o :ήc @sVddlZddlZddlmZeddddddZeddddd d Zdd dZdS)N)njitzf8[:, :](f8[:, :], f8)T)cachenogilc Csn|j\}}t||f}t|D]$}t|D]}tt| tt|||||||f<qq|S)N)shapenpzerosrangeexpsqrtsumsquare)XvnmKijrI/tmp/pip-target-vg8gfxp4/lib/python/pymatting/laplacian/lbdm_laplacian.pycalculate_kernel_matrixs   8rz1Tuple((f8[:], i4[:], i4[:]))(f8[:, :, :], f8, i4)c Cs|jdd\}}||}d|dd}t|||}t||df}tj||dftjd} tj||dftjd} t|||D]} t|||D]} | | |} t|df}d}t| || |dD]&}t| || |dD]}tdD] }||||f|||f<q~|d7}qxqk|| || |d| || |df}t ||j }tj ||t ||}t |jd|}| |j }t|D]}|| | |d|f<|| | |||||f<q||| <qOqF|| | fS)N)dtyper)rrarangereshaperint32ronesflattendotTlinalgsolveeyeravel)imageepsilonrhwrareaindicesvaluesi_indsj_indsyxrr ky2x2cwindow_indicesrftmp2tmp3rrr_lbdm_laplacians:   0  r;Hz>rc CsL|jdd\}}||}t|||\}}}tjj|||ff||fd} | S)a Calculate a Laplacian matrix based on :cite:`zheng2009learning`. Parameters ---------- image: numpy.ndarray Image with shape :math:`h\times w \times 3` epsilon: float Regularization strength, defaults to :math:`10^{-7}`. Strong regularization improves convergence but results in smoother alpha mattes. 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. Returns ------- L: scipy.sparse.csr_matrix Matting Laplacian Nr)r)rr;scipysparse csr_matrix) r'r(radiusr*r+rr.r/r0Lrrrlbdm_laplacian@s rB)r<r) numpyr scipy.sparser=numbarrr;rBrrrrs    0