# 3j RSSKrSSKrSSKJr SSKJr \"SSSS9S5rS Sjr g) N)weights_to_laplacian)njitz/Tuple((f8[:], i4[:], i4[:]))(f8[:,:,:], f8, i4)T)cachenogilc URSSup4X4-nUSU-S-S--n[R"U[RS9n[R"U[RS9n[R"U5n Sn [ U5Hn [ U5Hn [ U*US-5Hn [ U*US-5HnX-nX-n[ S[ US- U55n[ S[ US- U55nXU--nUUU--nX U 4nUUU4n[R"S[RRUU- 5S--5nUXz'UX'UX'U S- n M M M M XU4$)N)dtyperi|) shapenpemptyint32rangemaxminexplinalgnorm)imagesigmarhwnmi_indsj_indsvalueskyxdydxx2y2ijzizjwijs Z/home/wildlama/miniconda3/lib/python3.13/site-packages/pymatting/laplacian/rw_laplacian.py _rw_laplacianr,ss ;;r?DA A QUQY1 A XXarxx (F XXarxx (F XXa[F A 1XqAQBA&AE*BBBQAE2/BQAE2/BE AR!V A!tBr2vB&& rBw(?1(D!DEC !FI !FI #FIFA)+'2 6 !!cURSSupEXE-n[XU5upxn [RR XxU 44Xf4S9n [ XS9$)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 Nr)r )regularization)r r,scipysparse csr_matrixr) rrradiusr/rrrrrrWs r+ rw_laplacianr5/s[6 ;;r?DA A*5@FF &)9 :1&IA  AAr-)gL7A`?r g:0yE>) numpyr scipy.sparser0pymatting.util.utilrnumbarr,r5r-r+r;s547t4P%"Q%"P"Br-