def plot(self, fnum=None, pnum=(1, 1, 1), **kwargs):
import 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 visualize(normalizer, update=True, verbose=True, fnum=None):
"""
CommandLine:
python -m ibeis.algo.hots.score_normalization --test-visualize --index 0
--cmd
Example:
>>> # DISABLE_DOCTEST
>>> import plottool as pt
>>> from ibeis.algo.hots.score_normalization import * # NOQA
>>> #import ibeis
>>> index = ut.get_argval('--index', type_=int, default=0)
>>> normalizer = load_precomputed_normalizer(index, with_global=False)
>>> normalizer.visualize()
>>> six.exec_(pt.present(), globals(), locals())
"""
import plottool as pt
if verbose:
print(normalizer.get_infostr())
if normalizer.score_domain is None:
return
if fnum is None:
fnum = pt.next_fnum()
pt.figure(fnum=fnum, pnum=(2, 1, 1), doclf=True, docla=True)
normalizer.visualize_probs(fnum=fnum, pnum=(2, 1, 1), update=False)
normalizer.visualize_support(fnum=fnum, pnum=(2, 1, 2), update=False)
if update:
pt.update()
def inspect_pdfs(tn_support, tp_support, score_domain, p_tp_given_score,
p_tn_given_score, p_score_given_tp, p_score_given_tn, p_score,
with_scores=False):
import plottool as pt # NOQA
fnum = pt.next_fnum()
nRows = 2 + with_scores
pnum_ = pt.get_pnum_func(nRows=nRows, nCols=1)
#pnum_ = pt.get_pnum_func(nRows=3, nCols=1)
#def next_pnum():
# return pnum_(
def generate_pnum():
for px in range(nRows):
yield pnum_(px)
_pnumiter = generate_pnum().next
pt.figure(fnum=fnum, pnum=pnum_(0))
if with_scores:
plot_support(tn_support, tp_support, fnum=fnum, pnum=_pnumiter())
plot_prebayes_pdf(score_domain, p_score_given_tn, p_score_given_tp, p_score,
cfgstr='', fnum=fnum, pnum=_pnumiter())
plot_postbayes_pdf(score_domain, p_tn_given_score, p_tp_given_score,
cfgstr='', fnum=fnum, pnum=_pnumiter())
def show_single_coverage_mask(qreq_, cm, weight_mask_m, weight_mask, daids, fnum=None):
import plottool as pt
from ibeis 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=.5)
coverage_score = score_matching_mask(weight_mask_m, weight_mask)
pt.set_figtitle('score=%.4f' % (coverage_score,))
def draw_junction_tree(model, fnum=None, **kwargs):
import 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.node)
e = fixtupkeys(netx_graph.edge)
a = fixtupkeys(netx_graph.adj)
netx_graph.node = n
netx_graph.edge = e
netx_graph.adj = a
#netx_graph = model.to_markov_model()
#pos = netx.pygraphviz_layout(netx_graph)
#pos = netx.graphviz_layout(netx_graph)
pos = netx.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)
netx.draw(netx_graph, **drawkw)
if kwargs.get('show_title', True):
pt.set_figtitle('Junction / Clique Tree / Cluster Graph')
def show_notch_tips(depc, aid, config={}, fnum=None, pnum=None):
import plottool as pt
pt.figure(fnum=fnum, pnum=pnum)
notch = depc.get('Notch_Tips', aid, config=config)
chip = depc.get('chips', aid, 'img', config=config)
pt.imshow(chip)
pt.draw_kpts2(np.array(notch), pts=True, ell=False, pts_size=20)
def next_query_slot(self):
"""
callback used when all interactions are completed.
Generates the next incremental query and then tries
the automatic interactions
"""
try:
# Generate the query result
item = six.next(self.inc_query_gen)
(ibs, cm, qreq_, incinfo) = item
# update incinfo
self.cm = cm
self.qreq_ = qreq_
self.incinfo = incinfo
incinfo['count'] += 1
automatch.run_until_name_decision_signal(ibs, cm, qreq_, incinfo=incinfo)
#import plottool as pt
#pt.present()
except StopIteration:
#TODO: close this figure
# incinfo['fnum']
print('NO MORE QUERIES. CLOSE DOWN WINDOWS AND DISPLAY DONE MESSAGE')
import plottool as pt
fig1 = pt.figure(fnum=511)
fig2 = pt.figure(fnum=512)
pt.close_figure(fig1)
pt.close_figure(fig2)
if 'finish_callback' in self.incinfo:
self.incinfo['update_callback']()
if 'finish_callback' in self.incinfo:
self.incinfo['finish_callback']()
pass
def show_matches(fm,
fs,
fnum=1,
pnum=None,
title='',
key=None,
simp=None,
cmap='hot',
draw_lines=True,
**locals_):
#locals_ = locals()
import plottool as pt
from plottool import plot_helpers as ph
# hack keys out of namespace
keys = 'rchip1, rchip2, kpts1, kpts2'.split(', ')
rchip1, rchip2, kpts1, kpts2 = ut.dict_take(locals_, keys)
pt.figure(fnum=fnum, pnum=pnum)
#doclf=True, docla=True)
ax, xywh1, xywh2 = pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm=fm,
fs=fs,
fnum=fnum,
cmap=cmap,
draw_lines=draw_lines,
ori=True)
ph.set_plotdat(ax, 'viztype', 'matches')
ph.set_plotdat(ax, 'simp', simp)
ph.set_plotdat(ax, 'key', key)
title = title + '\n num=%d, sum=%.2f' % (len(fm), sum(fs))
pt.set_title(title)
return ax, xywh1, xywh2
def show_notch_tips(depc, aid, config={}, fnum=None, pnum=None):
import plottool as pt
pt.figure(fnum=fnum, pnum=pnum)
notch = depc.get('Notch_Tips', aid, config=config)
chip = depc.get('chips', aid, 'img', config=config)
pt.imshow(chip)
pt.draw_kpts2(np.array(notch), pts=True, ell=False, pts_size=20)
def show_chip_distinctiveness_plot(chip, kpts, dstncvs, fnum=1, pnum=None):
import 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=.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=.3, pts=not ell)
pt.plt.sca(ax)
def plot(self, fnum=None, pnum=(1, 1, 1), **kwargs):
import 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 show_normalizers(match, fnum=None, pnum=None, update=True):
import plottool as pt
from plottool import plot_helpers as ph
# hack keys out of namespace
keys = ["rchip", "kpts"]
rchip1, kpts1 = ut.dict_take(match.annot1.__dict__, keys)
rchip2, kpts2 = ut.dict_take(match.annot2.__dict__, keys)
fs, fm = match.fs, match.fm_norm
cmap = "cool"
draw_lines = True
if fnum is None:
fnum = pt.next_fnum()
pt.figure(fnum=fnum, pnum=pnum)
# doclf=True, docla=True)
ax, xywh1, xywh2 = pt.show_chipmatch2(
rchip1, rchip2, kpts1, kpts2, fm=fm, fs=fs, fnum=fnum, cmap=cmap, draw_lines=draw_lines
)
ph.set_plotdat(ax, "viztype", "matches")
ph.set_plotdat(ax, "key", match.key)
title = match.key + "\n num=%d, sum=%.2f" % (len(fm), sum(fs))
pt.set_title(title)
if update:
pt.update()
return ax, xywh1, xywh2
def show_normalizers(match, fnum=None, pnum=None, update=True):
import plottool as pt
from plottool import plot_helpers as ph
# hack keys out of namespace
keys = ['rchip', 'kpts']
rchip1, kpts1 = ut.dict_take(match.annot1.__dict__, keys)
rchip2, kpts2 = ut.dict_take(match.annot2.__dict__, keys)
fs, fm = match.fs, match.fm_norm
cmap = 'cool'
draw_lines = True
if fnum is None:
fnum = pt.next_fnum()
pt.figure(fnum=fnum, pnum=pnum)
#doclf=True, docla=True)
ax, xywh1, xywh2 = pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm=fm,
fs=fs,
fnum=fnum,
cmap=cmap,
draw_lines=draw_lines)
ph.set_plotdat(ax, 'viztype', 'matches')
ph.set_plotdat(ax, 'key', match.key)
title = match.key + '\n num=%d, sum=%.2f' % (len(fm), sum(fs))
pt.set_title(title)
if update:
pt.update()
return ax, xywh1, xywh2
def show_hist_submaxima(hist_, edges=None, centers=None, maxima_thresh=.8, pnum=(1, 1, 1)):
r"""
For C++ to show data
Args:
hist_ (?):
edges (None):
centers (None):
CommandLine:
python -m vtool.histogram --test-show_hist_submaxima --show
python -m pyhesaff._pyhesaff --test-test_rot_invar --show
python -m vtool.histogram --test-show_hist_submaxima --dpath figures --save ~/latex/crall-candidacy-2015/figures/show_hist_submaxima.jpg
Example:
>>> # DISABLE_DOCTEST
>>> import plottool as pt
>>> from vtool.histogram import * # NOQA
>>> # build test data
>>> hist_ = np.array(list(map(float, ut.get_argval('--hist', type_=list, default=[1, 4, 2, 5, 3, 3]))))
>>> edges = np.array(list(map(float, ut.get_argval('--edges', type_=list, default=[0, 1, 2, 3, 4, 5, 6]))))
>>> maxima_thresh = ut.get_argval('--maxima_thresh', type_=float, default=.8)
>>> centers = None
>>> # execute function
>>> show_hist_submaxima(hist_, edges, centers, maxima_thresh)
>>> pt.show_if_requested()
"""
#print(repr(hist_))
#print(repr(hist_.shape))
#print(repr(edges))
#print(repr(edges.shape))
#ut.embed()
import plottool as pt
#ut.embed()
if centers is None:
centers = hist_edges_to_centers(edges)
bin_colors = pt.get_orientation_color(centers)
pt.figure(fnum=pt.next_fnum(), pnum=pnum)
POLAR = False
if POLAR:
pt.df2.plt.subplot(*pnum, polar=True, axisbg='#000000')
pt.draw_hist_subbin_maxima(hist_, centers, bin_colors=bin_colors, maxima_thresh=maxima_thresh)
#pt.gca().set_rmax(hist_.max() * 1.1)
#pt.gca().invert_yaxis()
#pt.gca().invert_xaxis()
pt.dark_background()
#if ut.get_argflag('--legend'):
# pt.figure(fnum=pt.next_fnum())
# centers_ = np.append(centers, centers[0])
# r = np.ones(centers_.shape) * .2
# ax = pt.df2.plt.subplot(111, polar=True)
# pt.plots.colorline(centers_, r, cmap=pt.df2.plt.get_cmap('hsv'), linewidth=10)
# #ax.plot(centers_, r, 'm', color=bin_colors, linewidth=100)
# ax.set_rmax(.2)
# #ax.grid(True)
# #ax.set_title("Angle Colors", va='bottom')
title = ut.get_argval('--title', default='')
import plottool as pt
pt.set_figtitle(title)
def show_selected(self, event):
import plottool as pt
print('show_selected')
from ibeis.viz import viz_chip
fnum = pt.ensure_fnum(None)
print('fnum = %r' % (fnum,))
pt.figure(fnum=fnum)
viz_chip.show_many_chips(self.ibs, self.selected_aids)
pt.update()
def show_selected(self, event):
import plottool as pt
print('show_selected')
from ibeis.viz import viz_chip
fnum = pt.ensure_fnum(None)
print('fnum = %r' % (fnum, ))
pt.figure(fnum=fnum)
viz_chip.show_many_chips(self.ibs, self.selected_aids)
pt.update()
def draw_tree_model(model, **kwargs):
import 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 chipmatch_view(self, fnum=None, pnum=(1, 1, 1), verbose=None, **kwargs_):
"""
just visualizes the matches using some type of lines
"""
import plottool as pt
from plottool import plot_helpers as ph
if fnum is None:
fnum = self.fnum
if verbose is None:
verbose = ut.VERBOSE
if verbose:
print("-- CHIPMATCH VIEW --")
print("[ichipmatch_view] self.mode = %r" % (self.mode,))
mode = kwargs_.get("mode", self.mode)
draw_ell = mode >= 1
draw_lines = mode == 2
if verbose:
print("[ichipmatch_view] draw_lines = %r" % (draw_lines,))
print("[ichipmatch_view] draw_ell = %r" % (draw_ell,))
# pt.figure(fnum=fnum, docla=True, doclf=True)
# NOTE: i remove the clf here. might cause issues
pt.figure(fnum=fnum, docla=True, doclf=False)
# show_matches_kw = self.__dict__.copy()
show_matches_kw = dict(
# fnum=fnum, pnum=pnum,
draw_lines=draw_lines,
draw_ell=draw_ell,
colorbar_=True,
vert=self.vert,
)
show_matches_kw.update(kwargs_)
if verbose:
print("self.warp_homog = %r" % (self.warp_homog,))
if self.warp_homog:
show_matches_kw["H1"] = self.H1
show_matches_kw["H2"] = self.H2
if verbose:
print("show_matches_kw = %s" % (ut.dict_str(show_matches_kw, truncate=True)))
# tup = show_matches(fm, fs, **show_matches_kw)
ax, xywh1, xywh2 = pt.show_chipmatch2(
self.rchip1, self.rchip2, self.kpts1, self.kpts2, fm=self.fm, fs=self.fs, pnum=pnum, **show_matches_kw
)
self.xywh2 = xywh2
ph.set_plotdat(ax, "viztype", "matches")
if self.truth is not None and self.truth:
truth_color = pt.TRUE_BLUE # if else pt.FALSE_RED
pt.draw_border(ax, color=truth_color, lw=4)
if self.title is not None:
pt.set_title(self.title, ax=ax)
def chipmatch_view(self, fnum=None, pnum=(1, 1, 1), verbose=None, **kwargs_):
"""
just visualizes the matches using some type of lines
"""
import plottool as pt
from plottool import plot_helpers as ph
if fnum is None:
fnum = self.fnum
if verbose is None:
verbose = ut.VERBOSE
if verbose:
print('-- CHIPMATCH VIEW --')
print('[ichipmatch_view] self.mode = %r' % (self.mode,))
mode = kwargs_.get('mode', self.mode)
draw_ell = mode >= 1
draw_lines = mode == 2
if verbose:
print('[ichipmatch_view] draw_lines = %r' % (draw_lines,))
print('[ichipmatch_view] draw_ell = %r' % (draw_ell,))
#pt.figure(fnum=fnum, docla=True, doclf=True)
# NOTE: i remove the clf here. might cause issues
pt.figure(fnum=fnum, docla=True, doclf=False)
#show_matches_kw = self.__dict__.copy()
show_matches_kw = dict(
#fnum=fnum, pnum=pnum,
draw_lines=draw_lines,
draw_ell=draw_ell,
colorbar_=True,
vert=self.vert)
show_matches_kw.update(kwargs_)
if verbose:
print('self.warp_homog = %r' % (self.warp_homog,))
if self.warp_homog:
show_matches_kw['H1'] = self.H1
show_matches_kw['H2'] = self.H2
if verbose:
print('show_matches_kw = %s' % (ut.dict_str(show_matches_kw, truncate=True)))
#tup = show_matches(fm, fs, **show_matches_kw)
ax, xywh1, xywh2 = pt.show_chipmatch2(
self.rchip1, self.rchip2,
self.kpts1, self.kpts2,
fm=self.fm, fs=self.fs,
pnum=pnum, **show_matches_kw)
self.xywh2 = xywh2
ph.set_plotdat(ax, 'viztype', 'matches')
if self.truth is not None and self.truth:
truth_color = pt.TRUE_BLUE # if else pt.FALSE_RED
pt.draw_border(ax, color=truth_color, lw=4)
if self.title is not None:
pt.set_title(self.title, ax=ax)
def plot_search_surface(known_nd_data, known_target_points, given_data_dims, opt_model_params=None):
import plottool as pt
pt.figure(2, doclf=True)
# Interpolate uniform grid positions
unknown_nd_data, ug_shape = compute_interpolation_grid(known_nd_data, 0 * 5)
interpolated_error = interpolate_error(known_nd_data, known_target_points, unknown_nd_data)
ax = pt.plot_surface3d(
unknown_nd_data.T[0].reshape(ug_shape),
unknown_nd_data.T[1].reshape(ug_shape),
interpolated_error.reshape(ug_shape),
xlabel='nDaids',
ylabel='K',
zlabel='error',
rstride=1, cstride=1,
cmap=pt.plt.get_cmap('jet'),
wire=True,
#norm=pt.mpl.colors.Normalize(0, 1),
#shade=False,
#dark=False,
)
ax.scatter(known_nd_data.T[0], known_nd_data.T[1], known_target_points, s=100, c=pt.YELLOW)
assert len(given_data_dims) == 1, 'can only plot 1 given data dim'
xdim = given_data_dims[0]
ydim = (xdim + 1) % (len(known_nd_data.T))
known_nd_min = known_nd_data.min(axis=0)
known_nd_max = known_nd_data.max(axis=0)
xmin, xmax = known_nd_min[xdim], known_nd_max[xdim]
ymin, ymax = known_nd_min[ydim], known_nd_max[ydim]
zmin, zmax = known_target_points.min(), known_target_points.max()
if opt_model_params is not None:
# plot learned data if availabel
#given_known_nd_data = known_nd_data.take(given_data_dims, axis=1)
xdata = np.linspace(xmin, xmax)
ydata = compute_K(xdata, opt_model_params)
xydata = np.array((xdata, ydata)).T
zdata = interpolate_error(known_nd_data, known_target_points, xydata)
ax.plot(xdata, ydata, zdata, c=pt.ORANGE)
ymax = max(ymax, ydata.max())
ymin = min(ymin, ydata.min())
zmin = min(zmin, zdata.min())
zmax = max(zmax, zdata.max())
ax.scatter(xdata, ydata, zdata, s=100, c=pt.ORANGE)
#[t.set_color('white') for t in ax.xaxis.get_ticklines()]
#[t.set_color('white') for t in ax.xaxis.get_ticklabels()]
ax.set_aspect('auto')
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
ax.set_zlim(zmin, zmax)
import matplotlib.ticker as mtick
ax.zaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))
return ax
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:
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_single_coverage_mask(qreq_,
cm,
weight_mask_m,
weight_mask,
daids,
fnum=None):
import plottool as pt
from ibeis 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=.5)
coverage_score = score_matching_mask(weight_mask_m, weight_mask)
pt.set_figtitle('score=%.4f' % (coverage_score, ))
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 ibeis.viz.interact.interact_matches --test-chipmatch_view --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.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 show_edge(infr, edge, fnum=None, pnum=None, **kwargs):
import 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 draw_aids(infr, aids, fnum=None):
from ibeis.viz import viz_chip
import 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 draw_tree_model(model, **kwargs):
import 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'], '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.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()
}
n = fixtupkeys(netx_graph.node)
e = fixtupkeys(netx_graph.edge)
a = fixtupkeys(netx_graph.adj)
netx_graph.node = n
netx_graph.edge = e
netx_graph.adj = a
#netx_graph = model.to_markov_model()
#pos = netx.pygraphviz_layout(netx_graph)
#pos = netx.graphviz_layout(netx_graph)
pos = netx.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 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 plot_annotaiton_gps(X_data):
""" Plots gps coordinates on a map projection
InstallBasemap:
sudo apt-get install libgeos-dev
pip install git+https://github.com/matplotlib/basemap
Ignore:
pip install git+git://github.com/myuser/foo.git@v123
"""
import plottool as pt
from mpl_toolkits.basemap import Basemap
# lat = X_data[1:5, 1]
# lon = X_data[1:5, 2]
lat = X_data[:, 1] # NOQA
lon = X_data[:, 2] # NOQA
fnum = pt.ensure_fnum(None)
fig = pt.figure(fnum=fnum, doclf=True, docla=True) # NOQA
pt.close_figure(fig)
fig = pt.figure(fnum=fnum, doclf=True, docla=True)
# setup Lambert Conformal basemap.
m = Basemap(
llcrnrlon=lon.min(),
urcrnrlon=lon.max(),
llcrnrlat=lat.min(),
urcrnrlat=lat.max(),
projection="cea",
resolution="h",
)
# draw coastlines.
# m.drawcoastlines()
# m.drawstates()
# draw a boundary around the map, fill the background.
# this background will end up being the ocean color, since
# the continents will be drawn on top.
# m.bluemarble()
m.drawmapboundary(fill_color="aqua")
m.fillcontinents(color="coral", lake_color="aqua")
# Convert GPS to projected coordinates
x1, y1 = m(lon, lat) # convert to meters # lon==X, lat==Y
m.plot(x1, y1, "o")
fig.show()
def show_matches(fm, fs, fnum=1, pnum=None, title='', key=None, simp=None,
cmap='hot', draw_lines=True, **locals_):
#locals_ = locals()
import plottool as pt
from plottool import plot_helpers as ph
# hack keys out of namespace
keys = 'rchip1, rchip2, kpts1, kpts2'.split(', ')
rchip1, rchip2, kpts1, kpts2 = ut.dict_take(locals_, keys)
pt.figure(fnum=fnum, pnum=pnum)
#doclf=True, docla=True)
ax, xywh1, xywh2 = pt.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm=fm,
fs=fs, fnum=fnum, cmap=cmap,
draw_lines=draw_lines, ori=True)
ph.set_plotdat(ax, 'viztype', 'matches')
ph.set_plotdat(ax, 'simp', simp)
ph.set_plotdat(ax, 'key', key)
title = title + '\n num=%d, sum=%.2f' % (len(fm), sum(fs))
pt.set_title(title)
return ax, xywh1, xywh2
def 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 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 test_average_contrast():
import vtool as vt
ut.get_valid_test_imgkeys()
img_fpath_list = [ut.grab_test_imgpath(key) for key in ut.get_valid_test_imgkeys()]
img_list = [vt.imread(img, grayscale=True) for img in img_fpath_list]
avecontrast_list = np.array([compute_average_contrast(img) for img in img_list])
import plottool as pt
nCols = len(img_list)
fnum = None
if fnum is None:
fnum = pt.next_fnum()
pt.figure(fnum=fnum, pnum=(2, 1, 1))
sortx = avecontrast_list.argsort()
y_list = avecontrast_list[sortx]
x_list = np.arange(0, nCols) + .5
pt.plot(x_list, y_list, 'bo-')
sorted_imgs = ut.take(img_list, sortx)
for px, img in ut.ProgressIter(enumerate(sorted_imgs, start=1)):
pt.imshow(img, fnum=fnum, pnum=(2, nCols, nCols + px))
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 test_average_contrast():
import vtool as vt
ut.get_valid_test_imgkeys()
img_fpath_list = [
ut.grab_test_imgpath(key) for key in ut.get_valid_test_imgkeys()
]
img_list = [vt.imread(img, grayscale=True) for img in img_fpath_list]
avecontrast_list = np.array(
[compute_average_contrast(img) for img in img_list])
import plottool as pt
nCols = len(img_list)
fnum = None
if fnum is None:
fnum = pt.next_fnum()
pt.figure(fnum=fnum, pnum=(2, 1, 1))
sortx = avecontrast_list.argsort()
y_list = avecontrast_list[sortx]
x_list = np.arange(0, nCols) + .5
pt.plot(x_list, y_list, 'bo-')
sorted_imgs = ut.take(img_list, sortx)
for px, img in ut.ProgressIter(enumerate(sorted_imgs, start=1)):
pt.imshow(img, fnum=fnum, pnum=(2, nCols, nCols + px))
def draw_expanded_scales(imgL, sel_kpts, exkpts, exdesc_):
import plottool as pt
draw_keypoint_patch = pt.draw_keypoint_patch
get_warped_patch = pt.get_warped_patch # NOQA
# Rows are for different scales
# Cols are for different patches
# There is a prefix row showing the original image
nRows, nCols = len(exkpts), len(sel_kpts)
exkpts_ = np.vstack(exkpts)
fnum = 1
pt.figure(fnum=fnum, docla=True, doclf=True)
nPreRows = 1
nPreCols = (nPreRows * nCols) + 1
pt.show_keypoints(imgL,
exkpts_,
fnum=fnum,
pnum=(nRows + nPreRows, 1, 1),
color=pt.BLUE)
px = 0
for row, kpts_ in enumerate(exkpts):
for col, kp in enumerate(kpts_):
sift = exdesc_[px]
pnum = (nRows + nPreRows, nCols, px + nPreCols)
draw_keypoint_patch(imgL,
kp,
sift,
warped=True,
fnum=fnum,
pnum=pnum)
px += 1
#df2.draw()
print('nRows = %r' % nRows)
print('nCols = %r' % nCols)
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 next_query_slot(self):
"""
callback used when all interactions are completed.
Generates the next incremental query and then tries
the automatic interactions
"""
try:
# Generate the query result
item = six.next(self.inc_query_gen)
(ibs, cm, qreq_, incinfo) = item
# update incinfo
self.cm = cm
self.qreq_ = qreq_
self.incinfo = incinfo
incinfo['count'] += 1
automatch.run_until_name_decision_signal(ibs,
cm,
qreq_,
incinfo=incinfo)
#import plottool as pt
#pt.present()
except StopIteration:
#TODO: close this figure
# incinfo['fnum']
print(
'NO MORE QUERIES. CLOSE DOWN WINDOWS AND DISPLAY DONE MESSAGE')
import plottool as pt
fig1 = pt.figure(fnum=511)
fig2 = pt.figure(fnum=512)
pt.close_figure(fig1)
pt.close_figure(fig2)
if 'finish_callback' in self.incinfo:
self.incinfo['update_callback']()
if 'finish_callback' in self.incinfo:
self.incinfo['finish_callback']()
pass
def draw_junction_tree(model, fnum=None, **kwargs):
import 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.node)
e = fixtupkeys(netx_graph.edge)
a = fixtupkeys(netx_graph.adj)
netx_graph.node = 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):
import 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 draw_precision_recall_curve_(recall_range_, p_interp_curve, title_pref=None, fnum=1):
import 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)
print('Interplated Precision')
print(ut.repr2(list(zip(recall_range_, p_interp_curve))))
def draw_precision_recall_curve_(recall_range_, p_interp_curve, title_pref=None, fnum=1):
import 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)
print('Interplated Precision')
print(ut.list_str(list(zip(recall_range_, p_interp_curve))))
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:
#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 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 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:
#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 get_iters_vs_miou(harn):
from pysseg.util import jsonutil
import pysseg.backend.iface_caffe as iface
harn.prepare_test_model()
test_weight_dpaths = harn.find_test_weights_dpaths()
# for test_weights_dpath in test_weight_dpaths:
# harn.test_weights_dpath = test_weights_dpath
# harn.test_weights_fpath = ub.readfrom(join(test_weights_dpath, 'test_weights.caffemodel.lnk'))
# # if not exists(join(harn.test_weights_dpath, 'pred')):
# results_fpath = join(harn.test_weights_dpath, 'results.json')
# if exists(results_fpath):
# results = json.load(results_fpath)
iter_results = {}
for test_weights_dpath in test_weight_dpaths:
results_fpath = join(test_weights_dpath, 'results.json')
if exists(results_fpath):
iterno = iface.snapshot_iterno(test_weights_dpath)
results = json.load(open(results_fpath, 'r'))
ious = eval(results['ious'])
iter_results[iterno] = ious
iter_df = pd.DataFrame(iter_results)
iter_df.columns.name = 'iterno'
iter_df.index.name = 'class'
fpath = join(harn.test_dpath, 'iter_ious.json')
jsonutil.write_json(fpath, iter_df)
iter_df = iter_df.drop([57], axis=1)
iter_df.drop(harn.task.ignore_classnames).mean(axis=0)
if False:
"""
ffmpeg -y -f image2 -i ~/aretha/store/data/work/camvid/arch/segnet_proper/test/input_nqmmrhd/weights_abvroyo_segnet_proper_None_xwfmwfo_00040000/blend_pred/%*.png -crf 25 -vcodec libx264 -vf "setpts=4*PTS" camvid-results.avi
ffmpeg -y -f image2 -i out_haul83/%*.png -vcodec mpeg4 -vf "setpts=10*PTS" haul83-results.avi
"""
# move to computer with plottool
iter_df = pd.read_json(
'/home/joncrall/aretha/store/data/work/camvid/arch/segnet_proper/test/input_nqmmrhd/iter_ious.json'
)
import plottool as pt
pt.qtensure()
from pysseg.tasks import CamVid
task = CamVid()
iter_miou = iter_df.drop(task.ignore_classnames).mean(axis=0)
iter_miou = iter_miou.sort_index()
_set_mpl_rcparams()
fig = pt.figure(fnum=1, pnum=(1, 1, 1))
ax = pt.gca()
iter_miou.plot(ax=ax)
ax.set_xlabel('train iters')
ax.set_ylabel('test mIoU')
ax.set_title('Reproduced CamVid Results (init using VGG)')
ub.ensuredir('result_plots')
from pysseg.draw import render_figure_to_image
import cv2
cv2.imwrite('result_plots/miou.png',
render_figure_to_image(fig, dpi=100, transparent=True))
fig = pt.figure(fnum=2, pnum=(1, 1, 1))
ax = pt.gca()
iter_iou = iter_df.drop(task.ignore_classnames).T.sort_index()
# sort by results
iter_iou = iter_iou[iter_iou.iloc[-1].sort_values().index[::-1]]
colors = [
tuple(np.array(v[::-1]) / 255) + (1, )
for v in ub.take(task.class_colors, iter_iou.columns)
]
iter_iou.plot(ax=ax, colors=colors, lw=4)
ax.set_xlabel('train iters')
ax.set_ylabel('test IoU')
ax.set_title('Reproduced CamVid Results (init using VGG)')
ub.ensuredir('result_plots')
from pysseg.draw import render_figure_to_image
cv2.imwrite('result_plots/perclass_iou.png',
render_figure_to_image(fig, dpi=100, transparent=True))
def show_augmented_patches(Xb, Xb_, yb, yb_, data_per_label=1, shadows=None):
"""
from ibeis_cnn.augment import * # NOQA
std_ = center_std
mean_ = center_mean
"""
import plottool as pt
import vtool as vt
Xb_old = vt.rectify_to_float01(Xb)
Xb_new = vt.rectify_to_float01(Xb_)
# only look at ones that were actually augmented
sample1 = Xb_old[0::data_per_label]
sample2 = Xb_new[0::data_per_label]
diff = np.abs((sample1 - sample2))
diff_batches = diff.sum(-1).sum(-1).sum(-1) > 0
modified_indexes = np.where(diff_batches > 0)[0]
print('modified_indexes = %r' % (modified_indexes,))
#modified_indexes = np.arange(num_examples)
Xb_old = vt.rectify_to_uint8(Xb_old)
Xb_new = vt.rectify_to_uint8(Xb_new)
# Group data into n-tuples
grouped_idxs = [np.arange(n, len(Xb_), data_per_label)
for n in range(data_per_label)]
data_lists_old = vt.apply_grouping(Xb_old, grouped_idxs, axis=0)
data_lists_new = vt.apply_grouping(Xb_new, grouped_idxs, axis=0)
import six
#chunck_sizes = (4, 10)
import utool
with utool.embed_on_exception_context:
chunk_sizes = pt.get_square_row_cols(len(modified_indexes), max_cols=10,
fix=False, inclusive=False)
_iter = ut.iter_multichunks(modified_indexes, chunk_sizes)
multiindices = six.next(_iter)
from ibeis_cnn import draw_results
tup = draw_results.get_patch_multichunks(data_lists_old, yb, {},
multiindices)
orig_stack = tup[0]
#stacked_img, stacked_offsets, stacked_sfs = tup
tup = draw_results.get_patch_multichunks(data_lists_new, yb_, {},
multiindices)
warp_stack = tup[0]
#stacked_img, stacked_offsets, stacked_sfs = tup
#orig_stack = stacked_img_pairs(Xb_old, modified_indexes, yb)
#warp_stack = stacked_img_pairs(Xb_new, modified_indexes, yb_)
if shadows is not None:
# hack
shadow_stack = stacked_img_pairs(shadows, modified_indexes, yb_)
fnum = None
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum)
#next_pnum = pt.make_pnum_nextgen(nRows=2 + (shadows is not None), nCols=1)
next_pnum = pt.make_pnum_nextgen(nCols=2 + (shadows is not None), nRows=1)
pt.imshow(orig_stack, pnum=next_pnum(), title='before')
pt.imshow(warp_stack, pnum=next_pnum(), title='after')
if shadows is not None:
pt.imshow(shadow_stack, pnum=next_pnum(), title='shadow_stack')
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 viz_netx_chipgraph(ibs, netx_graph, fnum=None, with_images=False, zoom=ZOOM):
r"""
Args:
ibs (IBEISController): ibeis controller object
netx_graph (?):
fnum (int): figure number(default = None)
with_images (bool): (default = False)
zoom (float): (default = 0.4)
Returns:
?: pos
CommandLine:
python -m ibeis.viz.viz_graph --exec-viz_netx_chipgraph --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_graph import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> nid_list = ibs.get_valid_nids()[0:5]
>>> fnum = None
>>> with_images = True
>>> zoom = 0.4
>>> #pos = viz_netx_chipgraph(ibs, netx_graph, fnum, with_images, zoom)
>>> make_name_graph_interaction(ibs, None, ibs.get_valid_aids()[0:1])
>>> #make_name_graph_interaction(ibs, nid_list)
>>> ut.show_if_requested()
"""
if fnum is None:
fnum = pt.next_fnum()
#zoom = .8
print('[viz_graph] drawing chip graph')
pt.figure(fnum=fnum, pnum=(1, 1, 1))
ax = pt.gca()
#pos = netx.spring_layout(graph)
aid_list = netx_graph.nodes()
IMPLICIT_LAYOUT = len(set(ibs.get_annot_nids(aid_list))) != 1
# FIXME
print('zoom = %r' % (zoom,))
if IMPLICIT_LAYOUT:
# HACK:
# Use name edge to make pos (very bad)
aids1, aids2 = get_name_rowid_edges_from_aids(ibs, aid_list)
netx_graph_hack = make_netx_graph_from_aidpairs(ibs, aids1, aids2, unique_aids=aid_list)
pos = netx.graphviz_layout(netx_graph_hack)
else:
pos = netx.graphviz_layout(netx_graph)
#pos = netx.fruchterman_reingold_layout(netx_graph)
#pos = netx.spring_layout(netx_graph)
netx.draw(netx_graph, pos=pos, ax=ax)
with_nid_edges = True
if with_nid_edges:
import matplotlib as mpl
import scipy.sparse as spsparse
aids1, aids2 = get_name_rowid_edges_from_aids(ibs, aid_list)
edge_pts1 = np.array(ut.dict_take(pos, aids1), dtype=np.int32)
edge_pts2 = np.array(ut.dict_take(pos, aids2), dtype=np.int32)
if len(edge_pts1) == 0:
edge_pts1 = edge_pts1[:, None]
if len(edge_pts2) == 0:
edge_pts2 = edge_pts2[:, None]
I = np.array(aids1)
J = np.array(aids2)
if len(aid_list) > 0:
N = max(aid_list) + 1
else:
N = 1
forced_edge_idxs = ut.dict_take(dict(zip(zip(I, J), range(len(I)))), netx_graph.edges())
data = vt.L2(edge_pts1, edge_pts2)
if len(forced_edge_idxs) > 0:
data[forced_edge_idxs] = 0.00001
graph = spsparse.coo_matrix((data, (I, J)), shape=(N, N))
def extract_connected_compoments(graph):
import scipy.sparse as spsparse
import utool as ut
# I think this is how extraction is done?
# only returns edge info
# so singletons are not represented
shape = graph.shape
csr_graph = graph.tocsr()
num_components, labels = spsparse.csgraph.connected_components(csr_graph)
unique_labels = np.unique(labels)
group_flags_list = [labels == groupid for groupid in unique_labels]
subgraph_list = []
for label, group_flags in zip(unique_labels, group_flags_list):
num_members = group_flags.sum()
ixs = list(range(num_members))
if num_members == 0:
continue
group_rowix, group_cols = csr_graph[group_flags, :].nonzero()
if len(group_cols) == 0:
continue
ix2_row = dict(zip(ixs, np.nonzero(group_flags)[0]))
group_rows = ut.dict_take(ix2_row, group_rowix)
component = (group_rows, group_cols.tolist())
data = csr_graph[component].tolist()[0]
subgraph = spsparse.coo_matrix((data, component), shape=shape)
subgraph_list.append(subgraph)
#assert len(compoment_list) == num_components, 'bad impl'
return subgraph_list
subgraph_list = extract_connected_compoments(graph)
spantree_aids1_ = []
spantree_aids2_ = []
for subgraph in subgraph_list:
subgraph_spantree = spsparse.csgraph.minimum_spanning_tree(subgraph)
min_aids1_, min_aids2_ = subgraph_spantree.nonzero()
spantree_aids1_.extend(min_aids1_)
spantree_aids2_.extend(min_aids2_)
edge_pts1_ = np.array(ut.dict_take(pos, spantree_aids1_))
edge_pts2_ = np.array(ut.dict_take(pos, spantree_aids2_))
segments = list(zip(edge_pts1_, edge_pts2_))
#pt.distinct_colors
color_list = pt.DARK_ORANGE
#color_list = pt.BLACK
line_group = mpl.collections.LineCollection(segments, color=color_list, alpha=.3, lw=4)
ax.add_collection(line_group)
if with_images:
import cv2
pos_list = ut.dict_take(pos, aid_list)
img_list = ibs.get_annot_chips(aid_list)
img_list = [vt.resize_thumb(img, (220, 220)) for img in img_list]
img_list = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in img_list]
artist_list = netx_draw_images_at_positions(img_list, pos_list, zoom=zoom)
for artist, aid in zip(artist_list, aid_list):
pt.set_plotdat(artist, 'aid', aid)
return pos
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 netx
factor_list = kwargs.get('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
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)
netx.draw(model, **drawkw)
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 draw_markov_model(model, fnum=None, **kwargs):
import 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(
pos=pos,
ax=ax,
with_labels=True,
node_color=node_color, # NOQA
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.node[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.node[u]['patch']
n2 = G.node[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 draw_markov_model(model, fnum=None, **kwargs):
import 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 = netx.pydot_layout(markovmodel)
pos = netx.pygraphviz_layout(markovmodel)
# Referenecs:
# https://groups.google.com/forum/#!topic/networkx-discuss/FwYk0ixLDuY
# pos = netx.spring_layout(markovmodel)
# pos = netx.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(pos=pos, ax=ax, with_labels=True, node_color=node_color, # NOQA
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.node[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.node[u]['patch']
n2 = G.node[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
# netx.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 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=3.0,
**kwargs):
r"""
DEPRICATE or improve
Args:
ibs (IBEISController): ibeis 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 ibeis --tf viz_netx_chipgraph --show
Cand:
ibeis review_tagged_joins --save figures4/mergecase.png --figsize=15,15
--clipwhite --diskshow
ibeis compute_occurrence_groups --save figures4/occurgraph.png
--figsize=40,40 --clipwhite --diskshow
~/code/ibeis/ibeis/algo/preproc/preproc_occurrence.py
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_graph import * # NOQA
>>> import ibeis
>>> ibs = ibeis.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 plottool as pt
print('[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'
print('layout = %r' % (layout, ))
if use_image:
ensure_node_images(ibs, graph)
nx.set_node_attributes(graph, 'shape', '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 select_ith_match(self, mx):
"""
Selects the ith match and visualizes and prints information concerning
features weights, keypoint details, and sift descriptions
"""
import plottool as pt
from plottool import viz_featrow
from plottool import interact_helpers as ih
# <CLOSURE VARS>
fnum = self.fnum
same_fig = self.same_fig
rchip1 = self.rchip1
rchip2 = self.rchip2
# </CLOSURE VARS>
self.mx = mx
print("+--- SELECT --- ")
print("... selecting mx-th=%r feature match" % mx)
fsv = self.fsv
fs = self.fs
print("score stats:")
print(ut.get_stats_str(fsv, axis=0, newlines=True))
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
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_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 test_siamese_performance(model, data, labels, flat_metadata, dataname=''):
r"""
CommandLine:
utprof.py -m ibeis_cnn --tf pz_patchmatch --db liberty --test --weights=liberty:current --arch=siaml2_128 --test
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure --weights=new
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --train --weights=new
python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128 --test # NOQA
python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128
"""
import vtool as vt
import plottool as pt
# TODO: save in model.trainind_dpath/diagnostics/figures
ut.colorprint('\n[siam_perf] Testing Siamese Performance', 'white')
#epoch_dpath = model.get_epoch_diagnostic_dpath()
epoch_dpath = model.arch_dpath
ut.vd(epoch_dpath)
dataname += ' ' + model.get_history_hashid() + '\n'
history_text = ut.list_str(model.era_history, newlines=True)
ut.write_to(ut.unixjoin(epoch_dpath, 'era_history.txt'), history_text)
#if True:
# import matplotlib as mpl
# mpl.rcParams['agg.path.chunksize'] = 100000
#data = data[::50]
#labels = labels[::50]
#from ibeis_cnn import utils
#data, labels = utils.random_xy_sample(data, labels, 10000, model.data_per_label_input)
FULL = not ut.get_argflag('--quick')
fnum_gen = pt.make_fnum_nextgen()
ut.colorprint('[siam_perf] Show era history', 'white')
fig = model.show_era_loss(fnum=fnum_gen())
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
# hack
ut.colorprint('[siam_perf] Show weights image', 'white')
fig = model.show_weights_image(fnum=fnum_gen())
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
#model.draw_all_conv_layer_weights(fnum=fnum_gen())
#model.imwrite_weights(1)
#model.imwrite_weights(2)
# Compute each type of score
ut.colorprint('[siam_perf] Building Scores', 'white')
test_outputs = model.predict2(model, data)
network_output = test_outputs['network_output_determ']
# hack converting network output to distances for non-descriptor networks
if len(network_output.shape) == 2 and network_output.shape[1] == 1:
cnn_scores = network_output.T[0]
elif len(network_output.shape) == 1:
cnn_scores = network_output
elif len(network_output.shape) == 2 and network_output.shape[1] > 1:
assert model.data_per_label_output == 2
vecs1 = network_output[0::2]
vecs2 = network_output[1::2]
cnn_scores = vt.L2(vecs1, vecs2)
else:
assert False
cnn_scores = cnn_scores.astype(np.float64)
# Segfaults with the data passed in is large (AND MEMMAPPED apparently)
# Fixed in hesaff implementation
SIFT = FULL
if SIFT:
sift_scores, sift_list = test_sift_patchmatch_scores(data, labels)
sift_scores = sift_scores.astype(np.float64)
ut.colorprint('[siam_perf] Learning Encoders', 'white')
# Learn encoders
encoder_kw = {
#'monotonize': False,
'monotonize': True,
}
cnn_encoder = vt.ScoreNormalizer(**encoder_kw)
cnn_encoder.fit(cnn_scores, labels)
if SIFT:
sift_encoder = vt.ScoreNormalizer(**encoder_kw)
sift_encoder.fit(sift_scores, labels)
# Visualize
ut.colorprint('[siam_perf] Visualize Encoders', 'white')
viz_kw = dict(
with_scores=False,
with_postbayes=False,
with_prebayes=False,
target_tpr=.95,
)
inter_cnn = cnn_encoder.visualize(
figtitle=dataname + ' CNN scores. #data=' + str(len(data)),
fnum=fnum_gen(), **viz_kw)
if SIFT:
inter_sift = sift_encoder.visualize(
figtitle=dataname + ' SIFT scores. #data=' + str(len(data)),
fnum=fnum_gen(), **viz_kw)
# Save
pt.save_figure(fig=inter_cnn.fig, dpath=epoch_dpath)
if SIFT:
pt.save_figure(fig=inter_sift.fig, dpath=epoch_dpath)
# Save out examples of hard errors
#cnn_fp_label_indicies, cnn_fn_label_indicies =
#cnn_encoder.get_error_indicies(cnn_scores, labels)
#sift_fp_label_indicies, sift_fn_label_indicies =
#sift_encoder.get_error_indicies(sift_scores, labels)
with_patch_examples = FULL
if with_patch_examples:
ut.colorprint('[siam_perf] Visualize Confusion Examples', 'white')
cnn_indicies = cnn_encoder.get_confusion_indicies(cnn_scores, labels)
if SIFT:
sift_indicies = sift_encoder.get_confusion_indicies(sift_scores, labels)
warped_patch1_list, warped_patch2_list = list(zip(*ut.ichunks(data, 2)))
samp_args = (warped_patch1_list, warped_patch2_list, labels)
_sample = functools.partial(draw_results.get_patch_sample_img, *samp_args)
cnn_fp_img = _sample({'fs': cnn_scores}, cnn_indicies.fp)[0]
cnn_fn_img = _sample({'fs': cnn_scores}, cnn_indicies.fn)[0]
cnn_tp_img = _sample({'fs': cnn_scores}, cnn_indicies.tp)[0]
cnn_tn_img = _sample({'fs': cnn_scores}, cnn_indicies.tn)[0]
if SIFT:
sift_fp_img = _sample({'fs': sift_scores}, sift_indicies.fp)[0]
sift_fn_img = _sample({'fs': sift_scores}, sift_indicies.fn)[0]
sift_tp_img = _sample({'fs': sift_scores}, sift_indicies.tp)[0]
sift_tn_img = _sample({'fs': sift_scores}, sift_indicies.tn)[0]
#if ut.show_was_requested():
#def rectify(arr):
# return np.flipud(arr)
SINGLE_FIG = False
if SINGLE_FIG:
def dump_img(img_, lbl, fnum):
fig, ax = pt.imshow(img_, figtitle=dataname + ' ' + lbl, fnum=fnum)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
dump_img(cnn_fp_img, 'cnn_fp_img', fnum_gen())
dump_img(cnn_fn_img, 'cnn_fn_img', fnum_gen())
dump_img(cnn_tp_img, 'cnn_tp_img', fnum_gen())
dump_img(cnn_tn_img, 'cnn_tn_img', fnum_gen())
dump_img(sift_fp_img, 'sift_fp_img', fnum_gen())
dump_img(sift_fn_img, 'sift_fn_img', fnum_gen())
dump_img(sift_tp_img, 'sift_tp_img', fnum_gen())
dump_img(sift_tn_img, 'sift_tn_img', fnum_gen())
#vt.imwrite(dataname + '_' + 'cnn_fp_img.png', (cnn_fp_img))
#vt.imwrite(dataname + '_' + 'cnn_fn_img.png', (cnn_fn_img))
#vt.imwrite(dataname + '_' + 'sift_fp_img.png', (sift_fp_img))
#vt.imwrite(dataname + '_' + 'sift_fn_img.png', (sift_fn_img))
else:
print('Drawing TP FP TN FN')
fnum = fnum_gen()
pnum_gen = pt.make_pnum_nextgen(4, 2)
fig = pt.figure(fnum)
pt.imshow(cnn_fp_img, title='CNN FP', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_fp_img, title='SIFT FP', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_fn_img, title='CNN FN', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_fn_img, title='SIFT FN', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_tp_img, title='CNN TP', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_tp_img, title='SIFT TP', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_tn_img, title='CNN TN', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_tn_img, title='SIFT TN', fnum=fnum, pnum=pnum_gen())
pt.set_figtitle(dataname + ' confusions')
pt.adjust_subplots(left=0, right=1.0, bottom=0., wspace=.01, hspace=.05)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))
with_patch_desc = FULL
if with_patch_desc:
ut.colorprint('[siam_perf] Visualize Patch Descriptors', 'white')
fnum = fnum_gen()
fig = pt.figure(fnum=fnum, pnum=(1, 1, 1))
num_rows = 7
pnum_gen = pt.make_pnum_nextgen(num_rows, 3)
# Compare actual output descriptors
for index in ut.random_indexes(len(sift_list), num_rows):
vec_sift = sift_list[index]
vec_cnn = network_output[index]
patch = data[index]
pt.imshow(patch, fnum=fnum, pnum=pnum_gen())
pt.plot_descriptor_signature(vec_cnn, 'cnn vec', fnum=fnum, pnum=pnum_gen())
pt.plot_sift_signature(vec_sift, 'sift vec', fnum=fnum, pnum=pnum_gen())
pt.set_figtitle('Patch Descriptors')
pt.adjust_subplots(left=0, right=0.95, bottom=0., wspace=.1, hspace=.15)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))
def ewma():
import plottool as pt
import ubelt as ub
import numpy as np
pt.qtensure()
# Investigate the span parameter
span = 20
alpha = 2 / (span + 1)
# how long does it take for the estimation to hit 0?
# (ie, it no longer cares about the initial 1?)
# about 93 iterations to get to 1e-4
# about 47 iterations to get to 1e-2
# about 24 iterations to get to 1e-1
# 20 iterations goes to .135
data = ([1] + [0] * 20 + [1] * 40 + [0] * 20 + [1] * 50 + [0] * 20 +
[1] * 60 + [0] * 20 + [1] * 165 + [0] * 20 + [0])
mave = []
iter_ = iter(data)
current = next(iter_)
mave += [current]
for x in iter_:
current = (alpha * x) + (1 - alpha) * current
mave += [current]
if False:
pt.figure(fnum=1, doclf=True)
pt.plot(data)
pt.plot(mave)
np.where(np.array(mave) < 1e-1)
import sympy as sym
# span, alpha, n = sym.symbols('span, alpha, n')
n = sym.symbols('n', integer=True, nonnegative=True, finite=True)
span = sym.symbols('span', integer=True, nonnegative=True, finite=True)
thresh = sym.symbols('thresh', real=True, nonnegative=True, finite=True)
# alpha = 2 / (span + 1)
a, b, c = sym.symbols('a, b, c', real=True, nonnegative=True, finite=True)
sym.solve(sym.Eq(b**a, c), a)
current = 1
x = 0
steps = []
for _ in range(10):
current = (alpha * x) + (1 - alpha) * current
steps.append(current)
alpha = sym.symbols('alpha', real=True, nonnegative=True, finite=True)
base = sym.symbols('base', real=True, finite=True)
alpha = 2 / (span + 1)
thresh_expr = (1 - alpha)**n
thresthresh_exprh_expr = base**n
n_expr = sym.ceiling(sym.log(thresh) / sym.log(1 - 2 / (span + 1)))
sym.pprint(sym.simplify(thresh_expr))
sym.pprint(sym.simplify(n_expr))
print(sym.latex(sym.simplify(n_expr)))
# def calc_n2(span, thresh):
# return np.log(thresh) / np.log(1 - 2 / (span + 1))
def calc_n(span, thresh):
return np.log(thresh) / np.log((span - 1) / (span + 1))
def calc_thresh_val(n, span):
alpha = 2 / (span + 1)
return (1 - alpha)**n
span = np.arange(2, 200)
n_frac = calc_n(span, thresh=.5)
n = np.ceil(n_frac)
calc_thresh_val(n, span)
pt.figure(fnum=1, doclf=True)
ydatas = ut.odict([('thresh=%f' % thresh,
np.ceil(calc_n(span, thresh=thresh)))
for thresh in [1e-3, .01, .1, .2, .3, .4, .5]])
pt.multi_plot(
span,
ydatas,
xlabel='span',
ylabel='n iters to acheive thresh',
marker='',
# num_xticks=len(span),
fnum=1)
pt.gca().set_aspect('equal')
def both_sides(eqn, func):
return sym.Eq(func(eqn.lhs), func(eqn.rhs))
eqn = sym.Eq(thresh_expr, thresh)
n_expr = sym.solve(eqn,
n)[0].subs(base,
(1 - alpha)).subs(alpha, (2 / (span + 1)))
eqn = both_sides(eqn, lambda x: sym.log(x, (1 - alpha)))
lhs = eqn.lhs
from sympy.solvers.inequalities import solve_univariate_inequality
def eval_expr(span_value, n_value):
return np.array(
[thresh_expr.subs(span, span_value).subs(n, n_) for n_ in n_value],
dtype=np.float)
eval_expr(20, np.arange(20))
def linear(x, a, b):
return a * x + b
def sigmoidal_4pl(x, a, b, c, d):
return d + (a - d) / (1 + (x / c)**b)
def exponential(x, a, b, c):
return a + b * np.exp(-c * x)
import scipy.optimize
# Determine how to choose span, such that you get to .01 from 1
# in n timesteps
thresh_to_span_to_n = []
thresh_to_n_to_span = []
for thresh_value in ub.ProgIter([.0001, .001, .01, .1, .2, .3, .4, .5]):
print('')
test_vals = sorted([2, 3, 4, 5, 6])
n_to_span = []
for n_value in ub.ProgIter(test_vals):
# In n iterations I want to choose a span that the expression go
# less than a threshold
constraint = thresh_expr.subs(n, n_value) < thresh_value
solution = solve_univariate_inequality(constraint, span)
try:
lowbound = np.ceil(float(solution.args[0].lhs))
highbound = np.floor(float(solution.args[1].rhs))
assert lowbound <= highbound
span_value = lowbound
except AttributeError:
span_value = np.floor(float(solution.rhs))
n_to_span.append((n_value, span_value))
# Given a threshold, find a minimum number of steps
# that brings you up to that threshold given a span
test_vals = sorted(set(list(range(2, 1000, 50)) + [2, 3, 4, 5, 6]))
span_to_n = []
for span_value in ub.ProgIter(test_vals):
constraint = thresh_expr.subs(span, span_value) < thresh_value
solution = solve_univariate_inequality(constraint, n)
n_value = solution.lhs
span_to_n.append((span_value, n_value))
thresh_to_n_to_span.append((thresh_value, n_to_span))
thresh_to_span_to_n.append((thresh_value, span_to_n))
thresh_to_params = []
for thresh_value, span_to_n in thresh_to_span_to_n:
xdata, ydata = [np.array(_, dtype=np.float) for _ in zip(*span_to_n)]
p0 = (1 / np.diff((ydata - ydata[0])[1:]).mean(), ydata[0])
func = linear
popt, pcov = scipy.optimize.curve_fit(func, xdata, ydata, p0)
# popt, pcov = scipy.optimize.curve_fit(exponential, xdata, ydata)
if False:
yhat = func(xdata, *popt)
pt.figure(fnum=1, doclf=True)
pt.plot(xdata, ydata, label='measured')
pt.plot(xdata, yhat, label='predicteed')
pt.legend()
# slope = np.diff(ydata).mean()
# pt.plot(d)
thresh_to_params.append((thresh_value, popt))
# pt.plt.plot(*zip(*thresh_to_slope), 'x-')
# for thresh_value=.01, we get a rough line with slop ~2.302,
# for thresh_value=.5, we get a line with slop ~34.66
# if we want to get to 0 in n timesteps, with a thresh_value of
# choose span=f(thresh_value) * (n + 2))
# f is some inverse exponential
# 0.0001, 460.551314197147
# 0.001, 345.413485647860,
# 0.01, 230.275657098573,
# 0.1, 115.137828549287,
# 0.2, 80.4778885203347,
# 0.3, 60.2031233261536,
# 0.4, 45.8179484913827,
# 0.5, 34.6599400289520
# Seems to be 4PL symetrical sigmoid
# f(x) = -66500.85 + (66515.88 - -66500.85) / (1 + (x/0.8604672)^0.001503716)
# f(x) = -66500.85 + (66515.88 - -66500.85)/(1 + (x/0.8604672)^0.001503716)
def f(x):
return -66500.85 + (66515.88 -
-66500.85) / (1 + (x / 0.8604672)**0.001503716)
# return (10000 * (-6.65 + (13.3015) / (1 + (x/0.86) ** 0.00150)))
# f(.5) * (n - 1)
# f(
solve_rational_inequalities(thresh_expr < .01, n)
def show_qres(ibs, cm, qreq_=None, **kwargs):
"""
Display Query Result Logic
Defaults to: query chip, groundtruth matches, and top matches
Args:
ibs (ibeis.IBEISController): ibeis controller object
cm (ibeis.ChipMatch): object of feature correspondences and scores
Kwargs:
in_image (bool) show result in image view if True else chip view
annot_mode (int):
if annot_mode == 0, then draw lines and ellipse
elif annot_mode == 1, then dont draw lines or ellipse
elif annot_mode == 2, then draw only lines
See: viz_matches.show_name_matches, viz_helpers.get_query_text
Returns:
mpl.Figure: fig
CommandLine:
./main.py --query 1 -y --db PZ_MTEST --noshow-qtres
python -m ibeis.viz.viz_qres --test-show_qres --show
python -m ibeis.viz.viz_qres --test-show_qres --show --top-aids=10 --db=PZ_MTEST --sidebyside --annot_mode=0 --notitle --no-viz_name_score --qaids=5 --max_nCols=2 --adjust=.01,.01,.01
python -m ibeis.viz.viz_qres --test-show_qres --show --top-aids=10 --db=PZ_MTEST --sidebyside --annot_mode=0 --notitle --no-viz_name_score --qaids=5 --max_nCols=2 --adjust=.01,.01,.01
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_qres import * # NOQA
>>> import plottool as pt
>>> ibs, cm, qreq_, kwargs = testdata_show_qres()
>>> # execute function
>>> fig = show_qres(ibs, cm, show_query=False, qreq_=qreq_, **kwargs)
>>> # verify results
>>> #fig.show()
>>> pt.show_if_requested()
"""
#ut.print_dict(kwargs)
annot_mode = kwargs.get('annot_mode', 1) % 3 # this is toggled
figtitle = kwargs.get('figtitle', '')
make_figtitle = kwargs.get('make_figtitle', False)
aug = kwargs.get('aug', '')
top_aids = kwargs.get('top_aids', DEFAULT_NTOP)
gt_aids = kwargs.get('gt_aids', [])
all_kpts = kwargs.get('all_kpts', False)
show_query = kwargs.get('show_query', False)
in_image = kwargs.get('in_image', False)
sidebyside = kwargs.get('sidebyside', True)
#name_scoring = kwargs.get('name_scoring', False)
viz_name_score = kwargs.get('viz_name_score', qreq_ is not None)
max_nCols = kwargs.get('max_nCols', None)
failed_to_match = kwargs.get('failed_to_match', False)
fnum = pt.ensure_fnum(kwargs.get('fnum', None))
if ut.VERBOSE and ut.NOT_QUIET:
print('query_info = ' + ut.obj_str(
ibs.get_annot_info(cm.qaid, default=True, gname=False, name=False, notes=False,
exemplar=False), nl=4))
print('top_aids_info = ' + ut.obj_str(
ibs.get_annot_info(top_aids, default=True, gname=False, name=False, notes=False,
exemplar=False, reference_aid=cm.qaid), nl=4))
if make_figtitle is True:
pass
#figtitle = cm.make_title(pack=True)
#figtitle
fig = pt.figure(fnum=fnum, docla=True, doclf=True)
if isinstance(top_aids, int):
#if isinstance(cm, chip_match.ChipMatch):
top_aids = cm.get_top_aids(top_aids)
#else:
# top_aids = cm.get_top_aids(num=top_aids, name_scoring=name_scoring, ibs=ibs)
if failed_to_match:
# HACK to visually indicate failure to match in analysis
top_aids = [None] + top_aids
nTop = len(top_aids)
if max_nCols is None:
max_nCols = 5
if nTop in [6, 7]:
max_nCols = 3
if nTop in [8]:
max_nCols = 4
try:
assert len(list(set(top_aids).intersection(set(gt_aids)))) == 0, (
'gts should be missed. not in top')
except AssertionError as ex:
ut.printex(ex, keys=['top_aids', 'gt_aids'])
raise
if ut.DEBUG2:
print(cm.get_inspect_str())
#--------------------------------------------------
# Get grid / cell information to build subplot grid
#--------------------------------------------------
# Show query or not
nQuerySubplts = 1 if show_query else 0
# The top row is given slots for ground truths and querys
# all aids in gt_aids should not be in top aids
nGtSubplts = nQuerySubplts + (0 if gt_aids is None else len(gt_aids))
# The bottom rows are for the top results
nTopNSubplts = nTop
nTopNCols = min(max_nCols, nTopNSubplts)
nGTCols = min(max_nCols, nGtSubplts)
nGTCols = max(nGTCols, nTopNCols)
nTopNCols = nGTCols
# Get number of rows to show groundtruth
nGtRows = 0 if nGTCols == 0 else int(np.ceil(nGtSubplts / nGTCols))
# Get number of rows to show results
nTopNRows = 0 if nTopNCols == 0 else int(np.ceil(nTopNSubplts / nTopNCols))
nGtCells = nGtRows * nGTCols
# Total number of rows
nRows = nTopNRows + nGtRows
DEBUG_SHOW_QRES = 0
if DEBUG_SHOW_QRES:
allgt_aids = ibs.get_annot_groundtruth(cm.qaid)
nSelGt = len(gt_aids)
nAllGt = len(allgt_aids)
print('[show_qres]========================')
print('[show_qres]----------------')
print('[show_qres] * annot_mode=%r' % (annot_mode,))
print('[show_qres] #nTop=%r #missed_gts=%r/%r' % (nTop, nSelGt, nAllGt))
print('[show_qres] * -----')
print('[show_qres] * nRows=%r' % (nRows,))
print('[show_qres] * nGtSubplts=%r' % (nGtSubplts,))
print('[show_qres] * nTopNSubplts=%r' % (nTopNSubplts,))
print('[show_qres] * nQuerySubplts=%r' % (nQuerySubplts,))
print('[show_qres] * -----')
print('[show_qres] * nGTCols=%r' % (nGTCols,))
print('[show_qres] * -----')
print('[show_qres] * fnum=%r' % (fnum,))
print('[show_qres] * figtitle=%r' % (figtitle,))
print('[show_qres] * max_nCols=%r' % (max_nCols,))
print('[show_qres] * show_query=%r' % (show_query,))
print('[show_qres] * kwargs=%s' % (ut.dict_str(kwargs),))
# HACK:
_color_list = pt.distinct_colors(nTop)
aid2_color = {aid: _color_list[ox] for ox, aid in enumerate(top_aids)}
ranked_aids = cm.get_top_aids()
# Helpers
def _show_query_fn(plotx_shift, rowcols):
""" helper for show_qres """
plotx = plotx_shift + 1
pnum = (rowcols[0], rowcols[1], plotx)
#print('[viz] Plotting Query: pnum=%r' % (pnum,))
_kwshow = dict(draw_kpts=annot_mode)
_kwshow.update(kwargs)
_kwshow['prefix'] = 'q'
_kwshow['pnum'] = pnum
_kwshow['aid2_color'] = aid2_color
_kwshow['draw_ell'] = annot_mode >= 1
viz_chip.show_chip(ibs, cm.qaid, annote=False, qreq_=qreq_, **_kwshow)
def _plot_matches_aids(aid_list, plotx_shift, rowcols):
""" helper for show_qres to draw many aids """
_kwshow = dict(draw_ell=annot_mode, draw_pts=False, draw_lines=annot_mode,
ell_alpha=.5, all_kpts=all_kpts)
_kwshow.update(kwargs)
_kwshow['fnum'] = fnum
_kwshow['in_image'] = in_image
if sidebyside:
# Draw each match side by side the query
_kwshow['draw_ell'] = annot_mode == 1
_kwshow['draw_lines'] = annot_mode >= 1
else:
#print('annot_mode = %r' % (annot_mode,))
_kwshow['draw_ell'] = annot_mode == 1
#_kwshow['draw_pts'] = annot_mode >= 1
#_kwshow['draw_lines'] = False
_kwshow['show_query'] = False
def _show_matches_fn(aid, orank, pnum):
""" Helper function for drawing matches to one aid """
aug = 'rank=%r\n' % orank
_kwshow['pnum'] = pnum
_kwshow['title_aug'] = aug
#draw_ell = annot_mode == 1
#draw_lines = annot_mode >= 1
# If we already are showing the query dont show it here
if sidebyside:
# Draw each match side by side the query
if viz_name_score:
cm.show_single_namematch(qreq_, ibs.get_annot_nids(aid), **_kwshow)
else:
_kwshow['draw_border'] = False
_kwshow['draw_lbl'] = False
_kwshow['notitle'] = True
_kwshow['vert'] = False
cm.show_single_annotmatch(qreq_, aid, **_kwshow)
#viz_matches.show_matches(ibs, cm, aid, qreq_=qreq_, **_kwshow)
else:
# Draw each match by themselves
data_config2_ = None if qreq_ is None else qreq_.get_external_data_config2()
#_kwshow['draw_border'] = kwargs.get('draw_border', True)
#_kwshow['notitle'] = ut.get_argflag(('--no-title', '--notitle'))
viz_chip.show_chip(ibs, aid, annote=False, notitle=True,
data_config2_=data_config2_, **_kwshow)
if DEBUG_SHOW_QRES:
print('[show_qres()] Plotting Chips %s:' % vh.get_aidstrs(aid_list))
if aid_list is None:
return
# Do lazy load before show
#data_config2_ = None if qreq_ is None else qreq_.get_external_data_config2()
tblhack = getattr(qreq_, 'tablename', None)
# HACK FOR HUMPBACKS
# (Also in viz_matches)
if tblhack == 'vsone' or (qreq_ is not None and not qreq_._isnewreq):
# precompute
pass
#ibs.get_annot_chips(aid_list, config2_=data_config2_, ensure=True)
#ibs.get_annot_kpts(aid_list, config2_=data_config2_, ensure=True)
for ox, aid in enumerate(aid_list):
plotx = ox + plotx_shift + 1
pnum = (rowcols[0], rowcols[1], plotx)
oranks = np.where(ranked_aids == aid)[0]
# This pair has no matches between them.
if len(oranks) == 0:
orank = -1
if aid is None:
pt.imshow_null('Failed to find matches\nfor qaid=%r' % (cm.qaid), fnum=fnum, pnum=pnum, fontsize=18)
else:
_show_matches_fn(aid, orank, pnum)
#if DEBUG_SHOW_QRES:
# print('skipping pnum=%r' % (pnum,))
continue
if DEBUG_SHOW_QRES:
print('pnum=%r' % (pnum,))
orank = oranks[0] + 1
_show_matches_fn(aid, orank, pnum)
shift_topN = nGtCells
if nGtSubplts == 1:
nGTCols = 1
if nRows == 0:
pt.imshow_null('[viz_qres] No matches. nRows=0', fnum=fnum)
else:
fig = pt.figure(fnum=fnum, pnum=(nRows, nGTCols, 1), docla=True, doclf=True)
pt.plt.subplot(nRows, nGTCols, 1)
# Plot Query
if show_query:
_show_query_fn(0, (nRows, nGTCols))
# Plot Ground Truth (if given)
_plot_matches_aids(gt_aids, nQuerySubplts, (nRows, nGTCols))
# Plot Results
_plot_matches_aids(top_aids, shift_topN, (nRows, nTopNCols))
figtitle += aug
if failed_to_match:
figtitle += '\n No matches found'
incanvas = kwargs.get('with_figtitle', not vh.NO_LBL_OVERRIDE)
pt.set_figtitle(figtitle, incanvas=incanvas)
# Result Interaction
return fig
def bayesnet():
"""
References:
https://class.coursera.org/pgm-003/lecture/17
http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
http://www3.cs.stonybrook.edu/~sael/teaching/cse537/Slides/chapter14d_BP.pdf
http://www.cse.unsw.edu.au/~cs9417ml/Bayes/Pages/PearlPropagation.html
https://github.com/pgmpy/pgmpy.git
http://pgmpy.readthedocs.org/en/latest/
http://nipy.bic.berkeley.edu:5000/download/11
"""
# import operator as op
# # Enumerate all possible events
# varcard_list = list(map(op.attrgetter('variable_card'), cpd_list))
# _esdat = list(ut.iprod(*map(range, varcard_list)))
# _escol = list(map(op.attrgetter('variable'), cpd_list))
# event_space = pd.DataFrame(_esdat, columns=_escol)
# # Custom compression of event space to inspect a specific graph
# def compress_space_flags(event_space, var1, var2, var3, cmp12_):
# """
# var1, var2, cmp_ = 'Lj', 'Lk', op.eq
# """
# import vtool as vt
# data = event_space
# other_cols = ut.setdiff_ordered(data.columns.tolist(), [var1, var2, var3])
# case_flags12 = cmp12_(data[var1], data[var2]).values
# # case_flags23 = cmp23_(data[var2], data[var3]).values
# # case_flags = np.logical_and(case_flags12, case_flags23)
# case_flags = case_flags12
# case_flags = case_flags.astype(np.int64)
# subspace = np.hstack((case_flags[:, None], data[other_cols].values))
# sel_ = vt.unique_row_indexes(subspace)
# flags = np.logical_and(mask, case_flags)
# return flags
# # Build special cases
# case_same = event_space.loc[compress_space_flags(event_space, 'Li', 'Lj', 'Lk', op.eq)]
# case_diff = event_space.loc[compress_space_flags(event_space, 'Li', 'Lj', 'Lk', op.ne)]
# special_cases = [
# case_same,
# case_diff,
# ]
from pgmpy.factors import TabularCPD
from pgmpy.models import BayesianModel
import pandas as pd
from pgmpy.inference import BeliefPropagation # NOQA
from pgmpy.inference import VariableElimination # NOQA
name_nice = ['n1', 'n2', 'n3']
score_nice = ['low', 'high']
match_nice = ['diff', 'same']
num_names = len(name_nice)
num_scores = len(score_nice)
nid_basis = list(range(num_names))
score_basis = list(range(num_scores))
semtype2_nice = {
'score': score_nice,
'name': name_nice,
'match': match_nice,
}
var2_cpd = {
}
globals()['semtype2_nice'] = semtype2_nice
globals()['var2_cpd'] = var2_cpd
name_combo = np.array(list(ut.iprod(nid_basis, nid_basis)))
combo_is_same = name_combo.T[0] == name_combo.T[1]
def get_expected_scores_prob(level1, level2):
part1 = combo_is_same * level1
part2 = (1 - combo_is_same) * (1 - (level2))
expected_scores_level = part1 + part2
return expected_scores_level
# def make_cpd():
def name_cpd(aid):
from pgmpy.factors import TabularCPD
cpd = TabularCPD(
variable='N' + aid,
variable_card=num_names,
values=[[1.0 / num_names] * num_names])
cpd.semtype = 'name'
return cpd
name_cpds = [name_cpd('i'), name_cpd('j'), name_cpd('k')]
var2_cpd.update(dict(zip([cpd.variable for cpd in name_cpds], name_cpds)))
if True:
num_same_diff = 2
samediff_measure = np.array([
# get_expected_scores_prob(.12, .2),
# get_expected_scores_prob(.88, .8),
get_expected_scores_prob(0, 0),
get_expected_scores_prob(1, 1),
])
samediff_vals = (samediff_measure / samediff_measure.sum(axis=0)).tolist()
def samediff_cpd(aid1, aid2):
cpd = TabularCPD(
variable='A' + aid1 + aid2,
variable_card=num_same_diff,
values=samediff_vals,
evidence=['N' + aid1, 'N' + aid2], # [::-1],
evidence_card=[num_names, num_names]) # [::-1])
cpd.semtype = 'match'
return cpd
samediff_cpds = [samediff_cpd('i', 'j'), samediff_cpd('j', 'k'), samediff_cpd('k', 'i')]
var2_cpd.update(dict(zip([cpd.variable for cpd in samediff_cpds], samediff_cpds)))
if True:
def score_cpd(aid1, aid2):
semtype = 'score'
evidence = ['A' + aid1 + aid2, 'N' + aid1, 'N' + aid2]
evidence_cpds = [var2_cpd[key] for key in evidence]
evidence_nice = [semtype2_nice[cpd.semtype] for cpd in evidence_cpds]
evidence_card = list(map(len, evidence_nice))
evidence_states = list(ut.iprod(*evidence_nice))
variable_basis = semtype2_nice[semtype]
variable_values = []
for mystate in variable_basis:
row = []
for state in evidence_states:
if state[0] == state[1]:
if state[2] == 'same':
val = .2 if mystate == 'low' else .8
else:
val = 1
# val = .5 if mystate == 'low' else .5
elif state[0] != state[1]:
if state[2] == 'same':
val = .5 if mystate == 'low' else .5
else:
val = 1
# val = .9 if mystate == 'low' else .1
row.append(val)
variable_values.append(row)
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=len(variable_basis),
values=variable_values,
evidence=evidence, # [::-1],
evidence_card=evidence_card) # [::-1])
cpd.semtype = semtype
return cpd
else:
score_values = [
[.8, .1],
[.2, .9],
]
def score_cpd(aid1, aid2):
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=num_scores,
values=score_values,
evidence=['A' + aid1 + aid2], # [::-1],
evidence_card=[num_same_diff]) # [::-1])
cpd.semtype = 'score'
return cpd
score_cpds = [score_cpd('i', 'j'), score_cpd('j', 'k')]
cpd_list = name_cpds + score_cpds + samediff_cpds
else:
score_measure = np.array([get_expected_scores_prob(level1, level2)
for level1, level2 in
zip(np.linspace(.1, .9, num_scores),
np.linspace(.2, .8, num_scores))])
score_values = (score_measure / score_measure.sum(axis=0)).tolist()
def score_cpd(aid1, aid2):
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=num_scores,
values=score_values,
evidence=['N' + aid1, 'N' + aid2],
evidence_card=[num_names, num_names])
cpd.semtype = 'score'
return cpd
score_cpds = [score_cpd('i', 'j'), score_cpd('j', 'k')]
cpd_list = name_cpds + score_cpds
pass
input_graph = []
for cpd in cpd_list:
if cpd.evidence is not None:
for evar in cpd.evidence:
input_graph.append((evar, cpd.variable))
name_model = BayesianModel(input_graph)
name_model.add_cpds(*cpd_list)
var2_cpd.update(dict(zip([cpd.variable for cpd in cpd_list], cpd_list)))
globals()['var2_cpd'] = var2_cpd
varnames = [cpd.variable for cpd in cpd_list]
# --- PRINT CPDS ---
cpd = score_cpds[0]
def print_cpd(cpd):
print('CPT: %r' % (cpd,))
index = semtype2_nice[cpd.semtype]
if cpd.evidence is None:
columns = ['None']
else:
basis_lists = [semtype2_nice[var2_cpd[ename].semtype] for ename in cpd.evidence]
columns = [','.join(x) for x in ut.iprod(*basis_lists)]
data = cpd.get_cpd()
print(pd.DataFrame(data, index=index, columns=columns))
for cpd in name_model.get_cpds():
print('----')
print(cpd._str('phi'))
print_cpd(cpd)
# --- INFERENCE ---
Ni = name_cpds[0]
event_space_combos = {}
event_space_combos[Ni.variable] = 0 # Set ni to always be Fred
for cpd in cpd_list:
if cpd.semtype == 'score':
event_space_combos[cpd.variable] = list(range(cpd.variable_card))
evidence_dict = ut.all_dict_combinations(event_space_combos)
# Query about name of annotation k given different event space params
def pretty_evidence(evidence):
return [key + '=' + str(semtype2_nice[var2_cpd[key].semtype][val])
for key, val in evidence.items()]
def print_factor(factor):
row_cards = factor.cardinality
row_vars = factor.variables
values = factor.values.reshape(np.prod(row_cards), 1).flatten()
# col_cards = 1
# col_vars = ['']
basis_lists = list(zip(*list(ut.iprod(*[range(c) for c in row_cards]))))
nice_basis_lists = []
for varname, basis in zip(row_vars, basis_lists):
cpd = var2_cpd[varname]
_nice_basis = ut.take(semtype2_nice[cpd.semtype], basis)
nice_basis = ['%s=%s' % (varname, val) for val in _nice_basis]
nice_basis_lists.append(nice_basis)
row_lbls = [', '.join(sorted(x)) for x in zip(*nice_basis_lists)]
print(ut.repr3(dict(zip(row_lbls, values)), precision=3, align=True, key_order_metric='-val'))
# name_belief = BeliefPropagation(name_model)
name_belief = VariableElimination(name_model)
import pgmpy
import six # NOQA
def try_query(evidence):
print('--------')
query_vars = ut.setdiff_ordered(varnames, list(evidence.keys()))
evidence_str = ', '.join(pretty_evidence(evidence))
probs = name_belief.query(query_vars, evidence)
factor_list = probs.values()
joint_factor = pgmpy.factors.factor_product(*factor_list)
print('P(' + ', '.join(query_vars) + ' | ' + evidence_str + ')')
# print(six.text_type(joint_factor))
factor = joint_factor # NOQA
# print_factor(factor)
# import utool as ut
print(ut.hz_str([(f._str(phi_or_p='phi')) for f in factor_list]))
for evidence in evidence_dict:
try_query(evidence)
evidence = {'Aij': 1, 'Ajk': 1, 'Aki': 1, 'Ni': 0}
try_query(evidence)
evidence = {'Aij': 0, 'Ajk': 0, 'Aki': 0, 'Ni': 0}
try_query(evidence)
globals()['score_nice'] = score_nice
globals()['name_nice'] = name_nice
globals()['score_basis'] = score_basis
globals()['nid_basis'] = nid_basis
print('Independencies')
print(name_model.get_independencies())
print(name_model.local_independencies([Ni.variable]))
# name_belief = BeliefPropagation(name_model)
# # name_belief = VariableElimination(name_model)
# for case in special_cases:
# test_data = case.drop('Lk', axis=1)
# test_data = test_data.reset_index(drop=True)
# print('----')
# for i in range(test_data.shape[0]):
# evidence = test_data.loc[i].to_dict()
# probs = name_belief.query(['Lk'], evidence)
# factor = probs['Lk']
# probs = factor.values
# evidence_ = evidence.copy()
# evidence_['Li'] = name_nice[evidence['Li']]
# evidence_['Lj'] = name_nice[evidence['Lj']]
# evidence_['Sij'] = score_nice[evidence['Sij']]
# evidence_['Sjk'] = score_nice[evidence['Sjk']]
# nice2_prob = ut.odict(zip(name_nice, probs.tolist()))
# ut.print_python_code('P(Lk | {evidence}) = {cpt}'.format(
# evidence=(ut.repr2(evidence_, explicit=True, nobraces=True, strvals=True)),
# cpt=ut.repr3(nice2_prob, precision=3, align=True, key_order_metric='-val')
# ))
# for case in special_cases:
# test_data = case.drop('Lk', axis=1)
# test_data = test_data.drop('Lj', axis=1)
# test_data = test_data.reset_index(drop=True)
# print('----')
# for i in range(test_data.shape[0]):
# evidence = test_data.loc[i].to_dict()
# query_vars = ['Lk', 'Lj']
# probs = name_belief.query(query_vars, evidence)
# for queryvar in query_vars:
# factor = probs[queryvar]
# print(factor._str('phi'))
# probs = factor.values
# evidence_ = evidence.copy()
# evidence_['Li'] = name_nice[evidence['Li']]
# evidence_['Sij'] = score_nice[evidence['Sij']]
# evidence_['Sjk'] = score_nice[evidence['Sjk']]
# nice2_prob = ut.odict(zip([queryvar + '=' + x for x in name_nice], probs.tolist()))
# ut.print_python_code('P({queryvar} | {evidence}) = {cpt}'.format(
# query_var=query_var,
# evidence=(ut.repr2(evidence_, explicit=True, nobraces=True, strvals=True)),
# cpt=ut.repr3(nice2_prob, precision=3, align=True, key_order_metric='-val')
# ))
# _ draw model
import plottool as pt
import networkx as netx
fig = pt.figure() # NOQA
fig.clf()
ax = pt.gca()
netx_nodes = [(node, {}) for node in name_model.nodes()]
netx_edges = [(etup[0], etup[1], {}) for etup in name_model.edges()]
netx_graph = netx.DiGraph()
netx_graph.add_nodes_from(netx_nodes)
netx_graph.add_edges_from(netx_edges)
# pos = netx.graphviz_layout(netx_graph)
pos = netx.pydot_layout(netx_graph, prog='dot')
netx.draw(netx_graph, pos=pos, ax=ax, with_labels=True)
pt.plt.savefig('foo.png')
ut.startfile('foo.png')
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)