def double_depcache_graph():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw double_depcache_graph --show --testmode
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=.5
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=5
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = double_depcache_graph()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
ibs = ibeis.opendb('testdb1')
reduced = True
implicit = True
annot_graph = ibs.depc_annot.make_graph(reduced=reduced, implicit=implicit)
image_graph = ibs.depc_image.make_graph(reduced=reduced, implicit=implicit)
to_rename = ut.isect(image_graph.nodes(), annot_graph.nodes())
nx.relabel_nodes(annot_graph, {x: 'annot_' + x
for x in to_rename},
copy=False)
nx.relabel_nodes(image_graph, {x: 'image_' + x
for x in to_rename},
copy=False)
graph = nx.compose_all([image_graph, annot_graph])
#graph = nx.union_all([image_graph, annot_graph], rename=('image', 'annot'))
# userdecision = ut.nx_makenode(graph, 'user decision', shape='rect', color=pt.DARK_YELLOW, style='diagonals')
# userdecision = ut.nx_makenode(graph, 'user decision', shape='circle', color=pt.DARK_YELLOW)
userdecision = ut.nx_makenode(
graph,
'User decision',
shape='rect',
#width=100, height=100,
color=pt.YELLOW,
style='diagonals')
#longcat = True
longcat = False
#edge = ('feat', 'neighbor_index')
#data = graph.get_edge_data(*edge)[0]
#print('data = %r' % (data,))
#graph.remove_edge(*edge)
## hack
#graph.add_edge('featweight', 'neighbor_index', **data)
graph.add_edge('detections',
userdecision,
constraint=longcat,
color=pt.PINK)
graph.add_edge(userdecision,
'annotations',
constraint=longcat,
color=pt.PINK)
# graph.add_edge(userdecision, 'annotations', implicit=True, color=[0, 0, 0])
if not longcat:
pass
#graph.add_edge('images', 'annotations', style='invis')
#graph.add_edge('thumbnails', 'annotations', style='invis')
#graph.add_edge('thumbnails', userdecision, style='invis')
graph.remove_node('Has_Notch')
graph.remove_node('annotmask')
layoutkw = {
'ranksep': 5,
'nodesep': 5,
'dpi': 96,
# 'nodesep': 1,
}
ns = 1000
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', 'Ubuntu')
ut.nx_set_default_node_attributes(graph, 'style', 'filled')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
#for u, v, d in graph.edge(data=True):
for u, vkd in graph.edge.items():
for v, dk in vkd.items():
for k, d in dk.items():
localid = d.get('local_input_id')
if localid:
# d['headlabel'] = localid
if localid not in ['1']:
d['taillabel'] = localid
#d['label'] = localid
if d.get('taillabel') in {'1'}:
del d['taillabel']
node_alias = {
'chips': 'Chip',
'images': 'Image',
'feat': 'Feat',
'featweight': 'Feat Weights',
'thumbnails': 'Thumbnail',
'detections': 'Detections',
'annotations': 'Annotation',
'Notch_Tips': 'Notch Tips',
'probchip': 'Prob Chip',
'Cropped_Chips': 'Croped Chip',
'Trailing_Edge': 'Trailing\nEdge',
'Block_Curvature': 'Block\nCurvature',
# 'BC_DTW': 'block curvature /\n dynamic time warp',
'BC_DTW': 'DTW Distance',
'vsone': 'Hots vsone',
'feat_neighbs': 'Nearest\nNeighbors',
'neighbor_index': 'Neighbor\nIndex',
'vsmany': 'Hots vsmany',
'annot_labeler': 'Annot Labeler',
'labeler': 'Labeler',
'localizations': 'Localizations',
'classifier': 'Classifier',
'sver': 'Spatial\nVerification',
'Classifier': 'Existence',
'image_labeler': 'Image Labeler',
}
node_alias = {
'Classifier': 'existence',
'feat_neighbs': 'neighbors',
'sver': 'spatial_verification',
'Cropped_Chips': 'cropped_chip',
'BC_DTW': 'dtw_distance',
'Block_Curvature': 'curvature',
'Trailing_Edge': 'trailing_edge',
'Notch_Tips': 'notch_tips',
'thumbnails': 'thumbnail',
'images': 'image',
'annotations': 'annotation',
'chips': 'chip',
#userdecision: 'User de'
}
node_alias = ut.delete_dict_keys(
node_alias, ut.setdiff(node_alias.keys(), graph.nodes()))
nx.relabel_nodes(graph, node_alias, copy=False)
fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#pt.gca().set_aspect('equal')
#pt.figure()
pt.show_nx(graph, layoutkw=layoutkw, fontkw=fontkw)
pt.zoom_factory()
def makecluster(name, num, **attrkw):
return [
ut.nx_makenode(graph, name + str(n), **attrkw) for n in range(num)
]
def graphcut_flow():
r"""
Returns:
?: name
CommandLine:
python -m ibeis.scripts.specialdraw graphcut_flow --show --save cutflow.png --diskshow --clipwhite
python -m ibeis.scripts.specialdraw graphcut_flow --save figures4/cutiden.png --diskshow --clipwhite --dpath ~/latex/crall-candidacy-2015/ --figsize=24,10 --arrow-width=2.0
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> graphcut_flow()
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
pt.ensure_pylab_qt4()
import networkx as nx
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [
ut.nx_makenode(graph, name + str(n), **attrkw) for n in range(num)
]
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)
ns = 512
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
annot1 = ut.nx_makenode(graph,
'Unlabeled\nannotations\n(query)',
width=ns,
height=ns,
groupid='annot',
color=p_shade2)
annot2 = ut.nx_makenode(graph,
'Labeled\nannotations\n(database)',
width=ns,
height=ns,
groupid='annot',
color=s1_shade2)
occurprob = ut.nx_makenode(graph,
'Dense \nprobabilities',
color=lighten_hex(p_shade2, .1))
cacheprob = ut.nx_makenode(graph,
'Cached \nprobabilities',
color=lighten_hex(s1_shade2, .1))
sparseprob = ut.nx_makenode(graph,
'Sparse\nprobabilities',
color=lighten_hex(c_shade2, .1))
graph.add_edge(annot1, occurprob)
graph.add_edge(annot1, sparseprob)
graph.add_edge(annot2, sparseprob)
graph.add_edge(annot2, cacheprob)
matchgraph = ut.nx_makenode(graph,
'Graph of\npotential matches',
color=lighten_hex(s2_shade2, .1))
cutalgo = ut.nx_makenode(graph,
'Graph cut algorithm',
color=lighten_hex(s2_shade2, .2),
shape='ellipse')
cc_names = ut.nx_makenode(
graph,
'Identifications,\n splits, and merges are\nconnected compoments',
color=lighten_hex(s2_shade2, .3))
graph.add_edge(occurprob, matchgraph)
graph.add_edge(sparseprob, matchgraph)
graph.add_edge(cacheprob, matchgraph)
graph.add_edge(matchgraph, cutalgo)
graph.add_edge(cutalgo, cc_names)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
ut.nx_set_default_node_attributes(graph, 'style', 'filled,rounded')
ut.nx_set_default_node_attributes(graph, 'fixedsize', 'true')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
ut.nx_set_default_node_attributes(graph, 'regular', False)
layoutkw = {
'prog': 'dot',
'rankdir': 'LR',
'splines': 'line',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
}
fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=14)
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()
def double_depcache_graph():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw double_depcache_graph --show --testmode
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=.5
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=5
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = double_depcache_graph()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
ibs = ibeis.opendb('testdb1')
reduced = True
implicit = True
annot_graph = ibs.depc_annot.make_graph(reduced=reduced, implicit=implicit)
image_graph = ibs.depc_image.make_graph(reduced=reduced, implicit=implicit)
to_rename = ut.isect(image_graph.nodes(), annot_graph.nodes())
nx.relabel_nodes(annot_graph, {x: 'annot_' + x for x in to_rename}, copy=False)
nx.relabel_nodes(image_graph, {x: 'image_' + x for x in to_rename}, copy=False)
graph = nx.compose_all([image_graph, annot_graph])
#graph = nx.union_all([image_graph, annot_graph], rename=('image', 'annot'))
# userdecision = ut.nx_makenode(graph, 'user decision', shape='rect', color=pt.DARK_YELLOW, style='diagonals')
# userdecision = ut.nx_makenode(graph, 'user decision', shape='circle', color=pt.DARK_YELLOW)
userdecision = ut.nx_makenode(graph, 'User decision', shape='rect',
#width=100, height=100,
color=pt.YELLOW, style='diagonals')
#longcat = True
longcat = False
#edge = ('feat', 'neighbor_index')
#data = graph.get_edge_data(*edge)[0]
#print('data = %r' % (data,))
#graph.remove_edge(*edge)
## hack
#graph.add_edge('featweight', 'neighbor_index', **data)
graph.add_edge('detections', userdecision, constraint=longcat, color=pt.PINK)
graph.add_edge(userdecision, 'annotations', constraint=longcat, color=pt.PINK)
# graph.add_edge(userdecision, 'annotations', implicit=True, color=[0, 0, 0])
if not longcat:
pass
#graph.add_edge('images', 'annotations', style='invis')
#graph.add_edge('thumbnails', 'annotations', style='invis')
#graph.add_edge('thumbnails', userdecision, style='invis')
graph.remove_node('Has_Notch')
graph.remove_node('annotmask')
layoutkw = {
'ranksep': 5,
'nodesep': 5,
'dpi': 96,
# 'nodesep': 1,
}
ns = 1000
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', 'Ubuntu')
ut.nx_set_default_node_attributes(graph, 'style', 'filled')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
#for u, v, d in graph.edge(data=True):
for u, vkd in graph.edge.items():
for v, dk in vkd.items():
for k, d in dk.items():
localid = d.get('local_input_id')
if localid:
# d['headlabel'] = localid
if localid not in ['1']:
d['taillabel'] = localid
#d['label'] = localid
if d.get('taillabel') in {'1'}:
del d['taillabel']
node_alias = {
'chips': 'Chip',
'images': 'Image',
'feat': 'Feat',
'featweight': 'Feat Weights',
'thumbnails': 'Thumbnail',
'detections': 'Detections',
'annotations': 'Annotation',
'Notch_Tips': 'Notch Tips',
'probchip': 'Prob Chip',
'Cropped_Chips': 'Croped Chip',
'Trailing_Edge': 'Trailing\nEdge',
'Block_Curvature': 'Block\nCurvature',
# 'BC_DTW': 'block curvature /\n dynamic time warp',
'BC_DTW': 'DTW Distance',
'vsone': 'Hots vsone',
'feat_neighbs': 'Nearest\nNeighbors',
'neighbor_index': 'Neighbor\nIndex',
'vsmany': 'Hots vsmany',
'annot_labeler': 'Annot Labeler',
'labeler': 'Labeler',
'localizations': 'Localizations',
'classifier': 'Classifier',
'sver': 'Spatial\nVerification',
'Classifier': 'Existence',
'image_labeler': 'Image Labeler',
}
node_alias = {
'Classifier': 'existence',
'feat_neighbs': 'neighbors',
'sver': 'spatial_verification',
'Cropped_Chips': 'cropped_chip',
'BC_DTW': 'dtw_distance',
'Block_Curvature': 'curvature',
'Trailing_Edge': 'trailing_edge',
'Notch_Tips': 'notch_tips',
'thumbnails': 'thumbnail',
'images': 'image',
'annotations': 'annotation',
'chips': 'chip',
#userdecision: 'User de'
}
node_alias = ut.delete_dict_keys(node_alias, ut.setdiff(node_alias.keys(),
graph.nodes()))
nx.relabel_nodes(graph, node_alias, copy=False)
fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#pt.gca().set_aspect('equal')
#pt.figure()
pt.show_nx(graph, layoutkw=layoutkw, fontkw=fontkw)
pt.zoom_factory()
def makecluster(name, num, **attrkw):
return [ut.nx_makenode(graph, name + str(n), **attrkw) for n in range(num)]
def graphcut_flow():
r"""
Returns:
?: name
CommandLine:
python -m ibeis.scripts.specialdraw graphcut_flow --show --save cutflow.png --diskshow --clipwhite
python -m ibeis.scripts.specialdraw graphcut_flow --save figures4/cutiden.png --diskshow --clipwhite --dpath ~/latex/crall-candidacy-2015/ --figsize=24,10 --arrow-width=2.0
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> graphcut_flow()
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
pt.ensure_pylab_qt4()
import networkx as nx
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [ut.nx_makenode(graph, name + str(n), **attrkw) for n in range(num)]
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)
ns = 512
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
annot1 = ut.nx_makenode(graph, 'Unlabeled\nannotations\n(query)', width=ns, height=ns,
groupid='annot', color=p_shade2)
annot2 = ut.nx_makenode(graph, 'Labeled\nannotations\n(database)', width=ns, height=ns,
groupid='annot', color=s1_shade2)
occurprob = ut.nx_makenode(graph, 'Dense \nprobabilities', color=lighten_hex(p_shade2, .1))
cacheprob = ut.nx_makenode(graph, 'Cached \nprobabilities', color=lighten_hex(s1_shade2, .1))
sparseprob = ut.nx_makenode(graph, 'Sparse\nprobabilities', color=lighten_hex(c_shade2, .1))
graph.add_edge(annot1, occurprob)
graph.add_edge(annot1, sparseprob)
graph.add_edge(annot2, sparseprob)
graph.add_edge(annot2, cacheprob)
matchgraph = ut.nx_makenode(graph, 'Graph of\npotential matches', color=lighten_hex(s2_shade2, .1))
cutalgo = ut.nx_makenode(graph, 'Graph cut algorithm', color=lighten_hex(s2_shade2, .2), shape='ellipse')
cc_names = ut.nx_makenode(graph, 'Identifications,\n splits, and merges are\nconnected compoments', color=lighten_hex(s2_shade2, .3))
graph.add_edge(occurprob, matchgraph)
graph.add_edge(sparseprob, matchgraph)
graph.add_edge(cacheprob, matchgraph)
graph.add_edge(matchgraph, cutalgo)
graph.add_edge(cutalgo, cc_names)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
ut.nx_set_default_node_attributes(graph, 'style', 'filled,rounded')
ut.nx_set_default_node_attributes(graph, 'fixedsize', 'true')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
ut.nx_set_default_node_attributes(graph, 'regular', False)
layoutkw = {
'prog': 'dot',
'rankdir': 'LR',
'splines': 'line',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
}
fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=14)
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()
