hw done

2018-Python-Practice-master/main.py

@@ -4,11 +4,13 @@
 
 import tensorflow as tf
 
+
+
 '''
 1. import `Video` and `save_video` from the correct module of package "styler"
 '''
-from ... import Video
-from ... import save_video
+from styler.video import Video
+from styler.utils import save_video
 
 
 model_file = 'data/vg-30.pb'
@@ -38,11 +40,13 @@ def main():
         out = graph.get_tensor_by_name("import/%s/output:0" % model_name)
         shape = image.get_shape().as_list()
 
+
         '''
         2. set the `path` to  your input
         '''
-        with Video(...) as v:
+        with Video('input/jaguar.mp4') as v:
             frames = v.read_frames(image_h=shape[1], image_w=shape[2])
+
 
         logging.info("Processing image")
         start_time = datetime.now()
@@ -51,17 +55,19 @@ def main():
         3. Write a list comprehension to iterate through all frames,
            and make it be processed by Tensorflow.
         '''
+
         processed = [
             session.run(out, feed_dict={image: [frame]})
-            ...
+            for frame in frames
         ]
 
+
         '''
         4. Pass the results as a argument into function
         '''
         save_video('result.mp4',
                    fps=30, h=shape[1], w=shape[2],
-                   frames=...)
+                   frames=processed)
 
         logging.info("Processing took %f" % (
             (datetime.now() - start_time).total_seconds()))

2018-Python-Practice-master/styler/video.py

@@ -28,9 +28,16 @@ def read_frames(self, image_h, image_w):
         '''
         frames = []
         # 5-1 /5-2 Read video and collect them
-
-        self.frames = ...  # 5-3 let object have the result
-        return ...  # return your results
+        while(True):
+            ret, frame = self.cap.read()
+
+            if(frame is None):
+                break
+            res = resize(frame,512, 512);
+            frames.append(res)
+
+        self.frames = frames  # 5-3 let object have the result
+        return self.frames  # return your results
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         self.cap.release()