U !?h<ã@sVddlZddlZddlmZedddddd„ƒZeddddd d „ƒZdd d„ZdS)éN)Únjitzf8[:, :](f8[:, :], f8)T)ÚcacheZnogilcCsn|j\}}t ||f¡}t|ƒD]H}t|ƒD]:}t t |¡t t ||||¡¡¡|||f<q,q |S)N)ÚshapeÚnpÚzerosÚrangeÚexpÚsqrtÚsumZsquare)ÚXÚvÚnÚmÚKÚiÚj©rúT/var/www/html/venv/lib/python3.8/site-packages/pymatting/laplacian/lbdm_laplacian.pyÚcalculate_kernel_matrixs :rz1Tuple((f8[:], i4[:], i4[:]))(f8[:, :, :], f8, i4)c Cs|jdd…\}}||}d|dd}t |¡ ||¡}t ||df¡}tj||dftjd} tj||dftjd} t|||ƒD]b}t|||ƒD]L}|||} t |df¡}d}t||||dƒD]N}t||||dƒD]2}tdƒD]}||||f|||f<q|d7}qôqÚ|||||d…||||d…f ¡}t ||j ¡}tj ||t |¡|¡}t |jd¡|}| |j ¡}t|ƒD]4}|| | |d|…f<|| | |||||…f<q¬| ¡|| <q qŒ| ¡| ¡| ¡fS)Néé)Zdtypeéré)rrZarangeZreshaperZint32rZonesÚflattenÚdotÚTZlinalgZsolveÚeyeZravel)ÚimageÚepsilonÚrÚhÚwr ZareaÚindicesÚvaluesÚi_indsÚj_indsÚyÚxrrÚkÚy2Zx2ÚcZwindow_indicesrÚfZtmp2Ztmp3rrrÚ_lbdm_laplacians60 r,çH¯¼šò×z>rc CsL|jdd…\}}||}t|||ƒ\}}}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,ÚscipyÚsparseZ csr_matrix) rrZradiusr r!r r#r$r%ÚLrrrÚlbdm_laplacian@s r1)r-r) ÚnumpyrZscipy.sparser.Znumbarrr,r1rrrrÚs 0