74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
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: # float dtype
|
|
if signed_float:
|
|
inverted = -image
|
|
else:
|
|
inverted = np.subtract(1, image, dtype=image.dtype)
|
|
return inverted
|