# 3j@XSSKJr SSKJr SSKJr SSKJr SSKJ r SSK r S004Sjr g))sanity_check_image)make_linear_system) knn_laplacian)jacobi)cgNcUc[n[U5 [[U40UD6U5upV[ XV4SU"U50UD6n[ R "USS5RUR5nU$)a Estimate alpha from an input image and an input trimap using KNN Matting similar to :cite:`chen2013knn`. See `knn_laplacian` for more details. 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: jacobi) laplacian_kwargs: dictionary Arguments passed to the :code:`knn_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_knn( ... image, ... trimap, ... laplacian_kwargs={"n_neighbors": [15, 10]}, ... cg_kwargs={"maxiter":2000}) Mr) rrrrrnpclipreshapeshape) imagetrimappreconditionerlaplacian_kwargs cg_kwargsAbxalphas \/home/wildlama/miniconda3/lib/python3.13/site-packages/pymatting/alpha/estimate_alpha_knn.pyestimate_alpha_knnr spHu mEF5EF ODA 12>!$2 2A GGAq!  $ $V\\ 2E L) pymatting.util.utilrpymatting.laplacian.laplacianr!pymatting.laplacian.knn_laplacianrpymatting.preconditioner.jacobirpymatting.solver.cgrnumpyr rrrr"s%2<;2"#'r/r