def make_netx_graph_from_aid_groups(ibs,
aids_list,
only_reviewed_matches=True,
invis_edges=None,
ensure_edges=None,
temp_nids=None,
allow_directed=False):
r"""
Args:
ibs (ibeis.IBEISController): image analysis api
aids_list (list):
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_graph import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> aids_list = [[1, 2, 3, 4], [5, 6, 7]]
>>> invis_edges = [(1, 5)]
>>> only_reviewed_matches = True
>>> graph = make_netx_graph_from_aid_groups(ibs, aids_list,
>>> only_reviewed_matches,
>>> invis_edges)
>>> list(nx.connected_components(graph.to_undirected()))
"""
#aids_list, nid_list = ibs.group_annots_by_name(aid_list)
unique_aids = list(ut.flatten(aids_list))
# grouped version
unflat_edges = (list(itertools.product(aids, aids)) for aids in aids_list)
aid_pairs = [tup for tup in ut.iflatten(unflat_edges) if tup[0] != tup[1]]
aids1 = ut.get_list_column(aid_pairs, 0)
aids2 = ut.get_list_column(aid_pairs, 1)
if only_reviewed_matches:
annotmatch_rowids = ibs.get_annotmatch_rowid_from_superkey(
aids1, aids2)
annotmatch_rowids = ut.filter_Nones(annotmatch_rowids)
aids1 = ibs.get_annotmatch_aid1(annotmatch_rowids)
aids2 = ibs.get_annotmatch_aid2(annotmatch_rowids)
graph = make_netx_graph_from_aidpairs(ibs,
aids1,
aids2,
unique_aids=unique_aids)
if ensure_edges is not None:
if ensure_edges == 'all':
ensure_edges = list(ut.upper_diag_self_prodx(list(graph.nodes())))
ensure_edges_ = []
for edge in ensure_edges:
edge = tuple(edge)
redge = tuple(edge[::-1]) # HACK
if graph.has_edge(*edge):
ensure_edges_.append(edge)
pass
#nx.set_edge_attributes(graph, 'weight', {edge: .001})
elif (not allow_directed) and graph.has_edge(*redge):
ensure_edges_.append(redge)
#nx.set_edge_attributes(graph, 'weight', {redge: .001})
pass
else:
ensure_edges_.append(edge)
#graph.add_edge(*edge, weight=.001)
graph.add_edge(*edge)
if temp_nids is None:
unique_nids = ibs.get_annot_nids(list(graph.nodes()))
else:
# HACK
unique_nids = [1] * len(list(graph.nodes()))
#unique_nids = temp_nids
nx.set_node_attributes(graph, 'nid', dict(zip(graph.nodes(), unique_nids)))
import plottool as pt
ensure_names_are_connected(graph, aids_list)
# Color edges by nid
color_by_nids(graph, unique_nids=unique_nids)
if invis_edges:
for edge in invis_edges:
if graph.has_edge(*edge):
nx.set_edge_attributes(graph, 'style', {edge: 'invis'})
nx.set_edge_attributes(graph, 'invisible', {edge: True})
else:
graph.add_edge(*edge, style='invis', invisible=True)
# Hack color images orange
if ensure_edges:
nx.set_edge_attributes(
graph, 'color', {tuple(edge): pt.ORANGE
for edge in ensure_edges_})
return graph
def merge_viewpoint_graph():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw merge_viewpoint_graph --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = merge_viewpoint_graph()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
import ibeis
import networkx as nx
defaultdb = 'PZ_Master1'
ibs = ibeis.opendb(defaultdb=defaultdb)
#nids = None
aids = ibs.get_name_aids(4875)
ibs.print_annot_stats(aids)
left_aids = ibs.filter_annots_general(aids, view='left')[0:3]
right_aids = ibs.filter_annots_general(aids, view='right')
right_aids = list(set(right_aids) - {14517})[0:3]
back = ibs.filter_annots_general(aids, view='back')[0:4]
backleft = ibs.filter_annots_general(aids, view='backleft')[0:4]
backright = ibs.filter_annots_general(aids, view='backright')[0:4]
right_graph = nx.DiGraph(ut.upper_diag_self_prodx(right_aids))
left_graph = nx.DiGraph(ut.upper_diag_self_prodx(left_aids))
back_edges = [
tuple([back[0], backright[0]][::1]),
tuple([back[0], backleft[0]][::1]),
]
back_graph = nx.DiGraph(back_edges)
# Let the graph be a bit smaller
right_graph.edge[right_aids[1]][
right_aids[2]]['constraint'] = ut.get_argflag('--constraint')
left_graph.edge[left_aids[1]][left_aids[2]]['constraint'] = ut.get_argflag(
'--constraint')
#right_graph = right_graph.to_undirected().to_directed()
#left_graph = left_graph.to_undirected().to_directed()
nx.set_node_attributes(right_graph, 'groupid', 'right')
nx.set_node_attributes(left_graph, 'groupid', 'left')
#nx.set_node_attributes(right_graph, 'scale', .2)
#nx.set_node_attributes(left_graph, 'scale', .2)
#back_graph.node[back[0]]['scale'] = 2.3
nx.set_node_attributes(back_graph, 'groupid', 'back')
view_graph = nx.compose_all([left_graph, back_graph, right_graph])
view_graph.add_edges_from([
[backright[0], right_aids[0]][::-1],
[backleft[0], left_aids[0]][::-1],
])
pt.ensure_pylab_qt4()
graph = graph = view_graph # NOQA
#graph = graph.to_undirected()
nx.set_edge_attributes(graph, 'color', pt.DARK_ORANGE[0:3])
#nx.set_edge_attributes(graph, 'color', pt.BLACK)
nx.set_edge_attributes(graph, 'color',
{edge: pt.LIGHT_BLUE[0:3]
for edge in back_edges})
#pt.close_all_figures();
from ibeis.viz import viz_graph
layoutkw = {
'nodesep': 1,
}
viz_graph.viz_netx_chipgraph(ibs,
graph,
with_images=1,
prog='dot',
augment_graph=False,
layoutkw=layoutkw)
if False:
"""
#view_graph = left_graph
pt.close_all_figures(); viz_netx_chipgraph(ibs, view_graph, with_images=0, prog='neato')
#viz_netx_chipgraph(ibs, view_graph, layout='pydot', with_images=False)
#back_graph = make_name_graph_interaction(ibs, aids=back, with_all=False)
aids = left_aids + back + backleft + backright + right_aids
for aid, chip in zip(aids, ibs.get_annot_chips(aids)):
fpath = ut.truepath('~/slides/merge/aid_%d.jpg' % (aid,))
vt.imwrite(fpath, vt.resize_to_maxdims(chip, (400, 400)))
ut.copy_files_to(, )
aids = ibs.filterannots_by_tags(ibs.get_valid_aids(),
dict(has_any_annotmatch='splitcase'))
aid1 = ibs.group_annots_by_name_dict(aids)[252]
aid2 = ibs.group_annots_by_name_dict(aids)[6791]
aids1 = ibs.get_annot_groundtruth(aid1)[0][0:4]
aids2 = ibs.get_annot_groundtruth(aid2)[0]
make_name_graph_interaction(ibs, aids=aids1 + aids2, with_all=False)
ut.ensuredir(ut.truthpath('~/slides/split/))
for aid, chip in zip(aids, ibs.get_annot_chips(aids)):
fpath = ut.truepath('~/slides/merge/aidA_%d.jpg' % (aid,))
vt.imwrite(fpath, vt.resize_to_maxdims(chip, (400, 400)))
"""
pass
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
def fix_duplicates(drive):
r"""
for every duplicate file passing a (eg avi) filter, remove the file
that is in the smallest directory. On a tie use the smallest dpath.
This will filter all duplicate files in a folder into a single folder.
but... need to look at non-duplicates in that folder and decide if they
should be moved as well. So, should trigger on folders that have at
least 50% duplicate. Might not want to move curated folders.
Example:
cd ~/local/scripts
>>> from register_files import * # NOQA
>>> dpaths = ut.get_argval('--drives', type_=list, default=['E:/'])#'D:/', 'E:/', 'F:/'])
>>> drives = [Drive(root_dpath) for root_dpath in dpaths]
>>> E = drive = drives[0]
>>> #D, E, F = drives
"""
print('Fixing Duplicates in %r' % (drive,))
list_ = drive.fpath_hashX_list
multiindex_dict_ = build_multindex(list_)
duplicate_hashes = [
key for key, val in six.iteritems(multiindex_dict_)
if len(val) > 1
]
duplicate_idxs = ut.dict_take(multiindex_dict_, duplicate_hashes)
unflat_fpaths = ut.list_unflat_take(drive.fpath_list, duplicate_idxs)
# Check if any dups have been removed
still_exists = ut.unflat_map(exists, unflat_fpaths)
unflat_idxs2 = ut.zipcompress(duplicate_idxs, still_exists)
duplicate_idxs = [idxs for idxs in unflat_idxs2 if len(idxs) > 1]
# Look at duplicate files
unflat_fpaths = ut.list_unflat_take(drive.fpath_list, duplicate_idxs)
unflat_sizes = ut.list_unflat_take(drive.fpath_bytes_list, duplicate_idxs)
# Find highly coupled directories
if True:
coupled_dirs = []
for fpaths in unflat_fpaths:
#basedir = ut.longest_existing_path(commonprefix(fpaths))
dirs = sorted(list(map(dirname, fpaths)))
_list = list(range(len(dirs)))
idxs = ut.upper_diag_self_prodx(_list)
coupled_dirs.extend(list(map(tuple, ut.list_unflat_take(dirs, idxs))))
hist_ = ut.dict_hist(coupled_dirs)
coupled_idxs = ut.list_argsort(hist_.values())[::-1]
most_coupled = ut.take(list(hist_.keys()), coupled_idxs[0:100])
print('Coupled fpaths: ' + ut.list_str(most_coupled, nl=True))
print('%d unique files are duplicated' % (len(unflat_sizes),))
#print('Duplicate sizes: ' + ut.list_str(unflat_sizes[0:10], nl=True))
#print('Duplicate fpaths: ' + ut.list_str(unflat_fpaths[0:10], nl=True))
#print('Duplicate fpaths: ' + ut.list_str(unflat_fpaths[0::5], nl=True))
print('Duplicate fpaths: ' + ut.list_str(unflat_fpaths, nl=True))
# Find duplicate directories
dpath_list = list(drive.dpath_to_fidx.keys())
fidxs_list = ut.dict_take(drive.dpath_to_fidx, drive.dpath_list)
#exists_list = list(map(exists, drive.fpath_list))
#unflat_exists = ut.list_unflat_take(exists_list, fidxs_list)
fname_registry = [basename(fpath) for fpath in drive.fpath_list]
unflat_fnames = ut.list_unflat_take(fname_registry, fidxs_list)
def unsorted_list_hash(list_):
return ut.hashstr27(str(sorted(list_)))
unflat_fname_sets = list(map(unsorted_list_hash, ut.ProgIter(unflat_fnames, freq=10000)))
fname_based_duplicate_dpaths = []
multiindex_dict2_ = build_multindex(unflat_fname_sets)
fname_based_duplicate_hashes = [key for key, val in multiindex_dict2_.items() if len(val) > 1]
print('#fname_based_duplicate_dpaths = %r' % (len(fname_based_duplicate_hashes),))
fname_based_duplicate_didxs = ut.dict_take(multiindex_dict2_, fname_based_duplicate_hashes)
fname_based_duplicate_dpaths = ut.list_unflat_take(dpath_list, fname_based_duplicate_didxs)
print(ut.repr3(fname_based_duplicate_dpaths[0:10]))
def fix_duplicates(drive):
r"""
for every duplicate file passing a (eg avi) filter, remove the file
that is in the smallest directory. On a tie use the smallest dpath.
This will filter all duplicate files in a folder into a single folder.
but... need to look at non-duplicates in that folder and decide if they
should be moved as well. So, should trigger on folders that have at
least 50% duplicate. Might not want to move curated folders.
Example:
cd ~/local/scripts
>>> from register_files import * # NOQA
>>> dpaths = ut.get_argval('--drives', type_=list, default=['E:/'])#'D:/', 'E:/', 'F:/'])
>>> drives = [Drive(root_dpath) for root_dpath in dpaths]
>>> E = drive = drives[0]
>>> #D, E, F = drives
"""
print('Fixing Duplicates in %r' % (drive, ))
list_ = drive.fpath_hashX_list
multiindex_dict_ = build_multindex(list_)
duplicate_hashes = [
key for key, val in six.iteritems(multiindex_dict_) if len(val) > 1
]
duplicate_idxs = ut.dict_take(multiindex_dict_, duplicate_hashes)
unflat_fpaths = ut.list_unflat_take(drive.fpath_list, duplicate_idxs)
# Check if any dups have been removed
still_exists = ut.unflat_map(exists, unflat_fpaths)
unflat_idxs2 = ut.zipcompress(duplicate_idxs, still_exists)
duplicate_idxs = [idxs for idxs in unflat_idxs2 if len(idxs) > 1]
# Look at duplicate files
unflat_fpaths = ut.list_unflat_take(drive.fpath_list, duplicate_idxs)
unflat_sizes = ut.list_unflat_take(drive.fpath_bytes_list,
duplicate_idxs)
# Find highly coupled directories
if True:
coupled_dirs = []
for fpaths in unflat_fpaths:
#basedir = ut.longest_existing_path(commonprefix(fpaths))
dirs = sorted(list(map(dirname, fpaths)))
_list = list(range(len(dirs)))
idxs = ut.upper_diag_self_prodx(_list)
coupled_dirs.extend(
list(map(tuple, ut.list_unflat_take(dirs, idxs))))
hist_ = ut.dict_hist(coupled_dirs)
coupled_idxs = ut.list_argsort(hist_.values())[::-1]
most_coupled = ut.take(list(hist_.keys()), coupled_idxs[0:100])
print('Coupled fpaths: ' + ut.repr2(most_coupled, nl=True))
print('%d unique files are duplicated' % (len(unflat_sizes), ))
#print('Duplicate sizes: ' + ut.repr2(unflat_sizes[0:10], nl=True))
#print('Duplicate fpaths: ' + ut.repr2(unflat_fpaths[0:10], nl=True))
#print('Duplicate fpaths: ' + ut.repr2(unflat_fpaths[0::5], nl=True))
print('Duplicate fpaths: ' + ut.repr2(unflat_fpaths, nl=True))
# Find duplicate directories
dpath_list = list(drive.dpath_to_fidx.keys())
fidxs_list = ut.dict_take(drive.dpath_to_fidx, drive.dpath_list)
#exists_list = list(map(exists, drive.fpath_list))
#unflat_exists = ut.list_unflat_take(exists_list, fidxs_list)
fname_registry = [basename(fpath) for fpath in drive.fpath_list]
unflat_fnames = ut.list_unflat_take(fname_registry, fidxs_list)
def unsorted_list_hash(list_):
return ut.hashstr27(str(sorted(list_)))
unflat_fname_sets = list(
map(unsorted_list_hash, ut.ProgIter(unflat_fnames, freq=10000)))
fname_based_duplicate_dpaths = []
multiindex_dict2_ = build_multindex(unflat_fname_sets)
fname_based_duplicate_hashes = [
key for key, val in multiindex_dict2_.items() if len(val) > 1
]
print('#fname_based_duplicate_dpaths = %r' %
(len(fname_based_duplicate_hashes), ))
fname_based_duplicate_didxs = ut.dict_take(
multiindex_dict2_, fname_based_duplicate_hashes)
fname_based_duplicate_dpaths = ut.list_unflat_take(
dpath_list, fname_based_duplicate_didxs)
print(ut.repr3(fname_based_duplicate_dpaths[0:10]))
def make_netx_graph_from_aid_groups(ibs, aids_list, only_reviewed_matches=True,
invis_edges=None, ensure_edges=None,
temp_nids=None, allow_directed=False):
r"""
Args:
ibs (ibeis.IBEISController): image analysis api
aids_list (list):
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_graph import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> aids_list = [[1, 2, 3, 4], [5, 6, 7]]
>>> invis_edges = [(1, 5)]
>>> only_reviewed_matches = True
>>> graph = make_netx_graph_from_aid_groups(ibs, aids_list,
>>> only_reviewed_matches,
>>> invis_edges)
>>> list(nx.connected_components(graph.to_undirected()))
"""
#aids_list, nid_list = ibs.group_annots_by_name(aid_list)
unique_aids = list(ut.flatten(aids_list))
# grouped version
unflat_edges = (list(itertools.product(aids, aids)) for aids in aids_list)
aid_pairs = [tup for tup in ut.iflatten(unflat_edges) if tup[0] != tup[1]]
aids1 = ut.get_list_column(aid_pairs, 0)
aids2 = ut.get_list_column(aid_pairs, 1)
if only_reviewed_matches:
annotmatch_rowids = ibs.get_annotmatch_rowid_from_superkey(aids1, aids2)
annotmatch_rowids = ut.filter_Nones(annotmatch_rowids)
aids1 = ibs.get_annotmatch_aid1(annotmatch_rowids)
aids2 = ibs.get_annotmatch_aid2(annotmatch_rowids)
graph = make_netx_graph_from_aidpairs(ibs, aids1, aids2, unique_aids=unique_aids)
if ensure_edges is not None:
if ensure_edges == 'all':
ensure_edges = list(ut.upper_diag_self_prodx(list(graph.nodes())))
ensure_edges_ = []
for edge in ensure_edges:
edge = tuple(edge)
redge = tuple(edge[::-1]) # HACK
if graph.has_edge(*edge):
ensure_edges_.append(edge)
pass
#nx.set_edge_attributes(graph, 'weight', {edge: .001})
elif (not allow_directed) and graph.has_edge(*redge):
ensure_edges_.append(redge)
#nx.set_edge_attributes(graph, 'weight', {redge: .001})
pass
else:
ensure_edges_.append(edge)
#graph.add_edge(*edge, weight=.001)
graph.add_edge(*edge)
if temp_nids is None:
unique_nids = ibs.get_annot_nids(list(graph.nodes()))
else:
# HACK
unique_nids = [1] * len(list(graph.nodes()))
#unique_nids = temp_nids
nx.set_node_attributes(graph, 'nid', dict(zip(graph.nodes(), unique_nids)))
import plottool as pt
ensure_names_are_connected(graph, aids_list)
# Color edges by nid
color_by_nids(graph, unique_nids=unique_nids)
if invis_edges:
for edge in invis_edges:
if graph.has_edge(*edge):
nx.set_edge_attributes(graph, 'style', {edge: 'invis'})
nx.set_edge_attributes(graph, 'invisible', {edge: True})
else:
graph.add_edge(*edge, style='invis', invisible=True)
# Hack color images orange
if ensure_edges:
nx.set_edge_attributes(graph, 'color',
{tuple(edge): pt.ORANGE for edge in ensure_edges_})
return graph
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
def merge_viewpoint_graph():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw merge_viewpoint_graph --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = merge_viewpoint_graph()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
import ibeis
import networkx as nx
defaultdb = 'PZ_Master1'
ibs = ibeis.opendb(defaultdb=defaultdb)
#nids = None
aids = ibs.get_name_aids(4875)
ibs.print_annot_stats(aids)
left_aids = ibs.filter_annots_general(aids, view='left')[0:3]
right_aids = ibs.filter_annots_general(aids, view='right')
right_aids = list(set(right_aids) - {14517})[0:3]
back = ibs.filter_annots_general(aids, view='back')[0:4]
backleft = ibs.filter_annots_general(aids, view='backleft')[0:4]
backright = ibs.filter_annots_general(aids, view='backright')[0:4]
right_graph = nx.DiGraph(ut.upper_diag_self_prodx(right_aids))
left_graph = nx.DiGraph(ut.upper_diag_self_prodx(left_aids))
back_edges = [
tuple([back[0], backright[0]][::1]),
tuple([back[0], backleft[0]][::1]),
]
back_graph = nx.DiGraph(back_edges)
# Let the graph be a bit smaller
right_graph.edge[right_aids[1]][right_aids[2]]['constraint'] = ut.get_argflag('--constraint')
left_graph.edge[left_aids[1]][left_aids[2]]['constraint'] = ut.get_argflag('--constraint')
#right_graph = right_graph.to_undirected().to_directed()
#left_graph = left_graph.to_undirected().to_directed()
nx.set_node_attributes(right_graph, 'groupid', 'right')
nx.set_node_attributes(left_graph, 'groupid', 'left')
#nx.set_node_attributes(right_graph, 'scale', .2)
#nx.set_node_attributes(left_graph, 'scale', .2)
#back_graph.node[back[0]]['scale'] = 2.3
nx.set_node_attributes(back_graph, 'groupid', 'back')
view_graph = nx.compose_all([left_graph, back_graph, right_graph])
view_graph.add_edges_from([
[backright[0], right_aids[0]][::-1],
[backleft[0], left_aids[0]][::-1],
])
pt.ensure_pylab_qt4()
graph = graph = view_graph # NOQA
#graph = graph.to_undirected()
nx.set_edge_attributes(graph, 'color', pt.DARK_ORANGE[0:3])
#nx.set_edge_attributes(graph, 'color', pt.BLACK)
nx.set_edge_attributes(graph, 'color', {edge: pt.LIGHT_BLUE[0:3] for edge in back_edges})
#pt.close_all_figures();
from ibeis.viz import viz_graph
layoutkw = {
'nodesep': 1,
}
viz_graph.viz_netx_chipgraph(ibs, graph, with_images=1, prog='dot',
augment_graph=False, layoutkw=layoutkw)
if False:
"""
#view_graph = left_graph
pt.close_all_figures(); viz_netx_chipgraph(ibs, view_graph, with_images=0, prog='neato')
#viz_netx_chipgraph(ibs, view_graph, layout='pydot', with_images=False)
#back_graph = make_name_graph_interaction(ibs, aids=back, with_all=False)
aids = left_aids + back + backleft + backright + right_aids
for aid, chip in zip(aids, ibs.get_annot_chips(aids)):
fpath = ut.truepath('~/slides/merge/aid_%d.jpg' % (aid,))
vt.imwrite(fpath, vt.resize_to_maxdims(chip, (400, 400)))
ut.copy_files_to(, )
aids = ibs.filterannots_by_tags(ibs.get_valid_aids(),
dict(has_any_annotmatch='splitcase'))
aid1 = ibs.group_annots_by_name_dict(aids)[252]
aid2 = ibs.group_annots_by_name_dict(aids)[6791]
aids1 = ibs.get_annot_groundtruth(aid1)[0][0:4]
aids2 = ibs.get_annot_groundtruth(aid2)[0]
make_name_graph_interaction(ibs, aids=aids1 + aids2, with_all=False)
ut.ensuredir(ut.truthpath('~/slides/split/))
for aid, chip in zip(aids, ibs.get_annot_chips(aids)):
fpath = ut.truepath('~/slides/merge/aidA_%d.jpg' % (aid,))
vt.imwrite(fpath, vt.resize_to_maxdims(chip, (400, 400)))
"""
pass
