U !?h.@sXddlmZddlmZddlmZddlmZddlm Z ddl Z diifddZ dS) )sanity_check_image)make_linear_system)lbdm_laplacian)ichol)cgNc Cs^|dkr t}t|tt|f||\}}t||fd||i|}t|dd|j}|S)az 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}) NMr) rrrrrnpZclipZreshapeshape) imageZtrimapZpreconditionerZlaplacian_kwargsZ cg_kwargsAbxalpharU/var/www/html/venv/lib/python3.8/site-packages/pymatting/alpha/estimate_alpha_lbdm.pyestimate_alpha_lbdm s$r) Zpymatting.util.utilrZpymatting.laplacian.laplacianrZ"pymatting.laplacian.lbdm_laplacianrZpymatting.preconditioner.icholrZpymatting.solver.cgrnumpyr rrrrrs