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()
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)
