o :ήc ã@sPddlmZmZddlmZddlmZddlmZddl Z diifdd„Z dS)é)Úsanity_check_imageÚ trimap_split)Ú cf_laplacian)Úichol)ÚcgNcCsÚ|durt}t|ƒ|jdd…\}}t|ƒ\}}} } t|fi|¤d| i¤Ž} | | dd…fdd…| f} | | dd…fdd…| f} || }| ¡ ¡}t| |  |¡ fd|| ƒi|¤Ž|| <t   |dd¡  |j¡}|S)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}) NéÚis_knownÚMré) rrÚshaperrÚcopyÚravelrÚdotÚnpÚclipÚreshape)ÚimageÚtrimapÚpreconditionerÚlaplacian_kwargsÚ cg_kwargsÚhÚwÚis_fgÚis_bgrÚ is_unknownÚLÚL_UÚRÚmÚxÚalpha©r"úH/tmp/pip-target-vg8gfxp4/lib/python/pymatting/alpha/estimate_alpha_cf.pyÚestimate_alpha_cfs&  &r$) Úpymatting.util.utilrrÚ pymatting.laplacian.cf_laplacianrÚpymatting.preconditioner.icholrÚpymatting.solver.cgrÚnumpyrr$r"r"r"r#Ús   ÿ