+ Ai.Z^RIHt^RIHt^RIHt^RIHt^RIH t ^RI t R//3Rlt R#))sanity_check_image)make_linear_system)lbdm_laplacian)ichol)cgNcVf\p\V4\\V3/VBV4wrV\ WV3RV!V4/VBp\ P !V^^4PVP4pV#)a  Estimate alpha from an input image and an input trimap using Learning Based Digital Matting as proposed by :cite:`zheng2009learning`. Parameters ---------- image: numpy.ndarray Image with shape :math:`h \times w \times d` for which the alpha matte should be estimated trimap: numpy.ndarray Trimap with shape :math:`h \times w` of the image preconditioner: function or scipy.sparse.linalg.LinearOperator Function or sparse matrix that applies the preconditioner to a vector (default: ichol) laplacian_kwargs: dictionary Arguments passed to the :code:`lbdm_laplacian` function cg_kwargs: dictionary Arguments passed to the :code:`cg` solver Returns ------- alpha: numpy.ndarray Estimated alpha matte Example ------- >>> from pymatting import * >>> image = load_image("data/lemur/lemur.png", "RGB") >>> trimap = load_image("data/lemur/lemur_trimap.png", "GRAY") >>> alpha = estimate_alpha_lbdm( ... image, ... trimap, ... laplacian_kwargs={"epsilon": 1e-6}, ... cg_kwargs={"maxiter":2000}) M) rrrrrnpclipreshapeshape) imagetrimappreconditionerlaplacian_kwargs cg_kwargsAbxalphas &&&&& a/var/www/html/photoedit/myenv/lib/python3.14/site-packages/pymatting/alpha/estimate_alpha_lbdm.pyestimate_alpha_lbdmr spHu nUG6FG PDA 12>!$2 2A GGAq!  $ $V\\ 2E L) pymatting.util.utilrpymatting.laplacian.laplacianr"pymatting.laplacian.lbdm_laplacianrpymatting.preconditioner.icholrpymatting.solver.cgrnumpyr rrrr!s%2<=0"#'r/r