tta.py

import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

import numpy as np
import cv2
import pandas as pd
from glob import glob
from tqdm import tqdm
import tensorflow as tf
from tensorflow.keras.utils import CustomObjectScope
from sklearn.metrics import accuracy_score, f1_score, jaccard_score, precision_score, recall_score
from metrics import dice_loss, dice_coef, iou
from train import create_dir, load_data

H = 256
W = 256

def read_image(path):
    x = cv2.imread(path, cv2.IMREAD_COLOR)  ## (H, W, 3)
    x = cv2.resize(x, (W, H))
    ori_x = x
    x = x/255.0
    x = x.astype(np.float32)
    x = np.expand_dims(x, axis=0)
    return ori_x, x                                ## (1, 256, 256, 3)


def read_mask(path):
    x = cv2.imread(path, cv2.IMREAD_GRAYSCALE)  ## (H, W)
    x = cv2.resize(x, (W, H))
    ori_x = x
    x = x/255.0
    x = x.astype(np.int32)                    ## (256, 256)
    return ori_x, x

def save_results(ori_x, ori_y, y_pred, save_image_path):
    line = np.ones((H, 10, 3)) * 255

    ori_y = np.expand_dims(ori_y, axis=-1)  ## (256, 256, 1)
    ori_y = np.concatenate([ori_y, ori_y, ori_y], axis=-1) ## (256, 256, 3)

    y_pred = np.expand_dims(y_pred, axis=-1)  ## (256, 256, 1)
    y_pred = np.concatenate([y_pred, y_pred, y_pred], axis=-1) ## (256, 256, 3)

    cat_images = np.concatenate([ori_x, line, ori_y, line, y_pred*255], axis=1)
    cv2.imwrite(save_image_path, cat_images)
    

if __name__ == "__main__":
    """ Seeding """
    np.random.seed(42)
    tf.random.set_seed(42)

    """ Folder for saving results """
    create_dir("results_DLV3SA_inception")

    """ Load the model """
    with CustomObjectScope({'iou': iou, 'dice_coef': dice_coef}):
        model = tf.keras.models.load_model("files_DLV3SA/model_DLV3SA_inception.h5")

    """ Load the test data """
    dataset_path = "D:/medical_challenge/segmentation/CVC-612/data/"
    # dataset_path = "D:/medical_challenge/segmentation/2d/VIP_CUP_5fold/fold_1/train/"
    # dataset_path = "D:/medical_challenge/segmentation/CVC-612/CVC_ClinicDB"
    # dataset_path = "D:/medical_challenge/segmentation/2d/data_mbrain_5fold/fold_0/train/"
    (train_x, train_y), (valid_x, valid_y) = load_data(dataset_path)
    # print(len(test_x), len(test_y))
    print("=====================================================================================================")
    print(len(valid_x), len(valid_y))
    print("=====================================================================================================")
    print(len(train_x), len(train_y))
    SCORE = []
    for x, y in tqdm(zip(valid_x, valid_y), total=len(valid_x)):
        """ Exctracting the image name """
        # name = x.split("/")[-1]
        # print(x)
        name = x
        """ Read the image and mask """
        ori_x, x = read_image(x)
        ori_y, y = read_mask(y)

        """ Predicting the mask """
        y_pred = model.predict(x)[0] > 0.5
        y_pred = np.squeeze(y_pred, axis=-1)
        y_pred = y_pred.astype(np.int32)

        """ Saving the predicted mask """
        save_image_path = os.path.join(os.getcwd(), 'results_DLV3SA_inception', os.path.basename(name))
        ## os.getcwd: lấy path fiel hiện tại
        ## basename: lấy tên ảnh

        # print("*"*50, save_image_path)
        save_results(ori_x, ori_y, y_pred, save_image_path)

        """ Flatten the array """
        y = y.flatten()
        y_pred = y_pred.flatten()

        """ Calculating metrics values """
        # dice_coef_value = 1 - dice_loss(y, y_pred)
        acc_value = accuracy_score(y, y_pred)
        f1_value = f1_score(y, y_pred, labels=[0, 1], average="binary")
        jac_value = jaccard_score(y, y_pred, labels=[0, 1], average="binary")
        recall_value = recall_score(y, y_pred, labels=[0, 1], average="binary")
        precision_value = precision_score(y, y_pred, labels=[0, 1], average="binary")
        SCORE.append([name, acc_value, f1_value, jac_value, recall_value, precision_value])


    """ mean metrics values """
    score = [s[1:] for s in SCORE]
    score = np.mean(score, axis=0)
    # print(f"Dice score: {score[0]:0.5f}")
    print(f"Accuracy: {score[0]:0.5f}")
    print(f"F1: {score[1]:0.5f}")
    print(f"Jaccard: {score[2]:0.5f}")
    print(f"Recall: {score[3]:0.5f}")
    print(f"Precision: {score[4]:0.5f}")

    df = pd.DataFrame(SCORE, columns = ["Image Name", "Acc", "F1", "Jaccard", "Recall", "Precision"])
    df.to_csv("files_DLV3SA/score_DLV3SA_inception.csv")