stouputils.data_science.data_processing.image.binary_threshold module

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

Apply binary threshold to an image.

Parameters:

• image (NDArray[Any]) – Image to threshold

• threshold (float) – Threshold value (between 0 and 1)

• ignore_dtype (bool) – Ignore the dtype check

Returns:

Thresholded binary image

Return type:

NDArray[Any]

>>> ## Basic tests
>>> image = np.array([[100, 150, 200], [50, 125, 175], [25, 75, 225]])
>>> binary_threshold_image(image.astype(np.uint8), 0.5).tolist()
[[0, 255, 255], [0, 0, 255], [0, 0, 255]]

>>> np.random.seed(42)
>>> img = np.random.randint(0, 256, (4,4), dtype=np.uint8)
>>> thresholded = binary_threshold_image(img, 0.7)
>>> set(np.unique(thresholded).tolist()) <= {0, 255}  # Should only contain 0 and 255
True

>>> rgb = np.random.randint(0, 256, (3,3,3), dtype=np.uint8)
>>> thresh_rgb = binary_threshold_image(rgb, 0.5)
>>> thresh_rgb.shape == rgb.shape
True
>>> set(np.unique(thresh_rgb).tolist()) <= {0, 255}
True

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

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

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