--- a
+++ b/semseg_train/evaluate.py
@@ -0,0 +1,60 @@
+from pathlib import Path
+import argparse
+import cv2
+import numpy as np
+from tqdm import tqdm
+
+
+def general_dice(y_true, y_pred):
+    if y_true.sum() == 0:
+        if y_pred.sum() == 0:
+            return 1
+        else:
+            return 0
+
+    return dice(y_true == 1, y_pred == 1)
+
+
+def general_jaccard(y_true, y_pred):
+    if y_true.sum() == 0:
+        if y_pred.sum() == 0:
+            return 1
+        else:
+            return 0
+
+    return jaccard(y_true == 1, y_pred == 1)
+
+
+def jaccard(y_true, y_pred):
+    intersection = (y_true * y_pred).sum()
+    union = y_true.sum() + y_pred.sum() - intersection
+    return (intersection + 1e-15) / (union + 1e-15)
+
+
+def dice(y_true, y_pred):
+    return (2 * (y_true * y_pred).sum() + 1e-15) / (y_true.sum() + y_pred.sum() + 1e-15)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    arg = parser.add_argument
+
+    arg('--train_path', type=str, default='data/train/angyodysplasia/masks', help='path where train images with ground truth are located')
+    arg('--target_path', type=str, default='predictions/UNet', help='path with predictions')
+    args = parser.parse_args()
+
+    result_dice = []
+    result_jaccard = []
+
+    for file_name in tqdm(list(Path(args.train_path).glob('*'))):
+        y_true = (cv2.imread(str(file_name), 0) > 255 * 0.5).astype(np.uint8)
+
+        pred_file_name = Path(args.target_path) / (file_name.stem.replace('_a', '') + '.png')
+
+        y_pred = (cv2.imread(str(pred_file_name), 0) > 255 * 0.5).astype(np.uint8)
+
+        result_dice += [dice(y_true, y_pred)]
+        result_jaccard += [jaccard(y_true, y_pred)]
+
+    print('Dice = ', np.mean(result_dice), np.std(result_dice))
+    print('Jaccard = ', np.mean(result_jaccard), np.std(result_jaccard))