def random_short_side_scale_jitter_list()

in slowfast/datasets/cv2_transform.py [0:0]

• 29 lines of code
• 11 McCabe index (conditional complexity)

def random_short_side_scale_jitter_list(images, min_size, max_size, boxes=None):
    """
    Perform a spatial short scale jittering on the given images and
    corresponding boxes.
    Args:
        images (list): list of images to perform scale jitter. Dimension is
            `height` x `width` x `channel`.
        min_size (int): the minimal size to scale the frames.
        max_size (int): the maximal size to scale the frames.
        boxes (list): optional. Corresponding boxes to images. Dimension is
            `num boxes` x 4.
    Returns:
        (list): the list of scaled images with dimension of
            `new height` x `new width` x `channel`.
        (ndarray or None): the scaled boxes with dimension of
            `num boxes` x 4.
    """
    size = int(round(1.0 / np.random.uniform(1.0 / max_size, 1.0 / min_size)))

    height = images[0].shape[0]
    width = images[0].shape[1]
    if (width <= height and width == size) or (
        height <= width and height == size
    ):
        return images, boxes
    new_width = size
    new_height = size
    if width < height:
        new_height = int(math.floor((float(height) / width) * size))
        if boxes is not None:
            boxes = [
                proposal * float(new_height) / height for proposal in boxes
            ]
    else:
        new_width = int(math.floor((float(width) / height) * size))
        if boxes is not None:
            boxes = [proposal * float(new_width) / width for proposal in boxes]
    return (
        [
            cv2.resize(
                image, (new_width, new_height), interpolation=cv2.INTER_LINEAR
            ).astype(np.float32)
            for image in images
        ],
        boxes,
    )