Function get_distances

computer_vision/haralick_descriptors.py:352–388

Calculate all Euclidean distances between a selected base descriptor and all other Haralick descriptors The resulting comparison is return in decreasing order, showing which descriptor is the most similar to the selected base. Args: descriptors: Haralick descriptors to

(descriptors: np.ndarray, base: int)

def get_distances(descriptors: np.ndarray, base: int) -> list[tuple[int, float]]:
    """
    Calculate all Euclidean distances between a selected base descriptor
    and all other Haralick descriptors
    The resulting comparison is return in decreasing order,
    showing which descriptor is the most similar to the selected base.

    Args:
        descriptors: Haralick descriptors to compare with base index
        base: Haralick descriptor index to use as base when calculating respective
        euclidean distance to other descriptors.

    Returns:
        Ordered distances between descriptors

    Example:
        >>> index = 1
        >>> 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)
        >>> get_distances(get_descriptors(
        ...                 binary_mask(gray, morphological), (0, 1)),
        ...               index)
        [(0, 0.0), (1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), \
(6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), \
(13, 0.0), (14, 0.0), (15, 0.0)]
    """
    distances = np.array(
        [euclidean(descriptor, descriptors[base]) for descriptor in descriptors]
    )
    # Normalize distances between range [0, 1]
    normalized_distances: list[float] = normalize_array(distances, 1).tolist()
    enum_distances = list(enumerate(normalized_distances))
    enum_distances.sort(key=lambda tup: tup[1], reverse=True)
    return enum_distances


if __name__ == "__main__":