stouputils.data_science.data_processing.image.laplacian module

laplacian_image(image: ndarray[Any, dtype[Any]], kernel_size: int = 3, ignore_dtype: bool = False) → ndarray[Any, dtype[Any]]

Apply Laplacian edge detection to an image.

Parameters:

• image (NDArray[Any]) – Image to apply Laplacian edge detection

• kernel_size (int) – Size of the kernel (must be odd)

• ignore_dtype (bool) – Ignore the dtype check

Returns:

Image with Laplacian edge detection applied

Return type:

NDArray[Any]

>>> ## Basic tests
>>> image = np.array([[100, 150, 200], [50, 125, 175], [25, 75, 225]])
>>> edges = laplacian_image(image.astype(np.uint8))
>>> edges.shape == image.shape[:2]  # Laplacian returns single channel
True

>>> rgb = np.random.randint(0, 256, (3,3,3), dtype=np.uint8)
>>> edges_rgb = laplacian_image(rgb)
>>> edges_rgb.shape == rgb.shape[:2]  # Laplacian returns single channel
True

>>> ## Test invalid inputs
>>> laplacian_image("not an image")
Traceback (most recent call last):
        ...
AssertionError: Image must be a numpy array

>>> laplacian_image(image.astype(np.uint8), kernel_size=2)
Traceback (most recent call last):
        ...
AssertionError: kernel_size must be odd, got 2