def show_chip_distinctiveness_plot(chip, kpts, dstncvs, fnum=1, pnum=None):
import wbia.plottool as pt
pt.figure(fnum, pnum=pnum)
ax = pt.gca()
divider = pt.ensure_divider(ax)
# ax1 = divider.append_axes("left", size="50%", pad=0)
ax1 = ax
ax2 = divider.append_axes('bottom', size='100%', pad=0.05)
# f, (ax1, ax2) = pt.plt.subplots(1, 2, sharex=True)
cmapstr = 'rainbow' # 'hot'
color_list = pt.df2.plt.get_cmap(cmapstr)(ut.norm_zero_one(dstncvs))
sortx = dstncvs.argsort()
# pt.df2.plt.plot(qfx2_dstncvs[sortx], c=color_list[sortx])
pt.plt.sca(ax1)
pt.colorline(np.arange(len(sortx)), dstncvs[sortx], cmap=pt.plt.get_cmap(cmapstr))
pt.gca().set_xlim(0, len(sortx))
pt.dark_background()
pt.plt.sca(ax2)
pt.imshow(chip, darken=0.2)
# MATPLOTLIB BUG CANNOT SHOW DIFFERENT ALPHA FOR POINTS AND KEYPOINTS AT ONCE
# pt.draw_kpts2(kpts, pts_color=color_list, ell_color=color_list, ell_alpha=.1, ell=True, pts=True)
# pt.draw_kpts2(kpts, color_list=color_list, pts_alpha=1.0, pts_size=1.5,
# ell=True, ell_alpha=.1, pts=False)
ell = ut.get_argflag('--ell')
pt.draw_kpts2(
kpts,
color_list=color_list,
pts_alpha=1.0,
pts_size=1.5,
ell=ell,
ell_alpha=0.3,
pts=not ell,
)
pt.plt.sca(ax)
def plot(self, fnum=None, pnum=(1, 1, 1), **kwargs):
import wbia.plottool as pt
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum, docla=True, doclf=True)
show_keypoints(self.chip, self.kpts, fnum=fnum, pnum=pnum, **kwargs)
if self.figtitle is not None:
pt.set_figtitle(self.figtitle)
def draw_tree_model(model, **kwargs):
import wbia.plottool as pt
import networkx as netx
if not ut.get_argval('--hackjunc'):
fnum = pt.ensure_fnum(None)
fig = pt.figure(fnum=fnum, doclf=True) # NOQA
ax = pt.gca()
# name_nodes = sorted(ut.list_getattr(model.ttype2_cpds[NAME_TTYPE], 'variable'))
netx_graph = model.to_markov_model()
# pos = netx.pygraphviz_layout(netx_graph)
# pos = netx.graphviz_layout(netx_graph)
# pos = get_hacked_pos(netx_graph, name_nodes, prog='neato')
pos = netx.nx_pydot.pydot_layout(netx_graph)
node_color = [pt.WHITE] * len(pos)
drawkw = dict(pos=pos,
ax=ax,
with_labels=True,
node_color=node_color,
node_size=1100)
netx.draw(netx_graph, **drawkw)
if kwargs.get('show_title', True):
pt.set_figtitle('Markov Model')
if not ut.get_argval('--hackmarkov'):
fnum = pt.ensure_fnum(None)
fig = pt.figure(fnum=fnum, doclf=True) # NOQA
ax = pt.gca()
netx_graph = model.to_junction_tree()
# prettify nodes
def fixtupkeys(dict_):
return {
', '.join(k) if isinstance(k, tuple) else k: fixtupkeys(v)
for k, v in dict_.items()
}
# FIXME
n = fixtupkeys(netx_graph.node)
e = fixtupkeys(netx_graph.edge)
a = fixtupkeys(netx_graph.adj)
netx_graph.nodes.update(n)
netx_graph.edges.update(e)
netx_graph.adj.update(a)
# netx_graph = model.to_markov_model()
# pos = netx.pygraphviz_layout(netx_graph)
# pos = netx.graphviz_layout(netx_graph)
pos = netx.nx_pydot.pydot_layout(netx_graph)
node_color = [pt.WHITE] * len(pos)
drawkw = dict(pos=pos,
ax=ax,
with_labels=True,
node_color=node_color,
node_size=2000)
netx.draw(netx_graph, **drawkw)
if kwargs.get('show_title', True):
pt.set_figtitle('Junction/Clique Tree / Cluster Graph')
def _chip_view(mode=0, pnum=(1, 1, 1), **kwargs):
logger.info('... _chip_view mode=%r' % mode_ptr[0])
kwargs['ell'] = mode_ptr[0] == 1
kwargs['pts'] = mode_ptr[0] == 2
if not ischild:
pt.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)
pt.set_figtitle('Chip View')
def show_selected(self, event):
import wbia.plottool as pt
logger.info('show_selected')
from wbia.viz import viz_chip
fnum = pt.ensure_fnum(10)
logger.info('fnum = %r' % (fnum, ))
pt.figure(fnum=fnum)
pt.update()
viz_chip.show_many_chips(self.infr.ibs, self.selected_aids)
pt.update()
def draw_precision_recall_curve_(recall_range_,
p_interp_curve,
title_pref=None,
fnum=1):
import wbia.plottool as pt
if recall_range_ is None:
recall_range_ = np.array([])
p_interp_curve = np.array([])
fig = pt.figure(fnum=fnum, docla=True, doclf=True) # NOQA
if recall_range_ is None:
ave_p = np.nan
else:
ave_p = p_interp_curve.sum() / p_interp_curve.size
pt.plot2(
recall_range_,
p_interp_curve,
marker='o--',
x_label='recall',
y_label='precision',
unitbox=True,
flipx=False,
color='r',
title='Interplated Precision Vs Recall\n' + 'avep = %r' % ave_p,
)
logger.info('Interplated Precision')
logger.info(ut.repr2(list(zip(recall_range_, p_interp_curve))))
def draw_aids(infr, aids, fnum=None):
from wbia.viz import viz_chip
import wbia.plottool as pt
fnum = pt.ensure_fnum(None)
fig = pt.figure(fnum=fnum)
viz_chip.show_many_chips(infr.ibs, aids, fnum=fnum)
return fig
def show_edge(infr, edge, fnum=None, pnum=None, **kwargs):
import wbia.plottool as pt
match = infr._exec_pairwise_match([edge])[0]
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum, pnum=pnum)
ax = pt.gca()
showkw = dict(
vert=False,
heatmask=True,
show_lines=False,
show_ell=False,
show_ori=False,
show_eig=False,
modifysize=True,
)
showkw.update(kwargs)
match.show(ax, **showkw)
def prepare_page(self):
figkw = {
'fnum': self.fnum,
'doclf': True,
'docla': True,
}
self.fig = pt.figure(**figkw)
ih.disconnect_callback(self.fig, 'button_press_event')
ih.connect_callback(self.fig, 'button_press_event', self.figure_clicked)
def sv_view(self, dodraw=True):
"""spatial verification view"""
# fnum = viz.FNUMS['special']
aid = self.daid
fnum = pt.next_fnum()
fig = pt.figure(fnum=fnum, docla=True, doclf=True)
ih.disconnect_callback(fig, 'button_press_event')
viz.viz_sver.show_sver(self.ibs, self.qaid, aid2=aid, fnum=fnum)
if dodraw:
# self.draw()
pt.draw()
def draw_junction_tree(model, fnum=None, **kwargs):
import wbia.plottool as pt
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum)
ax = pt.gca()
from pgmpy.models import JunctionTree
if not isinstance(model, JunctionTree):
netx_graph = model.to_junction_tree()
else:
netx_graph = model
# prettify nodes
def fixtupkeys(dict_):
return {
', '.join(k) if isinstance(k, tuple) else k: fixtupkeys(v)
for k, v in dict_.items()
}
n = fixtupkeys(netx_graph.nodes)
e = fixtupkeys(netx_graph.edge)
a = fixtupkeys(netx_graph.adj)
netx_graph.nodes = n
netx_graph.edge = e
netx_graph.adj = a
# netx_graph = model.to_markov_model()
# pos = nx.nx_agraph.pygraphviz_layout(netx_graph)
# pos = nx.nx_agraph.graphviz_layout(netx_graph)
pos = nx.pydot_layout(netx_graph)
node_color = [pt.NEUTRAL] * len(pos)
drawkw = dict(pos=pos,
ax=ax,
with_labels=True,
node_color=node_color,
node_size=2000)
nx.draw(netx_graph, **drawkw)
if kwargs.get('show_title', True):
pt.set_figtitle('Junction / Clique Tree / Cluster Graph')
def prepare_page(self, fulldraw=True):
figkw = {
'fnum': self.fnum,
'doclf': fulldraw,
'docla': fulldraw,
}
if fulldraw:
self.fig = pt.figure(**figkw)
ih.disconnect_callback(self.fig, 'button_press_event')
ih.disconnect_callback(self.fig, 'key_press_event')
ih.connect_callback(self.fig, 'button_press_event',
self.figure_clicked)
ih.connect_callback(self.fig, 'key_press_event', self.on_key_press)
def dump_to_disk(self, dpath, num=None, prefix='temp_img'):
import numpy as np
import wbia.plottool as pt
dpath = ut.ensurepath(dpath)
num_zeros = np.ceil(np.log10(len(self.gpath_list)))
total = len(self.gpath_list)
if num is None:
num = total
fmtstr = prefix + '_%0' + str(num_zeros) + 'd.jpg'
fig = pt.figure(fnum=self.fnum)
for index in ut.ProgIter(range(num), lbl='dumping images to disk'):
fig = pt.figure(fnum=self.fnum)
fig.clf()
ax = self._plot_index(index, {'fnum': self.fnum})
fig = ax.figure
axes_extents = pt.extract_axes_extents(fig)
assert len(axes_extents) == 1, 'more than one axes'
extent = axes_extents[0]
fpath = ut.unixjoin(dpath, fmtstr % (index))
fig.savefig(fpath, bbox_inches=extent)
pt.plt.close(fig)
def examine(self, aid, event=None):
print(' examining aid %r against the query result' % aid)
figtitle = 'Examine a specific image against the query'
# fnum = 510
fnum = pt.next_fnum()
fig = pt.figure(fnum=fnum, pnum=(1, 1, 1), doclf=True, docla=True)
# can cause freezes should be False
INTERACT_EXAMINE = False
if INTERACT_EXAMINE:
# from wbia.viz.interact import interact_matches
# fig = interact_matches.ishow_matches(self.ibs, self.cm, aid, figtitle=figtitle, fnum=fnum)
fig = self.cm.ishow_matches(self.ibs, aid, figtitle=figtitle, fnum=fnum)
print('Finished interact')
# this is only relevant to matplotlib.__version__ < 1.4.2
# raise Exception(
# 'BLACK MAGIC: error intentionally included as a workaround that seems'
# 'to fix a gui hang on certain computers.')
else:
viz_matches.show_matches(self.ibs, self.cm, aid, figtitle=figtitle)
fig.show()
def prepare_page(self, fulldraw=True):
import wbia.plottool as pt
ih.disconnect_callback(self.fig, 'button_press_event')
ih.disconnect_callback(self.fig, 'button_release_event')
ih.disconnect_callback(self.fig, 'key_press_event')
ih.disconnect_callback(self.fig, 'motion_notify_event')
figkw = {
'fnum': self.fnum,
'doclf': fulldraw,
'docla': fulldraw,
}
if fulldraw:
self.fig = pt.figure(**figkw)
self.make_hud()
ih.connect_callback(self.fig, 'button_press_event', self.on_click)
ih.connect_callback(self.fig, 'button_release_event',
self.on_click_release)
ih.connect_callback(self.fig, 'key_press_event', self.on_key_press)
ih.connect_callback(self.fig, 'motion_notify_event', self.on_motion)
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 wbia.algo.hots.bayes --exec-show_model --show
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.bayes import * # NOQA
>>> model = '?'
>>> evidence = {}
>>> soft_evidence = {}
>>> result = show_model(model, evidence, soft_evidence)
>>> print(result)
>>> ut.quit_if_noshow()
>>> import wbia.plottool as pt
>>> ut.show_if_requested()
"""
if ut.get_argval('--hackmarkov') or ut.get_argval('--hackjunc'):
draw_tree_model(model, **kwargs)
return
import wbia.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': (0.5, 0),
'pos_offset': [0, dpy],
'bbox_offset': [dbx, dby]
}
takw2 = {
'bbox_align': (0.5, 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)
# logger.info('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, 0.4)
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, 0.4)
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()
# logger.info('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, 0.4) 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)
# logger.info('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 draw_markov_model(model, fnum=None, **kwargs):
import wbia.plottool as pt
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum, doclf=True)
ax = pt.gca()
from pgmpy.models import MarkovModel
if isinstance(model, MarkovModel):
markovmodel = model
else:
markovmodel = model.to_markov_model()
# pos = nx.nx_agraph.pydot_layout(markovmodel)
pos = nx.nx_agraph.pygraphviz_layout(markovmodel)
# Referenecs:
# https://groups.google.com/forum/#!topic/networkx-discuss/FwYk0ixLDuY
# pos = nx.spring_layout(markovmodel)
# pos = nx.circular_layout(markovmodel)
# curved-arrow
# markovmodel.edge_attr['curved-arrow'] = True
# markovmodel.graph.setdefault('edge', {})['splines'] = 'curved'
# markovmodel.graph.setdefault('graph', {})['splines'] = 'curved'
# markovmodel.graph.setdefault('edge', {})['splines'] = 'curved'
node_color = [pt.NEUTRAL] * len(pos)
drawkw = dict( # NOQA
pos=pos,
ax=ax,
with_labels=True,
node_color=node_color,
node_size=1100)
from matplotlib.patches import FancyArrowPatch, Circle
import numpy as np
def draw_network(G, pos, ax, sg=None):
for n in G:
c = Circle(pos[n], radius=10, alpha=0.5, color=pt.NEUTRAL_BLUE)
ax.add_patch(c)
G.nodes[n]['patch'] = c
x, y = pos[n]
pt.ax_absolute_text(x, y, n, ha='center', va='center')
seen = {}
for (u, v, d) in G.edges(data=True):
n1 = G.nodes[u]['patch']
n2 = G.nodes[v]['patch']
rad = 0.1
if (u, v) in seen:
rad = seen.get((u, v))
rad = (rad + np.sign(rad) * 0.1) * -1
alpha = 0.5
color = 'k'
e = FancyArrowPatch(
n1.center,
n2.center,
patchA=n1,
patchB=n2,
# arrowstyle='-|>',
arrowstyle='-',
connectionstyle='arc3,rad=%s' % rad,
mutation_scale=10.0,
lw=2,
alpha=alpha,
color=color,
)
seen[(u, v)] = rad
ax.add_patch(e)
return e
# nx.draw(markovmodel, **drawkw)
draw_network(markovmodel, pos, ax)
ax.autoscale()
pt.plt.axis('equal')
pt.plt.axis('off')
if kwargs.get('show_title', True):
pt.set_figtitle('Markov Model')
def show_multi_images(ibs, gid_list, fnum=None, **kwargs):
r"""
Args:
ibs (IBEISController): wbia controller object
gid_list (list):
fnum (int): figure number(default = None)
CommandLine:
python -m wbia.viz.viz_image --test-show_multi_images --db NNP_Master3 --gids=7409,7448,4670,7497,7496,7464,7446,7442 --show
python -m wbia.viz.viz_image --test-show_multi_images --db NNP_Master3 --gids=1,2,3 --show
Ignore:
>>> # print to 8 gids sorted by num aids
>>> import wbia
>>> ibs = wbia.opendb('NNP_Master3')
>>> gid_list = ibs.get_valid_gids()
>>> aids_list = ibs.get_image_aids(gid_list)
>>> index_list = ut.list_argsort(list(map(len, aids_list)))[::-1]
>>> gid_list = ut.take(gid_list, index_list[0:8])
>>> print(','.join(map(str, gid_list)))
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.viz.viz_image import * # NOQA
>>> import wbia
>>> ibs = wbia.opendb(defaultdb='testdb1')
>>> gid_list = ut.get_argval('--gids', list, default=[1, 2])
>>> fnum = None
>>> result = show_multi_images(ibs, gid_list, fnum, draw_lbls=False, notitle=True, sel_aids='all')
>>> print(result)
>>> ut.show_if_requested()
"""
fnum = pt.ensure_fnum(fnum)
nGids = len(gid_list)
if nGids == 0:
fig = pt.figure(fnum=fnum, pnum=(1, 1, 1), **kwargs)
pt.imshow_null(fnum=fnum, **kwargs)
return fig
# Trigger computation of all chips in parallel
# ibsfuncs.ensure_annotation_data(ibs, aid_list, chips=(not in_image or annote), feats=annote)
rc = ut.get_argval('--rc', type_=list, default=None)
if rc is None:
nRows, nCols = ph.get_square_row_cols(nGids)
else:
nRows, nCols = rc
# notitle = ut.get_argflag('--notitle')
# draw_lbls = not ut.get_argflag('--no-draw_lbls')
# show_chip_kw = dict(annote=annote, in_image=in_image, notitle=notitle, draw_lbls=draw_lbls)
# logger.info('[viz_name] * r=%r, c=%r' % (nRows, nCols))
# gs2 = gridspec.GridSpec(nRows, nCols)
pnum_ = pt.get_pnum_func(nRows, nCols)
fig = pt.figure(fnum=fnum, pnum=pnum_(0), **kwargs)
fig.clf()
for px, gid in enumerate(gid_list):
logger.info(pnum_(px))
_fig, _ax1 = show_image(ibs, gid, fnum=fnum, pnum=pnum_(px), **kwargs)
# ax = pt.gca()
# if aid in sel_aids:
# pt.draw_border(ax, pt.GREEN, 4)
# if ut.get_argflag('--numlbl') and not DOBOTH:
# ax.set_xlabel('(' + str(px + 1) + ')')
# plot_aid3(ibs, aid)
pass
def _dev_iters_until_threshold():
"""
INTERACTIVE DEVELOPMENT FUNCTION
How many iterations of ewma until you hit the poisson / biniomal threshold
This establishes a principled way to choose the threshold for the refresh
criterion in my thesis. There are paramters --- moving parts --- that we
need to work with: `a` the patience, `s` the span, and `mu` our ewma.
`s` is a span paramter indicating how far we look back.
`mu` is the average number of label-changing reviews in roughly the last
`s` manual decisions.
These numbers are used to estimate the probability that any of the next `a`
manual decisions will be label-chanigng. When that probability falls below
a threshold we terminate. The goal is to choose `a`, `s`, and the threshold
`t`, such that the probability will fall below the threshold after a maximum
of `a` consecutive non-label-chaning reviews. IE we want to tie the patience
paramter (how far we look ahead) to how far we actually are willing to go.
"""
import numpy as np
import utool as ut
import sympy as sym
i = sym.symbols('i', integer=True, nonnegative=True, finite=True)
# mu_i = sym.symbols('mu_i', integer=True, nonnegative=True, finite=True)
s = sym.symbols('s', integer=True, nonnegative=True, finite=True) # NOQA
thresh = sym.symbols('tau', real=True, nonnegative=True, finite=True) # NOQA
alpha = sym.symbols('alpha', real=True, nonnegative=True, finite=True) # NOQA
c_alpha = sym.symbols('c_alpha', real=True, nonnegative=True, finite=True)
# patience
a = sym.symbols('a', real=True, nonnegative=True, finite=True)
available_subs = {
a: 20,
s: a,
alpha: 2 / (s + 1),
c_alpha: (1 - alpha),
}
def subs(expr, d=available_subs):
""" recursive expression substitution """
expr1 = expr.subs(d)
if expr == expr1:
return expr1
else:
return subs(expr1, d=d)
# mu is either the support for the poisson distribution
# or is is the p in the binomial distribution
# It is updated at timestep i based on ewma, assuming each incoming responce is 0
mu_0 = 1.0
mu_i = c_alpha ** i
# Estimate probability that any event will happen in the next `a` reviews
# at time `i`.
poisson_i = 1 - sym.exp(-mu_i * a)
binom_i = 1 - (1 - mu_i) ** a
# Expand probabilities to be a function of i, s, and a
part = ut.delete_dict_keys(available_subs.copy(), [a, s])
mu_i = subs(mu_i, d=part)
poisson_i = subs(poisson_i, d=part)
binom_i = subs(binom_i, d=part)
if True:
# ewma of mu at time i if review is always not label-changing (meaningful)
mu_1 = c_alpha * mu_0 # NOQA
mu_2 = c_alpha * mu_1 # NOQA
if True:
i_vals = np.arange(0, 100)
mu_vals = np.array([subs(mu_i).subs({i: i_}).evalf() for i_ in i_vals]) # NOQA
binom_vals = np.array(
[subs(binom_i).subs({i: i_}).evalf() for i_ in i_vals]
) # NOQA
poisson_vals = np.array(
[subs(poisson_i).subs({i: i_}).evalf() for i_ in i_vals]
) # NOQA
# Find how many iters it actually takes my expt to terminate
thesis_draft_thresh = np.exp(-2)
np.where(mu_vals < thesis_draft_thresh)[0]
np.where(binom_vals < thesis_draft_thresh)[0]
np.where(poisson_vals < thesis_draft_thresh)[0]
sym.pprint(sym.simplify(mu_i))
sym.pprint(sym.simplify(binom_i))
sym.pprint(sym.simplify(poisson_i))
# Find the thresholds that force termination after `a` reviews have passed
# do this by setting i=a
poisson_thresh = poisson_i.subs({i: a})
binom_thresh = binom_i.subs({i: a})
logger.info('Poisson thresh')
logger.info(sym.latex(sym.Eq(thresh, poisson_thresh)))
logger.info(sym.latex(sym.Eq(thresh, sym.simplify(poisson_thresh))))
poisson_thresh.subs({a: 115, s: 30}).evalf()
sym.pprint(sym.Eq(thresh, poisson_thresh))
sym.pprint(sym.Eq(thresh, sym.simplify(poisson_thresh)))
logger.info('Binomial thresh')
sym.pprint(sym.simplify(binom_thresh))
sym.pprint(sym.simplify(poisson_thresh.subs({s: a})))
def taud(coeff):
return coeff * 360
if 'poisson_cache' not in vars():
poisson_cache = {}
binom_cache = {}
S, A = np.meshgrid(np.arange(1, 150, 1), np.arange(0, 150, 1))
import wbia.plottool as pt
SA_coords = list(zip(S.ravel(), A.ravel()))
for sval, aval in ut.ProgIter(SA_coords):
if (sval, aval) not in poisson_cache:
poisson_cache[(sval, aval)] = float(
poisson_thresh.subs({a: aval, s: sval}).evalf()
)
poisson_zdata = np.array(
[poisson_cache[(sval, aval)] for sval, aval in SA_coords]
).reshape(A.shape)
fig = pt.figure(fnum=1, doclf=True)
pt.gca().set_axis_off()
pt.plot_surface3d(
S,
A,
poisson_zdata,
xlabel='s',
ylabel='a',
rstride=3,
cstride=3,
zlabel='poisson',
mode='wire',
contour=True,
title='poisson3d',
)
pt.gca().set_zlim(0, 1)
pt.gca().view_init(elev=taud(1 / 16), azim=taud(5 / 8))
fig.set_size_inches(10, 6)
fig.savefig(
'a-s-t-poisson3d.png',
dpi=300,
bbox_inches=pt.extract_axes_extents(fig, combine=True),
)
for sval, aval in ut.ProgIter(SA_coords):
if (sval, aval) not in binom_cache:
binom_cache[(sval, aval)] = float(
binom_thresh.subs({a: aval, s: sval}).evalf()
)
binom_zdata = np.array(
[binom_cache[(sval, aval)] for sval, aval in SA_coords]
).reshape(A.shape)
fig = pt.figure(fnum=2, doclf=True)
pt.gca().set_axis_off()
pt.plot_surface3d(
S,
A,
binom_zdata,
xlabel='s',
ylabel='a',
rstride=3,
cstride=3,
zlabel='binom',
mode='wire',
contour=True,
title='binom3d',
)
pt.gca().set_zlim(0, 1)
pt.gca().view_init(elev=taud(1 / 16), azim=taud(5 / 8))
fig.set_size_inches(10, 6)
fig.savefig(
'a-s-t-binom3d.png',
dpi=300,
bbox_inches=pt.extract_axes_extents(fig, combine=True),
)
# Find point on the surface that achieves a reasonable threshold
# Sympy can't solve this
# sym.solve(sym.Eq(binom_thresh.subs({s: 50}), .05))
# sym.solve(sym.Eq(poisson_thresh.subs({s: 50}), .05))
# Find a numerical solution
def solve_numeric(expr, target, want, fixed, method=None, bounds=None):
"""
Args:
expr (Expr): symbolic expression
target (float): numberic value
fixed (dict): fixed values of the symbol
expr = poisson_thresh
expr.free_symbols
fixed = {s: 10}
solve_numeric(poisson_thresh, .05, {s: 30}, method=None)
solve_numeric(poisson_thresh, .05, {s: 30}, method='Nelder-Mead')
solve_numeric(poisson_thresh, .05, {s: 30}, method='BFGS')
"""
import scipy.optimize
# Find the symbol you want to solve for
want_symbols = expr.free_symbols - set(fixed.keys())
# TODO: can probably extend this to multiple params
assert len(want_symbols) == 1, 'specify all but one var'
assert want == list(want_symbols)[0]
fixed_expr = expr.subs(fixed)
def func(a1):
expr_value = float(fixed_expr.subs({want: a1}).evalf())
return (expr_value - target) ** 2
# if method is None:
# method = 'Nelder-Mead'
# method = 'Newton-CG'
# method = 'BFGS'
# Use one of the other params the startin gpoing
a1 = list(fixed.values())[0]
result = scipy.optimize.minimize(func, x0=a1, method=method, bounds=bounds)
if not result.success:
logger.info('\n')
logger.info(result)
logger.info('\n')
return result
# Numeric measurments of thie line
thresh_vals = [0.001, 0.01, 0.05, 0.1, 0.135]
svals = np.arange(1, 100)
target_poisson_plots = {}
for target in ut.ProgIter(thresh_vals, bs=False, freq=1):
poisson_avals = []
for sval in ut.ProgIter(svals, 'poisson', freq=1):
expr = poisson_thresh
fixed = {s: sval}
want = a
aval = solve_numeric(expr, target, want, fixed, method='Nelder-Mead').x[0]
poisson_avals.append(aval)
target_poisson_plots[target] = (svals, poisson_avals)
fig = pt.figure(fnum=3)
for target, dat in target_poisson_plots.items():
pt.plt.plot(*dat, label='prob={}'.format(target))
pt.gca().set_xlabel('s')
pt.gca().set_ylabel('a')
pt.legend()
pt.gca().set_title('poisson')
fig.set_size_inches(5, 3)
fig.savefig(
'a-vs-s-poisson.png',
dpi=300,
bbox_inches=pt.extract_axes_extents(fig, combine=True),
)
target_binom_plots = {}
for target in ut.ProgIter(thresh_vals, bs=False, freq=1):
binom_avals = []
for sval in ut.ProgIter(svals, 'binom', freq=1):
aval = solve_numeric(
binom_thresh, target, a, {s: sval}, method='Nelder-Mead'
).x[0]
binom_avals.append(aval)
target_binom_plots[target] = (svals, binom_avals)
fig = pt.figure(fnum=4)
for target, dat in target_binom_plots.items():
pt.plt.plot(*dat, label='prob={}'.format(target))
pt.gca().set_xlabel('s')
pt.gca().set_ylabel('a')
pt.legend()
pt.gca().set_title('binom')
fig.set_size_inches(5, 3)
fig.savefig(
'a-vs-s-binom.png',
dpi=300,
bbox_inches=pt.extract_axes_extents(fig, combine=True),
)
# ----
if True:
fig = pt.figure(fnum=5, doclf=True)
s_vals = [1, 2, 3, 10, 20, 30, 40, 50]
for sval in s_vals:
pp = poisson_thresh.subs({s: sval})
a_vals = np.arange(0, 200)
pp_vals = np.array(
[float(pp.subs({a: aval}).evalf()) for aval in a_vals]
) # NOQA
pt.plot(a_vals, pp_vals, label='s=%r' % (sval,))
pt.legend()
pt.gca().set_xlabel('a')
pt.gca().set_ylabel('poisson prob after a reviews')
fig.set_size_inches(5, 3)
fig.savefig(
'a-vs-thresh-poisson.png',
dpi=300,
bbox_inches=pt.extract_axes_extents(fig, combine=True),
)
fig = pt.figure(fnum=6, doclf=True)
s_vals = [1, 2, 3, 10, 20, 30, 40, 50]
for sval in s_vals:
pp = binom_thresh.subs({s: sval})
a_vals = np.arange(0, 200)
pp_vals = np.array(
[float(pp.subs({a: aval}).evalf()) for aval in a_vals]
) # NOQA
pt.plot(a_vals, pp_vals, label='s=%r' % (sval,))
pt.legend()
pt.gca().set_xlabel('a')
pt.gca().set_ylabel('binom prob after a reviews')
fig.set_size_inches(5, 3)
fig.savefig(
'a-vs-thresh-binom.png',
dpi=300,
bbox_inches=pt.extract_axes_extents(fig, combine=True),
)
# -------
fig = pt.figure(fnum=5, doclf=True)
a_vals = [1, 2, 3, 10, 20, 30, 40, 50]
for aval in a_vals:
pp = poisson_thresh.subs({a: aval})
s_vals = np.arange(1, 200)
pp_vals = np.array(
[float(pp.subs({s: sval}).evalf()) for sval in s_vals]
) # NOQA
pt.plot(s_vals, pp_vals, label='a=%r' % (aval,))
pt.legend()
pt.gca().set_xlabel('s')
pt.gca().set_ylabel('poisson prob')
fig.set_size_inches(5, 3)
fig.savefig(
's-vs-thresh-poisson.png',
dpi=300,
bbox_inches=pt.extract_axes_extents(fig, combine=True),
)
fig = pt.figure(fnum=5, doclf=True)
a_vals = [1, 2, 3, 10, 20, 30, 40, 50]
for aval in a_vals:
pp = binom_thresh.subs({a: aval})
s_vals = np.arange(1, 200)
pp_vals = np.array(
[float(pp.subs({s: sval}).evalf()) for sval in s_vals]
) # NOQA
pt.plot(s_vals, pp_vals, label='a=%r' % (aval,))
pt.legend()
pt.gca().set_xlabel('s')
pt.gca().set_ylabel('binom prob')
fig.set_size_inches(5, 3)
fig.savefig(
's-vs-thresh-binom.png',
dpi=300,
bbox_inches=pt.extract_axes_extents(fig, combine=True),
)
# ---------------------
# Plot out a table
mu_i.subs({s: 75, a: 75}).evalf()
poisson_thresh.subs({s: 75, a: 75}).evalf()
sval = 50
for target, dat in target_poisson_plots.items():
slope = np.median(np.diff(dat[1]))
aval = int(np.ceil(sval * slope))
thresh = float(poisson_thresh.subs({s: sval, a: aval}).evalf())
logger.info(
'aval={}, sval={}, thresh={}, target={}'.format(aval, sval, thresh, target)
)
for target, dat in target_binom_plots.items():
slope = np.median(np.diff(dat[1]))
aval = int(np.ceil(sval * slope))
def chipmatch_view(self,
fnum=None,
pnum=(1, 1, 1),
verbose=None,
**kwargs_):
"""
just visualizes the matches using some type of lines
"""
import wbia.plottool as pt
from wbia.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,
white_background=False,
)
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.repr2(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 select_ith_match(self, mx):
"""
Selects the ith match and visualizes and prints information concerning
features weights, keypoint details, and sift descriptions
"""
import wbia.plottool as pt
from wbia.plottool import viz_featrow
from wbia.plottool import interact_helpers as ih
fnum = self.fnum
same_fig = self.same_fig
rchip1 = self.rchip1
rchip2 = self.rchip2
self.mx = mx
print('+--- SELECT --- ')
print('... selecting mx-th=%r feature match' % mx)
fsv = self.fsv
fs = self.fs
print('score stats:')
print(ut.repr2(ut.get_stats(fsv, axis=0), nl=1))
print('fsv[mx] = %r' % (fsv[mx], ))
print('fs[mx] = %r' % (fs[mx], ))
# ----------------------
# Get info for the select_ith_match plot
self.mode = 1
# Get the mx-th feature match
fx1, fx2 = self.fm[mx]
# Older info
fscore2 = self.fs[mx]
fk2 = None if self.fk is None else self.fk[mx]
kp1, kp2 = self.kpts1[fx1], self.kpts2[fx2]
vecs1, vecs2 = self.vecs1[fx1], self.vecs2[fx2]
info1 = '\nquery'
info2 = '\nk=%r fscore=%r' % (fk2, fscore2)
# self.last_fx = fx1
self.last_fx = fx1
# Extracted keypoints to draw
extracted_list = [
(rchip1, kp1, vecs1, fx1, 'aid1', info1),
(rchip2, kp2, vecs2, fx2, 'aid2', info2),
]
# Normalizng Keypoint
# if hasattr(cm, 'filt2_meta') and 'lnbnn' in cm.filt2_meta:
# qfx2_norm = cm.filt2_meta['lnbnn']
# # Normalizing chip and feature
# (aid3, fx3, normk) = qfx2_norm[fx1]
# rchip3 = ibs.get_annot_chips(aid3)
# kp3 = ibs.get_annot_kpts(aid3)[fx3]
# sift3 = ibs.get_annot_vecs(aid3)[fx3]
# info3 = '\nnorm %s k=%r' % (vh.get_aidstrs(aid3), normk)
# extracted_list.append((rchip3, kp3, sift3, fx3, aid3, info3))
# else:
# pass
# #print('WARNING: meta doesnt exist')
# ----------------------
# Draw the select_ith_match plot
nRows, nCols = len(extracted_list) + same_fig, 3
# Draw matching chips and features
sel_fm = np.array([(fx1, fx2)])
pnum1 = (nRows, 1, 1) if same_fig else (1, 1, 1)
vert = self.vert if self.vert is not None else False
self.chipmatch_view(
pnum=pnum1,
ell_alpha=0.4,
ell_linewidth=1.8,
colors=pt.BLUE,
sel_fm=sel_fm,
vert=vert,
)
# Draw selected feature matches
px = nCols * same_fig # plot offset
prevsift = None
if not same_fig:
# fnum2 = fnum + len(viz.FNUMS)
fnum2 = self.fnum2
fig2 = pt.figure(fnum=fnum2, docla=True, doclf=True)
else:
fnum2 = fnum
for (rchip, kp, sift, fx, aid, info) in extracted_list:
px = viz_featrow.draw_feat_row(
rchip,
fx,
kp,
sift,
fnum2,
nRows,
nCols,
px,
prevsift=prevsift,
aid=aid,
info=info,
)
prevsift = sift
if not same_fig:
ih.connect_callback(fig2, 'button_press_event', self.on_click)
def show_sv_simple(chip1,
chip2,
kpts1,
kpts2,
fm,
inliers,
mx=None,
fnum=1,
vert=None,
**kwargs):
"""
CommandLine:
python -m wbia.plottool.draw_sv --test-show_sv_simple --show
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.plottool.draw_sv import * # NOQA
>>> import vtool as vt
>>> kpts1, kpts2, fm, aff_inliers, chip1, chip2, xy_thresh_sqrd = vt.testdata_matching_affine_inliers()
>>> inliers = aff_inliers
>>> mx = None
>>> fnum = 1
>>> vert = None # ut.get_argval('--vert', type_=bool, default=None)
>>> result = show_sv_simple(chip1, chip2, kpts1, kpts2, fm, inliers, mx, fnum, vert=vert)
>>> print(result)
>>> import wbia.plottool as pt
>>> pt.show_if_requested()
"""
import wbia.plottool as pt
import vtool as vt
colors = pt.distinct_colors(2, brightness=0.95)
color1, color2 = colors[0:2]
# Begin the drawing
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum, pnum=(1, 1, 1), docla=True, doclf=True)
# dmkwargs = dict(fs=None, title='Inconsistent Matches', all_kpts=False, draw_lines=True,
# docla=True, draw_border=True, fnum=fnum, pnum=(1, 1, 1), colors=pt.ORANGE)
inlier_mask = vt.index_to_boolmask(inliers, maxval=len(fm))
fm_inliers = fm.compress(inlier_mask, axis=0)
fm_outliers = fm.compress(np.logical_not(inlier_mask), axis=0)
xywh1, xywh2, sf_tup = pt.show_chipmatch2(chip1,
chip2,
vert=vert,
modifysize=True,
new_return=True)
sf1, sf2 = sf_tup
fmatch_kw = dict(ell_linewidth=2, ell_alpha=0.7, line_alpha=0.7)
pt.plot_fmatch(xywh1,
xywh2,
kpts1,
kpts2,
fm_inliers,
colors=color1,
scale_factor1=sf1,
scale_factor2=sf2,
**fmatch_kw)
pt.plot_fmatch(xywh1,
xywh2,
kpts1,
kpts2,
fm_outliers,
colors=color2,
scale_factor1=sf1,
scale_factor2=sf2,
**fmatch_kw)
def hackshow_names(ibs, aid_list, fnum=None):
r"""
Args:
ibs (IBEISController): wbia controller object
aid_list (list):
CommandLine:
python -m wbia.other.dbinfo --exec-hackshow_names --show
python -m wbia.other.dbinfo --exec-hackshow_names --show --db PZ_Master1
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.other.dbinfo import * # NOQA
>>> import wbia
>>> ibs = wbia.opendb(defaultdb='PZ_MTEST')
>>> aid_list = ibs.get_valid_aids()
>>> result = hackshow_names(ibs, aid_list)
>>> print(result)
>>> ut.show_if_requested()
"""
import wbia.plottool as pt
import vtool as vt
grouped_aids, nid_list = ibs.group_annots_by_name(aid_list)
grouped_aids = [aids for aids in grouped_aids if len(aids) > 1]
unixtimes_list = ibs.unflat_map(ibs.get_annot_image_unixtimes_asfloat, grouped_aids)
yaws_list = ibs.unflat_map(ibs.get_annot_yaws, grouped_aids)
# markers_list = [[(1, 2, yaw * 360 / (np.pi * 2)) for yaw in yaws] for yaws in yaws_list]
unixtime_list = ut.flatten(unixtimes_list)
timemax = np.nanmax(unixtime_list)
timemin = np.nanmin(unixtime_list)
timerange = timemax - timemin
unixtimes_list = [
((unixtimes[:] - timemin) / timerange) for unixtimes in unixtimes_list
]
for unixtimes in unixtimes_list:
num_nan = sum(np.isnan(unixtimes))
unixtimes[np.isnan(unixtimes)] = np.linspace(-1, -0.5, num_nan)
# ydata_list = [np.arange(len(aids)) for aids in grouped_aids]
sortx_list = vt.argsort_groups(unixtimes_list, reverse=False)
# markers_list = ut.list_ziptake(markers_list, sortx_list)
yaws_list = ut.list_ziptake(yaws_list, sortx_list)
ydatas_list = vt.ziptake(unixtimes_list, sortx_list)
# ydatas_list = sortx_list
# ydatas_list = vt.argsort_groups(unixtimes_list, reverse=False)
# Sort by num members
# ydatas_list = ut.take(ydatas_list, np.argsort(list(map(len, ydatas_list))))
xdatas_list = [
np.zeros(len(ydatas)) + count for count, ydatas in enumerate(ydatas_list)
]
# markers = ut.flatten(markers_list)
# yaws = np.array(ut.flatten(yaws_list))
y_data = np.array(ut.flatten(ydatas_list))
x_data = np.array(ut.flatten(xdatas_list))
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum)
ax = pt.gca()
# unique_yaws, groupxs = vt.group_indices(yaws)
ax.scatter(x_data, y_data, color=[1, 0, 0], s=1, marker='.')
# pt.draw_stems(x_data, y_data, marker=markers, setlims=True, linestyle='')
pt.dark_background()
ax = pt.gca()
ax.set_xlim(min(x_data) - 0.1, max(x_data) + 0.1)
ax.set_ylim(min(y_data) - 0.1, max(y_data) + 0.1)
def get_injured_sharks():
"""
>>> from wbia.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')
logger.info(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')
logger.info(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:
# logger.info('[%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
logger.info('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])
logger.info(ut.repr3(cat_to_keys))
logger.info(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)
# logger.info(ut.repr3(encdict))
# logger.info(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']
# logger.info(ut.repr3(info))
flags = ut.lmap(ut.fpath_has_imgext, clist['gpath'])
clist = clist.compress(flags)
import wbia
ibs = wbia.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'])
logger.info('# 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 wbia.plottool as pt
from wbia 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()
# logger.info(len(groupxs))
# if False:
# groupxs = ut.find_duplicate_items(ut.lmap(basename, suffix_list)).values()
# logger.info(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))
# logger.info('fpath = %r' % (fpath,))
# import wbia
# from wbia.dbio import ingest_dataset
# dbdir = wbia.sysres.lookup_dbdir('WS_ALL')
# self = ingest_dataset.Ingestable2(dbdir)
if False:
# Show overlap matrix
import wbia.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 wbia.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 wbia.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 chipmatch_view(self,
fnum=None,
pnum=(1, 1, 1),
verbose=None,
**kwargs_):
"""
just visualizes the matches using some type of lines
CommandLine:
python -m wbia.viz.interact.interact_matches --test-chipmatch_view --show
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.viz.interact.interact_matches import * # NOQA
>>> self = testdata_match_interact()
>>> self.chipmatch_view()
>>> pt.show_if_requested()
"""
if fnum is None:
fnum = self.fnum
if verbose is None:
verbose = ut.VERBOSE
ibs = self.ibs
aid = self.daid
qaid = self.qaid
figtitle = self.figtitle
# drawing mode draw: with/without lines/feats
mode = kwargs_.get('mode', self.mode)
draw_ell = mode >= 1
draw_lines = mode == 2
# self.mode = (self.mode + 1) % 3
pt.figure(fnum=fnum, docla=True, doclf=True)
show_matches_kw = self.kwargs.copy()
show_matches_kw.update(
dict(
fnum=fnum,
pnum=pnum,
draw_lines=draw_lines,
draw_ell=draw_ell,
colorbar_=True,
vert=self.vert,
))
show_matches_kw.update(kwargs_)
if self.warp_homog:
show_matches_kw['H1'] = self.H1
# show_matches_kw['score'] = self.score
show_matches_kw['rawscore'] = self.score
show_matches_kw['aid2_raw_rank'] = self.rank
tup = viz.viz_matches.show_matches2(
ibs,
self.qaid,
self.daid,
self.fm,
self.fs,
qreq_=self.qreq_,
**show_matches_kw,
)
ax, xywh1, xywh2 = tup
self.xywh2 = xywh2
pt.set_figtitle(figtitle + ' ' + vh.get_vsstr(qaid, aid))
def viz_netx_chipgraph(
ibs,
graph,
fnum=None,
use_image=False,
layout=None,
zoom=None,
prog='neato',
as_directed=False,
augment_graph=True,
layoutkw=None,
framewidth=True,
**kwargs,
):
r"""
DEPRICATE or improve
Args:
ibs (IBEISController): wbia controller object
graph (nx.DiGraph):
fnum (int): figure number(default = None)
use_image (bool): (default = False)
zoom (float): (default = 0.4)
Returns:
?: pos
CommandLine:
python -m wbia --tf viz_netx_chipgraph --show
Cand:
wbia review_tagged_joins --save figures4/mergecase.png --figsize=15,15
--clipwhite --diskshow
wbia compute_occurrence_groups --save figures4/occurgraph.png
--figsize=40,40 --clipwhite --diskshow
~/code/wbia/wbia/algo/preproc/preproc_occurrence.py
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.viz.viz_graph import * # NOQA
>>> import wbia
>>> ibs = wbia.opendb(defaultdb='PZ_MTEST')
>>> nid_list = ibs.get_valid_nids()[0:10]
>>> fnum = None
>>> use_image = True
>>> zoom = 0.4
>>> make_name_graph_interaction(ibs, nid_list, prog='neato')
>>> ut.show_if_requested()
"""
import wbia.plottool as pt
logger.info('[viz_graph] drawing chip graph')
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum, pnum=(1, 1, 1))
ax = pt.gca()
if layout is None:
layout = 'agraph'
logger.info('layout = %r' % (layout, ))
if use_image:
ensure_node_images(ibs, graph)
nx.set_node_attributes(graph, name='shape', values='rect')
if layoutkw is None:
layoutkw = {}
layoutkw['prog'] = layoutkw.get('prog', prog)
layoutkw.update(kwargs)
if prog == 'neato':
graph = graph.to_undirected()
plotinfo = pt.show_nx(
graph,
ax=ax,
# img_dict=img_dict,
layout=layout,
# hacknonode=bool(use_image),
layoutkw=layoutkw,
as_directed=as_directed,
framewidth=framewidth,
)
return plotinfo
def augment_nnindexer_experiment():
"""
References:
http://answers.opencv.org/question/44592/flann-index-training-fails-with-segfault/
CommandLine:
utprof.py -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_MTEST --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6 --nosave-flann --show
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6 --nosave-flann --show
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6 --nosave-flann --no-api-cache --nocache-uuids
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_MTEST --show
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --show
# RUNS THE SEGFAULTING CASE
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --show
# Debug it
gdb python
run -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --show
gdb python
run -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots._neighbor_experiment import * # NOQA
>>> # execute function
>>> augment_nnindexer_experiment()
>>> # verify results
>>> ut.show_if_requested()
"""
import wbia
# build test data
# ibs = wbia.opendb('PZ_MTEST')
ibs = wbia.opendb(defaultdb='PZ_Master0')
if ibs.get_dbname() == 'PZ_MTEST':
initial = 1
addition_stride = 4
max_ceiling = 100
elif ibs.get_dbname() == 'PZ_Master0':
initial = 128
# addition_stride = 64
# addition_stride = 128
addition_stride = 256
max_ceiling = 10000
# max_ceiling = 4000
# max_ceiling = 2000
# max_ceiling = 600
else:
assert False
all_daids = ibs.get_valid_aids(species='zebra_plains')
qreq_ = ibs.new_query_request(all_daids, all_daids)
max_num = min(max_ceiling, len(all_daids))
# Clear Caches
ibs.delete_flann_cachedir()
neighbor_index_cache.clear_memcache()
neighbor_index_cache.clear_uuid_cache(qreq_)
# Setup
all_randomize_daids_ = ut.deterministic_shuffle(all_daids[:])
# ensure all features are computed
nnindexer_list = []
addition_lbl = 'Addition'
_addition_iter = list(range(initial + 1, max_num, addition_stride))
addition_iter = iter(
ut.ProgressIter(_addition_iter,
lbl=addition_lbl,
freq=1,
autoadjust=False))
time_list_addition = []
# time_list_reindex = []
addition_count_list = []
tmp_cfgstr_list = []
# for _ in range(80):
# next(addition_iter)
try:
memtrack = ut.MemoryTracker(disable=False)
for count in addition_iter:
aid_list_ = all_randomize_daids_[0:count]
# Request an indexer which could be an augmented version of an existing indexer.
with ut.Timer(verbose=False) as t:
memtrack.report('BEFORE AUGMENT')
nnindexer_ = neighbor_index_cache.request_augmented_wbia_nnindexer(
qreq_, aid_list_)
memtrack.report('AFTER AUGMENT')
nnindexer_list.append(nnindexer_)
addition_count_list.append(count)
time_list_addition.append(t.ellapsed)
tmp_cfgstr_list.append(nnindexer_.cfgstr)
logger.info('===============\n\n')
logger.info(ut.repr2(time_list_addition))
logger.info(ut.repr2(list(map(id, nnindexer_list))))
logger.info(ut.repr2(tmp_cfgstr_list))
logger.info(
ut.repr2(list([nnindxer.cfgstr for nnindxer in nnindexer_list])))
IS_SMALL = False
if IS_SMALL:
nnindexer_list = []
reindex_label = 'Reindex'
# go backwards for reindex
_reindex_iter = list(range(initial + 1, max_num,
addition_stride))[::-1]
reindex_iter = ut.ProgressIter(_reindex_iter, lbl=reindex_label)
time_list_reindex = []
# time_list_reindex = []
reindex_count_list = []
for count in reindex_iter:
logger.info('\n+===PREDONE====================\n')
# check only a single size for memory leaks
# count = max_num // 16 + ((x % 6) * 1)
# x += 1
aid_list_ = all_randomize_daids_[0:count]
# Call the same code, but force rebuilds
memtrack.report('BEFORE REINDEX')
with ut.Timer(verbose=False) as t:
nnindexer_ = neighbor_index_cache.request_augmented_wbia_nnindexer(
qreq_, aid_list_, force_rebuild=True, memtrack=memtrack)
memtrack.report('AFTER REINDEX')
ibs.print_cachestats_str()
logger.info('[nnindex.MEMCACHE] size(NEIGHBOR_CACHE) = %s' %
(ut.get_object_size_str(
neighbor_index_cache.NEIGHBOR_CACHE.items()), ))
logger.info('[nnindex.MEMCACHE] len(NEIGHBOR_CACHE) = %s' %
(len(neighbor_index_cache.NEIGHBOR_CACHE.items()), ))
logger.info('[nnindex.MEMCACHE] size(UUID_MAP_CACHE) = %s' %
(ut.get_object_size_str(
neighbor_index_cache.UUID_MAP_CACHE), ))
logger.info('totalsize(nnindexer) = ' +
ut.get_object_size_str(nnindexer_))
memtrack.report_type(neighbor_index_cache.NeighborIndex)
ut.print_object_size_tree(nnindexer_, lbl='nnindexer_')
if IS_SMALL:
nnindexer_list.append(nnindexer_)
reindex_count_list.append(count)
time_list_reindex.append(t.ellapsed)
# import cv2
# import matplotlib as mpl
# logger.info(mem_top.mem_top(limit=30, width=120,
# #exclude_refs=[cv2.__dict__, mpl.__dict__]
# ))
logger.info('L___________________\n\n\n')
logger.info(ut.repr2(time_list_reindex))
if IS_SMALL:
logger.info(ut.repr2(list(map(id, nnindexer_list))))
logger.info(
ut.repr2(list([nnindxer.cfgstr
for nnindxer in nnindexer_list])))
except KeyboardInterrupt:
logger.info('\n[train] Caught CRTL+C')
resolution = ''
from six.moves import input
while not (resolution.isdigit()):
logger.info('\n[train] What do you want to do?')
logger.info('[train] 0 - Continue')
logger.info('[train] 1 - Embed')
logger.info('[train] ELSE - Stop network training')
resolution = input('[train] Resolution: ')
resolution = int(resolution)
# We have a resolution
if resolution == 0:
logger.info('resuming training...')
elif resolution == 1:
ut.embed()
import wbia.plottool as pt
next_fnum = iter(range(0, 1)).next # python3 PY3
pt.figure(fnum=next_fnum())
if len(addition_count_list) > 0:
pt.plot2(
addition_count_list,
time_list_addition,
marker='-o',
equal_aspect=False,
x_label='num_annotations',
label=addition_lbl + ' Time',
)
if len(reindex_count_list) > 0:
pt.plot2(
reindex_count_list,
time_list_reindex,
marker='-o',
equal_aspect=False,
x_label='num_annotations',
label=reindex_label + ' Time',
)
pt.set_figtitle('Augmented indexer experiment')
pt.legend()
def show_single_coverage_mask(qreq_,
cm,
weight_mask_m,
weight_mask,
daids,
fnum=None):
"""
DEPRICATE
"""
import wbia.plottool as pt
import vtool as vt
from wbia import viz
fnum = pt.ensure_fnum(fnum)
idx_list = ut.dict_take(cm.daid2_idx, daids)
nPlots = len(idx_list) + 1
nRows, nCols = pt.get_square_row_cols(nPlots)
pnum_ = pt.make_pnum_nextgen(nRows, nCols)
pt.figure(fnum=fnum, pnum=(1, 2, 1))
# Draw coverage masks with bbox
# <FlipHack>
# weight_mask_m = np.fliplr(np.flipud(weight_mask_m))
# weight_mask = np.fliplr(np.flipud(weight_mask))
# </FlipHack>
stacked_weights, offset_tup, sf_tup = vt.stack_images(weight_mask_m,
weight_mask,
return_sf=True)
(woff, hoff) = offset_tup[1]
wh1 = weight_mask_m.shape[0:2][::-1]
wh2 = weight_mask.shape[0:2][::-1]
pt.imshow(
255 * (stacked_weights),
fnum=fnum,
pnum=pnum_(0),
title='(query image) What did match vs what should match',
)
pt.draw_bbox((0, 0) + wh1, bbox_color=(0, 0, 1))
pt.draw_bbox((woff, hoff) + wh2, bbox_color=(0, 0, 1))
# Get contributing matches
qaid = cm.qaid
daid_list = daids
fm_list = ut.take(cm.fm_list, idx_list)
fs_list = ut.take(cm.fs_list, idx_list)
# Draw matches
for px, (daid, fm, fs) in enumerate(zip(daid_list, fm_list, fs_list),
start=1):
viz.viz_matches.show_matches2(
qreq_.ibs,
qaid,
daid,
fm,
fs,
draw_pts=False,
draw_lines=True,
draw_ell=False,
fnum=fnum,
pnum=pnum_(px),
darken=0.5,
)
coverage_score = score_matching_mask(weight_mask_m, weight_mask)
pt.set_figtitle('score=%.4f' % (coverage_score, ))
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 wbia.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=(4.0, 20.0),
# box_alignment=(0, 0),
box_alignment=(0.5, 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, -20.0),
# box_alignment=(1, 1),
box_alignment=(0.5, 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':
logger.info('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 flann_add_time_experiment():
"""
builds plot of number of annotations vs indexer build time.
TODO: time experiment
CommandLine:
python -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_MTEST --show
python -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_Master0 --show
utprof.py -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --show
valgrind --tool=memcheck --suppressions=valgrind-python.supp python -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_MTEST --no-with-reindex
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots._neighbor_experiment import * # NOQA
>>> import wbia
>>> #ibs = wbia.opendb('PZ_MTEST')
>>> result = flann_add_time_experiment()
>>> # verify results
>>> print(result)
>>> ut.show_if_requested()
"""
import wbia
import utool as ut
import numpy as np
import wbia.plottool as pt
def make_flann_index(vecs, flann_params):
flann = pyflann.FLANN()
flann.build_index(vecs, **flann_params)
return flann
db = ut.get_argval('--db')
ibs = wbia.opendb(db=db)
# Input
if ibs.get_dbname() == 'PZ_MTEST':
initial = 1
reindex_stride = 16
addition_stride = 4
max_ceiling = 120
elif ibs.get_dbname() == 'PZ_Master0':
# ibs = wbia.opendb(db='GZ_ALL')
initial = 32
reindex_stride = 32
addition_stride = 16
max_ceiling = 300001
else:
assert False
# max_ceiling = 32
all_daids = ibs.get_valid_aids()
max_num = min(max_ceiling, len(all_daids))
flann_params = vt.get_flann_params()
# Output
count_list, time_list_reindex = [], []
count_list2, time_list_addition = [], []
# Setup
# all_randomize_daids_ = ut.deterministic_shuffle(all_daids[:])
all_randomize_daids_ = all_daids
# ensure all features are computed
ibs.get_annot_vecs(all_randomize_daids_)
def reindex_step(count, count_list, time_list_reindex):
daids = all_randomize_daids_[0:count]
vecs = np.vstack(ibs.get_annot_vecs(daids))
with ut.Timer(verbose=False) as t:
flann = make_flann_index(vecs, flann_params) # NOQA
count_list.append(count)
time_list_reindex.append(t.ellapsed)
def addition_step(count, flann, count_list2, time_list_addition):
daids = all_randomize_daids_[count:count + 1]
vecs = np.vstack(ibs.get_annot_vecs(daids))
with ut.Timer(verbose=False) as t:
flann.add_points(vecs)
count_list2.append(count)
time_list_addition.append(t.ellapsed)
def make_initial_index(initial):
daids = all_randomize_daids_[0:initial + 1]
vecs = np.vstack(ibs.get_annot_vecs(daids))
flann = make_flann_index(vecs, flann_params)
return flann
WITH_REINDEX = not ut.get_argflag('--no-with-reindex')
if WITH_REINDEX:
# Reindex Part
reindex_lbl = 'Reindexing'
_reindex_iter = range(1, max_num, reindex_stride)
reindex_iter = ut.ProgressIter(_reindex_iter, lbl=reindex_lbl, freq=1)
for count in reindex_iter:
reindex_step(count, count_list, time_list_reindex)
# Add Part
flann = make_initial_index(initial)
addition_lbl = 'Addition'
_addition_iter = range(initial + 1, max_num, addition_stride)
addition_iter = ut.ProgressIter(_addition_iter, lbl=addition_lbl)
for count in addition_iter:
addition_step(count, flann, count_list2, time_list_addition)
logger.info('---')
logger.info('Reindex took time_list_reindex %.2s seconds' %
sum(time_list_reindex))
logger.info('Addition took time_list_reindex %.2s seconds' %
sum(time_list_addition))
logger.info('---')
statskw = dict(precision=2, newlines=True)
logger.info('Reindex stats ' +
ut.get_stats_str(time_list_reindex, **statskw))
logger.info('Addition stats ' +
ut.get_stats_str(time_list_addition, **statskw))
logger.info('Plotting')
# with pt.FigureContext:
next_fnum = iter(range(0, 2)).next # python3 PY3
pt.figure(fnum=next_fnum())
if WITH_REINDEX:
pt.plot2(
count_list,
time_list_reindex,
marker='-o',
equal_aspect=False,
x_label='num_annotations',
label=reindex_lbl + ' Time',
dark=False,
)
# pt.figure(fnum=next_fnum())
pt.plot2(
count_list2,
time_list_addition,
marker='-o',
equal_aspect=False,
x_label='num_annotations',
label=addition_lbl + ' Time',
)
pt
pt.legend()
