image_processing_common.py 文件源码

def distort_elastic_cv2(image, alpha=80, sigma=20, random_state=None):
    """Elastic deformation of images as per [Simard2003].
    """
    if random_state is None:
        random_state = np.random.RandomState(None)

    shape_size = image.shape[:2]

    # Downscaling the random grid and then upsizing post filter
    # improves performance. Approx 3x for scale of 4, diminishing returns after.
    grid_scale = 4
    alpha //= grid_scale  # Does scaling these make sense? seems to provide
    sigma //= grid_scale  # more similar end result when scaling grid used.
    grid_shape = (shape_size[0]//grid_scale, shape_size[1]//grid_scale)

    blur_size = int(4 * sigma) | 1
    rand_x = cv2.GaussianBlur(
        (random_state.rand(*grid_shape) * 2 - 1).astype(np.float32),
        ksize=(blur_size, blur_size), sigmaX=sigma) * alpha
    rand_y = cv2.GaussianBlur(
        (random_state.rand(*grid_shape) * 2 - 1).astype(np.float32),
        ksize=(blur_size, blur_size), sigmaX=sigma) * alpha
    if grid_scale > 1:
        rand_x = cv2.resize(rand_x, shape_size[::-1])
        rand_y = cv2.resize(rand_y, shape_size[::-1])

    grid_x, grid_y = np.meshgrid(np.arange(shape_size[1]), np.arange(shape_size[0]))
    grid_x = (grid_x + rand_x).astype(np.float32)
    grid_y = (grid_y + rand_y).astype(np.float32)

    distorted_img = cv2.remap(image, grid_x, grid_y,
        borderMode=cv2.BORDER_REFLECT_101, interpolation=cv2.INTER_LINEAR)

    return distorted_img