9iPSSKrSSKrSSKJr SSKJr SSKrSSK J r J r J r J r SSKJr SrSrS r"S S \R&5rSS jrSS jr\"SS5SSj5rg)N)ndimage)sparse)measure segmentationutilcolor)requirec## URSnURnURnURn[ U5H(n[ X5X5S-5HnXTUX&4v M M* g7f)aYield weighted edge triples for use by NetworkX from a CSR matrix. This function is a straight rewrite of `networkx.convert_matrix._csr_gen_triples`. Since that is a private function, it is safer to include our own here. Parameters ---------- csr_array : scipy.sparse.csr_array The input matrix. An edge (i, j, w) will be yielded if there is a data value for coordinates (i, j) in the matrix, even if that value is 0. Yields ------ i, j, w : (int, int, float) tuples Each value `w` in the matrix along with its coordinates (i, j). Examples -------- >>> dense = np.eye(2, dtype=float) >>> csr = sparse.csr_array(dense) >>> edges = _edge_generator_from_csr(csr) >>> list(edges) [(0, 0, 1.0), (1, 1, 1.0)] rN)shapedataindptrindicesrange) csr_arraynrowsvaluesr col_indicesijs R/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/graph/_rag.py_edge_generator_from_csrr sl8 OOA E ^^F   F##K 5\vy&Q-0A^VY. .1sA,A.cS[R0nXRX5SnXRX$5SnS[XV50$)a@Callback to handle merging nodes by choosing minimum weight. Returns a dictionary with `"weight"` set as either the weight between (`src`, `n`) or (`dst`, `n`) in `graph` or the minimum of the two when both exist. Parameters ---------- graph : RAG The graph under consideration. src, dst : int The verices in `graph` to be merged. n : int A neighbor of `src` or `dst` or both. Returns ------- data : dict A dict with the `"weight"` attribute set the weight between (`src`, `n`) or (`dst`, `n`) in `graph` or the minimum of the two when both exist. weight)npinfgetmin)graphsrcdstndefaultw1w2s r min_weightr'0sK4 G c #H -B c #H -B c"k ""cUR[5nU[U5S-nUH3nX2:wdM URX#5(aM"UR X#5 M5 g)aCreate edge in `graph` between central element of `values` and the rest. Add an edge between the middle element in `values` and all other elements of `values` into `graph`. ``values[len(values) // 2]`` is expected to be the central value of the footprint used. Parameters ---------- values : array The array to process. graph : RAG The graph to add edges in. Returns ------- 0 : float Always returns 0. The return value is required so that `generic_filter` can put it in the output array, but it is ignored by this filter. rg)astypeintlenhas_edgeadd_edge)rr centervalues r_add_edge_filterr1PsS(]]3 F CK1$ %F ?5>>&#@#@ NN6 ) r(c^\rSrSrSrSU4Sjjr\SSS4SjrSU4SjjrSU4Sjjr U4S jr S r S r U4S jr S rU=r$)RAGlaThe Region Adjacency Graph (RAG) of an image, subclasses :obj:`networkx.Graph`. Parameters ---------- label_image : array of int An initial segmentation, with each region labeled as a different integer. Every unique value in ``label_image`` will correspond to a node in the graph. connectivity : int in {1, ..., ``label_image.ndim``}, optional The connectivity between pixels in ``label_image``. For a 2D image, a connectivity of 1 corresponds to immediate neighbors up, down, left, and right, while a connectivity of 2 also includes diagonal neighbors. See :func:`scipy.ndimage.generate_binary_structure`. data : :obj:`networkx.Graph` specification, optional Initial or additional edges to pass to :obj:`networkx.Graph` constructor. Valid edge specifications include edge list (list of tuples), NumPy arrays, and SciPy sparse matrices. **attr : keyword arguments, optional Additional attributes to add to the graph. Nc >[TU]"U40UD6 UR5S:XaSUlO[ UR 55UlUbp[ R"URU5n[R"SUR5nURSS9 [ R"U[USUU4S9 gg)Nr?T)writenearest)function footprintmodeoutputextra_arguments)super__init__number_of_nodesmax_idmaxnodesndigenerate_binary_structurendimr broadcast_tor setflagsgeneric_filterr1)self label_image connectivityrattrfpr< __class__s rr? RAG.__init__s &&    !Q &DKdjjl+DK  "..{/?/?NB__S+*;*;LD MM(4M 8 " JJsOH % 3(A A 3!    S ! r(c >UcUnOURU5 [TU]"U40UD6 [XR5Ulg)z^Add node `n` while updating the maximum node id. .. seealso:: :obj:`networkx.Graph.add_node`.N)updater>rWrBrA)rJr#rRrMrOs rrW RAG.add_nodes@  I   T " (i(![[) r(c >UcUnOURU5 [TU]"X40UD6 [XUR5Ulg)ziAdd an edge between `u` and `v` while updating max node id. .. seealso:: :obj:`networkx.Graph.add_edge`.N)rcr>r.rBrA)rJuvrRrMrOs rr. RAG.add_edgesB  I   T " ++! , r(cF>[TU]5nURUlU$)zNCopy the graph with its max node id. .. seealso:: :obj:`networkx.Graph.copy`.)r>copyrA)rJgrOs rrjRAG.copys  GLN;;r(c[5$)a3Return a fresh copy graph with the same data structure. A fresh copy has no nodes, edges or graph attributes. It is the same data structure as the current graph. This method is typically used to create an empty version of the graph. This is required when subclassing Graph with networkx v2 and does not cause problems for v1. Here is more detail from the network migrating from 1.x to 2.x document:: With the new GraphViews (SubGraph, ReversedGraph, etc) you can't assume that ``G.__class__()`` will create a new instance of the same graph type as ``G``. In fact, the call signature for ``__class__`` differs depending on whether ``G`` is a view or a base class. For v2.x you should use ``G.fresh_copy()`` to create a null graph of the correct type---ready to fill with nodes and edges. )r3rJs r fresh_copyRAG.fresh_copys (u r(c URS-$)zReturns the `id` for the new node to be inserted. The current implementation returns one more than the maximum `id`. Returns ------- id : int The `id` of the new node to be inserted. r rArns rrV RAG.next_ids{{Qr(c$>[TU]U5 g)zAdd node `n` without updating the maximum node id. This is a convenience method used internally. .. seealso:: :obj:`networkx.Graph.add_node`.N)r>rW)rJr#rOs r_add_node_silentRAG._add_node_silent)s r(rr)Nr N)N)__name__ __module__ __qualname____firstlineno____doc__r?r'r`rWr.rjrorVru__static_attributes__ __classcell__)rOs@rr3r3lsE*>JX * -, r(r3c *[XS9nUHInURURU/S[R"/SQ[R S9S.5 MK [R "UR5H=nXnURUS==S- ss'URUS==X- ss'M? UH8nURUSURUS- URUS 'M: URS S 9Hupn URU S URU S - n [RRU 5n US :Xa[RU S -*U- -U S'MoUS:XaXS'M{[SUS35e U$)aCompute the Region Adjacency Graph using mean colors. Given an image and its initial segmentation, this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within `image` with the same label in `labels`. The weight between two adjacent regions represents how similar or dissimilar two regions are depending on the `mode` parameter. Parameters ---------- image : ndarray, shape(M, N[, ..., P], 3) Input image. labels : ndarray, shape(M, N[, ..., P]) The labelled image. This should have one dimension less than `image`. If `image` has dimensions `(M, N, 3)` `labels` should have dimensions `(M, N)`. connectivity : int, optional Pixels with a squared distance less than `connectivity` from each other are considered adjacent. It can range from 1 to `labels.ndim`. Its behavior is the same as `connectivity` parameter in ``scipy.ndimage.generate_binary_structure``. mode : {'distance', 'similarity'}, optional The strategy to assign edge weights. 'distance' : The weight between two adjacent regions is the :math:`|c_1 - c_2|`, where :math:`c_1` and :math:`c_2` are the mean colors of the two regions. It represents the Euclidean distance in their average color. 'similarity' : The weight between two adjacent is :math:`e^{-d^2/sigma}` where :math:`d=|c_1 - c_2|`, where :math:`c_1` and :math:`c_2` are the mean colors of the two regions. It represents how similar two regions are. sigma : float, optional Used for computation when `mode` is "similarity". It governs how close to each other two colors should be, for their corresponding edge weight to be significant. A very large value of `sigma` could make any two colors behave as though they were similar. Returns ------- out : RAG The region adjacency graph. Examples -------- >>> from skimage import data, segmentation, graph >>> img = data.astronaut() >>> labels = segmentation.slic(img) >>> rag = graph.rag_mean_color(img, labels) References ---------- .. [1] Alain Tremeau and Philippe Colantoni "Regions Adjacency Graph Applied To Color Image Segmentation" :DOI:`10.1109/83.841950` )rLr)rrr)dtype)rS pixel count total colorrr rz mean colorTr similarityrrdistancez The mode 'z' is not recognised)r3rCrcrarrayfloat64ndindexr edgeslinalgnormmathe ValueError) imagerSrLr;sigmar r#indexcurrentxyddiffs rrag_mean_colorr2st  2E  A# !xx D  FLL)- G]+q0+ G]+u|;+*  KKN= )EKKN=,I I  A|$ ;;D;)a{{1~l+ekk!n\.JJyy~~d# < &&tQwZ%%78AhK Z hKz$/BCD D* Lr(cX[R"URU5n[R"XS9n[R"XS9nX@:gnXP:gn[ R "XFX45n[ R "XXW45n [ R"U 5S-n [ R"SUR5n [R"XU 44[X4S9n [ R "XX45n [R"XU 445nU=RU R-sl [5nUR[!U5SS9 UR[!U 5SS9 UR#5H$n UR"U R%SU /05 M& U$) aComouter RAG based on region boundaries Given an image's initial segmentation and its edge map this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within the image with the same label in `labels`. The weight between two adjacent regions is the average value in `edge_map` along their boundary. labels : ndarray The labelled image. edge_map : ndarray This should have the same shape as that of `labels`. For all pixels along the boundary between 2 adjacent regions, the average value of the corresponding pixels in `edge_map` is the edge weight between them. connectivity : int, optional Pixels with a squared distance less than `connectivity` from each other are considered adjacent. It can range from 1 to `labels.ndim`. Its behavior is the same as `connectivity` parameter in `scipy.ndimage.generate_binary_structure`. Examples -------- >>> from skimage import data, segmentation, filters, color, graph >>> img = data.chelsea() >>> labels = segmentation.slic(img) >>> edge_map = filters.sobel(color.rgb2gray(img)) >>> rag = graph.rag_boundary(labels, edge_map) )r:r r6)rr r)rcountrS)rDrErF grey_erosion grey_dilationr concatenaterBrGr rrr+rr3add_weighted_edges_fromrrCrc)rSedge_maprLconnerodeddilated boundaries0 boundaries1 labels_small labels_larger#ones count_matrixr graph_matrixrags r rag_boundaryrs~>  ( (l CD   f 5F7G"K#K>>6#68K"LML>>6#68L"MNL |q A ??3 2 2 3D## l+,CvL >>80(2GH ID##T,+G$HIL*** %C 8 FxX 8 FwW YY[ ! XsO, Jr( matplotlibz>=3.3c \SSKJn SSKJn SSKJn U(dUR 5nUcU R 5up[R"USS9n Uc?URS:dURS S ;a S n[U5eUSS2SS2SS24n O;U RU5n[R"U5n U"U 5SS2SS2SS24n U RU5nS n[R "UR#5S -5nUR%SS 9HunnUSHnUUU'M US - nM UUn[&R("U5n[+UR%SS 9U5H(uunnn[-[/[0US55US'M* U R35nUb'UR5U5n[6R8"U UUS9n UR;U 5 UR=5VVs/sH6unnUR$USSSS2UR$USSSS2/PM8 nnnU "UXES9nUR=SS 9VVVs/sH unnnUSPM nnnnUR?[R@"U55 URCU5 U$s snnfs snnnf)aShow a Region Adjacency Graph on an image. Given a labelled image and its corresponding RAG, show the nodes and edges of the RAG on the image with the specified colors. Edges are displayed between the centroid of the 2 adjacent regions in the image. Parameters ---------- labels : ndarray, shape (M, N) The labelled image. rag : RAG The Region Adjacency Graph. image : ndarray, shape (M, N[, 3]) Input image. If `colormap` is `None`, the image should be in RGB format. border_color : color spec, optional Color with which the borders between regions are drawn. edge_width : float, optional The thickness with which the RAG edges are drawn. edge_cmap : :py:class:`matplotlib.colors.Colormap`, optional Any matplotlib colormap with which the edges are drawn. img_cmap : :py:class:`matplotlib.colors.Colormap`, optional Any matplotlib colormap with which the image is draw. If set to `None` the image is drawn as it is. in_place : bool, optional If set, the RAG is modified in place. For each node `n` the function will set a new attribute ``rag.nodes[n]['centroid']``. ax : :py:class:`matplotlib.axes.Axes`, optional The axes to draw on. If not specified, new axes are created and drawn on. Returns ------- lc : :py:class:`matplotlib.collections.LineCollection` A collection of lines that represent the edges of the graph. It can be passed to the :meth:`matplotlib.figure.Figure.colorbar` function. Examples -------- .. testsetup:: >>> import pytest; _ = pytest.importorskip('matplotlib') >>> from skimage import data, segmentation, graph >>> import matplotlib.pyplot as plt >>> >>> img = data.coffee() >>> labels = segmentation.slic(img) >>> g = graph.rag_mean_color(img, labels) >>> lc = graph.show_rag(labels, g, img) >>> cbar = plt.colorbar(lc) r)colors)pyplot)LineCollectionNT) force_copyr)rz;If colormap is `None`, an RGB or RGBA image should be givenr rrScentroid)r ) linewidthscmapr)"rrrmatplotlib.collectionsrrjsubplotsr img_as_floatrFr rget_cmapr rgb2grayrarangerBrCr regionpropsziptuplemapr+ColorConverterto_rgbrmark_boundariesimshowr set_arrayradd_collection) rSrr border_color edge_width edge_cmapimg_cmaprZaxrpltrfigoutmsgoffset map_arrayr#rlabel rag_labelsregionsrregionccn1n2lineslcrr edge_weightss rshow_ragrs~"(5 hhj z,,.   Ed 3C ::>U[[^69OCS/ !Aq"1"Ho<<)nnU#smAq"1"H% Y'IF &**,*+I t $1x[E%Ie !! % 6"J!!*-G !5w? D6 S&*Z@    Byy. **3 ,OIIcN  #HR 2z "4R4 (#))B- *CDbD*IJ#   * EB/2yydy/CD/CGAq!AhK/CLDLL,'(b I  Es 8=J!J')rrgo@)r)blackg?magmaboneTN)networkxnxnumpyrscipyrrDrrrrrr _shared.version_requirementsr rr'r1Graphr3rrrr(rrs  112"/J#@8C"((CLYx9x w    z zr(