script fix

CNN/convert.py

@@ -1,3 +1,5 @@
+#! /usr/bin/env python
+
 import sys
 import coremltools
 
@@ -40,7 +42,7 @@ def main(argv):
 if __name__ == '__main__':
     if len(sys.argv) != 3:
         print('Usage:')
-        print('python convert.py -c path_to_keras_classifier_model')
-        print('python convert.py -r path_to_keras_regressor_model')
+        print('convert.py -c path_to_keras_classifier_model')
+        print('convert.py -r path_to_keras_regressor_model')
     else:
         main(sys.argv[1:])

CNN/evaluate.py

@@ -1,3 +1,5 @@
+#! /usr/bin/env python
+
 import csv
 import os
 import sys
@@ -258,6 +260,6 @@ if __name__ == '__main__':
             eval_from_file(sys.argv[2])
             exit()
     print('Usage:')
-    print('python evaluate.py -c path_to_classifier_keras_model')
-    print('python evaluate.py -r path_to_regressor_keras_model')
-    print('python evaluate.py -f path_to_user_study_data_file')
+    print('evaluate.py -c path_to_classifier_keras_model')
+    print('evaluate.py -r path_to_regressor_keras_model')
+    print('evaluate.py -f path_to_user_study_data_file')

CNN/net.py

@@ -1,7 +1,6 @@
-#! /usr/local/bin/python3
+#! /usr/bin/env python
 
 import os
-
 import numpy as np
 from keras.applications.mobilenet import DepthwiseConv2D
 from keras.callbacks import ModelCheckpoint

CNN/process.py

@@ -1,3 +1,5 @@
+#! /usr/bin/env python
+
 import csv
 import os
 import cv2
@@ -67,11 +69,11 @@ def process_labels(p):
 if __name__ == '__main__':
     if '-i' not in sys.argv and '-l' not in sys.argv:
         print('Usage:')
-        print('python process.py')
-        print('                   -i    process images from AffectNet training.csv + validation.csv')
-        print('                         requires raw images in data dir and outputs to data_p')
-        print('                   -l    process labels from AffectNet training.csv + validation.csv')
-        print('                         outputs to [training/validation]_[paths/labels].npy')
+        print('process.py')
+        print('           -i    process images from AffectNet training.csv + validation.csv')
+        print('                 requires raw images in data dir and outputs to data_p')
+        print('           -l    process labels from AffectNet training.csv + validation.csv')
+        print('                 outputs to [training/validation]_[paths/labels].npy')
     if '-i' in sys.argv:
         process_images('training.csv')
         process_images('validation.csv')