--- a
+++ b/metrics.py
@@ -0,0 +1,36 @@
+
+import numpy as np
+def single_dice_coef(y_pred, y_true):
+    # shape of y_true and y_pred: (height, width)
+    intersection = np.sum(y_true * y_pred)
+    if (np.sum(y_true) == 0) and (np.sum(y_pred) == 0):
+        return 1
+    return (2*intersection) / (np.sum(y_true) + np.sum(y_pred))
+
+
+def mean_dice_coef(y_pred, y_true):
+    # shape of y_true and y_pred: (n_samples, height, width)
+    batch_size = y_true.shape[0]
+    mean_dice_channel = 0.
+    for i in range(batch_size):
+        channel_dice = single_dice_coef(y_pred[i, :, :], y_true[i, :, :])
+        mean_dice_channel += channel_dice/(batch_size)
+    return mean_dice_channel
+
+def mean_dice_coef_remove_empty(y_pred, y_true):
+    # shape of y_true and y_pred: (n_samples, height, width)
+    batch_size = y_true.shape[0]
+    mean_dice_channel = 0.
+    num_no_empty = batch_size
+    for i in range(batch_size):
+        if (np.sum(y_true[i, :, :]) == 0):
+            num_no_empty -= 1
+            continue
+
+        channel_dice = single_dice_coef(y_pred[i, :, :], y_true[i, :, :])
+        mean_dice_channel += channel_dice
+    
+    if num_no_empty == 0:
+        return None
+
+    return mean_dice_channel/(num_no_empty)    
\ No newline at end of file