forked from 170010011/fr
75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
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import numpy as np
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from .dtype import dtype_limits
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def invert(image, signed_float=False):
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"""Invert an image.
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Invert the intensity range of the input image, so that the dtype maximum
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is now the dtype minimum, and vice-versa. This operation is
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slightly different depending on the input dtype:
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- unsigned integers: subtract the image from the dtype maximum
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- signed integers: subtract the image from -1 (see Notes)
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- floats: subtract the image from 1 (if signed_float is False, so we
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assume the image is unsigned), or from 0 (if signed_float is True).
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See the examples for clarification.
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Parameters
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----------
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image : ndarray
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Input image.
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signed_float : bool, optional
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If True and the image is of type float, the range is assumed to
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be [-1, 1]. If False and the image is of type float, the range is
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assumed to be [0, 1].
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Returns
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-------
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inverted : ndarray
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Inverted image.
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Notes
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-----
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Ideally, for signed integers we would simply multiply by -1. However,
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signed integer ranges are asymmetric. For example, for np.int8, the range
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of possible values is [-128, 127], so that -128 * -1 equals -128! By
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subtracting from -1, we correctly map the maximum dtype value to the
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minimum.
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Examples
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--------
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>>> img = np.array([[100, 0, 200],
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... [ 0, 50, 0],
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... [ 30, 0, 255]], np.uint8)
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>>> invert(img)
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array([[155, 255, 55],
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[255, 205, 255],
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[225, 255, 0]], dtype=uint8)
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>>> img2 = np.array([[ -2, 0, -128],
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... [127, 0, 5]], np.int8)
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>>> invert(img2)
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array([[ 1, -1, 127],
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[-128, -1, -6]], dtype=int8)
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>>> img3 = np.array([[ 0., 1., 0.5, 0.75]])
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>>> invert(img3)
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array([[1. , 0. , 0.5 , 0.25]])
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>>> img4 = np.array([[ 0., 1., -1., -0.25]])
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>>> invert(img4, signed_float=True)
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array([[-0. , -1. , 1. , 0.25]])
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"""
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if image.dtype == 'bool':
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inverted = ~image
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elif np.issubdtype(image.dtype, np.unsignedinteger):
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max_val = dtype_limits(image, clip_negative=False)[1]
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inverted = np.subtract(max_val, image, dtype=image.dtype)
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elif np.issubdtype(image.dtype, np.signedinteger):
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inverted = np.subtract(-1, image, dtype=image.dtype)
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else: # float dtype
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if signed_float:
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inverted = -image
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else:
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inverted = np.subtract(1, image, dtype=image.dtype)
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return inverted
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