o :ήcd @sLddlZddlZddlmZddlmZeddddddZdd d Z dS)N)weights_to_laplacian)njitz/Tuple((f8[:], i4[:], i4[:]))(f8[:,:,:], f8, i4)T)cachenogilc CsT|jdd\}}||}|d|dd}tj|tjd}tj|tjd}t|} d} t|D]r} t|D]k} t| |dD]`} t| |dD]U}| |}| | }tdt|d|}tdt|d|}| | |}|||}|| | f}|||f}tdtj ||d}||| <||| <|| | <| d7} qLqBq8q2| ||fS)N)dtyperi|) shapenpemptyint32rangemaxminexplinalgnorm)imagesigmarhwnmi_indsj_indsvalueskyxdydxx2y2ijzizjwijr)G/tmp/pip-target-vg8gfxp4/lib/python/pymatting/laplacian/rw_laplacian.py _rw_laplacians8         r+L7A`?r:0yE>c CsT|jdd\}}||}t|||\}}} tjj||| ff||fd} t| |dS)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 mattes. Returns ------- L: scipy.sparse.spmatrix Matting Laplacian Nr)r )regularization)r r+scipysparse csr_matrixr) rrradiusr.rrrrrrWr)r)r* rw_laplacian/s  r4)r,rr-) numpyr scipy.sparser/pymatting.util.utilrnumbarr+r4r)r)r)r*s    (