def analyse_video(self, video_code):
video_images = []
timestamp = video_code_to_timestamp(video_code)
video_bouts = self.fish_bouts.get_group(video_code)
video_path = os.path.join(self.video_directory, timestamp + '.avi')
tracking_path = os.path.join(self.tracking_directory, video_code + '.csv')
if os.path.exists(tracking_path):
tracking_info = read_csv(tracking_path, **self.dtype)
cap = cv2.VideoCapture(video_path)
tracked_frames = tracking_info[tracking_info['tracked']].index
bout_starts, bout_ends = video_bouts[['start', 'end']].values.T
exclude_frames = np.concatenate([np.arange(start, end) for (start, end) in zip(bout_starts, bout_ends)])
frame_numbers = tracked_frames[np.isin(tracked_frames, exclude_frames, assume_unique=True, invert=True)]
random = np.random.choice(frame_numbers, 100, replace=False)
for f in random:
cap.set(cv2.CAP_PROP_POS_FRAMES, f)
ret, frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
bg = background_division(frame, self.background)
ret, threshed = cv2.threshold(bg, 8, 255, cv2.THRESH_BINARY)
midpoint, heading = tracking_info.loc[f, self.cols].values
midpoint = np.array(midpoint) / 2.
img = rotate_and_centre_image(threshed, midpoint, heading, 0)
video_images.append(img)
assert len(video_images) == 100
video_images = np.array(video_images, dtype='float32')
average = np.mean(video_images, axis=0)
variance = np.var(video_images, axis=0)
else: # in case there is an error with the tracking
average, variance = np.empty((2, 500, 504), dtype='float32') * np.nan
self.images[video_code] = [(average, variance)]
def analyse_video(self, video_code):
video_images = []
timestamp = video_code_to_timestamp(video_code)
video_bouts = self.fish_bouts.get_group(video_code)
video_path = os.path.join(self.video_directory, timestamp + '.avi')
tracking_path = os.path.join(self.tracking_directory, video_code + '.csv')
if os.path.exists(tracking_path):
tracking_info = read_csv(tracking_path, **self.dtype)
cap = cv2.VideoCapture(video_path)
for idx, bout_info in video_bouts.iterrows():
bout_images = []
for frame_number in (bout_info.start, bout_info.end):
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
ret, frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
bg = background_division(frame, self.background)
ret, threshed = cv2.threshold(bg, 8, 255, cv2.THRESH_BINARY)
left, right, heading = tracking_info.loc[frame_number, self.cols].values
midpoint = np.array([left, right]).mean(axis=0) / 2.
img = rotate_and_centre_image(threshed, midpoint, heading, 0)
bout_images.append(img)
video_images.append(bout_images)
self.images[video_code] = video_images
if __name__ == "__main__":
exemplars = pd.read_csv(paths['exemplars'],
dtype={
'ID': str,
'video_code': str
})
to_check = exemplars[pd.isnull(exemplars['clean'])]
for idx, bout_info in to_check.iterrows():
if idx % 100 == 0:
print 'Analysed: {}/{}'.format(idx, len(exemplars))
fish_info = experiment.data[experiment.data['ID'] ==
bout_info.ID].iloc[0]
date_folder = fish_info.date.replace('-', '_')
video_file = video_code_to_timestamp(bout_info.video_code) + '.avi'
video_path = os.path.join(experiment.video_directory, date_folder,
fish_info['name'], video_file)
v = Video(video_path)
v.scroll(first_frame=bout_info.start,
last_frame=bout_info.end,
name='exemplar_' + str(idx))
if v.enter():
exemplars.loc[idx, 'clean'] = True
elif v.space():
exemplars.loc[idx, 'clean'] = False
else:
break
exemplars.to_csv(paths['exemplars'], index=False)
'ID': str,
'video_code': str
})
random_examples = np.random.choice(capture_strike_info.index,
100,
replace=False)
random_examples = capture_strike_info.loc[random_examples]
strike_frames = []
index_order = []
for fish_ID, fish_strikes in random_examples.groupby('ID'):
fish_info = experiment.data[experiment.data['ID'] == fish_ID].iloc[0]
date_folder = fish_info.date.replace('-', '_')
for video_code, video_strikes in fish_strikes.groupby('video_code'):
video_file = video_code_to_timestamp(video_code) + '.avi'
video_path = os.path.join(experiment.video_directory, date_folder,
fish_info['name'], video_file)
v = Video(video_path)
for idx, bout_info in video_strikes.iterrows():
v.scroll(first_frame=bout_info.start, last_frame=bout_info.end)
if v.enter():
strike_frames.append(v.frame_number)
elif v.space():
strike_frames.append(np.nan)
else:
sys.exit()
index_order.append(idx)
reordered = random_examples.loc[index_order]
reordered['strike_frame'] = strike_frames