Python cv2.CAP_PROP_POS_FRAMES屬性代碼示例

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def __next__(self):
        self.count += 1

        # Respect FPS for files
        if self.isFile:
            delta = time.time() - self.ts
            self.sma.add(delta)
            time.sleep(max(0,(1.0 - self.sma.current*self.fps)/self.fps))
            self.ts = time.time()

        # Read image
        ret_val, img0 = self.cam.read()
        if not ret_val and self.isFile:
            self.cam.set(cv2.CAP_PROP_POS_FRAMES, 0)
            ret_val, img0 = self.cam.read()
        assert ret_val, 'Video Error'

        # Preprocess
        img = img0
        if not self.isFile:
            img = cv2.flip(img, 1)

        return self.count, img 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def count_frames(self, check_validity=False):
        offset = 0
        if self.vid_path.endswith('.flv'):
            offset = -1
        unverified_frame_count = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) + offset
        if check_validity:
            verified_frame_count = 0
            for i in range(unverified_frame_count):
                self.cap.set(cv2.CAP_PROP_POS_FRAMES, i)
                if not self.cap.grab():
                    logging.warning("VideoIter:: >> frame (start from 0) {} corrupted in {}".format(i, self.vid_path))
                    break
                verified_frame_count = i + 1
            self.frame_count = verified_frame_count
        else:
            self.frame_count = unverified_frame_count
        assert self.frame_count > 0, "VideoIter:: Video: `{}' has no frames".format(self.vid_path)
        return self.frame_count 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def search_switch(self, cap, key, thresh=0.5):
        left = 0
        right = cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1

        cap.set(cv2.CAP_PROP_POS_FRAMES, int((right + left) / 2))

        while right > left + 1:
            _, frame = cap.read()

            image = self.extractor.prepare_frame(frame, self.extractor.image_dict[key][0])

            if not self.extractor.exists(image, self.extractor.image_dict[key][1][0], thresh):
                left = int((right + left) / 2)
            else:
                right = int((right + left) / 2)

            cap.set(cv2.CAP_PROP_POS_FRAMES, int((right + left) / 2))

        cap.set(cv2.CAP_PROP_POS_FRAMES, left)

        return left 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def skip_from_launch(self, cap, key, time, thresh=None):
        """
        Move the capture to T+time (time can be negative) and returns the frame index.
        :param cap: OpenCV capture
        :param time: delta time from launch to skip to
        :return: index of requested frame
        """        
        if thresh is None:
            thresh = self.extractor.image_dict[key][2]

        number_of_frames = int(cap.get(cv2.CAP_PROP_FPS) * time) + self.search_switch(cap, key, thresh)

        number_of_frames = max(number_of_frames, 0)
        number_of_frames = min(number_of_frames, cap.get(cv2.CAP_PROP_FRAME_COUNT))

        cap.set(cv2.CAP_PROP_POS_FRAMES, number_of_frames)

        return number_of_frames 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def find_anchor(self, cap, start=0, end=1, maxiter=10):
        if not isinstance(self.extractor, RelativeExtract):
            return False

        original_location = cap.get(cv2.CAP_PROP_POS_FRAMES)

        for i in range(maxiter):
            pos = random.uniform(start, end)

            cap.set(cv2.CAP_PROP_POS_FRAMES,  pos*cap.get(cv2.CAP_PROP_FRAME_COUNT))
            _, frame = cap.read()

            if self.extractor.prepare_image_dict(frame):
                return True

        cap.set(cv2.CAP_PROP_POS_FRAMES, original_location)

        return False 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def show_frames(capture, frame_num_list, bbox_list=None, wait_flag=True):

    # Iterate through the frame_num_list, while using indexes for bboxes.
    for frame_index, frame_num in enumerate(frame_num_list):
        capture.set(cv2.CAP_PROP_POS_FRAMES, frame_num)
        _, frame = capture.read()
        if bbox_list:
            frame = cv2.rectangle(frame, bbox_list[frame_index][0],
                bbox_list[frame_index][1], [0, 0, 255], 6)
        cv2.imshow('frame', frame)
        if wait_flag:
            cv2.waitKey(0)
        else:
            cv2.waitKey(1)


# Given a list of bounding boxes, return the average bounding box. 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def read(self, position=None):
    """Reads a certain frame.

    NOTE: The returned frame is assumed to be with `RGB` channel order.

    Args:
      position: Optional. If set, the reader will read frames from the exact
        position. Otherwise, the reader will read next frames. (default: None)
    """
    if position is not None and position < self.length:
      self.video.set(cv2.CAP_PROP_POS_FRAMES, position)
      self.position = position

    success, frame = self.video.read()
    self.position = self.position + 1

    return frame[:, :, ::-1] if success else None 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def run_ocr(self, lang: str, time_start: str, time_end: str,
                conf_threshold: int, use_fullframe: bool) -> None:
        self.lang = lang
        self.use_fullframe = use_fullframe

        ocr_start = utils.get_frame_index(time_start, self.fps) if time_start else 0
        ocr_end = utils.get_frame_index(time_end, self.fps) if time_end else self.num_frames

        if ocr_end < ocr_start:
            raise ValueError('time_start is later than time_end')
        num_ocr_frames = ocr_end - ocr_start

        # get frames from ocr_start to ocr_end
        with Capture(self.path) as v, multiprocessing.Pool() as pool:
            v.set(cv2.CAP_PROP_POS_FRAMES, ocr_start)
            frames = (v.read()[1] for _ in range(num_ocr_frames))

            # perform ocr to frames in parallel
            it_ocr = pool.imap(self._image_to_data, frames, chunksize=10)
            self.pred_frames = [
                PredictedFrame(i + ocr_start, data, conf_threshold)
                for i, data in enumerate(it_ocr)
            ] 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def is_picture(self):
        sampling_interval = int(math.floor(self.scene_length / 5))
        sampling_frames = list(range(self.start_frame_no + sampling_interval,
                                     self.end_frame_no - sampling_interval + 1, sampling_interval))
        frames = []
        for frame_no in sampling_frames:
            self.video.set(cv2.CAP_PROP_POS_FRAMES, frame_no)
            ret, frame = self.video.read()
            frames.append(frame)

        diff = 0
        n_diff = 0
        for frame, next_frame in zip(frames, frames[1:]):
            diff += cv2.norm(frame, next_frame, cv2.NORM_L1)  # abs diff
            n_diff += 1
        diff /= n_diff
        self.debugging_info[4] = round(diff, 0)

        return diff < 3000000 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def write_to_trackfile(track_info):
    # st = time.time()
    with open(WORK_DIR + "/METADATA/" + "trackfile.txt", "a+") as f:
        for item in track_info:
            f.write(str(item) + "\n")
            # print(str(item) + "\n")
        f.close()
    # et = time.time()
    # print ("write time:", et - st)
    # sys.exit()
    
#uncomment to use as stand-alone file
# start_frame = 8*3
# vid = VIDEO_PATH + "/cam_8.avi"
# cap = cv2.VideoCapture(vid)
# cap.set(cv2.CAP_PROP_FPS, 8)
# cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
# ok, frame = cap.read()
# bbox = selectROI(frame)
# track(0,cap,bbox,2)


# cam_id, cap, bbox, data_inc 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def _get_frame_from_capture(cap, frame_number, filename, total_frames):
    frame_pos_const = cv2.cv.CV_CAP_PROP_POS_FRAMES if \
        cv2.__version__.startswith('2') else cv2.CAP_PROP_POS_FRAMES
    if (cap.get(frame_pos_const) != int(frame_number)):
        # Following is an expensive operation; avoid if possible
        cap.set(frame_pos_const, int(frame_number))
    success, I = cap.read()
    if not success:
        logger.error('Unable to read frame {} (out-of {}) from {}. Using 0s.'
                     .format(frame_number, total_frames, filename))
    elif I.max() > 256 or I.min() < 0:
        logger.error('Invalid values detected when reading frame {} '
                     'from {}. Using 0s.'.format(frame_number, filename))
    else:
        return I
    return cfg.PIXEL_MEANS 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def generate_dataset(videos_path, framerate, width, height):
    """Converts videos from specified path to ndarrays of shape [numberOfVideos, -1, width, height, 1]

    Args:
        videos_path: Inside the 'videos/' directory, the name of the subdirectory for videos.
        framerate: The desired framerate of the dataset.
        width: The width we will resize the videos to.
        height: The height we will resize the videos to.

    Returns:
        The dataset with the new size and framerate, and converted to monochromatic.

    """
    dataset = []
    video_index = 0
    for playlist in os.listdir('videos/' + videos_path):
        for video_name in os.listdir('videos/{}/{}'.format(videos_path, playlist)):
            dataset.append([])
            print('Video: {}'.format(video_name))
            video = cv2.VideoCapture('videos/{}/{}/{}'.format(videos_path, playlist, video_name))
            while video.isOpened():
                success, frame = video.read()
                if success:
                    frame = preprocess_image(frame, width, height)
                    dataset[video_index].append(frame)

                    frame_index = video.get(cv2.CAP_PROP_POS_FRAMES)
                    video_framerate = video.get(cv2.CAP_PROP_FPS)
                    video.set(cv2.CAP_PROP_POS_FRAMES, frame_index + video_framerate // framerate)
                    last_frame_index = video.get(cv2.CAP_PROP_FRAME_COUNT)
                    if frame_index >= last_frame_index:
                        # Video is over
                        break

                    
                else:
                    break
            dataset[video_index] = np.reshape(dataset[video_index], (-1, width, height, 1))
            video_index += 1
    return dataset 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def video_jump(video_cap: cv2.VideoCapture, frame_id: int):
    # IMPORTANT:
    # - frame is a range actually
    # - frame 1 's timestamp is the beginning of this frame
    #
    # video_jump(cap, 2) means: moving the pointer to the start point of frame 2 => the end point of frame 1

    # another -1 for re-read
    video_cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id - 1 - 1)
    video_cap.read()
    logger.debug(
        f"previous pointer: {get_current_frame_id(video_cap)}({get_current_frame_time(video_cap)})"
    ) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def get_current_frame_id(video_cap: cv2.VideoCapture) -> int:
    # IMPORTANT:
    # this id is the frame which has already been grabbed
    # we jump to 5, which means the next frame will be 5
    # so the current frame id is: 5 - 1 = 4
    return int(video_cap.get(cv2.CAP_PROP_POS_FRAMES)) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import CAP_PROP_POS_FRAMES [as 別名]
def setFramePosition(self, framePos):
        self.stream.set(cv2.CAP_PROP_POS_FRAMES, framePos)