o :ήc @sDddlZddlZddlmZddlmZddgddgdfd d ZdS) N)knn)normalize_rows g@g?binarycCs|jdd\}}|ddj\}}}||} |dvr#td|dttdd ||} ttdd ||} ggg} } }t||D]c\}}tj ||||| || gd tj | d ftj d d }t |||d \}}| tt| || |t|| }|dkr|t|| d|| djd d|jd 8}| |qFt| | }t| | }t||}tj|||ff| | f}t|}tj| }||}|S)a* This function calculates the KNN matting Laplacian matrix similar to :cite:`chen2013knn`. We use a kernel of 1 instead of a soft kernel by default since the former is faster to compute and both produce almost identical results in all our experiments, which is to be expected as the soft kernel is very close to 1 in most cases. Parameters ---------- image: numpy.ndarray Image with shape :math:`h\times w \times 3` n_neighbors: list of ints Number of neighbors to consider. If :code:`len(n_neighbors)>1` multiple nearest neighbor calculations are done and merged, defaults to `[20, 10]`, i.e. first 20 neighbors are considered and in the second run :math:`10` neighbors. The pixel distances are then weighted by the :code:`distance_weights`. distance_weights: list of floats Weight of distance in feature vector, defaults to `[2.0, 0.1]`. kernel: str Must be either "binary" or "soft". Default is "binary". Returns --------- L: scipy.sparse.spmatrix Matting Laplacian matrix N)rsoftz$kernel must be binary/soft, but not .r)dtype)axisout)kr )r)shapereshapeT ValueErrornptilelinspacerepeatzipstackzerosfloat32rappendarangeravelonesabssum concatenatescipysparse csr_matrixridentity)image n_neighborsdistance_weightskernelhwrgbnxyijcoo_datardistance_weightf_neighbor_indicesW_ijijjiWILrBH/tmp/pip-target-vg8gfxp4/lib/python/pymatting/laplacian/knn_laplacian.py knn_laplacians:4  rD) numpyr scipy.sparser%pymatting.util.kdtreerpymatting.util.utilrrDrBrBrBrCs