| import numpy as np |
| from .dtype import dtype_limits |
|
|
|
|
| def invert(image, signed_float=False): |
| """Invert an image. |
| |
| Invert the intensity range of the input image, so that the dtype maximum |
| is now the dtype minimum, and vice-versa. This operation is |
| slightly different depending on the input dtype: |
| |
| - unsigned integers: subtract the image from the dtype maximum |
| - signed integers: subtract the image from -1 (see Notes) |
| - floats: subtract the image from 1 (if signed_float is False, so we |
| assume the image is unsigned), or from 0 (if signed_float is True). |
| |
| See the examples for clarification. |
| |
| Parameters |
| ---------- |
| image : ndarray |
| Input image. |
| signed_float : bool, optional |
| If True and the image is of type float, the range is assumed to |
| be [-1, 1]. If False and the image is of type float, the range is |
| assumed to be [0, 1]. |
| |
| Returns |
| ------- |
| inverted : ndarray |
| Inverted image. |
| |
| Notes |
| ----- |
| Ideally, for signed integers we would simply multiply by -1. However, |
| signed integer ranges are asymmetric. For example, for np.int8, the range |
| of possible values is [-128, 127], so that -128 * -1 equals -128! By |
| subtracting from -1, we correctly map the maximum dtype value to the |
| minimum. |
| |
| Examples |
| -------- |
| >>> img = np.array([[100, 0, 200], |
| ... [ 0, 50, 0], |
| ... [ 30, 0, 255]], np.uint8) |
| >>> invert(img) |
| array([[155, 255, 55], |
| [255, 205, 255], |
| [225, 255, 0]], dtype=uint8) |
| >>> img2 = np.array([[ -2, 0, -128], |
| ... [127, 0, 5]], np.int8) |
| >>> invert(img2) |
| array([[ 1, -1, 127], |
| [-128, -1, -6]], dtype=int8) |
| >>> img3 = np.array([[ 0., 1., 0.5, 0.75]]) |
| >>> invert(img3) |
| array([[1. , 0. , 0.5 , 0.25]]) |
| >>> img4 = np.array([[ 0., 1., -1., -0.25]]) |
| >>> invert(img4, signed_float=True) |
| array([[-0. , -1. , 1. , 0.25]]) |
| """ |
| if image.dtype == 'bool': |
| inverted = ~image |
| elif np.issubdtype(image.dtype, np.unsignedinteger): |
| max_val = dtype_limits(image, clip_negative=False)[1] |
| inverted = np.subtract(max_val, image, dtype=image.dtype) |
| elif np.issubdtype(image.dtype, np.signedinteger): |
| inverted = np.subtract(-1, image, dtype=image.dtype) |
| else: |
| if signed_float: |
| inverted = -image |
| else: |
| inverted = np.subtract(1, image, dtype=image.dtype) |
| return inverted |
|
|
