) # make all masks the same size + # test for "_compute_overlap_masks" + masks1 = masks[0] + masks2 = masks[1] + n1, n2, _, _, _ = pcv.time_series._compute_overlaps_masks(masks1[:, :, 0], masks2) + n1_, n2_, _, _, _ = pcv.time_series._compute_overlaps_masks(masks1, masks2[:, :, 0]) + assert n1 == 1 and n2 == masks2.shape[2] and n1_ == masks1.shape[2] and n2_ == 1 + logic = 'IOS' thres = 0.1 @@ -6485,6 +6492,19 @@ def test_plantcv_time_series_time_series(): assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 +# def test_plantcv_time_series_compute_overlap_masks(): +# path_segmentation = TIME_SERIES_TEST_INSTANCE_SEG +# ext_seg = ".pkl" +# list_seg = glob.glob(os.path.join(path_segmentation, "2*{}".format(ext_seg))) +# loaded1 = pkl.load(open(list_seg[0], 'rb')) +# loaded2 = pkl.load(open(list_seg[1], 'rb')) +# masks1 = loaded1['masks'] +# masks2 = loaded2['masks'] +# n1, n2, _, _, _ = _compute_overlaps_masks(masks1[:,:,0], masks2) +# assert n1 == 1 and n2 == masks2.shape[2] +# n1_, n2_, _, _, _ = _compute_overlaps_masks(masks1, masks2[:, :, 0]) +# assert n1_ == masks1.shape[2] and n2_ == 1 + def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_visualize_overlay_two_imgs") From 881a538ee5be4f7ab1f0d5f7e0ce21e905073863 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 11:07:33 -0600 Subject: [PATCH 213/274] Create evaluation.py --- plantcv/plantcv/time_series/evaluation.py | 157 ++++++++++++++++++++++ 1 file changed, 157 insertions(+) create mode 100644 plantcv/plantcv/time_series/evaluation.py diff --git a/plantcv/plantcv/time_series/evaluation.py b/plantcv/plantcv/time_series/evaluation.py new file mode 100644 index 000000000..8b3aa3f95 --- /dev/null +++ b/plantcv/plantcv/time_series/evaluation.py @@ -0,0 +1,157 @@ +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Dec 7 2020 + +Functions used to evaluate time-series linking result + +@author: hudanyunsheng + +Notations and Definitions: +T: total time +N: total number of (unique) leaves in ground truth +N_: total number of (unique) leaves in tracking result +li (link info): a list of length (T-1) + every element of li, i.e. li[t] represents how n_t link to n_{t+1} + length of li[t]: n_t + li[t][i]=j: the i-th segment at time t is the j-th segment at time t+1 +li_gt (link info in ground truth): same definition as li, represents the same information in ground truth +ti (tracking info): a matrix (2d-array) of size (T,N) + tk-th element of ti: ti[t,k]: local index of leaf k at time t if k leaf k appears at time t, -1 others. + Total number of non-negative elements in a row t: # of leaves at time t + Every column k: local indices for leaf k at different timepoints(ts). +ti_gt (tracking info in ground truth): same definition as ti, represents the same information in ground truth +""" + +import numpy as np +from scipy.optimize import linear_sum_assignment +import copy +from plantcv.plantcv import fatal_error + + +def evaluate_link(li, li_gt): + """ + Evaluate the link_info by comparing the result to the ground truth + :param li: link info + :param li_gt: link info in ground truth + :return: + score: final score for linking, defined as total # of correct matches / total # of matches + num_insts: number of instances at every time point except the last one + num_matched: number of matched instances at every time point except the last one + """ + if not len(li) == len(li_gt): + fatal_error('Linking information not same length!!') + else: + max_t = len(li_gt) + if not sum([len(x) == len(y) for (x, y) in zip(li_gt, li)]) == max_t: + fatal_error('Different number of instances!!') + num_insts = [len(x) for x in li_gt] + # num_matched = [(sum(x == y)) for (x,y) in zip(li_gt, li)] + num_matched = [(len(x == y)) for (x, y) in zip(li_gt, li)] + score = sum(num_matched) / sum(num_insts) + return score, num_insts, num_matched + + +def mismatch_rate(ti, ti_gt): + """ + Calculate rates related to two types of mis-match: unmatched and fake-new + :param ti: tracking info + :param ti_gt: tracking info in ground truth + :return: + r_unmatched: 0 if N_ >= N + r_fake_new: 0 if N_ <= N + """ + r_unmatched, r_fake_new = 0, 0 + N, N_ = ti_gt.shape[1], ti.shape[1] + if N_ < N: + r_unmatched = (N - N_) / N + elif N_ > N: + r_fake_new = (N_ - N) / N + return r_unmatched, r_fake_new + + +def confusion(ti, ti_gt): + """ + Generate a confusion matrix based on tracking info and ground truth of tracking info + :param ti: tracking info + :param ti_gt: tracking info in ground truth + :return: + confu: confusion matrix of size (N,N_) + match: (list of length N) "diagonal" elements of confu. (If N_N, there will still be N elements in "match") + rate: (list of length N) match/existence_times + score: averate rate of all leaves + """ + N, N_ = ti_gt.shape[1], ti.shape[1] + # existance times for every leaf (unique id in ground truth) + life_gt = [len(np.where(ti_gt[:, i] > -1)[0]) for i in range(N)] + life = [len(np.where(ti[:, i] > -1)[0]) for i in range(N_)] + confu = np.zeros((N, N_), dtype=np.int64) + for t, (ti_t, ti_gt_t) in enumerate(zip(ti, ti_gt)): + temp = np.zeros((N, N_), dtype=np.int64) + for (uid_gt, cid_gt) in enumerate(ti_gt_t): + if cid_gt > -1: + uid_t = np.where(ti_t == cid_gt)[0][0] + temp[uid_gt, uid_t] = 1 + confu = confu + temp + + # if N_ Date: Mon, 21 Dec 2020 11:07:36 -0600 Subject: [PATCH 214/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index 1bb96b3ff..9509934f1 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -3,6 +3,10 @@ from plantcv.plantcv.time_series.time_series_linking import _get_ti from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking +from plantcv.plantcv.time_series.evaluation import evaluate_link +from plantcv.plantcv.time_series.evaluation import mismatch_rate +from plantcv.plantcv.time_series.evaluation import confusion +from plantcv.plantcv.time_series.evaluation import get_scores # add new functions to end of lists -__all__ = ["_get_link", "_get_emergence", "_get_ti", "_compute_overlaps_masks", "InstanceTimeSeriesLinking"] +__all__ = ["_get_link", "_get_emergence", "_get_ti", "_compute_overlaps_masks", "InstanceTimeSeriesLinking", "evaluate_link", "mismatch_rate", "confusion", "get_scores"] From 633ab4a9184eaf94a2cc0ffca2f0398aa71c93be Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 11:07:42 -0600 Subject: [PATCH 215/274] Update tests.py --- tests/tests.py | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index a49c973f0..698b7acc4 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -998,7 +998,7 @@ def test_plantcv_parallel_process_results_invalid_json(): TEST_INPUT_INSTANCE_IMG = "visualize_inst_seg_img.png" TEST_INPUT_INSTANCE_MASK = "visualize_inst_seg_mask.pkl" -TIME_SERIES_TEST_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "seires_data") +TIME_SERIES_TEST_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "time_seires_data") TIME_SERIES_TEST_RAW = os.path.join(TIME_SERIES_TEST_DIR, "raw_im") TIME_SERIES_TEST_INSTANCE_SEG = os.path.join(TIME_SERIES_TEST_DIR, "inst_seg") @@ -6492,18 +6492,19 @@ def test_plantcv_time_series_time_series(): assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 -# def test_plantcv_time_series_compute_overlap_masks(): -# path_segmentation = TIME_SERIES_TEST_INSTANCE_SEG -# ext_seg = ".pkl" -# list_seg = glob.glob(os.path.join(path_segmentation, "2*{}".format(ext_seg))) -# loaded1 = pkl.load(open(list_seg[0], 'rb')) -# loaded2 = pkl.load(open(list_seg[1], 'rb')) -# masks1 = loaded1['masks'] -# masks2 = loaded2['masks'] -# n1, n2, _, _, _ = _compute_overlaps_masks(masks1[:,:,0], masks2) -# assert n1 == 1 and n2 == masks2.shape[2] -# n1_, n2_, _, _, _ = _compute_overlaps_masks(masks1, masks2[:, :, 0]) -# assert n1_ == masks1.shape[2] and n2_ == 1 +def test_plantcv_time_series_evaluation(): + loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"result.pkl"),'rb')) + loaded_gt = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"gt.pkl"),'rb')) + li = loaded['li'] + ti = loaded['ti'] + li_gt = loaded_gt['li_gt'] + ti_gt = loaded_gt['ti_gt'] + + with pytest.raises(RuntimeError): + pcv.time_series.get_scores(li[0:-1], ti[0:-1,:], li_gt, ti_gt) + + scores = pcv.time_series.get_scores(li, ti, li_gt, ti_gt) + assert len(li) == ti.shape[0]-1 and len(li_gt) == ti_gt.shape[0]-1 and ti.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From 86d41bc67df9ee619084b67de051ff8feb4eefa0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 11:08:14 -0600 Subject: [PATCH 216/274] upload testdata --- .../plantcv/time_series/time_series_linking.py | 4 ++-- tests/time_seires_data/gt.pkl | Bin 0 -> 16699 bytes .../inst_seg/2019-10-21-21-05.pkl | Bin .../inst_seg/2019-10-22-08-05.pkl | Bin .../inst_seg/2019-10-22-11-05.pkl | Bin .../inst_seg/2019-10-22-14-05.pkl | Bin .../inst_seg/2019-10-22-17-05.pkl | Bin .../inst_seg/2019-10-22-21-05.pkl | Bin .../inst_seg/img_segment_.pkl | Bin 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(100%) rename tests/{seires_data => time_seires_data}/inst_seg/2019-10-22-21-05.pkl (100%) rename tests/{seires_data => time_seires_data}/inst_seg/img_segment_.pkl (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-21-21-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-08-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-11-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_mask-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-17-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_crop-img12.jpg (100%) rename tests/{seires_data => time_seires_data}/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-21-05_mask-img12.jpg (100%) create mode 100644 tests/time_seires_data/result.pkl diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 1c6f3b898..92826f637 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -1,11 +1,11 @@ # !/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Created on Tue Jun 9 09:53:34 2020 +Created on Tue Jun 9 2020 Functions used in time series linking after getting leaf instances segmented -@author: hudanyunsheng +@author: hudanyun sheng """ import numpy as np diff --git a/tests/time_seires_data/gt.pkl b/tests/time_seires_data/gt.pkl new file mode 100644 index 0000000000000000000000000000000000000000..18e5daa1a36dc6a2d58704355d60be750da2984e GIT 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tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg rename to tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_crop-img12.jpg diff --git a/tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_mask-img12.jpg b/tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_mask-img12.jpg similarity index 100% rename from tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_mask-img12.jpg rename to tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-14-05_mask-img12.jpg diff --git a/tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-17-05_crop-img12.jpg b/tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-17-05_crop-img12.jpg similarity index 100% rename from tests/seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-17-05_crop-img12.jpg rename to tests/time_seires_data/raw_im/10.9.1.241_pos-165-003-020_2019-10-22-17-05_crop-img12.jpg diff 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zD-k1Qufc$F6*5U5VxVLzY)28Fh%1wxoZk<7Z?q+0? zTMz@?tyZWh-EDAG$su-h2s}9${(Zc7y ztyZs7Lo1iszl<;I-_RXM_iEDh=l34cxt9L!jIZ=w4RvJJzoEL4{I#r)$$Pf>JF@<5 z-i_pcm)k%8-=E};$=;FiCF@u5(OLhBJCf{q*1u%^Dn9zT-ao%TZmj4dp z--DgQeVxZSy?;J#Kd#*DI!QU*&$*o2dA*)CKi~O$zRT@nwfi?(`20?B(mvLze;Hq) z(|#WGc^dBg;_Gj;^w0knz{fXS&emU{1EXbreLj7j{XeGs_2N8I^Xq*6ABbDx%=m_0 zf2H=%&tFOV=cSXnA}B6jq*JOjAl0gwOy literal 0 HcmV?d00001 From cf8cc47499a3ae52cec48a5a92e6a6a0ae51c9e6 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 16:55:18 -0600 Subject: [PATCH 217/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 92826f637..3ae64ad99 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -46,9 +46,9 @@ def _compute_overlaps_masks(masks1, masks2): unions: (numpy.ndarray of shape: [n1, n2]) unions between any pairs of instances in masks1 and masks2 """ - # If either set of masks is empty return empty result - if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: - return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # If either set of masks is empty return an empty result + # if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + # return np.zeros((masks1.shape[-1], masks2.shape[-1])) # If either set of masks contains only one mask, expand the 2nd dimension if len(masks1.shape) == 2: masks1 = np.expand_dims(masks1, 2) From f4445977e8162c03072b1e4ed3fdff688a5c48a0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 16:55:22 -0600 Subject: [PATCH 218/274] Update tests.py --- tests/tests.py | 47 +++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 41 insertions(+), 6 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 698b7acc4..978c8f67b 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6490,21 +6490,56 @@ def test_plantcv_time_series_time_series(): inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + inst_ts_linking.update_ti(delta_t=2) + assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + + inst_ts_linking.save_linked_series(path_save,'linked_series') + assert (len(os.listdir(path_save)) > 0) + + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + inst_ts_linking.import_linked_series(path_save, 'linked_series') + assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + + logic = 'IOU' + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) + assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + + logic = 'emd' + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + with pytest.raises(RuntimeError): + inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 def test_plantcv_time_series_evaluation(): - loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"result.pkl"),'rb')) + loaded17 = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"result_N_17.pkl"),'rb')) + loaded18 = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR, "result_N_18.pkl"), 'rb')) + loaded20 = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR, "result_N_20.pkl"), 'rb')) loaded_gt = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"gt.pkl"),'rb')) - li = loaded['li'] - ti = loaded['ti'] + li17 = loaded17['li'] + ti17 = loaded17['ti'] + li18 = loaded18['li'] + ti18 = loaded18['ti'] + li20 = loaded20['li'] + ti20 = loaded20['ti'] li_gt = loaded_gt['li_gt'] ti_gt = loaded_gt['ti_gt'] with pytest.raises(RuntimeError): - pcv.time_series.get_scores(li[0:-1], ti[0:-1,:], li_gt, ti_gt) + pcv.time_series.get_scores(li18[0:-1], ti18[0:-1,:], li_gt, ti_gt) + + with pytest.raises(RuntimeError): + pcv.time_series.get_scores(li18[1:], ti18[1:,:], li_gt, ti_gt) + + scores = pcv.time_series.get_scores(li18, ti18, li_gt, ti_gt) + assert len(li18) == ti18.shape[0]-1 and len(li_gt) == ti_gt.shape[0]-1 and ti18.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] + + scores = pcv.time_series.get_scores(li20, ti20, li_gt, ti_gt) + assert len(li20) == ti20.shape[0]-1 and len(li_gt) == ti_gt.shape[0]-1 and ti20.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] + + scores = pcv.time_series.get_scores(li17, ti17, li_gt, ti_gt) + assert len(li17) == ti17.shape[0] - 1 and len(li_gt) == ti_gt.shape[0] - 1 and ti17.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] - scores = pcv.time_series.get_scores(li, ti, li_gt, ti_gt) - assert len(li) == ti.shape[0]-1 and len(li_gt) == ti_gt.shape[0]-1 and ti.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From ad9755953ac16362c7e3576b62cb775331853eef Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 21 Dec 2020 16:55:31 -0600 Subject: [PATCH 219/274] load new test data --- tests/time_seires_data/result_N_17.pkl | Bin 0 -> 16181 bytes tests/time_seires_data/result_N_18.pkl | Bin 0 -> 16693 bytes .../{result.pkl => result_N_20.pkl} | Bin 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/time_seires_data/result_N_17.pkl create mode 100644 tests/time_seires_data/result_N_18.pkl rename tests/time_seires_data/{result.pkl => result_N_20.pkl} (100%) diff --git a/tests/time_seires_data/result_N_17.pkl 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z+x0^Eed?BVjytP8u5rFWE7Cs0>MydEf50fPYgg_3yg+hQ48!}AMo{W9qs2LOBIuHd%iCF z;&b;lRbGmGjAc&un-^*~HyUrq^Ex+7dHh*A?8&U5=V$yLSaPLwMWKJjHjk2HiPNR( z8&W?lx%fZ7G4i|K>xvKGg#(6%jQiESQAh?-2UzrdPi~WGq$a zBDUj#A`SYO*ij^$?!n}xQW1ur(IkRubmic1L!2I8`H5uV=~3*6A>-&CxGAbl?CL9g*$2zdM$^v)#gOsBKDsC{G`)B-A;L}?Ib)cf#PHMD X61#e06!ul7A&S Date: Tue, 22 Dec 2020 08:43:22 -0600 Subject: [PATCH 221/274] Update tests.py --- tests/tests.py | 43 ++++++++++++++++++++++++++++++++----------- 1 file changed, 32 insertions(+), 11 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 978c8f67b..de75e5f52 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6490,27 +6490,41 @@ def test_plantcv_time_series_time_series(): inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + + # test with update_ti = True + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=True) + assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + + # test on update_ti inst_ts_linking.update_ti(delta_t=2) assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) - inst_ts_linking.save_linked_series(path_save,'linked_series') + # test on save_linked_series + inst_ts_linking.save_linked_series(path_save,savename) assert (len(os.listdir(path_save)) > 0) - inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() - inst_ts_linking.import_linked_series(path_save, 'linked_series') - assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) - + # test on using IOU as linking logic logic = 'IOU' inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + # test on using logic that is currently unavailable logic = 'emd' inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() with pytest.raises(RuntimeError): inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 + # test on import_linked_series and then for the case where there is no potential updates + inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() + inst_ts_linking.import_linked_series(TIME_SERIES_TEST_DIR, 'link_series_no_updates') + assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + inst_ts_linking.update_ti() + assert inst_ts_linking.updated == 0 + + def test_plantcv_time_series_evaluation(): loaded17 = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR,"result_N_17.pkl"),'rb')) loaded18 = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR, "result_N_18.pkl"), 'rb')) @@ -6525,12 +6539,6 @@ def test_plantcv_time_series_evaluation(): li_gt = loaded_gt['li_gt'] ti_gt = loaded_gt['ti_gt'] - with pytest.raises(RuntimeError): - pcv.time_series.get_scores(li18[0:-1], ti18[0:-1,:], li_gt, ti_gt) - - with pytest.raises(RuntimeError): - pcv.time_series.get_scores(li18[1:], ti18[1:,:], li_gt, ti_gt) - scores = pcv.time_series.get_scores(li18, ti18, li_gt, ti_gt) assert len(li18) == ti18.shape[0]-1 and len(li_gt) == ti_gt.shape[0]-1 and ti18.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] @@ -6540,6 +6548,19 @@ def test_plantcv_time_series_evaluation(): scores = pcv.time_series.get_scores(li17, ti17, li_gt, ti_gt) assert len(li17) == ti17.shape[0] - 1 and len(li_gt) == ti_gt.shape[0] - 1 and ti17.shape[1] == scores['N_'] and ti_gt.shape[1] == scores['N'] + # 1st fatal_error in evaluate_link + with pytest.raises(RuntimeError): + pcv.time_series.evaluate_link(li18[0:-1], li_gt) + + with pytest.raises(RuntimeError): + pcv.time_series.evaluate_link(li18[1:], li_gt) + + # 2nd fatal_error in evaluate_link + li18_ = li18 + li18_[0] = np.delete(li18[0],2) + with pytest.raises(RuntimeError): + pcv.time_series.evaluate_link(li18_, li_gt) + def test_plantcv_visualize_overlay_two_imgs(): pcv.params.debug = None From 66b64f5413ef8b26bfcfbbe855095963eca1d2e7 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 22 Dec 2020 13:34:28 -0600 Subject: [PATCH 222/274] Delete time_series.md --- docs/time_series.md | 251 -------------------------------------------- 1 file changed, 251 deletions(-) delete mode 100644 docs/time_series.md diff --git a/docs/time_series.md b/docs/time_series.md deleted file mode 100644 index f868b7876..000000000 --- a/docs/time_series.md +++ /dev/null @@ -1,251 +0,0 @@ -## time_series - -This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every 4 hours for several days), and ideally there should be minimal or no movment of plant and camera. - -To use this class for generating time-series linking, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). - -When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. - -To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. - -For details of using this class, see examples below. - -*initialize an instance of InstanceTimeSeriesLinking class:* - -**inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking()** - -*run the callable object* - -**inst_ts_linking**(*images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance",update=False, max_delta_t=2*) -**returns** No returned value, the inst_ts_linking is an instance object which belongs to InstanceTimeSeriesLinking class. - -- **Parameters** - - images: a list of images. Every element of this list is an array represents one image - - masks: a list of instance segmentation masks. Every element of this list is an array represents instance segmentation masks correspond to one image. - - timepoints: a list of timepoints. The lengths for images, masks and timepoint should be the same and the elements are correspond to each other - - logic (optional): the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) - If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". Default value is "IOS". - - thres (optional): threshold used in the linking logic as mentioned above. Different threshold is needed when using different linking logic. Default value is 0.2. - - name_sub (optional): name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". - - update (optional): whether or not updating the time-series linking result. By default (not updating) the time-series linking would only examine the neighboring pairs of time points (time interval = 1). By updating the time-series linking we will be examining a larger interval of time. - - max_delta_t (optional): only needed when update=True. By default max_delta_t=2. - -- **Output:** - An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. - -```python -from plantcv import plantcv as pcv -# Below are examples of input variables, always adjust base on your own application. -logic = 'IOS' -thres = 0.1 -name_sub = 'leaf' -## Initialize an instance of class InstanceTimeSeriesLinking -inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() -inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub) -``` -Make sure all the images and masks are already sorted based on timepoints. - -To save the result, simply specify the directory of saving as well as the name (prefix) -```python -## Specify the desired directory to save results -dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' - -## Specify the desired name to save the result (prefix) -savename = 'linked_series' - -inst_ts_linking.save_linked_series(savedir=dir_save, savename=savename) -``` -To import a previously saved result: -```python -dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' -savename = 'linked_series' -inst_ts_linking = time_series_linking.InstanceTimeSeriesLinking() -inst_ts_linking.import_linked_series(savedir=dir_save, savename=savename) -``` - -The option to update the time-series linking result is also available: -```python -max_delta_t = 3 -inst_ts_linking.update_ti(delta_t=max_delta_t) -``` - -Alternatively, the time-series linking can be done with ```update=True``` -```python -inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=True, max_delta_t=3) -``` - - - - -**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) - - From 9766607080c37ce0ded295480ca34c3d311d74a5 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 22 Dec 2020 13:34:45 -0600 Subject: [PATCH 223/274] Create time_series_evaluation.md --- docs/time_series_evaluation.md | 62 ++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 docs/time_series_evaluation.md diff --git a/docs/time_series_evaluation.md b/docs/time_series_evaluation.md new file mode 100644 index 000000000..272591f5e --- /dev/null +++ b/docs/time_series_evaluation.md @@ -0,0 +1,62 @@ +## Evaluation of time-series linking result + +This set of functions are designed to evaluate the time-series leaf tracking result by comparing to the ground-truth. + +To start, get familiar with some notations and definitions: + +**T**: total time + +**N**: total number of unique leaves in ground truth + +**nt**: number of leaves at time t + +**li**: link info, a list of length (T-1) +- t-th element of li, i.e. lit represents how leaves at t (nt leaves) link to leaves at t+1 (nt+1 leaves) +- length of lit: nt +- lit,i=j: the i-th leaf at time t is the j-th leaf at time t+1 + +**ti**: tracking info, a matrix (2d-array) of size (T,N) +- tk-th element of ti (tit,k): (local) index of leaf k at time t if leaf k appears at time t, -1 others. +- Total number of non-negative elements in a t-th row: # of leaves at time t +- Every column k: (local) indices for the leaf k at different timepoints(ts). + +**N'**: total number of (unique) leaves in leaf tracking result + +- *note: N=N' not necessary holds.* + +**li'**: link info in tracking result + +**ti'**: tracking info in tracking result + +The performance of leaf tracking can be evaluated by 4 different scores: +1. linking score +``` +linking score = # correct matches / # total matches +``` + +2. unmatched leaf rate +``` +if N' < N, unmatched leaf rate = (N-N')/N +otherwise, unmatched leaf rate = 0 +``` + +3. fake new leaf rate +``` +if N < N' <= 2N, fake new leaf rate = (N'-N)/N +if N'> 2N, fake new leaf rate = 1 +otherwise, fake new leaf rate = 0 +``` +4. tracking score
+To define the tracking score, first define the confusion matrix **C** based on ti and ti'. + - size: N×N' + - ij-th element C (Ci,j): how many times for leaf i becomes leaf j when tracking. + - row sum (*sumi*): total existence of leaf i in ground truth (i = 1,2,...,N) + - column sum (*sumj*): total existence of leaf j in tracking result (j = 1,2,...,N') + - tracking score for leaf i (*tracking-scorei*): + tracking-scorei=Ci,i/total existence of leaf i in ground truth +``` +tracking score = summation tracking-score (for i = 1,...,N)/N +``` + +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/evaluation.py) + From 684c94eb0df6ae42b6d60f46890e55f6a485ecc1 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 22 Dec 2020 13:34:49 -0600 Subject: [PATCH 224/274] Create time_series_InstanceTimeSeriesLinking..md --- .../time_series_InstanceTimeSeriesLinking..md | 251 ++++++++++++++++++ 1 file changed, 251 insertions(+) create mode 100644 docs/time_series_InstanceTimeSeriesLinking..md diff --git a/docs/time_series_InstanceTimeSeriesLinking..md b/docs/time_series_InstanceTimeSeriesLinking..md new file mode 100644 index 000000000..f868b7876 --- /dev/null +++ b/docs/time_series_InstanceTimeSeriesLinking..md @@ -0,0 +1,251 @@ +## time_series + +This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every 4 hours for several days), and ideally there should be minimal or no movment of plant and camera. + +To use this class for generating time-series linking, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). + +When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. + +To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. + +For details of using this class, see examples below. + +*initialize an instance of InstanceTimeSeriesLinking class:* + +**inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking()** + +*run the callable object* + +**inst_ts_linking**(*images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance",update=False, max_delta_t=2*) +**returns** No returned value, the inst_ts_linking is an instance object which belongs to InstanceTimeSeriesLinking class. + +- **Parameters** + - images: a list of images. Every element of this list is an array represents one image + - masks: a list of instance segmentation masks. Every element of this list is an array represents instance segmentation masks correspond to one image. + - timepoints: a list of timepoints. The lengths for images, masks and timepoint should be the same and the elements are correspond to each other + - logic (optional): the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) + If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". Default value is "IOS". + - thres (optional): threshold used in the linking logic as mentioned above. Different threshold is needed when using different linking logic. Default value is 0.2. + - name_sub (optional): name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". + - update (optional): whether or not updating the time-series linking result. By default (not updating) the time-series linking would only examine the neighboring pairs of time points (time interval = 1). By updating the time-series linking we will be examining a larger interval of time. + - max_delta_t (optional): only needed when update=True. By default max_delta_t=2. + +- **Output:** + An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. + +```python +from plantcv import plantcv as pcv +# Below are examples of input variables, always adjust base on your own application. +logic = 'IOS' +thres = 0.1 +name_sub = 'leaf' +## Initialize an instance of class InstanceTimeSeriesLinking +inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() +inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub) +``` +Make sure all the images and masks are already sorted based on timepoints. + +To save the result, simply specify the directory of saving as well as the name (prefix) +```python +## Specify the desired directory to save results +dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' + +## Specify the desired name to save the result (prefix) +savename = 'linked_series' + +inst_ts_linking.save_linked_series(savedir=dir_save, savename=savename) +``` +To import a previously saved result: +```python +dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' +savename = 'linked_series' +inst_ts_linking = time_series_linking.InstanceTimeSeriesLinking() +inst_ts_linking.import_linked_series(savedir=dir_save, savename=savename) +``` + +The option to update the time-series linking result is also available: +```python +max_delta_t = 3 +inst_ts_linking.update_ti(delta_t=max_delta_t) +``` + +Alternatively, the time-series linking can be done with ```update=True``` +```python +inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=True, max_delta_t=3) +``` + + + + +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) + + From 5238a50e26db23888201d577a6e781d578ed60ef Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 6 Jan 2021 20:02:04 -0600 Subject: [PATCH 225/274] Update evaluation.py --- plantcv/plantcv/time_series/evaluation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/evaluation.py b/plantcv/plantcv/time_series/evaluation.py index 8b3aa3f95..8258dca94 100644 --- a/plantcv/plantcv/time_series/evaluation.py +++ b/plantcv/plantcv/time_series/evaluation.py @@ -47,7 +47,7 @@ def evaluate_link(li, li_gt): fatal_error('Different number of instances!!') num_insts = [len(x) for x in li_gt] # num_matched = [(sum(x == y)) for (x,y) in zip(li_gt, li)] - num_matched = [(len(x == y)) for (x, y) in zip(li_gt, li)] + num_matched = [(sum(x == y)) for (x, y) in zip(li_gt, li)] score = sum(num_matched) / sum(num_insts) return score, num_insts, num_matched From 4c236ef000ec9429d57c86d947ed45aa6ac98896 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 6 Jan 2021 20:02:07 -0600 Subject: [PATCH 226/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 3ae64ad99..00386dfce 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -76,7 +76,7 @@ def _get_link(masks1, masks2, logic, thres): """ Get link information between two sets of binary instance segmentation masks :param masks1: (numpy.ndarray of shape: [Height, Width, n1]), where n1 is the number of instances - :param masks2: (numpy.ndarray of shape: [Height, Width, n1]), where n1 is the number of instances + :param masks2: (numpy.ndarray of shape: [Height, Width, n2]), where n2 is the number of instances :param logic: linking logic to use. (currently) either "IOU" or "IOS" :param thres: threshold to link two masks (two binary segmentation masks can only be linked if the calculated overlap between is greater than the threshold) :return: @@ -192,6 +192,23 @@ def _get_ti(num_insts, uids, link_info): t += 1 return ti, t_appear, t_disappear +def _get_li_from_ti(ti): + """ + + :param ti: + :return: + """ + li = [] + for t in range(len(ti)-1): + ti_0 = ti[t,:] + ti_1 = ti[t+1,:] + l0 = [x for x in ti_0 if x >= 0] + l1 = [x for (x, y) in zip(ti_1, ti_0) if y >= 0] + link_t = -np.ones(len(l0), dtype=np.int64) + for (idx, x) in enumerate(l0): + link_t[x] = l1[idx] + li.append(link_t) + return li # def _visualize1(img, mask, savename=None): # """ From 51acb225ad2c439e777f143bfefbc667441d4ee0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 16:39:06 -0500 Subject: [PATCH 227/274] Delete time_lapse_video.py --- plantcv/plantcv/visualize/time_lapse_video.py | 168 ------------------ 1 file changed, 168 deletions(-) delete mode 100644 plantcv/plantcv/visualize/time_lapse_video.py diff --git a/plantcv/plantcv/visualize/time_lapse_video.py b/plantcv/plantcv/visualize/time_lapse_video.py deleted file mode 100644 index 759f450c5..000000000 --- a/plantcv/plantcv/visualize/time_lapse_video.py +++ /dev/null @@ -1,168 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Mon. July 06 08:00:34 2020 -Functions used to generate a time-lapse-video with provided directory of images -@author: hudanyunsheng -""" - -import os -import cv2 -import numpy as np -import copy -import mimetypes -from plantcv.plantcv import params -from plantcv.plantcv import fatal_error -import warnings - - -def _resize_img(img, size): - """Resize the image to the given new size - If the given size is larger than the original image size, zero-pad the image. - If the given size is smaller than the origina image size, crop the image (right & bottom). - """ - - # original image size - r_ori, c_ori = np.shape(img)[0], np.shape(img)[1] - r, c = size[0], size[1] - - if len(img.shape) > 2: - b_ori = np.shape(img)[2] - junk = copy.deepcopy(img) - else: - b_ori = 1 - junk = np.expand_dims(img, axis=2) - - # deal with rows - img1 = copy.deepcopy(junk) - if r < r_ori: - img1 = junk[0:r, :, :] - elif r > r_ori: - img1 = np.zeros_like(junk, shape=[r, c_ori, b_ori]) - img1[0:r_ori, :, :] = junk - - # update - r_ori, c_ori = np.shape(img1)[0], np.shape(img1)[1] - img2 = copy.deepcopy(img1) - - # deal with columns - if c < c_ori: - img2 = img1[:, 0:c, :] - elif c > c_ori: - img2 = np.zeros_like(img1, shape=[r_ori, c, b_ori]) - img2[:, 0:c_ori, :] = img1 - - if len(img.shape) == 2: - output_img = np.squeeze(img2, axis=2) - else: - output_img = img2 - return output_img - - -def time_lapse_video(img_directory, list_img=None, suffix_img=None, size_frame=None, fps=29.97, - name_video='time_lapse_video', path_video=None, display='on'): - """ - Generate time-lapse video given a folder of images - :param img_directory: the directory of folder of images to make the time-lapse video. - :param list_img: desired list of images in img_directory to create the video. If None is passed, all images would be included by default. - :param suffix_img: - :param size_frame: the desired size of every frame. - In a video, every frame should have the same size. - The assumption is that all images given should have same size. However, in some cases, the sizes of images are slightly differ from each other. - If the frame size is given, if an images is larger than the given size, the image would be cropped automatically; if an image is smaller than the given size, the image would be zero-padded automatically - If the frame size is not given, the largest size of all images would be used as the frame size. - :param fps: (frames per second) frame rate - Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 - :param name_video: - :param path_video: the desired saving path of output video. If not given, the video would be saved the the same directory of the images. - :param display: indicator of whether to display current status (by displaying saving directory and saving name) while running this function - :param kw: possible name and value: compress, and a number indicating what fraction compared to the original image. e.g. 0.5 represent compress the image to 1/2 of its original size - :return: - list_img: the list of images used to generate the video - size_frame: the frame size of the video - """ - - ## Get the list of image files in the given directory and sort them alphabetically by their names - temp_list = [] - for f in os.listdir(img_directory): - type_f = mimetypes.guess_type(f)[0] - if type_f: - if type_f.startswith('image'): - temp_list.append(f) - if len(temp_list) <= 0: - fatal_error("There is no file in the provided folder that is an image, please check the provided directory!") - else: - temp_list.sort() - # get an "extension free" list of images - temp_list_no_ext = [os.path.splitext(f)[0] if os.path.splitext(f)[1] else f for f in temp_list] - - ## if the list of images is provided, stick to it, but automatically handle the extension issue - if list_img is not None: - list_img.sort() - # make the list of images extension free - list_img_no_ext = [os.path.splitext(f)[0] if os.path.splitext(f)[1] else f for f in list_img] - list_img = [] - for (f_, f) in zip(temp_list_no_ext, temp_list): - try: - list_img_no_ext.index(f_) - list_img.append(f) - except: - continue - - if len(list_img) == 0: - fatal_error("The provided list of files is not contained in the given directory") - elif len(list_img) < len(list_img_no_ext): - warnings.warn( - "Warning: Some files in the provided list not found, the video will be created based on available files in the provided directory!") - - # if the list of images is not provided, check if suffix information is available - elif suffix_img is not None: - list_img = [f for f in temp_list if f.endswith(suffix_img)] - if len(list_img) == 0: - fatal_error("There is no file that matches the provided suffix in the provided directory, please check again!") - - # neither the list of image nor the suffix is provided - else: - list_img = temp_list - - imgs = [] - list_r = [] - list_c = [] - for file in list_img: - img = cv2.imread(os.path.join(img_directory, file)) - list_r.append(img.shape[0]) - list_c.append(img.shape[1]) - imgs.append(img) - max_c, max_r = np.max(list_c), np.max(list_r) - - # If the frame size is not provided, use the largest size of the images as the frame size - size_frame = size_frame or (max_c, max_r) - - if not (len(np.unique(list_r)) == 1 and len(np.unique(list_c)) == 1): - warnings.warn("The sizes of images are not the same, an image resizing (cropping or zero-padding) will be done to make all images the same size ({}x{}) before creating the video! If you assume the images should have the same size, please check the images used to generate this video!".format( - size_frame[0], size_frame[1])) - - # If the video saving directory is not provided, save it in the same directory of the images - if path_video is None: - path_video = img_directory - - # Full video save name - save_name = os.path.join(path_video, name_video + '.mp4') - - fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case - # if 'compress' in kw: - # ratio = kw['compress'] - # else: - # ratio = 1 - # size_frame_ = tuple(int(x*ratio) for x in size_frame) - out = cv2.VideoWriter(save_name, fourcc, fps, size_frame) - - for img in imgs: - # out.write(cv2.resize(_resize_img(img, (size_frame[-1],size_frame[0])), (size_frame_))) - out.write(_resize_img(img, (size_frame[-1], size_frame[0]))) - out.release() - cv2.destroyAllWindows() - if display == 'on': - print('The generated video: \n{},\nAnd is saved here:\n{}.'.format(save_name, path_video)) - - return list_img, size_frame From b73a2ec9b1d6d152cf3e549362d01abc93b7e56a Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 16:39:23 -0500 Subject: [PATCH 228/274] Update evaluation.py --- plantcv/plantcv/time_series/evaluation.py | 28 +++++++++++------------ 1 file changed, 13 insertions(+), 15 deletions(-) diff --git a/plantcv/plantcv/time_series/evaluation.py b/plantcv/plantcv/time_series/evaluation.py index 8258dca94..5c8d4efe7 100644 --- a/plantcv/plantcv/time_series/evaluation.py +++ b/plantcv/plantcv/time_series/evaluation.py @@ -43,12 +43,12 @@ def evaluate_link(li, li_gt): fatal_error('Linking information not same length!!') else: max_t = len(li_gt) - if not sum([len(x) == len(y) for (x, y) in zip(li_gt, li)]) == max_t: + if not sum([len(x)==len(y) for (x,y) in zip(li_gt, li)]) == max_t: fatal_error('Different number of instances!!') - num_insts = [len(x) for x in li_gt] + num_insts = [len(x) for x in li_gt] # num_matched = [(sum(x == y)) for (x,y) in zip(li_gt, li)] num_matched = [(sum(x == y)) for (x, y) in zip(li_gt, li)] - score = sum(num_matched) / sum(num_insts) + score = sum(num_matched)/sum(num_insts) return score, num_insts, num_matched @@ -64,9 +64,9 @@ def mismatch_rate(ti, ti_gt): r_unmatched, r_fake_new = 0, 0 N, N_ = ti_gt.shape[1], ti.shape[1] if N_ < N: - r_unmatched = (N - N_) / N + r_unmatched = (N-N_)/N elif N_ > N: - r_fake_new = (N_ - N) / N + r_fake_new = (N_-N)/N return r_unmatched, r_fake_new @@ -103,21 +103,19 @@ def confusion(ti, ti_gt): life = life + [0 for n in range(delta_N)] # delta matrix of lift and life_gt - delta_life = -np.ones((N, max(N_, N)), dtype=int) - for i in range(0, N): - delta_life[i, :] = abs(np.int64(life_gt[i]) - life) + # delta_life = -np.ones((N,max(N_,N)),dtype=int) + # for i in range(0,N): + # delta_life[i,:] = abs(np.int64(life_gt[i])-life) + # linear assignment based on delta_life: find most probable corresponding based on life - row_i, col_i = linear_sum_assignment(delta_life) + # row_i, col_i = linear_sum_assignment(delta_life) + + # linear assignment based on confusion matrix: find those with largest values in confusion matrix + row_i, col_i = linear_sum_assignment(confu_, maximize=True) match = [confu_[i, j] for (i, j) in zip(row_i, col_i)] # "diagonal" rate = [match_t / time_i for (match_t, time_i) in zip(match, life_gt)] - # row_ind, col_ind = linear_sum_assignment(confu, maximize=True) - - # match = [confu[i,j] for (i,j) in zip(row_ind, col_ind)] # "diagonal" - # # rate = [match[i]/existance_times[i] for i in col_ind] - # rate = [match_t/time_i for (match_t,time_i) in zip(match, existance_times)] - # # always make the same length of num_gt # if N_ < N: # for i in range(N_-N): From 56f926be5194d28b1100462dfdc1317128f29943 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 16:39:25 -0500 Subject: [PATCH 229/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 130 ++++++++---------- 1 file changed, 54 insertions(+), 76 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 00386dfce..7d14ea468 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -12,6 +12,7 @@ import matplotlib import matplotlib.pyplot as plt import os +import os.path as osp import sys import random import math @@ -142,7 +143,7 @@ def _get_ti(num_insts, uids, link_info): :param uids: unique ids at every time point :param link_info: :return: - ti: tracking information (np.ndarray of shape (T,max_uid)) + ti: tracking information (np.ndarray of shape (T,N)) ti[t,k]=j represents for: at time t the k-th leaf has a local index of j if ti[t,k]=-1, the k-th leaf does not appear at time t number of non-negative elements every row: number of instances @@ -152,8 +153,9 @@ def _get_ti(num_insts, uids, link_info): emergence, emerge_times = _get_emergence(uids) T = len(num_insts) - max_uid = max([max(temp) for temp in uids]) - N = max_uid + 1 + # max_uid = max([max(temp) for temp in uids]) + # N = max_uid + 1 + N = len(np.unique(np.concatenate( uids, axis=0 ))) ti = -np.ones((T, N), dtype=np.int64) for t in range(T): if t == 0: @@ -220,7 +222,7 @@ def _get_li_from_ti(ti): # if savename is not None: # pcv.print_image(masked_im, savename) # return masked_im - +# # def _visualize2(img, fig_title, mask=None, savename=None): # """ # Apply mask with an alpha channel to the original image @@ -241,11 +243,12 @@ def _get_li_from_ti(ti): # if savename is not None: # plt.savefig(savename) # return masked_im - +# # def _visualize3(img, masks, colors, figsize=(16, 16), ax=None, captions=None, savename=None, title="", show_bbox=True): # visualize_display_instances.display_instances(img, masks.astype(np.uint8), figsize=figsize, title=title, ax=ax, colors=colors, captions=captions, show_bbox=True) # if savename is not None: -# plt.savefig(savename) +# # plt.savefig(savename) +# plt.savefig(savename, bbox_inches="tight",pad_inches=0) class InstanceTimeSeriesLinking(object): """A class that links segmented instances throughout time @@ -267,7 +270,8 @@ def __init__(self): self.thres = None self.link_info = None self.uids = None - self.max_uid = None + # self.max_uid = None + self.N = None self.emergence = None self.emerge_times = None self.ti = None @@ -292,9 +296,7 @@ def save_linked_series(self, savedir, savename): """save linking information into a .pkl file with the same prefix of filename Inputs: savedir and savename """ - - # pkl.dump(self, open(os.path.join(savedir, savename + '.pkl'), 'wb')) - pkl.dump(vars(self), open(os.path.join(savedir, savename + ".pkl"), 'wb')) + pkl.dump(vars(self), open(osp.join(savedir, savename + ".pkl"), 'wb')) def import_linked_series(self, savedir, savename): """import a linked time-series from previously saved file @@ -303,37 +305,10 @@ def import_linked_series(self, savedir, savename): :param savename: saving name :return: """ - linked = pkl.load(open(os.path.join(savedir, savename + '.pkl'), "rb")) + linked = pkl.load(open(osp.join(savedir, savename + '.pkl'), "rb")) for key, value in linked.items(): setattr(self, key, value) - # def save_to_csv(self, savedir, csvname1="link_series.csv", csvname2="link_series"): - # """ save linking information into 2 .csv files - # 1. link_series.csv - # 2. link_info.csv - # """ - # # csvname1 = "link_series.csv" - # # csvname2 = "link_info.csv" - # l0 = ['', ''] + [x for (idx, x) in enumerate(self.timepoints)] - # l1 = ['unique_id', 'current_id'] + ['t{}'.format(idx) for (idx, x) in enumerate(self.timepoints)] - # csvfile1 = open(os.path.join(savedir, csvname1), 'w', newline='') - # writer1 = csv.writer(csvfile1, delimiter=',', quoting=csv.QUOTE_MINIMAL) - # writer1.writerow(l0) - # writer1.writerow(l1) - # csvfile2 = open(os.path.join(savedir, csvname2), 'w', newline='') - # writer_junk2 = csv.writer(csvfile2, delimiter=',', quoting=csv.QUOTE_MINIMAL) - # writer_junk2.writerow(['unique_id', 'emerging_time', 'file_name_emerge', 'current_time', 'file_name', 'current_id']) - # for (t0_, item) in self.link_series.items(): - # t0 = int(t0_.replace('t', '')) - # for uid, cid in zip(item['uids'], item['ids']): - # link_t0_i = item['inst{}'.format(cid)] - # new_line = ['{}'.format(uid), '{}'.format(cid)] + link_t0_i.tolist() - # writer1.writerow(new_line) - # for t in range(t0, self.T): - # writer_junk2.writerow([uid, t0, self.timepoints[t0], t, self.timepoints[t], link_t0_i[t]]) - # csvfile1.close() - # csvfile2.close() - def linking(self, t0): """ Link instance segments of t0 to those of t1 (t0+1) by calculating the overlaps of their masks @@ -354,16 +329,22 @@ def get_uid(self): """ self.uids = [-np.ones(n, dtype=np.int64) for n in self.n_insts] self.uids[0] = np.arange(len(self.link_info[0]), dtype=np.int64) - self.max_uid = max(self.uids[0]) + # self.max_uid = max(self.uids[0]) + max_uid = max(self.uids[0]) + self.N = len(self.uids[0]) for (t, link_t) in enumerate(self.link_info): for (cidt, cidt_) in enumerate(link_t): if cidt_ >= 0: self.uids[t + 1][cidt_] = self.uids[t][cidt] - if -1 in self.uids[t + 1]: # -1 means there is not predecessor from a former timepoint -> assign new uids + if -1 in self.uids[t + 1]: # -1 means there is no predecessor from a former timepoint -> potential to assign new uids temp = np.where(self.uids[t + 1] == -1)[0] for tid in temp: - self.max_uid += 1 - self.uids[t + 1][tid] = self.max_uid + # self.max_uid += 1 + # self.uids[t + 1][tid] = self.max_uid + max_uid += 1 + self.uids[t + 1][tid] = max_uid + self.N += 1 + def update_ti(self, delta_t=2): self.delta_t = delta_t if delta_t else self.delta_t @@ -417,9 +398,9 @@ def update_ti(self, delta_t=2): print("updates!!") # there will be updates, so save previous result in self.ti_, self.t_appear_, and self.t_disappear_, respectively self.ti_, self.t_appear_, self.t_disappear_ = ti_, t_appear_, t_disappear_ - for (idx, id_up) in enumerate(ids_update): + for (idx, id_up) in enumerate(reversed(ids_update)): if id_up != -1: - id_old = disp_uids[idx] + id_old = disp_uids[len(ids_update)-1-idx] t_ = t_appear_[id_up] self.ti[t_:, id_old] = self.ti[t_:, id_up] self.ti = np.delete(self.ti, to_del, 1) @@ -430,7 +411,9 @@ def update_ti(self, delta_t=2): self.disp_uids = disp_uids self.updated = 1 self.delta_t = delta_t - self.max_uid = self.max_uid - len(np.where(ids_update > -1)[0]) + # self.max_uid = self.max_uid - len(np.where(ids_update > -1)[0]) +1 + # self.max_uid = self.ti.shape[1]-1 + self.N = self.ti.shape[1] # def visualize(self, visualdir, colors=None, color_all=None): # """ @@ -445,29 +428,44 @@ def update_ti(self, delta_t=2): # visualdirs = dict() # for i in range(1, 4): # idx = str(i) - # visualdirs[idx] = os.path.join(visualdir, 'visualization{}'.format(idx)) - # if not os.path.exists(visualdirs[idx]): + # visualdirs[idx] = osp.join(visualdir, 'visualization{}'.format(idx)) + # if not osp.exists(visualdirs[idx]): # os.makedirs(visualdirs[idx]) # # if colors is None: - # colors = visualize_display_instances._random_colors(self.max_uid) # worest case: all leaves are unique + # # colors = visualize_display_instances._random_colors(self.max_uid + 1) # worest case: all leaves are unique + # colors = visualize_display_instances._random_colors(self.N) # worest case: all leaves are unique # if color_all is None: # color_all = [[tuple() for i in range(0, num)] for num in self.n_insts] # - # for (t,ti_t) in enumerate(self.ti): + # for (t, ti_t) in enumerate(self.ti): # uids_t = [x for x in ti_t if x >= 0] - # for (idx,uid) in enumerate(uids_t): + # for (idx, uid) in enumerate(uids_t): # color_all[t][idx] = colors[uid] # - # for (img, masks, colors_t, t) in zip(self.images, self.masks, color_all, self.timepoints): - # savename3 = os.path.join(visualdirs['3'], '{}.jpg'.format(t)) + # for id_t, (img, masks, colors_t, t) in enumerate(zip(self.images, self.masks, color_all, self.timepoints)): + # # find exists unique ids at t + # uids = np.where(self.ti[id_t] >= 0)[0] + # for uid in uids: + # cid = self.ti[id_t][uid] + # mask = masks[:, :, cid] + # savedir_uid1 = osp.join(visualdirs['1'], "{}_{}".format(self.name_sub, uid)) + # savedir_uid2 = osp.join(visualdirs['2'], "{}_{}".format(self.name_sub, uid)) + # savename1 = osp.join(savedir_uid1, '{}_{}.jpg'.format(t, cid)) + # title2 = '{}_{}'.format(t, cid) + # savename2 = os.path.join(savedir_uid2, '{}.jpg'.format(title2)) + # if not osp.exists(savedir_uid1): + # os.makedirs(savedir_uid1) + # if not osp.exists(savedir_uid2): + # os.makedirs(savedir_uid2) + # _visualize1(img, mask, savename=savename1) + # plt.close('all') + # _visualize2(img, fig_title=title2, mask=mask, savename=savename2) + # plt.close('all') + # savename3 = osp.join(visualdirs['3'], '{}.jpg'.format(t)) # _visualize3(img, masks, colors_t, savename=savename3) # plt.close("all") - # def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", - # colors=None, color_all=None, update=False, max_delta_t=2, savedir_=None, savename_=None): - # def __call__(self, images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance", - # update=False, max_delta_t=2, savedir_=None, savename_=None): def __call__(self, images, masks, timepoints, logic="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): # a list of images which are ndarrays self.images = images @@ -495,27 +493,7 @@ def __call__(self, images, masks, timepoints, logic="IOS", thres=0.2, name_sub=" self.get_uid() self.emergence, self.emerge_times = _get_emergence(self.uids) self.ti, self.t_appear, self.t_disappear = _get_ti(self.n_insts, self.uids, self.link_info) - # visualdir = os.path.join(savedir, "visualization") - # if not os.path.exists(visualdir): - # os.makedirs(visualdir) - # self.visualize(visualdir) # self.save_linked_series(savedir, savename) if update: self.update_ti(max_delta_t) - # if self.updated == 1: - # if savedir_ is not None and savedir_ != savedir: - # visualdir_ = os.path.join(savedir, "visualization") - # else: - # savedir_ = savedir - # visualdir_ = os.path.join(savedir, "updated_visualization") - # if not os.path.exists(visualdir_): - # os.makedirs(visualdir_) - # - # if savename_ is None: - # savename_ = "{}_{}".format(savename, max_delta_t) - # - # # self.visualize(visualdir_) - # self.save_linked_series(savedir_, savename_) - - # self.save_to_csv(savedir, "link_series_old.csv", "link_info_old.csv") From 1dc653936ee98a635366b0ba7880f3cf2d4cd397 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 17:11:02 -0500 Subject: [PATCH 230/274] Delete visualize_time_lapse_video.md --- docs/visualize_time_lapse_video.md | 89 ------------------------------ 1 file changed, 89 deletions(-) delete mode 100644 docs/visualize_time_lapse_video.md diff --git a/docs/visualize_time_lapse_video.md b/docs/visualize_time_lapse_video.md deleted file mode 100644 index b2592aac4..000000000 --- a/docs/visualize_time_lapse_video.md +++ /dev/null @@ -1,89 +0,0 @@ -## Automatically Generate Time-Lapse Videos - -This function automatically generates and saves time-lapse videos given the folder of images. - -**plantcv.visualize.time_lapse_video**(*path_img, list_img, suffix_img, size_frame=None, fps=29.97, name_video='time_lapse_video', - path_video=path_img, display='on'*) - -**returns** list of images, frame size - -(The generated video would be saved in the user specified directory.) - -- **Parameters:** - - img_directory - Directory of images desired to be made into a video - - list_img - desired list of images in img_directory to create the video. If None is passed, all images would be included by default. - - suffix_img - The suffix of all input images. e.g. suffix='.jpg' or suffix='.png' or suffix='img10.jpg'. Make sure all images have the same suffix - - If neigher list_img nor suffix_img is provided, all images in the directory will be included. - - - size_frame - The desired size of every frame. - - To generate a video, the image used in every frame shold have the same size, known as frame size. - In most cases where the images have the same size, the image size is frame size, e.g. ```size_frame=(640, 480)```. - In some cases where not all images have the same size, image resizing would be done automaticaly - to be able to generate the video, with a warning message generated. - - If no value is passed to this parameter, the largest size of all images would be used as the frame size. - - The resizing is done by cropping for those larger then desired sizes, zero-padding for those smaller than desired sizes. - - Note: The most commonly used definition of frame size/image size is different from most commonly - used definition of matrix size (e.g. numpy arrays). The frame size/image size is defined as (width, height), - while the matrix size is defined as (num_rows, num_columns). - If you are trying to get the frame size/image size from the size of matrix (array), remember of change the - order of first and second dimension of the matrix, i.e. frame size = (num_columns, num_rows). - - - fps: (frames per second) frame rate. By default fps=29.97. Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60 - - - name_video - The desired name of output video name. By default the name would be 'time_lapse_video' - - path_video - The desired saving path of the video file. By default, the video will be saved at the same directory - of the images. - - display - Indicator of whether to display current status (by displaying saving directory and saving name) while - running this function. By default the display is turned on. - -- **Context:** - - Used to generate time-lapse video given a bunch of images. - -- **Example Use:** - - Below - - [Use in Time Series Documentation](time_series.md) - -**Folder of images** - -As an example, you can used the sample saved inside PlantCV to test. You will need to know the directory of your PlantCV package. -You can find the test data here: "./plantcv/tests/seires_data/raw_im". We are to use all images inside the folder "raw_im" to generate a time-lapse video. -```python -from plantcv import plantcv as pcv -# Note you will have to change this part on your own -img_directory = './plantcv/tests/seires_data/raw_im' - -list_img = [img for img in os.listdir(dir_img) if img.endswith('.jpg')] - -# In this specific case, every image has a suffix of ".jpg" -suffix_img = '.jpg' - -# In this case I know the sizes for all of the images is from (499, 499) to (501, 501), and here I decide I want the frame size to be (500, 500). -size_frame = (500, 500) - -fps = 29.97 - -name_video = 'eg_time_lapse' - -# In this specific case the video will be saved in the save directory of the images -path_video = path_img - -display = 'on' - -list_img, frame_size = pcv.visualize.time_lapse_video(img_directory=img_directory, list_img=list_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) -list_img, frame_size = pcv.visualize.time_lapse_video(img_directory=img_directory, suffix_img=suffix_img, size_frame=size_frame, fps=fps, name_video=name_video, path_video=path_video, display=display) - - -``` - -**Video generated** - -The generated video is saved automatically in the user-specified directory (path_video). The generated video should look similar to the one below: - - - -**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/visualize/time_lapse_video.py) From ba49ace3ed68fbd32059480c9ac9d40f8904dab8 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 17:34:40 -0500 Subject: [PATCH 231/274] remove time_lapse_video related tests --- tests/tests.py | 99 -------------------------------------------------- 1 file changed, 99 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index de75e5f52..d3600bc7c 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6317,105 +6317,6 @@ def test_plantcv_visualize_colorspaces_bad_input(): with pytest.raises(RuntimeError): _ = pcv.visualize.colorspaces(rgb_img=img) -def test_plantcv_visualize_time_lapse_video_resize_img(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - resized_img = pcv.visualize._resize_img(img, (10,10)) - assert np.shape(resized_img) == (10,10,3) - -def test_plantcv_visualize_time_lapse_video_resize_img_large(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeRGB') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - resized_img = pcv.visualize._resize_img(img, (1000,1000)) - assert np.shape(resized_img) == (1000,1000,3) - -def test_plantcv_visualize_time_lapse_video_resize_img_gray(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) - resized_img = pcv.visualize._resize_img(img, (10,10)) - assert np.shape(resized_img) == (10,10) - -def test_plantcv_visualize_time_lapse_video_resize_img_gray_large(): - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_resizeGRAY') - os.mkdir(cache_dir) - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) - resized_img = pcv.visualize._resize_img(img, (1000,1000)) - assert np.shape(resized_img) == (1000,1000) - -# case1: only the correct directory of images is provided -def test_plantcv_visualize_time_lapse_video_case1(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case1') - os.mkdir(cache_dir) - with pytest.warns(UserWarning): - _, _ = pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case1.mp4')) - -# case1.5: no path_video provided and display "on" -def test_plantcv_visualize_time_lapse_video_case1(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case1') - os.mkdir(cache_dir) - with pytest.warns(UserWarning): - _, _ = pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, fps=29.97, name_video='time_lapse_video_case1', display='on') - assert os.path.exists(os.path.join(TIME_SERIES_TEST_RAW, 'time_lapse_video_case1.mp4')) - -# case2: the correct directory of images as well as the correct suffix of images is provided -def test_plantcv_visualize_time_lapse_video_case2(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case2') - os.mkdir(cache_dir) - with pytest.warns(UserWarning): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.jpg', fps=29.97, name_video='time_lapse_video_case2', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case2.mp4')) - -# case 3: the correct directory of images as well as the correct list of files are provided -def test_plantcv_visualize_time_lapse_video_case3(): - # Test cache directoy - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case3') - os.mkdir(cache_dir) - list_img = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] - with pytest.warns(UserWarning): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case3', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case3.mp4')) - -# case 4: the correct directory of images as well as a list of files are provided, however the list is incorrect (contains correct part, but also contains incorrect part) -def test_plantcv_visualize_time_lapse_video_case4(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_case4') - os.mkdir(cache_dir) - list_img = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] - list_img.append('junk.jpg') - with pytest.warns(UserWarning): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, name_video='time_lapse_video_case4', path_video=cache_dir, display='off') - assert os.path.exists(os.path.join(cache_dir, 'time_lapse_video_case4.mp4')) - -def test_plantcv_visualize_time_lapse_video_bad_dir(): - # Test cachae directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_dir') - os.mkdir(cache_dir) - with pytest.raises(RuntimeError): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_DIR, fps=29.97, path_video=cache_dir, display='off') - -def test_plantcv_visualize_time_lapse_video_bad_suffix(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_suffix') - os.mkdir(cache_dir) - with pytest.raises(RuntimeError): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, suffix_img='.png', fps=29.97, path_video=None, display='on') - -# the correct directory of images as well as a list of files are provided, however the list is incorrect (none of the files contained is correct) -def test_plantcv_visualize_time_lapse_video_bad_list(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, 'visualize_time_lapse_video_bad_list') - os.mkdir(cache_dir) - list_img_ = [img for img in os.listdir(TIME_SERIES_TEST_RAW) if img.endswith('.jpg')] - list_img = [img.replace('.jpg', '_.jpg') for img in list_img_] - with pytest.raises(RuntimeError): - pcv.visualize.time_lapse_video(img_directory=TIME_SERIES_TEST_RAW, list_img=list_img, fps=29.97, path_video=cache_dir, display='off') # ############################## # Tests for the time_series subpackage From a92b539dc82b1f4c746922c27014981082dd27a6 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 29 Apr 2021 17:43:41 -0500 Subject: [PATCH 232/274] Update evaluation.py --- plantcv/plantcv/time_series/evaluation.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/plantcv/plantcv/time_series/evaluation.py b/plantcv/plantcv/time_series/evaluation.py index 5c8d4efe7..45e94e188 100644 --- a/plantcv/plantcv/time_series/evaluation.py +++ b/plantcv/plantcv/time_series/evaluation.py @@ -1,12 +1,5 @@ -# !/usr/bin/env python3 -# -*- coding: utf-8 -*- +# Functions used to evaulate time-series linking result """ -Created on Mon Dec 7 2020 - -Functions used to evaluate time-series linking result - -@author: hudanyunsheng - Notations and Definitions: T: total time N: total number of (unique) leaves in ground truth From c05ce75b1778f7ed7a007efa6c0618d529245c15 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 29 Apr 2021 17:43:44 -0500 Subject: [PATCH 233/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 122 +++++++++++------- 1 file changed, 73 insertions(+), 49 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 7d14ea468..118744208 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -1,12 +1,4 @@ -# !/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Jun 9 2020 - -Functions used in time series linking after getting leaf instances segmented - -@author: hudanyun sheng -""" +# Link time-series import numpy as np import matplotlib @@ -35,16 +27,23 @@ def _compute_overlaps_masks(masks1, masks2): """Compute overlaps of two sets of binary masks. - The overlaps are represented by IoU (intersection over union) and IoS (intersection over self-area). - + The overlaps are represented by IoU (intersection over union) and IoS (intersection over self-area of the 1st mask). + Inputs: + masks1 = Binary masks data correspond to the 1st image + masks1 = Binary masks data correspond to the 2nd image + Outputs: + n1 = the number of instances in 1st set of binary masks + n2 = the number of instances in 2nd set of binary masks + ious = inversection over union between any pairs of instances in masks1 and masks2 + ioss = inversection over self-area (areas of instances in 1st set of masks) between any pairs of instances in masks1 and masks2 + unions = unions between any pairs of instances in masks1 and masks2 :param masks1: (numpy.ndarray of shape: [Height, Width, n1]) , where n1 is the number of instances :param masks2: (numpy.ndarray of shape: [Height, Width, n2]) , where n2 is the number of instances - :return: - n1: the number of instances in 1st set of binary masks - n2: the number of instances in 2nd set of binary masks - ious: (numpy.ndarray of shape: [n1, n2]) inversection over union between any pairs of instances in masks1 and masks2 - ioss: (numpy.ndarray of shape: [n1, n2]) inversection over self-area (areas of instances in 1st set of masks) between any pairs of instances in masks1 and masks2 - unions: (numpy.ndarray of shape: [n1, n2]) unions between any pairs of instances in masks1 and masks2 + :return: n1: int + :return: n2: int + :return: ious: numpy.ndarray of shape: [n1, n2] + :return: ioss: numpy.ndarray of shape: [n1, n2] + :return: unions: numpy.ndarray of shape: [n1, n2] """ # If either set of masks is empty return an empty result @@ -74,16 +73,27 @@ def _compute_overlaps_masks(masks1, masks2): def _get_link(masks1, masks2, logic, thres): - """ - Get link information between two sets of binary instance segmentation masks - :param masks1: (numpy.ndarray of shape: [Height, Width, n1]), where n1 is the number of instances - :param masks2: (numpy.ndarray of shape: [Height, Width, n2]), where n2 is the number of instances - :param logic: linking logic to use. (currently) either "IOU" or "IOS" - :param thres: threshold to link two masks (two binary segmentation masks can only be linked if the calculated overlap between is greater than the threshold) - :return: - weight (numpy.ndarray of shape: [n1,n2]): weight matrix (indicated by IoU or IoS) calculated based on two sets of masks - link (numpy.1darray of length n1): link[i] = j means that the i-th mask in masks1 should be linked to j-th mask in masks2 - row_ind, col_ind: selected row indices and column indices based on the weight matrix to finalize the "link" + """Get link information between two sets of binary instance segmentation masks + Inputs: + masks1 = Binary masks data correspond to the 1st image + masks2 = Binary masks data correspond to the 2nd image + logic = linking logic to use. (currently) either "IOU" or "IOS" + thres = threshold to link two masks (two binary segmentation masks can only be linked if the calculated overlap + between is greater than the threshold) + Outputs: + weight = weight matrix (indicated by IoU or IoS) calculated based on two sets of masks + link = linking information (numpy.1darray of length n1)). link[i] = j means that the i-th mask in masks1 should be linked to j-th mask in masks2 + row_ind = selected row indices based on the weight matrix to finalize the "link" + col_ind = selected column indices based on the weight matrix to finalize the "link" + + :param masks1: numpy.ndarray + :param masks2: numpy.ndarray + :param logic: str + :param thres: float + :return weight: numpy.ndarray + :return link: numpy.1darray of length n1 + :return row_ind: int + :return col_ind: int """ n1, n2, ious, ioss, _ = _compute_overlaps_masks(masks1, masks2) @@ -110,16 +120,19 @@ def _get_link(masks1, masks2, logic, thres): def _get_emergence(uids): - """ - get emergence as well as emerge times - :param uids: unique ids at every time point - (list of length T, where T is the total time) - every sub-list uids[t] (for t in range of (0,T)) is also a list of length nt, where nt is the number of instances at t - :return: - emergence: new segments (leaves) compared to previous time-point - (list of length T, where T is the total time) - every sub-list emergence[t] if also a list. If there is no new segments at t compared to (t-1), the emergence[t] is empty; otherwise, emergence[t] is a list of unique ids appear at t but not at (t-1). - emerge_times: (list) time points with emergence + """ Get the emerging information as well as emerging times + Inputs: + uids = unique indices present at every time point. + (list of length T, where T is the total time + every sub-list uids[t] (t is in range of (0,T)) is also a list of length nt, + where nt is the number of instances at t) + Outputs: + emergence = new unique indices that don't exist in the previous time-point + emerge_times = time points with new indices appear + + :param uids: list + :return emergence: list + :return emerge_times: list """ emergence = [[] for i in uids] @@ -137,18 +150,25 @@ def _get_emergence(uids): def _get_ti(num_insts, uids, link_info): - """ - getting ti (tracking information) - :param num_insts: (list) number of instances at every time-point t - :param uids: unique ids at every time point - :param link_info: - :return: - ti: tracking information (np.ndarray of shape (T,N)) + """ Get tracking information matrix (ti) + Inputs: + num_insts = number of instances at every time-point t + uids = unique ids at every time point + link_info = + Outputs: + ti (tracking information) = (numpy.ndarray of shape (T,N)) ti[t,k]=j represents for: at time t the k-th leaf has a local index of j if ti[t,k]=-1, the k-th leaf does not appear at time t number of non-negative elements every row: number of instances - t_appear: (list, length: N) the appear time of every unique id - t_disappear: (list, length: N) the disappear time of every unique id + t_appear = (list, length: N) the appear time of every unique id + t_disappear = (list, length: N) the disappear time of every unique id + + :param num_insts: list + :param uids: list + :param link_info: + :return ti: numpy.ndarray + :return t_appear: list + :return t_disappear: list """ emergence, emerge_times = _get_emergence(uids) @@ -195,11 +215,15 @@ def _get_ti(num_insts, uids, link_info): return ti, t_appear, t_disappear def _get_li_from_ti(ti): + """ Get linking information from tracking information + Inputs: + ti = tracking information + Outputs: + li = linking information + :param ti: numpy.ndarray + :return li: list """ - :param ti: - :return: - """ li = [] for t in range(len(ti)-1): ti_0 = ti[t,:] From 8abeef80520f36c8ae4e7e4191be60605ac1e55c Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 26 Jul 2021 16:36:06 -0500 Subject: [PATCH 234/274] Update __init__.py --- plantcv/plantcv/time_series/__init__.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/plantcv/plantcv/time_series/__init__.py b/plantcv/plantcv/time_series/__init__.py index 9509934f1..600e81949 100644 --- a/plantcv/plantcv/time_series/__init__.py +++ b/plantcv/plantcv/time_series/__init__.py @@ -1,7 +1,7 @@ -from plantcv.plantcv.time_series.time_series_linking import _get_link -from plantcv.plantcv.time_series.time_series_linking import _get_emergence -from plantcv.plantcv.time_series.time_series_linking import _get_ti -from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks +# from plantcv.plantcv.time_series.time_series_linking import _get_link +# from plantcv.plantcv.time_series.time_series_linking import _get_emergence +# from plantcv.plantcv.time_series.time_series_linking import _get_ti +# from plantcv.plantcv.time_series.time_series_linking import _compute_overlaps_masks from plantcv.plantcv.time_series.time_series_linking import InstanceTimeSeriesLinking from plantcv.plantcv.time_series.evaluation import evaluate_link from plantcv.plantcv.time_series.evaluation import mismatch_rate @@ -9,4 +9,5 @@ from plantcv.plantcv.time_series.evaluation import get_scores # add new functions to end of lists -__all__ = ["_get_link", "_get_emergence", "_get_ti", "_compute_overlaps_masks", "InstanceTimeSeriesLinking", "evaluate_link", "mismatch_rate", "confusion", "get_scores"] +__all__ = ["InstanceTimeSeriesLinking", "evaluate_link", "mismatch_rate", "confusion", "get_scores"] + # ["_get_link", "_get_emergence", "_get_ti", "_compute_overlaps_masks", ] From 45be8a36d36d152a2417a2a4165dda8ffb9ade64 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 26 Jul 2021 16:36:12 -0500 Subject: [PATCH 235/274] Update time_series_linking.py --- .../time_series/time_series_linking.py | 647 +++++++----------- 1 file changed, 236 insertions(+), 411 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 118744208..7d0b2e177 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -5,7 +5,6 @@ import matplotlib.pyplot as plt import os import os.path as osp -import sys import random import math import skimage.io @@ -18,261 +17,35 @@ import datetime import copy import colorsys -from plantcv.plantcv import fatal_error +from plantcv.plantcv import fatal_error, params, color_palette from scipy.optimize import linear_sum_assignment +from scipy.spatial import distance import csv -# from scipy.stats import wasserstein_distance +from plantcv.plantcv.visualize import display_instances +# import sys # sys.path.append('/shares/mgehan_share/hsheng/projects/test_plantcv/visualize_display_instances') -# import visualize_display_instances - -def _compute_overlaps_masks(masks1, masks2): - """Compute overlaps of two sets of binary masks. - The overlaps are represented by IoU (intersection over union) and IoS (intersection over self-area of the 1st mask). - Inputs: - masks1 = Binary masks data correspond to the 1st image - masks1 = Binary masks data correspond to the 2nd image - Outputs: - n1 = the number of instances in 1st set of binary masks - n2 = the number of instances in 2nd set of binary masks - ious = inversection over union between any pairs of instances in masks1 and masks2 - ioss = inversection over self-area (areas of instances in 1st set of masks) between any pairs of instances in masks1 and masks2 - unions = unions between any pairs of instances in masks1 and masks2 - :param masks1: (numpy.ndarray of shape: [Height, Width, n1]) , where n1 is the number of instances - :param masks2: (numpy.ndarray of shape: [Height, Width, n2]) , where n2 is the number of instances - :return: n1: int - :return: n2: int - :return: ious: numpy.ndarray of shape: [n1, n2] - :return: ioss: numpy.ndarray of shape: [n1, n2] - :return: unions: numpy.ndarray of shape: [n1, n2] - """ - - # If either set of masks is empty return an empty result - # if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: - # return np.zeros((masks1.shape[-1], masks2.shape[-1])) - # If either set of masks contains only one mask, expand the 2nd dimension - if len(masks1.shape) == 2: - masks1 = np.expand_dims(masks1, 2) - if len(masks2.shape) == 2: - masks2 = np.expand_dims(masks2, 2) - n1 = masks1.shape[2] - n2 = masks2.shape[2] - intersections = np.zeros((n1, n2)) - unions = np.zeros((n1, n2)) - ioss = np.zeros((n1, n2)) - for idx_m in range(0, n1): - maski = np.expand_dims(masks1[:, :, idx_m], axis=2) - masks_ = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) - maski_ = np.reshape(maski > .5, (-1, maski.shape[-1])).astype(np.float32) - intersection = np.dot(masks_.T, maski_).squeeze() - intersections[idx_m, :] = intersection - union = np.sum(masks_, 0) + np.sum(maski_) - intersection - unions[idx_m, :] = union - ioss[idx_m, :] = intersection / maski_.sum() - ious = np.divide(intersections, unions) - return n1, n2, ious, ioss, unions - - -def _get_link(masks1, masks2, logic, thres): - """Get link information between two sets of binary instance segmentation masks - Inputs: - masks1 = Binary masks data correspond to the 1st image - masks2 = Binary masks data correspond to the 2nd image - logic = linking logic to use. (currently) either "IOU" or "IOS" - thres = threshold to link two masks (two binary segmentation masks can only be linked if the calculated overlap - between is greater than the threshold) - Outputs: - weight = weight matrix (indicated by IoU or IoS) calculated based on two sets of masks - link = linking information (numpy.1darray of length n1)). link[i] = j means that the i-th mask in masks1 should be linked to j-th mask in masks2 - row_ind = selected row indices based on the weight matrix to finalize the "link" - col_ind = selected column indices based on the weight matrix to finalize the "link" - - :param masks1: numpy.ndarray - :param masks2: numpy.ndarray - :param logic: str - :param thres: float - :return weight: numpy.ndarray - :return link: numpy.1darray of length n1 - :return row_ind: int - :return col_ind: int - """ - - n1, n2, ious, ioss, _ = _compute_overlaps_masks(masks1, masks2) - if logic.upper() == 'IOU': - weight = ious - elif logic.upper() == 'IOS': - weight = ioss - else: - fatal_error("Currently only IOU and IOS as supported linking logic!") - weight_ = copy.deepcopy(weight) - link = -np.ones(n1, dtype=np.int64) - - idx_col = np.where(np.max(weight_, axis=0) < thres)[ - 0] # find those columns with maximum value < threshold (self.thres) - avail_col = [x for x in range(0, n2) if x not in idx_col] - - weight_ = np.delete(weight_, idx_col, 1) - - row_ind, col_ind = linear_sum_assignment(weight_, maximize=True) - for (r, c) in zip(row_ind, col_ind): - if weight_[r, c] >= thres: - link[r] = avail_col[c] - return weight, link, row_ind, col_ind - - -def _get_emergence(uids): - """ Get the emerging information as well as emerging times - Inputs: - uids = unique indices present at every time point. - (list of length T, where T is the total time - every sub-list uids[t] (t is in range of (0,T)) is also a list of length nt, - where nt is the number of instances at t) - Outputs: - emergence = new unique indices that don't exist in the previous time-point - emerge_times = time points with new indices appear - - :param uids: list - :return emergence: list - :return emerge_times: list - """ - - emergence = [[] for i in uids] - emergence[0] = list(uids[0]) - # uid_lst = [] - # uid_lst += list(uids[0]) - for (t, temp) in enumerate(uids): - if t >= 1: - # new = [x for x in temp if x not in uid_lst] - new = [x for x in temp if x not in uids[t - 1]] - emergence[t] = new - # uid_lst += new - emerge_times = [i for i in range(len(emergence)) if len(emergence[i]) > 0] - return emergence, emerge_times - - -def _get_ti(num_insts, uids, link_info): - """ Get tracking information matrix (ti) - Inputs: - num_insts = number of instances at every time-point t - uids = unique ids at every time point - link_info = - Outputs: - ti (tracking information) = (numpy.ndarray of shape (T,N)) - ti[t,k]=j represents for: at time t the k-th leaf has a local index of j - if ti[t,k]=-1, the k-th leaf does not appear at time t - number of non-negative elements every row: number of instances - t_appear = (list, length: N) the appear time of every unique id - t_disappear = (list, length: N) the disappear time of every unique id - - :param num_insts: list - :param uids: list - :param link_info: - :return ti: numpy.ndarray - :return t_appear: list - :return t_disappear: list - """ - - emergence, emerge_times = _get_emergence(uids) - T = len(num_insts) - # max_uid = max([max(temp) for temp in uids]) - # N = max_uid + 1 - N = len(np.unique(np.concatenate( uids, axis=0 ))) - ti = -np.ones((T, N), dtype=np.int64) - for t in range(T): - if t == 0: - ti[t, 0:num_insts[t]] = uids[0] - else: - link = link_info[t - 1] - prev = ti[t - 1] - cids = list(np.arange(0, num_insts[t])) - - for (uid, pid) in enumerate(prev): - # pid: previous local id - if pid >= 0: - cid = link[pid] - ti[t, uid] = cid - if cid >= 0: - cids.remove(cid) - - if t in emerge_times: - new_ids = emergence[t] - for (cid, new_id) in zip(cids, new_ids): - ti[t, new_id] = cid - - t_appear = np.zeros(N, dtype=np.int64) - t_disappear = -np.ones(N, dtype=np.int64) - for (t, uids_t) in enumerate(emergence): - if uids_t: - for uid in uids_t: - t_appear[uid] = t - for uid in range(0, N): - t = 0 - while t < T: - if (ti[t][uid] == -1) and (t > t_appear[uid]): - t_disappear[uid] = t - break - else: - t += 1 - return ti, t_appear, t_disappear - -def _get_li_from_ti(ti): - """ Get linking information from tracking information - Inputs: - ti = tracking information - Outputs: - li = linking information - :param ti: numpy.ndarray - :return li: list - """ - - li = [] - for t in range(len(ti)-1): - ti_0 = ti[t,:] - ti_1 = ti[t+1,:] - l0 = [x for x in ti_0 if x >= 0] - l1 = [x for (x, y) in zip(ti_1, ti_0) if y >= 0] - link_t = -np.ones(len(l0), dtype=np.int64) - for (idx, x) in enumerate(l0): - link_t[x] = l1[idx] - li.append(link_t) - return li - -# def _visualize1(img, mask, savename=None): -# """ -# Apply mask to original image -# """ -# mask_1 = np.zeros(img.shape[0:2], dtype=np.uint8) -# mask_1[np.where(mask)] = 255 -# masked_im = pcv.apply_mask(img, mask_1, mask_color='black') -# if savename is not None: -# pcv.print_image(masked_im, savename) -# return masked_im +# from visualize_display_instances import display_instances + + +# def compute_dist_weights(pts1, pts2): +# """ Compute weights between 2 sets of points based on their Euclidean distances +# Inputs: +# pts1: the list of point coordinates for set 1 +# pts2: the list of point coordinates for set 2 +# Outputs: +# weight: weight matrix +# n1: number of instances of set 1 +# n2: number of instances of set 2 # -# def _visualize2(img, fig_title, mask=None, savename=None): +# :param pts1: list +# :param pts2: list +# :return weight: numpy.ndarray +# :return n1: int +# :return n2: int # """ -# Apply mask with an alpha channel to the original image -# Can be replaced with "overlay two images in the future" -# img -# mask -# """ -# mask_2 = np.ones(img.shape[0:2]) * 0.5 -# if mask is not None: -# mask_2[np.where(mask == True)] = 1 -# masked_im = np.concatenate((img.astype(float) / 255, np.expand_dims(mask_2, axis=2)), axis=2) -# -# fig = plt.figure(figsize=(5, 5)) -# ax = fig.add_subplot(1, 1, 1) -# ax.imshow(masked_im) -# ax.axis('off') -# ax.set_title(fig_title, fontsize=16) -# if savename is not None: -# plt.savefig(savename) -# return masked_im -# -# def _visualize3(img, masks, colors, figsize=(16, 16), ax=None, captions=None, savename=None, title="", show_bbox=True): -# visualize_display_instances.display_instances(img, masks.astype(np.uint8), figsize=figsize, title=title, ax=ax, colors=colors, captions=captions, show_bbox=True) -# if savename is not None: -# # plt.savefig(savename) -# plt.savefig(savename, bbox_inches="tight",pad_inches=0) +# n1, n2 = len(pts1), len(pts2) +# weight = distance.cdist(pts1, pts2) +# return weight, n1, n2 class InstanceTimeSeriesLinking(object): """A class that links segmented instances throughout time @@ -280,12 +53,8 @@ class InstanceTimeSeriesLinking(object): """ def __init__(self): - # a list of images which are ndarrays - self.images = None # a list of masks which are ndarrays (of the same length of images) self.masks = None - # a list of timepoints (of the same length of images) - self.timepoints = None self.T = None # number of instances: a list in which every element represent for number of instances in corresponding image self.n_insts = None @@ -302,9 +71,9 @@ def __init__(self): self.t_appear = None self.t_disappear = None self.weights = None - self.logic = None - self.name_sub = None - self.key_id = None + self.metric = None + # self.name_sub = None + # self.key_id = None # update releated self.updated = 0 @@ -316,33 +85,123 @@ def __init__(self): self.t_appear_ = None self.t_disappear_ = None + @staticmethod + def get_link(weight, thres): + """Get the link (coordinates) between two sets of instances based on pre-calculated weight matrix + Inputs: + weight: weight matrix, the smaller the weight, the more possible two will be linked + thres: maximum value to link two instances + Outputs: + link: + row_ind: + col_ind: + + :param weight: numpy.ndarray + :param thres: float + :return link: list + :return row_ind: int + :return col_ind: int + """ + n1, n2 = weight.shape + link = -np.ones(n1, dtype=np.int64) + + idx_col = np.where(np.max(weight, axis=0) > thres)[0] # find those columns with maximum value < threshold + avail_col = [x for x in range(0, n2) if x not in idx_col] + + weight = np.delete(weight, idx_col, 1) + + row_ind, col_ind = linear_sum_assignment(weight) + for (r, c) in zip(row_ind, col_ind): + if weight[r, c] <= thres: + link[r] = avail_col[c] + return link, row_ind, col_ind + + + @staticmethod + def compute_overlaps_weights(masks1, masks2, metric): + """Compute weights between 2 sets of binary masks based on their overlaps + The overlaps are represented by IoU (intersection over union) and IoS (intersection over self-area of the 1st mask). + Inputs: + masks1 = Binary masks data correspond to the 1st image + masks1 = Binary masks data correspond to the 2nd image + metric = metric to evaluate the overlap between 2 sets of binary masks + Outputs: + n1 = the number of instances in 1st set of binary masks + n2 = the number of instances in 2nd set of binary masks + ious = inversection over union between any pairs of instances in masks1 and masks2 + ioss = inversection over self-area (areas of instances in 1st set of masks) between any pairs of instances in masks1 and masks2 + unions = unions between any pairs of instances in masks1 and masks2 + + :param masks1: (numpy.ndarray of shape: [Height, Width, n1]) , where n1 is the number of instances + :param masks2: (numpy.ndarray of shape: [Height, Width, n2]) , where n2 is the number of instances + :param metric: str + :return n1: int + :return n2: int + :return ious: numpy.ndarray of shape: [n1, n2] + :return ioss: numpy.ndarray of shape: [n1, n2] + :return unions: numpy.ndarray of shape: [n1, n2] + """ + + # If either set of masks is empty return an empty result + # if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: + # return np.zeros((masks1.shape[-1], masks2.shape[-1])) + # If either set of masks contains only one mask, expand the 2nd dimension + if len(masks1.shape) == 2: + masks1 = np.expand_dims(masks1, 2) + if len(masks2.shape) == 2: + masks2 = np.expand_dims(masks2, 2) + n1 = masks1.shape[2] + n2 = masks2.shape[2] + intersections = np.zeros((n1, n2)) + unions = np.zeros((n1, n2)) + ioss = np.zeros((n1, n2)) + for idx_m in range(0, n1): + maski = np.expand_dims(masks1[:, :, idx_m], axis=2) + masks_ = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) + maski_ = np.reshape(maski > .5, (-1, maski.shape[-1])).astype(np.float32) + intersection = np.dot(masks_.T, maski_).squeeze() + intersections[idx_m, :] = intersection + union = np.sum(masks_, 0) + np.sum(maski_) - intersection + unions[idx_m, :] = union + ioss[idx_m, :] = intersection / maski_.sum() + ious = np.divide(intersections, unions) + if metric.upper() == "IOU": + return ious, n1, n2, unions + if metric.upper() == "IOS": + return ioss, n1, n2, unions + def save_linked_series(self, savedir, savename): """save linking information into a .pkl file with the same prefix of filename - Inputs: savedir and savename + Inputs: + savedir = desired saving directory + savename = desired saving name + + :param savedir: str + :param savename: str + :return: """ - pkl.dump(vars(self), open(osp.join(savedir, savename + ".pkl"), 'wb')) + + pkl.dump(vars(self), open(osp.join(savedir, f"{savename}.pkl"), 'wb')) def import_linked_series(self, savedir, savename): """import a linked time-series from previously saved file + savedir = directory of saved file + savename = name of saved file - :param savedir: saving directory - :param savename: saving name + :param savedir: str + :param savename: str :return: """ + linked = pkl.load(open(osp.join(savedir, savename + '.pkl'), "rb")) for key, value in linked.items(): setattr(self, key, value) - def linking(self, t0): - """ - Link instance segments of t0 to those of t1 (t0+1) by calculating the overlaps of their masks - :param t0: starting time point - :return: None, but self.link_info would be updated - """ - masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy( - self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays - weight, self.link_info[t0], _, _ = _get_link(masks0, masks1, self.logic, self.thres) - self.weights.append(weight) + def linking(self,t0): + + masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays + self.weights[t0], _, _, _ = self.compute_overlaps_weights(masks0, masks1, self.metric) + self.link_info[t0], _, _ = self.get_link(-self.weights[t0], self.thres) def get_uid(self): """ @@ -369,155 +228,121 @@ def get_uid(self): self.uids[t + 1][tid] = max_uid self.N += 1 - - def update_ti(self, delta_t=2): - self.delta_t = delta_t if delta_t else self.delta_t - - """ make copies for tc, t_appear, t_disappear """ - - ti_ = copy.deepcopy(self.ti) - t_appear_ = copy.deepcopy(self.t_appear) - t_disappear_ = copy.deepcopy(self.t_disappear) - - # find disappeared uids - disp_uids = np.where((t_disappear_ > -1) & (t_disappear_ < (self.T - 2)))[0] - - if len(disp_uids) == 0: - print("\nno updates!") - else: - # use an array to record how to update tc - ids_update = -np.ones(len(disp_uids), dtype=np.int64) - for (uid_i, uid) in enumerate(disp_uids): - if not (self.ti[:, uid] == -1).all(): - disp_time = t_disappear_[uid] - t0 = disp_time - 1 # time last appear - t_ = t0 + 2 # start with t_ = t0+2 - - while (t_ <= t0 + self.delta_t) and (t_ <= self.T - 1): - uids_t = np.where(t_appear_ == t_)[0] # find if there is new emergence at t_ (t0+delta) - if len(uids_t) > 0: - cidt0 = self.ti[t0, uid] - maskt0 = self.masks[t0][:, :, cidt0] # there is only one mask - cidst_ = self.ti[t_, uids_t] # there can be more than one potential indices at t_ - maskst_ = self.masks[t_][:, :, cidst_] - weight, _, row_ind, col_ind = _get_link(maskt0, maskst_, self.logic, self.thres) - if len(row_ind) > 0: - for (r, c) in zip(row_ind, col_ind): - if weight[r, c] >= self.thres: - # cidt0 = cidt0 - # cidt_ = cidst_[c] - """find corresponding uid """ - uidt_ = uids_t[c] - """ recore how to update tc """ - ids_update[uid_i] = uidt_ - t_ += 1 - else: - t_ += 1 - else: - t_ += 1 - to_del = [idx for idx in ids_update if idx != -1] - if len(to_del) == 0: - print("\nno updates!") - else: - print("updates!!") - # there will be updates, so save previous result in self.ti_, self.t_appear_, and self.t_disappear_, respectively - self.ti_, self.t_appear_, self.t_disappear_ = ti_, t_appear_, t_disappear_ - for (idx, id_up) in enumerate(reversed(ids_update)): - if id_up != -1: - id_old = disp_uids[len(ids_update)-1-idx] - t_ = t_appear_[id_up] - self.ti[t_:, id_old] = self.ti[t_:, id_up] - self.ti = np.delete(self.ti, to_del, 1) - self.t_appear = np.delete(self.t_appear, to_del) - self.t_disappear = np.delete(self.t_disappear, to_del) - - self.ids_update = ids_update - self.disp_uids = disp_uids - self.updated = 1 - self.delta_t = delta_t - # self.max_uid = self.max_uid - len(np.where(ids_update > -1)[0]) +1 - # self.max_uid = self.ti.shape[1]-1 - self.N = self.ti.shape[1] - - # def visualize(self, visualdir, colors=None, color_all=None): - # """ - # Create 3 sets of visualization - # visualization set 1: one leaf per image (visualdir['1']) - # visualization set 2: show with an alpha channel (visualdir['2']) - # visualization method 3: show with bounding boxes (visualdir['3']) - # Also save a csv file called linking_info.csv (or other user defined name) that includes the linking information - # """ - # - # # create subfolders inside the provided directory of visualization - # visualdirs = dict() - # for i in range(1, 4): - # idx = str(i) - # visualdirs[idx] = osp.join(visualdir, 'visualization{}'.format(idx)) - # if not osp.exists(visualdirs[idx]): - # os.makedirs(visualdirs[idx]) - # - # if colors is None: - # # colors = visualize_display_instances._random_colors(self.max_uid + 1) # worest case: all leaves are unique - # colors = visualize_display_instances._random_colors(self.N) # worest case: all leaves are unique - # if color_all is None: - # color_all = [[tuple() for i in range(0, num)] for num in self.n_insts] - # - # for (t, ti_t) in enumerate(self.ti): - # uids_t = [x for x in ti_t if x >= 0] - # for (idx, uid) in enumerate(uids_t): - # color_all[t][idx] = colors[uid] - # - # for id_t, (img, masks, colors_t, t) in enumerate(zip(self.images, self.masks, color_all, self.timepoints)): - # # find exists unique ids at t - # uids = np.where(self.ti[id_t] >= 0)[0] - # for uid in uids: - # cid = self.ti[id_t][uid] - # mask = masks[:, :, cid] - # savedir_uid1 = osp.join(visualdirs['1'], "{}_{}".format(self.name_sub, uid)) - # savedir_uid2 = osp.join(visualdirs['2'], "{}_{}".format(self.name_sub, uid)) - # savename1 = osp.join(savedir_uid1, '{}_{}.jpg'.format(t, cid)) - # title2 = '{}_{}'.format(t, cid) - # savename2 = os.path.join(savedir_uid2, '{}.jpg'.format(title2)) - # if not osp.exists(savedir_uid1): - # os.makedirs(savedir_uid1) - # if not osp.exists(savedir_uid2): - # os.makedirs(savedir_uid2) - # _visualize1(img, mask, savename=savename1) - # plt.close('all') - # _visualize2(img, fig_title=title2, mask=mask, savename=savename2) - # plt.close('all') - # savename3 = osp.join(visualdirs['3'], '{}.jpg'.format(t)) - # _visualize3(img, masks, colors_t, savename=savename3) - # plt.close("all") - - def __call__(self, images, masks, timepoints, logic="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): - # a list of images which are ndarrays - self.images = images + def get_emergence(self): + self.emergence = [[] for _ in self.uids] + self.emergence[0] = list(self.uids[0]) + for (t, temp) in enumerate(self.uids): + if t >= 1: + new = [x for x in temp if x not in self.uids[t - 1]] + self.emergence[t] = new + self.emerge_times = [i for i in range(len(self.emergence)) if len(self.emergence[i]) > 0] + + def get_ti(self): + self.ti = -np.ones((self.T, self.N), dtype=np.int64) + self.ti[0,0:self.n_insts[0]] = self.uids[0] + for t in range(1,self.T): + link = self.link_info[t-1] # link_info from t-1 to t + prev = self.ti[t-1] # tracking info at previous timepoint (t-1) + cids = list(np.arange(0,self.n_insts[t])) # possible values of current indices + for (uid,pid) in enumerate(prev): + if pid >= 0: + cid = link[pid] + self.ti[t,uid] = cid + if cid >= 0: + cids.remove(cid) + # if t is a timepoint with new instances + if t in self.emerge_times: + new_ids = self.emergence[t] + for (cid,new_id) in zip(cids,new_ids): + self.ti[t,new_id] = cid + + # get appear and disappear information + self.t_appear = np.zeros(self.N,dtype=np.int64) + self.t_disappear = -np.ones(self.N, dtype=np.int64) + + for (t,uids_t) in enumerate(self.emergence): + if uids_t: + for uid in uids_t: + self.t_appear[uid] = t + for uid in range(0,self.N): + t = 0 + while t < self.T: + if (self.ti[t][uid] == -1) and (t > self.t_appear[uid]): + self.t_disappear[uid] = t + break + else: + t += 1 + + def get_li_from_ti(self): + self.T, self.N = self.ti.shape + self.link_info = [np.empty(0) for _ in range(0, self.T - 1)] + for t in range(self.T - 1): + ti_0 = self.ti[t, :] + ti_1 = self.ti[t + 1, :] + l0 = [x for x in ti_0 if x >= 0] + l1 = [x for (x, y) in zip(ti_1, ti_0) if y >= 0] + link_t = -np.ones(len(l0), dtype=np.int64) + for (idx, x) in enumerate(l0): + link_t[x] = l1[idx] + self.link_info[t] = link_t + + def visualize(self, imgs, timepoints, visualdir, color_all=None): + params.debug = "plot" + if not osp.exists(visualdir): + os.makedirs(visualdir) + if color_all is None: + colors_ = color_palette(self.N) + colors = [tuple([ci / 255 for ci in c]) for c in colors_] + color_all = [[tuple() for _ in range(0, num)] for num in self.n_insts] + for (t,ti_t) in enumerate(self.ti): + for (uid,cid) in enumerate(ti_t): + if cid > -1: + color_all[t][cid] = colors[uid] + for img,masks,t,colors_t in zip(imgs, self.masks, timepoints, color_all): + savename = osp.join(visualdir, '{}.jpg'.format(t)) + display_instances(img, masks, colors=colors_t) + plt.savefig(savename, bbox_inches="tight", pad_inches=0) + plt.close("all") + + def link(self, masks, metric="IOS", thres=0.2): + + # def __call__(self, masks, metric="IOS", thres=0.2): # a list of masks which are ndarrays (of the same length of images) self.masks = masks - # a list of timepoints (of the same length of images) - self.timepoints = timepoints - self.T = len(self.timepoints) + self.T = len(masks) # number of instances: a list in which every element represent for number of instances in corresponding image self.n_insts = [] - for i in range(0, len(self.masks)): + for i in range(0, self.T): self.n_insts.append(self.masks[i].shape[2]) # initialization for linking - self.thres = thres + self.thres = thres self.link_info = [-np.ones((self.n_insts[i]), dtype=np.int64) for i in range(0, self.T - 1)] - self.weights = [] - self.logic = logic.upper() - self.name_sub = name_sub - self.key_id = '{}_ids'.format(name_sub) + self.weights = [np.empty(0) for _ in range(self.T)] + self.metric = metric.upper() for t0 in range(0, self.T - 1): self.linking(t0) self.get_uid() - self.emergence, self.emerge_times = _get_emergence(self.uids) - self.ti, self.t_appear, self.t_disappear = _get_ti(self.n_insts, self.uids, self.link_info) - # self.save_linked_series(savedir, savename) - - if update: - self.update_ti(max_delta_t) + self.get_emergence() + self.get_ti() + + + + # def __call__(self, images, masks, timepoints, metric="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): + # # a list of images which are ndarrays + # self.images = images + # # a list of masks which are ndarrays (of the same length of images) + # self.masks = masks + # # a list of timepoints (of the same length of images) + # self.timepoints = timepoints + # self.T = len(self.timepoints) + # self.name_sub = name_sub + # self.key_id = '{}_ids'.format(name_sub) + # # number of instances: a list in which every element represent for number of instances in corresponding image + # self.n_insts = [] + # for i in range(0, len(self.masks)): + # self.n_insts.append(self.masks[i].shape[2]) + # if update: + # self.update_ti(max_delta_t) \ No newline at end of file From 08230fdf94e7d39d8714f9c9ebf0a6b92692097f Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 26 Jul 2021 16:36:24 -0500 Subject: [PATCH 236/274] Update __init__.py --- plantcv/plantcv/visualize/__init__.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/plantcv/plantcv/visualize/__init__.py b/plantcv/plantcv/visualize/__init__.py index 307f32563..9d0827bf3 100644 --- a/plantcv/plantcv/visualize/__init__.py +++ b/plantcv/plantcv/visualize/__init__.py @@ -10,8 +10,8 @@ __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "overlay_two_imgs","display_instances"] -from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video -from plantcv.plantcv.visualize.time_lapse_video import _resize_img - -# __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] -__all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video"] +# from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video +# from plantcv.plantcv.visualize.time_lapse_video import _resize_img +# +# # __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video", "_resize_img"] +# __all__ = ["pseudocolor", "colorize_masks", "histogram", "clustered_contours", "colorspaces", "auto_threshold_methods", "time_lapse_video"] From 17e80db01c4362d8facd9136a9454cbe94cf2c37 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 27 Jul 2021 08:10:32 -0500 Subject: [PATCH 237/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 7d0b2e177..68ee3e03a 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -105,7 +105,7 @@ def get_link(weight, thres): n1, n2 = weight.shape link = -np.ones(n1, dtype=np.int64) - idx_col = np.where(np.max(weight, axis=0) > thres)[0] # find those columns with maximum value < threshold + idx_col = np.where(np.max(weight, axis=0) < thres)[0] # find those columns with maximum value < threshold avail_col = [x for x in range(0, n2) if x not in idx_col] weight = np.delete(weight, idx_col, 1) From 1af6463e9f7d3dca15f4c1edce8d96ab11231137 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 27 Jul 2021 08:24:08 -0500 Subject: [PATCH 238/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 68ee3e03a..451ebd7c7 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -112,7 +112,7 @@ def get_link(weight, thres): row_ind, col_ind = linear_sum_assignment(weight) for (r, c) in zip(row_ind, col_ind): - if weight[r, c] <= thres: + if weight[r, c] >= thres: link[r] = avail_col[c] return link, row_ind, col_ind From eb15bbf80d90df7240806687c3a41d7cbdf4a25d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 27 Jul 2021 08:28:53 -0500 Subject: [PATCH 239/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 451ebd7c7..16a9c1559 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -110,7 +110,7 @@ def get_link(weight, thres): weight = np.delete(weight, idx_col, 1) - row_ind, col_ind = linear_sum_assignment(weight) + row_ind, col_ind = linear_sum_assignment(weight, maximuze=True) for (r, c) in zip(row_ind, col_ind): if weight[r, c] >= thres: link[r] = avail_col[c] From b7ed6c6ea04e6efade90b69f8844e508cb019d7c Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 27 Jul 2021 08:32:53 -0500 Subject: [PATCH 240/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 16a9c1559..966bb6962 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -110,7 +110,7 @@ def get_link(weight, thres): weight = np.delete(weight, idx_col, 1) - row_ind, col_ind = linear_sum_assignment(weight, maximuze=True) + row_ind, col_ind = linear_sum_assignment(weight, maximize=True) for (r, c) in zip(row_ind, col_ind): if weight[r, c] >= thres: link[r] = avail_col[c] From 42dc2b5f001395c5b42082c4099276f65c629f2d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 27 Jul 2021 08:43:10 -0500 Subject: [PATCH 241/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 966bb6962..e154966f3 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -201,7 +201,7 @@ def linking(self,t0): masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays self.weights[t0], _, _, _ = self.compute_overlaps_weights(masks0, masks1, self.metric) - self.link_info[t0], _, _ = self.get_link(-self.weights[t0], self.thres) + self.link_info[t0], _, _ = self.get_link(self.weights[t0], self.thres) def get_uid(self): """ From 4c6bd8d4784a8650095cce8e535fcf5a1d13db86 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 28 Jul 2021 08:22:52 -0500 Subject: [PATCH 242/274] Update time_series_linking.py - Change several class methods to be static methods - Add `status_report` method --- .../time_series/time_series_linking.py | 185 +++++++++++------- 1 file changed, 109 insertions(+), 76 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index e154966f3..8478b810a 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -74,6 +74,7 @@ def __init__(self): self.metric = None # self.name_sub = None # self.key_id = None + self.leaf_status_report = None # update releated self.updated = 0 @@ -171,120 +172,146 @@ def compute_overlaps_weights(masks1, masks2, metric): return ioss, n1, n2, unions def save_linked_series(self, savedir, savename): - """save linking information into a .pkl file with the same prefix of filename - Inputs: - savedir = desired saving directory - savename = desired saving name - - :param savedir: str - :param savename: str - :return: - """ - pkl.dump(vars(self), open(osp.join(savedir, f"{savename}.pkl"), 'wb')) def import_linked_series(self, savedir, savename): - """import a linked time-series from previously saved file - savedir = directory of saved file - savename = name of saved file - - :param savedir: str - :param savename: str - :return: - """ - linked = pkl.load(open(osp.join(savedir, savename + '.pkl'), "rb")) for key, value in linked.items(): setattr(self, key, value) def linking(self,t0): - + """ + Time-series linking for a given timepoint to the next time point + :param t0: + :return: + """ masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays self.weights[t0], _, _, _ = self.compute_overlaps_weights(masks0, masks1, self.metric) self.link_info[t0], _, _ = self.get_link(self.weights[t0], self.thres) - def get_uid(self): + @staticmethod + def get_uid(link_info, n_insts): """ - get unique ids for every timepoint based on link_info - uids: a list - arrays inside the list - every array has a length of num_insts for that corresponding time + Get unique ids for every timepoint + Inputs: + link_info = link information for every timepoint. If there are T timepoints in total, the length of link_info would be T-1 + n_insts = number of instances for every timepiont + Outputs: + uids = every array inside the list has a length of n_insts for that corresponding time + N = number of unique instances + :param link_info: list + :param n_insts: list + :return uids: list + :return N: int """ - self.uids = [-np.ones(n, dtype=np.int64) for n in self.n_insts] - self.uids[0] = np.arange(len(self.link_info[0]), dtype=np.int64) - # self.max_uid = max(self.uids[0]) - max_uid = max(self.uids[0]) - self.N = len(self.uids[0]) - for (t, link_t) in enumerate(self.link_info): + + uids = [-np.ones(n, dtype=np.int64) for n in n_insts] + uids[0] = np.arange(len(link_info[0]), dtype=np.int64) + max_uid = max(uids[0]) + N = len(uids[0]) # initial number of instances + for (t, link_t) in enumerate(link_info): for (cidt, cidt_) in enumerate(link_t): if cidt_ >= 0: - self.uids[t + 1][cidt_] = self.uids[t][cidt] - if -1 in self.uids[t + 1]: # -1 means there is no predecessor from a former timepoint -> potential to assign new uids - temp = np.where(self.uids[t + 1] == -1)[0] + uids[t + 1][cidt_] = uids[t][cidt] + if -1 in uids[t + 1]: # -1 means there is no predecessor from a former timepoint -> potential to assign new uids + temp = np.where(uids[t + 1] == -1)[0] for tid in temp: - # self.max_uid += 1 - # self.uids[t + 1][tid] = self.max_uid max_uid += 1 - self.uids[t + 1][tid] = max_uid - self.N += 1 + uids[t + 1][tid] = max_uid + N += 1 + return uids, N + + + @staticmethod + def get_emergence(uids): + """ + Get emergence information of new unique instances, and their emerging times + Inputs: + uids = every array inside the list has a length of n_insts for that corresponding time + Outputs: + emergence = + emerge_times = + :param uids: + :return emergence: + :return emerge_times: + """ - def get_emergence(self): - self.emergence = [[] for _ in self.uids] - self.emergence[0] = list(self.uids[0]) - for (t, temp) in enumerate(self.uids): + emergence = [[] for _ in uids] + emergence[0] = list(uids[0]) + for (t, temp) in enumerate(uids): if t >= 1: - new = [x for x in temp if x not in self.uids[t - 1]] - self.emergence[t] = new - self.emerge_times = [i for i in range(len(self.emergence)) if len(self.emergence[i]) > 0] - - def get_ti(self): - self.ti = -np.ones((self.T, self.N), dtype=np.int64) - self.ti[0,0:self.n_insts[0]] = self.uids[0] - for t in range(1,self.T): - link = self.link_info[t-1] # link_info from t-1 to t - prev = self.ti[t-1] # tracking info at previous timepoint (t-1) - cids = list(np.arange(0,self.n_insts[t])) # possible values of current indices + new = [x for x in temp if x not in uids[t - 1]] + emergence[t] = new + emerge_times = [i for i in range(len(emergence)) if len(emergence[i]) > 0] + return emergence, emerge_times + + + @staticmethod + def get_ti(T, N, n_insts, uids, link_info): + emergence, emerge_times = InstanceTimeSeriesLinking.get_emergence(uids) + ti = -np.ones((T, N), dtype=np.int64) + ti[0,0:n_insts[0]] = uids[0] # initialize ti for 1st timepoint as unique ids of the 1st timepoint + for t in range(1,T): + link = link_info[t-1] # link_info from t-1 to t + prev = ti[t-1] # tracking info at previous timepoint (t-1) + cids = list(np.arange(0,n_insts[t])) # possible values of current indices for (uid,pid) in enumerate(prev): if pid >= 0: cid = link[pid] - self.ti[t,uid] = cid + ti[t,uid] = cid if cid >= 0: cids.remove(cid) # if t is a timepoint with new instances - if t in self.emerge_times: - new_ids = self.emergence[t] + if t in emerge_times: + new_ids = emergence[t] for (cid,new_id) in zip(cids,new_ids): - self.ti[t,new_id] = cid + ti[t,new_id] = cid # get appear and disappear information - self.t_appear = np.zeros(self.N,dtype=np.int64) - self.t_disappear = -np.ones(self.N, dtype=np.int64) + t_appear = np.zeros(N,dtype=np.int64) + t_disappear = -np.ones(N, dtype=np.int64) - for (t,uids_t) in enumerate(self.emergence): + for (t,uids_t) in enumerate(emergence): if uids_t: for uid in uids_t: - self.t_appear[uid] = t - for uid in range(0,self.N): + t_appear[uid] = t + for uid in range(0,N): t = 0 - while t < self.T: - if (self.ti[t][uid] == -1) and (t > self.t_appear[uid]): - self.t_disappear[uid] = t + while t < T: + if (ti[t][uid] == -1) and (t > t_appear[uid]): + t_disappear[uid] = t break else: t += 1 + return ti, t_appear, t_disappear - def get_li_from_ti(self): - self.T, self.N = self.ti.shape - self.link_info = [np.empty(0) for _ in range(0, self.T - 1)] - for t in range(self.T - 1): - ti_0 = self.ti[t, :] - ti_1 = self.ti[t + 1, :] + + @staticmethod + def get_li_from_ti(ti): + T, N = ti.shape + link_info = [np.empty(0) for _ in range(0, T - 1)] + for t in range(T - 1): + ti_0 = ti[t, :] + ti_1 = ti[t + 1, :] l0 = [x for x in ti_0 if x >= 0] l1 = [x for (x, y) in zip(ti_1, ti_0) if y >= 0] link_t = -np.ones(len(l0), dtype=np.int64) for (idx, x) in enumerate(l0): link_t[x] = l1[idx] - self.link_info[t] = link_t + link_info[t] = link_t + return link_info + + + @staticmethod + def status_report(ti, masks): + leaf_status_report = np.zeros(ti.shape) + for (t, masks_t) in enumerate(masks): + ti_t = ti[t, :] + for cid in range(masks_t.shape[2]): + uid = np.where(ti_t == cid)[0][0] + leaf_status_report[t, uid] = np.sum(masks[:, :, cid]) + return leaf_status_report + def visualize(self, imgs, timepoints, visualdir, color_all=None): params.debug = "plot" @@ -319,14 +346,20 @@ def link(self, masks, metric="IOS", thres=0.2): self.thres = thres self.link_info = [-np.ones((self.n_insts[i]), dtype=np.int64) for i in range(0, self.T - 1)] - self.weights = [np.empty(0) for _ in range(self.T)] + self.weights = [np.empty(0) for _ in range(self.T-1)] self.metric = metric.upper() for t0 in range(0, self.T - 1): self.linking(t0) - self.get_uid() - self.get_emergence() - self.get_ti() + + self.uids, self.N = self.get_uid(self.link_info, self.n_insts) + self.emergence, self.emerge_times = self.get_emergence(self.uids) + self.ti, self.t_appear, self.t_disappear = self.get_ti(self.T, self.N, self.n_insts, self.uids, self.link_info) + + self.leaf_status_report = InstanceTimeSeriesLinking.status_report(self.ti, self.masks) + + + From 573fbeeb167def12c88dc2a9743131c2a60acd1e Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 28 Jul 2021 08:41:53 -0500 Subject: [PATCH 243/274] Update time_series_linking.py fix minor bug --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 8478b810a..8d17cea4c 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -309,7 +309,7 @@ def status_report(ti, masks): ti_t = ti[t, :] for cid in range(masks_t.shape[2]): uid = np.where(ti_t == cid)[0][0] - leaf_status_report[t, uid] = np.sum(masks[:, :, cid]) + leaf_status_report[t, uid] = np.sum(masks_t[:, :, cid]) return leaf_status_report From 22275bb8e38c9596622c88d7965ecb9ae7dbe724 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 28 Jul 2021 14:47:57 -0500 Subject: [PATCH 244/274] Update time_series_linking.py - fix minor issues - make `visualize` a static method --- .../time_series/time_series_linking.py | 58 ++++++++++++++----- 1 file changed, 45 insertions(+), 13 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 8d17cea4c..dd7bb3929 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -124,7 +124,7 @@ def compute_overlaps_weights(masks1, masks2, metric): The overlaps are represented by IoU (intersection over union) and IoS (intersection over self-area of the 1st mask). Inputs: masks1 = Binary masks data correspond to the 1st image - masks1 = Binary masks data correspond to the 2nd image + masks2 = Binary masks data correspond to the 2nd image metric = metric to evaluate the overlap between 2 sets of binary masks Outputs: n1 = the number of instances in 1st set of binary masks @@ -312,25 +312,57 @@ def status_report(ti, masks): leaf_status_report[t, uid] = np.sum(masks_t[:, :, cid]) return leaf_status_report - - def visualize(self, imgs, timepoints, visualdir, color_all=None): + @staticmethod + def visualize(imgs, masks_all, tps, savedir, ti = None, color_all = None): params.debug = "plot" - if not osp.exists(visualdir): - os.makedirs(visualdir) - if color_all is None: - colors_ = color_palette(self.N) + if not osp.exists(savedir): + os.makedirs(savedir) + + n_insts = [masks.shape[2] for masks in masks_all] + if not color_all: + if not ti: # if no tracking information provided, the color assignment would base on local id (cid) solely + N = max(n_insts) + T = len(imgs) + else: + T, N = ti.shape + colors_ = color_palette(N) colors = [tuple([ci / 255 for ci in c]) for c in colors_] - color_all = [[tuple() for _ in range(0, num)] for num in self.n_insts] - for (t,ti_t) in enumerate(self.ti): - for (uid,cid) in enumerate(ti_t): - if cid > -1: - color_all[t][cid] = colors[uid] - for img,masks,t,colors_t in zip(imgs, self.masks, timepoints, color_all): + if not ti: + color_all = [[colors[i] for i in range(0, num)] for num in n_insts] + else: + color_all = [[tuple() for _ in range(0, num)] for num in n_insts] + for (t,ti_t) in enumerate(ti): + for (uid,cid) in enumerate(ti_t): + if cid > -1: + color_all[t][cid] = colors[uid] + for img, masks, t, colors_t in zip(imgs, masks, tps, color_all): savename = osp.join(visualdir, '{}.jpg'.format(t)) display_instances(img, masks, colors=colors_t) plt.savefig(savename, bbox_inches="tight", pad_inches=0) plt.close("all") + + + + + # def visualize(self, imgs, timepoints, visualdir, color_all=None): + # params.debug = "plot" + # if not osp.exists(visualdir): + # os.makedirs(visualdir) + # if color_all is None: + # colors_ = color_palette(self.N) + # colors = [tuple([ci / 255 for ci in c]) for c in colors_] + # color_all = [[tuple() for _ in range(0, num)] for num in self.n_insts] + # for (t,ti_t) in enumerate(self.ti): + # for (uid,cid) in enumerate(ti_t): + # if cid > -1: + # color_all[t][cid] = colors[uid] + # for img,masks,t,colors_t in zip(imgs, self.masks, timepoints, color_all): + # savename = osp.join(visualdir, '{}.jpg'.format(t)) + # display_instances(img, masks, colors=colors_t) + # plt.savefig(savename, bbox_inches="tight", pad_inches=0) + # plt.close("all") + def link(self, masks, metric="IOS", thres=0.2): # def __call__(self, masks, metric="IOS", thres=0.2): From 602419a8eda1aad66c2ca50792afc5dcded96200 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 9 Aug 2021 15:13:54 -0500 Subject: [PATCH 245/274] Update time_series_linking.py --- plantcv/plantcv/time_series/helper.py | 130 ++++++++++++++++++ .../time_series/time_series_linking.py | 16 +-- 2 files changed, 138 insertions(+), 8 deletions(-) create mode 100644 plantcv/plantcv/time_series/helper.py diff --git a/plantcv/plantcv/time_series/helper.py b/plantcv/plantcv/time_series/helper.py new file mode 100644 index 000000000..919f7aac3 --- /dev/null +++ b/plantcv/plantcv/time_series/helper.py @@ -0,0 +1,130 @@ +import os +import pickle as pkl +import glob +import numpy as np +import cv2 +import re +import skimage +import datetime +from plantcv.plantcv.time_series import time_series_linking, evaluation +import matplotlib.pyplot as plt +import pandas as pd + +plant = "Bean" +path_img_ = "/shares/bioinformatics/nfahlgren/projects/mrcnn/old/training_data_downloads/MSU/Dataset/Images" +path_gt_ = "/shares/mgehan_share/hsheng/projects/synthetic_data_MSU/label" +ext_img = "rgb.png" +ext_seg = "mask.pkl" +pattern_datetime = "day_.+_hour_.+_" + +metric = "IOU" +thres = 0.05 + +path_save_ = path_gt_.replace("label", "time_series_linking") +junk = datetime.datetime.now() +subfolder = "{}-{}_{}-{}-{}-{}-{}".format(metric, thres, junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), + str(junk.hour).zfill(2), str(junk.minute).zfill(2), str(junk.second).zfill(2)) +path_save = os.path.join(path_save_, plant, subfolder) +if not os.path.exists(path_save): + os.makedirs(path_save) + +df_results = pd.DataFrame( + columns=["Plant-ID", "num_leaves_gt", "num_leaves", "link_score", "track_score", "unmatched_rate", "fake_new_rate"]) + +for idx_p in range(1, 17): + path_img = f"{path_img_}/{plant}/plant_{idx_p}" + path_gt = path_img.replace(path_img_, path_gt_) + path_seg = os.path.join(path_gt, "masks") + + # Get timepoints from available segmentation results + list_seg = glob.glob(os.path.join(path_seg, "*{}".format(ext_seg))) + + # sort timepoints + timepoints = [] + for f_seg in list_seg: + tp_temp = re.search(pattern_datetime, f_seg).group() + timepoints.append(tp_temp) + timepoints.sort() + + # load images + temp_imgs = [] + sz = [] + imgs = [] + for tp in timepoints: + filename_ = "*{}{}".format(tp, ext_img) + if "_09" in filename_: + filename_ = filename_.replace("09", "9") + filename = glob.glob(os.path.join(path_img, filename_))[0] + junk = skimage.io.imread(os.path.join(path_img, filename)) + if len(junk.shape) == 2: + junk = cv2.cvtColor(junk, cv2.COLOR_GRAY2BGR) + temp_imgs.append(junk) + sz.append(np.min(junk.shape[0:2])) + min_dim = np.min(sz) + for junk in temp_imgs: + img = junk[0:min_dim, 0:min_dim, :] # make all images the same size (if not the same size) + imgs.append(img) + + # load instance segmentation results + masks = [] + for tp in timepoints: + filename_ = "*{}{}".format(tp, ext_seg) + filename = glob.glob(os.path.join(path_seg, filename_))[0] + r = pkl.load(open(filename, 'rb')) + masks.append(r['masks'][0: min_dim, 0:min_dim, :]) # make all masks the same size (if not already) + + inst_ts_linking = time_series_linking.InstanceTimeSeriesLinking() + + inst_ts_linking.images = imgs + inst_ts_linking.masks = masks + inst_ts_linking.timepoints = timepoints + inst_ts_linking.T = len(inst_ts_linking.timepoints) + # inst_ts_linking.name_sub = name_sub + # inst_ts_linking.key_id = '{}_ids'.format(name_sub) + inst_ts_linking.link(masks, metric, thres) + + ## Leaf status report + # extract timepoints + tps_dd, tps_hh = [], [] + for tp in timepoints: + temp = tp.split("_") + idx_day = temp.index("day") + idx_hour = temp.index("hour") + dd = temp[idx_day + 1] + hh = temp[idx_hour + 1] + if hh.startswith("0"): + hh = hh.replace("0", "") + tps_dd.append(dd) + tps_hh.append(hh) + days = np.unique(tps_dd) + locs = [tps_dd.index(d) for d in days] + + # plot leaf status report + leaf_report = inst_ts_linking.leaf_status_report + leaf_report[np.where(leaf_report == 0)] = np.nan + fig, ax = plt.subplots(figsize=(10, 8)) + for uid in range(leaf_report.shape[1]): + ax.plot(leaf_report[:, uid], ".-", label=f"leaf{uid}") + ax.legend() + ax.set_xlabel("Days", fontsize=15) + ax.set_ylabel("Leaf Area", fontsize=15) + ax.set_xticks(locs) + ax.set_xticklabels(days, fontsize=14) + ax.yaxis.set_tick_params(labelsize=14) + plt.savefig(os.path.join(path_save, f"leaf_status_Plant{idx_p}.png")) + plt.close("all") + + ## results evaluation + # load ground truth + loaded_gt = pkl.load(open(os.path.join(path_gt, "gt_link_info.pkl"), "rb")) + li_gt = loaded_gt["link_info"] + ti_gt = loaded_gt["ti"] + score_link, num_insts, num_matched = evaluation.evaluate_link(inst_ts_linking.link_info, li_gt) + r_unmatched, r_fake_new = evaluation.mismatch_rate(inst_ts_linking.ti, ti_gt) + N, N_, confu, track_match, track_rate, score_track = evaluation.confusion(inst_ts_linking.ti, ti_gt) + + df_results = df_results.append({ + "Plant-ID": idx_p, "num_leaves_gt": N, "num_leaves": N_, "link_score": score_link, "track_score": score_track, + "unmatched_rate": r_unmatched, "fake_new_rate": r_fake_new}, ignore_index=True) + +df_results.to_csv(os.path.join(path_save, "summary.csv"), index=False) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index dd7bb3929..7c74f873d 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -313,14 +313,14 @@ def status_report(ti, masks): return leaf_status_report @staticmethod - def visualize(imgs, masks_all, tps, savedir, ti = None, color_all = None): + def visualize(imgs, masks, tps, savedir, ti = None, color_all = None): params.debug = "plot" if not osp.exists(savedir): os.makedirs(savedir) - n_insts = [masks.shape[2] for masks in masks_all] + n_insts = [masks_t.shape[2] for masks_t in masks] if not color_all: - if not ti: # if no tracking information provided, the color assignment would base on local id (cid) solely + if ti is None: # if no tracking information provided, the color assignment would base on local id (cid) solely N = max(n_insts) T = len(imgs) else: @@ -331,13 +331,13 @@ def visualize(imgs, masks_all, tps, savedir, ti = None, color_all = None): color_all = [[colors[i] for i in range(0, num)] for num in n_insts] else: color_all = [[tuple() for _ in range(0, num)] for num in n_insts] - for (t,ti_t) in enumerate(ti): - for (uid,cid) in enumerate(ti_t): + for (t, ti_t) in enumerate(ti): + for (uid, cid) in enumerate(ti_t): if cid > -1: color_all[t][cid] = colors[uid] - for img, masks, t, colors_t in zip(imgs, masks, tps, color_all): - savename = osp.join(visualdir, '{}.jpg'.format(t)) - display_instances(img, masks, colors=colors_t) + for img_t, masks_t, t, colors_t in zip(imgs, masks, tps, color_all): + savename = osp.join(savedir, '{}.jpg'.format(t)) + display_instances(img_t, masks_t, colors=colors_t) plt.savefig(savename, bbox_inches="tight", pad_inches=0) plt.close("all") From 7a5615b573835e7290dfc0204b2d8dac833dd113 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 9 Aug 2021 15:25:26 -0500 Subject: [PATCH 246/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 7c74f873d..5ec48c018 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -327,7 +327,7 @@ def visualize(imgs, masks, tps, savedir, ti = None, color_all = None): T, N = ti.shape colors_ = color_palette(N) colors = [tuple([ci / 255 for ci in c]) for c in colors_] - if not ti: + if ti is None: color_all = [[colors[i] for i in range(0, num)] for num in n_insts] else: color_all = [[tuple() for _ in range(0, num)] for num in n_insts] From a9ced2247c833684688d9a60e01165e268f5f3e3 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 12 Aug 2021 20:04:58 -0500 Subject: [PATCH 247/274] Several updates in time_series_linking.py - remove unused methods; - change from `get_uid` to `get_sorted_uids`; - change from `get_emergence` to `get_emerg_disap_info`; - update `get_ti` method - add static method `update_ti` that is used to update time-series tracking by comparing timepoints whose distance is greater than 1. --- .../time_series/time_series_linking.py | 276 +++++++++--------- 1 file changed, 144 insertions(+), 132 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 5ec48c018..d38e96568 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -27,26 +27,6 @@ # from visualize_display_instances import display_instances -# def compute_dist_weights(pts1, pts2): -# """ Compute weights between 2 sets of points based on their Euclidean distances -# Inputs: -# pts1: the list of point coordinates for set 1 -# pts2: the list of point coordinates for set 2 -# Outputs: -# weight: weight matrix -# n1: number of instances of set 1 -# n2: number of instances of set 2 -# -# :param pts1: list -# :param pts2: list -# :return weight: numpy.ndarray -# :return n1: int -# :return n2: int -# """ -# n1, n2 = len(pts1), len(pts2) -# weight = distance.cdist(pts1, pts2) -# return weight, n1, n2 - class InstanceTimeSeriesLinking(object): """A class that links segmented instances throughout time Assumption: the timepoints are all sorted, the images and masks are also sorted by timepoints (chronologically) @@ -64,27 +44,26 @@ def __init__(self): self.link_info = None self.uids = None # self.max_uid = None - self.N = None - self.emergence = None - self.emerge_times = None + # self.N = None + # self.emergence = None + # self.emerge_times = None self.ti = None - self.t_appear = None - self.t_disappear = None + # self.t_appear = None + # self.t_disappear = None self.weights = None self.metric = None # self.name_sub = None # self.key_id = None self.leaf_status_report = None - # update releated - self.updated = 0 - self.delta_t = None - self.disp_uids = None - self.ids_update = None - - self.ti_ = None - self.t_appear_ = None - self.t_disappear_ = None + # # update releated + # self.updated = 0 + # self.delta_t = None + # self.disp_uids = None + # self.ids_update = None + # self.ti_ = None + # self.t_appear_ = None + # self.t_disappear_ = None @staticmethod def get_link(weight, thres): @@ -190,100 +169,94 @@ def linking(self,t0): self.link_info[t0], _, _ = self.get_link(self.weights[t0], self.thres) @staticmethod - def get_uid(link_info, n_insts): - """ - Get unique ids for every timepoint - Inputs: - link_info = link information for every timepoint. If there are T timepoints in total, the length of link_info would be T-1 - n_insts = number of instances for every timepiont - Outputs: - uids = every array inside the list has a length of n_insts for that corresponding time - N = number of unique instances - :param link_info: list - :param n_insts: list - :return uids: list - :return N: int - """ - - uids = [-np.ones(n, dtype=np.int64) for n in n_insts] - uids[0] = np.arange(len(link_info[0]), dtype=np.int64) - max_uid = max(uids[0]) - N = len(uids[0]) # initial number of instances - for (t, link_t) in enumerate(link_info): - for (cidt, cidt_) in enumerate(link_t): - if cidt_ >= 0: - uids[t + 1][cidt_] = uids[t][cidt] - if -1 in uids[t + 1]: # -1 means there is no predecessor from a former timepoint -> potential to assign new uids - temp = np.where(uids[t + 1] == -1)[0] - for tid in temp: + def get_sorted_uids(li, n_insts): + uids_sort = [-1 * np.ones(num, dtype=np.int64) for num in n_insts] + uids_sort[0] = np.arange(inst_ts_linking.n_insts[0]) + max_uid = max(uids_sort[0]) + for t in range(1, len(li) + 1): + + li_t = li[t - 1] + uids_sort_t = uids_sort[t] + uids_sort_t_ = uids_sort[t - 1] + for cidt_, cidt in enumerate(li_t): + if cidt > -1: + uids_sort_t[cidt] = uids_sort_t_[cidt_] + if -1 in uids_sort_t: + ids = np.where(uids_sort_t == -1)[0] + for i in ids: max_uid += 1 - uids[t + 1][tid] = max_uid - N += 1 - return uids, N - + uids_sort_t[i] = max_uid + uids_sort[t] = uids_sort_t + return uids_sort @staticmethod - def get_emergence(uids): - """ - Get emergence information of new unique instances, and their emerging times - Inputs: - uids = every array inside the list has a length of n_insts for that corresponding time - Outputs: - emergence = - emerge_times = - :param uids: - :return emergence: - :return emerge_times: - """ + def get_uid_from_ti(ti): + # uids: a list of length T, where every sub-list has a length of n_t (# of instances at time t). Every sub-list is + # contains the unique indices present at time t - emergence = [[] for _ in uids] + # uids_sort: basically the contains the same information as uids, however, in every sub-list of uids_sort, the + # location of every unique-id represent the index of the leaf in the image (cid) + T, N = ti.shape + uids = [np.where(ti_t > -1)[0] for ti_t in ti] + uids_sort = [[np.where(ti_t > -1)[0][i] for i in np.argsort(ti_t[np.where(ti_t > -1)])] for ti_t in ti] + + return uids, uids_sort, T, N + + @staticmethod + def get_emerg_disap_info(uids): + emergence, disappearance = dict(), dict() emergence[0] = list(uids[0]) for (t, temp) in enumerate(uids): if t >= 1: - new = [x for x in temp if x not in uids[t - 1]] - emergence[t] = new - emerge_times = [i for i in range(len(emergence)) if len(emergence[i]) > 0] - return emergence, emerge_times + emerg = [x for x in temp if x not in uids[t - 1]] + if len(emerg) > 0: + emergence[t] = emerg + if t < len(uids) - 1: + disap = [x for x in temp if x not in uids[t + 1]] + if len(disap) > 0: + disappearance[t] = disap + return emergence, disappearance @staticmethod - def get_ti(T, N, n_insts, uids, link_info): - emergence, emerge_times = InstanceTimeSeriesLinking.get_emergence(uids) + def get_ti(uids, link_info, n_insts): + emergence, _ = InstanceTimeSeriesLinking.get_emerg_disap_info(uids) + N = max([max(uid) for uid in uids]) + 1 ti = -np.ones((T, N), dtype=np.int64) ti[0,0:n_insts[0]] = uids[0] # initialize ti for 1st timepoint as unique ids of the 1st timepoint for t in range(1,T): - link = link_info[t-1] # link_info from t-1 to t + li_t = link_info[t-1] # link_info from t-1 to t prev = ti[t-1] # tracking info at previous timepoint (t-1) cids = list(np.arange(0,n_insts[t])) # possible values of current indices for (uid,pid) in enumerate(prev): if pid >= 0: - cid = link[pid] + cid = li_t[pid] ti[t,uid] = cid if cid >= 0: cids.remove(cid) # if t is a timepoint with new instances - if t in emerge_times: + if t in emergence.keys(): new_ids = emergence[t] for (cid,new_id) in zip(cids,new_ids): ti[t,new_id] = cid - # get appear and disappear information - t_appear = np.zeros(N,dtype=np.int64) - t_disappear = -np.ones(N, dtype=np.int64) - - for (t,uids_t) in enumerate(emergence): - if uids_t: - for uid in uids_t: - t_appear[uid] = t - for uid in range(0,N): - t = 0 - while t < T: - if (ti[t][uid] == -1) and (t > t_appear[uid]): - t_disappear[uid] = t - break - else: - t += 1 - return ti, t_appear, t_disappear + # # get appear and disappear information + # t_appear = np.zeros(N,dtype=np.int64) + # t_disappear = -np.ones(N, dtype=np.int64) + # + # for (t,uids_t) in enumerate(emergence): + # if uids_t: + # for uid in uids_t: + # t_appear[uid] = t + # for uid in range(0,N): + # t = 0 + # while t < T: + # if (ti[t][uid] == -1) and (t > t_appear[uid]): + # t_disappear[uid] = t + # break + # else: + # t += 1 + return ti @staticmethod @@ -342,27 +315,6 @@ def visualize(imgs, masks, tps, savedir, ti = None, color_all = None): plt.close("all") - - - - # def visualize(self, imgs, timepoints, visualdir, color_all=None): - # params.debug = "plot" - # if not osp.exists(visualdir): - # os.makedirs(visualdir) - # if color_all is None: - # colors_ = color_palette(self.N) - # colors = [tuple([ci / 255 for ci in c]) for c in colors_] - # color_all = [[tuple() for _ in range(0, num)] for num in self.n_insts] - # for (t,ti_t) in enumerate(self.ti): - # for (uid,cid) in enumerate(ti_t): - # if cid > -1: - # color_all[t][cid] = colors[uid] - # for img,masks,t,colors_t in zip(imgs, self.masks, timepoints, color_all): - # savename = osp.join(visualdir, '{}.jpg'.format(t)) - # display_instances(img, masks, colors=colors_t) - # plt.savefig(savename, bbox_inches="tight", pad_inches=0) - # plt.close("all") - def link(self, masks, metric="IOS", thres=0.2): # def __call__(self, masks, metric="IOS", thres=0.2): @@ -384,16 +336,76 @@ def link(self, masks, metric="IOS", thres=0.2): for t0 in range(0, self.T - 1): self.linking(t0) - self.uids, self.N = self.get_uid(self.link_info, self.n_insts) - self.emergence, self.emerge_times = self.get_emergence(self.uids) - self.ti, self.t_appear, self.t_disappear = self.get_ti(self.T, self.N, self.n_insts, self.uids, self.link_info) + # self.ti, self.t_appear, self.t_disappear = self.get_ti(self.T, self.N, self.n_insts, self.uids, self.link_info) + # self.uids, uids_sort, _, self.N = self.get_uid(self.ti) + # self.emergence, self.emerge_times = self.get_emerg_disap_info(uids_sort) + self.uids = InstanceTimeSeriesLinking.get_sorted_uids(self.link_info, self.n_insts) + self.ti = self.get_ti(self.uids, self.link_info, self.n_insts) self.leaf_status_report = InstanceTimeSeriesLinking.status_report(self.ti, self.masks) - - - - + @staticmethod + def update_ti(ti, metric, thres, max_gap=5): + ti_ = copy.deepcopy(ti) + uids, uids_sort, T, N = InstanceTimeSeriesLinking.get_uid(ti) + emergence, disappearance = InstanceTimeSeriesLinking.get_emerg_disap_info(uids_sort) + t_emerg, t_disap = emergence.keys(), disappearance.keys() + + # loop over timepoints with disappearing leaves (in reversed order) + for t in reversed(sorted(t_disap)): + # unique indices(index) that last appear at t + uids_disap = disappearance[t] + # corresponding cid(s) (i.e. indices for masks) + cids_disap = [uids_sort[t].index(i) for i in uids_disap] + # pull out masks + masks_t = np.take(masks[t], cids_disap, axis=2) + + # cids_emerg = dict() + # ts_pot = pot_link_time[t] + + # timepoints with potential link with t + ts_pot = [te for te in t_emerg if t < te < t + max_gap] + # loop over timepoints for a potential link and get cids and masks for every timepoint + for t_ in ts_pot: + uids_emerg = emergence[t_] + # cids_emerg[t_] = [uids_sort[t_].index(i) for i in uids_emerg] + cids_emerg = [uids_sort[t_].index(i) for i in uids_emerg] + + # masks_t_ = np.take(masks[t_], cids_emerg[t_], axis=2) + masks_t_ = np.take(masks[t_], cids_emerg, axis=2) + + # calculate weight to calculate the link + weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) + li_ts, _, _ = inst_ts_linking.get_link(weights, thres) + + uids_undisap = [] + for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices + li_t = li_ts[idx] + if li_t > -1: + print(f"\n{t} -> {t_}: ") + print(f"{uid_disap} <- {uids_emerg[li_t]}") + # update ti + # ti[t_, uid_disap] = cids_emerg[t_][li_t] + ti_[t_:, uid_disap] = ti_[t_:, uids_emerg[li_t]] + ti_[t_:, uids_emerg[li_t]] = -np.ones(T - t_, dtype=np.int64) + + uids_undisap.append(uid_disap) + uids_disap = list(set(uids_disap).difference(set(uids_undisap))) + + if len(uids_disap) == 0: + # remove key + disappearance.pop(t) + break + else: + # update + disappearance[t] = uids_disap + remove_uids = [] + for uid in range(N): + if (ti_[:, uid] == 1).all(): + remove_uids.append(uid) + ti_ = np.delete(ti_, remove_uids, axis=1) + if not np.array_equal(ti_, ti): + return InstanceTimeSeriesLinking.update_ti(ti_, metric, thres, max_gap) # def __call__(self, images, masks, timepoints, metric="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): # # a list of images which are ndarrays @@ -410,4 +422,4 @@ def link(self, masks, metric="IOS", thres=0.2): # for i in range(0, len(self.masks)): # self.n_insts.append(self.masks[i].shape[2]) # if update: - # self.update_ti(max_delta_t) \ No newline at end of file + # self.update_ti(max_delta_t) From 65b36d3cc00e4c1cc54e28a731189de74ed38d40 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 12 Aug 2021 20:11:27 -0500 Subject: [PATCH 248/274] bug fix for method `get_sorted_uids` --- plantcv/plantcv/time_series/time_series_linking.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index d38e96568..c662c54ef 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -171,7 +171,7 @@ def linking(self,t0): @staticmethod def get_sorted_uids(li, n_insts): uids_sort = [-1 * np.ones(num, dtype=np.int64) for num in n_insts] - uids_sort[0] = np.arange(inst_ts_linking.n_insts[0]) + uids_sort[0] = np.arange(n_insts[0]) max_uid = max(uids_sort[0]) for t in range(1, len(li) + 1): @@ -376,7 +376,7 @@ def update_ti(ti, metric, thres, max_gap=5): # calculate weight to calculate the link weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) - li_ts, _, _ = inst_ts_linking.get_link(weights, thres) + li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, thres) uids_undisap = [] for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices From 7c3aa7dd0df2952542e506ef639aaf4ea7783822 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 12 Aug 2021 21:58:33 -0500 Subject: [PATCH 249/274] bug fix for static method `update_ti` --- plantcv/plantcv/time_series/time_series_linking.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index c662c54ef..c5b366ec4 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -190,7 +190,7 @@ def get_sorted_uids(li, n_insts): return uids_sort @staticmethod - def get_uid_from_ti(ti): + def get_uids_from_ti(ti): # uids: a list of length T, where every sub-list has a length of n_t (# of instances at time t). Every sub-list is # contains the unique indices present at time t @@ -222,6 +222,7 @@ def get_emerg_disap_info(uids): def get_ti(uids, link_info, n_insts): emergence, _ = InstanceTimeSeriesLinking.get_emerg_disap_info(uids) N = max([max(uid) for uid in uids]) + 1 + T = len(uids) ti = -np.ones((T, N), dtype=np.int64) ti[0,0:n_insts[0]] = uids[0] # initialize ti for 1st timepoint as unique ids of the 1st timepoint for t in range(1,T): @@ -347,7 +348,7 @@ def link(self, masks, metric="IOS", thres=0.2): @staticmethod def update_ti(ti, metric, thres, max_gap=5): ti_ = copy.deepcopy(ti) - uids, uids_sort, T, N = InstanceTimeSeriesLinking.get_uid(ti) + uids, uids_sort, T, N = InstanceTimeSeriesLinking.get_uids_from_ti(ti) emergence, disappearance = InstanceTimeSeriesLinking.get_emerg_disap_info(uids_sort) t_emerg, t_disap = emergence.keys(), disappearance.keys() @@ -401,11 +402,14 @@ def update_ti(ti, metric, thres, max_gap=5): disappearance[t] = uids_disap remove_uids = [] for uid in range(N): - if (ti_[:, uid] == 1).all(): + if (ti_[:, uid] == -1).all(): remove_uids.append(uid) ti_ = np.delete(ti_, remove_uids, axis=1) - if not np.array_equal(ti_, ti): - return InstanceTimeSeriesLinking.update_ti(ti_, metric, thres, max_gap) + return ti_ + # if np.array_equal(ti_, ti): + # return ti_ + # return InstanceTimeSeriesLinking.update_ti(ti_, metric, thres, max_gap) + # def __call__(self, images, masks, timepoints, metric="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): # # a list of images which are ndarrays From 706042f60567e4181e1b7ab2a6eb47159aa9ad8d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 13 Aug 2021 15:28:51 -0500 Subject: [PATCH 250/274] Change `update_ti` from static method to class method; update docstrings for multiple methods --- .../time_series/time_series_linking.py | 225 ++++++++++++------ 1 file changed, 156 insertions(+), 69 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index c5b366ec4..aa0742779 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -35,26 +35,19 @@ class InstanceTimeSeriesLinking(object): def __init__(self): # a list of masks which are ndarrays (of the same length of images) self.masks = None - self.T = None + self.T, self.N, self.max_uid = None, None, None # number of instances: a list in which every element represent for number of instances in corresponding image self.n_insts = None # initialization for linking - self.thres = None - self.link_info = None - self.uids = None - # self.max_uid = None - # self.N = None + self.thres, self.metric, self.uids, self.link_info, self.weights = None, None, None, None, None # self.emergence = None # self.emerge_times = None - self.ti = None # self.t_appear = None # self.t_disappear = None - self.weights = None - self.metric = None + self.ti, self.tracking_report = None, None # self.name_sub = None # self.key_id = None - self.leaf_status_report = None # # update releated # self.updated = 0 @@ -65,42 +58,55 @@ def __init__(self): # self.t_appear_ = None # self.t_disappear_ = None + def save_linked_series(self, savedir, savename): + pkl.dump(vars(self), open(osp.join(savedir, f"{savename}.pkl"), 'wb')) + + def import_linked_series(self, savedir, savename): + linked = pkl.load(open(osp.join(savedir, savename + '.pkl'), "rb")) + for key, value in linked.items(): + setattr(self, key, value) + + def linking(self,t0): + """ + Time-series linking for a given timepoint to the next time point + :param t0: + :return: + """ + masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays + self.weights[t0], _, _, _ = self.compute_overlaps_weights(masks0, masks1, self.metric) + # self.link_info[t0], _, _ = self.get_link(self.weights[t0], self.thres) + self.link_info[t0] = self.get_link(self.weights[t0], self.thres) + @staticmethod def get_link(weight, thres): """Get the link (coordinates) between two sets of instances based on pre-calculated weight matrix Inputs: - weight: weight matrix, the smaller the weight, the more possible two will be linked - thres: maximum value to link two instances + weight = weight matrix, the smaller the weight, the more possible two will be linked + thres = minimum weight value for two instances to be considered as the same one Outputs: - link: - row_ind: - col_ind: + link = a list containing link information: e.g. [0,2,3,1] -> item 0 link to item 0, item 1 link to item 2, item 2 link to item 3, item 3 link to item 1 :param weight: numpy.ndarray :param thres: float :return link: list - :return row_ind: int - :return col_ind: int """ n1, n2 = weight.shape link = -np.ones(n1, dtype=np.int64) - idx_col = np.where(np.max(weight, axis=0) < thres)[0] # find those columns with maximum value < threshold avail_col = [x for x in range(0, n2) if x not in idx_col] - weight = np.delete(weight, idx_col, 1) - row_ind, col_ind = linear_sum_assignment(weight, maximize=True) for (r, c) in zip(row_ind, col_ind): if weight[r, c] >= thres: link[r] = avail_col[c] - return link, row_ind, col_ind + return link#, row_ind, col_ind @staticmethod def compute_overlaps_weights(masks1, masks2, metric): - """Compute weights between 2 sets of binary masks based on their overlaps - The overlaps are represented by IoU (intersection over union) and IoS (intersection over self-area of the 1st mask). + """ + Compute weights between 2 sets of binary masks based on their overlaps + The overlaps are represented by either IoU (intersection over union) and IoS (intersection over self-area of the 1st mask). Inputs: masks1 = Binary masks data correspond to the 1st image masks2 = Binary masks data correspond to the 2nd image @@ -150,32 +156,29 @@ def compute_overlaps_weights(masks1, masks2, metric): if metric.upper() == "IOS": return ioss, n1, n2, unions - def save_linked_series(self, savedir, savename): - pkl.dump(vars(self), open(osp.join(savedir, f"{savename}.pkl"), 'wb')) - def import_linked_series(self, savedir, savename): - linked = pkl.load(open(osp.join(savedir, savename + '.pkl'), "rb")) - for key, value in linked.items(): - setattr(self, key, value) - - def linking(self,t0): - """ - Time-series linking for a given timepoint to the next time point - :param t0: - :return: + @staticmethod + def get_sorted_uids(link_info, n_insts): """ - masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays - self.weights[t0], _, _, _ = self.compute_overlaps_weights(masks0, masks1, self.metric) - self.link_info[t0], _, _ = self.get_link(self.weights[t0], self.thres) + Get unique indices at every timestamp based on link information and number of instances at every timepoint + Inputs: + link_info = a list (length: T-1) of linking information, every sub-list contains the information of how every instance link to instances to the next timepoint + n_insts = a list (length: T) contains the information of number of instances at every timepoint. + Outputs: + uids_sort = a list of unique indices at every timepoint. Every element in the list is a numpy array. Every array contains the information of unique indices & location. + e.g. [2,0,3,20] means that unique indices 0,2,3,20 are present in this timepoint, specifically, 2 is at location 0, 0 is at location 1, 3 is at location 2, and 20 is at location 3 - @staticmethod - def get_sorted_uids(li, n_insts): + :param link_info: list + :param n_insts: list + :return uids_sort: list + """ uids_sort = [-1 * np.ones(num, dtype=np.int64) for num in n_insts] uids_sort[0] = np.arange(n_insts[0]) max_uid = max(uids_sort[0]) + N = len(np.unique(uids_sort[0])) for t in range(1, len(li) + 1): - li_t = li[t - 1] + li_t = link_info[t - 1] uids_sort_t = uids_sort[t] uids_sort_t_ = uids_sort[t - 1] for cidt_, cidt in enumerate(li_t): @@ -185,12 +188,19 @@ def get_sorted_uids(li, n_insts): ids = np.where(uids_sort_t == -1)[0] for i in ids: max_uid += 1 + N += 1 uids_sort_t[i] = max_uid uids_sort[t] = uids_sort_t - return uids_sort + return uids_sort, max_uid, N + @staticmethod def get_uids_from_ti(ti): + """ + Get unique indices at every timestamp based on tracking information + :param ti: numpy.array + :return uids_sort: list + """ # uids: a list of length T, where every sub-list has a length of n_t (# of instances at time t). Every sub-list is # contains the unique indices present at time t @@ -200,10 +210,21 @@ def get_uids_from_ti(ti): uids = [np.where(ti_t > -1)[0] for ti_t in ti] uids_sort = [[np.where(ti_t > -1)[0][i] for i in np.argsort(ti_t[np.where(ti_t > -1)])] for ti_t in ti] - return uids, uids_sort, T, N + return uids_sort#, uids, T, N @staticmethod def get_emerg_disap_info(uids): + """ + Get emergence and disappearence indices and corresponding timepoints based on uids + Inputs: + uids = unique indices present at evert timepoint + Outputs: + emergence = new unique indices and their emerging times. e.g. emergence = {0: [0,1,2,3], 4, [4]} means that at t0, new uids 0,1,2,3 first appear, at t4, new uid 4 first appear + disappearance = disappearence of unique indices and the last timepoint they exist + :param uids: list + :return emergence: dictionary + :return disappearance: dictionary + """ emergence, disappearance = dict(), dict() emergence[0] = list(uids[0]) for (t, temp) in enumerate(uids): @@ -215,11 +236,18 @@ def get_emerg_disap_info(uids): disap = [x for x in temp if x not in uids[t + 1]] if len(disap) > 0: disappearance[t] = disap - return emergence, disappearance + @staticmethod def get_ti(uids, link_info, n_insts): + """ + Get tracking information from linking information, number of instances, and unique indices at every timepoint + :param uids: list + :param link_info: list + :param n_insts: list + :return ti: numpy.array + """ emergence, _ = InstanceTimeSeriesLinking.get_emerg_disap_info(uids) N = max([max(uid) for uid in uids]) + 1 T = len(uids) @@ -262,6 +290,11 @@ def get_ti(uids, link_info, n_insts): @staticmethod def get_li_from_ti(ti): + """ + Get linking information from tracking information + :param ti: numpy.array + :return link_info: list + """ T, N = ti.shape link_info = [np.empty(0) for _ in range(0, T - 1)] for t in range(T - 1): @@ -277,14 +310,15 @@ def get_li_from_ti(ti): @staticmethod - def status_report(ti, masks): - leaf_status_report = np.zeros(ti.shape) + def get_tracking_report(ti, masks): + tracking_report = np.zeros(ti.shape) for (t, masks_t) in enumerate(masks): ti_t = ti[t, :] for cid in range(masks_t.shape[2]): uid = np.where(ti_t == cid)[0][0] leaf_status_report[t, uid] = np.sum(masks_t[:, :, cid]) - return leaf_status_report + return tracking_report + @staticmethod def visualize(imgs, masks, tps, savedir, ti = None, color_all = None): @@ -317,8 +351,6 @@ def visualize(imgs, masks, tps, savedir, ti = None, color_all = None): def link(self, masks, metric="IOS", thres=0.2): - - # def __call__(self, masks, metric="IOS", thres=0.2): # a list of masks which are ndarrays (of the same length of images) self.masks = masks self.T = len(masks) @@ -341,17 +373,16 @@ def link(self, masks, metric="IOS", thres=0.2): # self.uids, uids_sort, _, self.N = self.get_uid(self.ti) # self.emergence, self.emerge_times = self.get_emerg_disap_info(uids_sort) - self.uids = InstanceTimeSeriesLinking.get_sorted_uids(self.link_info, self.n_insts) + self.uids, self.max_uid, self.N = InstanceTimeSeriesLinking.get_sorted_uids(self.link_info, self.n_insts) self.ti = self.get_ti(self.uids, self.link_info, self.n_insts) - self.leaf_status_report = InstanceTimeSeriesLinking.status_report(self.ti, self.masks) + self.tracking_report = InstanceTimeSeriesLinking.get_tracking_report(self.ti, self.masks) - @staticmethod - def update_ti(ti, metric, thres, max_gap=5): + def update_ti(self, ti, max_gap=5): ti_ = copy.deepcopy(ti) - uids, uids_sort, T, N = InstanceTimeSeriesLinking.get_uids_from_ti(ti) + # uids, uids_sort, T, N = InstanceTimeSeriesLinking.get_uids_from_ti(ti) + uids_sort = InstanceTimeSeriesLinking.get_uids_from_ti(ti) emergence, disappearance = InstanceTimeSeriesLinking.get_emerg_disap_info(uids_sort) t_emerg, t_disap = emergence.keys(), disappearance.keys() - # loop over timepoints with disappearing leaves (in reversed order) for t in reversed(sorted(t_disap)): # unique indices(index) that last appear at t @@ -359,25 +390,20 @@ def update_ti(ti, metric, thres, max_gap=5): # corresponding cid(s) (i.e. indices for masks) cids_disap = [uids_sort[t].index(i) for i in uids_disap] # pull out masks - masks_t = np.take(masks[t], cids_disap, axis=2) - - # cids_emerg = dict() - # ts_pot = pot_link_time[t] + masks_t = np.take(self.masks[t], cids_disap, axis=2) # timepoints with potential link with t ts_pot = [te for te in t_emerg if t < te < t + max_gap] # loop over timepoints for a potential link and get cids and masks for every timepoint for t_ in ts_pot: uids_emerg = emergence[t_] - # cids_emerg[t_] = [uids_sort[t_].index(i) for i in uids_emerg] cids_emerg = [uids_sort[t_].index(i) for i in uids_emerg] - - # masks_t_ = np.take(masks[t_], cids_emerg[t_], axis=2) masks_t_ = np.take(masks[t_], cids_emerg, axis=2) # calculate weight to calculate the link - weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) - li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, thres) + weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, self.metric) + # li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, self.thres) + li_ts = InstanceTimeSeriesLinking.get_link(weights, self.thres) uids_undisap = [] for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices @@ -386,10 +412,8 @@ def update_ti(ti, metric, thres, max_gap=5): print(f"\n{t} -> {t_}: ") print(f"{uid_disap} <- {uids_emerg[li_t]}") # update ti - # ti[t_, uid_disap] = cids_emerg[t_][li_t] ti_[t_:, uid_disap] = ti_[t_:, uids_emerg[li_t]] ti_[t_:, uids_emerg[li_t]] = -np.ones(T - t_, dtype=np.int64) - uids_undisap.append(uid_disap) uids_disap = list(set(uids_disap).difference(set(uids_undisap))) @@ -406,9 +430,72 @@ def update_ti(ti, metric, thres, max_gap=5): remove_uids.append(uid) ti_ = np.delete(ti_, remove_uids, axis=1) return ti_ - # if np.array_equal(ti_, ti): - # return ti_ - # return InstanceTimeSeriesLinking.update_ti(ti_, metric, thres, max_gap) + + + # @staticmethod + # def update_ti(ti, metric, thres, max_gap=5): + # ti_ = copy.deepcopy(ti) + # uids, uids_sort, T, N = InstanceTimeSeriesLinking.get_uids_from_ti(ti) + # emergence, disappearance = InstanceTimeSeriesLinking.get_emerg_disap_info(uids_sort) + # t_emerg, t_disap = emergence.keys(), disappearance.keys() + # + # # loop over timepoints with disappearing leaves (in reversed order) + # for t in reversed(sorted(t_disap)): + # # unique indices(index) that last appear at t + # uids_disap = disappearance[t] + # # corresponding cid(s) (i.e. indices for masks) + # cids_disap = [uids_sort[t].index(i) for i in uids_disap] + # # pull out masks + # masks_t = np.take(masks[t], cids_disap, axis=2) + # + # # cids_emerg = dict() + # # ts_pot = pot_link_time[t] + # + # # timepoints with potential link with t + # ts_pot = [te for te in t_emerg if t < te < t + max_gap] + # # loop over timepoints for a potential link and get cids and masks for every timepoint + # for t_ in ts_pot: + # uids_emerg = emergence[t_] + # # cids_emerg[t_] = [uids_sort[t_].index(i) for i in uids_emerg] + # cids_emerg = [uids_sort[t_].index(i) for i in uids_emerg] + # + # # masks_t_ = np.take(masks[t_], cids_emerg[t_], axis=2) + # masks_t_ = np.take(masks[t_], cids_emerg, axis=2) + # + # # calculate weight to calculate the link + # weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) + # li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, thres) + # + # uids_undisap = [] + # for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices + # li_t = li_ts[idx] + # if li_t > -1: + # print(f"\n{t} -> {t_}: ") + # print(f"{uid_disap} <- {uids_emerg[li_t]}") + # # update ti + # # ti[t_, uid_disap] = cids_emerg[t_][li_t] + # ti_[t_:, uid_disap] = ti_[t_:, uids_emerg[li_t]] + # ti_[t_:, uids_emerg[li_t]] = -np.ones(T - t_, dtype=np.int64) + # + # uids_undisap.append(uid_disap) + # uids_disap = list(set(uids_disap).difference(set(uids_undisap))) + # + # if len(uids_disap) == 0: + # # remove key + # disappearance.pop(t) + # break + # else: + # # update + # disappearance[t] = uids_disap + # remove_uids = [] + # for uid in range(N): + # if (ti_[:, uid] == -1).all(): + # remove_uids.append(uid) + # ti_ = np.delete(ti_, remove_uids, axis=1) + # return ti_ + # # if np.array_equal(ti_, ti): + # # return ti_ + # # return InstanceTimeSeriesLinking.update_ti(ti_, metric, thres, max_gap) # def __call__(self, images, masks, timepoints, metric="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): From 94f3d6ad52564c621b5d31854b29c0bb79759296 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 08:54:29 -0500 Subject: [PATCH 251/274] Delete helper.py --- plantcv/plantcv/time_series/helper.py | 130 -------------------------- 1 file changed, 130 deletions(-) delete mode 100644 plantcv/plantcv/time_series/helper.py diff --git a/plantcv/plantcv/time_series/helper.py b/plantcv/plantcv/time_series/helper.py deleted file mode 100644 index 919f7aac3..000000000 --- a/plantcv/plantcv/time_series/helper.py +++ /dev/null @@ -1,130 +0,0 @@ -import os -import pickle as pkl -import glob -import numpy as np -import cv2 -import re -import skimage -import datetime -from plantcv.plantcv.time_series import time_series_linking, evaluation -import matplotlib.pyplot as plt -import pandas as pd - -plant = "Bean" -path_img_ = "/shares/bioinformatics/nfahlgren/projects/mrcnn/old/training_data_downloads/MSU/Dataset/Images" -path_gt_ = "/shares/mgehan_share/hsheng/projects/synthetic_data_MSU/label" -ext_img = "rgb.png" -ext_seg = "mask.pkl" -pattern_datetime = "day_.+_hour_.+_" - -metric = "IOU" -thres = 0.05 - -path_save_ = path_gt_.replace("label", "time_series_linking") -junk = datetime.datetime.now() -subfolder = "{}-{}_{}-{}-{}-{}-{}".format(metric, thres, junk.year, str(junk.month).zfill(2), str(junk.day).zfill(2), - str(junk.hour).zfill(2), str(junk.minute).zfill(2), str(junk.second).zfill(2)) -path_save = os.path.join(path_save_, plant, subfolder) -if not os.path.exists(path_save): - os.makedirs(path_save) - -df_results = pd.DataFrame( - columns=["Plant-ID", "num_leaves_gt", "num_leaves", "link_score", "track_score", "unmatched_rate", "fake_new_rate"]) - -for idx_p in range(1, 17): - path_img = f"{path_img_}/{plant}/plant_{idx_p}" - path_gt = path_img.replace(path_img_, path_gt_) - path_seg = os.path.join(path_gt, "masks") - - # Get timepoints from available segmentation results - list_seg = glob.glob(os.path.join(path_seg, "*{}".format(ext_seg))) - - # sort timepoints - timepoints = [] - for f_seg in list_seg: - tp_temp = re.search(pattern_datetime, f_seg).group() - timepoints.append(tp_temp) - timepoints.sort() - - # load images - temp_imgs = [] - sz = [] - imgs = [] - for tp in timepoints: - filename_ = "*{}{}".format(tp, ext_img) - if "_09" in filename_: - filename_ = filename_.replace("09", "9") - filename = glob.glob(os.path.join(path_img, filename_))[0] - junk = skimage.io.imread(os.path.join(path_img, filename)) - if len(junk.shape) == 2: - junk = cv2.cvtColor(junk, cv2.COLOR_GRAY2BGR) - temp_imgs.append(junk) - sz.append(np.min(junk.shape[0:2])) - min_dim = np.min(sz) - for junk in temp_imgs: - img = junk[0:min_dim, 0:min_dim, :] # make all images the same size (if not the same size) - imgs.append(img) - - # load instance segmentation results - masks = [] - for tp in timepoints: - filename_ = "*{}{}".format(tp, ext_seg) - filename = glob.glob(os.path.join(path_seg, filename_))[0] - r = pkl.load(open(filename, 'rb')) - masks.append(r['masks'][0: min_dim, 0:min_dim, :]) # make all masks the same size (if not already) - - inst_ts_linking = time_series_linking.InstanceTimeSeriesLinking() - - inst_ts_linking.images = imgs - inst_ts_linking.masks = masks - inst_ts_linking.timepoints = timepoints - inst_ts_linking.T = len(inst_ts_linking.timepoints) - # inst_ts_linking.name_sub = name_sub - # inst_ts_linking.key_id = '{}_ids'.format(name_sub) - inst_ts_linking.link(masks, metric, thres) - - ## Leaf status report - # extract timepoints - tps_dd, tps_hh = [], [] - for tp in timepoints: - temp = tp.split("_") - idx_day = temp.index("day") - idx_hour = temp.index("hour") - dd = temp[idx_day + 1] - hh = temp[idx_hour + 1] - if hh.startswith("0"): - hh = hh.replace("0", "") - tps_dd.append(dd) - tps_hh.append(hh) - days = np.unique(tps_dd) - locs = [tps_dd.index(d) for d in days] - - # plot leaf status report - leaf_report = inst_ts_linking.leaf_status_report - leaf_report[np.where(leaf_report == 0)] = np.nan - fig, ax = plt.subplots(figsize=(10, 8)) - for uid in range(leaf_report.shape[1]): - ax.plot(leaf_report[:, uid], ".-", label=f"leaf{uid}") - ax.legend() - ax.set_xlabel("Days", fontsize=15) - ax.set_ylabel("Leaf Area", fontsize=15) - ax.set_xticks(locs) - ax.set_xticklabels(days, fontsize=14) - ax.yaxis.set_tick_params(labelsize=14) - plt.savefig(os.path.join(path_save, f"leaf_status_Plant{idx_p}.png")) - plt.close("all") - - ## results evaluation - # load ground truth - loaded_gt = pkl.load(open(os.path.join(path_gt, "gt_link_info.pkl"), "rb")) - li_gt = loaded_gt["link_info"] - ti_gt = loaded_gt["ti"] - score_link, num_insts, num_matched = evaluation.evaluate_link(inst_ts_linking.link_info, li_gt) - r_unmatched, r_fake_new = evaluation.mismatch_rate(inst_ts_linking.ti, ti_gt) - N, N_, confu, track_match, track_rate, score_track = evaluation.confusion(inst_ts_linking.ti, ti_gt) - - df_results = df_results.append({ - "Plant-ID": idx_p, "num_leaves_gt": N, "num_leaves": N_, "link_score": score_link, "track_score": score_track, - "unmatched_rate": r_unmatched, "fake_new_rate": r_fake_new}, ignore_index=True) - -df_results.to_csv(os.path.join(path_save, "summary.csv"), index=False) From 3060aba35de5a6c7b1c74423b5c9bbb643c93502 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 08:59:59 -0500 Subject: [PATCH 252/274] remove redundant code in `time_series_linking.py`, finalize `update_ti` method --- .../time_series/time_series_linking.py | 135 ++++-------------- 1 file changed, 31 insertions(+), 104 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index aa0742779..595d93342 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -41,23 +41,10 @@ def __init__(self): # initialization for linking self.thres, self.metric, self.uids, self.link_info, self.weights = None, None, None, None, None - # self.emergence = None - # self.emerge_times = None - # self.t_appear = None - # self.t_disappear = None - self.ti, self.tracking_report = None, None + self.ti, self.ti_old, self.tracking_report = None, None, None # self.name_sub = None # self.key_id = None - # # update releated - # self.updated = 0 - # self.delta_t = None - # self.disp_uids = None - # self.ids_update = None - # self.ti_ = None - # self.t_appear_ = None - # self.t_disappear_ = None - def save_linked_series(self, savedir, savename): pkl.dump(vars(self), open(osp.join(savedir, f"{savename}.pkl"), 'wb')) @@ -128,6 +115,9 @@ def compute_overlaps_weights(masks1, masks2, metric): :return unions: numpy.ndarray of shape: [n1, n2] """ + if not (metric.upper() == "IOU" or metric.upper() == "IOS"): + fatal_error("Currently only calculating metrics 'IOU' and 'IOS' are available!") + # If either set of masks is empty return an empty result # if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: # return np.zeros((masks1.shape[-1], masks2.shape[-1])) @@ -153,10 +143,11 @@ def compute_overlaps_weights(masks1, masks2, metric): ious = np.divide(intersections, unions) if metric.upper() == "IOU": return ious, n1, n2, unions - if metric.upper() == "IOS": + else: return ioss, n1, n2, unions + @staticmethod def get_sorted_uids(link_info, n_insts): """ @@ -176,7 +167,7 @@ def get_sorted_uids(link_info, n_insts): uids_sort[0] = np.arange(n_insts[0]) max_uid = max(uids_sort[0]) N = len(np.unique(uids_sort[0])) - for t in range(1, len(li) + 1): + for t in range(1, len(link_info) + 1): li_t = link_info[t - 1] uids_sort_t = uids_sort[t] @@ -268,23 +259,6 @@ def get_ti(uids, link_info, n_insts): new_ids = emergence[t] for (cid,new_id) in zip(cids,new_ids): ti[t,new_id] = cid - - # # get appear and disappear information - # t_appear = np.zeros(N,dtype=np.int64) - # t_disappear = -np.ones(N, dtype=np.int64) - # - # for (t,uids_t) in enumerate(emergence): - # if uids_t: - # for uid in uids_t: - # t_appear[uid] = t - # for uid in range(0,N): - # t = 0 - # while t < T: - # if (ti[t][uid] == -1) and (t > t_appear[uid]): - # t_disappear[uid] = t - # break - # else: - # t += 1 return ti @@ -316,7 +290,7 @@ def get_tracking_report(ti, masks): ti_t = ti[t, :] for cid in range(masks_t.shape[2]): uid = np.where(ti_t == cid)[0][0] - leaf_status_report[t, uid] = np.sum(masks_t[:, :, cid]) + tracking_report[t, uid] = np.sum(masks_t[:, :, cid]) return tracking_report @@ -377,9 +351,11 @@ def link(self, masks, metric="IOS", thres=0.2): self.ti = self.get_ti(self.uids, self.link_info, self.n_insts) self.tracking_report = InstanceTimeSeriesLinking.get_tracking_report(self.ti, self.masks) - def update_ti(self, ti, max_gap=5): + + @staticmethod + def _update_ti(masks, metric, thres, ti, min_gap, max_gap): ti_ = copy.deepcopy(ti) - # uids, uids_sort, T, N = InstanceTimeSeriesLinking.get_uids_from_ti(ti) + T, N = ti.shape uids_sort = InstanceTimeSeriesLinking.get_uids_from_ti(ti) emergence, disappearance = InstanceTimeSeriesLinking.get_emerg_disap_info(uids_sort) t_emerg, t_disap = emergence.keys(), disappearance.keys() @@ -390,10 +366,10 @@ def update_ti(self, ti, max_gap=5): # corresponding cid(s) (i.e. indices for masks) cids_disap = [uids_sort[t].index(i) for i in uids_disap] # pull out masks - masks_t = np.take(self.masks[t], cids_disap, axis=2) + masks_t = np.take(masks[t], cids_disap, axis=2) # timepoints with potential link with t - ts_pot = [te for te in t_emerg if t < te < t + max_gap] + ts_pot = [te for te in t_emerg if t + min_gap < te < t + max_gap] # loop over timepoints for a potential link and get cids and masks for every timepoint for t_ in ts_pot: uids_emerg = emergence[t_] @@ -401,9 +377,9 @@ def update_ti(self, ti, max_gap=5): masks_t_ = np.take(masks[t_], cids_emerg, axis=2) # calculate weight to calculate the link - weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, self.metric) + weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) # li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, self.thres) - li_ts = InstanceTimeSeriesLinking.get_link(weights, self.thres) + li_ts = InstanceTimeSeriesLinking.get_link(weights, thres) uids_undisap = [] for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices @@ -432,70 +408,21 @@ def update_ti(self, ti, max_gap=5): return ti_ - # @staticmethod - # def update_ti(ti, metric, thres, max_gap=5): - # ti_ = copy.deepcopy(ti) - # uids, uids_sort, T, N = InstanceTimeSeriesLinking.get_uids_from_ti(ti) - # emergence, disappearance = InstanceTimeSeriesLinking.get_emerg_disap_info(uids_sort) - # t_emerg, t_disap = emergence.keys(), disappearance.keys() - # - # # loop over timepoints with disappearing leaves (in reversed order) - # for t in reversed(sorted(t_disap)): - # # unique indices(index) that last appear at t - # uids_disap = disappearance[t] - # # corresponding cid(s) (i.e. indices for masks) - # cids_disap = [uids_sort[t].index(i) for i in uids_disap] - # # pull out masks - # masks_t = np.take(masks[t], cids_disap, axis=2) - # - # # cids_emerg = dict() - # # ts_pot = pot_link_time[t] - # - # # timepoints with potential link with t - # ts_pot = [te for te in t_emerg if t < te < t + max_gap] - # # loop over timepoints for a potential link and get cids and masks for every timepoint - # for t_ in ts_pot: - # uids_emerg = emergence[t_] - # # cids_emerg[t_] = [uids_sort[t_].index(i) for i in uids_emerg] - # cids_emerg = [uids_sort[t_].index(i) for i in uids_emerg] - # - # # masks_t_ = np.take(masks[t_], cids_emerg[t_], axis=2) - # masks_t_ = np.take(masks[t_], cids_emerg, axis=2) - # - # # calculate weight to calculate the link - # weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) - # li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, thres) - # - # uids_undisap = [] - # for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices - # li_t = li_ts[idx] - # if li_t > -1: - # print(f"\n{t} -> {t_}: ") - # print(f"{uid_disap} <- {uids_emerg[li_t]}") - # # update ti - # # ti[t_, uid_disap] = cids_emerg[t_][li_t] - # ti_[t_:, uid_disap] = ti_[t_:, uids_emerg[li_t]] - # ti_[t_:, uids_emerg[li_t]] = -np.ones(T - t_, dtype=np.int64) - # - # uids_undisap.append(uid_disap) - # uids_disap = list(set(uids_disap).difference(set(uids_undisap))) - # - # if len(uids_disap) == 0: - # # remove key - # disappearance.pop(t) - # break - # else: - # # update - # disappearance[t] = uids_disap - # remove_uids = [] - # for uid in range(N): - # if (ti_[:, uid] == -1).all(): - # remove_uids.append(uid) - # ti_ = np.delete(ti_, remove_uids, axis=1) - # return ti_ - # # if np.array_equal(ti_, ti): - # # return ti_ - # # return InstanceTimeSeriesLinking.update_ti(ti_, metric, thres, max_gap) + # def update_ti(self, ti, max_gap=5): + def update_ti(self, max_gap=5): + # self.ti_old = ti + self.ti_old = self.ti + min_gap = 1 + # ti_ = InstanceTimeSeriesLinking._update_ti(self.masks, self.metric, self.thres, ti, min_gap, max_gap) + ti_ = InstanceTimeSeriesLinking._update_ti(self.masks, self.metric, self.thres, self.ti_old, min_gap, max_gap) + while True: + min_gap += 1 + ti = ti_ + ti_ = InstanceTimeSeriesLinking._update_ti(self.masks, self.metric, self.thres, ti, min_gap, max_gap) + if np.array_equal(ti_, ti) or min_gap == max_gap - 2: + # return ti_ + self.ti = ti_ + break # def __call__(self, images, masks, timepoints, metric="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): From b4634d07a15b8706b2b235e1af655c24fece3556 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 09:00:22 -0500 Subject: [PATCH 253/274] add test data for `time_series` module --- tests/time_seires_data/link_series.pkl | Bin 0 -> 75842236 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/time_seires_data/link_series.pkl diff --git a/tests/time_seires_data/link_series.pkl b/tests/time_seires_data/link_series.pkl new file mode 100644 index 0000000000000000000000000000000000000000..253ebacda9f661e8656c58044a64f48b2208efcb GIT binary patch literal 75842236 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z;7wG)yNT#|R8PDQ2u?RD9?U3w#fTk$Q0hBj1{uFjWPWlW`)@;P9yBKNw-cGaziZ-$ zAHNa2b5-!xA$nQpiMI>EdxgRqP2s&m`TGauK*&MxRwnC~DT!-mvLC?m_MV!QAH&CK R_;NXgUnMRN)Ol3R{{x0dhu#1H literal 0 HcmV?d00001 From 14ecbf5cb0e685db40748faabbc75de0c32163fd Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 09:00:52 -0500 Subject: [PATCH 254/274] update tests for `time_series/time_series_linking` --- tests/tests.py | 143 +++++++++++++++++++++++++++++-------------------- 1 file changed, 86 insertions(+), 57 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index dd1e50839..8e1150c96 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6725,30 +6725,21 @@ def test_plantcv_visualize_colorspaces_bad_input(): _ = pcv.visualize.colorspaces(rgb_img=img) -# ############################## +# ##################################### # Tests for the time_series subpackage -# ############################## - -# def test_plantcv_time_series_display_instances(): -# # Test cache directory -# loaded = pkl.load(open(os.path.join(TIME_SERIES_TEST_INSTANCE_SEG, "img_segment_.pkl"), 'rb')) -# img = loaded['img'] -# masks = loaded['segment']['masks'] -# pcv.time_series._display_instances(img, masks, figsize=(16, 16), title="", ax=None, colors=None) +# ##################################### -def test_plantcv_time_series_time_series(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_time_series") - os.mkdir(cache_dir) +def test_plantcv_time_series_inst_ts_linking(tmpdir): + cache_dir = tmpdir.mkdir("sub") pcv.params.debug_outdir = cache_dir path_img = TIME_SERIES_TEST_RAW path_segmentation = TIME_SERIES_TEST_INSTANCE_SEG - path_save = cache_dir + + ext_img = "_crop-img12.jpg" + ext_seg = ".pkl" savename = "link_series" pattern_datetime = "\d{4}-\d{2}-\d{2}-\d{2}-\d{2}" # YYYY-MM-DD-hh - ext = "_crop-img12.jpg" - ext_seg = ".pkl" list_seg = glob.glob(os.path.join(path_segmentation, "2*{}".format(ext_seg))) timepoints = [] @@ -6756,13 +6747,14 @@ def test_plantcv_time_series_time_series(): tp_temp = re.search(pattern_datetime, f_seg).group() timepoints.append(tp_temp) timepoints.sort() + # Load original images images = [] temp_imgs = [] sz = [] for tp in timepoints: - filename_ = "*_{}{}".format(tp, ext) + filename_ = "*_{}{}".format(tp, ext_img) filename = glob.glob(os.path.join(path_img, filename_))[0] junk_ = skimage.io.imread(filename) @@ -6783,54 +6775,91 @@ def test_plantcv_time_series_time_series(): r = pkl.load(open(filename, 'rb')) masks.append(r['masks'][0: min_dim, 0:min_dim, :]) # make all masks the same size - # test for "_compute_overlap_masks" - masks1 = masks[0] - masks2 = masks[1] - n1, n2, _, _, _ = pcv.time_series._compute_overlaps_masks(masks1[:, :, 0], masks2) - n1_, n2_, _, _, _ = pcv.time_series._compute_overlaps_masks(masks1, masks2[:, :, 0]) - assert n1 == 1 and n2 == masks2.shape[2] and n1_ == masks1.shape[2] and n2_ == 1 - - logic = 'IOS' - thres = 0.1 - - # create an instance of Instance_timeseries_linking - name_sub = 'leaf' inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() - inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) - assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + inst_ts_linking.link(masks, metric="IOU", thres=0.05) + inst_ts_linking.save_linked_series(cache_dir, savename) + + vis_dir = os.path.join(cache_dir, "vis") + os.makedirs(vis_dir) + colors_ = pcv.color_palette(inst_ts_linking.N) + colors = [tuple([ci / 255 for ci in c]) for c in colors_] + n_insts = inst_ts_linking.n_insts[0:3] + ti = inst_ts_linking.ti[0:3] + color_all = [[tuple() for _ in range(0, num)] for num in n_insts] + for (t, ti_t) in enumerate(ti): + for (uid, cid) in enumerate(ti_t): + if cid > -1: + color_all[t][cid] = colors[uid] + + pcv.time_series.InstanceTimeSeriesLinking.visualize(images[0:3], masks[0:3], timepoints[0:3], vis_dir, ti, color_all) + assert inst_ts_linking.ti.shape[0] == len(timepoints) and os.path.isfile(os.path.join(cache_dir, f"{savename}.pkl")) \ + and inst_ts_linking.ti_old is None and len(os.listdir(vis_dir)) > 0 + +def test_plantcv_time_series_inst_ts_linking_import_update(tmpdir): + cache_dir = tmpdir.mkdir("sub") + pcv.params.debug_outdir = cache_dir - # test with update_ti = True inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() - inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=True) - assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) - - # test on update_ti - inst_ts_linking.update_ti(delta_t=2) - assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) + path_save = TIME_SERIES_TEST_DIR + name_save = "link_series" + inst_ts_linking.import_linked_series(path_save, savename=name_save) + inst_ts_linking.update_ti(max_gap=3) + assert (inst_ts_linking.ti is not None) and (inst_ts_linking.ti_old is not None) - # test on save_linked_series - inst_ts_linking.save_linked_series(path_save,savename) - assert (len(os.listdir(path_save)) > 0) +def test_plantcv_time_series_inst_ts_linking_compute_overlap(tmpdir): + # test for the static method "compute_overlap_weights" + # metric = IOS (not tested when testing the class) + seg_name1 = os.path.join(TIME_SERIES_TEST_INSTANCE_SEG, "2019-10-21-21-05.pkl") + # seg_name2 = os.path.join(TIME_SERIES_TEST_INSTANCE_SEG, "2019-10-22-11-05.pkl") + masks1 = pkl.load(open(seg_name1, 'rb'))['masks'] + # masks2 = pkl.load(open(seg_name2, 'rb'))['masks'] + ioss, n1, n2, unions = pcv.time_series.InstanceTimeSeriesLinking.compute_overlaps_weights(masks1[:,:,0], masks1[:,:,1], "IOS") + assert ioss.shape == (n1,n2) and (n1==n2) and (n1==1) - # test on using IOU as linking logic - logic = 'IOU' - inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() - inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) - assert inst_ts_linking.updated == 0 and len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) - # test on using logic that is currently unavailable - logic = 'emd' - inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() +def test_plantcv_time_series_inst_ts_linking_compute_overlap_bad_metric(tmpdir): + # test for the static method "compute_overlap_weights", bad metric with pytest.raises(RuntimeError): - inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=False) - # assert (len(os.listdir(path_save)) > 0) and inst_ts_linking.updated == 0 + _ = pcv.time_series.InstanceTimeSeriesLinking.compute_overlaps_weights(np.ones((5,5)), np.zeros((5,5)), "") - # test on import_linked_series and then for the case where there is no potential updates - inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() - inst_ts_linking.import_linked_series(TIME_SERIES_TEST_DIR, 'link_series_no_updates') - assert len(inst_ts_linking.timepoints)-1==len(inst_ts_linking.link_info) - inst_ts_linking.update_ti() - assert inst_ts_linking.updated == 0 + +def test_plantcv_time_series_inst_ts_linking_visualize(tmpdir): + # test for the static method "visualize" + cache_dir = tmpdir.mkdir("sub") + pcv.params.debug_outdir = cache_dir + path_img = TIME_SERIES_TEST_RAW + path_segmentation = TIME_SERIES_TEST_INSTANCE_SEG + temp_imgs, imgs, masks = [], [], [] + tps = ["2019-10-21-21-05", "2019-10-22-11-05"] + sz = [] + for tp in tps: + filename_ = f"*_{tp}_crop-img12.jpg" + filename = glob.glob(os.path.join(path_img, filename_))[0] + + junk_ = skimage.io.imread(filename) + junk = junk_ + if len(junk_.shape) == 2: + junk = cv2.cvtColor(junk_, cv2.COLOR_GRAY2BGR) + temp_imgs.append(junk) + sz.append(np.min(junk.shape[0:2])) + min_dim = np.min(sz) + for junk in temp_imgs: + img = junk[0: min_dim, 0:min_dim, :] # make all images the same size + imgs.append(img) + for tp in tps: + filename_ = f"*{tp}.pkl" + filename = glob.glob(os.path.join(path_segmentation, filename_))[0] + r = pkl.load(open(filename, 'rb')) + masks.append(r['masks'][0: min_dim, 0:min_dim, :]) # make all masks the same size + pcv.time_series.InstanceTimeSeriesLinking.visualize(imgs, masks, tps, cache_dir) + assert len(os.listdir(cache_dir)) > 0 + + +def test_plantcv_time_series_inst_ts_linking_get_li_from_ti(tmpdir): + # test for the static method "get_li_from_ti" + ti_gt = pkl.load(open(os.path.join(TIME_SERIES_TEST_DIR, "gt.pkl"), "rb"))["ti_gt"] + li_gt = pcv.time_series.InstanceTimeSeriesLinking.get_li_from_ti(ti_gt) + assert len(li_gt) == (ti_gt.shape[0]-1) def test_plantcv_time_series_evaluation(): From 8eb4988d9418f198054d96bf31edfe23961d8fec Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 09:01:31 -0500 Subject: [PATCH 255/274] update plantcv/plantcv/__init__.py for `time_series' subpackage --- plantcv/plantcv/__init__.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py index 4c60146b4..a29bd56cd 100644 --- a/plantcv/plantcv/__init__.py +++ b/plantcv/plantcv/__init__.py @@ -86,7 +86,6 @@ from plantcv.plantcv import time_series from plantcv.plantcv.spatial_clustering import spatial_clustering from plantcv.plantcv import photosynthesis -from plantcv.plantcv import time_series # add new functions to end of lists # Auto versioning @@ -106,7 +105,6 @@ 'x_axis_pseudolandmarks', 'y_axis_pseudolandmarks', 'cluster_contours', 'visualize', 'cluster_contour_splitimg', 'rotate', 'shift_img', 'output_mask', 'auto_crop', 'canny_edge_detect', 'background_subtraction', 'naive_bayes_classifier', 'acute', 'distance_transform', 'params', - - 'cluster_contour_mask', 'analyze_thermal_values', 'opening','closing', 'within_frame', 'fill_holes', 'get_kernel', + 'cluster_contour_mask', 'analyze_thermal_values', 'opening','closing', 'within_frame', 'fill_holes', 'get_kernel', 'Spectral_data', 'crop', 'stdev_filter', 'spatial_clustering' 'time_series', 'photosynthesis'] From c8c1500e18c6ce4adf32f9cd0c7e28f9492ffff6 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 09:50:38 -0500 Subject: [PATCH 256/274] update class method `update_ti` so that the tracking_report is updated when `update_ti` is called --- plantcv/plantcv/time_series/time_series_linking.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 595d93342..c161d5871 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -41,7 +41,7 @@ def __init__(self): # initialization for linking self.thres, self.metric, self.uids, self.link_info, self.weights = None, None, None, None, None - self.ti, self.ti_old, self.tracking_report = None, None, None + self.ti, self.ti_old, self.tracking_report, self.tracking_report_old = None, None, None, None # self.name_sub = None # self.key_id = None @@ -412,6 +412,7 @@ def _update_ti(masks, metric, thres, ti, min_gap, max_gap): def update_ti(self, max_gap=5): # self.ti_old = ti self.ti_old = self.ti + self.tracking_report_old = self.tracking_report min_gap = 1 # ti_ = InstanceTimeSeriesLinking._update_ti(self.masks, self.metric, self.thres, ti, min_gap, max_gap) ti_ = InstanceTimeSeriesLinking._update_ti(self.masks, self.metric, self.thres, self.ti_old, min_gap, max_gap) @@ -422,6 +423,8 @@ def update_ti(self, max_gap=5): if np.array_equal(ti_, ti) or min_gap == max_gap - 2: # return ti_ self.ti = ti_ + # update tracking_report + self.tracking_report = InstanceTimeSeriesLinking.get_tracking_report(self.ti, self.masks) break From d667c2b4a13cd8f1fc7fd2800962e22a8d93b63f Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 09:51:10 -0500 Subject: [PATCH 257/274] update tests.py for updates in time_series/time_series_linking --- tests/tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index 8e1150c96..e7b408ff3 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6804,7 +6804,7 @@ def test_plantcv_time_series_inst_ts_linking_import_update(tmpdir): name_save = "link_series" inst_ts_linking.import_linked_series(path_save, savename=name_save) inst_ts_linking.update_ti(max_gap=3) - assert (inst_ts_linking.ti is not None) and (inst_ts_linking.ti_old is not None) + assert (inst_ts_linking.ti is not None) and (inst_ts_linking.ti_old is not None) and (inst_ts_linking.tracking_report_old is not None) def test_plantcv_time_series_inst_ts_linking_compute_overlap(tmpdir): # test for the static method "compute_overlap_weights" From 12d31c6e3d580528828e522c3344df82bd1ae177 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 17:16:56 -0500 Subject: [PATCH 258/274] Update continuous-integration.yml --- .github/workflows/continuous-integration.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/continuous-integration.yml b/.github/workflows/continuous-integration.yml index 8114c0921..f0a3bd5e5 100644 --- a/.github/workflows/continuous-integration.yml +++ b/.github/workflows/continuous-integration.yml @@ -6,10 +6,10 @@ name: builds on: push: # Run when master is updated - branches: [ master, 4.x ] + branches: [ master, 4.x, Visualize show instances ] pull_request: # Run on pull requests against master - branches: [ master, 4.x ] + branches: [ master, 4.x, visualize_show_instances ] jobs: build: From ce2db6a5d8a6986ea83ee4332e36a42c0c4ae8b1 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 17:41:25 -0500 Subject: [PATCH 259/274] add static method to compute weights based on distances --- .../plantcv/time_series/time_series_linking.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index c161d5871..e802713c5 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -89,6 +89,13 @@ def get_link(weight, thres): return link#, row_ind, col_ind + # @staticmethod + # def compute_dist_weights(pts1, pts2): + # n1, n2 = len(pts1), len(pts2) + # weight = distance.cdist(pts1, pts2) + # return weight, n1, n2 + + @staticmethod def compute_overlaps_weights(masks1, masks2, metric): """ @@ -147,6 +154,16 @@ def compute_overlaps_weights(masks1, masks2, metric): return ioss, n1, n2, unions + # @staticmethod + # def compute_weights(measure1, measure2, metric): + # if metric.upper() == "IOU" or metric.upper() == "IOS": + # weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(measure1, measure2, metric) + # elif metric.upper() == "DIST": + # weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_dist_weights(measure1, measure2, metric) + # else: + # fatal_error("Currently only calculating metrics 'IOU', 'IOS', or 'DIST' are available!") + # return weights, n1, n2 + @staticmethod def get_sorted_uids(link_info, n_insts): From ee13562df6a8c0a5b9a4a0773440aa676bd3c054 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 17:41:54 -0500 Subject: [PATCH 260/274] update tests to improve coverage --- tests/tests.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index e7b408ff3..9e8d6ab6b 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6792,8 +6792,11 @@ def test_plantcv_time_series_inst_ts_linking(tmpdir): color_all[t][cid] = colors[uid] pcv.time_series.InstanceTimeSeriesLinking.visualize(images[0:3], masks[0:3], timepoints[0:3], vis_dir, ti, color_all) + # an extra test for visualize without "color_all", with "ti" + vis_dir2 = os.path.join(vis_dir, "extra") + pcv.time_series.InstanceTimeSeriesLinking.visualize(images[0:3], masks[0:3], timepoints[0:3], vis_dir2, ti, color_all=None) assert inst_ts_linking.ti.shape[0] == len(timepoints) and os.path.isfile(os.path.join(cache_dir, f"{savename}.pkl")) \ - and inst_ts_linking.ti_old is None and len(os.listdir(vis_dir)) > 0 + and inst_ts_linking.ti_old is None and len(os.listdir(vis_dir)) > 0 and len(os.listdir(vis_dir2)) > 0 def test_plantcv_time_series_inst_ts_linking_import_update(tmpdir): cache_dir = tmpdir.mkdir("sub") From 2db93196ddc56c5ed47488d8682aa593bb60df0d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 19:05:49 -0500 Subject: [PATCH 261/274] include distance based metric for time-series tracking --- .github/workflows/continuous-integration.yml | 6 +- .../time_series/time_series_linking.py | 84 ++++++++++++++----- 2 files changed, 65 insertions(+), 25 deletions(-) diff --git a/.github/workflows/continuous-integration.yml b/.github/workflows/continuous-integration.yml index f0a3bd5e5..573a2a1c9 100644 --- a/.github/workflows/continuous-integration.yml +++ b/.github/workflows/continuous-integration.yml @@ -6,10 +6,12 @@ name: builds on: push: # Run when master is updated - branches: [ master, 4.x, Visualize show instances ] + branches: [ master, 4.x ] +# branches: [ master, 4.x, Visualize show instances ] pull_request: # Run on pull requests against master - branches: [ master, 4.x, visualize_show_instances ] + branches: [ master, 4.x ] +# branches: [ master, 4.x, visualize_show_instances ] jobs: build: diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index e802713c5..9043825a9 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -34,7 +34,7 @@ class InstanceTimeSeriesLinking(object): def __init__(self): # a list of masks which are ndarrays (of the same length of images) - self.masks = None + self.masks, self.tips = None, None self.T, self.N, self.max_uid = None, None, None # number of instances: a list in which every element represent for number of instances in corresponding image self.n_insts = None @@ -53,17 +53,6 @@ def import_linked_series(self, savedir, savename): for key, value in linked.items(): setattr(self, key, value) - def linking(self,t0): - """ - Time-series linking for a given timepoint to the next time point - :param t0: - :return: - """ - masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays - self.weights[t0], _, _, _ = self.compute_overlaps_weights(masks0, masks1, self.metric) - # self.link_info[t0], _, _ = self.get_link(self.weights[t0], self.thres) - self.link_info[t0] = self.get_link(self.weights[t0], self.thres) - @staticmethod def get_link(weight, thres): """Get the link (coordinates) between two sets of instances based on pre-calculated weight matrix @@ -89,13 +78,6 @@ def get_link(weight, thres): return link#, row_ind, col_ind - # @staticmethod - # def compute_dist_weights(pts1, pts2): - # n1, n2 = len(pts1), len(pts2) - # weight = distance.cdist(pts1, pts2) - # return weight, n1, n2 - - @staticmethod def compute_overlaps_weights(masks1, masks2, metric): """ @@ -301,7 +283,7 @@ def get_li_from_ti(ti): @staticmethod - def get_tracking_report(ti, masks): + def area_tracking_report(ti, masks): tracking_report = np.zeros(ti.shape) for (t, masks_t) in enumerate(masks): ti_t = ti[t, :] @@ -311,6 +293,17 @@ def get_tracking_report(ti, masks): return tracking_report + # @staticmethod + # def length_tracking_report(ti, masks): + # tracking_report = np.zeros(ti.shape) + # for (t, masks_t) in enumerate(masks): + # ti_t = ti[t, :] + # for cid in range(masks_t.shape[2]): + # uid = np.where(ti_t == cid)[0][0] + # tracking_report[t, uid] = np.sum(masks_t[:, :, cid]) + # return tracking_report + + @staticmethod def visualize(imgs, masks, tps, savedir, ti = None, color_all = None): params.debug = "plot" @@ -341,6 +334,18 @@ def visualize(imgs, masks, tps, savedir, ti = None, color_all = None): plt.close("all") + def link_t(self,t0): + """ + Time-series linking for a given timepoint to the next time point + :param t0: + :return: + """ + masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays + self.weights[t0], _, _, _ = self.compute_overlaps_weights(masks0, masks1, self.metric) + # self.link_info[t0], _, _ = self.get_link(self.weights[t0], self.thres) + self.link_info[t0] = self.get_link(self.weights[t0], self.thres) + + def link(self, masks, metric="IOS", thres=0.2): # a list of masks which are ndarrays (of the same length of images) self.masks = masks @@ -358,7 +363,7 @@ def link(self, masks, metric="IOS", thres=0.2): self.metric = metric.upper() for t0 in range(0, self.T - 1): - self.linking(t0) + self.link_t(t0) # self.ti, self.t_appear, self.t_disappear = self.get_ti(self.T, self.N, self.n_insts, self.uids, self.link_info) # self.uids, uids_sort, _, self.N = self.get_uid(self.ti) @@ -366,7 +371,40 @@ def link(self, masks, metric="IOS", thres=0.2): self.uids, self.max_uid, self.N = InstanceTimeSeriesLinking.get_sorted_uids(self.link_info, self.n_insts) self.ti = self.get_ti(self.uids, self.link_info, self.n_insts) - self.tracking_report = InstanceTimeSeriesLinking.get_tracking_report(self.ti, self.masks) + self.tracking_report = InstanceTimeSeriesLinking.area_tracking_report(self.ti, self.masks) + + + @staticmethod + def compute_dist_weights(pts1, pts2): + n1, n2 = len(pts1), len(pts2) + weight = distance.cdist(pts1, pts2) + return weight, n1, n2 + + + def link_dist_t(self,t0): + tips0, tips1 = copy.deepcopy(self.tips[t0]), copy.deepcopy(self.tips[t0 + 1]) # both masks0 and masks1 are ndarrays + weights, _, _, _ = self.compute_dist_weights(tips0, tips1) + self.weights[t0] = -weights + self.link_info[t0] = self.get_link(self.weights[t0], self.thres) + + def link_dist(self, tips, thres=0.0): + self.tips = tips + self.T = len(tips) + # number of instances: a list in which every element represent for number of instances in corresponding image + self.n_insts = [] + for i in range(0, self.T): + self.n_insts.append(self.tips[i].shape[2]) + # initialization for linking + self.thres = thres + self.link_info = [-np.ones((self.n_insts[i]), dtype=np.int64) for i in range(0, self.T - 1)] + self.weights = [np.empty(0) for _ in range(self.T - 1)] + + for t0 in range(0, self.T - 1): + self.link_dist_t(t0) + + self.uids, self.max_uid, self.N = InstanceTimeSeriesLinking.get_sorted_uids(self.link_info, self.n_insts) + self.ti = self.get_ti(self.uids, self.link_info, self.n_insts) + self.tracking_report = InstanceTimeSeriesLinking.area_tracking_report(self.ti, self.masks) @staticmethod @@ -441,7 +479,7 @@ def update_ti(self, max_gap=5): # return ti_ self.ti = ti_ # update tracking_report - self.tracking_report = InstanceTimeSeriesLinking.get_tracking_report(self.ti, self.masks) + self.tracking_report = InstanceTimeSeriesLinking.area_tracking_report(self.ti, self.masks) break From dce343e70596b0c2aa7ee50ec175361e2ae36f19 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 23:47:48 -0500 Subject: [PATCH 262/274] fix bug for `link_dist` --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 9043825a9..9cbbb7f4d 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -393,7 +393,7 @@ def link_dist(self, tips, thres=0.0): # number of instances: a list in which every element represent for number of instances in corresponding image self.n_insts = [] for i in range(0, self.T): - self.n_insts.append(self.tips[i].shape[2]) + self.n_insts.append(len(self.tips[i])) # initialization for linking self.thres = thres self.link_info = [-np.ones((self.n_insts[i]), dtype=np.int64) for i in range(0, self.T - 1)] From c53a9f65f363796d21e8f46ceae7a48e8a7656f8 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 23:51:22 -0500 Subject: [PATCH 263/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 9cbbb7f4d..68f421977 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -383,7 +383,7 @@ def compute_dist_weights(pts1, pts2): def link_dist_t(self,t0): tips0, tips1 = copy.deepcopy(self.tips[t0]), copy.deepcopy(self.tips[t0 + 1]) # both masks0 and masks1 are ndarrays - weights, _, _, _ = self.compute_dist_weights(tips0, tips1) + weights, _, _ = self.compute_dist_weights(tips0, tips1) self.weights[t0] = -weights self.link_info[t0] = self.get_link(self.weights[t0], self.thres) From 57c4514891bcb18f221406148b968561757c10b8 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 18 Aug 2021 23:58:58 -0500 Subject: [PATCH 264/274] Update time_series_linking.py --- plantcv/plantcv/time_series/time_series_linking.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 68f421977..306b918bd 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -387,7 +387,7 @@ def link_dist_t(self,t0): self.weights[t0] = -weights self.link_info[t0] = self.get_link(self.weights[t0], self.thres) - def link_dist(self, tips, thres=0.0): + def link_dist(self, tips, thres=10000): self.tips = tips self.T = len(tips) # number of instances: a list in which every element represent for number of instances in corresponding image @@ -395,7 +395,7 @@ def link_dist(self, tips, thres=0.0): for i in range(0, self.T): self.n_insts.append(len(self.tips[i])) # initialization for linking - self.thres = thres + self.thres = -thres self.link_info = [-np.ones((self.n_insts[i]), dtype=np.int64) for i in range(0, self.T - 1)] self.weights = [np.empty(0) for _ in range(self.T - 1)] @@ -404,7 +404,7 @@ def link_dist(self, tips, thres=0.0): self.uids, self.max_uid, self.N = InstanceTimeSeriesLinking.get_sorted_uids(self.link_info, self.n_insts) self.ti = self.get_ti(self.uids, self.link_info, self.n_insts) - self.tracking_report = InstanceTimeSeriesLinking.area_tracking_report(self.ti, self.masks) + # self.tracking_report = InstanceTimeSeriesLinking.area_tracking_report(self.ti, self.masks) @staticmethod From b675930ea7794294eb0635b22cae599d8dda89b0 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 19 Aug 2021 14:00:14 -0500 Subject: [PATCH 265/274] fix the bug for static method `_update_ti` --- plantcv/plantcv/time_series/time_series_linking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 306b918bd..aab1b1abb 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -439,7 +439,7 @@ def _update_ti(masks, metric, thres, ti, min_gap, max_gap): uids_undisap = [] for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices li_t = li_ts[idx] - if li_t > -1: + if li_t > -1 and uid_disap != uids_emerg[li_t]: print(f"\n{t} -> {t_}: ") print(f"{uid_disap} <- {uids_emerg[li_t]}") # update ti From 9ca6c86df431b3cd37783368dc040dcafeac865d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 19 Aug 2021 15:55:37 -0500 Subject: [PATCH 266/274] fix bug in static method `_update_ti` --- .../time_series/time_series_linking.py | 84 ++++++++++--------- 1 file changed, 46 insertions(+), 38 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index aab1b1abb..ac0a2e4a0 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -417,44 +417,52 @@ def _update_ti(masks, metric, thres, ti, min_gap, max_gap): # loop over timepoints with disappearing leaves (in reversed order) for t in reversed(sorted(t_disap)): # unique indices(index) that last appear at t - uids_disap = disappearance[t] - # corresponding cid(s) (i.e. indices for masks) - cids_disap = [uids_sort[t].index(i) for i in uids_disap] - # pull out masks - masks_t = np.take(masks[t], cids_disap, axis=2) - - # timepoints with potential link with t - ts_pot = [te for te in t_emerg if t + min_gap < te < t + max_gap] - # loop over timepoints for a potential link and get cids and masks for every timepoint - for t_ in ts_pot: - uids_emerg = emergence[t_] - cids_emerg = [uids_sort[t_].index(i) for i in uids_emerg] - masks_t_ = np.take(masks[t_], cids_emerg, axis=2) - - # calculate weight to calculate the link - weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) - # li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, self.thres) - li_ts = InstanceTimeSeriesLinking.get_link(weights, thres) - - uids_undisap = [] - for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices - li_t = li_ts[idx] - if li_t > -1 and uid_disap != uids_emerg[li_t]: - print(f"\n{t} -> {t_}: ") - print(f"{uid_disap} <- {uids_emerg[li_t]}") - # update ti - ti_[t_:, uid_disap] = ti_[t_:, uids_emerg[li_t]] - ti_[t_:, uids_emerg[li_t]] = -np.ones(T - t_, dtype=np.int64) - uids_undisap.append(uid_disap) - uids_disap = list(set(uids_disap).difference(set(uids_undisap))) - - if len(uids_disap) == 0: - # remove key - disappearance.pop(t) - break - else: - # update - disappearance[t] = uids_disap + uids_disap_ = disappearance[t] + idx = [] + for (i, uid_disap) in enumerate(uids_disap_): + if (ti[t+1:, uid_disap]==-1).all(): + idx.append(i) + uids_disap = [uids_disap_[i] for i in idx] + if len(uids_disap) > 0: + # corresponding cid(s) (i.e. indices for masks) + cids_disap = [uids_sort[t].index(i) for i in uids_disap] + # pull out masks + masks_t = np.take(masks[t], cids_disap, axis=2) + + # timepoints with potential link with t + ts_pot = [te for te in t_emerg if t + min_gap < te < t + max_gap] + # loop over timepoints for a potential link and get cids and masks for every timepoint + for t_ in ts_pot: + uids_emerg = emergence[t_] + cids_emerg = [uids_sort[t_].index(i) for i in uids_emerg] + masks_t_ = np.take(masks[t_], cids_emerg, axis=2) + + # calculate weight to calculate the link + weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) + # li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, self.thres) + li_ts = InstanceTimeSeriesLinking.get_link(weights, thres) + + uids_undisap = [] + uids_reemerged = [] + for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices + li_t = li_ts[idx] + if li_t > -1 and uid_disap != uids_emerg[li_t] and uids_emerg[li_t] not in uids_reemerged: + uids_reemerged.append(uid_disap) + print(f"\n{t} -> {t_}: ") + print(f"{uid_disap} <- {uids_emerg[li_t]}") + # update ti + ti_[t_:, uid_disap] = ti_[t_:, uids_emerg[li_t]] + ti_[t_:, uids_emerg[li_t]] = -np.ones(T - t_, dtype=np.int64) + uids_undisap.append(uid_disap) + uids_disap = list(set(uids_disap).difference(set(uids_undisap))) + + if len(uids_disap) == 0: + # remove key + disappearance.pop(t) + break + else: + # update + disappearance[t] = uids_disap remove_uids = [] for uid in range(N): if (ti_[:, uid] == -1).all(): From 8daf1a4a28ab1528bc66ef8d63f051eb416e8153 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 19 Aug 2021 16:08:06 -0500 Subject: [PATCH 267/274] fix bug in static method `_update_ti` --- .../time_series/time_series_linking.py | 66 ++++++++++--------- 1 file changed, 36 insertions(+), 30 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index ac0a2e4a0..58a415f87 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -433,36 +433,42 @@ def _update_ti(masks, metric, thres, ti, min_gap, max_gap): ts_pot = [te for te in t_emerg if t + min_gap < te < t + max_gap] # loop over timepoints for a potential link and get cids and masks for every timepoint for t_ in ts_pot: - uids_emerg = emergence[t_] - cids_emerg = [uids_sort[t_].index(i) for i in uids_emerg] - masks_t_ = np.take(masks[t_], cids_emerg, axis=2) - - # calculate weight to calculate the link - weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) - # li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, self.thres) - li_ts = InstanceTimeSeriesLinking.get_link(weights, thres) - - uids_undisap = [] - uids_reemerged = [] - for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices - li_t = li_ts[idx] - if li_t > -1 and uid_disap != uids_emerg[li_t] and uids_emerg[li_t] not in uids_reemerged: - uids_reemerged.append(uid_disap) - print(f"\n{t} -> {t_}: ") - print(f"{uid_disap} <- {uids_emerg[li_t]}") - # update ti - ti_[t_:, uid_disap] = ti_[t_:, uids_emerg[li_t]] - ti_[t_:, uids_emerg[li_t]] = -np.ones(T - t_, dtype=np.int64) - uids_undisap.append(uid_disap) - uids_disap = list(set(uids_disap).difference(set(uids_undisap))) - - if len(uids_disap) == 0: - # remove key - disappearance.pop(t) - break - else: - # update - disappearance[t] = uids_disap + uids_emerg_ = emergence[t_] + idx = [] + for (i, uid_emerg) in enumerate(uids_emerg_): + if (ti[0:t:, uid_emerg] == -1).all(): + idx.append(i) + uids_emerg = [uids_emerg_[i] for i in idx] + if len(uids_emerg) > 0: + cids_emerg = [uids_sort[t_].index(i) for i in uids_emerg] + masks_t_ = np.take(masks[t_], cids_emerg, axis=2) + + # calculate weight to calculate the link + weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) + # li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, self.thres) + li_ts = InstanceTimeSeriesLinking.get_link(weights, thres) + + uids_undisap = [] + uids_reemerged = [] + for (idx, uid_disap) in enumerate(uids_disap): # loop over all disappeared indices + li_t = li_ts[idx] + if li_t > -1 and uid_disap != uids_emerg[li_t] and uids_emerg[li_t] not in uids_reemerged: + uids_reemerged.append(uid_disap) + print(f"\n{t} -> {t_}: ") + print(f"{uid_disap} <- {uids_emerg[li_t]}") + # update ti + ti_[t_:, uid_disap] = ti_[t_:, uids_emerg[li_t]] + ti_[t_:, uids_emerg[li_t]] = -np.ones(T - t_, dtype=np.int64) + uids_undisap.append(uid_disap) + uids_disap = list(set(uids_disap).difference(set(uids_undisap))) + + if len(uids_disap) == 0: + # remove key + disappearance.pop(t) + break + else: + # update + disappearance[t] = uids_disap remove_uids = [] for uid in range(N): if (ti_[:, uid] == -1).all(): From f36816a3469d747f8713f3db7ce997ca55a526a9 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 24 Aug 2021 10:33:08 -0500 Subject: [PATCH 268/274] Remove redundant in 'plantcv/time_series/evaluation.py` --- plantcv/plantcv/time_series/evaluation.py | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/plantcv/plantcv/time_series/evaluation.py b/plantcv/plantcv/time_series/evaluation.py index 45e94e188..da393f3d8 100644 --- a/plantcv/plantcv/time_series/evaluation.py +++ b/plantcv/plantcv/time_series/evaluation.py @@ -95,25 +95,11 @@ def confusion(ti, ti_gt): confu_ = np.append(confu_, new_col, 1) life = life + [0 for n in range(delta_N)] - # delta matrix of lift and life_gt - # delta_life = -np.ones((N,max(N_,N)),dtype=int) - # for i in range(0,N): - # delta_life[i,:] = abs(np.int64(life_gt[i])-life) - - # linear assignment based on delta_life: find most probable corresponding based on life - # row_i, col_i = linear_sum_assignment(delta_life) - - # linear assignment based on confusion matrix: find those with largest values in confusion matrix row_i, col_i = linear_sum_assignment(confu_, maximize=True) match = [confu_[i, j] for (i, j) in zip(row_i, col_i)] # "diagonal" rate = [match_t / time_i for (match_t, time_i) in zip(match, life_gt)] - # # always make the same length of num_gt - # if N_ < N: - # for i in range(N_-N): - # match.append(0) - # rate.append(0) score = sum(rate) / N return N, N_, confu, match, rate, score From 0c445a7bcdfca7988c1c26885e30c9ac3eb8e405 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 24 Aug 2021 10:33:55 -0500 Subject: [PATCH 269/274] change name from "IOS" to "IOF" and make "IOU" the default metric --- .../time_series/time_series_linking.py | 20 +++++++++---------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 58a415f87..719e9604b 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -22,9 +22,6 @@ from scipy.spatial import distance import csv from plantcv.plantcv.visualize import display_instances -# import sys -# sys.path.append('/shares/mgehan_share/hsheng/projects/test_plantcv/visualize_display_instances') -# from visualize_display_instances import display_instances class InstanceTimeSeriesLinking(object): @@ -91,7 +88,8 @@ def compute_overlaps_weights(masks1, masks2, metric): n1 = the number of instances in 1st set of binary masks n2 = the number of instances in 2nd set of binary masks ious = inversection over union between any pairs of instances in masks1 and masks2 - ioss = inversection over self-area (areas of instances in 1st set of masks) between any pairs of instances in masks1 and masks2 + iofs = inversection over first timepoint area (areas of instances in 1st set of masks) between any pairs of + instances in masks1 and masks2 unions = unions between any pairs of instances in masks1 and masks2 :param masks1: (numpy.ndarray of shape: [Height, Width, n1]) , where n1 is the number of instances @@ -100,12 +98,12 @@ def compute_overlaps_weights(masks1, masks2, metric): :return n1: int :return n2: int :return ious: numpy.ndarray of shape: [n1, n2] - :return ioss: numpy.ndarray of shape: [n1, n2] + :return iofs: numpy.ndarray of shape: [n1, n2] :return unions: numpy.ndarray of shape: [n1, n2] """ - if not (metric.upper() == "IOU" or metric.upper() == "IOS"): - fatal_error("Currently only calculating metrics 'IOU' and 'IOS' are available!") + if not (metric.upper() == "IOU" or metric.upper() == "IOF"): + fatal_error("Currently only calculating metrics 'IOU' and 'IOF' are available!") # If either set of masks is empty return an empty result # if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: @@ -119,7 +117,7 @@ def compute_overlaps_weights(masks1, masks2, metric): n2 = masks2.shape[2] intersections = np.zeros((n1, n2)) unions = np.zeros((n1, n2)) - ioss = np.zeros((n1, n2)) + iofs = np.zeros((n1, n2)) for idx_m in range(0, n1): maski = np.expand_dims(masks1[:, :, idx_m], axis=2) masks_ = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32) @@ -128,12 +126,12 @@ def compute_overlaps_weights(masks1, masks2, metric): intersections[idx_m, :] = intersection union = np.sum(masks_, 0) + np.sum(maski_) - intersection unions[idx_m, :] = union - ioss[idx_m, :] = intersection / maski_.sum() + iofs[idx_m, :] = intersection / maski_.sum() ious = np.divide(intersections, unions) if metric.upper() == "IOU": return ious, n1, n2, unions else: - return ioss, n1, n2, unions + return iofs, n1, n2, unions # @staticmethod @@ -346,7 +344,7 @@ def link_t(self,t0): self.link_info[t0] = self.get_link(self.weights[t0], self.thres) - def link(self, masks, metric="IOS", thres=0.2): + def link(self, masks, metric="IOU", thres=0.2): # a list of masks which are ndarrays (of the same length of images) self.masks = masks self.T = len(masks) From 98297d28f6f4e6982fda95b717cb8adbdff65dcd Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 24 Aug 2021 10:34:29 -0500 Subject: [PATCH 270/274] Update time_series_InstanceTimeSeriesLinking.md --- ... time_series_InstanceTimeSeriesLinking.md} | 124 +++++++++++++----- 1 file changed, 88 insertions(+), 36 deletions(-) rename docs/{time_series_InstanceTimeSeriesLinking..md => time_series_InstanceTimeSeriesLinking.md} (73%) diff --git a/docs/time_series_InstanceTimeSeriesLinking..md b/docs/time_series_InstanceTimeSeriesLinking.md similarity index 73% rename from docs/time_series_InstanceTimeSeriesLinking..md rename to docs/time_series_InstanceTimeSeriesLinking.md index f868b7876..a1c63326a 100644 --- a/docs/time_series_InstanceTimeSeriesLinking..md +++ b/docs/time_series_InstanceTimeSeriesLinking.md @@ -1,12 +1,15 @@ -## time_series +## Time-series Tracking -This class is designed to link segmented instances over time. Images should be taken across a time (e.g. every 4 hours for several days), and ideally there should be minimal or no movment of plant and camera. +This class is designed to track segmented instances over time. Images should be taken across a time (e.g. every 4 hours +for several days), and ideally either plant or camera should have minimal or no movment. -To use this class for generating time-series linking, the instance segmentation for every image is required. For more information on instance segmentation and a demo of instance segmentation, check here: [Instance Segmentation](instance_segmentation_tutorial.md). +To use this class for generating time-series linking, instance segmentation for every image is required. For more +information of instance segmentation, check out here: [Instance Segmentation](instance_segmentation_tutorial.md) for +a demo of instance segmentation. -When using instance segmentation algorithms like maskRCNN, the assignment for the instance labels is random, i.e. the same index does not necessarily represents the same instance in different images. - -To understand the growth of every leaf instance of a plant, we need to re-assign the instance labels so that the same leaf always has the same instance label (identifier) across the whole time period. +The goals are: +1) Assign global unique indices to every instance at every timepoint +2) Learn how instances connect with each other between consective timepoints For details of using this class, see examples below. @@ -14,66 +17,115 @@ For details of using this class, see examples below. **inst_ts_linking = plantcv.time_series.InstanceTimeSeriesLinking()** -*run the callable object* +*use the class method `link` to track time-series* -**inst_ts_linking**(*images, masks, timepoints, savedir, savename, logic="IOS", thres=0.2, name_sub="instance",update=False, max_delta_t=2*) +**inst_ts_linking.link**(*masks, metric, thres*) **returns** No returned value, the inst_ts_linking is an instance object which belongs to InstanceTimeSeriesLinking class. - **Parameters** - - images: a list of images. Every element of this list is an array represents one image - - masks: a list of instance segmentation masks. Every element of this list is an array represents instance segmentation masks correspond to one image. - - timepoints: a list of timepoints. The lengths for images, masks and timepoint should be the same and the elements are correspond to each other - - logic (optional): the logic used in linking. Segments from different timepoints are believed to be the same instance appeared in different timepoints based on either their IOU (intersection-over-union) or IOS (Intersection-over-self_area) - If the value is larger than the threshold, they will be connected. The logic can be either "IOU" or "IOS". Default value is "IOS". - - thres (optional): threshold used in the linking logic as mentioned above. Different threshold is needed when using different linking logic. Default value is 0.2. - - name_sub (optional): name of the main subject we care about. By default name_sub = 'instance', which means the instances we care about in images are called "instance". Other examples can be "leaf" which means that we call one instance in images a "leaf". - - update (optional): whether or not updating the time-series linking result. By default (not updating) the time-series linking would only examine the neighboring pairs of time points (time interval = 1). By updating the time-series linking we will be examining a larger interval of time. - - max_delta_t (optional): only needed when update=True. By default max_delta_t=2. + - masks: a list of instance segmentation masks. Every element of this list is a numpy array represents instance + segmentation masks correspond to one image (one timepoint). To be specific, a numpy array of size `r*c*n` represents + that there are `n` segmented instances in the image (hence `n` masks), and the size of the original image is `r*c`. + + - metric (optional): the metric to measure how likely two instances (segmentation masks) appear in two timepoints + can be considered as the same object appear in different timepoints. Currently, two overlap-based metrics are + available: IoU (intersection-over-union) and IoF (Intersection-over-first timepoint area). + Default value is "IOU". + + - thres (optional): how large the weight `W_t1_i_t2_j` (calculated based on the metric of choice) should be to for the + connection from i-th segment from t1 to j-th segment from t2 to be considered as a potential link. + Different threshold should be chosen when using different metric. Default value is 0.2. - **Output:** - An instance of InstanceTimeSeriesLinking class is returned, with all information (original image series, mask series, link information, etc.) included. + An instance object of InstanceTimeSeriesLinking class, with all information (original, mask series, link information, etc.) included. + +- images: a list of images. Every element of this list is an array represents one image +- timepoints: a list of timepoints. The lengths for images, masks and timepoint should be the same and the elements are correspond to each other +Note: when comparing instances from two timepoints, we are comparing n1 masks from t1 and n2 masks from t2, + ```python -from plantcv import plantcv as pcv +from plantcv.plantcv import time_series_linking as tsl +## Load all segmentation masks and put them into a list in the correct order here +# masks = + # Below are examples of input variables, always adjust base on your own application. -logic = 'IOS' +metric = 'IOU' thres = 0.1 -name_sub = 'leaf' + ## Initialize an instance of class InstanceTimeSeriesLinking -inst_ts_linking = pcv.time_series.InstanceTimeSeriesLinking() -inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub) +inst_ts_linking = tsl.InstanceTimeSeriesLinking() +inst_ts_linking.link(masks=masks, metric=metric, thres=thres) ``` -Make sure all the images and masks are already sorted based on timepoints. +Make sure the list of all the masks is temporarily sorted. -To save the result, simply specify the directory of saving as well as the name (prefix) +To save the instance object, simply specify the directory of saving as well as the name (prefix) ```python ## Specify the desired directory to save results -dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' +dir_save = "./results" ## Specify the desired name to save the result (prefix) -savename = 'linked_series' +savename = "linked_series" inst_ts_linking.save_linked_series(savedir=dir_save, savename=savename) ``` + To import a previously saved result: ```python -dir_save = '/shares/mgehan_share/hsheng/projects/maskRCNN/results/output_10.1.9.214_wtCol_512/index12/2020-08-24-07-29/time_series_linking' -savename = 'linked_series' -inst_ts_linking = time_series_linking.InstanceTimeSeriesLinking() +dir_save = "./results" +savename = "linked_series" +## Initialize an instance of class InstanceTimeSeriesLinking +inst_ts_linking = tsl.InstanceTimeSeriesLinking() inst_ts_linking.import_linked_series(savedir=dir_save, savename=savename) ``` -The option to update the time-series linking result is also available: +To visualize the time-series tracking result, a list of images corresponding to every set of masks in the list of masks +must be provided. Besides, a list of timepoints also need to be provided. +The visualization assigns colors based on unique object indices information, in other words, all objects in the time-series +with the same unique index will be assigned the same color. +The information can be achieved from `inst_ts_linking.ti`. +The visualization results are saved in the provided directory `savedir`. ```python -max_delta_t = 3 -inst_ts_linking.update_ti(delta_t=max_delta_t) +## Load all original images and put them into a list in the correct order here (same order as masks) +# imgs = + +## Get a list of timepoints (usually from the file names of original images) +# timepoints = + +# specify the directory to save visualization +visualdir = "./results/visualization" +inst_ts_linking.visualize(imgs=imgs, masks=masks, tps=timepoints, savedir=visualdir, ti=inst_ts_linking.ti, color_all=None) ``` +Note: `visualize` is a static method of class `InstanceTimeSeriesLinking`. In other words, it can be called without initialization +of a class object, as long as all required parameters as passed in. See example below. If `ti` is not provided, the color +assignment is based on local indices. +```python +## Load all segmentation masks and put them into a list in the correct order here +# masks = + +## Load all original images and put them into a list in the correct order here (same order as masks) +# imgs = + +## Get a list of timepoints (usually from the file names of original images) +# timepoints = + +# specify the directory to save visualization +visualdir = "./results/visualization" + +tsl.InstanceTimeSeriesLinking.visualize(imgs=imgs, masks=masks, tps=timepoints, savedir=visualdir) +``` + +In some cases, objects disappear in one or several timepoints and re-appear. To lower the rate of assigning new indices +(false positive) to those objects, updating the time-series tracking is also possible by indicating the expected maximum +time gap of disappearance `max_gap`. By default `max_gap=5`. A larger number of `max_gap` is not recommended. -Alternatively, the time-series linking can be done with ```update=True``` ```python -inst_ts_linking(images=images, masks=masks, timepoints=timepoints, logic=logic, thres=thres, name_sub=name_sub, update=True, max_delta_t=3) +nax_gap = 3 +inst_ts_linking.update_ti(max_gap=nax_gap) ``` +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series_linking.py) + -**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/time_series/time_series.py) + From 7d7b14037e6cf8d20b83182e47afd3ee88cac175 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 24 Aug 2021 10:34:32 -0500 Subject: [PATCH 271/274] Update time_series_evaluation.md --- docs/time_series_evaluation.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/docs/time_series_evaluation.md b/docs/time_series_evaluation.md index 272591f5e..c53259c5a 100644 --- a/docs/time_series_evaluation.md +++ b/docs/time_series_evaluation.md @@ -1,12 +1,13 @@ -## Evaluation of time-series linking result +## Evaluation of time-series tracking result -This set of functions are designed to evaluate the time-series leaf tracking result by comparing to the ground-truth. +This set of functions are designed to evaluate the time-series tracking result by comparing to the time-series ground-truth. -To start, get familiar with some notations and definitions: +First, get familiar with some notations and definitions: -**T**: total time +**T**: total time (length of the time-series) **N**: total number of unique leaves in ground truth +- *note*: in python indices start with 0 so the maximum unique index is N-1. **nt**: number of leaves at time t @@ -22,7 +23,7 @@ To start, get familiar with some notations and definitions: **N'**: total number of (unique) leaves in leaf tracking result -- *note: N=N' not necessary holds.* +- *note*: N=N' does not necessary hold. **li'**: link info in tracking result From f03b348bea6dea12a0700f04a2b2241e0099bfa3 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Wed, 25 Aug 2021 17:21:27 -0500 Subject: [PATCH 272/274] fix error in time_series_InstanceTimeSeriesLinking.md --- docs/time_series_InstanceTimeSeriesLinking.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/time_series_InstanceTimeSeriesLinking.md b/docs/time_series_InstanceTimeSeriesLinking.md index a1c63326a..2e7028dd5 100644 --- a/docs/time_series_InstanceTimeSeriesLinking.md +++ b/docs/time_series_InstanceTimeSeriesLinking.md @@ -45,7 +45,7 @@ Note: when comparing instances from two timepoints, we are comparing n1 masks fr ```python -from plantcv.plantcv import time_series_linking as tsl +from plantcv.plantcv.time_series import time_series_linking as tsl ## Load all segmentation masks and put them into a list in the correct order here # masks = From aa0a33d9139377d36ade32133d82ef4c7d449bb2 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 26 Aug 2021 10:50:39 -0500 Subject: [PATCH 273/274] remove unused code; add methods relationships for each method --- .../time_series/time_series_linking.py | 44 ++++++++----------- 1 file changed, 18 insertions(+), 26 deletions(-) diff --git a/plantcv/plantcv/time_series/time_series_linking.py b/plantcv/plantcv/time_series/time_series_linking.py index 719e9604b..a32d4c0be 100644 --- a/plantcv/plantcv/time_series/time_series_linking.py +++ b/plantcv/plantcv/time_series/time_series_linking.py @@ -53,6 +53,7 @@ def import_linked_series(self, savedir, savename): @staticmethod def get_link(weight, thres): """Get the link (coordinates) between two sets of instances based on pre-calculated weight matrix + Called by static method "_update_ti", class methods "link_t", "link_dist_t". Inputs: weight = weight matrix, the smaller the weight, the more possible two will be linked thres = minimum weight value for two instances to be considered as the same one @@ -78,6 +79,7 @@ def get_link(weight, thres): @staticmethod def compute_overlaps_weights(masks1, masks2, metric): """ + Called by class method "link_t" and static method "_update_ti" Compute weights between 2 sets of binary masks based on their overlaps The overlaps are represented by either IoU (intersection over union) and IoS (intersection over self-area of the 1st mask). Inputs: @@ -148,6 +150,7 @@ def compute_overlaps_weights(masks1, masks2, metric): @staticmethod def get_sorted_uids(link_info, n_insts): """ + Called by class methods "link" and "link_dist" Get unique indices at every timestamp based on link information and number of instances at every timepoint Inputs: link_info = a list (length: T-1) of linking information, every sub-list contains the information of how every instance link to instances to the next timepoint @@ -185,6 +188,7 @@ def get_sorted_uids(link_info, n_insts): @staticmethod def get_uids_from_ti(ti): """ + Called by static method "_update_ti". Get unique indices at every timestamp based on tracking information :param ti: numpy.array :return uids_sort: list @@ -203,6 +207,7 @@ def get_uids_from_ti(ti): @staticmethod def get_emerg_disap_info(uids): """ + Called by static methods "get_ti" and "_update_ti". Get emergence and disappearence indices and corresponding timepoints based on uids Inputs: uids = unique indices present at evert timepoint @@ -230,6 +235,7 @@ def get_emerg_disap_info(uids): @staticmethod def get_ti(uids, link_info, n_insts): """ + Called by class methods "link" and "link_dist" Get tracking information from linking information, number of instances, and unique indices at every timepoint :param uids: list :param link_info: list @@ -282,6 +288,9 @@ def get_li_from_ti(ti): @staticmethod def area_tracking_report(ti, masks): + """ + Called by class methods "link" and "update_ti". + """ tracking_report = np.zeros(ti.shape) for (t, masks_t) in enumerate(masks): ti_t = ti[t, :] @@ -334,13 +343,13 @@ def visualize(imgs, masks, tps, savedir, ti = None, color_all = None): def link_t(self,t0): """ + Called by class method "link" Time-series linking for a given timepoint to the next time point :param t0: :return: """ masks0, masks1 = copy.deepcopy(self.masks[t0]), copy.deepcopy(self.masks[t0 + 1]) # both masks0 and masks1 are ndarrays self.weights[t0], _, _, _ = self.compute_overlaps_weights(masks0, masks1, self.metric) - # self.link_info[t0], _, _ = self.get_link(self.weights[t0], self.thres) self.link_info[t0] = self.get_link(self.weights[t0], self.thres) @@ -363,10 +372,6 @@ def link(self, masks, metric="IOU", thres=0.2): for t0 in range(0, self.T - 1): self.link_t(t0) - # self.ti, self.t_appear, self.t_disappear = self.get_ti(self.T, self.N, self.n_insts, self.uids, self.link_info) - # self.uids, uids_sort, _, self.N = self.get_uid(self.ti) - # self.emergence, self.emerge_times = self.get_emerg_disap_info(uids_sort) - self.uids, self.max_uid, self.N = InstanceTimeSeriesLinking.get_sorted_uids(self.link_info, self.n_insts) self.ti = self.get_ti(self.uids, self.link_info, self.n_insts) self.tracking_report = InstanceTimeSeriesLinking.area_tracking_report(self.ti, self.masks) @@ -374,12 +379,17 @@ def link(self, masks, metric="IOU", thres=0.2): @staticmethod def compute_dist_weights(pts1, pts2): + """ + Called by class method "link_dist_t" + """ n1, n2 = len(pts1), len(pts2) weight = distance.cdist(pts1, pts2) return weight, n1, n2 - def link_dist_t(self,t0): + """ + Called by class method "link_dist" + """ tips0, tips1 = copy.deepcopy(self.tips[t0]), copy.deepcopy(self.tips[t0 + 1]) # both masks0 and masks1 are ndarrays weights, _, _ = self.compute_dist_weights(tips0, tips1) self.weights[t0] = -weights @@ -407,6 +417,8 @@ def link_dist(self, tips, thres=10000): @staticmethod def _update_ti(masks, metric, thres, ti, min_gap, max_gap): + """ Called by class method "update_ti" + """ ti_ = copy.deepcopy(ti) T, N = ti.shape uids_sort = InstanceTimeSeriesLinking.get_uids_from_ti(ti) @@ -443,7 +455,6 @@ def _update_ti(masks, metric, thres, ti, min_gap, max_gap): # calculate weight to calculate the link weights, n1, n2, _ = InstanceTimeSeriesLinking.compute_overlaps_weights(masks_t, masks_t_, metric) - # li_ts, _, _ = InstanceTimeSeriesLinking.get_link(weights, self.thres) li_ts = InstanceTimeSeriesLinking.get_link(weights, thres) uids_undisap = [] @@ -475,7 +486,6 @@ def _update_ti(masks, metric, thres, ti, min_gap, max_gap): return ti_ - # def update_ti(self, ti, max_gap=5): def update_ti(self, max_gap=5): # self.ti_old = ti self.ti_old = self.ti @@ -493,21 +503,3 @@ def update_ti(self, max_gap=5): # update tracking_report self.tracking_report = InstanceTimeSeriesLinking.area_tracking_report(self.ti, self.masks) break - - - # def __call__(self, images, masks, timepoints, metric="IOS", thres=0.2, name_sub="instance", update=False, max_delta_t=2): - # # a list of images which are ndarrays - # self.images = images - # # a list of masks which are ndarrays (of the same length of images) - # self.masks = masks - # # a list of timepoints (of the same length of images) - # self.timepoints = timepoints - # self.T = len(self.timepoints) - # self.name_sub = name_sub - # self.key_id = '{}_ids'.format(name_sub) - # # number of instances: a list in which every element represent for number of instances in corresponding image - # self.n_insts = [] - # for i in range(0, len(self.masks)): - # self.n_insts.append(self.masks[i].shape[2]) - # if update: - # self.update_ti(max_delta_t) From ce3c5bf952f9992ca8c72b6e1badeb52acdaaf1d Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Thu, 26 Aug 2021 10:51:21 -0500 Subject: [PATCH 274/274] fix bug in tests when accepted metric changed from "IOS" to "IOF" --- tests/tests.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index 9e8d6ab6b..281ac60d0 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6816,7 +6816,7 @@ def test_plantcv_time_series_inst_ts_linking_compute_overlap(tmpdir): # seg_name2 = os.path.join(TIME_SERIES_TEST_INSTANCE_SEG, "2019-10-22-11-05.pkl") masks1 = pkl.load(open(seg_name1, 'rb'))['masks'] # masks2 = pkl.load(open(seg_name2, 'rb'))['masks'] - ioss, n1, n2, unions = pcv.time_series.InstanceTimeSeriesLinking.compute_overlaps_weights(masks1[:,:,0], masks1[:,:,1], "IOS") + ioss, n1, n2, unions = pcv.time_series.InstanceTimeSeriesLinking.compute_overlaps_weights(masks1[:,:,0], masks1[:,:,1], "IOF") assert ioss.shape == (n1,n2) and (n1==n2) and (n1==1)

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