eval.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 tensorflow as tf
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_2value_no_early_stop")

    """ Load the model """
    with CustomObjectScope({'iou': iou, 'dice_coef': dice_coef, 'dice_loss': dice_loss}):
        model = tf.keras.models.load_model("files_DLV3SA/model_DLV3SA_2value_no_early_stop.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_2value_no_early_stop', 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_2value_no_early_stop.csv")