# 3j PSSKJrJr SSKJr SSKJr SSKJr SSK r S004Sjr g))sanity_check_image trimap_split) cf_laplacian)ichol)cgNcUc[n[U5 URSSupV[U5upxp[ U40UDSU 0D6n XSS24SS2U 4n XSS24SS2U 4n XynUR 5R 5n[XRU5*4SU"U 50UD6X'[R"USS5RUR5nU$)a Estimate alpha from an input image and an input trimap using Closed-Form Alpha Matting as proposed by :cite:`levin2007closed`. 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:`cf_laplacian` function cg_kwargs: dictionary Arguments passed to the :code:`cg` solver is_known: numpy.ndarray Binary mask of pixels for which to compute the laplacian matrix. Providing this parameter might improve performance if few pixels are unknown. 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_cf( ... image, ... trimap, ... laplacian_kwargs={"epsilon": 1e-6}, ... cg_kwargs={"maxiter":2000}) Nis_knownMr) rrshaperrcopyravelrdotnpclipreshape)imagetrimappreconditionerlaplacian_kwargs cg_kwargshwis_fgis_bgr is_unknownLL_URmxalphas [/home/wildlama/miniconda3/lib/python3.13/site-packages/pymatting/alpha/estimate_alpha_cf.pyestimate_alpha_cfr%sLu << DA)5f)=&E(UB.BBA M 1j= )C a-H%A A AsUU1XIJ)<J JAM GGAq!  $ $V\\ 2E L) pymatting.util.utilrr pymatting.laplacian.cf_laplacianrpymatting.preconditioner.icholrpymatting.solver.cgrnumpyrr%r&r$r-s#@90"#'rFr&