def get_video_frames_preload(
video_path,
frame_numbers=None,
mask=Ellipsis,
as_list=False,
func=lambda x: x,
):
"""
Obtain numpy array corresponding to a particular video frame in video_path.
Fetching and returning a list is about 33% faster but may be less memory controlled. NB: Any
gain in speed will be lost if subsequently converted to array.
:param video_path: URL or local path to mp4 file
:param frame_numbers: video frames to be returned. If None, return all frames.
:param mask: a logical mask or slice to apply to frames
:param as_list: if true the frames are returned as a list, this is faster but may be less
memory efficient
:param func: Function to be applied to each frame. Applied after masking if applicable.
:return: numpy array corresponding to frame of interest, or list if as_list is True.
Default dimensions are (n, w, h, 3) where n = len(frame_numbers)
Example - Load first 1000 frames, keeping only the first colour channel:
frames = get_video_frames_preload(video_path, range(1000), mask=np.s_[:, :, 0])
"""
is_url = isinstance(video_path, str) and video_path.startswith('http')
cap = VideoStreamer(video_path).cap if is_url else cv2.VideoCapture(
str(video_path))
assert cap.isOpened(), 'Failed to open video'
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_numbers = frame_numbers if frame_numbers is not None else range(
frame_count)
# Setting the index is extremely slow; determine where frame index must be set
# The first index is always explicitly set.
to_set = np.insert(np.diff(frame_numbers), 0, 0) != 1
if as_list:
frame_images = [None] * len(frame_numbers)
else:
ret, frame = cap.read()
frame_images = np.empty(
(len(frame_numbers), *func(frame[mask or ...]).shape), np.uint8)
for ii, i in enumerate(frame_numbers):
sys.stdout.write(f'\rloading frame {ii}/{len(frame_numbers)}')
sys.stdout.flush()
if to_set[ii]:
cap.set(cv2.CAP_PROP_POS_FRAMES, i)
ret, frame = cap.read()
if ret:
frame_images[ii] = func(frame[mask or ...])
else:
print(f'failed to read frame #{i}')
cap.release()
sys.stdout.write('\x1b[2K\r') # Erase current line in stdout
return frame_images
def get_video_frame(video_path, frame_number):
"""
Obtain numpy array corresponding to a particular video frame in video_path
:param video_path: local path to mp4 file
:param frame_number: video frame to be returned
:return: numpy array corresponding to frame of interest. Dimensions are (w, h, 3)
"""
is_url = isinstance(video_path, str) and video_path.startswith('http')
cap = VideoStreamer(video_path).cap if is_url else cv2.VideoCapture(str(video_path))
# 0-based index of the frame to be decoded/captured next.
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
ret, frame_image = cap.read()
cap.release()
return frame_image