+ Ai ^RIt^RIHt^RIHt^RIHt^RIH t H t H t .ROt Rt ]R4t]RR /R l4tR R/R ltR tR^d/RltR#)N)entropy) dispatchable) dtype_range)_supported_float_typecheck_shape_equalitywarnc\VPVP34p\P!WR7p\P!WR7pW3#)zC Promote im1, im2 to nearest appropriate floating point precision. dtype)rr npasarray)image0image1 float_types&& \/var/www/html/photoedit/myenv/lib/python3.14/site-packages/skimage/metrics/simple_metrics.py _as_floatsrs?' fll'CDJ ZZ 1F ZZ 1F >c\W4\W4wr\P!W, ^,\PR7#)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``. r )rrr meanfloat64)rrs&&rmean_squared_errorrs4,(/NF 77FO) <>#33 & !011 77%j= > FFr data_rangec\W4VfVPVP8wd \R4\VPP,wr4\ P !V4\ P!V4reWd8gWS8d \R4hV^8dTpMWC, p\W4wr\W4p\V4p^ \ P!V^,V, 4,#)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.) rr rrtyper rrrrrfloatlog10)r r!r$dmindmaxtrue_mintrue_maxerrs&&$ rpeak_signal_noise_ratior.ss>0   z// /  !!1!1!6!67 VVJ/ 1C( ?hoM  q=JJ' ?J Z 4Cz"J *a-3./ //rc\;QJd/R\WP44F 'dK RM$ RM !R\WP444'g\RV RVP R24h\WP4UUu.uFwr#^W#, 3NK ppp\P !WR^R7#uuppi) 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."TF wrW8xK R#5i)N).0sis& r _pad_to..s8"7$!qv"7sFTz Target shape z# cannot be smaller than inputshape z along any axis.constant) pad_widthmodeconstant_values)allzipshaperr pad)arrr=r3r4paddings&& r_pad_torAs( 38#eYY"783338#eYY"78 8 8E7#YYK/ 1  '*%&;<&;da15z&;G< 66#z1 MM=sCbinscVPVP8wd'\RVP RVP R24hVPVP8wdC\P!VPVP4p\ W4p\ W4pMYrT\P !\P!VR4\P!VR4.VRR7wrg\\P!V^R74p\\P!V^R74p \\P!VR44p W,V , #)aCompute the normalized mutual information (NMI). The normalized mutual information of :math:`A` and :math:`B` is given by: .. math:: Y(A, B) = \frac{H(A) + H(B)}{H(A, B)} where :math:`H(X) := - \sum_{x \in X}{p(x) \log p(x)}` is the entropy, :math:`X` is the set of image values, and :math:`p(x)` is the probability of occurrence of value :math:`x \in X`. It was proposed to be useful in registering images by Colin Studholme and colleagues [1]_. It ranges from 1 (perfectly uncorrelated image values) to 2 (perfectly correlated image values, whether positively or negatively). Parameters ---------- image0, image1 : ndarray Images to be compared. The two input images must have the same number of dimensions. bins : int or sequence of int, optional The number of bins along each axis of the joint histogram. Returns ------- nmi : float The normalized mutual information between the two arrays, computed at the granularity given by ``bins``. Higher NMI implies more similar input images. Raises ------ ValueError If the images don't have the same number of dimensions. Notes ----- If the two input images are not the same shape, the smaller image is padded with zeros. References ---------- .. [1] C. Studholme, D.L.G. Hill, & D.J. Hawkes (1999). An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1):71-86 :DOI:`10.1016/S0031-3203(98)00091-0` z6NMI requires images of same number of dimensions. Got zD for `image0` and zD for `image1`.T)rBdensity)axis) ndimrr=r maximumrA histogramddreshapersum) rrrB max_shapepadded0padded1hist bin_edgesH0H1H01s &&$ rnormalized_mutual_informationrTsb{{fkk!;;-2{{m? ,  ||v||#JJv||V\\: &,&,!nn GR "**Wb"9: OD 1% &B 1% &B "**T2& 'C Gs?r)rr#r.rT)numpyr scipy.statsrutil._backendsr util.dtyper _shared.utilsrrr__all__rrr#r.rArTr1rrr[sw)$MM ==4:G:G:Gz80$80vN:H#Hr