Upload 3 files

metrics.py

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+
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import backend as K
+
+smooth = 1e-15
+def dice_coef(y_true, y_pred):
+    y_true = tf.keras.layers.Flatten()(y_true)
+    y_pred = tf.keras.layers.Flatten()(y_pred)
+    intersection = tf.reduce_sum(y_true * y_pred)
+    return (2. * intersection + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth)
+
+def dice_loss(y_true, y_pred):
+    return 1.0 - dice_coef(y_true, y_pred)

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:16922585059a7dc54791c56674640054a8575a4a0282923a2ec77a38553cba2c
+size 372930696

test.py

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+
+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")