1-Ph$lddlZddlmZddlmZddlmZddlm Z ddl m Z dZ ddd dd d d Z ddZdS)N)sparse)csgraph)_raveled_offsets_and_distances) map_array)_safe_downcast_indicesc6tj||z |zS)aA default edge function for complete image graphs. A pixel graph on an image with no edge values and no mask is a very boring regular lattice, so we define a default edge weight to be the absolute difference between values *weighted* by the distance between them. Parameters ---------- values0 : array The pixel values for each node. values1 : array The pixel values for each neighbor. distances : array The distance between each node and its neighbor. Returns ------- edge_values : array of float The computed values: abs(values0 - values1) * distances. )npabs)values0values1 distancess T/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/graph/_graph.py_weighted_abs_diffr s, 6'G# $ $y 00matrix)mask edge_function connectivityspacing sparse_typec|.|jtkr|}ntj|t}||jtkrd}nt}tj|ddd}tj|}t|j||\}} |ddtj f|z} tj | | j} tj|} tj | j } t| || }|d | }tj|d }tj| |}tj| |}||}| |}t|| | }|d }||||||}| j }t#j|||ff||f }|d krt#j|}n|d krd|}t)||| fS)aCreate an adjacency graph of pixels in an image. Pixels where the mask is True are nodes in the returned graph, and they are connected by edges to their neighbors according to the connectivity parameter. By default, the *value* of an edge when a mask is given, or when the image is itself the mask, is the Euclidean distance between the pixels. However, if an int- or float-valued image is given with no mask, the value of the edges is the absolute difference in intensity between adjacent pixels, weighted by the Euclidean distance. Parameters ---------- image : array The input image. If the image is of type bool, it will be used as the mask as well. mask : array of bool Which pixels to use. If None, the graph for the whole image is used. edge_function : callable A function taking an array of pixel values, and an array of neighbor pixel values, and an array of distances, and returning a value for the edge. If no function is given, the value of an edge is just the distance. connectivity : int The square connectivity of the pixel neighborhood: the number of orthogonal steps allowed to consider a pixel a neighbor. See `scipy.ndimage.generate_binary_structure` for details. spacing : tuple of float The spacing between pixels along each axis. sparse_type : {"matrix", "array"}, optional The return type of `graph`, either `scipy.sparse.csr_array` or `scipy.sparse.csr_matrix` (default). Returns ------- graph : scipy.sparse.csr_matrix or scipy.sparse.csr_array A sparse adjacency matrix in which entry (i, j) is 1 if nodes i and j are neighbors, 0 otherwise. Depending on `sparse_type`, this can be returned as a `scipy.sparse.csr_array`. nodes : array of int The nodes of the graph. These correspond to the raveled indices of the nonzero pixels in the mask. N)dtypec|S)N)xyrs rrz"pixel_graph..edge_function_s  rrconstantF)modeconstant_values)rraxis)shaperarrayz/`sparse_type` must be 'array' or 'matrix', got )rboolr ones_likerpad flatnonzerorr%newaxis broadcast_toarangesizerreshapesumrepeatr csr_array csr_matrix ValueError)imagerrrrrpadded nodes_paddedneighbor_offsets_paddeddistances_paddedneighbors_paddedneighbor_distances_fullnodesnodes_sequential neighborsneighbors_mask num_neighborsindicesindices_sequentialneighbor_indicesneighbor_distancesneighbor_indices_sequentialimage_rdatamgraphmsgs r pixel_graphrK"sGh | ;$  DD<T222D ;$   ! ! ! !/M2VD!*e D D DF>&))L0N <111--$AAArzM25LL o.>@P@VWW N4 Ey,,*L%@@I^^B''(89NF>222Mi}--G#3]CC 00@"+,rls ======''''''LLLLLL1118  ~~~~~B9,9,9,9,9,9,r