o :ήc@s@ddlZddlZddlmZeddddddZd d d ZdS) N)njitz>void(f8[:, :, :], f8, i8, f8[:, :, :], i8[:], i8[:], b1[:, :])T)cachenogilc3 Cs|j\}}} | dks Jd|d} | | } t|D]i} t|D]b} | | |}|d|dd}t| d|| d|dD]:}t| d|| d|dD](}|||}d|krd|krvnnd|krp|krvnn|||<|d7}qRqA|||d<q qtd|dd|ddf}t|||D]C}t|||D]8}t|||||d||||dfrqtdD]Q}d}t| D]}t| D]}|||||||||f7}qqtd|dD]%}td|dD]}||||||||f|| ||||f<qqq|}d}d}|}d}|}t| D]k}t| D]c}||||df|||df7}||||df|||df7}||||df|||df7}||||df|||df7}||||df|||df7}||||df|||df7}q.q(|| }|| }|| }|| }|| }|| }||||||||||||d|||} d| }!|||||!}"|||||!}#|||||!}$|||||!}%|||||!}&|||||!}'td|dD]}(td|dD]})||(|)df}||(|)df}*||(|)df}+|"||#|*|$|+},|#||%|*|&|+}-|$||&|*|'|+}.td|dD]x}/td|dD]l}0||)|} ||(|} ||0|}||/|}| | |}|||}|,||/|0df|-||/|0df|.||/|0df}1||krdndd|1| }2|| d|}|| d|}||||f|27<qkqaqqqqdS)Nrgg?)shaperangenpzerosall)3imageepsilonrvaluesindicesindptris_knownhwdsize window_areayixiikyjxjjcyxdcsdydxa00a01a02a11a12a22detinv_detm00m01m02m11m12m22dyidxituc0c1c2dyjdxjtempvaluerAG/tmp/pip-target-vg8gfxp4/lib/python/pymatting/laplacian/cf_laplacian.py _cf_laplacians      "" 0  2   $$       $          rCHz>rc Cs|j\}}}||}|durtj||ftjd}|||}tj|dtjd}tj|d|ddtjd} tj|d|dd|dftjd} t|||| | ||tj | | |f||f} | S)a This function implements the alpha estimator for closed-form alpha matting as proposed by :cite:`levin2007closed`. 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. is_known: numpy.ndarray Binary mask of pixels for which to compute the laplacian matrix. Laplacian entries for known pixels will have undefined values. Returns ------- L: scipy.sparse.spmatrix Matting Laplacian N)dtyperrr) r r r bool8reshapeint64float64rCscipysparse csr_matrixravel) rrradiusrrrrnrrrLrArArB cf_laplacians   &rQ)rDrN)numpyr scipy.sparserJnumbarrCrQrArArArBs    ~