Python imageio.get_reader方法代碼示例

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def avi_to_frame_list(avi_filename, gray):
    """Creates a list of frames starting from an AVI movie.
    Inverts axes to have num_channels, height, width in this order.

    Parameters
    ----------

    avi_filename: name of the AVI movie
    gray: if True, the resulting images are treated as grey images with only
          one channel. If False, the images have three channels.
    """
    print('Loading {}'.format(avi_filename))
    vid = imageio.get_reader(avi_filename, 'ffmpeg')
    if gray:
        data = [np.mean(np.moveaxis(im, 2, 0), axis=0, keepdims=True)
                for im in vid.iter_data()]
        print('Loaded grayscale images.')

    else:
        data = [np.moveaxis(im, 2, 0) for im in vid.iter_data()]
        print('Loaded RGB images.')
    return data 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def main():
    reader = imageio.get_reader("person_0_tube.mp4")
    frames = []
    for cur_frame in reader:
        frames.append(cur_frame)

    input_tube = np.stack(frames[:32], axis=0)
    input_tube = np.expand_dims(input_tube, axis=0) # batch dimension
    detector_dict = set_up_detector()
    prediction_probabilites = detect_on_tube(input_tube, detector_dict)

    top_k = 5
    top_classes = np.argsort(prediction_probabilites[0,:])[:-top_k-1:-1]

    print("Results")
    for ii in range(top_k):
        class_id = top_classes[ii]
        class_str = act.ACTION_STRINGS[class_id]
        class_prob = prediction_probabilites[0,class_id]
        print("%.10s : %.3f" % (class_str, class_prob))
    
    cv2.imshow('midframe', input_tube[0,16,:,:,::-1])
    cv2.waitKey(0) 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def test_screenrecord(d: u2.Device):
    with pytest.raises(RuntimeError):
        d.screenrecord.stop()

    d.screenrecord("output.mp4", fps=10)
    start = time.time()

    with pytest.raises(RuntimeError):
        d.screenrecord("output2.mp4")

    time.sleep(3.0)
    d.screenrecord.stop()
    print("Time used:", time.time() - start)

    # check
    with imageio.get_reader("output.mp4") as f:
        meta = f.get_meta_data()
        assert isinstance(meta, dict)
        from pprint import pprint
        pprint(meta) 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def main(n):
    """Stream the video into a Kafka producer in an infinite loop"""
    
    topic = choose_channel(n)
    video_reader = imageio.get_reader(DATA + topic + '.mp4', 'ffmpeg')
    metadata = video_reader.get_meta_data()
    fps = metadata['fps']

    producer = KafkaProducer(bootstrap_servers='localhost:9092',
                             batch_size=15728640,
                             linger_ms=1000,
                             max_request_size=15728640,
                             value_serializer=lambda v: json.dumps(v.tolist()))
    
    while True:
        video_loop(video_reader, producer, topic, fps) 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def _get_fps(self):
        """ Get the Frames per Second.

        If the input is a folder of images than 25.0 will be returned, as it is not possible to
        calculate the fps just from frames alone. For video files the correct FPS will be returned.

        Returns
        -------
        float: The Frames per Second of the input sources
        """
        if self._is_video:
            reader = imageio.get_reader(self.location, "ffmpeg")
            retval = reader.get_meta_data()["fps"]
            reader.close()
        else:
            retval = 25.0
        logger.debug(retval)
        return retval 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def _from_video(self):
        """ Generator for loading frames from a video

        Yields
        ------
        filename: str
            The dummy filename of the loaded video frame.
        image: numpy.ndarray
            The loaded video frame.
        """
        logger.debug("Loading frames from video: '%s'", self.location)
        reader = imageio.get_reader(self.location, "ffmpeg")
        for idx, frame in enumerate(reader):
            if idx in self._skip_list:
                logger.trace("Skipping frame %s due to skip list", idx)
                continue
            # Convert to BGR for cv2 compatibility
            frame = frame[:, :, ::-1]
            filename = self._dummy_video_framename(idx)
            logger.trace("Loading video frame: '%s'", filename)
            yield filename, frame
        reader.close() 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def _load_one_video_frame(self, frame_no):
        """ Obtain a single frame from a video file.

        Parameters
        ----------
        frame_no: int
            The frame index for the required frame

        Returns
        ------
        :class:`numpy.ndarray`
            The image for the requested frame index,
        """
        logger.trace("Loading video frame: %s", frame_no)
        reader = imageio.get_reader(self._args.input_dir, "ffmpeg")
        reader.set_image_index(frame_no - 1)
        frame = reader.get_next_data()[:, :, ::-1]
        reader.close()
        return frame 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def __init__(self, fname, format=None, resize=None, anti_aliasing=False,
                 electrodes=None, metadata=None, compress=False,
                 interp_method='linear', extrapolate=False):
        # Open the video reader:
        reader = video_reader(fname, format=format)
        # Combine video metadata with user-specified metadata:
        meta = reader.get_meta_data()
        if metadata is not None:
            meta.update(metadata)
        meta['source'] = fname
        # Read the video:
        vid = [frame for frame in reader]
        # Consider downscaling before doing anything else (with anti-aliasing,
        # this can take a while):
        if resize is not None:
            vid = parfor(img_resize, vid, func_args=[resize],
                         func_kwargs={'anti_aliasing': anti_aliasing})
        if vid[0].ndim == 3 and vid[0].shape[-1] == 3:
            vid = parfor(rgb2gray, vid)
        vid = np.array(parfor(img_as_float, vid)).transpose((1, 2, 0))
        # Infer the time points from the video frame rate:
        n_frames = vid.shape[-1]
        time = np.arange(n_frames) * meta['fps']
        # Call the Stimulus constructor:
        super(VideoStimulus, self).__init__(vid.reshape((-1, n_frames)),
                                            time=time, electrodes=electrodes,
                                            metadata=meta, compress=compress,
                                            interp_method=interp_method,
                                            extrapolate=extrapolate) 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def __init__(self, path):
        self.path = path
        self.container = imageio.get_reader(path, 'ffmpeg')
        self.length = self.container.count_frames()
        self.fps = self.container.get_meta_data()['fps'] 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def __init__(self, vid_path, start=0, seq_len=1, fps=29.97):
    """ The VideoReader serves a generator of frame-sequences as needed. To increase performance, sequential
    frame access is enforced, and frames are cached into a buffer to allow for quicker repeated and
    sequential accesses within the allocated buffer.
    TODO: Buffer implementation. Could be implemented by taking a cache_size param for max(caption_size) and
    keeping track lo/hi indices for when to update. See `_updateCache` for draft.

    Note memory usage may grow significantly with high-resolution video or large cache sizes, calculated by the
    following formula:
    ```
    bytes = seq_len * height * width * channels
    ```

    A 1080p (1920x1080) video with sequence length of 30, or approximately 1 second of 30fps footage equates to:
    ```
    30 * 1080 * 1920 * 3 bytes ~ 186MB
    ```

    :param vid_path: Path of the video to read from.
    :param start: The starting frame index to begin reading from.
    :param seq_len: The length of the sequence of frames to serve.
    """
    assert os.path.isfile(vid_path)
    reader = imageio.get_reader(vid_path)
    vid_len = reader.get_length()

    # State.
    self.lo = start
    self._reader = reader
    self.cache = collections.deque(maxlen=seq_len)
    self.buf = np.empty(shape=(seq_len,), dtype=np.ndarray)

    # For convenience.
    self._seq_len = seq_len
    self._vid_len = vid_len
    self._fps = fps 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def test_local_video():
    main_folder_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) 
    obj_detection_graph =  os.path.join(main_folder_path, 'object_detection/weights/batched_zoo/faster_rcnn_nas_coco_2018_01_28/batched_graph/frozen_inference_graph.pb')

    print("Loading object detection model at %s" % obj_detection_graph)

    Obj_Detector = obj.Object_Detector(obj_detection_graph)

    test_vid_path = "chase1Person1View3Point0.mp4"
    print('Testing on %s' % test_vid_path)

    reader = imageio.get_reader(test_vid_path, 'ffmpeg')
    fps = reader.get_meta_data()['fps'] // 2

    out_vid_path = "chase1Person1View3Point0_out.mp4"
    writer = imageio.get_writer(out_vid_path, fps=fps)
    print("Writing output video on %s" %out_vid_path)

    frame_cnt = 0
    for test_img in reader:
        frame_cnt += 1
        if frame_cnt % 2 == 0:
            continue
        print("frame_cnt: %i" %frame_cnt)
        expanded_img = np.expand_dims(test_img, axis=0)
        detection_list = Obj_Detector.detect_objects_in_np(expanded_img)
        out_img = visualize_results(test_img, detection_list, display=False)
        writer.append_data(out_img)
        
    writer.close() 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def test_tracking_local_video():
    main_folder_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) 
    # obj_detection_graph =  os.path.join(main_folder_path, 'object_detection/weights/batched_zoo/faster_rcnn_nas_coco_2018_01_28/batched_graph/frozen_inference_graph.pb')
    obj_detection_graph =  os.path.join(main_folder_path, 'object_detection/weights/batched_zoo/faster_rcnn_nas_lowproposals_coco_2018_01_28/batched_graph/frozen_inference_graph.pb')

    print("Loading object detection model at %s" % obj_detection_graph)

    Obj_Detector = obj.Object_Detector(obj_detection_graph)
    Tracker = obj.Tracker()

    #test_vid_path = "chase1Person1View3Point0.mp4"
    #test_vid_path = "VIRAT_S_000003_9_00.mp4"
    test_vid_path = "VIRAT_S_000101_1_00.mp4"
    print('Testing on %s' % test_vid_path)

    reader = imageio.get_reader(test_vid_path, 'ffmpeg')
    fps = reader.get_meta_data()['fps'] // 2

    #out_vid_path = "chase1Person1View3Point0_out.mp4"
    #out_vid_path = "VIRAT_S_000003_9_00_out.mp4"
    out_vid_path = "VIRAT_S_000101_1_00_out.mp4"
    writer = imageio.get_writer(out_vid_path, fps=fps)
    print("Writing output video on %s" %out_vid_path)

    frame_cnt = 0
    for test_img in reader:
        frame_cnt += 1
        if frame_cnt % 2 == 0:
            continue
        print("frame_cnt: %i" %frame_cnt)
        expanded_img = np.expand_dims(test_img, axis=0)
        detection_list = Obj_Detector.detect_objects_in_np(expanded_img)
        detection_info = [info[0] for info in detection_list]
        Tracker.update_tracker(detection_info, test_img)
        #print(Tracker.active_actors)
        out_img = visualize_results_from_tracking(test_img, Tracker.active_actors, Tracker.inactive_actors, display=False)
        writer.append_data(out_img)
        
    writer.close() 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def make_gif(image, iteration_number, save_path, model_name, max_frames_per_gif=100):

    # Make gif
    gif_frames = []

    # Read old gif frames
    try:
        gif_frames_reader = imageio.get_reader(os.path.join(save_path, model_name + ".gif"))
        for frame in gif_frames_reader:
            gif_frames.append(frame[:, :, :3])
    except:
        pass

    # Append new frame
    im = cv2.putText(np.concatenate((np.zeros((32, image.shape[1], image.shape[2])), image), axis=0),
                     'iter %s' % str(iteration_number), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1, cv2.LINE_AA).astype('uint8')
    gif_frames.append(im)

    # If frames exceeds, save as different file
    if len(gif_frames) > max_frames_per_gif:
        print("Splitting the GIF...")
        gif_frames_00 = gif_frames[:max_frames_per_gif]
        num_of_gifs_already_saved = len(glob.glob(os.path.join(save_path, model_name + "_*.gif")))
        print("Saving", os.path.join(save_path, model_name + "_%05d.gif" % (num_of_gifs_already_saved)))
        imageio.mimsave(os.path.join(save_path, model_name + "_%05d.gif" % (num_of_gifs_already_saved)), gif_frames_00)
        gif_frames = gif_frames[max_frames_per_gif:]

    # Save gif
    # print("Saving", os.path.join(save_path, model_name + ".gif"))
    imageio.mimsave(os.path.join(save_path, model_name + ".gif"), gif_frames) 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def extract_frames(video_path):

    def convert_frame(arg):
        return Image.fromarray(arg[:, :, :3], mode='RGB')

    video_reader = imageio.get_reader(video_path)
    fps = video_reader.get_meta_data().get('fps', None)
    frames = [convert_frame(x) for x in video_reader]

    return frames, fps 

# 需要導入模塊: import imageio [as 別名]
# 或者: from imageio import get_reader [as 別名]
def _get_data(file: Path, index: int) -> np.ndarray:
        with imageio.get_reader(file) as reader:
            return np.asarray(reader.get_data(index))