skimage2.util.invert#

skimage2.util.invert(image, signed_float=False)[source]#

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:

imagendarray: Input image.
signed_floatbool, 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:

invertedndarray: 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]])