+ Ai Z^RIt^RIt^RIHt^RIHt]!RRRR7R4tR Rlt R#) N)weights_to_laplacian)njitz/Tuple((f8[:], i4[:], i4[:]))(f8[:,:,:], f8, i4)T)cachenogilc VPR,wr4W4,pV^V,^,^,,p\P!V\PR7p\P!V\PR7p\P!V4p ^p \ V4EF)p \ V4EFp \ V)V^,4Fp \ V)V^,4FpW,pW,p\ ^\ V^, V44p\ ^\ V^, V44pWV,,pVVV,,pW V 3,pVVV3,p\P!R\PPVV, 4^,,4pVWz&VW&VW&V ^, p K K EK EK, WV3#)NN)dtypei|) shapenpemptyint32rangemaxminexplinalgnorm)imagesigmarhwnmi_indsj_indsvalueskyxdydxx2y2ijzizjwijs&&& ^/var/www/html/photoedit/myenv/lib/python3.14/site-packages/pymatting/laplacian/rw_laplacian.py _rw_laplacianr,sq ;;r?DA A QUQY1 A XXarxx (F XXarxx (F XXa[F A 1XqAQBA&AE*BBBQAE2/BQAE2/BE AR!V A!tBr2vB&& rBw(?1(D!DEC !FI !FI #FIFA)+'2 6 !!cVPR,wrEWE,p\WV4wrxp \PP WxV 33Wf3R7p \ WR7#)a This function implements the alpha estimator for random walk alpha matting as described in :cite:`grady2005random`. Parameters ---------- image: numpy.ndarray Image with shape :math:`h\times w \times 3` sigma: float Sigma used to calculate the weights (see Equation 4 in :cite:`grady2005random`), defaults to :math:`0.033` radius: int Radius of local window size, defaults to :math:`1`, i.e. only adjacent pixels are considered. The size of the local window is given as :math:`(2 r + 1)^2`, where :math:`r` denotes the radius. A larger radius might lead to violated color line constraints, but also favors further propagation of information within the image. regularization: float Regularization strength, defaults to :math:`10^{-8}`. Strong regularization improves convergence but results in smoother alpha matte. Returns ------- L: scipy.sparse.spmatrix Matting Laplacian r)r )regularization)r r,scipysparse csr_matrixr) rrradiusr/rrrrrrWs &&&& r+ rw_laplacianr5/sY6 ;;r?DA A*5@FF &)9 :1&IA  AAr-)gL7A`?g:0yE>) numpyr scipy.sparser0pymatting.util.utilrnumbarr,r5r-r+r<s547t4P%"Q%"P"Br-