9i7 |SSKrSSKJr SSKJr SSKJrJrJ r /SQr Sr Sr S S .S jr SS .S jrSrSS.Sjrg)N)entropy) dtype_range)_supported_float_typecheck_shape_equalitywarn)mean_squared_errornormalized_root_msepeak_signal_noise_rationormalized_mutual_informationc[URUR45n[R"XS9n[R"XS9nX4$)zC Promote im1, im2 to nearest appropriate floating point precision. dtype)rrnpasarray)image0image1 float_types ^/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/metrics/simple_metrics.py _as_floatsrs?' fll'CDJ ZZ 1F ZZ 1F >c[X5 [X5up[R"X- S-[RS9$)az Compute the mean-squared error between two images. Parameters ---------- image0, image1 : ndarray Images. Any dimensionality, must have same shape. Returns ------- mse : float The mean-squared error (MSE) metric. Notes ----- .. versionchanged:: 0.16 This function was renamed from ``skimage.measure.compare_mse`` to ``skimage.metrics.mean_squared_error``. rr)rrrmeanfloat64)rrs rr r s4*(/NF 77FO) <>#33 & !011 77%j= > FFr) data_rangec[X5 UcURUR:wa [S5 [URRup4[ R "U5[ R"U5peXd:dXS:a [S5eUS:aUnOXC- n[X5up[X5n[U5nS[ R"US-U- 5-$)a Compute the peak signal to noise ratio (PSNR) for an image. Parameters ---------- image_true : ndarray Ground-truth image, same shape as im_test. image_test : ndarray Test image. data_range : int, optional The data range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type. Returns ------- psnr : float The PSNR metric. Notes ----- .. versionchanged:: 0.16 This function was renamed from ``skimage.measure.compare_psnr`` to ``skimage.metrics.peak_signal_noise_ratio``. References ---------- .. [1] https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio zFInputs have mismatched dtype. Setting data_range based on image_true.zuimage_true has intensity values outside the range expected for its data type. Please manually specify the data_range.r r) rrrrtyperr!r r"rr floatlog10)r#r$r&dmindmaxtrue_mintrue_maxerrs rr r ps>0   z// /  !!1!1!6!67 VVJ/ 1C( ?hoM  q=JJ' ?J Z 4Cz"J *a-3./ //rc [S[XR555(d[SUSURS35e[XR5VVs/sH up#SX#- 4PM nnn[R "XSSS9$s snnf)a&Pad an array with trailing zeros to a given target shape. Parameters ---------- arr : ndarray The input array. shape : tuple The target shape. Returns ------- padded : ndarray The padded array. Examples -------- >>> _pad_to(np.ones((1, 1), dtype=int), (1, 3)) array([[1, 0, 0]]) c3.# UH upX:v M g7f)N).0sis r _pad_to..s8"7$!qv"7sz Target shape z# cannot be smaller than inputshape z along any axis.rconstant) pad_widthmodeconstant_values)allzipshaper"rpad)arrr?r5r6paddings r_pad_torCs( 8#eYY"78 8 8E7#YYK/ 1  '*%&;<&;da15z&;G< 66#z1 MM=sB d)binscURUR:wa&[SURSURS35eURUR:waB[R"URUR5n[ X5n[ X5nOXpT[R "[R"US5[R"US5/USS9upg[[R"USS95n[[R"US S95n [[R"US55n X-U - $) a0Compute the normalized mutual information (NMI). The normalized mutual information of :math:`A` and :math:`B` is given by:: .. math:: Y(A, B) = \frac{H(A) + H(B)}{H(A, B)} where :math:`H(X) := - \sum_{x \in X}{x \log x}` is the entropy. It was proposed to be useful in registering images by Colin Studholme and colleagues [1]_. It ranges from 1 (perfectly uncorrelated image values) to 2 (perfectly correlated image values, whether positively or negatively). Parameters ---------- image0, image1 : ndarray Images to be compared. The two input images must have the same number of dimensions. bins : int or sequence of int, optional The number of bins along each axis of the joint histogram. Returns ------- nmi : float The normalized mutual information between the two arrays, computed at the granularity given by ``bins``. Higher NMI implies more similar input images. Raises ------ ValueError If the images don't have the same number of dimensions. Notes ----- If the two input images are not the same shape, the smaller image is padded with zeros. References ---------- .. [1] C. Studholme, D.L.G. Hill, & D.J. Hawkes (1999). An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1):71-86 :DOI:`10.1016/S0031-3203(98)00091-0` z6NMI requires images of same number of dimensions. Got zD for `image0` and zD for `image1`.T)rEdensityr)axis) ndimr"r?rmaximumrC histogramddreshapersum) rrrE max_shapepadded0padded1hist bin_edgesH0H1H01s rr r s^{{fkk!;;-2{{m? ,  ||v||#JJv||V\\: &,&,!nn GR "**Wb"9: OD 1% &B 1% &B "**T2& 'C Gs?r)numpyr scipy.statsr util.dtyper _shared.utilsrrr__all__rr r r rCr r3rrr]sQ$MM =4BM:GzCG80vN:;>Fr