invert(image, signed_float=False)
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.
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.
Input image.
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].
Inverted image.
Invert an image.
>>> img = np.array([[100, 0, 200],This example is valid syntax, but we were not able to check execution
... [ 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],This example is valid syntax, but we were not able to check execution
... [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]])This example is valid syntax, but we were not able to check execution
... invert(img3) array([[1. , 0. , 0.5 , 0.25]])
>>> img4 = np.array([[ 0., 1., -1., -0.25]])See :
... invert(img4, signed_float=True) array([[-0. , -1. , 1. , 0.25]])
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.util._invert.invert
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