from __future__ import absolute_import, division, print_function
import utool as ut
(print, rrr, profile) = ut.inject2(__name__, '[specialdraw]')
[docs]def multidb_montage():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw multidb_montage --save montage.jpg --dpath ~/slides --diskshow --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> multidb_montage()
"""
import ibeis
import plottool as pt
import vtool as vt
import numpy as np
pt.ensure_pylab_qt4()
ibs1 = ibeis.opendb('PZ_MTEST')
ibs2 = ibeis.opendb('GZ_ALL')
ibs3 = ibeis.opendb('GIRM_Master1')
chip_lists = []
aids_list = []
for ibs in [ibs1, ibs2, ibs3]:
aids = ibs.sample_annots_general(minqual='good', sample_size=400)
aids_list.append(aids)
print(ut.depth_profile(aids_list))
for ibs, aids in zip([ibs1, ibs2, ibs3], aids_list):
chips = ibs.get_annot_chips(aids)
chip_lists.append(chips)
chip_list = ut.flatten(chip_lists)
np.random.shuffle(chip_list)
widescreen_ratio = 16 / 9
ratio = ut.PHI
ratio = widescreen_ratio
fpath = pt.get_save_directions()
#height = 6000
width = 6000
#width = int(height * ratio)
height = int(width / ratio)
dsize = (width, height)
dst = vt.montage(chip_list, dsize)
vt.imwrite(fpath, dst)
if ut.get_argflag('--show'):
pt.imshow(dst)
[docs]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()
[docs]def lighten_hex(hexcolor, amount):
import plottool as pt
import matplotlib.colors as colors
return pt.color_funcs.lighten_rgb(colors.hex2color(hexcolor), amount)
[docs]def general_identify_flow():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw general_identify_flow --show --save pairsim.png --dpi=100 --diskshow --clipwhite
python -m ibeis.scripts.specialdraw general_identify_flow --dpi=200 --diskshow --clipwhite --dpath ~/latex/cand/ --figsize=20,10 --save figures4/pairprob.png --arrow-width=2.0
Example:
>>> # SCRIPT
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> general_identify_flow()
>>> ut.quit_if_noshow()
>>> ut.show_if_requested()
"""
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [ut.nx_makenode(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)
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
ns = 512
ut.inject_func_as_method(graph, ut.nx_makenode)
annot1_color = p_shade2
annot2_color = s1_shade2
#annot1_color2 = pt.color_funcs.lighten_rgb(colors.hex2color(annot1_color), .01)
annot1 = graph.nx_makenode('Annotation X', width=ns, height=ns, groupid='annot', color=annot1_color)
annot2 = graph.nx_makenode('Annotation Y', width=ns, height=ns, groupid='annot', color=annot2_color)
featX = graph.nx_makenode('Features X', size=(ns / 1.2, ns / 2), groupid='feats', color=lighten_hex(annot1_color, .1))
featY = graph.nx_makenode('Features Y', size=(ns / 1.2, ns / 2), groupid='feats', color=lighten_hex(annot2_color, .1))
#'#4771B3')
global_pairvec = graph.nx_makenode('Global similarity\n(viewpoint, quality, ...)', width=ns * ut.PHI * 1.2, color=s2_shade2)
findnn = graph.nx_makenode('Find correspondences\n(nearest neighbors)', shape='ellipse', color=c_shade2)
local_pairvec = graph.nx_makenode('Local similarities\n(LNBNN, spatial error, ...)',
size=(ns * 2.2, ns), color=lighten_hex(c_shade2, .1))
agglocal = graph.nx_makenode('Aggregate', size=(ns / 1.1, ns / 2), shape='ellipse', color=lighten_hex(c_shade2, .2))
catvecs = graph.nx_makenode('Concatenate', size=(ns / 1.1, ns / 2), shape='ellipse', color=lighten_hex(s2_shade2, .1))
pairvec = graph.nx_makenode('Vector of\npairwise similarities', color=lighten_hex(s2_shade2, .2))
classifier = graph.nx_makenode('Classifier\n(SVM/RF/DNN)', color=lighten_hex(s2_shade2, .3))
prob = graph.nx_makenode('Matching Probability\n(same individual given\nsimilar viewpoint)', color=lighten_hex(s2_shade2, .4))
graph.add_edge(annot1, global_pairvec)
graph.add_edge(annot2, global_pairvec)
add_edge2(annot1, featX)
add_edge2(annot2, featY)
add_edge2(featX, findnn)
add_edge2(featY, findnn)
add_edge2(findnn, local_pairvec)
graph.add_edge(local_pairvec, agglocal, constraint=True)
graph.add_edge(agglocal, catvecs, constraint=False)
graph.add_edge(global_pairvec, catvecs)
graph.add_edge(catvecs, pairvec)
# graph.add_edge(annot1, classifier, style='invis')
# graph.add_edge(pairvec, classifier , constraint=False)
graph.add_edge(pairvec, classifier)
graph.add_edge(classifier, prob)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
#ut.nx_set_default_node_attributes(graph, 'fillcolor', nx.get_node_attributes(graph, 'color'))
#ut.nx_set_default_node_attributes(graph, 'style', 'rounded')
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, 'xlabel', nx.get_node_attributes(graph, 'label'))
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns)
ut.nx_set_default_node_attributes(graph, 'regular', False)
#font = 'MonoDyslexic'
#font = 'Mono_Dyslexic'
font = 'Ubuntu'
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', font)
#ut.nx_delete_node_attr(graph, 'width')
#ut.nx_delete_node_attr(graph, 'height')
#ut.nx_delete_node_attr(graph, 'fixedsize')
#ut.nx_delete_node_attr(graph, 'style')
#ut.nx_delete_node_attr(graph, 'regular')
#ut.nx_delete_node_attr(graph, 'shape')
#graph.node[annot1]['label'] = "<f0> left|<f1> mid\ dle|<f2> right"
#graph.node[annot2]['label'] = ut.codeblock(
# '''
# <<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
# <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR>
# </TABLE>>
# ''')
#graph.node[annot1]['label'] = ut.codeblock(
# '''
# <<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
# <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR>
# </TABLE>>
# ''')
#graph.node[annot1]['shape'] = 'none'
#graph.node[annot1]['margin'] = '0'
layoutkw = {
'forcelabels': True,
'prog': 'dot',
'rankdir': 'LR',
# 'splines': 'curved',
'splines': 'line',
'samplepoints': 20,
'showboxes': 1,
# 'splines': 'polyline',
#'splines': 'spline',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
#'inputscale': 72,
# 'inputscale': 1,
# 'dpi': 72,
# 'concentrate': 'true', # merges edge lines
# 'splines': 'ortho',
# 'aspect': 1,
# 'ratio': 'compress',
# 'size': '5,4000',
# 'rank': 'max',
}
#fontkw = dict(fontfamilty='sans-serif', fontweight='normal', fontsize=12)
#fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=20)
fontkw = dict(fontname=font, fontweight='light', fontsize=12)
#prop = fm.FontProperties(fname='/usr/share/fonts/truetype/groovygh.ttf')
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()
[docs]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()
[docs]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
[docs]def setcover_example():
"""
CommandLine:
python -m ibeis.scripts.specialdraw setcover_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = setcover_example()
>>> 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='testdb2')
if False:
# Select a good set
aids = ibs.get_name_aids(ibs.get_valid_nids())
# ibeis.testdata_aids('testdb2', a='default:mingt=2')
aids = [a for a in aids if len(a) > 1]
for a in aids:
print(ut.repr3(ibs.get_annot_stats_dict(a)))
print(aids[-2])
#aids = [78, 79, 80, 81, 88, 91]
aids = [78, 79, 81, 88, 91]
qreq_ = ibs.depc.new_request('vsone', aids, aids, cfgdict={})
cm_list = qreq_.execute()
from ibeis.algo.hots import graph_iden
infr = graph_iden.AnnotInference(cm_list)
unique_aids, prob_annots = infr.make_prob_annots()
import numpy as np
print(ut.hz_str('prob_annots = ', ut.array2string2(prob_annots, precision=2, max_line_width=140, suppress_small=True)))
# ut.setcover_greedy(candidate_sets_dict)
max_weight = 3
prob_annots[np.diag_indices(len(prob_annots))] = np.inf
prob_annots = prob_annots
thresh_points = np.sort(prob_annots[np.isfinite(prob_annots)])
# probably not the best way to go about searching for these thresholds
# but when you have a hammer...
if False:
quant = sorted(np.diff(thresh_points))[(len(thresh_points) - 1) // 2 ]
candset = {point: thresh_points[np.abs(thresh_points - point) < quant] for point in thresh_points}
check_thresholds = len(aids) * 2
thresh_points2 = np.array(ut.setcover_greedy(candset, max_weight=check_thresholds).keys())
thresh_points = thresh_points2
# pt.plot(sorted(thresh_points), 'rx')
# pt.plot(sorted(thresh_points2), 'o')
# prob_annots = prob_annots.T
# thresh_start = np.mean(thresh_points)
current_idxs = []
current_covers = []
current_val = np.inf
for thresh in thresh_points:
covering_sets = [np.where(row >= thresh)[0] for row in (prob_annots)]
candidate_sets_dict = {ax: others for ax, others in enumerate(covering_sets)}
soln_cover = ut.setcover_ilp(candidate_sets_dict, max_weight=max_weight)
exemplar_idxs = list(soln_cover.keys())
soln_weight = len(exemplar_idxs)
val = max_weight - soln_weight
# print('val = %r' % (val,))
# print('soln_weight = %r' % (soln_weight,))
if val < current_val:
current_val = val
current_covers = covering_sets
current_idxs = exemplar_idxs
exemplars = ut.take(aids, current_idxs)
ensure_edges = [(aids[ax], aids[ax2]) for ax, other_xs in enumerate(current_covers) for ax2 in other_xs]
graph = viz_graph.make_netx_graph_from_aid_groups(
ibs, [aids], allow_directed=True, ensure_edges=ensure_edges,
temp_nids=[1] * len(aids))
viz_graph.ensure_node_images(ibs, graph)
nx.set_node_attributes(graph, 'framewidth', False)
nx.set_node_attributes(graph, 'framewidth', {aid: 4.0 for aid in exemplars})
nx.set_edge_attributes(graph, 'color', pt.ORANGE)
nx.set_node_attributes(graph, 'color', pt.LIGHT_BLUE)
nx.set_node_attributes(graph, 'shape', 'rect')
layoutkw = {
'sep' : 1 / 10,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw)
pt.zoom_factory()
[docs]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()
# The database exemplars
# TODO: match these along with the intra encounter set
#interact = viz_graph.make_name_graph_interaction(
# ibs, aids=dbaids, with_all=False, prog='neato', framewidth=True)
#print(interact)
# Groupid only works for dot
#nx.set_node_attributes(unlabeled_graph, 'groupid', 'unlabeled')
#nx.set_node_attributes(exemplars, 'groupid', 'exemplars')
#exemplars = exemplars.to_undirected()
#add_clique(exemplars, aids_, edgeattrs=dict(constraint=False))
#layoutkw = {}
#pt.show_nx(exemplars, layout='agraph', layoutkw=layoutkw,
# as_directed=False, framewidth=True,)
[docs]def scalespace():
r"""
THIS DOES NOT SHOW A REAL SCALE SPACE PYRAMID YET. FIXME.
Returns:
?: imgBGRA_warped
CommandLine:
python -m ibeis.scripts.specialdraw scalespace --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> imgBGRA_warped = scalespace()
>>> result = ('imgBGRA_warped = %s' % (ut.repr2(imgBGRA_warped),))
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import numpy as np
# import matplotlib.pyplot as plt
import cv2
import vtool as vt
import plottool as pt
pt.qt4ensure()
#imgBGR = vt.imread(ut.grab_test_imgpath('lena.png'))
imgBGR = vt.imread(ut.grab_test_imgpath('zebra.png'))
# imgBGR = vt.imread(ut.grab_test_imgpath('carl.jpg'))
# Convert to colored intensity image
imgGray = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2GRAY)
imgBGR = cv2.cvtColor(imgGray, cv2.COLOR_GRAY2BGR)
imgRaw = imgBGR
# TODO: # stack images in pyramid # boarder?
initial_sigma = 1.6
num_intervals = 4
def makepyramid_octave(imgRaw, level, num_intervals):
# Downsample image to take sigma to a power of level
step = (2 ** (level))
img_level = imgRaw[::step, ::step]
# Compute interval relative scales
interval = np.array(list(range(num_intervals)))
relative_scales = (2 ** ((interval / num_intervals)))
sigma_intervals = initial_sigma * relative_scales
octave_intervals = []
for sigma in sigma_intervals:
sizex = int(6. * sigma + 1.) + int(1 - (int(6. * sigma + 1.) % 2))
ksize = (sizex, sizex)
img_blur = cv2.GaussianBlur(img_level, ksize, sigmaX=sigma,
sigmaY=sigma,
borderType=cv2.BORDER_REPLICATE)
octave_intervals.append(img_blur)
return octave_intervals
pyramid = []
num_octaves = 4
for level in range(num_octaves):
octave = makepyramid_octave(imgRaw, level, num_intervals)
pyramid.append(octave)
def makewarp(imgBGR):
# hack a projection matrix using dummy homogrpahy
imgBGRA = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2BGRA)
imgBGRA[:, :, 3] = .87 * 255 # hack alpha
imgBGRA = vt.pad_image(imgBGRA, 2, value=[0, 0, 255, 255])
size = np.array(vt.get_size(imgBGRA))
pts1 = np.array([(0, 0), (0, 1), (1, 1), (1, 0)]) * size
x_adjust = .15
y_adjust = .5
pts2 = np.array([(x_adjust, 0), (0, 1 - y_adjust), (1, 1 - y_adjust), (1 - x_adjust, 0)]) * size
H = cv2.findHomography(pts1, pts2)[0]
dsize = np.array(vt.bbox_from_verts(pts2)[2:4]).astype(np.int)
warpkw = dict(flags=cv2.INTER_LANCZOS4, borderMode=cv2.BORDER_CONSTANT)
imgBGRA_warped = cv2.warpPerspective(imgBGRA, H, tuple(dsize), **warpkw)
return imgBGRA_warped
framesize = (700, 500)
steps = np.array([.04, .03, .02, .01]) * 1.3
numintervals = 4
octave_ty_starts = [1.0]
for i in range(1, 4):
prev_ty = octave_ty_starts[-1]
prev_base = pyramid[i - 1][0]
next_ty = prev_ty - ((prev_base.shape[0] / framesize[1]) / 2 + (numintervals - 1) * (steps[i - 1]))
octave_ty_starts.append(next_ty)
def temprange(stop, step, num):
return [stop - (x * step) for x in range(num)]
layers = []
for i in range(0, 4):
ty_start = octave_ty_starts[i]
step = steps[i]
intervals = pyramid[i]
ty_range = temprange(ty_start, step, numintervals)
nextpart = [
vt.embed_in_square_image(makewarp(interval), framesize, img_origin=(.5, .5),
target_origin=(.5, ty / 2))
for ty, interval in zip(ty_range, intervals)
]
layers += nextpart
for layer in layers:
pt.imshow(layer)
pt.plt.grid(False)
[docs]def event_space():
"""
pip install matplotlib-venn
"""
from matplotlib import pyplot as plt
# import numpy as np
from matplotlib_venn import venn3, venn2, venn3_circles
plt.figure(figsize=(4, 4))
v = venn3(subsets=(1, 1, 1, 1, 1, 1, 1), set_labels=('A', 'B', 'C'))
v.get_patch_by_id('100').set_alpha(1.0)
v.get_patch_by_id('100').set_color('white')
v.get_label_by_id('100').set_text('Unknown')
v.get_label_by_id('A').set_text('Set "A"')
c = venn3_circles(subsets=(1, 1, 1, 1, 1, 1, 1), linestyle='dashed')
c[0].set_lw(1.0)
c[0].set_ls('dotted')
plt.show()
same = set(['comparable', 'incomparable', 'same'])
diff = set(['comparable', 'incomparable', 'diff'])
# comparable = set(['comparable', 'same', 'diff'])
# incomparable = set(['incomparable', 'same', 'diff'])
subsets = [same, diff] # , comparable, incomparable]
set_labels = ('same', 'diff') # , 'comparable', 'incomparable')
venn3(subsets=subsets, set_labels=set_labels)
plt.show()
import plottool as pt
pt.ensure_pylab_qt4()
from matplotlib_subsets import treesets_rectangles
tree = (
(120, 'Same', None), [
((50, 'comparable', None), []),
((50, 'incomparable', None), [])
]
(120, 'Diff', None), [
((50, 'comparable', None), []),
((50, 'incomparable', None), [])
]
)
treesets_rectangles(tree)
plt.show()
from matplotlib import pyplot as plt
from matplotlib_venn import venn2, venn2_circles # NOQA
# Subset sizes
s = (
2, # Ab
3, # aB
1, # AB
)
v = venn2(subsets=s, set_labels=('A', 'B'))
# Subset labels
v.get_label_by_id('10').set_text('A but not B')
v.get_label_by_id('01').set_text('B but not A')
v.get_label_by_id('11').set_text('A and B')
# Subset colors
v.get_patch_by_id('10').set_color('c')
v.get_patch_by_id('01').set_color('#993333')
v.get_patch_by_id('11').set_color('blue')
# Subset alphas
v.get_patch_by_id('10').set_alpha(0.4)
v.get_patch_by_id('01').set_alpha(1.0)
v.get_patch_by_id('11').set_alpha(0.7)
# Border styles
c = venn2_circles(subsets=s, linestyle='solid')
c[0].set_ls('dashed') # Line style
c[0].set_lw(2.0) # Line width
plt.show()
# plt.savefig('example_tree.pdf', bbox_inches='tight')
# plt.close()
# venn2(subsets=(25, 231+65, 8+15))
# # Find out the location of the two circles
# # (you can look up how its done in the first lines
# # of the venn2 function)
# from matplotlib_venn._venn2 import compute_venn2_areas, solve_venn2_circles
# subsets = (25, 231+65, 8+15)
# areas = compute_venn2_areas(subsets, normalize_to=1.0)
# centers, radii = solve_venn2_circles(areas)
# # Now draw the third circle.
# # Its area is (15+65)/(25+8+15) times
# # that of the first circle,
# # hence its radius must be
# r3 = radii[0]*sqrt((15+65.0)/(25+8+15))
# # Its position must be such that the intersection
# # area with C1 is 15/(15+8+25) of C1's area.
# # The way to compute the distance between
# # the circles by area can be looked up in
# # solve_venn2_circles
# from matplotlib_venn._math import find_distance_by_area
# distance = find_distance_by_area(radii[0], r3,
# 15.0/(15+8+25)*np.pi*radii[0]*radii[0])
# ax = gca()
# ax.add_patch(Circle(centers[0] + np.array([distance, 0]),
# r3, alpha=0.5, edgecolor=None,
# facecolor='red', linestyle=None,
# linewidth=0))
if __name__ == '__main__':
r"""
CommandLine:
python -m ibeis.scripts.specialdraw
python -m ibeis.scripts.specialdraw --allexamples
"""
import multiprocessing
multiprocessing.freeze_support() # for win32
import utool as ut # NOQA
ut.doctest_funcs()