def show_matches(fm,
fs,
fnum=1,
pnum=None,
title='',
key=None,
simp=None,
cmap='hot',
draw_lines=True,
**locals_):
#locals_ = locals()
import plottool as pt
from plottool import plot_helpers as ph
# hack keys out of namespace
keys = 'rchip1, rchip2, kpts1, kpts2'.split(', ')
rchip1, rchip2, kpts1, kpts2 = ut.dict_take(locals_, keys)
pt.figure(fnum=fnum, pnum=pnum)
#doclf=True, docla=True)
ax, xywh1, xywh2 = pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm=fm,
fs=fs,
fnum=fnum,
cmap=cmap,
draw_lines=draw_lines,
ori=True)
ph.set_plotdat(ax, 'viztype', 'matches')
ph.set_plotdat(ax, 'simp', simp)
ph.set_plotdat(ax, 'key', key)
title = title + '\n num=%d, sum=%.2f' % (len(fm), sum(fs))
pt.set_title(title)
return ax, xywh1, xywh2
def show_normalizers(match, fnum=None, pnum=None, update=True):
import plottool as pt
from plottool import plot_helpers as ph
# hack keys out of namespace
keys = ["rchip", "kpts"]
rchip1, kpts1 = ut.dict_take(match.annot1.__dict__, keys)
rchip2, kpts2 = ut.dict_take(match.annot2.__dict__, keys)
fs, fm = match.fs, match.fm_norm
cmap = "cool"
draw_lines = True
if fnum is None:
fnum = pt.next_fnum()
pt.figure(fnum=fnum, pnum=pnum)
# doclf=True, docla=True)
ax, xywh1, xywh2 = pt.show_chipmatch2(
rchip1, rchip2, kpts1, kpts2, fm=fm, fs=fs, fnum=fnum, cmap=cmap, draw_lines=draw_lines
)
ph.set_plotdat(ax, "viztype", "matches")
ph.set_plotdat(ax, "key", match.key)
title = match.key + "\n num=%d, sum=%.2f" % (len(fm), sum(fs))
pt.set_title(title)
if update:
pt.update()
return ax, xywh1, xywh2
def show_normalizers(match, fnum=None, pnum=None, update=True):
import plottool as pt
from plottool import plot_helpers as ph
# hack keys out of namespace
keys = ['rchip', 'kpts']
rchip1, kpts1 = ut.dict_take(match.annot1.__dict__, keys)
rchip2, kpts2 = ut.dict_take(match.annot2.__dict__, keys)
fs, fm = match.fs, match.fm_norm
cmap = 'cool'
draw_lines = True
if fnum is None:
fnum = pt.next_fnum()
pt.figure(fnum=fnum, pnum=pnum)
#doclf=True, docla=True)
ax, xywh1, xywh2 = pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm=fm,
fs=fs,
fnum=fnum,
cmap=cmap,
draw_lines=draw_lines)
ph.set_plotdat(ax, 'viztype', 'matches')
ph.set_plotdat(ax, 'key', match.key)
title = match.key + '\n num=%d, sum=%.2f' % (len(fm), sum(fs))
pt.set_title(title)
if update:
pt.update()
return ax, xywh1, xywh2
def chipmatch_view(self, fnum=None, pnum=(1, 1, 1), verbose=None, **kwargs_):
"""
just visualizes the matches using some type of lines
"""
import plottool as pt
from plottool import plot_helpers as ph
if fnum is None:
fnum = self.fnum
if verbose is None:
verbose = ut.VERBOSE
if verbose:
print('-- CHIPMATCH VIEW --')
print('[ichipmatch_view] self.mode = %r' % (self.mode,))
mode = kwargs_.get('mode', self.mode)
draw_ell = mode >= 1
draw_lines = mode == 2
if verbose:
print('[ichipmatch_view] draw_lines = %r' % (draw_lines,))
print('[ichipmatch_view] draw_ell = %r' % (draw_ell,))
#pt.figure(fnum=fnum, docla=True, doclf=True)
# NOTE: i remove the clf here. might cause issues
pt.figure(fnum=fnum, docla=True, doclf=False)
#show_matches_kw = self.__dict__.copy()
show_matches_kw = dict(
#fnum=fnum, pnum=pnum,
draw_lines=draw_lines,
draw_ell=draw_ell,
colorbar_=True,
vert=self.vert)
show_matches_kw.update(kwargs_)
if verbose:
print('self.warp_homog = %r' % (self.warp_homog,))
if self.warp_homog:
show_matches_kw['H1'] = self.H1
show_matches_kw['H2'] = self.H2
if verbose:
print('show_matches_kw = %s' % (ut.dict_str(show_matches_kw, truncate=True)))
#tup = show_matches(fm, fs, **show_matches_kw)
ax, xywh1, xywh2 = pt.show_chipmatch2(
self.rchip1, self.rchip2,
self.kpts1, self.kpts2,
fm=self.fm, fs=self.fs,
pnum=pnum, **show_matches_kw)
self.xywh2 = xywh2
ph.set_plotdat(ax, 'viztype', 'matches')
if self.truth is not None and self.truth:
truth_color = pt.TRUE_BLUE # if else pt.FALSE_RED
pt.draw_border(ax, color=truth_color, lw=4)
if self.title is not None:
pt.set_title(self.title, ax=ax)
def chipmatch_view(self, fnum=None, pnum=(1, 1, 1), verbose=None, **kwargs_):
"""
just visualizes the matches using some type of lines
"""
import plottool as pt
from plottool import plot_helpers as ph
if fnum is None:
fnum = self.fnum
if verbose is None:
verbose = ut.VERBOSE
if verbose:
print("-- CHIPMATCH VIEW --")
print("[ichipmatch_view] self.mode = %r" % (self.mode,))
mode = kwargs_.get("mode", self.mode)
draw_ell = mode >= 1
draw_lines = mode == 2
if verbose:
print("[ichipmatch_view] draw_lines = %r" % (draw_lines,))
print("[ichipmatch_view] draw_ell = %r" % (draw_ell,))
# pt.figure(fnum=fnum, docla=True, doclf=True)
# NOTE: i remove the clf here. might cause issues
pt.figure(fnum=fnum, docla=True, doclf=False)
# show_matches_kw = self.__dict__.copy()
show_matches_kw = dict(
# fnum=fnum, pnum=pnum,
draw_lines=draw_lines,
draw_ell=draw_ell,
colorbar_=True,
vert=self.vert,
)
show_matches_kw.update(kwargs_)
if verbose:
print("self.warp_homog = %r" % (self.warp_homog,))
if self.warp_homog:
show_matches_kw["H1"] = self.H1
show_matches_kw["H2"] = self.H2
if verbose:
print("show_matches_kw = %s" % (ut.dict_str(show_matches_kw, truncate=True)))
# tup = show_matches(fm, fs, **show_matches_kw)
ax, xywh1, xywh2 = pt.show_chipmatch2(
self.rchip1, self.rchip2, self.kpts1, self.kpts2, fm=self.fm, fs=self.fs, pnum=pnum, **show_matches_kw
)
self.xywh2 = xywh2
ph.set_plotdat(ax, "viztype", "matches")
if self.truth is not None and self.truth:
truth_color = pt.TRUE_BLUE # if else pt.FALSE_RED
pt.draw_border(ax, color=truth_color, lw=4)
if self.title is not None:
pt.set_title(self.title, ax=ax)
def draw_map_histogram(top_assignments, fnum=None, pnum=(1, 1, 1)):
import plottool as pt
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
fnum = pt.ensure_fnum(fnum)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(bin_labels, bin_vals, fnum=fnum, pnum=pnum,
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob', xlabel='assignment')
pt.set_title('Assignment probabilities')
def draw_map_histogram(top_assignments, fnum=None, pnum=(1, 1, 1)):
import plottool as pt
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
fnum = pt.ensure_fnum(fnum)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(
bin_labels,
bin_vals,
fnum=fnum,
pnum=pnum,
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob',
xlabel='assignment')
pt.set_title('Assignment probabilities')
def show_matches(fm, fs, fnum=1, pnum=None, title='', key=None, simp=None,
cmap='hot', draw_lines=True, **locals_):
#locals_ = locals()
import plottool as pt
from plottool import plot_helpers as ph
# hack keys out of namespace
keys = 'rchip1, rchip2, kpts1, kpts2'.split(', ')
rchip1, rchip2, kpts1, kpts2 = ut.dict_take(locals_, keys)
pt.figure(fnum=fnum, pnum=pnum)
#doclf=True, docla=True)
ax, xywh1, xywh2 = pt.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm=fm,
fs=fs, fnum=fnum, cmap=cmap,
draw_lines=draw_lines, ori=True)
ph.set_plotdat(ax, 'viztype', 'matches')
ph.set_plotdat(ax, 'simp', simp)
ph.set_plotdat(ax, 'key', key)
title = title + '\n num=%d, sum=%.2f' % (len(fm), sum(fs))
pt.set_title(title)
return ax, xywh1, xywh2
def show_model(model, evidence={}, soft_evidence={}, **kwargs):
"""
References:
http://stackoverflow.com/questions/22207802/pygraphviz-networkx-set-node-level-or-layer
Ignore:
pkg-config --libs-only-L libcgraph
sudo apt-get install libgraphviz-dev -y
sudo apt-get install libgraphviz4 -y
# sudo apt-get install pkg-config
sudo apt-get install libgraphviz-dev
# pip install git+git://github.com/pygraphviz/pygraphviz.git
pip install pygraphviz
python -c "import pygraphviz; print(pygraphviz.__file__)"
sudo pip3 install pygraphviz --install-option="--include-path=/usr/include/graphviz" --install-option="--library-path=/usr/lib/graphviz/"
python3 -c "import pygraphviz; print(pygraphviz.__file__)"
CommandLine:
python -m ibeis.algo.hots.bayes --exec-show_model --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.bayes import * # NOQA
>>> model = '?'
>>> evidence = {}
>>> soft_evidence = {}
>>> result = show_model(model, evidence, soft_evidence)
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
if ut.get_argval('--hackmarkov') or ut.get_argval('--hackjunc'):
draw_tree_model(model, **kwargs)
return
import plottool as pt
import networkx as netx
fnum = pt.ensure_fnum(None)
netx_graph = (model)
#netx_graph.graph.setdefault('graph', {})['size'] = '"10,5"'
#netx_graph.graph.setdefault('graph', {})['rankdir'] = 'LR'
pos_dict = get_hacked_pos(netx_graph)
#pos_dict = netx.nx_agraph.pygraphviz_layout(netx_graph)
#pos = netx.nx_agraph.nx_pydot.pydot_layout(netx_graph, prog='dot')
#pos_dict = netx.nx_agraph.graphviz_layout(netx_graph)
textprops = {
'family': 'monospace',
'horizontalalignment': 'left',
#'horizontalalignment': 'center',
#'size': 12,
'size': 8,
}
netx_nodes = model.nodes(data=True)
node_key_list = ut.get_list_column(netx_nodes, 0)
pos_list = ut.dict_take(pos_dict, node_key_list)
var2_post = {f.variables[0]: f for f in kwargs.get('factor_list', [])}
prior_text = None
post_text = None
evidence_tas = []
post_tas = []
prior_tas = []
node_color = []
has_inferred = evidence or var2_post
if has_inferred:
ignore_prior_with_ttype = [SCORE_TTYPE, MATCH_TTYPE]
show_prior = False
else:
ignore_prior_with_ttype = []
#show_prior = True
show_prior = False
dpy = 5
dbx, dby = (20, 20)
takw1 = {
'bbox_align': (.5, 0),
'pos_offset': [0, dpy],
'bbox_offset': [dbx, dby]
}
takw2 = {
'bbox_align': (.5, 1),
'pos_offset': [0, -dpy],
'bbox_offset': [-dbx, -dby]
}
name_colors = pt.distinct_colors(max(model.num_names, 10))
name_colors = name_colors[:model.num_names]
#cmap_ = 'hot' #mx = 0.65 #mn = 0.15
cmap_, mn, mx = 'plasma', 0.15, 1.0
_cmap = pt.plt.get_cmap(cmap_)
def cmap(x):
return _cmap((x * mx) + mn)
for node, pos in zip(netx_nodes, pos_list):
variable = node[0]
cpd = model.var2_cpd[variable]
prior_marg = (cpd if cpd.evidence is None else cpd.marginalize(
cpd.evidence, inplace=False))
show_evidence = variable in evidence
show_prior = cpd.ttype not in ignore_prior_with_ttype
show_post = variable in var2_post
show_prior |= cpd.ttype not in ignore_prior_with_ttype
post_marg = None
if show_post:
post_marg = var2_post[variable]
def get_name_color(phi):
order = phi.values.argsort()[::-1]
if len(order) < 2:
dist_next = phi.values[order[0]]
else:
dist_next = phi.values[order[0]] - phi.values[order[1]]
dist_total = (phi.values[order[0]])
confidence = (dist_total * dist_next)**(2.5 / 4)
#print('confidence = %r' % (confidence,))
color = name_colors[order[0]]
color = pt.color_funcs.desaturate_rgb(color, 1 - confidence)
color = np.array(color)
return color
if variable in evidence:
if cpd.ttype == SCORE_TTYPE:
cmap_index = evidence[variable] / (cpd.variable_card - 1)
color = cmap(cmap_index)
color = pt.lighten_rgb(color, .4)
color = np.array(color)
node_color.append(color)
elif cpd.ttype == NAME_TTYPE:
color = name_colors[evidence[variable]]
color = np.array(color)
node_color.append(color)
else:
color = pt.FALSE_RED
node_color.append(color)
#elif variable in soft_evidence:
# color = pt.LIGHT_PINK
# show_prior = True
# color = get_name_color(prior_marg)
# node_color.append(color)
else:
if cpd.ttype == NAME_TTYPE and post_marg is not None:
color = get_name_color(post_marg)
node_color.append(color)
elif cpd.ttype == MATCH_TTYPE and post_marg is not None:
color = cmap(post_marg.values[1])
color = pt.lighten_rgb(color, .4)
color = np.array(color)
node_color.append(color)
else:
#color = pt.WHITE
color = pt.NEUTRAL
node_color.append(color)
if show_prior:
if variable in soft_evidence:
prior_color = pt.LIGHT_PINK
else:
prior_color = None
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
prior_tas.append(
dict(text=prior_text, pos=pos, color=prior_color, **takw2))
if show_evidence:
_takw1 = takw1
if cpd.ttype == SCORE_TTYPE:
_takw1 = takw2
evidence_text = cpd.variable_statenames[evidence[variable]]
if isinstance(evidence_text, int):
evidence_text = '%d/%d' % (evidence_text + 1,
cpd.variable_card)
evidence_tas.append(
dict(text=evidence_text, pos=pos, color=color, **_takw1))
if show_post:
_takw1 = takw1
if cpd.ttype == MATCH_TTYPE:
_takw1 = takw2
post_text = pgm_ext.make_factor_text(post_marg, 'post')
post_tas.append(dict(text=post_text, pos=pos, color=None,
**_takw1))
def trnps_(dict_list):
""" tranpose dict list """
list_dict = ut.ddict(list)
for dict_ in dict_list:
for key, val in dict_.items():
list_dict[key + '_list'].append(val)
return list_dict
takw1_ = trnps_(post_tas + evidence_tas)
takw2_ = trnps_(prior_tas)
# Draw graph
if has_inferred:
pnum1 = (3, 1, (slice(0, 2), 0))
else:
pnum1 = None
fig = pt.figure(fnum=fnum, pnum=pnum1, doclf=True) # NOQA
ax = pt.gca()
#print('node_color = %s' % (ut.repr3(node_color),))
drawkw = dict(pos=pos_dict,
ax=ax,
with_labels=True,
node_size=1500,
node_color=node_color)
netx.draw(netx_graph, **drawkw)
hacks = []
if len(post_tas + evidence_tas):
hacks.append(pt.draw_text_annotations(textprops=textprops, **takw1_))
if prior_tas:
hacks.append(pt.draw_text_annotations(textprops=textprops, **takw2_))
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
num_annots = len(model.ttype2_cpds[NAME_TTYPE])
if num_annots > 4:
ax.set_xlim((xmin - 40, xmax + 40))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(30, 7)
else:
ax.set_xlim((xmin - 42, xmax + 42))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(23, 7)
fig = pt.gcf()
title = 'num_names=%r, num_annots=%r' % (
model.num_names,
num_annots,
)
map_assign = kwargs.get('map_assign', None)
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
map_assign, map_prob = top_assignments[0]
if map_assign is not None:
def word_insert(text):
return '' if len(text) == 0 else text + ' '
title += '\n%sMAP: ' % (word_insert(kwargs.get('method', '')))
title += map_assign + ' @' + '%.2f%%' % (100 * map_prob, )
if kwargs.get('show_title', True):
pt.set_figtitle(title, size=14)
for hack in hacks:
hack()
# Hack in colorbars
if has_inferred:
pt.colorbar(np.linspace(0, 1, len(name_colors)),
name_colors,
lbl=NAME_TTYPE,
ticklabels=model.ttype2_template[NAME_TTYPE].basis,
ticklocation='left')
basis = model.ttype2_template[SCORE_TTYPE].basis
scalars = np.linspace(0, 1, len(basis))
scalars = np.linspace(0, 1, 100)
colors = pt.scores_to_color(scalars,
cmap_=cmap_,
reverse_cmap=False,
cmap_range=(mn, mx))
colors = [pt.lighten_rgb(c, .4) for c in colors]
if ut.list_type(basis) is int:
pt.colorbar(scalars,
colors,
lbl=SCORE_TTYPE,
ticklabels=np.array(basis) + 1)
else:
pt.colorbar(scalars, colors, lbl=SCORE_TTYPE, ticklabels=basis)
#print('basis = %r' % (basis,))
# Draw probability hist
if has_inferred and top_assignments is not None:
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(
bin_labels,
bin_vals,
fnum=fnum,
pnum=(3, 8, (2, slice(4, None))),
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob',
xlabel='assignment')
pt.set_title('Assignment probabilities')
def show_nx(graph, with_labels=True, fnum=None, pnum=None, layout='agraph',
ax=None, pos=None, img_dict=None, title=None, layoutkw=None,
verbose=None, **kwargs):
r"""
Args:
graph (networkx.Graph):
with_labels (bool): (default = True)
node_size (int): (default = 1100)
fnum (int): figure number(default = None)
pnum (tuple): plot number(default = None)
CommandLine:
python -m plottool.nx_helpers --exec-show_nx --show
python -m dtool --tf DependencyCache.make_graph --show
python -m ibeis.scripts.specialdraw double_depcache_graph --show --testmode
python -m vtool.clustering2 unsupervised_multicut_labeling --show
Example:
>>> # DISABLE_DOCTEST
>>> from plottool.nx_helpers import * # NOQA
>>> import networkx as nx
>>> graph = nx.DiGraph()
>>> graph.add_nodes_from(['a', 'b', 'c', 'd'])
>>> graph.add_edges_from({'a': 'b', 'b': 'c', 'b': 'd', 'c': 'd'}.items())
>>> nx.set_node_attributes(graph, 'shape', 'rect')
>>> nx.set_node_attributes(graph, 'image', {'a': ut.grab_test_imgpath('carl.jpg')})
>>> nx.set_node_attributes(graph, 'image', {'d': ut.grab_test_imgpath('lena.png')})
>>> #nx.set_node_attributes(graph, 'height', 100)
>>> with_labels = True
>>> fnum = None
>>> pnum = None
>>> e = show_nx(graph, with_labels, fnum, pnum, layout='agraph')
>>> ut.show_if_requested()
"""
import plottool as pt
import networkx as nx
if ax is None:
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum, pnum=pnum)
ax = pt.gca()
if img_dict is None:
img_dict = nx.get_node_attributes(graph, 'image')
if verbose is None:
verbose = ut.VERBOSE
use_image = kwargs.get('use_image', True)
if verbose:
print('Getting layout')
layout_info = get_nx_layout(graph, layout, layoutkw=layoutkw,
verbose=verbose)
if verbose:
print('Drawing graph')
# zoom = kwargs.pop('zoom', .4)
framewidth = kwargs.pop('framewidth', 1.0)
patch_dict = draw_network2(graph, layout_info, ax, verbose=verbose, **kwargs)
layout_info.update(patch_dict)
if kwargs.get('modify_ax', True):
ax.grid(False)
pt.plt.axis('equal')
ax.axesPatch.set_facecolor('white')
ax.autoscale()
ax.autoscale_view(True, True, True)
#axes.facecolor
node_size = layout_info['node']['size']
node_pos = layout_info['node']['pos']
if node_size is not None:
half_size_arr = np.array(ut.take(node_size, graph.nodes())) / 2.
pos_arr = np.array(ut.take(node_pos, graph.nodes()))
# autoscale does not seem to work
#ul_pos = pos_arr - half_size_arr
#br_pos = pos_arr + half_size_arr
# hack because edges are cut off.
# need to take into account extent of edges as well
ul_pos = pos_arr - half_size_arr * 1.5
br_pos = pos_arr + half_size_arr * 1.5
xmin, ymin = ul_pos.min(axis=0)
xmax, ymax = br_pos.max(axis=0)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
#pt.plt.axis('off')
ax.set_xticks([])
ax.set_yticks([])
if use_image and img_dict is not None and len(img_dict) > 0:
if verbose:
print('Drawing images')
node_list = sorted(img_dict.keys())
pos_list = ut.dict_take(node_pos, node_list)
img_list = ut.dict_take(img_dict, node_list)
size_list = ut.dict_take(node_size, node_list)
#color_list = ut.dict_take(nx.get_node_attributes(graph, 'color'), node_list, None)
color_list = ut.dict_take(nx.get_node_attributes(graph, 'framecolor'), node_list, None)
framewidth_list = ut.dict_take(nx.get_node_attributes(graph, 'framewidth'),
node_list, framewidth)
# TODO; frames without images
imgdat = pt.netx_draw_images_at_positions(img_list, pos_list,
size_list, color_list,
framewidth_list=framewidth_list)
imgdat['node_list'] = node_list
layout_info['imgdat'] = imgdat
else:
if verbose:
print('Not drawing images')
if title is not None:
pt.set_title(title)
return layout_info
def show_model(model, evidence={}, soft_evidence={}, **kwargs):
"""
References:
http://stackoverflow.com/questions/22207802/pygraphviz-networkx-set-node-level-or-layer
Ignore:
pkg-config --libs-only-L libcgraph
sudo apt-get install libgraphviz-dev -y
sudo apt-get install libgraphviz4 -y
# sudo apt-get install pkg-config
sudo apt-get install libgraphviz-dev
# pip install git+git://github.com/pygraphviz/pygraphviz.git
pip install pygraphviz
python -c "import pygraphviz; print(pygraphviz.__file__)"
sudo pip3 install pygraphviz --install-option="--include-path=/usr/include/graphviz" --install-option="--library-path=/usr/lib/graphviz/"
python3 -c "import pygraphviz; print(pygraphviz.__file__)"
CommandLine:
python -m ibeis.algo.hots.bayes --exec-show_model --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.bayes import * # NOQA
>>> model = '?'
>>> evidence = {}
>>> soft_evidence = {}
>>> result = show_model(model, evidence, soft_evidence)
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
if ut.get_argval('--hackmarkov') or ut.get_argval('--hackjunc'):
draw_tree_model(model, **kwargs)
return
import plottool as pt
import networkx as netx
fnum = pt.ensure_fnum(None)
netx_graph = (model)
#netx_graph.graph.setdefault('graph', {})['size'] = '"10,5"'
#netx_graph.graph.setdefault('graph', {})['rankdir'] = 'LR'
pos_dict = get_hacked_pos(netx_graph)
#pos_dict = netx.pygraphviz_layout(netx_graph)
#pos = netx.pydot_layout(netx_graph, prog='dot')
#pos_dict = netx.graphviz_layout(netx_graph)
textprops = {
'family': 'monospace',
'horizontalalignment': 'left',
#'horizontalalignment': 'center',
#'size': 12,
'size': 8,
}
netx_nodes = model.nodes(data=True)
node_key_list = ut.get_list_column(netx_nodes, 0)
pos_list = ut.dict_take(pos_dict, node_key_list)
var2_post = {f.variables[0]: f for f in kwargs.get('factor_list', [])}
prior_text = None
post_text = None
evidence_tas = []
post_tas = []
prior_tas = []
node_color = []
has_infered = evidence or var2_post
if has_infered:
ignore_prior_with_ttype = ['score', 'match']
show_prior = False
else:
ignore_prior_with_ttype = []
#show_prior = True
show_prior = False
dpy = 5
dbx, dby = (20, 20)
takw1 = {'bbox_align': (.5, 0), 'pos_offset': [0, dpy], 'bbox_offset': [dbx, dby]}
takw2 = {'bbox_align': (.5, 1), 'pos_offset': [0, -dpy], 'bbox_offset': [-dbx, -dby]}
name_colors = pt.distinct_colors(max(model.num_names, 10))
name_colors = name_colors[:model.num_names]
#cmap_ = 'hot' #mx = 0.65 #mn = 0.15
cmap_, mn, mx = 'plasma', 0.15, 1.0
_cmap = pt.plt.get_cmap(cmap_)
def cmap(x):
return _cmap((x * mx) + mn)
for node, pos in zip(netx_nodes, pos_list):
variable = node[0]
cpd = model.var2_cpd[variable]
prior_marg = (cpd if cpd.evidence is None else
cpd.marginalize(cpd.evidence, inplace=False))
show_evidence = variable in evidence
show_prior = cpd.ttype not in ignore_prior_with_ttype
show_post = variable in var2_post
show_prior |= cpd.ttype not in ignore_prior_with_ttype
post_marg = None
if show_post:
post_marg = var2_post[variable]
def get_name_color(phi):
order = phi.values.argsort()[::-1]
if len(order) < 2:
dist_next = phi.values[order[0]]
else:
dist_next = phi.values[order[0]] - phi.values[order[1]]
dist_total = (phi.values[order[0]])
confidence = (dist_total * dist_next) ** (2.5 / 4)
#print('confidence = %r' % (confidence,))
color = name_colors[order[0]]
color = pt.color_funcs.desaturate_rgb(color, 1 - confidence)
color = np.array(color)
return color
if variable in evidence:
if cpd.ttype == 'score':
cmap_index = evidence[variable] / (cpd.variable_card - 1)
color = cmap(cmap_index)
color = pt.lighten_rgb(color, .4)
color = np.array(color)
node_color.append(color)
elif cpd.ttype == 'name':
color = name_colors[evidence[variable]]
color = np.array(color)
node_color.append(color)
else:
color = pt.FALSE_RED
node_color.append(color)
#elif variable in soft_evidence:
# color = pt.LIGHT_PINK
# show_prior = True
# color = get_name_color(prior_marg)
# node_color.append(color)
else:
if cpd.ttype == 'name' and post_marg is not None:
color = get_name_color(post_marg)
node_color.append(color)
elif cpd.ttype == 'match' and post_marg is not None:
color = cmap(post_marg.values[1])
color = pt.lighten_rgb(color, .4)
color = np.array(color)
node_color.append(color)
else:
#color = pt.WHITE
color = pt.NEUTRAL
node_color.append(color)
if show_prior:
if variable in soft_evidence:
prior_color = pt.LIGHT_PINK
else:
prior_color = None
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
prior_tas.append(dict(text=prior_text, pos=pos, color=prior_color, **takw2))
if show_evidence:
_takw1 = takw1
if cpd.ttype == 'score':
_takw1 = takw2
evidence_text = cpd.variable_statenames[evidence[variable]]
if isinstance(evidence_text, int):
evidence_text = '%d/%d' % (evidence_text + 1, cpd.variable_card)
evidence_tas.append(dict(text=evidence_text, pos=pos, color=color, **_takw1))
if show_post:
_takw1 = takw1
if cpd.ttype == 'match':
_takw1 = takw2
post_text = pgm_ext.make_factor_text(post_marg, 'post')
post_tas.append(dict(text=post_text, pos=pos, color=None, **_takw1))
def trnps_(dict_list):
""" tranpose dict list """
list_dict = ut.ddict(list)
for dict_ in dict_list:
for key, val in dict_.items():
list_dict[key + '_list'].append(val)
return list_dict
takw1_ = trnps_(post_tas + evidence_tas)
takw2_ = trnps_(prior_tas)
# Draw graph
if has_infered:
pnum1 = (3, 1, (slice(0, 2), 0))
else:
pnum1 = None
fig = pt.figure(fnum=fnum, pnum=pnum1, doclf=True) # NOQA
ax = pt.gca()
#print('node_color = %s' % (ut.repr3(node_color),))
drawkw = dict(pos=pos_dict, ax=ax, with_labels=True, node_size=1500,
node_color=node_color)
netx.draw(netx_graph, **drawkw)
hacks = []
if len(post_tas + evidence_tas):
hacks.append(pt.draw_text_annotations(textprops=textprops, **takw1_))
if prior_tas:
hacks.append(pt.draw_text_annotations(textprops=textprops, **takw2_))
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
num_annots = len(model.ttype2_cpds['name'])
if num_annots > 4:
ax.set_xlim((xmin - 40, xmax + 40))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(30, 7)
else:
ax.set_xlim((xmin - 42, xmax + 42))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(23, 7)
fig = pt.gcf()
title = 'num_names=%r, num_annots=%r' % (model.num_names, num_annots,)
map_assign = kwargs.get('map_assign', None)
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
map_assign, map_prob = top_assignments[0]
if map_assign is not None:
def word_insert(text):
return '' if len(text) == 0 else text + ' '
title += '\n%sMAP: ' % (word_insert(kwargs.get('method', '')))
title += map_assign + ' @' + '%.2f%%' % (100 * map_prob,)
if kwargs.get('show_title', True):
pt.set_figtitle(title, size=14)
for hack in hacks:
hack()
# Hack in colorbars
if has_infered:
pt.colorbar(np.linspace(0, 1, len(name_colors)), name_colors, lbl='name',
ticklabels=model.ttype2_template['name'].basis, ticklocation='left')
basis = model.ttype2_template['score'].basis
scalars = np.linspace(0, 1, len(basis))
scalars = np.linspace(0, 1, 100)
colors = pt.scores_to_color(scalars, cmap_=cmap_, reverse_cmap=False,
cmap_range=(mn, mx))
colors = [pt.lighten_rgb(c, .4) for c in colors]
if ut.list_type(basis) is int:
pt.colorbar(scalars, colors, lbl='score', ticklabels=np.array(basis) + 1)
else:
pt.colorbar(scalars, colors, lbl='score', ticklabels=basis)
#print('basis = %r' % (basis,))
# Draw probability hist
if has_infered and top_assignments is not None:
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(bin_labels, bin_vals, fnum=fnum, pnum=(3, 8, (2, slice(4, None))),
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob', xlabel='assignment')
pt.set_title('Assignment probabilities')
def show_chip(ibs,
aid,
in_image=False,
annote=True,
title_suffix='',
weight_label=None,
weights=None,
config2_=None,
**kwargs):
r""" Driver function to show chips
Args:
ibs (ibeis.IBEISController):
aid (int): annotation rowid
in_image (bool): displays annotation with the context of its source image
annote (bool): enables overlay annoations
title_suffix (str):
weight_label (None): (default = None)
weights (None): (default = None)
config2_ (dict): (default = None)
Kwargs:
enable_chip_title_prefix, nokpts, kpts_subset, kpts, text_color,
notitle, draw_lbls, show_aidstr, show_gname, show_name, show_nid,
show_exemplar, show_num_gt, show_quality_text, show_yawtext, fnum,
title, figtitle, pnum, interpolation, cmap, heatmap, data_colorbar,
darken, update, xlabel, redraw_image, ax, alpha, docla, doclf,
projection, use_gridspec, pts, ell
color (3/4-tuple, ndarray, or str): colors for keypoints
CommandLine:
python -m ibeis.viz.viz_chip --test-show_chip --show --ecc
python -c "import utool as ut; ut.print_auto_docstr('ibeis.viz.viz_chip', 'show_chip')"
python -m ibeis.viz.viz_chip --test-show_chip --show --db NNP_Master3 --aids 14047 --no-annote
python -m ibeis.viz.viz_chip --test-show_chip --show --db NNP_Master3 --aids 14047 --no-annote
python -m ibeis.viz.viz_chip --test-show_chip --show --db PZ_MTEST --aid 1 --bgmethod=cnn
python -m ibeis.viz.viz_chip --test-show_chip --show --db PZ_MTEST --aid 1 --bgmethod=cnn --scale_max=30
Example:
>>> # VIZ_TEST
>>> from ibeis.viz.viz_chip import * # NOQA
>>> import numpy as np
>>> import vtool as vt
>>> in_image = False
>>> ibs, aid_list, kwargs, config2_ = testdata_showchip()
>>> aid = aid_list[0]
>>> if ut.get_argflag('--ecc'):
>>> kpts = ibs.get_annot_kpts(aid, config2_=config2_)
>>> weights = ibs.get_annot_fgweights([aid], ensure=True, config2_=config2_)[0]
>>> kpts = ut.random_sample(kpts[weights > .9], 200, seed=0)
>>> ecc = vt.get_kpts_eccentricity(kpts)
>>> scale = 1 / vt.get_scales(kpts)
>>> s = ecc if config2_.affine_invariance else scale
>>> colors = pt.scores_to_color(s, cmap_='jet')
>>> kwargs['color'] = colors
>>> kwargs['kpts'] = kpts
>>> show_chip(ibs, aid, in_image=in_image, config2_=config2_, **kwargs)
>>> pt.show_if_requested()
"""
if ut.VERBOSE:
print('[viz] show_chip(aid=%r)' % (aid, ))
#ibs.assert_valid_aids((aid,))
# Get chip
#print('in_image = %r' % (in_image,))
chip = vh.get_chips(ibs, aid, in_image=in_image, config2_=config2_)
# Create chip title
chip_text = vh.get_annot_texts(ibs, [aid], **kwargs)[0]
if kwargs.get('enable_chip_title_prefix', True):
chip_title_text = chip_text + title_suffix
else:
chip_title_text = title_suffix
chip_title_text = chip_title_text.strip('\n')
# Draw chip
fig, ax = pt.imshow(chip, **kwargs)
# Populate axis user data
vh.set_ibsdat(ax, 'viztype', 'chip')
vh.set_ibsdat(ax, 'aid', aid)
if annote and not kwargs.get('nokpts', False):
# Get and draw keypoints
if 'color' not in kwargs:
if weight_label == 'fg_weights':
if weights is None and ibs.has_species_detector(
ibs.get_annot_species_texts(aid)):
weight_label = 'fg_weights'
weights = ibs.get_annot_fgweights([aid],
ensure=True,
config2_=config2_)[0]
if weights is not None:
cmap_ = 'hot'
#if weight_label == 'dstncvs':
# cmap_ = 'rainbow'
color = pt.scores_to_color(weights,
cmap_=cmap_,
reverse_cmap=False)
kwargs['color'] = color
kwargs['ell_color'] = color
kwargs['pts_color'] = color
kpts_ = vh.get_kpts(ibs,
aid,
in_image,
config2_=config2_,
kpts_subset=kwargs.get('kpts_subset', None),
kpts=kwargs.get('kpts', None))
try:
del kwargs['kpts']
except KeyError:
pass
pt.viz_keypoints._annotate_kpts(kpts_, **kwargs)
if not ut.get_argflag('--noaidlabel'):
pt.upperleft_text(chip_text, color=kwargs.get('text_color', None))
use_title = not kwargs.get('notitle', False)
if use_title:
pt.set_title(chip_title_text)
if in_image:
gid = ibs.get_annot_gids(aid)
aid_list = ibs.get_image_aids(gid)
annotekw = viz_image.get_annot_annotations(ibs,
aid_list,
sel_aids=[aid],
draw_lbls=kwargs.get(
'draw_lbls', True))
# Put annotation centers in the axis
ph.set_plotdat(ax, 'annotation_bbox_list', annotekw['bbox_list'])
ph.set_plotdat(ax, 'aid_list', aid_list)
pt.viz_image2.draw_image_overlay(ax, **annotekw)
zoom_ = ut.get_argval('--zoom', type_=float, default=None)
if zoom_ is not None:
import vtool as vt
# Zoom into the chip for some image context
rotated_verts = ibs.get_annot_rotated_verts(aid)
bbox = ibs.get_annot_bboxes(aid)
#print(bbox)
#print(rotated_verts)
rotated_bbox = vt.bbox_from_verts(rotated_verts)
imgw, imgh = ibs.get_image_sizes(gid)
pad_factor = zoom_
pad_length = min(bbox[2], bbox[3]) * pad_factor
minx = max(rotated_bbox[0] - pad_length, 0)
miny = max(rotated_bbox[1] - pad_length, 0)
maxx = min((rotated_bbox[0] + rotated_bbox[2]) + pad_length, imgw)
maxy = min((rotated_bbox[1] + rotated_bbox[3]) + pad_length, imgh)
#maxy = imgh - maxy
#miny = imgh - miny
ax = pt.gca()
ax.set_xlim(minx, maxx)
ax.set_ylim(miny, maxy)
ax.invert_yaxis()
else:
ph.set_plotdat(ax, 'chipshape', chip.shape)
#if 'featweights' in vars() and 'color' in kwargs:
if weights is not None and weight_label is not None:
## HACK HACK HACK
if len(weights) > 0:
cb = pt.colorbar(weights, kwargs['color'])
cb.set_label(weight_label)
return fig, ax
def show_chip(ibs, aid, in_image=False, annote=True, title_suffix='',
weight_label=None, weights=None, config2_=None, **kwargs):
r""" Driver function to show chips
Args:
ibs (ibeis.IBEISController):
aid (int): annotation rowid
in_image (bool): displays annotation with the context of its source image
annote (bool): enables overlay annoations
title_suffix (str):
weight_label (None): (default = None)
weights (None): (default = None)
config2_ (dict): (default = None)
Kwargs:
enable_chip_title_prefix, nokpts, kpts_subset, kpts, text_color,
notitle, draw_lbls, show_aidstr, show_gname, show_name, show_nid,
show_exemplar, show_num_gt, show_quality_text, show_yawtext, fnum,
title, figtitle, pnum, interpolation, cmap, heatmap, data_colorbar,
darken, update, xlabel, redraw_image, ax, alpha, docla, doclf,
projection, use_gridspec, pts, ell
color (3/4-tuple, ndarray, or str): colors for keypoints
CommandLine:
python -m ibeis.viz.viz_chip --test-show_chip --show --ecc
python -c "import utool as ut; ut.print_auto_docstr('ibeis.viz.viz_chip', 'show_chip')"
python -m ibeis.viz.viz_chip --test-show_chip --show --db NNP_Master3 --aids 14047 --no-annote
python -m ibeis.viz.viz_chip --test-show_chip --show --db NNP_Master3 --aids 14047 --no-annote
python -m ibeis.viz.viz_chip --test-show_chip --show --db PZ_MTEST --aid 1 --bgmethod=cnn
python -m ibeis.viz.viz_chip --test-show_chip --show --db PZ_MTEST --aid 1 --bgmethod=cnn --scale_max=30
Example:
>>> # VIZ_TEST
>>> from ibeis.viz.viz_chip import * # NOQA
>>> import numpy as np
>>> import vtool as vt
>>> in_image = False
>>> ibs, aid_list, kwargs, config2_ = testdata_showchip()
>>> aid = aid_list[0]
>>> if ut.get_argflag('--ecc'):
>>> kpts = ibs.get_annot_kpts(aid, config2_=config2_)
>>> weights = ibs.get_annot_fgweights([aid], ensure=True, config2_=config2_)[0]
>>> kpts = ut.random_sample(kpts[weights > .9], 200, seed=0)
>>> ecc = vt.get_kpts_eccentricity(kpts)
>>> scale = 1 / vt.get_scales(kpts)
>>> s = ecc if config2_.affine_invariance else scale
>>> colors = pt.scores_to_color(s, cmap_='jet')
>>> kwargs['color'] = colors
>>> kwargs['kpts'] = kpts
>>> show_chip(ibs, aid, in_image=in_image, config2_=config2_, **kwargs)
>>> pt.show_if_requested()
"""
if ut.VERBOSE:
print('[viz] show_chip(aid=%r)' % (aid,))
#ibs.assert_valid_aids((aid,))
# Get chip
#print('in_image = %r' % (in_image,))
chip = vh.get_chips(ibs, aid, in_image=in_image, config2_=config2_)
# Create chip title
chip_text = vh.get_annot_texts(ibs, [aid], **kwargs)[0]
if kwargs.get('enable_chip_title_prefix', True):
chip_title_text = chip_text + title_suffix
else:
chip_title_text = title_suffix
chip_title_text = chip_title_text.strip('\n')
# Draw chip
fig, ax = pt.imshow(chip, **kwargs)
# Populate axis user data
vh.set_ibsdat(ax, 'viztype', 'chip')
vh.set_ibsdat(ax, 'aid', aid)
if annote and not kwargs.get('nokpts', False):
# Get and draw keypoints
if 'color' not in kwargs:
if weight_label == 'fg_weights':
if weights is None and ibs.has_species_detector(ibs.get_annot_species_texts(aid)):
weight_label = 'fg_weights'
weights = ibs.get_annot_fgweights([aid], ensure=True, config2_=config2_)[0]
if weights is not None:
cmap_ = 'hot'
#if weight_label == 'dstncvs':
# cmap_ = 'rainbow'
color = pt.scores_to_color(weights, cmap_=cmap_, reverse_cmap=False)
kwargs['color'] = color
kwargs['ell_color'] = color
kwargs['pts_color'] = color
kpts_ = vh.get_kpts(ibs, aid, in_image, config2_=config2_,
kpts_subset=kwargs.get('kpts_subset', None),
kpts=kwargs.get('kpts', None))
try:
del kwargs['kpts']
except KeyError:
pass
pt.viz_keypoints._annotate_kpts(kpts_, **kwargs)
if not ut.get_argflag('--noaidlabel'):
pt.upperleft_text(chip_text, color=kwargs.get('text_color', None))
use_title = not kwargs.get('notitle', False)
if use_title:
pt.set_title(chip_title_text)
if in_image:
gid = ibs.get_annot_gids(aid)
aid_list = ibs.get_image_aids(gid)
annotekw = viz_image.get_annot_annotations(
ibs, aid_list, sel_aids=[aid], draw_lbls=kwargs.get('draw_lbls', True))
# Put annotation centers in the axis
ph.set_plotdat(ax, 'annotation_bbox_list', annotekw['bbox_list'])
ph.set_plotdat(ax, 'aid_list', aid_list)
pt.viz_image2.draw_image_overlay(ax, **annotekw)
zoom_ = ut.get_argval('--zoom', type_=float, default=None)
if zoom_ is not None:
import vtool as vt
# Zoom into the chip for some image context
rotated_verts = ibs.get_annot_rotated_verts(aid)
bbox = ibs.get_annot_bboxes(aid)
#print(bbox)
#print(rotated_verts)
rotated_bbox = vt.bbox_from_verts(rotated_verts)
imgw, imgh = ibs.get_image_sizes(gid)
pad_factor = zoom_
pad_length = min(bbox[2], bbox[3]) * pad_factor
minx = max(rotated_bbox[0] - pad_length, 0)
miny = max(rotated_bbox[1] - pad_length, 0)
maxx = min((rotated_bbox[0] + rotated_bbox[2]) + pad_length, imgw)
maxy = min((rotated_bbox[1] + rotated_bbox[3]) + pad_length, imgh)
#maxy = imgh - maxy
#miny = imgh - miny
ax = pt.gca()
ax.set_xlim(minx, maxx)
ax.set_ylim(miny, maxy)
ax.invert_yaxis()
else:
ph.set_plotdat(ax, 'chipshape', chip.shape)
#if 'featweights' in vars() and 'color' in kwargs:
if weights is not None and weight_label is not None:
## HACK HACK HACK
if len(weights) > 0:
cb = pt.colorbar(weights, kwargs['color'])
cb.set_label(weight_label)
return fig, ax
def show_graph(infr, title, final=False, selected_edges=None):
if not VISUALIZE:
return
# TODO: rich colored text?
latest = '\n'.join(infr.latest_logs())
showkw = dict(
# fontsize=infr.graph.graph['fontsize'],
# fontname=infr.graph.graph['fontname'],
show_unreviewed_edges=True,
show_inferred_same=False,
show_inferred_diff=False,
outof=(len(infr.aids)),
# show_inferred_same=True,
# show_inferred_diff=True,
selected_edges=selected_edges,
show_labels=True,
simple_labels=True,
# show_recent_review=not final,
show_recent_review=False,
# splines=infr.graph.graph['splines'],
reposition=False,
# with_colorbar=True
)
verbose = infr.verbose
infr.verbose = 0
infr_ = infr.copy()
infr_ = infr
infr_.verbose = verbose
infr_.show(pickable=True, verbose=0, **showkw)
infr.verbose = verbose
# print('status ' + ut.repr4(infr_.status()))
# infr.show(**showkw)
ax = pt.gca()
pt.set_title(title, fontsize=20)
fig = pt.gcf()
fontsize = 22
if True:
# postprocess xlabel
lines = []
for line in latest.split('\n'):
if False and line.startswith('ORACLE ERROR'):
lines += ['ORACLE ERROR']
else:
lines += [line]
latest = '\n'.join(lines)
if len(lines) > 10:
fontsize = 16
if len(lines) > 12:
fontsize = 14
if len(lines) > 14:
fontsize = 12
if len(lines) > 18:
fontsize = 10
if len(lines) > 23:
fontsize = 8
if True:
pt.adjust_subplots(top=.95, left=0, right=1, bottom=.45, fig=fig)
ax.set_xlabel('\n' + latest)
xlabel = ax.get_xaxis().get_label()
xlabel.set_horizontalalignment('left')
# xlabel.set_x(.025)
xlabel.set_x(-.6)
# xlabel.set_fontname('CMU Typewriter Text')
xlabel.set_fontname('Inconsolata')
xlabel.set_fontsize(fontsize)
ax.set_aspect('equal')
# ax.xaxis.label.set_color('red')
from os.path import join
fpath = join(dpath, 'demo_{:04d}.png'.format(next(fig_counter)))
fig.savefig(
fpath,
dpi=300,
# transparent=True,
edgecolor='none')
# pt.save_figure(dpath=dpath, dpi=300)
infr.latest_logs()
def annotate_matches2(
ibs,
aid1,
aid2,
fm,
fs,
offset1=(0, 0),
offset2=(0, 0),
xywh2=None, # (0, 0, 0, 0),
xywh1=None, # (0, 0, 0, 0),
qreq_=None,
**kwargs):
"""
TODO: use this as the main function.
"""
if True:
aid_list = [aid1, aid2]
bbox_list = [xywh1, xywh2]
offset_list = [offset1, offset2]
name_fm_list = [fm]
name_fs_list = [fs]
return annotate_matches3(ibs,
aid_list,
bbox_list,
offset_list,
name_fm_list,
name_fs_list,
qreq_=qreq_,
**kwargs)
else:
# TODO: make sure all of this functionality is incorporated into annotate_matches3
in_image = kwargs.get('in_image', False)
show_query = kwargs.get('show_query', True)
draw_border = kwargs.get('draw_border', True)
draw_lbl = kwargs.get('draw_lbl', True)
notitle = kwargs.get('notitle', False)
truth = ibs.get_match_truth(aid1, aid2)
truth_color = vh.get_truth_color(truth)
# Build title
title = vh.get_query_text(ibs, None, aid2, truth, qaid=aid1, **kwargs)
# Build xlbl
ax = pt.gca()
ph.set_plotdat(ax, 'viztype', 'matches')
ph.set_plotdat(ax, 'qaid', aid1)
ph.set_plotdat(ax, 'aid1', aid1)
ph.set_plotdat(ax, 'aid2', aid2)
if draw_lbl:
name1, name2 = ibs.get_annot_names([aid1, aid2])
nid1, nid2 = ibs.get_annot_name_rowids([aid1, aid2],
distinguish_unknowns=False)
#lbl1 = repr(name1) + ' : ' + 'q' + vh.get_aidstrs(aid1)
#lbl2 = repr(name2) + ' : ' + vh.get_aidstrs(aid2)
lbl1_list = []
lbl2_list = []
if kwargs.get('show_aid', True):
lbl1_list.append('q' + vh.get_aidstrs(aid1))
lbl2_list.append(vh.get_aidstrs(aid2))
if kwargs.get('show_name', True):
lbl1_list.append(repr((name1)))
lbl2_list.append(repr((name2)))
if kwargs.get('show_nid', True):
lbl1_list.append(vh.get_nidstrs(nid1))
lbl2_list.append(vh.get_nidstrs(nid2))
lbl1 = ' : '.join(lbl1_list)
lbl2 = ' : '.join(lbl2_list)
else:
lbl1, lbl2 = None, None
if vh.NO_LBL_OVERRIDE:
title = ''
if not notitle:
pt.set_title(title, ax)
# Plot annotations over images
if in_image:
bbox1, bbox2 = vh.get_bboxes(ibs, [aid1, aid2], [offset1, offset2])
theta1, theta2 = ibs.get_annot_thetas([aid1, aid2])
# HACK!
if show_query:
pt.draw_bbox(bbox1,
bbox_color=pt.ORANGE,
lbl=lbl1,
theta=theta1)
bbox_color2 = truth_color if draw_border else pt.ORANGE
pt.draw_bbox(bbox2, bbox_color=bbox_color2, lbl=lbl2, theta=theta2)
else:
xy, w, h = pt.get_axis_xy_width_height(ax)
bbox2 = (xy[0], xy[1], w, h)
theta2 = 0
if xywh2 is None:
#xywh2 = (xy[0], xy[1], w, h)
# weird when sidebyside is off y seems to be inverted
xywh2 = (0, 0, w, h)
if not show_query and xywh1 is None:
data_config2 = (None if qreq_ is None else
qreq_.get_external_data_config2())
# FIXME, pass data in
kpts2 = ibs.get_annot_kpts([aid2], config2_=data_config2)[0]
#pt.draw_kpts2(kpts2.take(fm.T[1], axis=0))
# Draw any selected matches
#sm_kw = dict(rect=True, colors=pt.BLUE)
pt.plot_fmatch(None, xywh2, None, kpts2, fm, fs=fs, **kwargs)
if draw_border:
pt.draw_border(ax, truth_color, 4, offset=offset2)
if draw_lbl:
# Custom user lbl for chips 1 and 2
if show_query:
(x1, y1, w1, h1) = xywh1
pt.absolute_lbl(x1 + w1, y1, lbl1)
(x2, y2, w2, h2) = xywh2
pt.absolute_lbl(x2 + w2, y2, lbl2)
if True:
# No matches draw a red box
if fm is None or len(fm) == 0:
if draw_border:
pass
def annotate_matches3(ibs, aid_list, bbox_list, offset_list, name_fm_list,
name_fs_list, qreq_=None, **kwargs):
"""
TODO: use this as the main function.
"""
# TODO Use this function when you clean show_matches
in_image = kwargs.get('in_image', False)
#show_query = kwargs.get('show_query', True)
draw_border = kwargs.get('draw_border', True)
draw_lbl = kwargs.get('draw_lbl', True)
notitle = kwargs.get('notitle', False)
# List of annotation scores for each annot in the name
#printDBG('[viz] annotate_matches3()')
#truth = ibs.get_match_truth(aid1, aid2)
#name_equality = (
# np.array(ibs.get_annot_nids(aid_list[1:])) == ibs.get_annot_nids(aid_list[0])
#).tolist()
#truth = 1 if all(name_equality) else (2 if any(name_equality) else 0)
#truth_color = vh.get_truth_color(truth)
## Build title
#score = kwargs.pop('score', None)
#rawscore = kwargs.pop('rawscore', None)
#aid2_raw_rank = kwargs.pop('aid2_raw_rank', None)
#print(kwargs)
#title = vh.get_query_text(ibs, None, aid2, truth, qaid=aid1, **kwargs)
# Build xlbl
ax = pt.gca()
ph.set_plotdat(ax, 'viztype', 'multi_match')
ph.set_plotdat(ax, 'qaid', aid_list[0])
ph.set_plotdat(ax, 'num_matches', len(aid_list) - 1)
ph.set_plotdat(ax, 'aid_list', aid_list[1:])
for count, aid in enumerate(aid_list, start=1):
ph.set_plotdat(ax, 'aid%d' % (count,), aid)
#name_equality = (ibs.get_annot_nids(aid_list[0]) ==
# np.array(ibs.get_annot_nids(aid_list[1:])))
#truth = 1 if np.all(name_equality) else (2 if np.any(name_equality) else 0)
truth = get_multitruth(ibs, aid_list)
if any(ibs.is_aid_unknown(aid_list[1:])) or ibs.is_aid_unknown(aid_list[0]):
truth = ibs.const.TRUTH_UNKNOWN
truth_color = vh.get_truth_color(truth)
name_annot_scores = kwargs.get('name_annot_scores', None)
if len(aid_list) == 2:
# HACK; generalize to multple annots
title = vh.get_query_text(ibs, None, aid_list[1], truth, qaid=aid_list[0], **kwargs)
if not notitle:
pt.set_title(title, ax)
if draw_lbl:
# Build labels
nid_list = ibs.get_annot_nids(aid_list, distinguish_unknowns=False)
name_list = ibs.get_annot_names(aid_list)
lbls_list = [[] for _ in range(len(aid_list))]
if kwargs.get('show_name', False):
for count, (lbls, name) in enumerate(zip(lbls_list, name_list)):
lbls.append(ut.repr2((name)))
if kwargs.get('show_nid', True):
for count, (lbls, nid) in enumerate(zip(lbls_list, nid_list)):
# only label the first two images with nids
LABEL_ALL_NIDS = False
if count <= 1 or LABEL_ALL_NIDS:
#lbls.append(vh.get_nidstrs(nid))
lbls.append(('q' if count == 0 else '') + vh.get_nidstrs(nid))
if kwargs.get('show_aid', True):
for count, (lbls, aid) in enumerate(zip(lbls_list, aid_list)):
lbls.append(('q' if count == 0 else '') + vh.get_aidstrs(aid))
if (kwargs.get('show_annot_score', True) and
name_annot_scores is not None):
max_digits = kwargs.get('score_precision', None)
for (lbls, score) in zip(lbls_list[1:], name_annot_scores):
lbls.append(ut.num_fmt(score, max_digits=max_digits))
lbl_list = [' : '.join(lbls) for lbls in lbls_list]
else:
lbl_list = [None] * len(aid_list)
#pt.set_title(title, ax)
# Plot annotations over images
if in_image:
in_image_bbox_list = vh.get_bboxes(ibs, aid_list, offset_list)
in_image_theta_list = ibs.get_annot_thetas(aid_list)
# HACK!
#if show_query:
# pt.draw_bbox(bbox1, bbox_color=pt.ORANGE, lbl=lbl1, theta=theta1)
bbox_color = pt.ORANGE
bbox_color = truth_color if draw_border else pt.ORANGE
for bbox, theta, lbl in zip(in_image_bbox_list, in_image_theta_list,
lbl_list):
pt.draw_bbox(bbox, bbox_color=bbox_color, lbl=lbl, theta=theta)
pass
else:
xy, w, h = pt.get_axis_xy_width_height(ax)
#theta2 = 0
#if xywh2 is None:
# #xywh2 = (xy[0], xy[1], w, h)
# # weird when sidebyside is off y seems to be inverted
# xywh2 = (0, 0, w, h)
#if not show_query and xywh1 is None:
# data_config2 = None if qreq_ is None else
# qreq_.get_external_data_config2()
# kpts2 = ibs.get_annot_kpts([aid2], config2_=data_config2)[0]
# #pt.draw_kpts2(kpts2.take(fm.T[1], axis=0))
# # Draw any selected matches
# #sm_kw = dict(rect=True, colors=pt.BLUE)
# pt.plot_fmatch(None, xywh2, None, kpts2, fm, fs=fs, **kwargs)
#if draw_border:
# pt.draw_border(ax, truth_color, 4, offset=offset2)
if draw_border:
pt.draw_border(ax, color=truth_color, lw=4)
if draw_lbl:
# Custom user lbl for chips 1 and 2
#if show_query:
# (x1, y1, w1, h1) = xywh1
# pt.absolute_lbl(x1 + w1, y1, lbl1)
for bbox, lbl in zip(bbox_list, lbl_list):
(x, y, w, h) = bbox
pt.absolute_lbl(x + w, y, lbl)
# No matches draw a red box
if True:
no_matches = name_fm_list is None or all([True if fm is None else len(fm) == 0 for fm in name_fm_list])
if no_matches:
xy, w, h = pt.get_axis_xy_width_height(ax)
#axes_bbox = (xy[0], xy[1], w, h)
if draw_border:
pass
def annotate_matches2(ibs, aid1, aid2, fm, fs,
offset1=(0, 0),
offset2=(0, 0),
xywh2=None, # (0, 0, 0, 0),
xywh1=None, # (0, 0, 0, 0),
qreq_=None,
**kwargs):
"""
TODO: use this as the main function.
"""
if True:
aid_list = [aid1, aid2]
bbox_list = [xywh1, xywh2]
offset_list = [offset1, offset2]
name_fm_list = [fm]
name_fs_list = [fs]
return annotate_matches3(ibs, aid_list, bbox_list, offset_list, name_fm_list, name_fs_list, qreq_=qreq_, **kwargs)
else:
# TODO: make sure all of this functionality is incorporated into annotate_matches3
in_image = kwargs.get('in_image', False)
show_query = kwargs.get('show_query', True)
draw_border = kwargs.get('draw_border', True)
draw_lbl = kwargs.get('draw_lbl', True)
notitle = kwargs.get('notitle', False)
truth = ibs.get_match_truth(aid1, aid2)
truth_color = vh.get_truth_color(truth)
# Build title
title = vh.get_query_text(ibs, None, aid2, truth, qaid=aid1, **kwargs)
# Build xlbl
ax = pt.gca()
ph.set_plotdat(ax, 'viztype', 'matches')
ph.set_plotdat(ax, 'qaid', aid1)
ph.set_plotdat(ax, 'aid1', aid1)
ph.set_plotdat(ax, 'aid2', aid2)
if draw_lbl:
name1, name2 = ibs.get_annot_names([aid1, aid2])
nid1, nid2 = ibs.get_annot_name_rowids([aid1, aid2],
distinguish_unknowns=False)
#lbl1 = repr(name1) + ' : ' + 'q' + vh.get_aidstrs(aid1)
#lbl2 = repr(name2) + ' : ' + vh.get_aidstrs(aid2)
lbl1_list = []
lbl2_list = []
if kwargs.get('show_aid', True):
lbl1_list.append('q' + vh.get_aidstrs(aid1))
lbl2_list.append(vh.get_aidstrs(aid2))
if kwargs.get('show_name', True):
lbl1_list.append(repr((name1)))
lbl2_list.append(repr((name2)))
if kwargs.get('show_nid', True):
lbl1_list.append(vh.get_nidstrs(nid1))
lbl2_list.append(vh.get_nidstrs(nid2))
lbl1 = ' : '.join(lbl1_list)
lbl2 = ' : '.join(lbl2_list)
else:
lbl1, lbl2 = None, None
if vh.NO_LBL_OVERRIDE:
title = ''
if not notitle:
pt.set_title(title, ax)
# Plot annotations over images
if in_image:
bbox1, bbox2 = vh.get_bboxes(ibs, [aid1, aid2], [offset1, offset2])
theta1, theta2 = ibs.get_annot_thetas([aid1, aid2])
# HACK!
if show_query:
pt.draw_bbox(bbox1, bbox_color=pt.ORANGE, lbl=lbl1, theta=theta1)
bbox_color2 = truth_color if draw_border else pt.ORANGE
pt.draw_bbox(bbox2, bbox_color=bbox_color2, lbl=lbl2, theta=theta2)
else:
xy, w, h = pt.get_axis_xy_width_height(ax)
bbox2 = (xy[0], xy[1], w, h)
theta2 = 0
if xywh2 is None:
#xywh2 = (xy[0], xy[1], w, h)
# weird when sidebyside is off y seems to be inverted
xywh2 = (0, 0, w, h)
if not show_query and xywh1 is None:
data_config2 = (None if qreq_ is None else
qreq_.get_external_data_config2())
# FIXME, pass data in
kpts2 = ibs.get_annot_kpts([aid2], config2_=data_config2)[0]
#pt.draw_kpts2(kpts2.take(fm.T[1], axis=0))
# Draw any selected matches
#sm_kw = dict(rect=True, colors=pt.BLUE)
pt.plot_fmatch(None, xywh2, None, kpts2, fm, fs=fs, **kwargs)
if draw_border:
pt.draw_border(ax, truth_color, 4, offset=offset2)
if draw_lbl:
# Custom user lbl for chips 1 and 2
if show_query:
(x1, y1, w1, h1) = xywh1
pt.absolute_lbl(x1 + w1, y1, lbl1)
(x2, y2, w2, h2) = xywh2
pt.absolute_lbl(x2 + w2, y2, lbl2)
if True:
# No matches draw a red box
if fm is None or len(fm) == 0:
if draw_border:
pass
def show_name_matches(ibs, qaid, name_daid_list, name_fm_list, name_fs_list,
name_H1_list, name_featflag_list, qreq_=None, **kwargs):
"""
Called from chip_match.py
Args:
ibs (IBEISController): ibeis controller object
qaid (int): query annotation id
name_daid_list (list):
name_fm_list (list):
name_fs_list (list):
name_H1_list (list):
name_featflag_list (list):
qreq_ (QueryRequest): query request object with hyper-parameters(default = None)
Kwargs:
draw_fmatches, name_rank, fnum, pnum, colorbar_, nonvote_mode,
fastmode, show_matches, fs, fm_norm, lbl1, lbl2, rect, draw_border,
cmap, H1, H2, scale_factor1, scale_factor2, draw_pts, draw_ell,
draw_lines, show_nMatches, all_kpts, in_image, show_query, draw_lbl,
name_annot_scores, score, rawscore, aid2_raw_rank, show_name,
show_nid, show_aid, show_annot_score, show_truth, name_score,
show_name_score, show_name_rank, show_timedelta
CommandLine:
python -m ibeis.viz.viz_matches --exec-show_name_matches
python -m ibeis.viz.viz_matches --test-show_name_matches --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_matches import * # NOQA
>>> from ibeis.algo.hots import chip_match
>>> from ibeis.algo.hots import name_scoring
>>> import vtool as vt
>>> from ibeis.algo.hots import _pipeline_helpers as plh # NOQA
>>> import numpy as np
>>> func = chip_match.ChipMatch.show_single_namematch
>>> sourcecode = ut.get_func_sourcecode(func, stripdef=True, stripret=True,
>>> strip_docstr=True)
>>> setup = ut.regex_replace('viz_matches.show_name_matches', '#', sourcecode)
>>> homog = False
>>> print(ut.indent(setup, '>>> '))
>>> ibs, qreq_, cm_list = plh.testdata_post_sver('PZ_MTEST', qaid_list=[1])
>>> cm = cm_list[0]
>>> cm.score_nsum(qreq_)
>>> dnid = ibs.get_annot_nids(cm.qaid)
>>> # +--- COPIED SECTION
>>> locals_ = locals()
>>> var_list = ut.exec_func_src(
>>> func, locals_=locals_,
>>> sentinal='name_annot_scores = cm.annot_score_list.take(sorted_groupxs')
>>> exec(ut.execstr_dict(var_list))
>>> # L___ COPIED SECTION
>>> kwargs = {}
>>> show_name_matches(ibs, qaid, name_daid_list, name_fm_list,
>>> name_fs_list, name_h1_list, name_featflag_list,
>>> qreq_=qreq_, **kwargs)
>>> ut.quit_if_noshow()
>>> ut.show_if_requested()
"""
#print("SHOW NAME MATCHES")
#print(ut.repr2(kwargs, nl=True))
#from ibeis import constants as const
from ibeis import tag_funcs
draw_fmatches = kwargs.pop('draw_fmatches', True)
rchip1, kpts1 = get_query_annot_pair_info(ibs, qaid, qreq_, draw_fmatches)
rchip2_list, kpts2_list = get_data_annot_pair_info(ibs, name_daid_list,
qreq_, draw_fmatches)
fm_list = name_fm_list
fs_list = name_fs_list
featflag_list = name_featflag_list
offset_list, sf_list, bbox_list = show_multichip_match(rchip1, rchip2_list,
kpts1, kpts2_list,
fm_list, fs_list,
featflag_list,
**kwargs)
aid_list = [qaid] + name_daid_list
annotate_matches3(ibs, aid_list, bbox_list, offset_list, name_fm_list,
name_fs_list, qreq_=None, **kwargs)
ax = pt.gca()
title = vh.get_query_text(ibs, None, name_daid_list, False, qaid=qaid,
**kwargs)
pt.set_title(title, ax)
# Case tags
annotmatch_rowid_list = ibs.get_annotmatch_rowid_from_superkey(
[qaid] * len(name_daid_list), name_daid_list)
annotmatch_rowid_list = ut.filter_Nones(annotmatch_rowid_list)
tags_list = ibs.get_annotmatch_case_tags(annotmatch_rowid_list)
if not ut.get_argflag('--show'): # False:
tags_list = tag_funcs.consolodate_annotmatch_tags(tags_list)
tag_list = ut.unique_ordered(ut.flatten(tags_list))
name_rank = kwargs.get('name_rank', None)
truth = get_multitruth(ibs, aid_list)
xlabel = {1: 'Correct ID', 0: 'Incorrect ID', 2: 'Unknown ID'}[truth]
if False:
if name_rank is None:
xlabel = {1: 'Genuine', 0: 'Imposter', 2: 'Unknown'}[truth]
#xlabel = {1: 'True', 0: 'False', 2: 'Unknown'}[truth]
else:
if name_rank == 0:
xlabel = {
1: 'True Positive', 0: 'False Positive', 2: 'Unknown'}[truth]
else:
xlabel = {
1: 'False Negative', 0: 'True Negative', 2: 'Unknown'}[truth]
if len(tag_list) > 0:
xlabel += '\n' + ', '.join(tag_list)
pt.set_xlabel(xlabel)
return ax
def show_model(model, evidence={}, soft_evidence={}, **kwargs):
"""
References:
http://stackoverflow.com/questions/22207802/pygraphviz-networkx-set-node-level-or-layer
Ignore:
pkg-config --libs-only-L libcgraph
sudo apt-get install libgraphviz-dev -y
sudo apt-get install libgraphviz4 -y
# sudo apt-get install pkg-config
sudo apt-get install libgraphviz-dev
# pip install git+git://github.com/pygraphviz/pygraphviz.git
pip install pygraphviz
python -c "import pygraphviz; print(pygraphviz.__file__)"
sudo pip3 install pygraphviz --install-option="--include-path=/usr/include/graphviz" --install-option="--library-path=/usr/lib/graphviz/"
python3 -c "import pygraphviz; print(pygraphviz.__file__)"
"""
if ut.get_argval('--hackmarkov') or ut.get_argval('--hackjunc'):
draw_tree_model(model, **kwargs)
return
import plottool as pt
import networkx as netx
import matplotlib as mpl
fnum = pt.ensure_fnum(None)
fig = pt.figure(fnum=fnum, pnum=(3, 1, (slice(0, 2), 0)), doclf=True) # NOQA
#fig = pt.figure(fnum=fnum, pnum=(3, 2, (1, slice(1, 2))), doclf=True) # NOQA
ax = pt.gca()
var2_post = {f.variables[0]: f for f in kwargs.get('factor_list', [])}
netx_graph = (model)
#netx_graph.graph.setdefault('graph', {})['size'] = '"10,5"'
#netx_graph.graph.setdefault('graph', {})['rankdir'] = 'LR'
pos = get_hacked_pos(netx_graph)
#netx.pygraphviz_layout(netx_graph)
#pos = netx.pydot_layout(netx_graph, prog='dot')
#pos = netx.graphviz_layout(netx_graph)
drawkw = dict(pos=pos, ax=ax, with_labels=True, node_size=1500)
if evidence is not None:
node_colors = [
# (pt.TRUE_BLUE
(pt.WHITE
if node not in soft_evidence else
pt.LIGHT_PINK)
if node not in evidence
else pt.FALSE_RED
for node in netx_graph.nodes()]
for node in netx_graph.nodes():
cpd = model.var2_cpd[node]
if cpd.ttype == 'score':
pass
drawkw['node_color'] = node_colors
netx.draw(netx_graph, **drawkw)
show_probs = True
if show_probs:
textprops = {
'family': 'monospace',
'horizontalalignment': 'left',
#'horizontalalignment': 'center',
#'size': 12,
'size': 8,
}
textkw = dict(
xycoords='data', boxcoords='offset points', pad=0.25,
frameon=True, arrowprops=dict(arrowstyle='->'),
#bboxprops=dict(fc=node_attr['fillcolor']),
)
netx_nodes = model.nodes(data=True)
node_key_list = ut.get_list_column(netx_nodes, 0)
pos_list = ut.dict_take(pos, node_key_list)
artist_list = []
offset_box_list = []
for pos_, node in zip(pos_list, netx_nodes):
x, y = pos_
variable = node[0]
cpd = model.var2_cpd[variable]
prior_marg = (cpd if cpd.evidence is None else
cpd.marginalize(cpd.evidence, inplace=False))
prior_text = None
text = None
if variable in evidence:
text = cpd.variable_statenames[evidence[variable]]
elif variable in var2_post:
post_marg = var2_post[variable]
text = pgm_ext.make_factor_text(post_marg, 'post')
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
else:
if len(evidence) == 0 and len(soft_evidence) == 0:
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
show_post = kwargs.get('show_post', False)
show_prior = kwargs.get('show_prior', False)
show_prior = True
show_post = True
show_ev = (evidence is not None and variable in evidence)
if (show_post or show_ev) and text is not None:
offset_box = mpl.offsetbox.TextArea(text, textprops)
artist = mpl.offsetbox.AnnotationBbox(
# offset_box, (x + 5, y), xybox=(20., 5.),
offset_box, (x, y + 5), xybox=(4., 20.),
#box_alignment=(0, 0),
box_alignment=(.5, 0),
**textkw)
offset_box_list.append(offset_box)
artist_list.append(artist)
if show_prior and prior_text is not None:
offset_box2 = mpl.offsetbox.TextArea(prior_text, textprops)
artist2 = mpl.offsetbox.AnnotationBbox(
# offset_box2, (x - 5, y), xybox=(-20., -15.),
# offset_box2, (x, y - 5), xybox=(-15., -20.),
offset_box2, (x, y - 5), xybox=(-4, -20.),
#box_alignment=(1, 1),
box_alignment=(.5, 1),
**textkw)
offset_box_list.append(offset_box2)
artist_list.append(artist2)
for artist in artist_list:
ax.add_artist(artist)
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
num_annots = len(model.ttype2_cpds['name'])
if num_annots > 4:
ax.set_xlim((xmin - 40, xmax + 40))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(30, 7)
else:
ax.set_xlim((xmin - 42, xmax + 42))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(23, 7)
fig = pt.gcf()
title = 'num_names=%r, num_annots=%r' % (model.num_names, num_annots,)
map_assign = kwargs.get('map_assign', None)
#max_marginal_list = []
#for name, marginal in marginalized_joints.items():
# states = list(ut.iprod(*marginal.statenames))
# vals = marginal.values.ravel()
# x = vals.argmax()
# max_marginal_list += ['P(' + ', '.join(states[x]) + ') = ' + str(vals[x])]
# title += str(marginal)
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
map_assign, map_prob = top_assignments[0]
if map_assign is not None:
# title += '\nMAP=' + ut.repr2(map_assign, strvals=True)
title += '\nMAP: ' + map_assign + ' @' + '%.2f%%' % (100 * map_prob,)
if kwargs.get('show_title', True):
pt.set_figtitle(title, size=14)
#pt.set_xlabel()
def hack_fix_centeralign():
if textprops['horizontalalignment'] == 'center':
print('Fixing centeralign')
fig = pt.gcf()
fig.canvas.draw()
# Superhack for centered text. Fix bug in
# /usr/local/lib/python2.7/dist-packages/matplotlib/offsetbox.py
# /usr/local/lib/python2.7/dist-packages/matplotlib/text.py
for offset_box in offset_box_list:
offset_box.set_offset
z = offset_box._text.get_window_extent()
(z.x1 - z.x0) / 2
offset_box._text
T = offset_box._text.get_transform()
A = mpl.transforms.Affine2D()
A.clear()
A.translate((z.x1 - z.x0) / 2, 0)
offset_box._text.set_transform(T + A)
hack_fix_centeralign()
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(bin_labels, bin_vals, fnum=fnum, pnum=(3, 8, (2, slice(4, None))),
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob', xlabel='assignment')
pt.set_title('Assignment probabilities')
def show_model(model, evidence={}, soft_evidence={}, **kwargs):
"""
References:
http://stackoverflow.com/questions/22207802/pygraphviz-networkx-set-node-level-or-layer
Ignore:
pkg-config --libs-only-L libcgraph
sudo apt-get install libgraphviz-dev -y
sudo apt-get install libgraphviz4 -y
# sudo apt-get install pkg-config
sudo apt-get install libgraphviz-dev
# pip install git+git://github.com/pygraphviz/pygraphviz.git
pip install pygraphviz
python -c "import pygraphviz; print(pygraphviz.__file__)"
sudo pip3 install pygraphviz --install-option="--include-path=/usr/include/graphviz" --install-option="--library-path=/usr/lib/graphviz/"
python3 -c "import pygraphviz; print(pygraphviz.__file__)"
"""
if ut.get_argval('--hackmarkov') or ut.get_argval('--hackjunc'):
draw_tree_model(model, **kwargs)
return
import plottool as pt
import networkx as netx
import matplotlib as mpl
fnum = pt.ensure_fnum(None)
fig = pt.figure(fnum=fnum, pnum=(3, 1, (slice(0, 2), 0)),
doclf=True) # NOQA
#fig = pt.figure(fnum=fnum, pnum=(3, 2, (1, slice(1, 2))), doclf=True) # NOQA
ax = pt.gca()
var2_post = {f.variables[0]: f for f in kwargs.get('factor_list', [])}
netx_graph = (model)
#netx_graph.graph.setdefault('graph', {})['size'] = '"10,5"'
#netx_graph.graph.setdefault('graph', {})['rankdir'] = 'LR'
pos = get_hacked_pos(netx_graph)
#netx.nx_agraph.pygraphviz_layout(netx_graph)
#pos = netx.nx_agraph.pydot_layout(netx_graph, prog='dot')
#pos = netx.nx_agraph.graphviz_layout(netx_graph)
drawkw = dict(pos=pos, ax=ax, with_labels=True, node_size=1500)
if evidence is not None:
node_colors = [
# (pt.TRUE_BLUE
(pt.WHITE if node not in soft_evidence else pt.LIGHT_PINK)
if node not in evidence else pt.FALSE_RED
for node in netx_graph.nodes()
]
for node in netx_graph.nodes():
cpd = model.var2_cpd[node]
if cpd.ttype == 'score':
pass
drawkw['node_color'] = node_colors
netx.draw(netx_graph, **drawkw)
show_probs = True
if show_probs:
textprops = {
'family': 'monospace',
'horizontalalignment': 'left',
#'horizontalalignment': 'center',
#'size': 12,
'size': 8,
}
textkw = dict(
xycoords='data',
boxcoords='offset points',
pad=0.25,
framewidth=True,
arrowprops=dict(arrowstyle='->'),
#bboxprops=dict(fc=node_attr['fillcolor']),
)
netx_nodes = model.nodes(data=True)
node_key_list = ut.get_list_column(netx_nodes, 0)
pos_list = ut.dict_take(pos, node_key_list)
artist_list = []
offset_box_list = []
for pos_, node in zip(pos_list, netx_nodes):
x, y = pos_
variable = node[0]
cpd = model.var2_cpd[variable]
prior_marg = (cpd if cpd.evidence is None else cpd.marginalize(
cpd.evidence, inplace=False))
prior_text = None
text = None
if variable in evidence:
text = cpd.variable_statenames[evidence[variable]]
elif variable in var2_post:
post_marg = var2_post[variable]
text = pgm_ext.make_factor_text(post_marg, 'post')
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
else:
if len(evidence) == 0 and len(soft_evidence) == 0:
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
show_post = kwargs.get('show_post', False)
show_prior = kwargs.get('show_prior', False)
show_prior = True
show_post = True
show_ev = (evidence is not None and variable in evidence)
if (show_post or show_ev) and text is not None:
offset_box = mpl.offsetbox.TextArea(text, textprops)
artist = mpl.offsetbox.AnnotationBbox(
# offset_box, (x + 5, y), xybox=(20., 5.),
offset_box,
(x, y + 5),
xybox=(4., 20.),
#box_alignment=(0, 0),
box_alignment=(.5, 0),
**textkw)
offset_box_list.append(offset_box)
artist_list.append(artist)
if show_prior and prior_text is not None:
offset_box2 = mpl.offsetbox.TextArea(prior_text, textprops)
artist2 = mpl.offsetbox.AnnotationBbox(
# offset_box2, (x - 5, y), xybox=(-20., -15.),
# offset_box2, (x, y - 5), xybox=(-15., -20.),
offset_box2,
(x, y - 5),
xybox=(-4, -20.),
#box_alignment=(1, 1),
box_alignment=(.5, 1),
**textkw)
offset_box_list.append(offset_box2)
artist_list.append(artist2)
for artist in artist_list:
ax.add_artist(artist)
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
num_annots = len(model.ttype2_cpds['name'])
if num_annots > 4:
ax.set_xlim((xmin - 40, xmax + 40))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(30, 7)
else:
ax.set_xlim((xmin - 42, xmax + 42))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(23, 7)
fig = pt.gcf()
title = 'num_names=%r, num_annots=%r' % (
model.num_names,
num_annots,
)
map_assign = kwargs.get('map_assign', None)
#max_marginal_list = []
#for name, marginal in marginalized_joints.items():
# states = list(ut.iprod(*marginal.statenames))
# vals = marginal.values.ravel()
# x = vals.argmax()
# max_marginal_list += ['P(' + ', '.join(states[x]) + ') = ' + str(vals[x])]
# title += str(marginal)
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
map_assign, map_prob = top_assignments[0]
if map_assign is not None:
# title += '\nMAP=' + ut.repr2(map_assign, strvals=True)
title += '\nMAP: ' + map_assign + ' @' + '%.2f%%' % (
100 * map_prob, )
if kwargs.get('show_title', True):
pt.set_figtitle(title, size=14)
#pt.set_xlabel()
def hack_fix_centeralign():
if textprops['horizontalalignment'] == 'center':
print('Fixing centeralign')
fig = pt.gcf()
fig.canvas.draw()
# Superhack for centered text. Fix bug in
# /usr/local/lib/python2.7/dist-packages/matplotlib/offsetbox.py
# /usr/local/lib/python2.7/dist-packages/matplotlib/text.py
for offset_box in offset_box_list:
offset_box.set_offset
z = offset_box._text.get_window_extent()
(z.x1 - z.x0) / 2
offset_box._text
T = offset_box._text.get_transform()
A = mpl.transforms.Affine2D()
A.clear()
A.translate((z.x1 - z.x0) / 2, 0)
offset_box._text.set_transform(T + A)
hack_fix_centeralign()
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(
bin_labels,
bin_vals,
fnum=fnum,
pnum=(3, 8, (2, slice(4, None))),
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob',
xlabel='assignment')
pt.set_title('Assignment probabilities')
def annotate_matches3(ibs,
aid_list,
bbox_list,
offset_list,
name_fm_list,
name_fs_list,
qreq_=None,
**kwargs):
"""
TODO: use this as the main function.
"""
# TODO Use this function when you clean show_matches
in_image = kwargs.get('in_image', False)
#show_query = kwargs.get('show_query', True)
draw_border = kwargs.get('draw_border', True)
draw_lbl = kwargs.get('draw_lbl', True)
notitle = kwargs.get('notitle', False)
# List of annotation scores for each annot in the name
#printDBG('[viz] annotate_matches3()')
#truth = ibs.get_match_truth(aid1, aid2)
#name_equality = (
# np.array(ibs.get_annot_nids(aid_list[1:])) == ibs.get_annot_nids(aid_list[0])
#).tolist()
#truth = 1 if all(name_equality) else (2 if any(name_equality) else 0)
#truth_color = vh.get_truth_color(truth)
## Build title
#score = kwargs.pop('score', None)
#rawscore = kwargs.pop('rawscore', None)
#aid2_raw_rank = kwargs.pop('aid2_raw_rank', None)
#print(kwargs)
#title = vh.get_query_text(ibs, None, aid2, truth, qaid=aid1, **kwargs)
# Build xlbl
ax = pt.gca()
ph.set_plotdat(ax, 'viztype', 'multi_match')
ph.set_plotdat(ax, 'qaid', aid_list[0])
ph.set_plotdat(ax, 'num_matches', len(aid_list) - 1)
ph.set_plotdat(ax, 'aid_list', aid_list[1:])
for count, aid in enumerate(aid_list, start=1):
ph.set_plotdat(ax, 'aid%d' % (count, ), aid)
#name_equality = (ibs.get_annot_nids(aid_list[0]) ==
# np.array(ibs.get_annot_nids(aid_list[1:])))
#truth = 1 if np.all(name_equality) else (2 if np.any(name_equality) else 0)
truth = get_multitruth(ibs, aid_list)
if any(ibs.is_aid_unknown(aid_list[1:])) or ibs.is_aid_unknown(
aid_list[0]):
truth = ibs.const.TRUTH_UNKNOWN
truth_color = vh.get_truth_color(truth)
name_annot_scores = kwargs.get('name_annot_scores', None)
if len(aid_list) == 2:
# HACK; generalize to multple annots
title = vh.get_query_text(ibs,
None,
aid_list[1],
truth,
qaid=aid_list[0],
**kwargs)
if not notitle:
pt.set_title(title, ax)
if draw_lbl:
# Build labels
nid_list = ibs.get_annot_nids(aid_list, distinguish_unknowns=False)
name_list = ibs.get_annot_names(aid_list)
lbls_list = [[] for _ in range(len(aid_list))]
if kwargs.get('show_name', False):
for count, (lbls, name) in enumerate(zip(lbls_list, name_list)):
lbls.append(ut.repr2((name)))
if kwargs.get('show_nid', True):
for count, (lbls, nid) in enumerate(zip(lbls_list, nid_list)):
# only label the first two images with nids
LABEL_ALL_NIDS = False
if count <= 1 or LABEL_ALL_NIDS:
#lbls.append(vh.get_nidstrs(nid))
lbls.append(('q' if count == 0 else '') +
vh.get_nidstrs(nid))
if kwargs.get('show_aid', True):
for count, (lbls, aid) in enumerate(zip(lbls_list, aid_list)):
lbls.append(('q' if count == 0 else '') + vh.get_aidstrs(aid))
if (kwargs.get('show_annot_score', True)
and name_annot_scores is not None):
max_digits = kwargs.get('score_precision', None)
for (lbls, score) in zip(lbls_list[1:], name_annot_scores):
lbls.append(ut.num_fmt(score, max_digits=max_digits))
lbl_list = [' : '.join(lbls) for lbls in lbls_list]
else:
lbl_list = [None] * len(aid_list)
#pt.set_title(title, ax)
# Plot annotations over images
if in_image:
in_image_bbox_list = vh.get_bboxes(ibs, aid_list, offset_list)
in_image_theta_list = ibs.get_annot_thetas(aid_list)
# HACK!
#if show_query:
# pt.draw_bbox(bbox1, bbox_color=pt.ORANGE, lbl=lbl1, theta=theta1)
bbox_color = pt.ORANGE
bbox_color = truth_color if draw_border else pt.ORANGE
for bbox, theta, lbl in zip(in_image_bbox_list, in_image_theta_list,
lbl_list):
pt.draw_bbox(bbox, bbox_color=bbox_color, lbl=lbl, theta=theta)
pass
else:
xy, w, h = pt.get_axis_xy_width_height(ax)
#theta2 = 0
#if xywh2 is None:
# #xywh2 = (xy[0], xy[1], w, h)
# # weird when sidebyside is off y seems to be inverted
# xywh2 = (0, 0, w, h)
#if not show_query and xywh1 is None:
# data_config2 = None if qreq_ is None else
# qreq_.get_external_data_config2()
# kpts2 = ibs.get_annot_kpts([aid2], config2_=data_config2)[0]
# #pt.draw_kpts2(kpts2.take(fm.T[1], axis=0))
# # Draw any selected matches
# #sm_kw = dict(rect=True, colors=pt.BLUE)
# pt.plot_fmatch(None, xywh2, None, kpts2, fm, fs=fs, **kwargs)
#if draw_border:
# pt.draw_border(ax, truth_color, 4, offset=offset2)
if draw_border:
pt.draw_border(ax, color=truth_color, lw=4)
if draw_lbl:
# Custom user lbl for chips 1 and 2
#if show_query:
# (x1, y1, w1, h1) = xywh1
# pt.absolute_lbl(x1 + w1, y1, lbl1)
for bbox, lbl in zip(bbox_list, lbl_list):
(x, y, w, h) = bbox
pt.absolute_lbl(x + w, y, lbl)
# No matches draw a red box
if True:
no_matches = name_fm_list is None or all(
[True if fm is None else len(fm) == 0 for fm in name_fm_list])
if no_matches:
xy, w, h = pt.get_axis_xy_width_height(ax)
#axes_bbox = (xy[0], xy[1], w, h)
if draw_border:
pass
def do_infr_test(ccs, edges, new_edges):
"""
Creates a graph with `ccs` + `edges` and then adds `new_edges`
"""
# import networkx as nx
import plottool as pt
infr = demo.make_demo_infr(ccs, edges)
if ut.show_was_requested():
pt.qtensure()
# Preshow
fnum = 1
if ut.show_was_requested():
infr.set_node_attrs('shape', 'circle')
infr.show(pnum=(2, 1, 1),
fnum=fnum,
show_unreviewed_edges=True,
show_reviewed_cuts=True,
splines='spline',
show_inferred_diff=True,
groupby='name_label',
show_labels=True,
pickable=True)
pt.set_title('pre-review')
pt.gca().set_aspect('equal')
infr.set_node_attrs('pin', 'true')
# fig1 = pt.gcf()
# fig1.canvas.mpl_connect('pick_event', ut.partial(on_pick, infr=infr))
infr1 = infr
infr2 = infr.copy()
for new_edge in new_edges:
aid1, aid2, data = new_edge
evidence_decision = data['evidence_decision']
infr2.add_feedback((aid1, aid2), evidence_decision)
infr2.relabel_using_reviews(rectify=False)
infr2.apply_nondynamic_update()
# Postshow
if ut.show_was_requested():
infr2.show(pnum=(2, 1, 2),
fnum=fnum,
show_unreviewed_edges=True,
show_inferred_diff=True,
show_labels=True)
pt.gca().set_aspect('equal')
pt.set_title('post-review')
# fig2 = pt.gcf()
# if fig2 is not fig1:
# fig2.canvas.mpl_connect('pick_event', ut.partial(on_pick, infr=infr2))
class Checker(object):
"""
Asserts pre and post test properties of the graph
"""
def __init__(self, infr1, infr2):
self._errors = []
self.infr1 = infr1
self.infr2 = infr2
def __call__(self, infr, u, v, key, val, msg):
data = infr.get_nonvisual_edge_data((u, v))
if data is None:
assert infr.graph.has_edge(u, v), ('uv=%r, %r does not exist' %
(u, v))
got = data.get(key)
if got != val:
msg1 = 'key=%s %r!=%r, ' % (key, got, val)
errmsg = ''.join([
msg1, msg, '\nedge=',
ut.repr2((u, v)), '\n',
infr.repr_edge_data(data)
])
self._errors.append(errmsg)
def custom_precheck(self, func):
try:
func(self.infr1)
except AssertionError as ex:
self._errors.append(str(ex))
def after(self, errors=[]):
"""
Delays error reporting until after visualization
prints errors, then shows you the graph, then
finally if any errors were discovered they are raised
"""
errors = errors + self._errors
if errors:
ut.cprint('PRINTING %d FAILURE' % (len(errors)), 'red')
for msg in errors:
print(msg)
ut.cprint('HAD %d FAILURE' % (len(errors)), 'red')
if ut.show_was_requested():
pt.all_figures_tile(percent_w=.5)
ut.show_if_requested()
if errors:
raise AssertionError('There were errors')
check = Checker(infr1, infr2)
return infr1, infr2, check
def show_name_matches(ibs,
qaid,
name_daid_list,
name_fm_list,
name_fs_list,
name_H1_list,
name_featflag_list,
qreq_=None,
**kwargs):
"""
Called from chip_match.py
Args:
ibs (IBEISController): ibeis controller object
qaid (int): query annotation id
name_daid_list (list):
name_fm_list (list):
name_fs_list (list):
name_H1_list (list):
name_featflag_list (list):
qreq_ (QueryRequest): query request object with hyper-parameters(default = None)
Kwargs:
draw_fmatches, name_rank, fnum, pnum, colorbar_, nonvote_mode,
fastmode, show_matches, fs, fm_norm, lbl1, lbl2, rect, draw_border,
cmap, H1, H2, scale_factor1, scale_factor2, draw_pts, draw_ell,
draw_lines, show_nMatches, all_kpts, in_image, show_query, draw_lbl,
name_annot_scores, score, rawscore, aid2_raw_rank, show_name,
show_nid, show_aid, show_annot_score, show_truth, name_score,
show_name_score, show_name_rank, show_timedelta
CommandLine:
python -m ibeis.viz.viz_matches --exec-show_name_matches
python -m ibeis.viz.viz_matches --test-show_name_matches --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_matches import * # NOQA
>>> from ibeis.algo.hots import chip_match
>>> from ibeis.algo.hots import name_scoring
>>> import vtool as vt
>>> from ibeis.algo.hots import _pipeline_helpers as plh # NOQA
>>> import numpy as np
>>> func = chip_match.ChipMatch.show_single_namematch
>>> sourcecode = ut.get_func_sourcecode(func, stripdef=True, stripret=True,
>>> strip_docstr=True)
>>> setup = ut.regex_replace('viz_matches.show_name_matches', '#', sourcecode)
>>> homog = False
>>> print(ut.indent(setup, '>>> '))
>>> ibs, qreq_, cm_list = plh.testdata_post_sver('PZ_MTEST', qaid_list=[1])
>>> cm = cm_list[0]
>>> cm.score_nsum(qreq_)
>>> dnid = ibs.get_annot_nids(cm.qaid)
>>> # +--- COPIED SECTION
>>> locals_ = locals()
>>> var_list = ut.exec_func_src(
>>> func, locals_=locals_,
>>> sentinal='name_annot_scores = cm.annot_score_list.take(sorted_groupxs')
>>> exec(ut.execstr_dict(var_list))
>>> # L___ COPIED SECTION
>>> kwargs = {}
>>> show_name_matches(ibs, qaid, name_daid_list, name_fm_list,
>>> name_fs_list, name_h1_list, name_featflag_list,
>>> qreq_=qreq_, **kwargs)
>>> ut.quit_if_noshow()
>>> ut.show_if_requested()
"""
#print("SHOW NAME MATCHES")
#print(ut.repr2(kwargs, nl=True))
#from ibeis import constants as const
from ibeis import tag_funcs
draw_fmatches = kwargs.pop('draw_fmatches', True)
rchip1, kpts1 = get_query_annot_pair_info(ibs, qaid, qreq_, draw_fmatches)
rchip2_list, kpts2_list = get_data_annot_pair_info(ibs, name_daid_list,
qreq_, draw_fmatches)
fm_list = name_fm_list
fs_list = name_fs_list
featflag_list = name_featflag_list
offset_list, sf_list, bbox_list = show_multichip_match(
rchip1, rchip2_list, kpts1, kpts2_list, fm_list, fs_list,
featflag_list, **kwargs)
aid_list = [qaid] + name_daid_list
annotate_matches3(ibs,
aid_list,
bbox_list,
offset_list,
name_fm_list,
name_fs_list,
qreq_=None,
**kwargs)
ax = pt.gca()
title = vh.get_query_text(ibs,
None,
name_daid_list,
False,
qaid=qaid,
**kwargs)
pt.set_title(title, ax)
# Case tags
annotmatch_rowid_list = ibs.get_annotmatch_rowid_from_superkey(
[qaid] * len(name_daid_list), name_daid_list)
annotmatch_rowid_list = ut.filter_Nones(annotmatch_rowid_list)
tags_list = ibs.get_annotmatch_case_tags(annotmatch_rowid_list)
if not ut.get_argflag('--show'): # False:
tags_list = tag_funcs.consolodate_annotmatch_tags(tags_list)
tag_list = ut.unique_ordered(ut.flatten(tags_list))
name_rank = kwargs.get('name_rank', None)
truth = get_multitruth(ibs, aid_list)
xlabel = {1: 'Correct ID', 0: 'Incorrect ID', 2: 'Unknown ID'}[truth]
if False:
if name_rank is None:
xlabel = {1: 'Genuine', 0: 'Imposter', 2: 'Unknown'}[truth]
#xlabel = {1: 'True', 0: 'False', 2: 'Unknown'}[truth]
else:
if name_rank == 0:
xlabel = {
1: 'True Positive',
0: 'False Positive',
2: 'Unknown'
}[truth]
else:
xlabel = {
1: 'False Negative',
0: 'True Negative',
2: 'Unknown'
}[truth]
if len(tag_list) > 0:
xlabel += '\n' + ', '.join(tag_list)
pt.set_xlabel(xlabel)
return ax
def shadowform_probability():
""" its hearthstone, but whatev
probability of
raza + no shadowform on turn 5 +
probability of
raza + shadowform on turn 5 +
probability of
kazakus turn 4, raza turn 5, + no shadowform
"""
from scipy.stats import hypergeom
def p_badstuff_shadowform(turn=5, hand_size=3):
deck_size = 30
num_shadowform = 2
def prob_nohave_card_never_mulled(copies=2, hand_size=3):
deck_size = 30
prb = hypergeom(deck_size, copies, hand_size)
# P(initial_miss)
p_none_premul = prb.cdf(0)
# GIVEN that we mul our first 3 what is prob we still are unlucky
# P(miss_turn0 | initial_miss)
prb = hypergeom(deck_size - hand_size, copies, hand_size)
p_none_in_mul = prb.cdf(0)
# TODO: add constraints about 2 drops
# P(miss_turn0) = P(miss_turn0 | initial_miss) * P(initial_miss)
p_none_at_start = p_none_in_mul * p_none_premul
return p_none_at_start
def prob_nohave_card_always_mulled(copies=2, hand_size=3):
# probability of getting the card initially
p_none_premul = hypergeom(deck_size, copies, hand_size).cdf(0)
# probability of getting the card if everything is thrown away
# (TODO: factor in the probability that you need to keep something)
# for now its fine because if we keep shadowform the end calculation is fine
p_nohave_postmul_given_nohave = hypergeom(deck_size - hand_size, copies, hand_size).cdf(0)
# not necessary, but it shows the theory
p_nohave_postmul_given_had = 1
p_nohave_turn0 = (
p_nohave_postmul_given_nohave * p_none_premul + (1 - p_none_premul) * p_nohave_postmul_given_had
)
return p_nohave_turn0
def prob_nohave_by_turn(p_none_turn0, turn, copies, hand_size):
# P(miss_turnN | miss_mul)
p_none_turnN_given_mulmis = hypergeom(deck_size - hand_size, copies, turn).cdf(0)
# P(miss_turnN) = P(miss_turnN | miss_mul) P(miss_mul)
p_none_turnN = p_none_turnN_given_mulmis * p_none_turn0
return p_none_turnN
p_no_shadowform_on_turn0 = prob_nohave_card_never_mulled(copies=num_shadowform, hand_size=hand_size)
no_shadowform_turnN = prob_nohave_by_turn(p_no_shadowform_on_turn0, turn, num_shadowform, hand_size)
# Assume you always mul raza
p_noraza_initial = prob_nohave_card_always_mulled(copies=1, hand_size=hand_size)
p_noraza_turnN = prob_nohave_by_turn(p_noraza_initial, turn, copies=1, hand_size=hand_size)
p_raza_turnN = 1 - p_noraza_turnN
# probability that you have raza and no shadowform by turn 5
p_raza_and_noshadowform_turnN = p_raza_turnN * no_shadowform_turnN
return p_raza_and_noshadowform_turnN
import plottool as pt # NOQA
turns = list(range(0, 26))
probs = [p_badstuff_shadowform(turn, hand_size=3) for turn in turns]
pt.plot(turns, probs, label="on play")
probs = [p_badstuff_shadowform(turn, hand_size=4) for turn in turns]
pt.plot(turns, probs, label="with coin")
pt.set_xlabel("turn")
pt.set_ylabel("probability")
pt.set_title("Probability of Having Raza without a Shadowform")
pt.legend()
pt.gca().set_ylim(0, 1)
