stouputils.data_science.data_processing.image.clahe module

clahe_image(image: ndarray[Any, dtype[Any]], clip_limit: float = 2.0, tile_grid_size: int = 8, ignore_dtype: bool = False) → ndarray[Any, dtype[Any]]

Apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to an image.

Parameters:

• image (NDArray[Any]) – Image to apply CLAHE to

• clip_limit (float) – Threshold for contrast limiting (1.0-4.0 recommended)

• tile_grid_size (int) – Size of grid for histogram equalization (2-16 recommended)

• ignore_dtype (bool) – Ignore the dtype check

Returns:

Image with CLAHE applied

Return type:

NDArray[Any]

>>> ## Basic tests
>>> image = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> clahed = clahe_image(image.astype(np.uint8), 2.0, 1)
>>> clahed.tolist()
[[28, 57, 85], [113, 142, 170], [198, 227, 255]]
>>> clahed.shape == image.shape
True

>>> img = np.full((10,10), 128, dtype=np.uint8)
>>> img[2:8, 2:8] = 200  # Create a bright region
>>> clahed = clahe_image(img, 2.0, 4)
>>> bool(np.mean(clahed) > np.mean(img))  # Should enhance contrast
True
>>> bool(np.std(clahed) > np.std(img))  # Should enhance contrast
True

>>> rgb = np.full((10,10,3), 128, dtype=np.uint8)
>>> rgb[2:8, 2:8, :] = 50  # Create a dark region
>>> clahed_rgb = clahe_image(rgb, 2.0, 4)
>>> bool(np.mean(clahed_rgb) > np.mean(rgb))  # Should enhance contrast
True
>>> bool(np.std(clahed_rgb) > np.std(rgb))  # Should enhance contrast
True
>>> clahed_rgb.shape == rgb.shape
True

>>> ## Test invalid inputs
>>> clahe_image("not an image", 2.0, 8)
Traceback (most recent call last):
        ...
AssertionError: Image must be a numpy array

>>> clahe_image(image.astype(np.uint8), "2.0", 8)
Traceback (most recent call last):
        ...
AssertionError: clip_limit must be a number, got <class 'str'>

>>> clahe_image(image.astype(np.uint8), 2.0, -1)
Traceback (most recent call last):
        ...
AssertionError: tile_grid_size must be positive, got -1