def make_prob_annots(infr):
cm_list = infr.cm_list
unique_aids = sorted(ut.list_union(*[cm.daid_list for cm in cm_list] +
[[cm.qaid for cm in cm_list]]))
aid2_aidx = ut.make_index_lookup(unique_aids)
prob_annots = np.zeros((len(unique_aids), len(unique_aids)))
for count, cm in enumerate(cm_list):
idx = aid2_aidx[cm.qaid]
annot_scores = ut.dict_take(cm.aid2_annot_score, unique_aids, 0)
prob_annots[idx][:] = annot_scores
prob_annots[np.diag_indices(len(prob_annots))] = np.inf
prob_annots += 1E-9
#print(ut.hz_str('prob_names = ', ut.array2string2(prob_names,
#precision=2, max_line_width=140, suppress_small=True)))
return unique_aids, prob_annots
def make_prob_annots(infr):
cm_list = infr.cm_list
unique_aids = sorted(
ut.list_union(*[cm.daid_list
for cm in cm_list] + [[cm.qaid
for cm in cm_list]]))
aid2_aidx = ut.make_index_lookup(unique_aids)
prob_annots = np.zeros((len(unique_aids), len(unique_aids)))
for count, cm in enumerate(cm_list):
idx = aid2_aidx[cm.qaid]
annot_scores = ut.dict_take(cm.aid2_annot_score, unique_aids, 0)
prob_annots[idx][:] = annot_scores
prob_annots[np.diag_indices(len(prob_annots))] = np.inf
prob_annots += 1E-9
#print(ut.hz_str('prob_names = ', ut.array2string2(prob_names,
#precision=2, max_line_width=140, suppress_small=True)))
return unique_aids, prob_annots
def make_prob_names(self):
cm_list = self.cm_list
# Consolodate information from a series of chip matches
unique_nids = sorted(ut.list_union(*[cm.unique_nids for cm in cm_list]))
# nid2_nidx = ut.make_index_lookup(unique_nids)
# Populate matrix of raw name scores
prob_names = np.zeros((len(cm_list), len(unique_nids)))
for count, cm in enumerate(cm_list):
try:
name_scores = ut.dict_take(cm.nid2_name_score, unique_nids, 0)
except AttributeError:
unique_nidxs = ut.take(cm.nid2_nidx, unique_nids)
name_scores = ut.take(cm.name_score_list, unique_nidxs)
prob_names[count][:] = name_scores
# Normalize to row stochastic matrix
prob_names /= prob_names.sum(axis=1)[:, None]
# logger.info(ut.hz_str('prob_names = ', ut.repr2(prob_names,
# precision=2, max_line_width=140, suppress_small=True)))
return unique_nids, prob_names
def make_prob_names(infr):
cm_list = infr.cm_list
# Consolodate information from a series of chip matches
unique_nids = sorted(ut.list_union(*[cm.unique_nids for cm in cm_list]))
#nid2_nidx = ut.make_index_lookup(unique_nids)
# Populate matrix of raw name scores
prob_names = np.zeros((len(cm_list), len(unique_nids)))
for count, cm in enumerate(cm_list):
try:
name_scores = ut.dict_take(cm.nid2_name_score, unique_nids, 0)
except AttributeError:
unique_nidxs = ut.take(cm.nid2_nidx, unique_nids)
name_scores = ut.take(cm.name_score_list, unique_nidxs)
prob_names[count][:] = name_scores
# Normalize to row stochastic matrix
prob_names /= prob_names.sum(axis=1)[:, None]
#print(ut.hz_str('prob_names = ', ut.array2string2(prob_names,
#precision=2, max_line_width=140, suppress_small=True)))
return unique_nids, prob_names
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
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