def execute_test():
func = find_expt_func(e)
assert func is not None, 'unknown experiment e=%r' % (e, )
argspec = ut.get_func_argspec(func)
if (len(argspec.args) >= 2 and argspec.args[0] == 'ibs'
and argspec.args[1] == 'testres'):
# most experiments need a testres
expts_kw = dict(
defaultdb=db,
dbdir=dbdir,
a=a,
t=t,
qaid_override=qaid_override,
daid_override=daid_override,
initial_aids=initial_aids,
)
testdata_expts_func = functools.partial(
main_helpers.testdata_expts, **expts_kw)
ibs, testres = testdata_expts_func()
# Build the requested drawing funciton
draw_func = functools.partial(func, ibs, testres, **kwargs)
testres.draw_func = draw_func
ut.inject_func_as_method(testres, draw_cases)
ut.inject_func_as_method(testres, draw_taghist)
# testres.draw_cases = draw_cases
return testres
else:
raise AssertionError('Unknown type of function for experiment')
def simple_api_item_widget():
r"""
Very simple example of basic APIItemWidget widget with CustomAPI
CommandLine:
python -m wbia.guitool.api_item_widget --test-simple_api_item_widget
python -m wbia.guitool.api_item_widget --test-simple_api_item_widget --show
Example:
>>> # ENABLE_DOCTEST
>>> # xdoctest: +REQUIRES(--gui)
>>> from wbia.guitool.api_item_widget import * # NOQA
>>> import wbia.guitool as gt
>>> gt.ensure_qapp() # must be ensured before any embeding
>>> wgt = simple_api_item_widget()
>>> # xdoctest: +REQUIRES(--show)
>>> ut.quit_if_noshow()
>>> wgt.show()
>>> gt.qtapp_loop(wgt, frequency=100)
"""
import wbia.guitool as gt
gt.ensure_qapp()
col_getter_dict = {
'col1': [1, 2, 3],
'col2': ['a', 'b', 'c'],
'col3': ['e', 'f', 'g'],
}
sortby = 'col1'
# col_display_role_func_dict = {
# 'col1': lambda x: 'banana %d' % ((x * 100 % 23)),
# }
api = gt.CustomAPI(
col_getter_dict=col_getter_dict,
# col_display_role_func_dict=col_display_role_func_dict,
editable_colnames=['col3'],
sortby=sortby,
)
headers = api.make_headers(tblnice='Simple Example')
wgt = gt.APIItemWidget()
wgt.change_headers(headers)
# gt.qtapp_loop(qwin=wgt, ipy=ipy, frequency=loop_freq)
# for testing
wgt.menubar = gt.newMenubar(wgt)
wgt.menuFile = wgt.menubar.newMenu('Dev')
def wgt_embed(wgt):
view = wgt.view # NOQA
import utool
utool.embed()
ut.inject_func_as_method(wgt, wgt_embed)
wgt.menuFile.newAction(triggered=wgt.wgt_embed)
return wgt
def make_vsone_tuner(
ibs, edge=None, qreq_=None, autoupdate=True, info_text=None, cfgdict=None
):
"""
Makes a qt widget for inspecting one-vs-one matches
CommandLine:
python -m wbia.gui.inspect_gui make_vsone_tuner --show
Example:
>>> # xdoctest: +REQUIRES(--gui)
>>> from wbia.gui.inspect_gui import * # NOQA
>>> import wbia
>>> gt.ensure_qapp()
>>> ut.qtensure()
>>> ibs = wbia.opendb(defaultdb='PZ_MTEST')
>>> edge = ut.get_argval('--aids', default=[1, 2], type_=list)
>>> self = make_vsone_tuner(ibs, edge, autoupdate=False)
>>> ut.quit_if_noshow()
>>> self.show()
>>> gt.qtapp_loop(qwin=self, freq=10)
"""
from vtool import inspect_matches
import vtool as vt
if cfgdict is not None:
assert qreq_ is None, 'specify only one cfg or qreq_'
else:
cfgdict = {}
def set_edge(self, edge, info_text=None):
aid1, aid2 = edge
if qreq_ is None:
qreq2_ = ibs.new_query_request([aid1], [aid2], cfgdict=cfgdict, verbose=False)
else:
qreq2_ = ibs.new_query_request(
[aid1], [aid2], cfgdict=qreq_.qparams, verbose=False
)
qconfig2_ = qreq2_.extern_query_config2
dconfig2_ = qreq2_.extern_data_config2
annot1 = ibs.annots([aid1], config=qconfig2_)[0]._make_lazy_dict()
annot2 = ibs.annots([aid2], config=dconfig2_)[0]._make_lazy_dict()
match = vt.PairwiseMatch(annot1, annot2)
def on_context():
from wbia.gui import inspect_gui
return inspect_gui.make_annotpair_context_options(ibs, aid1, aid2, None)
self.set_match(match, on_context, info_text)
self = inspect_matches.MatchInspector(autoupdate=autoupdate, cfgdict=cfgdict)
ut.inject_func_as_method(self, set_edge)
if edge is not None:
self.set_edge(edge, info_text)
return self
def unmonkeypatch_encounters(ibs):
from wbia.other import ibsfuncs
ut.inject_func_as_method(
ibs, ibsfuncs.get_annot_encounter_text, 'get_annot_encounter_text', force=True
)
ut.inject_func_as_method(
ibs, ibsfuncs.get_annot_occurrence_text, 'get_annot_occurrence_text', force=True
)
def execute_test():
func = find_expt_func(e)
assert func is not None, 'unknown experiment e=%r' % (e,)
argspec = ut.get_func_argspec(func)
if len(argspec.args) >= 2 and argspec.args[0] == 'ibs' and argspec.args[1] == 'testres':
# most experiments need a testres
expts_kw = dict(defaultdb=db, a=a, t=t,
qaid_override=qaid_override,
daid_override=daid_override,
initial_aids=initial_aids
)
testdata_expts_func = functools.partial(main_helpers.testdata_expts, **expts_kw)
ibs, testres = testdata_expts_func()
# Build the requested drawing funciton
draw_func = functools.partial(func, ibs, testres, **kwargs)
testres.draw_func = draw_func
ut.inject_func_as_method(testres, draw_cases)
ut.inject_func_as_method(testres, draw_taghist)
#testres.draw_cases = draw_cases
return testres
else:
raise AssertionError('Unknown type of function for experiment')
def customize_model(model):
model.var2_cpd = {cpd.variable: cpd for cpd in model.cpds}
model.ttype2_cpds = ut.groupby_attr(model.cpds, 'ttype')
model._templates = list(
set([cpd._template_ for cpd in model.var2_cpd.values()]))
model.ttype2_template = {t.ttype: t for t in model._templates}
def pretty_evidence(model, evidence):
return [
evar + '=' + str(model.var2_cpd[evar].variable_statenames[val])
for evar, val in evidence.items()
]
def print_templates(model, ignore_ttypes=[]):
templates = model._templates
ut.colorprint('\n --- CPD Templates ---', 'blue')
for temp_cpd in templates:
if temp_cpd.ttype not in ignore_ttypes:
ut.colorprint(temp_cpd._cpdstr('psql'), 'brightcyan')
def print_priors(model, ignore_ttypes=[], title='Priors', color='blue'):
ut.colorprint('\n --- %s ---' % (title, ), color=color)
for ttype, cpds in model.ttype2_cpds.items():
if ttype not in ignore_ttypes:
for fs_ in ut.ichunks(cpds, 4):
ut.colorprint(ut.hz_str([f._cpdstr('psql') for f in fs_]),
color)
ut.inject_func_as_method(model, print_priors)
ut.inject_func_as_method(model, print_templates)
ut.inject_func_as_method(model, pretty_evidence)
ut.inject_func_as_method(model, pgm_viz.show_model)
ut.inject_func_as_method(model, pgm_viz.show_markov_model)
ut.inject_func_as_method(model, pgm_viz.show_junction_tree)
return model
def __init__(self,
ibs,
cm,
aid2=None,
fnum=None,
figtitle='Match Interaction',
same_fig=True,
qreq_=None,
**kwargs):
self.qres = cm
self.ibs = ibs
self.cm = cm
self.qreq_ = qreq_
self.fnum = pt.ensure_fnum(fnum)
# Unpack Args
if aid2 is None:
index = 0
# FIXME: no sortself
cm.sortself()
self.rank = index
else:
index = cm.daid2_idx.get(aid2, None)
# TODO: rank?
self.rank = None
if index is not None:
self.qaid = self.cm.qaid
self.daid = self.cm.daid_list[index]
self.fm = self.cm.fm_list[index]
self.fk = self.cm.fk_list[index]
self.fsv = self.cm.fsv_list[index]
if self.cm.fs_list is None:
fs_list = self.cm.get_fsv_prod_list()
else:
fs_list = self.cm.fs_list
self.fs = None if fs_list is None else fs_list[index]
self.score = None if self.cm.score_list is None else self.cm.score_list[
index]
self.H1 = None if self.cm.H_list is None else cm.H_list[index]
else:
self.qaid = self.cm.qaid
self.daid = aid2
self.fm = np.empty((0, 2), dtype=hstypes.FM_DTYPE)
self.fk = np.empty(0, dtype=hstypes.FK_DTYPE)
self.fsv = np.empty((0, 2), dtype=hstypes.FS_DTYPE)
self.fs = np.empty(0, dtype=hstypes.FS_DTYPE)
self.score = None
self.H1 = None
# Read properties
self.query_config2_ = (None if self.qreq_ is None else
self.qreq_.get_external_query_config2())
self.data_config2_ = (None if self.qreq_ is None else
self.qreq_.get_external_data_config2())
self.rchip1 = vh.get_chips(ibs, [self.qaid],
config2_=self.query_config2_)[0]
self.rchip2 = vh.get_chips(ibs, [self.daid],
config2_=self.data_config2_)[0]
# Begin Interaction
# call doclf docla and make figure
self.fig = ih.begin_interaction('matches', self.fnum)
self.xywh2_ptr = [None]
self.mode = kwargs.pop('mode', 0)
# New state vars
self.same_fig = same_fig
self.use_homog = False
self.vert = kwargs.pop('vert', None)
self.mx = kwargs.pop('mx', None)
self.last_fx = 0
self.fnum2 = pt.next_fnum()
self.figtitle = figtitle
self.kwargs = kwargs
abstract_interaction.register_interaction(self)
ut.inject_func_as_method(self,
AbstractInteraction.append_button.im_func)
ut.inject_func_as_method(self,
AbstractInteraction.show_popup_menu.im_func)
self.scope = []
if not kwargs.get('nobegin', False):
dodraw = kwargs.get('dodraw', True)
self.begin(dodraw=dodraw)
def testdata_tree_view():
r"""
CommandLine:
python -m wbia.guitool.api_tree_view testdata_tree_view
python -m wbia.guitool.api_tree_view testdata_tree_view --show
Example:
>>> # DISABLE_DOCTEST
>>> # xdoctest: +REQUIRES(--gui)
>>> import wbia.guitool as gt
>>> from wbia.guitool.api_tree_view import * # NOQA
>>> wgt = testdata_tree_view()
>>> view = wgt.view
>>> rows = view.selectedRows()
>>> print('rows = %r' % (rows,))
>>> # xdoctest: +REQUIRES(--show)
>>> ut.quit_if_noshow()
>>> gt.qtapp_loop(qwin=wgt)
"""
import wbia.guitool as gt
gt.ensure_qapp()
col_name_list = ['name', 'num_annots', 'annots']
col_getter_dict = {
'name': ['fred', 'sue', 'tom', 'mary', 'paul'],
'num_annots': [2, 1, 3, 5, 1],
}
# make consistent data
grouped_data = [[
col_getter_dict['name'][index] + '-' + str(i) for i in range(num)
] for index, num in enumerate(col_getter_dict['num_annots'])]
flat_data, reverse_list = ut.invertible_flatten1(grouped_data)
col_getter_dict['annots'] = flat_data
iders = [list(range(len(col_getter_dict['name']))), reverse_list]
col_level_dict = {
'name': 0,
'num_annots': 0,
'annots': 1,
}
sortby = 'name'
api = gt.CustomAPI(
col_name_list=col_name_list,
col_getter_dict=col_getter_dict,
sortby=sortby,
iders=iders,
col_level_dict=col_level_dict,
)
headers = api.make_headers(tblnice='Tree Example')
wgt = gt.APIItemWidget(view_class=APITreeView)
wgt.change_headers(headers)
wgt.menubar = gt.newMenubar(wgt)
wgt.menuFile = wgt.menubar.newMenu('Dev')
def wgt_embed(wgt):
view = wgt.view # NOQA
import utool
utool.embed()
ut.inject_func_as_method(wgt, wgt_embed)
wgt.menuFile.newAction(triggered=wgt.wgt_embed)
return wgt
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()
def to_string_monkey(df, highlight_cols=[0, 1]):
""" monkey patch to pandas to highlight the maximum value in specified
cols of a row """
kwds = dict(buf=None,
columns=None,
col_space=None,
header=True,
index=True,
na_rep='NaN',
formatters=None,
float_format=None,
sparsify=None,
index_names=True,
justify=None,
line_width=None,
max_rows=None,
max_cols=None,
show_dimensions=False)
self = pd.formats.format.DataFrameFormatter(df, **kwds)
self.highlight_cols = highlight_cols
ut.inject_func_as_method(self,
monkey_to_str_columns,
'_to_str_columns',
override=True,
force=True)
def strip_ansi(text):
import re
ansi_escape = re.compile(r'\x1b[^m]*m')
return ansi_escape.sub('', text)
def justify_ansi(self, texts, max_len, mode='right'):
if mode == 'left':
return [
x.ljust(max_len + (len(x) - len(strip_ansi(x)))) for x in texts
]
elif mode == 'center':
return [
x.center(max_len + (len(x) - len(strip_ansi(x))))
for x in texts
]
else:
return [
x.rjust(max_len + (len(x) - len(strip_ansi(x)))) for x in texts
]
def strlen_ansii(self, text):
return pd.compat.strlen(strip_ansi(text), encoding=self.encoding)
ut.inject_func_as_method(self.adj,
strlen_ansii,
'len',
override=True,
force=True)
ut.inject_func_as_method(self.adj,
justify_ansi,
'justify',
override=True,
force=True)
# strcols = monkey_to_str_columns(self)
# texts = strcols[2]
# str_ = self.adj.adjoin(1, *strcols)
# print(str_)
# print(strip_ansi(str_))
self.to_string()
result = self.buf.getvalue()
return result
def monkeypatch_encounters(ibs, aids, cache=None, **kwargs):
"""
Hacks in a temporary custom definition of encounters for this controller
50 days for PZ_MTEST
kwargs = dict(days=50)
if False:
name_mindeltas = []
for name in annots.group_items(annots.nids).values():
times = name.image_unixtimes_asfloat
deltas = [ut.unixtime_to_timedelta(np.abs(t1 - t2))
for t1, t2 in ut.combinations(times, 2)]
if deltas:
name_mindeltas.append(min(deltas))
print(ut.repr3(ut.lmap(ut.get_timedelta_str,
sorted(name_mindeltas))))
"""
from ibeis.algo.preproc.occurrence_blackbox import cluster_timespace_sec
import numpy as np
import datetime
if len(aids) == 0:
return
annots = ibs.annots(sorted(set(aids)))
thresh_sec = datetime.timedelta(**kwargs).total_seconds()
# thresh_sec = datetime.timedelta(minutes=30).seconds
if cache is None:
cache = True
# cache = len(aids) > 200
cfgstr = str(ut.combine_uuids(annots.visual_uuids)) + str(thresh_sec)
cacher = ut.Cacher('occurrence_labels', cfgstr=cfgstr, enabled=cache)
data = cacher.tryload()
if data is None:
print('Computing occurrences for monkey patch for %d aids' %
(len(aids)))
posixtimes = annots.image_unixtimes_asfloat
latlons = annots.gps
data = cluster_timespace_sec(posixtimes,
latlons,
thresh_sec=thresh_sec,
km_per_sec=.002)
cacher.save(data)
occurrence_ids = data
if occurrence_ids is None:
# return
# each annot is its own occurrence
occurrence_ids = list(range(len(annots)))
ndec = int(np.ceil(np.log10(max(occurrence_ids))))
suffmt = '-monkey-occur%0' + str(ndec) + 'd'
encounter_labels = [
n + suffmt % (o, ) for o, n in zip(occurrence_ids, annots.names)
]
occurrence_labels = [suffmt[1:] % (o, ) for o in occurrence_ids]
enc_lookup = ut.dzip(annots.aids, encounter_labels)
occur_lookup = ut.dzip(annots.aids, occurrence_labels)
# annots_per_enc = ut.dict_hist(encounter_labels, ordered=True)
# ut.get_stats(list(annots_per_enc.values()))
# encounters = ibs._annot_groups(annots.group(encounter_labels)[1])
# enc_names = ut.take_column(encounters.nids, 0)
# name_to_encounters = ut.group_items(encounters, enc_names)
# print('name_to_encounters = %s' % (ut.repr3(name_to_encounters)),)
# print('Names to num encounters')
# name_to_num_enc = ut.dict_hist(
# ut.map_dict_vals(len, name_to_encounters).values())
# monkey patch to override encounter info
def _monkey_get_annot_occurrence_text(ibs, aids):
return ut.dict_take(occur_lookup, aids)
def _monkey_get_annot_encounter_text(ibs, aids):
return ut.dict_take(enc_lookup, aids)
ut.inject_func_as_method(ibs,
_monkey_get_annot_encounter_text,
'get_annot_encounter_text',
force=True)
ut.inject_func_as_method(ibs,
_monkey_get_annot_occurrence_text,
'get_annot_occurrence_text',
force=True)
def customize_model(model):
model.var2_cpd = {cpd.variable: cpd for cpd in model.cpds}
model.ttype2_cpds = ut.groupby_attr(model.cpds, 'ttype')
model._templates = list(set([cpd._template_
for cpd in model.var2_cpd.values()]))
model.ttype2_template = {t.ttype: t for t in model._templates}
def pretty_evidence(model, evidence):
return [evar + '=' + str(model.var2_cpd[evar].variable_statenames[val])
for evar, val in evidence.items()]
def print_templates(model, ignore_ttypes=[]):
templates = model._templates
ut.colorprint('\n --- CPD Templates ---', 'blue')
for temp_cpd in templates:
if temp_cpd.ttype not in ignore_ttypes:
ut.colorprint(temp_cpd._cpdstr('psql'), 'turquoise')
def print_priors(model, ignore_ttypes=[], title='Priors', color='darkblue'):
ut.colorprint('\n --- %s ---' % (title,), color=color)
for ttype, cpds in model.ttype2_cpds.items():
if ttype not in ignore_ttypes:
for fs_ in ut.ichunks(cpds, 4):
ut.colorprint(ut.hz_str([f._cpdstr('psql') for f in fs_]), color)
ut.inject_func_as_method(model, print_priors)
ut.inject_func_as_method(model, print_templates)
ut.inject_func_as_method(model, pretty_evidence)
ut.inject_func_as_method(model, pgm_viz.show_model)
ut.inject_func_as_method(model, pgm_viz.show_markov_model)
ut.inject_func_as_method(model, pgm_viz.show_junction_tree)
return model
def __init__(self, ibs, cm, aid2=None, fnum=None,
figtitle='Match Interaction', same_fig=True,
qreq_=None, **kwargs):
self.qres = cm
self.ibs = ibs
self.cm = cm
self.qreq_ = qreq_
self.fnum = pt.ensure_fnum(fnum)
# Unpack Args
if aid2 is None:
index = 0
# FIXME: no sortself
cm.sortself()
self.rank = index
else:
index = cm.daid2_idx.get(aid2, None)
# TODO: rank?
self.rank = None
if index is not None:
self.qaid = self.cm.qaid
self.daid = self.cm.daid_list[index]
self.fm = self.cm.fm_list[index]
self.fk = self.cm.fk_list[index]
self.fsv = self.cm.fsv_list[index]
if self.cm.fs_list is None:
fs_list = self.cm.get_fsv_prod_list()
else:
fs_list = self.cm.fs_list
self.fs = None if fs_list is None else fs_list[index]
self.score = None if self.cm.score_list is None else self.cm.score_list[index]
self.H1 = None if self.cm.H_list is None else cm.H_list[index]
else:
self.qaid = self.cm.qaid
self.daid = aid2
self.fm = np.empty((0, 2), dtype=hstypes.FM_DTYPE)
self.fk = np.empty(0, dtype=hstypes.FK_DTYPE)
self.fsv = np.empty((0, 2), dtype=hstypes.FS_DTYPE)
self.fs = np.empty(0, dtype=hstypes.FS_DTYPE)
self.score = None
self.H1 = None
# Read properties
self.query_config2_ = (None if self.qreq_ is None else
self.qreq_.get_external_query_config2())
self.data_config2_ = (None if self.qreq_ is None else
self.qreq_.get_external_data_config2())
self.rchip1 = vh.get_chips(ibs, [self.qaid], config2_=self.query_config2_)[0]
self.rchip2 = vh.get_chips(ibs, [self.daid], config2_=self.data_config2_)[0]
# Begin Interaction
# call doclf docla and make figure
self.fig = ih.begin_interaction('matches', self.fnum)
self.xywh2_ptr = [None]
self.mode = kwargs.pop('mode', 0)
# New state vars
self.same_fig = same_fig
self.use_homog = False
self.vert = kwargs.pop('vert', None)
self.mx = kwargs.pop('mx', None)
self.last_fx = 0
self.fnum2 = pt.next_fnum()
self.figtitle = figtitle
self.kwargs = kwargs
abstract_interaction.register_interaction(self)
ut.inject_func_as_method(self, AbstractInteraction.append_button.im_func)
ut.inject_func_as_method(self, AbstractInteraction.show_popup_menu.im_func)
self.scope = []
if not kwargs.get('nobegin', False):
dodraw = kwargs.get('dodraw', True)
self.begin(dodraw=dodraw)
def to_string_monkey(df, highlight_cols=None):
""" monkey patch to pandas to highlight the maximum value in specified
cols of a row
df = pd.DataFrame(
np.array([[ 0.87031269, 0.86886931, 0.86842073, 0.91981975],
[ 0.34196218, 0.34289191, 0.34206377, 0.34252863],
[ 0.34827074, 0.34829214, 0.35032833, 0.28857126],
[ 0.76979453, 0.77214855, 0.77547518, 0.38850962]]),
columns=['sum(fgweights)', 'sum(weighted_ratio)', 'len(matches)', 'score_lnbnn_1vM'],
index=['match_state(match-v-rest)', 'match_state(nomatch-v-rest)', 'match_state(notcomp-v-rest)', 'photobomb_state']
)
highlight_cols = 'all'
ut.editfile(pd.formats.printing.adjoin)
"""
import pandas as pd
import utool as ut
import numpy as np
import six
if isinstance(highlight_cols, six.string_types) and highlight_cols == 'all':
highlight_cols = np.arange(len(df.columns))
# kwds = dict(buf=None, columns=None, col_space=None, header=True,
# index=True, na_rep='NaN', formatters=None,
# float_format=None, sparsify=None, index_names=True,
# justify=None, line_width=None, max_rows=None,
# max_cols=None, show_dimensions=False)
# self = pd.formats.format.DataFrameFormatter(df, **kwds)
self = pd.formats.format.DataFrameFormatter(df)
self.highlight_cols = highlight_cols
ut.inject_func_as_method(self, monkey_to_str_columns, '_to_str_columns', override=True, force=True)
def strip_ansi(text):
import re
ansi_escape = re.compile(r'\x1b[^m]*m')
return ansi_escape.sub('', text)
def justify_ansi(self, texts, max_len, mode='right'):
if mode == 'left':
return [x.ljust(max_len + (len(x) - len(strip_ansi(x)))) for x in texts]
elif mode == 'center':
return [x.center(max_len + (len(x) - len(strip_ansi(x)))) for x in texts]
else:
return [x.rjust(max_len + (len(x) - len(strip_ansi(x)))) for x in texts]
ut.inject_func_as_method(self.adj, justify_ansi, 'justify', override=True, force=True)
def strlen_ansii(self, text):
return pd.compat.strlen(strip_ansi(text), encoding=self.encoding)
ut.inject_func_as_method(self.adj, strlen_ansii, 'len', override=True, force=True)
if False:
strlen = ut.partial(strlen_ansii, self.adj) # NOQA
justfunc = ut.partial(justify_ansi, self.adj) # NOQA
# Essentially what to_string does
strcols = monkey_to_str_columns(self)
# texts = strcols[2]
space = 1
lists = strcols
str_ = self.adj.adjoin(space, *lists)
print(str_)
print(strip_ansi(str_))
self.to_string()
result = self.buf.getvalue()
# hack because adjoin is not working correctly with injected strlen
result = '\n'.join([x.rstrip() for x in result.split('\n')])
return result
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()
