stouputils.data_science.data_processing.image.shearing module

shear_image(image: ndarray[Any, dtype[Any]], x: float, y: float, ignore_dtype: bool = False) → ndarray[Any, dtype[Any]]

Shear an image

Parameters:

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

• x (float) – Shearing along the x axis (between -180 and 180)

• y (float) – Shearing along the y axis (between -180 and 180)

• ignore_dtype (bool) – Ignore the dtype check

Returns:

Sheared image

Return type:

NDArray[Any]

>>> ## Basic tests
>>> image = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> shear_image(image.astype(np.uint8), 15, 0).tolist()
[[1, 2, 3], [3, 5, 6], [3, 7, 8]]

>>> shear_image(image.astype(np.float32), 0, 15).tolist()
[[1.0, 1.4375, 1.40625], [4.0, 4.15625, 4.40625], [7.0, 7.15625, 7.40625]]

>>> ## Test invalid inputs
>>> shear_image(image.astype(np.uint8), 200, 0)
Traceback (most recent call last):
        ...
AssertionError: x must be between -180 and 180, got 200

>>> shear_image(image.astype(np.uint8), 0, -200)
Traceback (most recent call last):
        ...
AssertionError: y must be between -180 and 180, got -200

>>> shear_image("not an image", 0, 0)
Traceback (most recent call last):
        ...
AssertionError: Image must be a numpy array