def run(video_id, video):
'''
Main function for running all feature extraction steps.
TODO: move this to "__main__"
ARGS:
video_id: unique video identifier
video: path to video file (mp4 format)
RETURNS:
dataframe with frame-by-frame analysis
'''
## Go through every frame in the video and extract features
cap = cv2.VideoCapture(video)
frame = None
## Output dataframes
video_df = pd.DataFrame()
img_quality_df = pd.DataFrame()
num_frames = cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT)
#num_frames = 3
debug = 0
if debug == 0:
i_frame = 0
while i_frame < num_frames:
## Progress logging
if i_frame % 100 == 0:
print i_frame
if frame is not None:
prev_frame = frame.copy()
else:
prev_frame = None
ret,frame = cap.read()
## If there are no more frames, break out of loop
if ret == False:
break
frame_series = pd.DataFrame(index=[0])
frame_series['video_id'] = video_id
frame_series['frame_number'] = i_frame
frame_series['time'] = cap.get(cv2.cv.CV_CAP_PROP_POS_MSEC) / 1000.
##=======================
## Image quality metrics
##=======================
## Blur
blur_df = SceneAnalysis.get_blur(frame)
## Color Spectrum
color_df = SceneAnalysis.get_hsv_hists(frame)
img_quality_df = img_quality_df.append(pd.concat([frame_series, blur_df, color_df], axis=1), ignore_index=True)
#img_quality_df = img_quality_df.append(frame_series)
##=======================
## Video/motion metrics
##=======================
## Optical Flow
if prev_frame is not None:
flow_df = VideoUtilities.optical_flow_on_frame(frame, prev_frame)
else:
flow_df = None
## Merge results into single dataframe
#video_df = video_df.append(pd.concat([frame_series, blur_df, color_df, flow_df], axis=1, ignore_index=True))
#video_df = video_df.append(frame_series)
#cat['video_id'] = video_id
#cat = pd.concat([blur_df, color_df, flow_df], axis=1)
if flow_df is not None:
flow_series = flow_df.copy()
##NOTE: the below code is duplicated above - bad style
flow_series['video_id'] = video_id
flow_series['frame_number'] = i_frame #cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES-1)
flow_series['time'] = cap.get(cv2.cv.CV_CAP_PROP_POS_MSEC) / 1000.
#cat = pd.concat([frame_series, cat], axis=1)
video_df = video_df.append(flow_series, ignore_index=True)
i_frame += 1
else:
img_quality_df = pd.read_pickle(video_id + '.img_quality.pkl')
## After completing the frame-by-frame analysis, run video metrics
## scene changes
scene_change_df = VideoAnalysis.detect_cut(video, img_quality_df['time'])
img_quality_df['is_scene_transition'] = scene_change_df['is_scene_transition'].copy()
## Pickle model and save it to S3 or local directory
img_quality_df.to_pickle(video_id + '.img_quality.pkl')
video_df.to_pickle(video_id + '.flow.pkl')
return img_quality_df, video_df
def run(video_id, video):
'''
Main function for running all feature extraction steps.
TODO: move this to "__main__"
ARGS:
video_id: unique video identifier
video: path to video file (mp4 format)
RETURNS:
dataframe with frame-by-frame analysis
'''
## Go through every frame in the video and extract features
cap = cv2.VideoCapture(video)
frame = None
## Output dataframes
video_df = pd.DataFrame()
img_quality_df = pd.DataFrame()
num_frames = cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT)
#num_frames = 3
debug = 0
if debug == 0:
i_frame = 0
while i_frame < num_frames:
## Progress logging
if i_frame % 100 == 0:
print i_frame
if frame is not None:
prev_frame = frame.copy()
else:
prev_frame = None
ret, frame = cap.read()
## If there are no more frames, break out of loop
if ret == False:
break
frame_series = pd.DataFrame(index=[0])
frame_series['video_id'] = video_id
frame_series['frame_number'] = i_frame
frame_series['time'] = cap.get(
cv2.cv.CV_CAP_PROP_POS_MSEC) / 1000.
##=======================
## Image quality metrics
##=======================
## Blur
blur_df = SceneAnalysis.get_blur(frame)
## Color Spectrum
color_df = SceneAnalysis.get_hsv_hists(frame)
img_quality_df = img_quality_df.append(pd.concat(
[frame_series, blur_df, color_df], axis=1),
ignore_index=True)
#img_quality_df = img_quality_df.append(frame_series)
##=======================
## Video/motion metrics
##=======================
## Optical Flow
if prev_frame is not None:
flow_df = VideoUtilities.optical_flow_on_frame(
frame, prev_frame)
else:
flow_df = None
## Merge results into single dataframe
#video_df = video_df.append(pd.concat([frame_series, blur_df, color_df, flow_df], axis=1, ignore_index=True))
#video_df = video_df.append(frame_series)
#cat['video_id'] = video_id
#cat = pd.concat([blur_df, color_df, flow_df], axis=1)
if flow_df is not None:
flow_series = flow_df.copy()
##NOTE: the below code is duplicated above - bad style
flow_series['video_id'] = video_id
flow_series[
'frame_number'] = i_frame #cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES-1)
flow_series['time'] = cap.get(
cv2.cv.CV_CAP_PROP_POS_MSEC) / 1000.
#cat = pd.concat([frame_series, cat], axis=1)
video_df = video_df.append(flow_series, ignore_index=True)
i_frame += 1
else:
img_quality_df = pd.read_pickle(video_id + '.img_quality.pkl')
## After completing the frame-by-frame analysis, run video metrics
## scene changes
scene_change_df = VideoAnalysis.detect_cut(video,
img_quality_df['time'])
img_quality_df['is_scene_transition'] = scene_change_df[
'is_scene_transition'].copy()
## Pickle model and save it to S3 or local directory
img_quality_df.to_pickle(video_id + '.img_quality.pkl')
video_df.to_pickle(video_id + '.flow.pkl')
return img_quality_df, video_df
flow_series = flow_df.copy()
##NOTE: the below code is duplicated above - bad style
flow_series['video_id'] = video_id
flow_series['frame_number'] = i_frame #cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES-1)
flow_series['time'] = cap.get(cv2.cv.CV_CAP_PROP_POS_MSEC) / 1000.
#cat = pd.concat([frame_series, cat], axis=1)
video_df = video_df.append(flow_series, ignore_index=True)
i_frame += 1
else:
img_quality_df = pd.read_pickle(video_id + '.img_quality.pkl')
## After completing the frame-by-frame analysis, run video metrics
## scene changes
scene_change_df = VideoAnalysis.detect_cut(video, img_quality_df['time'])
img_quality_df['is_scene_transition'] = scene_change_df['is_scene_transition'].copy()
## Pickle model and save it to S3 or local directory
img_quality_df.to_pickle(video_id + '.img_quality.pkl')
video_df.to_pickle(video_id + '.flow.pkl')
return img_quality_df, video_df
if __name__ == "__main__":
video = "../media/CKeLfaOl0Qk.mp4"
video_df = pd.read_pickle('CKeLfaOl0Qk.img_quality.pkl')
video_series = video_df['time']
scene_change_df = VideoAnalysis.detect_cut(video, video_series)
#print scene_change_df[scene_change_df['is_scene_transition'] == 1]
video_df['is_scene_transition'] = scene_change_df['is_scene_transition'].copy()
print video_df[video_df['is_scene_transition'] == 1]
cv2.cv.CV_CAP_PROP_POS_MSEC) / 1000.
#cat = pd.concat([frame_series, cat], axis=1)
video_df = video_df.append(flow_series, ignore_index=True)
i_frame += 1
else:
img_quality_df = pd.read_pickle(video_id + '.img_quality.pkl')
## After completing the frame-by-frame analysis, run video metrics
## scene changes
scene_change_df = VideoAnalysis.detect_cut(video,
img_quality_df['time'])
img_quality_df['is_scene_transition'] = scene_change_df[
'is_scene_transition'].copy()
## Pickle model and save it to S3 or local directory
img_quality_df.to_pickle(video_id + '.img_quality.pkl')
video_df.to_pickle(video_id + '.flow.pkl')
return img_quality_df, video_df
if __name__ == "__main__":
video = "../media/CKeLfaOl0Qk.mp4"
video_df = pd.read_pickle('CKeLfaOl0Qk.img_quality.pkl')
video_series = video_df['time']
scene_change_df = VideoAnalysis.detect_cut(video, video_series)
#print scene_change_df[scene_change_df['is_scene_transition'] == 1]
video_df['is_scene_transition'] = scene_change_df[
'is_scene_transition'].copy()
print video_df[video_df['is_scene_transition'] == 1]
