U ?h+&@sddlZddlZddlmZddlmZddlm Z m Z ddd Z ed d id d ddddddZ ddZ ddZddZddZdS)N)linalg)deprecate_kwarg)_ncut_ncut_cyTc s|s |}fdd|jddD}||t|}tj|d|jd}t |D].\}}|D] } |j | dD] } ||| <qxqfqZ||S)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` cs&g|]\}}}|dkr||fqSweight).0xydthreshr J/var/www/html/venv/lib/python3.8/site-packages/skimage/graph/_graph_cut.py 6s z!cut_threshold..Tdatardtypelabels) copyedgesZremove_edges_fromnxZconnected_componentsnpZarangemaxr enumeratenodes) rragrin_placeZ to_removecomps map_arrayirnodelabelr rr cut_threshold s)  r&Z random_staterngz0.21z0.23)Zdeprecated_versionZremoved_versionMbP? ?)r'c Cstj|}|s|}|D]}|j|||dq t||||tj|d|j d}|jddD]\} } | d|| d<qh||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. rrrTr ncut labelr) rrandomZ default_rngrrZadd_edge _ncut_relabelZzerosrr) rrrnum_cutsr Zmax_edger'r$r"nrr r rcut_normalizedHs<  r0csPfddt|D}fddt|D}||}||}||fS)aRCompute resulting subgraphs from given bi-partition. Parameters ---------- cut : array A array of booleans. Elements set to `True` belong to one set. rag : RAG The Region Adjacency Graph. Returns ------- sub1, sub2 : RAG The two resulting subgraphs from the bi-partition. csg|]\}}|r|qSr r r r#r/cutr rrsz$partition_by_cut..csg|]\}}|s|qSr r r1r2r rrs)rrZsubgraph)r3rZnodes1Znodes2sub1sub2r r2rpartition_by_cuts   r6c Cs~tj}|}|}tj|td}t||r8||fStj|||ddD]*}||k} t | ||} | |krJ| }| }qJ||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. rF)Zendpoint) rinfminrZ zeros_likeboolZallcloseZlinspacerZ ncut_cost) evrwr.mcutZmnZmxZmin_masktmaskZcostr r r get_min_ncuts r?cCs@t|}|j|dd}|jddD]\}}|||<q*dS)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. rrTrN)r8r)r attr_namer$Z new_labelr/rr r r _label_alls rAcCst|\}}|jd}|dkr|}tjtj|j|jd|jd|_||||}||jd} t j |d| t d|dd\} } t | t | } } t | } | dd| f} t| |||\}}||krt||\}}t||||t||||dSt|ddS) 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. rr)outZSMd)whichv0kNr+)rZ DW_matricesshaperrZ reciprocalsqrtrr,rZeigshr8realrZargmin2r?r6r-rA)rrr.Zrandom_generatorrr;mZd2ArEvalsZvectorsindex2r:Zcut_maskr<r4r5r r rr-s(       r-)T)r(r)Tr*)ZnetworkxrnumpyrZ scipy.sparserZ _shared.utilsrrrr&r0r6r?rAr-r r r rs"   ? K#,