--- a
+++ b/prepare_hdf5_cutedge.py
@@ -0,0 +1,124 @@
+from medpy.io import load
+import numpy as np
+import os
+import h5py
+
+#Path to your dataset (img, hdr files)
+data_path = '/media/toanhoi/Study/databaseSeg/ISeg/iSeg-2017-Training'
+#Saved path
+target_path = '/media/toanhoi/Study/databaseSeg/ISeg/hdf5_iseg_data'
+#Reference https://github.com/zhengyang-wang/Unet_3D/tree/master/preprocessing
+def cut_edge(data, keep_margin):
+    '''
+    function that cuts zero edge
+    '''
+    D, H, W = data.shape
+    D_s, D_e = 0, D - 1
+    H_s, H_e = 0, H - 1
+    W_s, W_e = 0, W - 1
+
+    while D_s < D:
+        if data[D_s].sum() != 0:
+            break
+        D_s += 1
+    while D_e > D_s:
+        if data[D_e].sum() != 0:
+            break
+        D_e -= 1
+    while H_s < H:
+        if data[:, H_s].sum() != 0:
+            break
+        H_s += 1
+    while H_e > H_s:
+        if data[:, H_e].sum() != 0:
+            break
+        H_e -= 1
+    while W_s < W:
+        if data[:, :, W_s].sum() != 0:
+            break
+        W_s += 1
+    while W_e > W_s:
+        if data[:, :, W_e].sum() != 0:
+            break
+        W_e -= 1
+
+    if keep_margin != 0:
+        D_s = max(0, D_s - keep_margin)
+        D_e = min(D - 1, D_e + keep_margin)
+        H_s = max(0, H_s - keep_margin)
+        H_e = min(H - 1, H_e + keep_margin)
+        W_s = max(0, W_s - keep_margin)
+        W_e = min(W - 1, W_e + keep_margin)
+
+    return int(D_s), int(D_e), int(H_s), int(H_e), int(W_s), int(W_e)
+
+def convert_label(label_img):
+    label_processed=np.zeros(label_img.shape[0:]).astype(np.uint8)
+    for i in range(label_img.shape[2]):
+        label_slice=label_img[:, :, i]
+        label_slice[label_slice == 10] = 1
+        label_slice[label_slice == 150] = 2
+        label_slice[label_slice == 250] = 3
+        label_processed[:, :, i]=label_slice
+    return label_processed
+
+def build_h5_dataset(data_path, target_path):
+    '''
+    Build HDF5 Image Dataset.
+    '''
+    for i in range(10):
+        #Skip subject 9 for validation
+        if (i==8):
+            continue
+        subject_name = 'subject-%d-' % (i + 1)
+        f_T1 = os.path.join(data_path, subject_name + 'T1.hdr')
+        img_T1, header_T1 = load(f_T1)
+        f_T2 = os.path.join(data_path, subject_name + 'T2.hdr')
+        img_T2, header_T2 = load(f_T2)
+        f_l = os.path.join(data_path, subject_name + 'label.hdr')
+        labels, header_label = load(f_l)
+
+        inputs_T1 = img_T1.astype(np.float32)
+        inputs_T2 = img_T2.astype(np.float32)
+        labels = labels.astype(np.uint8)
+        labels=convert_label(labels)
+        mask=labels>0
+        # Normalization
+        inputs_T1_norm = (inputs_T1 - inputs_T1[mask].mean()) / inputs_T1[mask].std()
+        inputs_T2_norm = (inputs_T2 - inputs_T2[mask].mean()) / inputs_T2[mask].std()
+
+        # Cut edge
+        margin = 64/2   # training_patch_size / 2
+        mask = mask.astype(np.uint8)
+        min_D_s, max_D_e, min_H_s, max_H_e, min_W_s, max_W_e = cut_edge(mask, margin)
+        inputs_tmp_T1 = inputs_T1_norm[min_D_s:max_D_e + 1, min_H_s: max_H_e + 1, min_W_s:max_W_e + 1]
+        inputs_tmp_T2 = inputs_T2_norm[min_D_s:max_D_e + 1, min_H_s: max_H_e + 1, min_W_s:max_W_e + 1]
+
+        labels_tmp = labels[min_D_s:max_D_e + 1, min_H_s: max_H_e + 1, min_W_s:max_W_e + 1]
+
+        inputs_tmp_T1 = inputs_tmp_T1[:, :, :, None]
+        inputs_tmp_T2 = inputs_tmp_T2[:, :, :, None]
+        labels_tmp = labels_tmp[:, :, :, None]
+
+        inputs = np.concatenate((inputs_tmp_T1, inputs_tmp_T2), axis=3)
+
+        print (inputs.shape, labels_tmp.shape)
+
+        inputs_caffe = inputs[None, :, :, :, :]
+        labels_caffe = labels_tmp[None, :, :, :, :]
+        inputs_caffe = inputs_caffe.transpose(0, 4, 3, 1, 2)
+        labels_caffe = labels_caffe.transpose(0, 4, 3, 1, 2)
+
+        with h5py.File(os.path.join(target_path, 'train_iseg_norm_cutedge_weight_%s.h5' % (i+1)), 'w') as f:
+            f['data'] = inputs_caffe  # for caffe num channel x d x h x w
+            f['label'] = labels_caffe
+
+        with open('./train_list.txt', 'a') as f:
+            f.write(os.path.join(target_path, 'train_iseg_norm_cutedge_weight_%s.h5\n' % (i+1)))
+
+if __name__ == '__main__':
+    if not os.path.exists(target_path):
+        os.makedirs(target_path)
+    if os.path.exists("./train_list.txt"):
+        os.remove("./train_list.txt")
+    build_h5_dataset(data_path, target_path)