
    #
3j<                     b    S SK rS SKrS SKJr  \" SSSS9S 5       r\" SSSS9S 5       rS
S	 jrg)    N)njitzf8[:, :](f8[:, :], f8)T)cachenogilc                 Z   U R                   u  p#[        R                  " X"45      n[        U5       Hv  n[        U5       Hd  n[        R                  " [        R
                  " U5      * [        R                  " [        R                  " X   X   -
  5      5      -  5      XEU4'   Mf     Mx     U$ )N)shapenpzerosrangeexpsqrtsumsquare)XvnmKijs          \/home/wildlama/miniconda3/lib/python3.13/site-packages/pymatting/laplacian/lbdm_laplacian.pycalculate_kernel_matrixr      s}    77DA
!A1XqAffbggaj[266"))AD14K2H+IIJAdG   H    z1Tuple((f8[:], i4[:], i4[:]))(f8[:, :, :], f8, i4)c           	         U R                   S S u  p4X4-  nSU-  S-   S-  n[        R                  " U5      R                  X45      n[        R                  " XVS-  45      n[        R                  " XVS-  4[        R
                  S9n	[        R                  " XVS-  4[        R
                  S9n
[        X#U-
  5       GH  n[        X$U-
  5       GHz  nXU-  -   n[        R                  " US45      nSn[        X-
  X-   S-   5       H@  n[        X-
  X-   S-   5       H&  n[        S5       H  nU UUU4   XU4'   M     US-  nM(     MB     X{U-
  X-   S-   2X-
  X-   S-   24   R                  5       n[        R                  " XR                  5      n[        R                  R                  UU[        R                  " U5      -  -   U5      n[        R                  " UR                   S   5      U-
  nUR                  UR                  5      n[        U5       H  nUXUS U24'   UXX-  X-  U-   24'   M     UR                  5       X'   GM}     GM     UR                  5       U	R                  5       U
R                  5       4$ )N      )dtype   r      )r   r   arangereshaper	   int32r
   onesflattendotTlinalgsolveeyeravel)imageepsilonrhwr   areaindicesvaluesi_indsj_indsyxr   r   ky2x2cwindow_indicesr   ftmp2tmp3s                           r   _lbdm_laplacianr>      s7   ;;r?DA	AEAI!Diil""1(GXXq!)n%FXXq!)nBHH5FXXq!)nBHH5F
 1!e_qa%AE	Au&AAAE1519-quqy1B"1X"'B	"2Q$ &FA 2 . %UQUQY%6	8I%IJRRTN
 q##A		GbffTl$: :A>A66!''!*%)D88DFF#D4[%3!'T'z"8F!(QX_445 ! 

FI7 ! < <<>6<<>6<<>99r   c                     U R                   SS u  p4X4-  n[        XU5      u  pgn[        R                  R	                  XgU44XU4S9n	U	$ )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   )r   r>   scipysparse
csr_matrix)
r*   r+   radiusr-   r.   r   r1   r2   r3   Ls
             r   lbdm_laplacianrE   @   sU    . ;;r?DA	A,UVDFF&)9 :1&IAHr   )gHz>r   )	numpyr   scipy.sparser@   numbar   r   r>   rE    r   r   <module>rJ      sO      d$7 8 9TR-: S-:`r   