fixed bug

2018-Python-Practice-master/main.py

@@ -1,72 +1,78 @@
-import logging
-import sys
-from datetime import datetime
-
-import tensorflow as tf
-
-'''
-1. import `Video` and `save_video` from the correct module of package "styler"
-'''
-from ... import Video
-from ... import save_video
-
-
-model_file = 'data/vg-30.pb'
-model_name = 'vg-30'
-logging.basicConfig(
-    stream=sys.stdout,
-    format='%(asctime)s %(levelname)s:%(message)s',
-    level=logging.INFO,
-    datefmt='%I:%M:%S')
-
-
-def main():
-
-    with open(model_file, 'rb') as f:
-        graph_def = tf.GraphDef()
-        graph_def.ParseFromString(f.read())
-        tf.import_graph_def(graph_def)
-        graph = tf.get_default_graph()
-
-    with tf.Session(config=tf.ConfigProto(
-            intra_op_parallelism_threads=4)) as session:
-
-        logging.info("Initializing graph")
-        session.run(tf.global_variables_initializer())
-
-        image = graph.get_tensor_by_name("import/%s/image_in:0" % model_name)
-        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:
-            frames = v.read_frames(image_h=shape[1], image_w=shape[2])
-
-        logging.info("Processing image")
-        start_time = datetime.now()
-
-        '''
-        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]})
-            ...
-        ]
-
-        '''
-        4. Pass the results as a argument into function
-        '''
-        save_video('result.mp4',
-                   fps=30, h=shape[1], w=shape[2],
-                   frames=...)
-
-        logging.info("Processing took %f" % (
-            (datetime.now() - start_time).total_seconds()))
-        logging.info("Done")
-
-
-if __name__ == '__main__':
-    main()
+import logging
+import sys
+from datetime import datetime
+
+import tensorflow as tf
+
+
+
+'''
+1. import `Video` and `save_video` from the correct module of package "styler"
+'''
+from styler.video import Video
+from styler.utils import save_video
+
+
+model_file = 'data/vg-30.pb'
+model_name = 'vg-30'
+logging.basicConfig(
+    stream=sys.stdout,
+    format='%(asctime)s %(levelname)s:%(message)s',
+    level=logging.INFO,
+    datefmt='%I:%M:%S')
+
+
+def main():
+
+    with open(model_file, 'rb') as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        tf.import_graph_def(graph_def)
+        graph = tf.get_default_graph()
+
+    with tf.Session(config=tf.ConfigProto(
+            intra_op_parallelism_threads=4)) as session:
+
+        logging.info("Initializing graph")
+        session.run(tf.global_variables_initializer())
+
+        image = graph.get_tensor_by_name("import/%s/image_in:0" % model_name)
+        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('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()
+
+        '''
+        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=processed)
+
+        logging.info("Processing took %f" % (
+            (datetime.now() - start_time).total_seconds()))
+        logging.info("Done")
+
+
+if __name__ == '__main__':
+    main()

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

@@ -1,48 +1,48 @@
-import cv2
-import numpy as np
-
-MEAN_VALUES = np.array([123, 117, 104]).reshape((1,1,3))
-
-
-def resize(img, image_h, image_w, zoom=False):
-    # crop image from center
-    ratio = float(image_h) / image_w
-    height, width = int(img.shape[0]), int(img.shape[1])
-    yy, xx = 0, 0
-    if height > width * ratio:  #too tall
-        yy = int(height - width * ratio) // 2
-        height = int(width * ratio)
-    else:  # too wide
-        xx = int(width - height / ratio) // 2
-        width = int(height / ratio)
-    if zoom:
-        yy += int(height / 6)
-        xx += int(height / 6)
-        height = int(height * 2 / 3)
-        width = int(width * 2 / 3)
-    crop_img = img[yy:yy + height, xx:xx + width]
-    # resize
-    resized_img = cv2.resize(crop_img, (image_h, image_w))
-    centered_img = resized_img - MEAN_VALUES
-
-    return centered_img
-
-
-def save_video(filepath, fps, w, h, frames):
-    codecs = ['WMV1', 'MJPG', 'XVID', 'PIM1']
-    '''
-      If you cannot write video file, you may change the used codec
-    '''
-    used_codec = codecs[2]  # change the index from codecs
-    fourcc = cv2.VideoWriter_fourcc(*used_codec)
-    out = cv2.VideoWriter(filepath, fourcc, fps, (w, h))
-    for frame in frames:
-        f = frame[0, :, :, :]
-        out.write(post_process(f))
-    out.release()
-
-
-def post_process(image):
-    img = image + MEAN_VALUES
-    img = np.clip(img, 0, 255).astype('uint8')
-    return cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+import cv2
+import numpy as np
+
+MEAN_VALUES = np.array([123, 117, 104]).reshape((1,1,3))
+
+
+def resize(img, image_h, image_w, zoom=False):
+    # crop image from center
+    ratio = float(image_h) / image_w
+    height, width = int(img.shape[0]), int(img.shape[1])
+    yy, xx = 0, 0
+    if height > width * ratio:  #too tall
+        yy = int(height - width * ratio) // 2
+        height = int(width * ratio)
+    else:  # too wide
+        xx = int(width - height / ratio) // 2
+        width = int(height / ratio)
+    if zoom:
+        yy += int(height / 6)
+        xx += int(height / 6)
+        height = int(height * 2 / 3)
+        width = int(width * 2 / 3)
+    crop_img = img[yy:yy + height, xx:xx + width]
+    # resize
+    resized_img = cv2.resize(crop_img, (image_h, image_w))
+    centered_img = resized_img - MEAN_VALUES
+
+    return centered_img
+
+
+def save_video(filepath, fps, w, h, frames):
+    codecs = ['WMV1', 'MJPG', 'XVID', 'PIM1']
+    '''
+      If you cannot write video file, you may change the used codec
+    '''
+    used_codec = codecs[2]  # change the index from codecs
+    fourcc = cv2.VideoWriter_fourcc(*used_codec)
+    out = cv2.VideoWriter(filepath, fourcc, fps, (w, h))
+    for frame in frames:
+        f = frame[0, :, :, :]
+        out.write(post_process(f))
+    out.release()
+
+
+def post_process(image):
+    img = image + MEAN_VALUES
+    img = np.clip(img, 0, 255).astype('uint8')
+    return cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

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

@@ -1,36 +1,43 @@
-import cv2
-
-from styler.utils import resize
-
-
-class Video:
-
-    def __init__(self, path):
-        self.path = path
-        self.cap = cv2.VideoCapture(self.path)
-        self.frames = []
-
-    def __enter__(self):
-        if not self.cap.isOpened():
-            raise Exception('Cannot open video: {}'.format(self.path))
-        return self
-
-    def __len__(self):
-        return len(self.frames)
-
-    def read_frames(self, image_h, image_w):
-        '''
-        5.
-         - Read video frames from `self.cap` and collect frames into list
-         - Apply `resize()` on each frame before add it to list
-         - Also assign frames to "self" object
-         - Return your results
-        '''
-        frames = []
-        # 5-1 /5-2 Read video and collect them
-
-        self.frames = ...  # 5-3 let object have the result
-        return ...  # return your results
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.cap.release()
+import cv2
+
+from styler.utils import resize
+
+
+class Video:
+
+    def __init__(self, path):
+        self.path = path
+        self.cap = cv2.VideoCapture(self.path)
+        self.frames = []
+
+    def __enter__(self):
+        if not self.cap.isOpened():
+            raise Exception('Cannot open video: {}'.format(self.path))
+        return self
+
+    def __len__(self):
+        return len(self.frames)
+
+    def read_frames(self, image_h, image_w):
+        '''
+        5.
+         - Read video frames from `self.cap` and collect frames into list
+         - Apply `resize()` on each frame before add it to list
+         - Also assign frames to "self" object
+         - Return your results
+        '''
+        frames = []
+        # 5-1 /5-2 Read video and collect them
+        while(True):
+            ret, frame = self.cap.read()
+
+            if(frame is None):
+                break
+            res = resize(frame,image_h, image_w);
+            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()