stouputils.data_science.data_processing.image.noise module

noise_image(image: ndarray[Any, dtype[Any]], amount: float, ignore_dtype: bool = False) → ndarray[Any, dtype[Any]]

Add Gaussian noise to an image.

Parameters:

• image (NDArray[Any]) – Image to add noise to

• amount (float) – Amount of noise to add (between 0 and 1)

• ignore_dtype (bool) – Ignore the dtype check

Returns:

Noisy image

Return type:

NDArray[Any]

>>> ## Basic tests
>>> image = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> noisy = noise_image(image.astype(np.uint8), 0.5)
>>> noisy.shape == image.shape
True
>>> bool(np.all(noisy >= 0) and np.all(noisy <= 255))
True

>>> np.random.seed(0)
>>> image = np.array([[128] * 3] * 3)
>>> noise_image(image.astype(np.uint8), 0.1).tolist()
[[172, 138, 152], [185, 175, 104], [152, 125, 126]]

>>> rgb = np.full((3,3,3), 128, dtype=np.uint8)
>>> noisy_rgb = noise_image(rgb, 0.1)
>>> noisy_rgb.shape == (3,3,3)
True

>>> ## Test invalid inputs
>>> noise_image("not an image", 0.5)
Traceback (most recent call last):
        ...
AssertionError: Image must be a numpy array

>>> noise_image(image.astype(np.uint8), "0.5")
Traceback (most recent call last):
        ...
AssertionError: amount must be a number, got <class 'str'>

>>> noise_image(image.astype(np.uint8), 1.5)
Traceback (most recent call last):
        ...
AssertionError: amount must be between 0 and 1, got 1.5