U ?h @s"ddlZddlmZdddZdS)N) dtype_limitsFcCs|jdkr|}nvt|jtjrFt|ddd}tj|||jd}nBt|jtjrjtjd||jd}n|rv| }ntjd||jd}|S)aInvert 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]]) boolF)Z clip_negativer)dtype)rnpZ issubdtypeZunsignedintegerrsubtractZ signedinteger)imageZ signed_floatinvertedZmax_valr F/var/www/html/venv/lib/python3.8/site-packages/skimage/util/_invert.pyinverts9 r )F)numpyrrrr r r r r s