+ Ai(r^RIt^RIt^RIHt^RIHt^RIH t H t R Rlt R RR/Rllt Rt R tR tR tR#)N)linalg)SCIPY_GE_1_17_0_DEV0)_ncut_ncut_cycV'gVP4pVPRR7UUUu.uFwrEqfR,V8gKWE3NK ppppVPV4\P!V4p\ P !VP4^,VPR7p \V4F3wrV F(p VPV ,R,Fp WV &K K* K5 W,#uupppi)a-Combine 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` Tdataweightdtypelabels) copyedgesremove_edges_fromnxconnected_componentsnparangemaxr enumeratenodes)r ragthreshin_placexyd to_removecomps map_arrayirnodelabels&&&& V/var/www/html/photoedit/myenv/lib/python3.14/site-packages/skimage/graph/_graph_cut.py cut_thresholdr% sR hhj(+yydy';U';GA!{f?T!';IU)$  # #C (E  &**,*&,,?Ie$D422#$% 3%  Vs C9C9rngc\PPV4pV'gVP4pVP 4FpVP WwVR7K \ WW64\P!VP4^,VPR7pVP RR7FwrV R,WR,&K W,#)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 r Tr ncut labelr ) rrandom default_rngrradd_edge _ncut_relabelzerosrr ) r rrnum_cutsrmax_edger&r"r nrs &&&&&&$ r$cut_normalizedr1Gs~ ))   $C hhj  T 1#x-)>I t $!"< H+%  cT\VP44UUu.uFwr#W,'gKVNK ppp\VP44UUu.uFwr#W,'dKVNK pppVPV4pVPV4pWg3#uuppiuuppi)a"Compute resulting subgraphs from given bi-partition. Parameters ---------- cut : array A array of booleans. Elements set to `True` belong to one set. rag : RAG The Region Adjacency Graph. Returns ------- sub1, sub2 : RAG The two resulting subgraphs from the bi-partition. )rrsubgraph)cutrr!r0nodes1nodes2sub1sub2s&& r$partition_by_cutr:s4&ciik2 =2DAcffaa2F =%ciik2 A2DA#&&aa2F A << D << D : > AsBBB$0B$cl\PpVP4pVP4p\P!V\ R7p\P !WV4'dWt3#\P!WVVRR7F*pW8p \P!WV4p W8gK&T pT pK, Wt3#)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_ncutrMs* 66D B B }}Rt,H {{2~[[E :vt* ;HD ; >r2c\VP44pVPV,R,^,pVPRR7F wrEW5V&K R#)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. r TrN)r>r)r attr_namer" new_labelr0rs&& r$ _label_allrQsI syy{ D $)!,I t $ ) %r2c z\P!V4wrEVP^,pV^8Ed|WE8gP^8EdhVP 4p\ P !\ P!VPVPR7VPR7VnWtV, ,V,pVPVP^,4p \'dRV/M/p \P!V3RRRV R\^dV^, 4/V Bwr\ P!V 4\ P!V 4r\P !V 4p V RV 3,p\#WWR4wppVV8d+\%W4wpp\'VWV4\'VWV4R#\)VR 4R#) 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. )outr&whichSMv0k:NNNNr()r DW_matricesshapennzrr reciprocalsqrtr r)rreigshr>realrargmin2rMr:r,rQ)rrr.random_generatorrrEmd2ArVrng_kwvalsvectorsindex2rDcut_maskrFr8r9s&&&& r$r,r,se(   S !DA  A AAF<rpsC7;|M M` F)X!$G"r2