include the Apache license header for process_data.py

examples/largedataset_cnn/process_data.py

@@ -1,3 +1,22 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+
 import os
 import imghdr
 import numpy as np
@@ -10,15 +29,15 @@ def paths_to_images(paths, image_size):
     for i in range(num_images):
         temp = np.array(Image.open(paths[i]).convert('RGB').resize((image_size, image_size), Image.BILINEAR))
         temp = np.moveaxis(temp,-1,0)
-        im[i] = temp  
+        im[i] = temp
 
     im /= 255
 
     return im
 
 
 def process_data(dataset_root, classes):
-  # load class names
+    # load class names
     with open(classes, 'r', encoding='utf-8') as f:
         classes = f.readlines()
         classes = list(map(lambda x: x.strip(), classes))
@@ -52,16 +71,16 @@ def process_data(dataset_root, classes):
     val_labels = labels[border:]
     train_input_paths = input_paths[:border]
     val_input_paths = input_paths[border:]
-    
+
 
     print("Training on %d images and labels" % (len(train_input_paths)))
     print("Validation on %d images and labels" % (len(val_input_paths)))
-    
+
     return train_input_paths, train_labels, val_input_paths, val_labels
 
 def loaddata():
     dataset_root = '/Dataset/Data/'
-    classes = '/Dataset/classes.txt'    
+    classes = '/Dataset/classes.txt'
     return process_data(dataset_root, classes)
 
 if __name__ == '__main__':
@@ -72,9 +91,8 @@ def loaddata():
     print(train_input_paths.shape)
     print(train_labels.shape)
     print(val_input_paths.shape)
-    print(val_labels.shape)   
+    print(val_labels.shape)
 
     a=paths_to_images(paths=train_input_paths[0:5], image_size=299)
     print(a)
     print(a.shape)
-