Function binary_mask

computer_vision/haralick_descriptors.py:195–219

Apply binary mask, or thresholding based on bit mask value (mapping mask is binary). Returns the mapped true value mask and its complementary false value mask. Example: >>> img = np.array([[[108, 201, 72], [255, 11, 127]], ... [[56, 56, 56], [128

(
    image_gray: np.ndarray, image_map: np.ndarray
)

def binary_mask(
    image_gray: np.ndarray, image_map: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
    """
    Apply binary mask, or thresholding based
    on bit mask value (mapping mask is binary).

    Returns the mapped true value mask and its complementary false value mask.

    Example:
        >>> img = np.array([[[108, 201, 72], [255, 11,  127]],
        ...                 [[56,  56,  56], [128, 255, 107]]])
        >>> gray = grayscale(img)
        >>> binary = binarize(gray)
        >>> morphological = opening_filter(binary)
        >>> binary_mask(gray, morphological)
        (array([[1, 1],
               [1, 1]], dtype=uint8), array([[158,  97],
               [ 56, 200]], dtype=uint8))
    """
    true_mask, false_mask = image_gray.copy(), image_gray.copy()
    true_mask[image_map == 1] = 1
    false_mask[image_map == 0] = 0

    return true_mask, false_mask


def matrix_concurrency(image: np.ndarray, coordinate: tuple[int, int]) -> np.ndarray: