Added support for pspnet100_cityscapes, alpha blending, keras saving & loading and much more

utils.py

@@ -1,20 +1,23 @@
+from __future__ import print_function
 import colorsys
 import numpy as np
 
 from keras.models import Model
 
+
 def add_color(img):
-    h,w = img.shape
-    img_color = np.zeros((h,w,3))
-    for i in xrange(1,151):
+    h, w = img.shape
+    img_color = np.zeros((h, w, 3))
+    for i in xrange(1, 151):
         img_color[img == i] = to_color(i)
     return img_color
 
+
 def to_color(category):
     # Maps each category a good distance away
     # from each other on the HSV color space
     v = (category-1)*(137.5/360)
-    return colorsys.hsv_to_rgb(v,1,1)
+    return colorsys.hsv_to_rgb(v, 1, 1)
 
 
 # For printing the activations in each layer
@@ -23,10 +26,14 @@ def debug(model, data):
     names = [layer.name for layer in model.layers]
     for name in names[:]:
         print_activation(model, name, data)
+
+
 def print_activation(model, layer_name, data):
     intermediate_layer_model = Model(inputs=model.input,
                                      outputs=model.get_layer(layer_name).output)
     io = intermediate_layer_model.predict(data)
-    print layer_name, array_to_str(io)
+    print (layer_name, array_to_str(io))
+
+
 def array_to_str(a):
-    return "{} {} {} {} {}".format(a.dtype, a.shape, np.min(a), np.max(a), np.mean(a))
+    return "{} {} {} {} {}".format(a.dtype, a.shape, np.min(a), np.max(a), np.mean(a))