79j(rddlZddlZddlmZddlmZddlm Z m Z d dZ ddddZ d Z d Zd Zd Zy)N)linalg)SCIPY_GE_1_17_0_DEV0)_ncut_ncut_cyc|s|j}|jdDcgc]\}}}|d|k\s||f}}}}|j|tj|}t j |jdz|j} t|D](\} } | D]} |j| dD]} | | | <  *| |Scc}}}w)aCombine regions separated by weight less than threshold. Given an image's labels and its RAG, output new labels by combining regions whose nodes are separated by a weight less than the given threshold. Parameters ---------- labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : float The threshold. Regions connected by edges with smaller weights are combined. in_place : bool If set, modifies `rag` in place. The function will remove the edges with weights less that `thresh`. If set to `False` the function makes a copy of `rag` before proceeding. Returns ------- out : ndarray The new labelled array. Examples -------- >>> from skimage import data, segmentation, graph >>> img = data.astronaut() >>> labels = segmentation.slic(img) >>> rag = graph.rag_mean_color(img, labels) >>> new_labels = graph.cut_threshold(labels, rag, 10) References ---------- .. [1] Alain Tremeau and Philippe Colantoni "Regions Adjacency Graph Applied To Color Image Segmentation" :DOI:`10.1109/83.841950` Tdataweightrdtypelabels) copyedgesremove_edges_fromnxconnected_componentsnparangemaxr enumeratenodes)rragthreshin_placexyd to_removecomps map_arrayirnodelabels X/media/conek/DATA/Code/OCR/venv/lib/python3.12/site-packages/skimage/graph/_graph_cut.py cut_thresholdr& sR hhj(+yydy';UUGAq!q{f?T!QUIU)$  # #C (E  &**,*&,,?Ie$%5 %D42 %#$ %  % %% V Vs CC)rngctjj|}|s|j}|j D]}|j |||t ||||tj|jdz|j}|j dD]\} } | d|| d<||S)aPerform Normalized Graph cut on the Region Adjacency Graph. Given an image's labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output. Parameters ---------- labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : float The threshold. A subgraph won't be further subdivided if the value of the N-cut exceeds `thresh`. num_cuts : int The number or N-cuts to perform before determining the optimal one. in_place : bool If set, modifies `rag` in place. For each node `n` the function will set a new attribute ``rag.nodes[n]['ncut label']``. max_edge : float, optional The maximum possible value of an edge in the RAG. This corresponds to an edge between identical regions. This is used to put self edges in the RAG. rng : {`numpy.random.Generator`, int}, optional Pseudo-random number generator. By default, a PCG64 generator is used (see :func:`numpy.random.default_rng`). If `rng` is an int, it is used to seed the generator. The `rng` is used to determine the starting point of `scipy.sparse.linalg.eigsh`. Returns ------- out : ndarray The new labeled array. Examples -------- >>> from skimage import data, segmentation, graph >>> img = data.astronaut() >>> labels = segmentation.slic(img) >>> rag = graph.rag_mean_color(img, labels, mode='similarity') >>> new_labels = graph.cut_normalized(labels, rag) References ---------- .. [1] Shi, J.; Malik, J., "Normalized cuts and image segmentation", Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000. )r rr Tr ncut labelr) rrandom default_rngrradd_edge _ncut_relabelzerosrr ) rrrnum_cutsrmax_edger'r#r!nrs r%cut_normalizedr2Gs~ ))   $C hhj 2 T4 12#vx-)>I t $11!"< !H+1 V c*t|jDcgc] \}}||s |}}}t|jDcgc] \}}||r |}}}|j|}|j|}||fScc}}wcc}}w)aRCompute resulting subgraphs from given bi-partition. Parameters ---------- cut : array A array of booleans. Elements set to `True` belong to one set. rag : RAG The Region Adjacency Graph. Returns ------- sub1, sub2 : RAG The two resulting subgraphs from the bi-partition. )rrsubgraph)cutrr"r1nodes1nodes2sub1sub2s r%partition_by_cutr;s4&ciik2 =DAqc!fa =F =%ciik2 ADAq#a&a AF A << D << D : > As B B  BBc\tj}|j}|j}tj|t }tj ||r||fStj|||dD](}||kD} tj| ||} | |ks%| }| }*||fS)aThreshold an eigenvector evenly, to determine minimum ncut. Parameters ---------- ev : array The eigenvector to threshold. d : ndarray The diagonal matrix of the graph. w : ndarray The weight matrix of the graph. num_cuts : int The number of evenly spaced thresholds to check for. Returns ------- mask : array The array of booleans which denotes the bi-partition. mcut : float The value of the minimum ncut. r F)endpoint) rinfminr zeros_likeboolallcloselinspacer ncut_cost) evrwr/mcutmnmxmin_masktmaskcosts r% get_min_ncutrNs* 66D B B }}Rt,H {{2r~[[RE :AvtQ* $;HD  T>r3ct|j}|j|dd}|jdD] \}}|||< y)aqAssign a unique integer to the given attribute in the RAG. This function assumes that all labels in `rag` are unique. It picks up a random label from them and assigns it to the `attr_name` attribute of all the nodes. rag : RAG The Region Adjacency Graph. attr_name : string The attribute to which a unique integer is assigned. rrTr N)r?r)r attr_namer# new_labelr1rs r% _label_allrRsR syy{ D $)!,I t $!1 ) !r3c ,tj|\}}|jd}|dkDr[||k7jdkDrH|j }t j t j|j|j|j|_|||z z|z}|j|jd} trd|ini} tj|fd| td|dz d| \} } t j| t j| } } tj | } | dd| f}t#||||\}}||kr,t%||\}}t'||||t'||||yt)|d y) aPerform Normalized Graph cut on the Region Adjacency Graph. Recursively partition the graph into 2, until further subdivision yields a cut greater than `thresh` or such a cut cannot be computed. For such a subgraph, indices to labels of all its nodes map to a single unique value. Parameters ---------- rag : RAG The region adjacency graph. thresh : float The threshold. A subgraph won't be further subdivided if the value of the N-cut exceeds `thresh`. num_cuts : int The number or N-cuts to perform before determining the optimal one. random_generator : `numpy.random.Generator` Provides initial values for eigenvalue solver. r)outr'SMd)whichv0kNr))r DW_matricesshapennzrr reciprocalsqrtr r*rreigshr?realrargmin2rNr;r-rR)rrr/random_generatorrrFmd2ArYrng_kwvalsvectorsindex2rEcut_maskrGr9r:s r%r-r-sp(   S !DAq  A AAF<rssQ7;B     M M` F)X!$G"r3