Update test.py

test.py

@@ -1,103 +1,2 @@
 
-import os
 
-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 f1_score, jaccard_score, precision_score, recall_score
-from sklearn.model_selection import train_test_split
-from metrics import dice_loss, dice_coef
-from train import load_dataset
-from unet import build_unet
-
-""" Global parameters """
-H = 256
-W = 256
-
-""" Creating a directory """
-def create_dir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-def save_results(image, mask, y_pred, save_image_path):
-    mask = np.expand_dims(mask, axis=-1)
-    mask = np.concatenate([mask, mask, mask], axis=-1)
-
-    y_pred = np.expand_dims(y_pred, axis=-1)
-    y_pred = np.concatenate([y_pred, y_pred, y_pred], axis=-1)
-    y_pred = y_pred * 255
-
-    line = np.ones((H, 10, 3)) * 255
-
-    cat_images = np.concatenate([image, line, mask, line, y_pred], axis=1)
-    cv2.imwrite(save_image_path, cat_images)
-
-
-if __name__ == "__main__":
-    """ Seeding """
-    np.random.seed(42)
-    tf.random.set_seed(42)
-
-    """ Directory for storing files """
-    create_dir("results")
-
-    """ Load the model """
-    with CustomObjectScope({"dice_coef": dice_coef, "dice_loss": dice_loss}):
-        model = tf.keras.models.load_model(os.path.join("files", "model.h5"))
-
-    """ Dataset """
-    dataset_path = "/media/nikhil/Seagate Backup Plus Drive/ML_DATASET/brain_tumor_dataset/data"
-    (train_x, train_y), (valid_x, valid_y), (test_x, test_y) = load_dataset(dataset_path)
-
-    """ Prediction and Evaluation """
-    SCORE = []
-    for x, y in tqdm(zip(test_x, test_y), total=len(test_y)):
-        """ Extracting the name """
-        name = x.split("/")[-1]
-
-        """ Reading the image """
-        image = cv2.imread(x, cv2.IMREAD_COLOR) ## [H, w, 3]
-        image = cv2.resize(image, (W, H))       ## [H, w, 3]
-        x = image/255.0                         ## [H, w, 3]
-        x = np.expand_dims(x, axis=0)           ## [1, H, w, 3]
-
-        """ Reading the mask """
-        mask = cv2.imread(y, cv2.IMREAD_GRAYSCALE)
-        mask = cv2.resize(mask, (W, H))
-
-        """ Prediction """
-        y_pred = model.predict(x, verbose=0)[0]
-        y_pred = np.squeeze(y_pred, axis=-1)
-        y_pred = y_pred >= 0.5
-        y_pred = y_pred.astype(np.int32)
-
-        """ Saving the prediction """
-        save_image_path = os.path.join("results", name)
-        save_results(image, mask, y_pred, save_image_path)
-
-        """ Flatten the array """
-        mask = mask/255.0
-        mask = (mask > 0.5).astype(np.int32).flatten()
-        y_pred = y_pred.flatten()
-
-        """ Calculating the metrics values """
-        f1_value = f1_score(mask, y_pred, labels=[0, 1], average="binary")
-        jac_value = jaccard_score(mask, y_pred, labels=[0, 1], average="binary")
-        recall_value = recall_score(mask, y_pred, labels=[0, 1], average="binary", zero_division=0)
-        precision_value = precision_score(mask, y_pred, labels=[0, 1], average="binary", zero_division=0)
-        SCORE.append([name, f1_value, jac_value, recall_value, precision_value])
-
-    """ Metrics values """
-    score = [s[1:]for s in SCORE]
-    score = np.mean(score, axis=0)
-    print(f"F1: {score[0]:0.5f}")
-    print(f"Jaccard: {score[1]:0.5f}")
-    print(f"Recall: {score[2]:0.5f}")
-    print(f"Precision: {score[3]:0.5f}")
-
-    df = pd.DataFrame(SCORE, columns=["Image", "F1", "Jaccard", "Recall", "Precision"])
-    df.to_csv("files/score.csv")