def _update_state_gco(model, weight_key='cut_weight', name_label_key='name_label'): import networkx as nx # Get nx graph properties external_nodes = sorted(list(model.graph.nodes())) external_edges = list(model.graph.edges()) edge_to_weights = nx.get_edge_attributes(model.graph, weight_key) node_to_labeling = nx.get_node_attributes(model.graph, name_label_key) edge_weights = ut.dict_take(edge_to_weights, external_edges, 0) external_labeling = [ node_to_labeling.get(node, -node) for node in external_nodes ] # Map to internal ids for pygco internal_nodes = ut.rebase_labels(external_nodes) extern2_intern = dict(zip(external_nodes, internal_nodes)) internal_edges = ut.unflat_take(extern2_intern, external_edges) internal_labeling = ut.rebase_labels(external_labeling) internal_labeling = np.array(internal_labeling) internal_edges = np.array(internal_edges) n_nodes = len(internal_nodes) # Model state model.n_nodes = n_nodes model.extern2_intern = extern2_intern model.intern2_extern = ut.invert_dict(extern2_intern) model.edges = internal_edges model.edge_weights = edge_weights # Model parameters model.labeling = np.zeros(model.n_nodes, dtype=np.int32) model._update_labels(labeling=internal_labeling) model._update_weights()
def nx_topsort_nodes(graph, nodes): import utool as ut node_rank = ut.nx_topsort_rank(graph, nodes) node_idx = ut.rebase_labels(node_rank) sorted_nodes = ut.take(nodes, node_idx) return sorted_nodes
def initialize_graph_and_model(infr): """ Unused in internal split stuff pt.qt4ensure() layout_info = pt.show_nx(graph, as_directed=False, fnum=1, layoutkw=dict(prog='neato'), use_image=True, verbose=0) ax = pt.gca() pt.zoom_factory() pt.interactions.PanEvents() """ #import networkx as nx #import itertools cm_list = infr.cm_list hack = True hack = False if hack: cm_list = cm_list[:10] qaid_list = [cm.qaid for cm in cm_list] daids_list = [cm.daid_list for cm in cm_list] unique_aids = sorted(ut.list_union(*daids_list + [qaid_list])) if hack: unique_aids = sorted(ut.isect(unique_aids, qaid_list)) aid2_aidx = ut.make_index_lookup(unique_aids) # Construct K-broken graph edges = [] edge_weights = [] #top = (infr.qreq_.qparams.K + 1) * 2 #top = (infr.qreq_.qparams.K) * 2 top = (infr.qreq_.qparams.K + 2) for count, cm in enumerate(cm_list): qidx = aid2_aidx[cm.qaid] score_list = cm.annot_score_list sortx = ut.argsort(score_list)[::-1] score_list = ut.take(score_list, sortx)[:top] daid_list = ut.take(cm.daid_list, sortx)[:top] for score, daid in zip(score_list, daid_list): if daid not in qaid_list: continue didx = aid2_aidx[daid] edge_weights.append(score) edges.append((qidx, didx)) # make symmetric directed_edges = dict(zip(edges, edge_weights)) # Find edges that point in both directions undirected_edges = {} for (u, v), w in directed_edges.items(): if (v, u) in undirected_edges: undirected_edges[(v, u)] += w undirected_edges[(v, u)] /= 2 else: undirected_edges[(u, v)] = w edges = list(undirected_edges.keys()) edge_weights = list(undirected_edges.values()) nodes = list(range(len(unique_aids))) nid_labeling = infr.qreq_.ibs.get_annot_nids(unique_aids) labeling = ut.rebase_labels(nid_labeling) import networkx as nx from ibeis.viz import viz_graph set_node_attrs = nx.set_node_attributes set_edge_attrs = nx.set_edge_attributes # Create match-based graph structure graph = nx.DiGraph() graph.add_nodes_from(nodes) graph.add_edges_from(edges) # Important properties nid_list = infr.qreq_.ibs.get_annot_nids(unique_aids) labeling = ut.rebase_labels(nid_list) set_node_attrs(graph, 'name_label', dict(zip(nodes, labeling))) set_edge_attrs(graph, 'weight', dict(zip(edges, edge_weights))) # Visualization properties import plottool as pt ax2_aid = ut.invert_dict(aid2_aidx) set_node_attrs(graph, 'aid', ax2_aid) viz_graph.ensure_node_images(infr.qreq_.ibs, graph) set_node_attrs(graph, 'framewidth', dict(zip(nodes, [3.0] * len(nodes)))) set_node_attrs(graph, 'framecolor', dict(zip(nodes, [pt.DARK_BLUE] * len(nodes)))) ut.color_nodes(graph, labelattr='name_label') edge_colors = pt.scores_to_color(np.array(edge_weights), cmap_='viridis') #import utool #utool.embed() #edge_colors = [pt.color_funcs.ensure_base255(color) for color in edge_colors] #print('edge_colors = %r' % (edge_colors,)) set_edge_attrs(graph, 'color', dict(zip(edges, edge_colors))) # Build inference model from ibeis.algo.hots import graph_iden #graph_iden.rrr() model = graph_iden.InfrModel(graph) #model = graph_iden.InfrModel(len(nodes), edges, edge_weights, labeling=labeling) infr.model = model