def on_click(self, event):
print('[inter] clicked in expandable interact')
ax = event.inaxes
if ih.clicked_inside_axis(event):
func = pt.get_plotdat(ax, 'plot_func', None)
if ut.VERBOSE:
print('func = %r' % (func,))
if func is not None:
if ut.VERBOSE:
print('calling func = %r' % (func,))
fnum = pt.next_fnum()
#pt.figure(fnum=fnum)
pnum = (1, 1, 1)
index = pt.get_plotdat(ax, 'expandable_index', None)
if index is not None:
ishow_func = self.ishow_func_list[index]
else:
ishow_func = None
if ishow_func is not None:
inter = ishow_func(fnum=fnum)
else:
if check_if_subinteract(func):
inter = func(fnum=fnum)
inter.show_page()
elif hasattr(func, 'plot'):
inter = func
inter.start()
#func.plot(fnum=self.fnum, pnum=pnum)
else:
func(fnum=fnum, pnum=pnum)
#inter.show_page()
fig = pt.gcf()
pt.show_figure(fig)
def query_last_feature(self):
ibs = self.ibs
qaid = self.qaid
viz.show_nearest_descriptors(ibs, qaid, self.last_fx, pt.next_fnum(),
qreq_=self.qreq_, draw_chip=True)
fig3 = pt.gcf()
ih.connect_callback(fig3, 'button_press_event', self.on_click)
pt.draw()
def visualize(encoder):
import plottool as pt
# is_timedata = False
is_timedelta = True
p_xdata = encoder.p_xdata
xdata_domain = encoder.xdata_domain
# if is_timedata:
# xdata_domain_ = [ut.unixtime_to_datetimeobj(unixtime) for unixtime in xdata_domain]
if is_timedelta:
# xdata_domain_ = [ut.unixtime_to_timedelta(unixtime) for unixtime in xdata_domain]
pass
else:
pass
# xdata_domain_ = xdata_domain
pt.plot_probabilities([p_xdata], [""], xdata=xdata_domain)
ax = pt.gca()
# HISTOGRAM
if False:
X = encoder.support["X"]
xdata = X[~np.isnan(X)]
n, bins, patches = pt.plt.hist(xdata, 1000)
ax.set_xlabel("xdata")
if is_timedelta:
ax.set_xlabel("Time Delta")
ax.set_title("Timedelta distribution")
def timeTicks(x, pos):
import datetime
d = datetime.timedelta(seconds=x)
return str(d)
import matplotlib as mpl
formatter = mpl.ticker.FuncFormatter(timeTicks)
ax.xaxis.set_major_formatter(formatter)
pt.gcf().autofmt_xdate()
def visualize(encoder):
import plottool as pt
#is_timedata = False
is_timedelta = True
p_xdata = encoder.p_xdata
xdata_domain = encoder.xdata_domain
#if is_timedata:
# xdata_domain_ = [ut.unixtime_to_datetimeobj(unixtime) for unixtime in xdata_domain]
if is_timedelta:
#xdata_domain_ = [ut.unixtime_to_timedelta(unixtime) for unixtime in xdata_domain]
pass
else:
pass
#xdata_domain_ = xdata_domain
pt.plot_probabilities([p_xdata], [''], xdata=xdata_domain)
ax = pt.gca()
# HISTOGRAM
if False:
X = encoder.support['X']
xdata = X[~np.isnan(X)]
n, bins, patches = pt.plt.hist(xdata, 1000)
ax.set_xlabel('xdata')
if is_timedelta:
ax.set_xlabel('Time Delta')
ax.set_title('Timedelta distribution')
def timeTicks(x, pos):
import datetime
d = datetime.timedelta(seconds=x)
return str(d)
import matplotlib as mpl
formatter = mpl.ticker.FuncFormatter(timeTicks)
ax.xaxis.set_major_formatter(formatter)
pt.gcf().autofmt_xdate()
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)
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)
def show_graph(infr,
graph=None,
use_image=False,
update_attrs=True,
with_colorbar=False,
pnum=(1, 1, 1),
zoomable=True,
pickable=False,
**kwargs):
r"""
Args:
infr (?):
graph (None): (default = None)
use_image (bool): (default = False)
update_attrs (bool): (default = True)
with_colorbar (bool): (default = False)
pnum (tuple): plot number(default = (1, 1, 1))
zoomable (bool): (default = True)
pickable (bool): (de = False)
**kwargs: verbose, with_labels, fnum, layout, ax, pos, img_dict,
title, layoutkw, framewidth, modify_ax, as_directed,
hacknoedge, hacknode, node_labels, arrow_width, fontsize,
fontweight, fontname, fontfamilty, fontproperties
CommandLine:
python -m ibeis.algo.graph.mixin_viz GraphVisualization.show_graph --show
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.graph.mixin_viz import * # NOQA
>>> from ibeis.algo.graph import demo
>>> import plottool as pt
>>> infr = demo.demodata_infr(ccs=ut.estarmap(
>>> range, [(1, 6), (6, 10), (10, 13), (13, 15), (15, 16),
>>> (17, 20)]))
>>> pnum_ = pt.make_pnum_nextgen(nRows=1, nCols=3)
>>> infr.show_graph(show_cand=True, simple_labels=True, pickable=True, fnum=1, pnum=pnum_())
>>> infr.add_feedback((1, 5), INCMP)
>>> infr.add_feedback((14, 18), INCMP)
>>> infr.refresh_candidate_edges()
>>> infr.show_graph(show_cand=True, simple_labels=True, pickable=True, fnum=1, pnum=pnum_())
>>> infr.add_feedback((17, 18), NEGTV) # add inconsistency
>>> infr.apply_nondynamic_update()
>>> infr.show_graph(show_cand=True, simple_labels=True, pickable=True, fnum=1, pnum=pnum_())
>>> ut.show_if_requested()
"""
import plottool as pt
if graph is None:
graph = infr.graph
# kwargs['fontsize'] = kwargs.get('fontsize', 8)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# default_update_kw = ut.get_func_kwargs(infr.update_visual_attrs)
# update_kw = ut.update_existing(default_update_kw, kwargs)
# infr.update_visual_attrs(**update_kw)
if update_attrs:
infr.update_visual_attrs(graph=graph, **kwargs)
verbose = kwargs.pop('verbose', infr.verbose)
pt.show_nx(graph,
layout='custom',
as_directed=False,
modify_ax=False,
use_image=use_image,
pnum=pnum,
verbose=verbose,
**kwargs)
if zoomable:
pt.zoom_factory()
pt.pan_factory(pt.gca())
# if with_colorbar:
# # Draw a colorbar
# _normal_ticks = np.linspace(0, 1, num=11)
# _normal_scores = np.linspace(0, 1, num=500)
# _normal_colors = infr.get_colored_weights(_normal_scores)
# cb = pt.colorbar(_normal_scores, _normal_colors, lbl='weights',
# ticklabels=_normal_ticks)
# # point to threshold location
# thresh = None
# if thresh is not None:
# xy = (1, thresh)
# xytext = (2.5, .3 if thresh < .5 else .7)
# cb.ax.annotate('threshold', xy=xy, xytext=xytext,
# arrowprops=dict(
# alpha=.5, fc="0.6",
# connectionstyle="angle3,angleA=90,angleB=0"),)
# infr.graph
if graph.graph.get('dark_background', None):
pt.dark_background(force=True)
if pickable:
fig = pt.gcf()
fig.canvas.mpl_connect('pick_event', ut.partial(on_pick,
infr=infr))
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 draw_bayesian_model(model,
evidence={},
soft_evidence={},
fnum=None,
pnum=None,
**kwargs):
from pgmpy.models import BayesianModel
if not isinstance(model, BayesianModel):
model = model.to_bayesian_model()
import plottool as pt
import networkx as nx
kwargs = kwargs.copy()
factor_list = kwargs.pop('factor_list', [])
ttype_colors, ttype_scalars = make_colorcodes(model)
textprops = {
'horizontalalignment': 'left',
'family': 'monospace',
'size': 8,
}
# build graph attrs
tup = get_node_viz_attrs(model, evidence, soft_evidence, factor_list,
ttype_colors, **kwargs)
node_color, pos_list, pos_dict, takws = tup
# draw graph
has_infered = evidence or 'factor_list' in kwargs
if False:
fig = pt.figure(fnum=fnum, pnum=pnum, doclf=True) # NOQA
ax = pt.gca()
drawkw = dict(pos=pos_dict,
ax=ax,
with_labels=True,
node_size=1100,
node_color=node_color)
nx.draw(model, **drawkw)
else:
# BE VERY CAREFUL
if 1:
graph = model.copy()
graph.__class__ = nx.DiGraph
graph.graph['groupattrs'] = ut.ddict(dict)
#graph = model.
if getattr(graph, 'ttype2_cpds', None) is not None:
# Add invis edges and ttype groups
for ttype in model.ttype2_cpds.keys():
ttype_cpds = model.ttype2_cpds[ttype]
# use defined ordering
ttype_nodes = ut.list_getattr(ttype_cpds, 'variable')
# ttype_nodes = sorted(ttype_nodes)
invis_edges = list(ut.itertwo(ttype_nodes))
graph.add_edges_from(invis_edges)
nx.set_edge_attributes(
graph, 'style',
{edge: 'invis'
for edge in invis_edges})
nx.set_node_attributes(
graph, 'groupid',
{node: ttype
for node in ttype_nodes})
graph.graph['groupattrs'][ttype]['rank'] = 'same'
graph.graph['groupattrs'][ttype]['cluster'] = False
else:
graph = model
pt.show_nx(graph,
layout_kw={'prog': 'dot'},
fnum=fnum,
pnum=pnum,
verbose=0)
pt.zoom_factory()
fig = pt.gcf()
ax = pt.gca()
pass
hacks = [
pt.draw_text_annotations(textprops=textprops, **takw) for takw in takws
if takw
]
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
if 'name' in model.ttype2_template:
num_names = len(model.ttype2_template['name'].basis)
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)
title = 'num_names=%r, num_annots=%r' % (
num_names,
num_annots,
)
else:
title = ''
map_assign = kwargs.get('map_assign', None)
def word_insert(text):
return '' if len(text) == 0 else text + ' '
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 += '\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()
if has_infered:
# Hack in colorbars
# 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)
keys = ['name', 'score']
locs = ['left', 'right']
for key, loc in zip(keys, locs):
if key in ttype_colors:
basis = model.ttype2_template[key].basis
# scalars =
colors = ttype_colors[key]
scalars = ttype_scalars[key]
pt.colorbar(scalars,
colors,
lbl=key,
ticklabels=basis,
ticklocation=loc)
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 ishow_chip(ibs, aid, fnum=2, fx=None, dodraw=True, config2_=None,
ischild=False, **kwargs):
r"""
# TODO:
split into two interactions
interact chip and interact chip features
Args:
ibs (IBEISController): ibeis controller object
aid (int): annotation id
fnum (int): figure number
fx (None):
CommandLine:
python -m ibeis.viz.interact.interact_chip --test-ishow_chip --show
python -m ibeis.viz.interact.interact_chip --test-ishow_chip --show --aid 2
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.interact.interact_chip import * # NOQA
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> aid = ut.get_argval('--aid', type_=int, default=1)
>>> fnum = 2
>>> fx = None
>>> # execute function
>>> dodraw = ut.show_was_requested()
>>> result = ishow_chip(ibs, aid, fnum, fx, dodraw)
>>> # verify results
>>> pt.show_if_requested()
>>> print(result)
"""
fnum = pt.ensure_fnum(fnum)
vh.ibsfuncs.assert_valid_aids(ibs, (aid,))
# TODO: Reconcile this with interact keypoints.
# Preferably this will call that but it will set some fancy callbacks
if not ischild:
fig = ih.begin_interaction('chip', fnum)
else:
fig = pt.gcf()
#fig = pt.figure(fnum=fnum, pnum=pnum)
# Get chip info (make sure get_chips is called first)
#mode_ptr = [1]
mode_ptr = [0]
def _select_fxth_kpt(fx):
# Get the fx-th keypiont
chip = ibs.get_annot_chips(aid, config2_=config2_)
kp = ibs.get_annot_kpts(aid, config2_=config2_)[fx]
sift = ibs.get_annot_vecs(aid, config2_=config2_)[fx]
# Draw chip + keypoints + highlighted plots
_chip_view(pnum=(2, 1, 1), sel_fx=fx)
#ishow_chip(ibs, aid, fnum=None, fx=fx, config2_=config2_, **kwargs)
# Draw the selected feature plots
nRows, nCols, px = (2, 3, 3)
draw_feat_row(chip, fx, kp, sift, fnum, nRows, nCols, px, None)
def _chip_view(mode=0, pnum=(1, 1, 1), **kwargs):
print('... _chip_view mode=%r' % mode_ptr[0])
kwargs['ell'] = mode_ptr[0] == 1
kwargs['pts'] = mode_ptr[0] == 2
if not ischild:
df2.figure(fnum=fnum, pnum=pnum, docla=True, doclf=True)
# Toggle no keypoints view
viz.show_chip(ibs, aid, fnum=fnum, pnum=pnum, config2_=config2_,
**kwargs)
df2.set_figtitle('Chip View')
def _on_chip_click(event):
print('[inter] clicked chip')
ax, x, y = event.inaxes, event.xdata, event.ydata
if ih.clicked_outside_axis(event):
if not ischild:
print('... out of axis')
mode_ptr[0] = (mode_ptr[0] + 1) % 3
_chip_view(**kwargs)
else:
if event.button == 3: # right-click
import guitool
#from ibeis.viz.interact import interact_chip
height = fig.canvas.geometry().height()
qpoint = guitool.newQPoint(event.x, height - event.y)
refresh_func = partial(_chip_view, **kwargs)
callback_list = build_annot_context_options(
ibs, aid, refresh_func=refresh_func,
with_interact_chip=False,
config2_=config2_)
qwin = fig.canvas
guitool.popup_menu(qwin, qpoint, callback_list)
#interact_chip.show_annot_context_menu(
# ibs, aid, fig.canvas, qpoint, refresh_func=refresh_func,
# with_interact_chip=False, config2_=config2_)
else:
viztype = vh.get_ibsdat(ax, 'viztype')
print('[ic] viztype=%r' % viztype)
if viztype == 'chip' and event.key == 'shift':
_chip_view(**kwargs)
ih.disconnect_callback(fig, 'button_press_event')
elif viztype == 'chip':
kpts = ibs.get_annot_kpts(aid, config2_=config2_)
if len(kpts) > 0:
fx = vt.nearest_point(
x, y, kpts, conflict_mode='next')[0]
print('... clicked fx=%r' % fx)
_select_fxth_kpt(fx)
else:
print('... len(kpts) == 0')
elif viztype in ['warped', 'unwarped']:
fx = vh.get_ibsdat(ax, 'fx')
if fx is not None and viztype == 'warped':
viz.show_keypoint_gradient_orientations(
ibs, aid, fx, fnum=df2.next_fnum())
else:
print('...Unknown viztype: %r' % viztype)
viz.draw()
# Draw without keypoints the first time
if fx is not None:
_select_fxth_kpt(fx)
else:
_chip_view(**kwargs)
if dodraw:
viz.draw()
if not ischild:
ih.connect_callback(fig, 'button_press_event', _on_chip_click)
def draw_bayesian_model(model, evidence={}, soft_evidence={}, fnum=None,
pnum=None, **kwargs):
from pgmpy.models import BayesianModel
if not isinstance(model, BayesianModel):
model = model.to_bayesian_model()
import plottool as pt
import networkx as nx
kwargs = kwargs.copy()
factor_list = kwargs.pop('factor_list', [])
ttype_colors, ttype_scalars = make_colorcodes(model)
textprops = {
'horizontalalignment': 'left', 'family': 'monospace', 'size': 8, }
# build graph attrs
tup = get_node_viz_attrs(
model, evidence, soft_evidence, factor_list, ttype_colors, **kwargs)
node_color, pos_list, pos_dict, takws = tup
# draw graph
has_infered = evidence or 'factor_list' in kwargs
if False:
fig = pt.figure(fnum=fnum, pnum=pnum, doclf=True) # NOQA
ax = pt.gca()
drawkw = dict(pos=pos_dict, ax=ax, with_labels=True, node_size=1100,
node_color=node_color)
nx.draw(model, **drawkw)
else:
# BE VERY CAREFUL
if 1:
graph = model.copy()
graph.__class__ = nx.DiGraph
graph.graph['groupattrs'] = ut.ddict(dict)
#graph = model.
if getattr(graph, 'ttype2_cpds', None) is not None:
# Add invis edges and ttype groups
for ttype in model.ttype2_cpds.keys():
ttype_cpds = model.ttype2_cpds[ttype]
# use defined ordering
ttype_nodes = ut.list_getattr(ttype_cpds, 'variable')
# ttype_nodes = sorted(ttype_nodes)
invis_edges = list(ut.itertwo(ttype_nodes))
graph.add_edges_from(invis_edges)
nx.set_edge_attributes(graph, 'style', {edge: 'invis' for edge in invis_edges})
nx.set_node_attributes(graph, 'groupid', {node: ttype for node in ttype_nodes})
graph.graph['groupattrs'][ttype]['rank'] = 'same'
graph.graph['groupattrs'][ttype]['cluster'] = False
else:
graph = model
pt.show_nx(graph, layout_kw={'prog': 'dot'}, fnum=fnum, pnum=pnum, verbose=0)
pt.zoom_factory()
fig = pt.gcf()
ax = pt.gca()
pass
hacks = [pt.draw_text_annotations(textprops=textprops, **takw)
for takw in takws if takw]
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
if 'name' in model.ttype2_template:
num_names = len(model.ttype2_template['name'].basis)
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)
title = 'num_names=%r, num_annots=%r' % (num_names, num_annots,)
else:
title = ''
map_assign = kwargs.get('map_assign', None)
def word_insert(text):
return '' if len(text) == 0 else text + ' '
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 += '\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()
if has_infered:
# Hack in colorbars
# 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)
keys = ['name', 'score']
locs = ['left', 'right']
for key, loc in zip(keys, locs):
if key in ttype_colors:
basis = model.ttype2_template[key].basis
# scalars =
colors = ttype_colors[key]
scalars = ttype_scalars[key]
pt.colorbar(scalars, colors, lbl=key, ticklabels=basis,
ticklocation=loc)
def get_injured_sharks():
"""
>>> from ibeis.scripts.getshark import * # NOQA
"""
import requests
url = 'http://www.whaleshark.org/getKeywordImages.jsp'
resp = requests.get(url)
assert resp.status_code == 200
keywords = resp.json()['keywords']
key_list = ut.take_column(keywords, 'indexName')
key_to_nice = {k['indexName']: k['readableName'] for k in keywords}
injury_patterns = [
'injury',
'net',
'hook',
'trunc',
'damage',
'scar',
'nicks',
'bite',
]
injury_keys = [
key for key in key_list if any([pat in key for pat in injury_patterns])
]
noninjury_keys = ut.setdiff(key_list, injury_keys)
injury_nice = ut.lmap(lambda k: key_to_nice[k], injury_keys) # NOQA
noninjury_nice = ut.lmap(lambda k: key_to_nice[k], noninjury_keys) # NOQA
key_list = injury_keys
keyed_images = {}
for key in ut.ProgIter(key_list, lbl='reading index', bs=True):
key_url = url + '?indexName={indexName}'.format(indexName=key)
key_resp = requests.get(key_url)
assert key_resp.status_code == 200
key_imgs = key_resp.json()['images']
keyed_images[key] = key_imgs
key_hist = {key: len(imgs) for key, imgs in keyed_images.items()}
key_hist = ut.sort_dict(key_hist, 'vals')
print(ut.repr3(key_hist))
nice_key_hist = ut.map_dict_keys(lambda k: key_to_nice[k], key_hist)
nice_key_hist = ut.sort_dict(nice_key_hist, 'vals')
print(ut.repr3(nice_key_hist))
key_to_urls = {
key: ut.take_column(vals, 'url')
for key, vals in keyed_images.items()
}
overlaps = {}
import itertools
overlap_img_list = []
for k1, k2 in itertools.combinations(key_to_urls.keys(), 2):
overlap_imgs = ut.isect(key_to_urls[k1], key_to_urls[k2])
num_overlap = len(overlap_imgs)
overlaps[(k1, k2)] = num_overlap
overlaps[(k1, k1)] = len(key_to_urls[k1])
if num_overlap > 0:
#print('[%s][%s], overlap=%r' % (k1, k2, num_overlap))
overlap_img_list.extend(overlap_imgs)
all_img_urls = list(set(ut.flatten(key_to_urls.values())))
num_all = len(all_img_urls) # NOQA
print('num_all = %r' % (num_all, ))
# Determine super-categories
categories = ['nicks', 'scar', 'trunc']
# Force these keys into these categories
key_to_cat = {'scarbite': 'other_injury'}
cat_to_keys = ut.ddict(list)
for key in key_to_urls.keys():
flag = 1
if key in key_to_cat:
cat = key_to_cat[key]
cat_to_keys[cat].append(key)
continue
for cat in categories:
if cat in key:
cat_to_keys[cat].append(key)
flag = 0
if flag:
cat = 'other_injury'
cat_to_keys[cat].append(key)
cat_urls = ut.ddict(list)
for cat, keys in cat_to_keys.items():
for key in keys:
cat_urls[cat].extend(key_to_urls[key])
cat_hist = {}
for cat in list(cat_urls.keys()):
cat_urls[cat] = list(set(cat_urls[cat]))
cat_hist[cat] = len(cat_urls[cat])
print(ut.repr3(cat_to_keys))
print(ut.repr3(cat_hist))
key_to_cat = dict([(val, key) for key, vals in cat_to_keys.items()
for val in vals])
#ingestset = {
# '__class__': 'ImageSet',
# 'images': ut.ddict(dict)
#}
#for key, key_imgs in keyed_images.items():
# for imgdict in key_imgs:
# url = imgdict['url']
# encid = imgdict['correspondingEncounterNumber']
# # Make structure
# encdict = encounters[encid]
# encdict['__class__'] = 'Encounter'
# imgdict = ut.delete_keys(imgdict.copy(), ['correspondingEncounterNumber'])
# imgdict['__class__'] = 'Image'
# cat = key_to_cat[key]
# annotdict = {'relative_bbox': [.01, .01, .98, .98], 'tags': [cat, key]}
# annotdict['__class__'] = 'Annotation'
# # Ensure structures exist
# encdict['images'] = encdict.get('images', [])
# imgdict['annots'] = imgdict.get('annots', [])
# # Add an image to this encounter
# encdict['images'].append(imgdict)
# # Add an annotation to this image
# imgdict['annots'].append(annotdict)
##http://springbreak.wildbook.org/rest/org.ecocean.Encounter/1111
#get_enc_url = 'http://www.whaleshark.org/rest/org.ecocean.Encounter/%s' % (encid,)
#resp = requests.get(get_enc_url)
#print(ut.repr3(encdict))
#print(ut.repr3(encounters))
# Download the files to the local disk
#fpath_list =
all_urls = ut.unique(
ut.take_column(
ut.flatten(
ut.dict_subset(keyed_images,
ut.flatten(cat_to_keys.values())).values()),
'url'))
dldir = ut.truepath('~/tmpsharks')
from os.path import commonprefix, basename # NOQA
prefix = commonprefix(all_urls)
suffix_list = [url_[len(prefix):] for url_ in all_urls]
fname_list = [suffix.replace('/', '--') for suffix in suffix_list]
fpath_list = []
for url, fname in ut.ProgIter(zip(all_urls, fname_list),
lbl='downloading imgs',
freq=1):
fpath = ut.grab_file_url(url,
download_dir=dldir,
fname=fname,
verbose=False)
fpath_list.append(fpath)
# Make sure we keep orig info
#url_to_keys = ut.ddict(list)
url_to_info = ut.ddict(dict)
for key, imgdict_list in keyed_images.items():
for imgdict in imgdict_list:
url = imgdict['url']
info = url_to_info[url]
for k, v in imgdict.items():
info[k] = info.get(k, [])
info[k].append(v)
info['keys'] = info.get('keys', [])
info['keys'].append(key)
#url_to_keys[url].append(key)
info_list = ut.take(url_to_info, all_urls)
for info in info_list:
if len(set(info['correspondingEncounterNumber'])) > 1:
assert False, 'url with two different encounter nums'
# Combine duplicate tags
hashid_list = [
ut.get_file_uuid(fpath_, stride=8)
for fpath_ in ut.ProgIter(fpath_list, bs=True)
]
groupxs = ut.group_indices(hashid_list)[1]
# Group properties by duplicate images
#groupxs = [g for g in groupxs if len(g) > 1]
fpath_list_ = ut.take_column(ut.apply_grouping(fpath_list, groupxs), 0)
url_list_ = ut.take_column(ut.apply_grouping(all_urls, groupxs), 0)
info_list_ = [
ut.map_dict_vals(ut.flatten, ut.dict_accum(*info_))
for info_ in ut.apply_grouping(info_list, groupxs)
]
encid_list_ = [
ut.unique(info_['correspondingEncounterNumber'])[0]
for info_ in info_list_
]
keys_list_ = [ut.unique(info_['keys']) for info_ in info_list_]
cats_list_ = [ut.unique(ut.take(key_to_cat, keys)) for keys in keys_list_]
clist = ut.ColumnLists({
'gpath': fpath_list_,
'url': url_list_,
'encid': encid_list_,
'key': keys_list_,
'cat': cats_list_,
})
#for info_ in ut.apply_grouping(info_list, groupxs):
# info = ut.dict_accum(*info_)
# info = ut.map_dict_vals(ut.flatten, info)
# x = ut.unique(ut.flatten(ut.dict_accum(*info_)['correspondingEncounterNumber']))
# if len(x) > 1:
# info = info.copy()
# del info['keys']
# print(ut.repr3(info))
flags = ut.lmap(ut.fpath_has_imgext, clist['gpath'])
clist = clist.compress(flags)
import ibeis
ibs = ibeis.opendb('WS_Injury', allow_newdir=True)
gid_list = ibs.add_images(clist['gpath'])
clist['gid'] = gid_list
failed_flags = ut.flag_None_items(clist['gid'])
print('# failed %s' % (sum(failed_flags)), )
passed_flags = ut.not_list(failed_flags)
clist = clist.compress(passed_flags)
ut.assert_all_not_None(clist['gid'])
#ibs.get_image_uris_original(clist['gid'])
ibs.set_image_uris_original(clist['gid'], clist['url'], overwrite=True)
#ut.zipflat(clist['cat'], clist['key'])
if False:
# Can run detection instead
clist['tags'] = ut.zipflat(clist['cat'])
aid_list = ibs.use_images_as_annotations(clist['gid'],
adjust_percent=0.01,
tags_list=clist['tags'])
aid_list
import plottool as pt
from ibeis import core_annots
pt.qt4ensure()
#annots = ibs.annots()
#aids = [1, 2]
#ibs.depc_annot.get('hog', aids , 'hog')
#ibs.depc_annot.get('chip', aids, 'img')
for aid in ut.InteractiveIter(ibs.get_valid_aids()):
hogs = ibs.depc_annot.d.get_hog_hog([aid])
chips = ibs.depc_annot.d.get_chips_img([aid])
chip = chips[0]
hogimg = core_annots.make_hog_block_image(hogs[0])
pt.clf()
pt.imshow(hogimg, pnum=(1, 2, 1))
pt.imshow(chip, pnum=(1, 2, 2))
fig = pt.gcf()
fig.show()
fig.canvas.draw()
#print(len(groupxs))
#if False:
#groupxs = ut.find_duplicate_items(ut.lmap(basename, suffix_list)).values()
#print(ut.repr3(ut.apply_grouping(all_urls, groupxs)))
# # FIX
# for fpath, fname in zip(fpath_list, fname_list):
# if ut.checkpath(fpath):
# ut.move(fpath, join(dirname(fpath), fname))
# print('fpath = %r' % (fpath,))
#import ibeis
#from ibeis.dbio import ingest_dataset
#dbdir = ibeis.sysres.lookup_dbdir('WS_ALL')
#self = ingest_dataset.Ingestable2(dbdir)
if False:
# Show overlap matrix
import plottool as pt
import pandas as pd
import numpy as np
dict_ = overlaps
s = pd.Series(dict_, index=pd.MultiIndex.from_tuples(overlaps))
df = s.unstack()
lhs, rhs = df.align(df.T)
df = lhs.add(rhs, fill_value=0).fillna(0)
label_texts = df.columns.values
def label_ticks(label_texts):
import plottool as pt
truncated_labels = [repr(lbl[0:100]) for lbl in label_texts]
ax = pt.gca()
ax.set_xticks(list(range(len(label_texts))))
ax.set_xticklabels(truncated_labels)
[lbl.set_rotation(-55) for lbl in ax.get_xticklabels()]
[
lbl.set_horizontalalignment('left')
for lbl in ax.get_xticklabels()
]
#xgrid, ygrid = np.meshgrid(range(len(label_texts)), range(len(label_texts)))
#pt.plot_surface3d(xgrid, ygrid, disjoint_mat)
ax.set_yticks(list(range(len(label_texts))))
ax.set_yticklabels(truncated_labels)
[
lbl.set_horizontalalignment('right')
for lbl in ax.get_yticklabels()
]
[
lbl.set_verticalalignment('center')
for lbl in ax.get_yticklabels()
]
#[lbl.set_rotation(20) for lbl in ax.get_yticklabels()]
#df = df.sort(axis=0)
#df = df.sort(axis=1)
sortx = np.argsort(df.sum(axis=1).values)[::-1]
df = df.take(sortx, axis=0)
df = df.take(sortx, axis=1)
fig = pt.figure(fnum=1)
fig.clf()
mat = df.values.astype(np.int32)
mat[np.diag_indices(len(mat))] = 0
vmax = mat[(1 - np.eye(len(mat))).astype(np.bool)].max()
import matplotlib.colors
norm = matplotlib.colors.Normalize(vmin=0, vmax=vmax, clip=True)
pt.plt.imshow(mat, cmap='hot', norm=norm, interpolation='none')
pt.plt.colorbar()
pt.plt.grid('off')
label_ticks(label_texts)
fig.tight_layout()
#overlap_df = pd.DataFrame.from_dict(overlap_img_list)
class TmpImage(ut.NiceRepr):
pass
from skimage.feature import hog
from skimage import data, color, exposure
import plottool as pt
image2 = color.rgb2gray(data.astronaut()) # NOQA
fpath = './GOPR1120.JPG'
import vtool as vt
for fpath in [fpath]:
"""
http://scikit-image.org/docs/dev/auto_examples/plot_hog.html
"""
image = vt.imread(fpath, grayscale=True)
image = pt.color_funcs.to_base01(image)
fig = pt.figure(fnum=2)
fd, hog_image = hog(image,
orientations=8,
pixels_per_cell=(16, 16),
cells_per_block=(1, 1),
visualise=True)
fig, (ax1, ax2) = pt.plt.subplots(1,
2,
figsize=(8, 4),
sharex=True,
sharey=True)
ax1.axis('off')
ax1.imshow(image, cmap=pt.plt.cm.gray)
ax1.set_title('Input image')
ax1.set_adjustable('box-forced')
# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image,
in_range=(0, 0.02))
ax2.axis('off')
ax2.imshow(hog_image_rescaled, cmap=pt.plt.cm.gray)
ax2.set_title('Histogram of Oriented Gradients')
ax1.set_adjustable('box-forced')
pt.plt.show()
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 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 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_time_distributions(ibs, unixtime_list):
r"""
"""
#import vtool as vt
import plottool as pt
unixtime_list = np.array(unixtime_list)
num_nan = np.isnan(unixtime_list).sum()
num_total = len(unixtime_list)
unixtime_list = unixtime_list[~np.isnan(unixtime_list)]
if False:
from matplotlib import dates as mpldates
#data_list = list(map(ut.unixtime_to_datetimeobj, unixtime_list))
n, bins, patches = pt.plt.hist(unixtime_list, 365)
#n_ = list(map(ut.unixtime_to_datetimeobj, n))
#bins_ = list(map(ut.unixtime_to_datetimeobj, bins))
pt.plt.setp(patches, 'facecolor', 'g', 'alpha', 0.75)
ax = pt.gca()
#ax.xaxis.set_major_locator(mpldates.YearLocator())
#hfmt = mpldates.DateFormatter('%y/%m/%d')
#ax.xaxis.set_major_formatter(hfmt)
mpldates.num2date(unixtime_list)
#pt.gcf().autofmt_xdate()
#y = pt.plt.normpdf( bins, unixtime_list.mean(), unixtime_list.std())
#ax.set_xticks(bins_)
#l = pt.plt.plot(bins_, y, 'k--', linewidth=1.5)
else:
pt.draw_time_distribution(unixtime_list)
#pt.draw_histogram()
ax = pt.gca()
ax.set_xlabel('Date')
ax.set_title('Timestamp distribution of %s. #nan=%d/%d' % (
ibs.get_dbname_alias(),
num_nan, num_total))
pt.gcf().autofmt_xdate()
icon = ibs.get_database_icon()
if icon is not None:
#import matplotlib as mpl
#import vtool as vt
ax = pt.gca()
# Overlay a species icon
# http://matplotlib.org/examples/pylab_examples/demo_annotation_box.html
#icon = vt.convert_image_list_colorspace([icon], 'RGB', 'BGR')[0]
pt.overlay_icon(icon, coords=(0, 1), bbox_alignment=(0, 1))
#imagebox = mpl.offsetbox.OffsetImage(icon, zoom=1.0)
##xy = [ax.get_xlim()[0] + 5, ax.get_ylim()[1]]
##ax.set_xlim(1, 100)
##ax.set_ylim(0, 100)
##x = np.array(ax.get_xlim()).sum() / 2
##y = np.array(ax.get_ylim()).sum() / 2
##xy = [x, y]
##print('xy = %r' % (xy,))
##x = np.nanmin(unixtime_list)
##xy = [x, y]
##print('xy = %r' % (xy,))
##ax.get_ylim()[0]]
#xy = [ax.get_xlim()[0], ax.get_ylim()[1]]
#ab = mpl.offsetbox.AnnotationBbox(
# imagebox, xy, xycoords='data',
# xybox=(-0., 0.),
# boxcoords="offset points",
# box_alignment=(0, 1), pad=0.0)
#ax.add_artist(ab)
if ut.get_argflag('--contextadjust'):
#pt.adjust_subplots2(left=.08, bottom=.1, top=.9, wspace=.3, hspace=.1)
pt.adjust_subplots2(use_argv=True)
def ishow_chip(ibs, aid, fnum=2, fx=None, dodraw=True, config2_=None,
ischild=False, **kwargs):
r"""
# TODO:
split into two interactions
interact chip and interact chip features
Args:
ibs (IBEISController): ibeis controller object
aid (int): annotation id
fnum (int): figure number
fx (None):
CommandLine:
python -m ibeis.viz.interact.interact_chip --test-ishow_chip --show
python -m ibeis.viz.interact.interact_chip --test-ishow_chip --show --aid 2
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.interact.interact_chip import * # NOQA
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> aid = ut.get_argval('--aid', type_=int, default=1)
>>> fnum = 2
>>> fx = None
>>> # execute function
>>> dodraw = ut.show_was_requested()
>>> result = ishow_chip(ibs, aid, fnum, fx, dodraw)
>>> # verify results
>>> pt.show_if_requested()
>>> print(result)
"""
fnum = pt.ensure_fnum(fnum)
vh.ibsfuncs.assert_valid_aids(ibs, (aid,))
# TODO: Reconcile this with interact keypoints.
# Preferably this will call that but it will set some fancy callbacks
if not ischild:
fig = ih.begin_interaction('chip', fnum)
else:
fig = pt.gcf()
#fig = pt.figure(fnum=fnum, pnum=pnum)
# Get chip info (make sure get_chips is called first)
#mode_ptr = [1]
mode_ptr = [0]
def _select_fxth_kpt(fx):
# Get the fx-th keypiont
chip = ibs.get_annot_chips(aid, config2_=config2_)
kp = ibs.get_annot_kpts(aid, config2_=config2_)[fx]
sift = ibs.get_annot_vecs(aid, config2_=config2_)[fx]
# Draw chip + keypoints + highlighted plots
_chip_view(pnum=(2, 1, 1), sel_fx=fx)
#ishow_chip(ibs, aid, fnum=None, fx=fx, config2_=config2_, **kwargs)
# Draw the selected feature plots
nRows, nCols, px = (2, 3, 3)
draw_feat_row(chip, fx, kp, sift, fnum, nRows, nCols, px, None)
def _chip_view(mode=0, pnum=(1, 1, 1), **kwargs):
print('... _chip_view mode=%r' % mode_ptr[0])
kwargs['ell'] = mode_ptr[0] == 1
kwargs['pts'] = mode_ptr[0] == 2
if not ischild:
df2.figure(fnum=fnum, pnum=pnum, docla=True, doclf=True)
# Toggle no keypoints view
viz.show_chip(ibs, aid, fnum=fnum, pnum=pnum, config2_=config2_,
**kwargs)
df2.set_figtitle('Chip View')
def _on_chip_click(event):
print('[inter] clicked chip')
ax, x, y = event.inaxes, event.xdata, event.ydata
if ih.clicked_outside_axis(event):
if not ischild:
print('... out of axis')
mode_ptr[0] = (mode_ptr[0] + 1) % 3
_chip_view(**kwargs)
else:
if event.button == 3: # right-click
import guitool
#from ibeis.viz.interact import interact_chip
height = fig.canvas.geometry().height()
qpoint = guitool.newQPoint(event.x, height - event.y)
refresh_func = partial(_chip_view, **kwargs)
callback_list = build_annot_context_options(
ibs, aid, refresh_func=refresh_func,
with_interact_chip=False,
config2_=config2_)
qwin = fig.canvas
guitool.popup_menu(qwin, qpoint, callback_list)
#interact_chip.show_annot_context_menu(
# ibs, aid, fig.canvas, qpoint, refresh_func=refresh_func,
# with_interact_chip=False, config2_=config2_)
else:
viztype = vh.get_ibsdat(ax, 'viztype')
print('[ic] viztype=%r' % viztype)
if viztype == 'chip' and event.key == 'shift':
_chip_view(**kwargs)
ih.disconnect_callback(fig, 'button_press_event')
elif viztype == 'chip':
kpts = ibs.get_annot_kpts(aid, config2_=config2_)
if len(kpts) > 0:
fx = vt.nearest_point(
x, y, kpts, conflict_mode='next')[0]
print('... clicked fx=%r' % fx)
_select_fxth_kpt(fx)
else:
print('... len(kpts) == 0')
elif viztype in ['warped', 'unwarped']:
fx = vh.get_ibsdat(ax, 'fx')
if fx is not None and viztype == 'warped':
viz.show_keypoint_gradient_orientations(
ibs, aid, fx, fnum=df2.next_fnum())
else:
print('...Unknown viztype: %r' % viztype)
viz.draw()
# Draw without keypoints the first time
if fx is not None:
_select_fxth_kpt(fx)
else:
_chip_view(**kwargs)
if dodraw:
viz.draw()
if not ischild:
ih.connect_callback(fig, 'button_press_event', _on_chip_click)
def viz_overlay_layers(task):
"""
>>> from pysseg.tasks import *
>>> task = DivaV1(clean=0)
"""
for scene in ub.ProgIter(task.scene_ids, label='scene', verbose=3):
scene_path = join(task.scene_base, scene, 'static')
frame_image_fpaths = sorted(glob.glob(join(scene_path, '*.png')))
scene_json_fpath = join(scene_path, 'static.json')
frame_to_class_coords = task.parse_scene_elements(scene_json_fpath)
from pysseg.util import imutil
def new_layer(shape, classname, poly_coords):
coords = np.round(np.array([poly_coords])).astype(np.int)
alpha = int(.5 * 255)
color = list(task.class_colors[classname]) + [alpha]
# Initialize groundtruth image
layer = np.full((shape[0], shape[1], 4),
fill_value=0,
dtype=np.uint8)
layer = cv2.fillPoly(layer, coords, color)
layer = imutil.ensure_float01(layer)
yield layer
# outline to see more clearly
alpha = int(.95 * 255)
color = list(task.class_colors[classname]) + [alpha]
layer = np.full((shape[0], shape[1], 4),
fill_value=0,
dtype=np.uint8)
layer = cv2.drawContours(layer, [coords], -1, color, 3)
layer = imutil.ensure_float01(layer)
yield layer
priority = [
'Crosswalk', 'Intersection', 'Trees', 'Grass', 'Parking_Lot'
]
for frame_id, class_coords in frame_to_class_coords.items():
frame_fpath = frame_image_fpaths[0]
frame = cv2.imread(frame_fpath)
shape = frame.shape[:2]
# {c[0] for c in class_coords}
layers = []
boarder_layers = []
class_coords = sorted(
class_coords,
key=lambda t: 900
if t[0] not in priority else priority.index(t[0]))
classnames = set([p[0] for p in class_coords])
for classname, poly_coords in reversed(class_coords):
layer, layer_border = list(
new_layer(shape, classname, poly_coords))
layers.append(layer)
boarder_layers.append(layer_border)
layers = boarder_layers + layers
topdown = layers[0]
for layer in ub.ProgIter(layers[1:], label='blending'):
topdown = imutil.overlay_alpha_images(topdown, layer)
blend = imutil.overlay_alpha_images(topdown,
imutil.ensure_grayscale(frame))
import plottool as pt
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['legend.fontsize'] = 20
mpl.rcParams['legend.loc'] = 'center'
mpl.rcParams['axes.titlesize'] = 20
mpl.rcParams['figure.titlesize'] = 20
handles = [
patches.Patch(color=np.array(bgr[::-1]) / 255, label=classname)
for classname, bgr in ub.dict_subset(task.class_colors,
classnames).items()
]
n_cols = 5
n = 1
pt.imshow(blend, pnum=(1, n_cols, slice(0, n_cols - n)), fnum=1)
ax = pt.gca()
ax.set_title('Scene {}, frame {}'.format(scene, frame_id))
pt.figure(fnum=1, pnum=(1, n_cols, slice(n_cols - n, n_cols)))
ax = pt.gca()
ax.grid(False)
ax.set_xticks([])
ax.set_yticks([])
plt.legend(handles=handles)
mplutil.adjust_subplots(top=.9,
bottom=0,
left=0,
right=1,
wspace=.01)
fig = pt.gcf()
inches = np.array(blend.shape[:2][::-1]) / fig.dpi
fig.set_size_inches(*inches)
ub.ensuredir('scene_plots')
cv2.imwrite(
'scene_plots/scene_{}_{}.png'.format(scene, frame_id),
mplutil.render_figure_to_image(fig, dpi=100, transparent=True))