--- a
+++ b/tf_utilities.py
@@ -0,0 +1,136 @@
+# ==============================================================================
+# Copyright (C) 2020 Vladimir Juras, Ravinder Regatte and Cem M. Deniz
+#
+# This file is part of 2019_IWOAI_Challenge
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published
+# by the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+# ==============================================================================
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import tensorflow.python.platform
+
+import numpy as np
+import h5py
+
+def zeroMeanUnitVariance(input_image):
+	# zero mean unit variance
+	augmented_image = np.zeros(input_image.shape)
+	for ci in range(input_image.shape[0]):
+		mn = np.mean(input_image[ci, ...])
+		sd = np.std(input_image[ci, ...])
+		augmented_image[ci, ...] = (input_image[ci, ...] - mn) / np.maximum(sd, 1e-5)
+	return augmented_image
+
+def compute_weights_multiClass(y,num_class):
+	flat_y = y.reshape([-1, num_class])
+	weight=np.zeros(num_class)
+	for i in range(num_class):
+		weight[i] = flat_y.sum() / flat_y[:,i].sum()
+	return weight
+
+def loadData_list_h5(X_train,y_train,num_channels):
+	train_X = []
+	train_y = []
+	train_info = []
+	for ii in range(len(X_train)):
+		hf = h5py.File(str(X_train[ii]),'r')
+		tmp_X = np.array(hf['data'])
+
+		hf = h5py.File(str(y_train[ii]),'r')
+		tmp_y = np.array(hf['data'])
+		## here we add the background layer for softmax
+		background = 1-(np.sum(tmp_y,axis=3)>0)
+		addBG = np.zeros((tmp_y.shape[0],tmp_y.shape[1],tmp_y.shape[2],tmp_y.shape[3]+1))
+		addBG[...,0]=background
+		addBG[...,1:7] = tmp_y
+		tmp_y=addBG
+
+		if tmp_y.shape[0]!=384:
+			print ("######")
+		if tmp_y.shape[1]!=384:
+			print ("######")
+		print(ii,tmp_X.shape,tmp_y.shape,str(X_train[ii]),str(y_train[ii]))
+		#tmp_y[tmp_y==3]=2
+		train_X.append(tmp_X)
+		train_y.append(tmp_y)
+		train_info.append('Filename:%s'%(X_train[ii]))
+
+	tmpp=np.asarray(train_X)
+	#tmpp=tmpp[...,np.newaxis]
+	return tmpp, np.asarray(train_y), train_info
+
+def loadData_list_h5_single(X_train,y_train,num_channels):
+	train_X = []
+	train_y = []
+	train_info = []
+
+	hf = h5py.File(str(X_train),'r')
+	tmp_X = np.array(hf['data'])
+
+	hf = h5py.File(str(y_train),'r')
+	tmp_y = np.array(hf['data'])
+	## here we add the background layer for softmax
+	background = 1-(np.sum(tmp_y,axis=3)>0)
+	addBG = np.zeros((tmp_y.shape[0],tmp_y.shape[1],tmp_y.shape[2],tmp_y.shape[3]+1))
+	addBG[...,0]=background
+	addBG[...,1:7] = tmp_y
+	tmp_y=addBG
+
+	if tmp_y.shape[0]!=384:
+		print ("######")
+	if tmp_y.shape[1]!=384:
+		print ("######")
+	train_X.append(tmp_X)
+	train_y.append(tmp_y)
+	train_info.append('Filename:%s'%(X_train))
+
+	tmpp=np.asarray(train_X)
+	return tmpp, np.asarray(train_y), train_info
+
+def loadData_list_h5_image(X_train,num_channels):
+	train_X = []
+	train_info = []
+	for ii in range(len(X_train)):
+		hf = h5py.File(str(X_train[ii]),'r')
+		tmp_X = np.array(hf['data'])
+
+		if tmp_X.shape[0]!=384:
+			print ("######")
+		if tmp_X.shape[1]!=384:
+			print ("######")
+
+		train_X.append(tmp_X)
+		train_info.append('Filename:%s'%(X_train[ii]))
+
+	tmpp=np.asarray(train_X)
+	return tmpp, train_info
+
+def crop_to_shape(data, shape):
+    """
+    Crops the array to the given image shape by removing the border (expects a tensor of shape [batches, nx, ny,nz, channels].
+
+    :param data: the array to crop
+    :param shape: the target shape
+    """
+    offset0 = (data.shape[1] - shape[1]) // 2
+    offset1 = (data.shape[2] - shape[2]) // 2
+    offset2 = (data.shape[3] - shape[3]) // 2
+
+    if offset0 == 0 and offset1 == 0 and offset2 == 0:
+        return data
+    else:
+    	return data[:, offset0:(-offset0), offset1:(-offset1),offset2:(-offset2),:]
+
+