ó  ‰\c@sÃyddlZWn+ek r=ddlmZedƒnXddlZddlmZddlm Z ddl m Z e d „Z d d e d d „Zd„Zd„Zd„Zd„ZdS(iÿÿÿÿNi(twarnsRAGs require networkxi(t_ncut(t_ncut_cy(tlinalgcCsó|s|jƒ}ng|jdtƒD]+\}}}|d|kr(||f^q(}|j|ƒtj|ƒ}tj|jƒdd|j ƒ} xQt |ƒD]C\} } x4| D],} x#|j | dD]} | | | >> from skimage import data, segmentation >>> from skimage.future import 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" http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5274 tdatatweightitdtypetlabels( tcopytedgestTruetremove_edges_fromtnxtconnected_componentstnptarangetmaxRt enumeratetnode(Rtragtthreshtin_placetxtytdt to_removetcompst map_arraytitnodesRtlabel((s=lib/python2.7/site-packages/skimage/future/graph/graph_cut.pyt cut_threshold s*"" " gü©ñÒMbP?i gð?c Cs±|s|jƒ}nx*|jƒD]}|j||d|ƒq"Wt|||ƒtj|jƒdd|jƒ}x2|jdtƒD]\}} | d|| d>> from skimage import data, segmentation >>> from skimage.future import 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. RiRRs ncut labelR( RRtadd_edget _ncut_relabelRtzerosRRR ( RRRtnum_cutsRtmax_edgeRRtnR((s=lib/python2.7/site-packages/skimage/future/graph/graph_cut.pytcut_normalizedLs1"cCs’gt|jƒƒD]\}}||r|^q}gt|jƒƒD]\}}||sH|^qH}|j|ƒ}|j|ƒ}||fS(sQCompute resulting subgraphs from given bi-parition. 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. (RRtsubgraph(tcutRRR%tnodes1tnodes2tsub1tsub2((s=lib/python2.7/site-packages/skimage/future/graph/graph_cut.pytpartition_by_cuts 55c CsÁtj}|jƒ}|jƒ}tj|dtjƒ}tj||ƒrU||fSx_tj|||dtƒD]B}||k} t j | ||ƒ} | |krq| }| }qqqqW||fS(söThreshold 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. Rtendpoint( RtinftminRt zeros_liketbooltallclosetlinspacetFalseRt ncut_cost( tevRtwR#tmcuttmntmxtmin_masktttmasktcost((s=lib/python2.7/site-packages/skimage/future/graph/graph_cut.pyt get_min_ncut°s    "   cCsXt|jƒƒ}|j|dd}x*|jdtƒD]\}}|||s   @  @ # ,