--- a
+++ b/semseg/utils.py
@@ -0,0 +1,59 @@
+import numpy as np
+
+
+# from config.paths import train_images_folder, train_labels_folder
+
+
+def dice_coeff(gt, pred, eps=1e-5):
+    dice = np.sum(pred[gt == 1]) * 2.0 / (np.sum(pred) + np.sum(gt))
+    return dice
+
+
+def multi_dice_coeff(gt, pred, num_classes):
+    labels = one_hot_encode_np(gt, num_classes)
+    outputs = one_hot_encode_np(pred, num_classes)
+    dices = list()
+    for cls in range(1, num_classes):
+        outputs_ = outputs[:, cls]
+        labels_  = labels[:, cls]
+        dice_ = dice_coeff(outputs_, labels_)
+        dices.append(dice_)
+    return sum(dices) / (num_classes-1)
+
+
+def one_hot_encode_np(label, num_classes):
+    """ Numpy One Hot Encode
+    :param label: Numpy Array of shape BxHxW or BxDxHxW
+    :param num_classes: K classes
+    :return: label_ohe, Numpy Array of shape BxKxHxW or BxKxDxHxW
+    """
+    assert len(label.shape) == 3 or len(label.shape) == 4, 'Invalid Label Shape {}'.format(label.shape)
+    label_ohe = None
+    if len(label.shape) == 3:
+        label_ohe = np.zeros((label.shape[0], num_classes, label.shape[1], label.shape[2]))
+    elif len(label.shape) == 4:
+        label_ohe = np.zeros((label.shape[0], num_classes, label.shape[1], label.shape[2], label.shape[3]))
+    for batch_idx, batch_el_label in enumerate(label):
+        for cls in range(num_classes):
+            label_ohe[batch_idx, cls] = (batch_el_label == cls)
+    return label_ohe
+
+
+def min_max_normalization(input):
+    return (input - input.min()) / (input.max() - input.min())
+
+
+def z_score_normalization(input):
+    input_mean = np.mean(input)
+    input_std = np.std(input)
+    # print("Mean = {:.2f} - Std = {:.2f}".format(input_mean,input_std))
+    return (input - input_mean)/input_std
+
+
+def zero_pad_3d_image(image, pad_ref=(64,64,64), value_to_pad = 0):
+    if value_to_pad == 0:
+        image_padded = np.zeros(pad_ref)
+    else:
+        image_padded = value_to_pad * np.ones(pad_ref)
+    image_padded[:image.shape[0],:image.shape[1],:image.shape[2]] = image
+    return image_padded