def build_matches_sep(X_fxs, Y_fxs, scores_list): r""" Just build matches. Scores have already been broken up. No need to do that. Returns: tuple: (fm, fs) CommandLine: python -m wbia.algo.smk.smk_funcs build_matches_agg --show Example: >>> # ENABLE_DOCTEST >>> from wbia.algo.smk.smk_funcs import * # NOQA >>> map_int = ut.partial(ut.lmap, ut.partial(np.array, dtype=np.int32)) >>> map_float = ut.partial(ut.lmap, ut.partial(np.array, dtype=np.float32)) >>> X_fxs = map_int([[0, 1], [2, 3, 4], [5]]) >>> Y_fxs = map_int([[8], [0, 4], [99]]) >>> scores_list = map_float([ >>> [[.1], [.2],], >>> [[.3, .4], [.4, .6], [.5, .9],], >>> [[.4]], >>> ]) >>> (fm, fs) = build_matches_sep(X_fxs, Y_fxs, scores_list) >>> print('fm = ' + ut.repr2(fm)) >>> print('fs = ' + ut.repr2(fs)) >>> assert len(fm) == len(fs) >>> assert np.isclose(np.sum(ut.total_flatten(scores_list)), fs.sum()) """ fs = np.array(ut.total_flatten(scores_list), dtype=np.float32) unflat_fm = (ut.product(fxs1, fxs2) for fxs1, fxs2 in zip(X_fxs, Y_fxs)) fm = np.array(ut.flatten(unflat_fm), dtype=np.int32) isvalid = np.greater(fs, 0) fm = fm.compress(isvalid, axis=0) fs = fs.compress(isvalid, axis=0) return fm, fs
def build_matches_agg(X_fxs, Y_fxs, X_maws, Y_maws, score_list): r""" Builds explicit features matches. Break and distribute up each aggregate score amongst its contributing features. Returns: tuple: (fm, fs) CommandLine: python -m wbia.algo.smk.smk_funcs build_matches_agg --show Example: >>> # ENABLE_DOCTEST >>> from wbia.algo.smk.smk_funcs import * # NOQA >>> map_int = ut.partial(ut.lmap, ut.partial(np.array, dtype=np.int32)) >>> map_float = ut.partial(ut.lmap, ut.partial(np.array, dtype=np.float32)) >>> X_fxs = map_int([[0, 1], [2, 3, 4], [5]]) >>> Y_fxs = map_int([[8], [0, 4], [99]]) >>> X_maws = map_float([[1, 1], [1, 1, 1], [1]]) >>> Y_maws = map_float([[1], [1, 1], [1]]) >>> score_list = np.array([1, 2, 3], dtype=np.float32) >>> (fm, fs) = build_matches_agg(X_fxs, Y_fxs, X_maws, Y_maws, score_list) >>> print('fm = ' + ut.repr2(fm)) >>> print('fs = ' + ut.repr2(fs)) >>> assert len(fm) == len(fs) >>> assert score_list.sum() == fs.sum() """ # Build feature matches # Spread word score according to contriubtion (maw) weight unflat_contrib = [ maws1[:, None].dot(maws2[:, None].T).ravel() for maws1, maws2 in zip(X_maws, Y_maws) ] unflat_factor = [contrib / contrib.sum() for contrib in unflat_contrib] # factor_list = np.divide(score_list, factor_list, out=factor_list) for score, factor in zip(score_list, unflat_factor): np.multiply(score, factor, out=factor) unflat_fs = unflat_factor # itertools.product seems fastest for small arrays unflat_fm = (ut.product(fxs1, fxs2) for fxs1, fxs2 in zip(X_fxs, Y_fxs)) fm = np.array(ut.flatten(unflat_fm), dtype=np.int32) fs = np.array(ut.flatten(unflat_fs), dtype=np.float32) isvalid = np.greater(fs, 0) fm = fm.compress(isvalid, axis=0) fs = fs.compress(isvalid, axis=0) return fm, fs
def draw(ChapX, expt_name, dbnames, *args): """ CommandLine: python -m wbia Chap3.draw nsum --dbs=GZ_Master1,PZ_Master1 python -m wbia Chap3.draw foregroundness --dbs=GZ_Master1,PZ_Master1 --diskshow python -m wbia Chap3.draw kexpt --dbs=GZ_Master1 --diskshow python -m wbia Chap4.draw importance GZ_Master1 python -m wbia Chap4.draw hard_cases GZ_Master1,PZ_Master1 match_state,photobomb_state --diskshow # Example: # >>> # Script # >>> from wbia.scripts.thesis import * # NOQA # >>> expt_name = ut.get_argval('--expt', type_=str, pos=1) # >>> dbnames = ut.get_argval(('--dbs', '--db'), type_=list, default=[]) # >>> Chap3.draw(expt_name, dbnames) """ logger.info('expt_name = %r' % (expt_name, )) logger.info('dbnames = %r' % (dbnames, )) logger.info('args = %r' % (args, )) dbnames = ut.smart_cast(dbnames, list) if len(dbnames) > 1: # parallelize drawing tasks from concurrent import futures multi_args = [ut.smart_cast(a, list) for a in args] with futures.ProcessPoolExecutor(max_workers=6) as executor: list( futures.as_completed([ executor.submit(ChapX.draw_serial, expt_name, *fsargs) for fsargs in ut.product(dbnames, *multi_args) ])) logger.info('\n\n Completed multiple tasks') else: ChapX.draw_serial(expt_name, dbnames, *args)
def intraoccurrence_connected(): r""" CommandLine: python -m ibeis.scripts.specialdraw intraoccurrence_connected --show python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --postcut python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --smaller Example: >>> # DISABLE_DOCTEST >>> from ibeis.scripts.specialdraw import * # NOQA >>> result = intraoccurrence_connected() >>> print(result) >>> ut.quit_if_noshow() >>> import plottool as pt >>> ut.show_if_requested() """ import ibeis import plottool as pt from ibeis.viz import viz_graph import networkx as nx pt.ensure_pylab_qt4() ibs = ibeis.opendb(defaultdb='PZ_Master1') nid2_aid = { #4880: [3690, 3696, 3703, 3706, 3712, 3721], 4880: [3690, 3696, 3703], 6537: [3739], 6653: [7671], 6610: [7566, 7408], #6612: [7664, 7462, 7522], #6624: [7465, 7360], #6625: [7746, 7383, 7390, 7477, 7376, 7579], 6630: [7586, 7377, 7464, 7478], #6677: [7500] } nid2_dbaids = {4880: [33, 6120, 7164], 6537: [7017, 7206], 6653: [7660]} if ut.get_argflag('--small') or ut.get_argflag('--smaller'): del nid2_aid[6630] del nid2_aid[6537] del nid2_dbaids[6537] if ut.get_argflag('--smaller'): nid2_dbaids[4880].remove(33) nid2_aid[4880].remove(3690) nid2_aid[6610].remove(7408) #del nid2_aid[4880] #del nid2_dbaids[4880] aids = ut.flatten(nid2_aid.values()) temp_nids = [1] * len(aids) postcut = ut.get_argflag('--postcut') aids_list = ibs.group_annots_by_name(aids)[0] ensure_edges = 'all' if True or not postcut else None unlabeled_graph = viz_graph.make_netx_graph_from_aid_groups( ibs, aids_list, #invis_edges=invis_edges, ensure_edges=ensure_edges, temp_nids=temp_nids) viz_graph.color_by_nids(unlabeled_graph, unique_nids=[1] * len(list(unlabeled_graph.nodes()))) viz_graph.ensure_node_images(ibs, unlabeled_graph) nx.set_node_attributes(unlabeled_graph, 'shape', 'rect') #unlabeled_graph = unlabeled_graph.to_undirected() # Find the "database exemplars for these annots" if False: gt_aids = ibs.get_annot_groundtruth(aids) gt_aids = [ut.setdiff(s, aids) for s in gt_aids] dbaids = ut.unique(ut.flatten(gt_aids)) dbaids = ibs.filter_annots_general(dbaids, minqual='good') ibs.get_annot_quality_texts(dbaids) else: dbaids = ut.flatten(nid2_dbaids.values()) exemplars = nx.DiGraph() #graph = exemplars # NOQA exemplars.add_nodes_from(dbaids) def add_clique(graph, nodes, edgeattrs={}, nodeattrs={}): edge_list = ut.upper_diag_self_prodx(nodes) graph.add_edges_from(edge_list, **edgeattrs) return edge_list for aids_, nid in zip(*ibs.group_annots_by_name(dbaids)): add_clique(exemplars, aids_) viz_graph.ensure_node_images(ibs, exemplars) viz_graph.color_by_nids(exemplars, ibs=ibs) nx.set_node_attributes(unlabeled_graph, 'framewidth', False) nx.set_node_attributes(exemplars, 'framewidth', 4.0) nx.set_node_attributes(unlabeled_graph, 'group', 'unlab') nx.set_node_attributes(exemplars, 'group', 'exemp') #big_graph = nx.compose_all([unlabeled_graph]) big_graph = nx.compose_all([exemplars, unlabeled_graph]) # add sparse connections from unlabeled to exemplars import numpy as np rng = np.random.RandomState(0) if True or not postcut: for aid_ in unlabeled_graph.nodes(): flags = rng.rand(len(exemplars)) > .5 nid_ = ibs.get_annot_nids(aid_) exnids = np.array(ibs.get_annot_nids(list(exemplars.nodes()))) flags = np.logical_or(exnids == nid_, flags) exmatches = ut.compress(list(exemplars.nodes()), flags) big_graph.add_edges_from(list(ut.product([aid_], exmatches)), color=pt.ORANGE, implicit=True) else: for aid_ in unlabeled_graph.nodes(): flags = rng.rand(len(exemplars)) > .5 exmatches = ut.compress(list(exemplars.nodes()), flags) nid_ = ibs.get_annot_nids(aid_) exnids = np.array(ibs.get_annot_nids(exmatches)) exmatches = ut.compress(exmatches, exnids == nid_) big_graph.add_edges_from(list(ut.product([aid_], exmatches))) pass nx.set_node_attributes(big_graph, 'shape', 'rect') #if False and postcut: # ut.nx_delete_node_attr(big_graph, 'nid') # ut.nx_delete_edge_attr(big_graph, 'color') # viz_graph.ensure_graph_nid_labels(big_graph, ibs=ibs) # viz_graph.color_by_nids(big_graph, ibs=ibs) # big_graph = big_graph.to_undirected() layoutkw = { 'sep': 1 / 5, 'prog': 'neato', 'overlap': 'false', #'splines': 'ortho', 'splines': 'spline', } as_directed = False #as_directed = True #hacknode = True hacknode = 0 graph = big_graph ut.nx_ensure_agraph_color(graph) if hacknode: nx.set_edge_attributes(graph, 'taillabel', {e: str(e[0]) for e in graph.edges()}) nx.set_edge_attributes(graph, 'headlabel', {e: str(e[1]) for e in graph.edges()}) explicit_graph = pt.get_explicit_graph(graph) _, layout_info = pt.nx_agraph_layout(explicit_graph, orig_graph=graph, inplace=True, **layoutkw) if ut.get_argflag('--smaller'): graph.node[7660]['pos'] = np.array([550, 350]) graph.node[6120]['pos'] = np.array([200, 600]) + np.array([350, -400]) graph.node[7164]['pos'] = np.array([200, 480]) + np.array([350, -400]) nx.set_node_attributes(graph, 'pin', 'true') _, layout_info = pt.nx_agraph_layout(graph, inplace=True, **layoutkw) elif ut.get_argflag('--small'): graph.node[7660]['pos'] = np.array([750, 350]) graph.node[33]['pos'] = np.array([300, 600]) + np.array([350, -400]) graph.node[6120]['pos'] = np.array([500, 600]) + np.array([350, -400]) graph.node[7164]['pos'] = np.array([410, 480]) + np.array([350, -400]) nx.set_node_attributes(graph, 'pin', 'true') _, layout_info = pt.nx_agraph_layout(graph, inplace=True, **layoutkw) if not postcut: #pt.show_nx(graph.to_undirected(), layout='agraph', layoutkw=layoutkw, # as_directed=False) #pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, # as_directed=as_directed, hacknode=hacknode) pt.show_nx(graph, layout='custom', layoutkw=layoutkw, as_directed=as_directed, hacknode=hacknode) else: #explicit_graph = pt.get_explicit_graph(graph) #_, layout_info = pt.nx_agraph_layout(explicit_graph, orig_graph=graph, # **layoutkw) #layout_info['edge']['alpha'] = .8 #pt.apply_graph_layout_attrs(graph, layout_info) #graph_layout_attrs = layout_info['graph'] ##edge_layout_attrs = layout_info['edge'] ##node_layout_attrs = layout_info['node'] #for key, vals in layout_info['node'].items(): # #print('[special] key = %r' % (key,)) # nx.set_node_attributes(graph, key, vals) #for key, vals in layout_info['edge'].items(): # #print('[special] key = %r' % (key,)) # nx.set_edge_attributes(graph, key, vals) #nx.set_edge_attributes(graph, 'alpha', .8) #graph.graph['splines'] = graph_layout_attrs.get('splines', 'line') #graph.graph['splines'] = 'polyline' # graph_layout_attrs.get('splines', 'line') #graph.graph['splines'] = 'line' cut_graph = graph.copy() edge_list = list(cut_graph.edges()) edge_nids = np.array(ibs.unflat_map(ibs.get_annot_nids, edge_list)) cut_flags = edge_nids.T[0] != edge_nids.T[1] cut_edges = ut.compress(edge_list, cut_flags) cut_graph.remove_edges_from(cut_edges) ut.nx_delete_node_attr(cut_graph, 'nid') viz_graph.ensure_graph_nid_labels(cut_graph, ibs=ibs) #ut.nx_get_default_node_attributes(exemplars, 'color', None) ut.nx_delete_node_attr(cut_graph, 'color', nodes=unlabeled_graph.nodes()) aid2_color = ut.nx_get_default_node_attributes(cut_graph, 'color', None) nid2_colors = ut.group_items(aid2_color.values(), ibs.get_annot_nids(aid2_color.keys())) nid2_colors = ut.map_dict_vals(ut.filter_Nones, nid2_colors) nid2_colors = ut.map_dict_vals(ut.unique, nid2_colors) #for val in nid2_colors.values(): # assert len(val) <= 1 # Get initial colors nid2_color_ = { nid: colors_[0] for nid, colors_ in nid2_colors.items() if len(colors_) == 1 } graph = cut_graph viz_graph.color_by_nids(cut_graph, ibs=ibs, nid2_color_=nid2_color_) nx.set_node_attributes(cut_graph, 'framewidth', 4) pt.show_nx(cut_graph, layout='custom', layoutkw=layoutkw, as_directed=as_directed, hacknode=hacknode) pt.zoom_factory()
def add_edge2(u, v, *args, **kwargs): v = ut.ensure_iterable(v) u = ut.ensure_iterable(u) for _u, _v in ut.product(u, v): graph.add_edge(_u, _v, *args, **kwargs)
def make_graph(infr, show=False): import networkx as nx import itertools cm_list = infr.cm_list unique_nids, prob_names = infr.make_prob_names() thresh = infr.choose_thresh() # Simply cut any edge with a weight less than a threshold qaid_list = [cm.qaid for cm in cm_list] postcut = prob_names > thresh qxs, nxs = np.where(postcut) if False: kw = dict(precision=2, max_line_width=140, suppress_small=True) print( ut.hz_str('prob_names = ', ut.array2string2((prob_names), **kw))) print( ut.hz_str('postcut = ', ut.array2string2((postcut).astype(np.int), **kw))) matching_qaids = ut.take(qaid_list, qxs) matched_nids = ut.take(unique_nids, nxs) qreq_ = infr.qreq_ nodes = ut.unique(qreq_.qaids.tolist() + qreq_.daids.tolist()) if not hasattr(qreq_, 'dnids'): qreq_.dnids = qreq_.ibs.get_annot_nids(qreq_.daids) qreq_.qnids = qreq_.ibs.get_annot_nids(qreq_.qaids) dnid2_daids = ut.group_items(qreq_.daids, qreq_.dnids) grouped_aids = dnid2_daids.values() matched_daids = ut.take(dnid2_daids, matched_nids) name_cliques = [ list(itertools.combinations(aids, 2)) for aids in grouped_aids ] aid_matches = [ list(ut.product([qaid], daids)) for qaid, daids in zip(matching_qaids, matched_daids) ] graph = nx.Graph() graph.add_nodes_from(nodes) graph.add_edges_from(ut.flatten(name_cliques)) graph.add_edges_from(ut.flatten(aid_matches)) #matchless_quries = ut.take(qaid_list, ut.index_complement(qxs, len(qaid_list))) name_nodes = [('nid', l) for l in qreq_.dnids] db_aid_nid_edges = list(zip(qreq_.daids, name_nodes)) #query_aid_nid_edges = list(zip(matching_qaids, [('nid', l) for l in matched_nids])) #G = nx.Graph() #G.add_nodes_from(matchless_quries) #G.add_edges_from(db_aid_nid_edges) #G.add_edges_from(query_aid_nid_edges) graph.add_edges_from(db_aid_nid_edges) if infr.user_feedback is not None: user_feedback = ut.map_dict_vals(np.array, infr.user_feedback) p_bg = 0.0 part1 = user_feedback['p_match'] * (1 - user_feedback['p_notcomp']) part2 = p_bg * user_feedback['p_notcomp'] p_same_list = part1 + part2 for aid1, aid2, p_same in zip(user_feedback['aid1'], user_feedback['aid2'], p_same_list): if p_same > .5: if not graph.has_edge(aid1, aid2): graph.add_edge(aid1, aid2) else: if graph.has_edge(aid1, aid2): graph.remove_edge(aid1, aid2) if show: import plottool as pt nx.set_node_attributes(graph, 'color', {aid: pt.LIGHT_PINK for aid in qreq_.daids}) nx.set_node_attributes(graph, 'color', {aid: pt.TRUE_BLUE for aid in qreq_.qaids}) nx.set_node_attributes( graph, 'color', { aid: pt.LIGHT_PURPLE for aid in np.intersect1d(qreq_.qaids, qreq_.daids) }) nx.set_node_attributes( graph, 'label', {node: 'n%r' % (node[1], ) for node in name_nodes}) nx.set_node_attributes( graph, 'color', {node: pt.LIGHT_GREEN for node in name_nodes}) if show: import plottool as pt pt.show_nx(graph, layoutkw={'prog': 'neato'}, verbose=False) return graph
def make_graph(infr, show=False): import networkx as nx import itertools cm_list = infr.cm_list unique_nids, prob_names = infr.make_prob_names() thresh = infr.choose_thresh() # Simply cut any edge with a weight less than a threshold qaid_list = [cm.qaid for cm in cm_list] postcut = prob_names > thresh qxs, nxs = np.where(postcut) if False: kw = dict(precision=2, max_line_width=140, suppress_small=True) print(ut.hz_str('prob_names = ', ut.array2string2((prob_names), **kw))) print(ut.hz_str('postcut = ', ut.array2string2((postcut).astype(np.int), **kw))) matching_qaids = ut.take(qaid_list, qxs) matched_nids = ut.take(unique_nids, nxs) qreq_ = infr.qreq_ nodes = ut.unique(qreq_.qaids.tolist() + qreq_.daids.tolist()) if not hasattr(qreq_, 'dnids'): qreq_.dnids = qreq_.ibs.get_annot_nids(qreq_.daids) qreq_.qnids = qreq_.ibs.get_annot_nids(qreq_.qaids) dnid2_daids = ut.group_items(qreq_.daids, qreq_.dnids) grouped_aids = dnid2_daids.values() matched_daids = ut.take(dnid2_daids, matched_nids) name_cliques = [list(itertools.combinations(aids, 2)) for aids in grouped_aids] aid_matches = [list(ut.product([qaid], daids)) for qaid, daids in zip(matching_qaids, matched_daids)] graph = nx.Graph() graph.add_nodes_from(nodes) graph.add_edges_from(ut.flatten(name_cliques)) graph.add_edges_from(ut.flatten(aid_matches)) #matchless_quries = ut.take(qaid_list, ut.index_complement(qxs, len(qaid_list))) name_nodes = [('nid', l) for l in qreq_.dnids] db_aid_nid_edges = list(zip(qreq_.daids, name_nodes)) #query_aid_nid_edges = list(zip(matching_qaids, [('nid', l) for l in matched_nids])) #G = nx.Graph() #G.add_nodes_from(matchless_quries) #G.add_edges_from(db_aid_nid_edges) #G.add_edges_from(query_aid_nid_edges) graph.add_edges_from(db_aid_nid_edges) if infr.user_feedback is not None: user_feedback = ut.map_dict_vals(np.array, infr.user_feedback) p_bg = 0.0 part1 = user_feedback['p_match'] * (1 - user_feedback['p_notcomp']) part2 = p_bg * user_feedback['p_notcomp'] p_same_list = part1 + part2 for aid1, aid2, p_same in zip(user_feedback['aid1'], user_feedback['aid2'], p_same_list): if p_same > .5: if not graph.has_edge(aid1, aid2): graph.add_edge(aid1, aid2) else: if graph.has_edge(aid1, aid2): graph.remove_edge(aid1, aid2) if show: import plottool as pt nx.set_node_attributes(graph, 'color', {aid: pt.LIGHT_PINK for aid in qreq_.daids}) nx.set_node_attributes(graph, 'color', {aid: pt.TRUE_BLUE for aid in qreq_.qaids}) nx.set_node_attributes(graph, 'color', { aid: pt.LIGHT_PURPLE for aid in np.intersect1d(qreq_.qaids, qreq_.daids)}) nx.set_node_attributes(graph, 'label', {node: 'n%r' % (node[1],) for node in name_nodes}) nx.set_node_attributes(graph, 'color', {node: pt.LIGHT_GREEN for node in name_nodes}) if show: import plottool as pt pt.show_nx(graph, layoutkw={'prog': 'neato'}, verbose=False) return graph
def intraoccurrence_connected(): r""" CommandLine: python -m ibeis.scripts.specialdraw intraoccurrence_connected --show python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --postcut python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --smaller Example: >>> # DISABLE_DOCTEST >>> from ibeis.scripts.specialdraw import * # NOQA >>> result = intraoccurrence_connected() >>> print(result) >>> ut.quit_if_noshow() >>> import plottool as pt >>> ut.show_if_requested() """ import ibeis import plottool as pt from ibeis.viz import viz_graph import networkx as nx pt.ensure_pylab_qt4() ibs = ibeis.opendb(defaultdb='PZ_Master1') nid2_aid = { #4880: [3690, 3696, 3703, 3706, 3712, 3721], 4880: [3690, 3696, 3703], 6537: [3739], 6653: [7671], 6610: [7566, 7408], #6612: [7664, 7462, 7522], #6624: [7465, 7360], #6625: [7746, 7383, 7390, 7477, 7376, 7579], 6630: [7586, 7377, 7464, 7478], #6677: [7500] } nid2_dbaids = { 4880: [33, 6120, 7164], 6537: [7017, 7206], 6653: [7660] } if ut.get_argflag('--small') or ut.get_argflag('--smaller'): del nid2_aid[6630] del nid2_aid[6537] del nid2_dbaids[6537] if ut.get_argflag('--smaller'): nid2_dbaids[4880].remove(33) nid2_aid[4880].remove(3690) nid2_aid[6610].remove(7408) #del nid2_aid[4880] #del nid2_dbaids[4880] aids = ut.flatten(nid2_aid.values()) temp_nids = [1] * len(aids) postcut = ut.get_argflag('--postcut') aids_list = ibs.group_annots_by_name(aids)[0] ensure_edges = 'all' if True or not postcut else None unlabeled_graph = viz_graph.make_netx_graph_from_aid_groups( ibs, aids_list, #invis_edges=invis_edges, ensure_edges=ensure_edges, temp_nids=temp_nids) viz_graph.color_by_nids(unlabeled_graph, unique_nids=[1] * len(list(unlabeled_graph.nodes()))) viz_graph.ensure_node_images(ibs, unlabeled_graph) nx.set_node_attributes(unlabeled_graph, 'shape', 'rect') #unlabeled_graph = unlabeled_graph.to_undirected() # Find the "database exemplars for these annots" if False: gt_aids = ibs.get_annot_groundtruth(aids) gt_aids = [ut.setdiff(s, aids) for s in gt_aids] dbaids = ut.unique(ut.flatten(gt_aids)) dbaids = ibs.filter_annots_general(dbaids, minqual='good') ibs.get_annot_quality_texts(dbaids) else: dbaids = ut.flatten(nid2_dbaids.values()) exemplars = nx.DiGraph() #graph = exemplars # NOQA exemplars.add_nodes_from(dbaids) def add_clique(graph, nodes, edgeattrs={}, nodeattrs={}): edge_list = ut.upper_diag_self_prodx(nodes) graph.add_edges_from(edge_list, **edgeattrs) return edge_list for aids_, nid in zip(*ibs.group_annots_by_name(dbaids)): add_clique(exemplars, aids_) viz_graph.ensure_node_images(ibs, exemplars) viz_graph.color_by_nids(exemplars, ibs=ibs) nx.set_node_attributes(unlabeled_graph, 'framewidth', False) nx.set_node_attributes(exemplars, 'framewidth', 4.0) nx.set_node_attributes(unlabeled_graph, 'group', 'unlab') nx.set_node_attributes(exemplars, 'group', 'exemp') #big_graph = nx.compose_all([unlabeled_graph]) big_graph = nx.compose_all([exemplars, unlabeled_graph]) # add sparse connections from unlabeled to exemplars import numpy as np rng = np.random.RandomState(0) if True or not postcut: for aid_ in unlabeled_graph.nodes(): flags = rng.rand(len(exemplars)) > .5 nid_ = ibs.get_annot_nids(aid_) exnids = np.array(ibs.get_annot_nids(list(exemplars.nodes()))) flags = np.logical_or(exnids == nid_, flags) exmatches = ut.compress(list(exemplars.nodes()), flags) big_graph.add_edges_from(list(ut.product([aid_], exmatches)), color=pt.ORANGE, implicit=True) else: for aid_ in unlabeled_graph.nodes(): flags = rng.rand(len(exemplars)) > .5 exmatches = ut.compress(list(exemplars.nodes()), flags) nid_ = ibs.get_annot_nids(aid_) exnids = np.array(ibs.get_annot_nids(exmatches)) exmatches = ut.compress(exmatches, exnids == nid_) big_graph.add_edges_from(list(ut.product([aid_], exmatches))) pass nx.set_node_attributes(big_graph, 'shape', 'rect') #if False and postcut: # ut.nx_delete_node_attr(big_graph, 'nid') # ut.nx_delete_edge_attr(big_graph, 'color') # viz_graph.ensure_graph_nid_labels(big_graph, ibs=ibs) # viz_graph.color_by_nids(big_graph, ibs=ibs) # big_graph = big_graph.to_undirected() layoutkw = { 'sep' : 1 / 5, 'prog': 'neato', 'overlap': 'false', #'splines': 'ortho', 'splines': 'spline', } as_directed = False #as_directed = True #hacknode = True hacknode = 0 graph = big_graph ut.nx_ensure_agraph_color(graph) if hacknode: nx.set_edge_attributes(graph, 'taillabel', {e: str(e[0]) for e in graph.edges()}) nx.set_edge_attributes(graph, 'headlabel', {e: str(e[1]) for e in graph.edges()}) explicit_graph = pt.get_explicit_graph(graph) _, layout_info = pt.nx_agraph_layout(explicit_graph, orig_graph=graph, inplace=True, **layoutkw) if ut.get_argflag('--smaller'): graph.node[7660]['pos'] = np.array([550, 350]) graph.node[6120]['pos'] = np.array([200, 600]) + np.array([350, -400]) graph.node[7164]['pos'] = np.array([200, 480]) + np.array([350, -400]) nx.set_node_attributes(graph, 'pin', 'true') _, layout_info = pt.nx_agraph_layout(graph, inplace=True, **layoutkw) elif ut.get_argflag('--small'): graph.node[7660]['pos'] = np.array([750, 350]) graph.node[33]['pos'] = np.array([300, 600]) + np.array([350, -400]) graph.node[6120]['pos'] = np.array([500, 600]) + np.array([350, -400]) graph.node[7164]['pos'] = np.array([410, 480]) + np.array([350, -400]) nx.set_node_attributes(graph, 'pin', 'true') _, layout_info = pt.nx_agraph_layout(graph, inplace=True, **layoutkw) if not postcut: #pt.show_nx(graph.to_undirected(), layout='agraph', layoutkw=layoutkw, # as_directed=False) #pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, # as_directed=as_directed, hacknode=hacknode) pt.show_nx(graph, layout='custom', layoutkw=layoutkw, as_directed=as_directed, hacknode=hacknode) else: #explicit_graph = pt.get_explicit_graph(graph) #_, layout_info = pt.nx_agraph_layout(explicit_graph, orig_graph=graph, # **layoutkw) #layout_info['edge']['alpha'] = .8 #pt.apply_graph_layout_attrs(graph, layout_info) #graph_layout_attrs = layout_info['graph'] ##edge_layout_attrs = layout_info['edge'] ##node_layout_attrs = layout_info['node'] #for key, vals in layout_info['node'].items(): # #print('[special] key = %r' % (key,)) # nx.set_node_attributes(graph, key, vals) #for key, vals in layout_info['edge'].items(): # #print('[special] key = %r' % (key,)) # nx.set_edge_attributes(graph, key, vals) #nx.set_edge_attributes(graph, 'alpha', .8) #graph.graph['splines'] = graph_layout_attrs.get('splines', 'line') #graph.graph['splines'] = 'polyline' # graph_layout_attrs.get('splines', 'line') #graph.graph['splines'] = 'line' cut_graph = graph.copy() edge_list = list(cut_graph.edges()) edge_nids = np.array(ibs.unflat_map(ibs.get_annot_nids, edge_list)) cut_flags = edge_nids.T[0] != edge_nids.T[1] cut_edges = ut.compress(edge_list, cut_flags) cut_graph.remove_edges_from(cut_edges) ut.nx_delete_node_attr(cut_graph, 'nid') viz_graph.ensure_graph_nid_labels(cut_graph, ibs=ibs) #ut.nx_get_default_node_attributes(exemplars, 'color', None) ut.nx_delete_node_attr(cut_graph, 'color', nodes=unlabeled_graph.nodes()) aid2_color = ut.nx_get_default_node_attributes(cut_graph, 'color', None) nid2_colors = ut.group_items(aid2_color.values(), ibs.get_annot_nids(aid2_color.keys())) nid2_colors = ut.map_dict_vals(ut.filter_Nones, nid2_colors) nid2_colors = ut.map_dict_vals(ut.unique, nid2_colors) #for val in nid2_colors.values(): # assert len(val) <= 1 # Get initial colors nid2_color_ = {nid: colors_[0] for nid, colors_ in nid2_colors.items() if len(colors_) == 1} graph = cut_graph viz_graph.color_by_nids(cut_graph, ibs=ibs, nid2_color_=nid2_color_) nx.set_node_attributes(cut_graph, 'framewidth', 4) pt.show_nx(cut_graph, layout='custom', layoutkw=layoutkw, as_directed=as_directed, hacknode=hacknode) pt.zoom_factory()