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 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 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 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 show_probability_chip(ibs, aid, species=None, fnum=None, config2_=None, **kwargs):
"""
TODO: allow species override in controller
CommandLine:
python -m ibeis.viz.viz_hough --exec-show_probability_chip --cnn --show
python -m ibeis.viz.viz_hough --exec-show_probability_chip --cnn --show --db PZ_Master1
python -m ibeis.viz.viz_hough --exec-show_probability_chip --cnn --show --db PZ_Master1 --aid 9970
Example:
>>> # SCRIPT
>>> from ibeis.viz.viz_hough import * # NOQA
>>> import ibeis
>>> from ibeis.viz import viz_chip
>>> ibs, aid_list, kwargs, config2_ = viz_chip.testdata_showchip()
>>> fnum = 1
>>> species = None
>>> aid = aid_list[0]
>>> fig, ax = show_probability_chip(ibs, aid, species, fnum, **kwargs)
>>> ut.show_if_requested()
"""
fnum = pt.ensure_fnum(fnum)
title = 'Probability Chip: ' + ', '.join(vh.get_annot_text(ibs, [aid], True))
hough_cpath = ibs.get_annot_probchip_fpath(aid, config2_=config2_)
img = gtool.imread(hough_cpath)
fig, ax = viz_image2.show_image(img, title=title, fnum=fnum, **kwargs)
return fig, ax
def show_sv_simple(chip1, chip2, kpts1, kpts2, fm, inliers, mx=None, fnum=1, vert=None, **kwargs):
"""
CommandLine:
python -m plottool.draw_sv --test-show_sv_simple --show
Example:
>>> # DISABLE_DOCTEST
>>> from plottool.draw_sv import * # NOQA
>>> import vtool as vt
>>> kpts1, kpts2, fm, aff_inliers, chip1, chip2, xy_thresh_sqrd = vt.testdata_matching_affine_inliers()
>>> inliers = aff_inliers
>>> mx = None
>>> fnum = 1
>>> vert = None # ut.get_argval('--vert', type_=bool, default=None)
>>> result = show_sv_simple(chip1, chip2, kpts1, kpts2, fm, inliers, mx, fnum, vert=vert)
>>> print(result)
>>> ut.show_if_requested()
"""
import plottool as pt
colors = df2.distinct_colors(2, brightness=.95)
color1, color2 = colors[0:2]
# Begin the drawing
fnum = pt.ensure_fnum(fnum)
df2.figure(fnum=fnum, pnum=(1, 1, 1), docla=True, doclf=True)
#dmkwargs = dict(fs=None, title='Inconsistent Matches', all_kpts=False, draw_lines=True,
# docla=True, draw_border=True, fnum=fnum, pnum=(1, 1, 1), colors=df2.ORANGE)
inlier_mask = vt.index_to_boolmask(inliers, maxval=len(fm))
fm_inliers = fm.compress(inlier_mask, axis=0)
fm_outliers = fm.compress(np.logical_not(inlier_mask), axis=0)
ax, xywh1, xywh2 = df2.show_chipmatch2(chip1, chip2, vert=vert)
fmatch_kw = dict(ell_linewidth=2, ell_alpha=.7, line_alpha=.7)
df2.plot_fmatch(xywh1, xywh2, kpts1, kpts2, fm_inliers, colors=color1, **fmatch_kw)
df2.plot_fmatch(xywh1, xywh2, kpts1, kpts2, fm_outliers, colors=color2, **fmatch_kw)
def gridsearch_image_function(param_info,
test_func,
args=tuple(),
show_func=None):
"""
gridsearch for a function that produces a single image
"""
import plottool as pt
cfgdict_list, cfglbl_list = param_info.get_gridsearch_input(
defaultslice=slice(0, 10))
fnum = pt.ensure_fnum(None)
if show_func is None:
show_func = pt.imshow
lbl = ut.get_funcname(test_func)
cfgresult_list = [
test_func(*args, **cfgdict)
for cfgdict in ut.ProgressIter(cfgdict_list, lbl=lbl)
]
onclick_func = None
ut.interact_gridsearch_result_images(show_func,
cfgdict_list,
cfglbl_list,
cfgresult_list,
fnum=fnum,
figtitle=lbl,
unpack=False,
max_plots=25,
onclick_func=onclick_func)
pt.iup()
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_probability_chip(ibs, aid, species=None, fnum=None, config2_=None,
blend=False, **kwargs):
"""
TODO: allow species override in controller
CommandLine:
python -m ibeis.viz.viz_hough --exec-show_probability_chip --cnn --show
python -m ibeis.viz.viz_hough --exec-show_probability_chip --cnn --show --db PZ_Master1
python -m ibeis.viz.viz_hough --exec-show_probability_chip --cnn --show --db PZ_Master1 --aid 9970
Example:
>>> # SCRIPT
>>> from ibeis.viz.viz_hough import * # NOQA
>>> import ibeis
>>> from ibeis.viz import viz_chip
>>> ibs, aid_list, kwargs, config2_ = viz_chip.testdata_showchip()
>>> fnum = 1
>>> species = None
>>> aid = aid_list[0]
>>> fig, ax = show_probability_chip(ibs, aid, species, fnum, blend=True, **kwargs)
>>> ut.show_if_requested()
"""
fnum = pt.ensure_fnum(fnum)
title = 'Probability Chip: ' + ', '.join(vh.get_annot_text(ibs, [aid], True))
hough_cpath = ibs.get_annot_probchip_fpath(aid, config2_=config2_)
img = vt.imread(hough_cpath)
if blend:
chip = ibs.get_annot_chips(aid, config2_=config2_)
img = vt.blend_images_multiply(chip, vt.resize_mask(img, chip))
fig, ax = viz_image2.show_image(img, title=title, fnum=fnum, **kwargs)
return fig, 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 __init__(self, rchip1, rchip2, kpts1, kpts2, fm, fs, fsv, vecs1, vecs2,
H1=None, H2=None, fnum=None, **kwargs):
# Drawing Data
self.rchip1 = rchip1
self.rchip2 = rchip2
self.kpts1 = kpts1
self.kpts2 = kpts2
self.fm = fm
self.fs = fs
self.fsv = fsv
self.vecs1 = vecs1
self.vecs2 = vecs2
self.H1 = H1
self.H2 = H2
# Drawing settings
kwargs = kwargs.copy()
self.warp_homog = False
self.mode = kwargs.pop('mode', 0)
self.mx = kwargs.pop('mx', None)
self.vert = kwargs.pop('vert', None)
self.same_fig = kwargs.get('same_fig', True)
self.last_fx = 0
# self.figtitle = kwargs.get('figtitle', 'Inspect Matches')
self.xywh2 = None
import plottool as pt
self.fnum2 = pt.ensure_fnum(fnum)
self.title = kwargs.get('title', True)
self.truth = kwargs.pop('truth', None)
#self.fnum2 = pt.next_fnum()
#if BASE_CLASS is not object:
kwargs['interaction_name'] = 'matches'
super(MatchInteraction2, self).__init__(**kwargs)
def __init__(self, rchip1, rchip2, kpts1, kpts2, fm, fs, fsv, vecs1, vecs2, H1=None, H2=None, fnum=None, **kwargs):
# Drawing Data
self.rchip1 = rchip1
self.rchip2 = rchip2
self.kpts1 = kpts1
self.kpts2 = kpts2
self.fm = fm
self.fs = fs
self.fsv = fsv
self.vecs1 = vecs1
self.vecs2 = vecs2
self.H1 = H1
self.H2 = H2
# Drawing settings
kwargs = kwargs.copy()
self.warp_homog = False
self.mode = kwargs.pop("mode", 0)
self.mx = kwargs.pop("mx", None)
self.vert = kwargs.pop("vert", None)
self.same_fig = kwargs.get("same_fig", True)
self.last_fx = 0
# self.figtitle = kwargs.get('figtitle', 'Inspect Matches')
self.xywh2 = None
import plottool as pt
self.fnum2 = pt.ensure_fnum(fnum)
self.title = kwargs.get("title", True)
self.truth = kwargs.pop("truth", None)
# self.fnum2 = pt.next_fnum()
# if BASE_CLASS is not object:
kwargs["interaction_name"] = "matches"
super(MatchInteraction2, self).__init__(**kwargs)
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 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 visualize_vocab_word(ibs, invassign, wx, fnum=None):
"""
Example:
>>> from ibeis.new_annots import * # NOQA
>>> import plottool as pt
>>> pt.qt4ensure()
>>> ibs, aid_list, vocab = testdata_vocab()
>>> #aid_list = aid_list[0:1]
>>> fstack = StackedFeatures(ibs, aid_list)
>>> nAssign = 2
>>> invassign = fstack.inverted_assignment(vocab, nAssign)
>>> sortx = ut.argsort(invassign.num_list)[::-1]
>>> wx_list = ut.take(invassign.wx_list, sortx)
>>> wx = wx_list[0]
"""
import plottool as pt
pt.qt4ensure()
vecs = invassign.get_vecs(wx)
word = invassign.vocab.wx2_word[wx]
word_patches = invassign.get_patches(wx)
average_patch = np.mean(word_patches, axis=0)
average_vec = vecs.mean(axis=0)
average_vec = word
word
with_sift = True
fnum = 2
fnum = pt.ensure_fnum(fnum)
if with_sift:
patch_img = pt.render_sift_on_patch(average_patch, average_vec)
#sift_word_patches = [pt.render_sift_on_patch(patch, vec) for patch, vec in ut.ProgIter(list(zip(word_patches, vecs)))]
#stacked_patches = vt.stack_square_images(word_patches)
#stacked_patches = vt.stack_square_images(sift_word_patches)
else:
patch_img = average_patch
stacked_patches = vt.stack_square_images(word_patches)
solidbar = np.zeros((patch_img.shape[0], int(patch_img.shape[1] * .1), 3),
dtype=patch_img.dtype)
border_color = (100, 10, 10) # bgr, darkblue
if ut.is_float(solidbar):
solidbar[:, :, :] = (np.array(border_color) / 255)[None, None]
else:
solidbar[:, :, :] = np.array(border_color)[None, None]
word_img = vt.stack_image_list([patch_img, solidbar, stacked_patches],
vert=False,
modifysize=True)
pt.imshow(word_img, fnum=fnum)
#pt.imshow(patch_img, pnum=(1, 2, 1), fnum=fnum)
#patch_size = 64
#half_size = patch_size / 2
#pt.imshow(stacked_patches, pnum=(1, 2, 2), fnum=fnum)
pt.iup()
def show_sv_simple(chip1,
chip2,
kpts1,
kpts2,
fm,
inliers,
mx=None,
fnum=1,
vert=None,
**kwargs):
"""
CommandLine:
python -m plottool.draw_sv --test-show_sv_simple --show
Example:
>>> # DISABLE_DOCTEST
>>> from plottool.draw_sv import * # NOQA
>>> import vtool as vt
>>> kpts1, kpts2, fm, aff_inliers, chip1, chip2, xy_thresh_sqrd = vt.testdata_matching_affine_inliers()
>>> inliers = aff_inliers
>>> mx = None
>>> fnum = 1
>>> vert = None # ut.get_argval('--vert', type_=bool, default=None)
>>> result = show_sv_simple(chip1, chip2, kpts1, kpts2, fm, inliers, mx, fnum, vert=vert)
>>> print(result)
>>> ut.show_if_requested()
"""
import plottool as pt
colors = df2.distinct_colors(2, brightness=.95)
color1, color2 = colors[0:2]
# Begin the drawing
fnum = pt.ensure_fnum(fnum)
df2.figure(fnum=fnum, pnum=(1, 1, 1), docla=True, doclf=True)
#dmkwargs = dict(fs=None, title='Inconsistent Matches', all_kpts=False, draw_lines=True,
# docla=True, draw_border=True, fnum=fnum, pnum=(1, 1, 1), colors=df2.ORANGE)
inlier_mask = vt.index_to_boolmask(inliers, maxval=len(fm))
fm_inliers = fm.compress(inlier_mask, axis=0)
fm_outliers = fm.compress(np.logical_not(inlier_mask), axis=0)
ax, xywh1, xywh2 = df2.show_chipmatch2(chip1, chip2, vert=vert)
fmatch_kw = dict(ell_linewidth=2, ell_alpha=.7, line_alpha=.7)
df2.plot_fmatch(xywh1,
xywh2,
kpts1,
kpts2,
fm_inliers,
colors=color1,
**fmatch_kw)
df2.plot_fmatch(xywh1,
xywh2,
kpts1,
kpts2,
fm_outliers,
colors=color2,
**fmatch_kw)
def show_bayesian_model(model,
evidence={},
soft_evidence={},
fnum=None,
**kwargs):
r"""
References:
http://stackoverflow.com/questions/22207802/networkx-node-level-or-layer
Ignore:
import nx2tikz
print(nx2tikz.dumps_tikz(model, layout='layered', use_label=True))
Ignore:
sudo apt-get install libgraphviz4 libgraphviz-dev -y
sudo apt-get install libgraphviz-dev
pip install pygraphviz
sudo pip3 install pygraphviz \
--install-option="--include-path=/usr/include/graphviz" \
--install-option="--library-path=/usr/lib/graphviz/"
python -c "import pygraphviz; print(pygraphviz.__file__)"
python3 -c "import pygraphviz; print(pygraphviz.__file__)"
CommandLine:
python -m ibeis.algo.hots.pgm_viz --exec-show_model --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.pgm_viz 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()
"""
import plottool as pt
fnum = pt.ensure_fnum(fnum)
top_assignments = kwargs.get('top_assignments', None)
if evidence and top_assignments and 'factor_list' in kwargs:
pnum1 = (3, 1, (slice(0, 2), 0))
pnum2 = (3, 8, (2, slice(4, None)))
else:
pnum1 = (1, 1, 1)
draw_bayesian_model(model,
evidence,
soft_evidence,
fnum=fnum,
pnum=pnum1,
**kwargs)
# Draw probability hist
if top_assignments is not None:
draw_map_histogram(top_assignments, fnum=fnum, pnum=pnum2)
def __init__(self, fnum=None, _pnumiter=None, interactive=None, **kwargs):
self.nRows = kwargs.get('nRows', None)
self.nCols = kwargs.get('nCols', None)
self._pnumiter = _pnumiter
self.pnum_list = []
self.interactive = interactive
self.ishow_func_list = []
self.func_list = []
self.fnum = pt.ensure_fnum(fnum)
self.fig = None
autostart = False
super(ExpandableInteraction, self).__init__(autostart=autostart, **kwargs)
def visualize_vocab_word(ibs, invassign, wx, fnum=None):
"""
Example:
>>> from ibeis.new_annots import * # NOQA
>>> import plottool as pt
>>> pt.qt4ensure()
>>> ibs, aid_list, vocab = testdata_vocab()
>>> #aid_list = aid_list[0:1]
>>> fstack = StackedFeatures(ibs, aid_list)
>>> nAssign = 2
>>> invassign = fstack.inverted_assignment(vocab, nAssign)
>>> sortx = ut.argsort(invassign.num_list)[::-1]
>>> wx_list = ut.take(invassign.wx_list, sortx)
>>> wx = wx_list[0]
"""
import plottool as pt
pt.qt4ensure()
vecs = invassign.get_vecs(wx)
word = invassign.vocab.wx2_word[wx]
word_patches = invassign.get_patches(wx)
average_patch = np.mean(word_patches, axis=0)
average_vec = vecs.mean(axis=0)
average_vec = word
word
with_sift = True
fnum = 2
fnum = pt.ensure_fnum(fnum)
if with_sift:
patch_img = pt.render_sift_on_patch(average_patch, average_vec)
#sift_word_patches = [pt.render_sift_on_patch(patch, vec) for patch, vec in ut.ProgIter(list(zip(word_patches, vecs)))]
#stacked_patches = vt.stack_square_images(word_patches)
#stacked_patches = vt.stack_square_images(sift_word_patches)
else:
patch_img = average_patch
stacked_patches = vt.stack_square_images(word_patches)
solidbar = np.zeros((patch_img.shape[0], int(patch_img.shape[1] * .1), 3), dtype=patch_img.dtype)
border_color = (100, 10, 10) # bgr, darkblue
if ut.is_float(solidbar):
solidbar[:, :, :] = (np.array(border_color) / 255)[None, None]
else:
solidbar[:, :, :] = np.array(border_color)[None, None]
word_img = vt.stack_image_list([patch_img, solidbar, stacked_patches], vert=False, modifysize=True)
pt.imshow(word_img, fnum=fnum)
#pt.imshow(patch_img, pnum=(1, 2, 1), fnum=fnum)
#patch_size = 64
#half_size = patch_size / 2
#pt.imshow(stacked_patches, pnum=(1, 2, 2), fnum=fnum)
pt.iup()
def draw_map_histogram(top_assignments, fnum=None, pnum=(1, 1, 1)):
import plottool as pt
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
fnum = pt.ensure_fnum(fnum)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(bin_labels, bin_vals, fnum=fnum, pnum=pnum,
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob', xlabel='assignment')
pt.set_title('Assignment probabilities')
def show_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 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 show_bayesian_model(model, evidence={}, soft_evidence={}, fnum=None, **kwargs):
r"""
References:
http://stackoverflow.com/questions/22207802/networkx-node-level-or-layer
Ignore:
import nx2tikz
print(nx2tikz.dumps_tikz(model, layout='layered', use_label=True))
Ignore:
sudo apt-get install libgraphviz4 libgraphviz-dev -y
sudo apt-get install libgraphviz-dev
pip install pygraphviz
sudo pip3 install pygraphviz \
--install-option="--include-path=/usr/include/graphviz" \
--install-option="--library-path=/usr/lib/graphviz/"
python -c "import pygraphviz; print(pygraphviz.__file__)"
python3 -c "import pygraphviz; print(pygraphviz.__file__)"
CommandLine:
python -m ibeis.algo.hots.pgm_viz --exec-show_model --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.pgm_viz 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()
"""
import plottool as pt
fnum = pt.ensure_fnum(fnum)
top_assignments = kwargs.get('top_assignments', None)
if evidence and top_assignments and 'factor_list' in kwargs:
pnum1 = (3, 1, (slice(0, 2), 0))
pnum2 = (3, 8, (2, slice(4, None)))
else:
pnum1 = (1, 1, 1)
draw_bayesian_model(model, evidence, soft_evidence, fnum=fnum, pnum=pnum1, **kwargs)
# Draw probability hist
if top_assignments is not None:
draw_map_histogram(top_assignments, fnum=fnum, pnum=pnum2)
def draw_map_histogram(top_assignments, fnum=None, pnum=(1, 1, 1)):
import plottool as pt
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
fnum = pt.ensure_fnum(fnum)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(
bin_labels,
bin_vals,
fnum=fnum,
pnum=pnum,
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob',
xlabel='assignment')
pt.set_title('Assignment probabilities')
def 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 gridsearch_image_function(param_info, test_func, args=tuple(), show_func=None):
"""
gridsearch for a function that produces a single image
"""
import plottool as pt
cfgdict_list, cfglbl_list = param_info.get_gridsearch_input(defaultslice=slice(0, 10))
fnum = pt.ensure_fnum(None)
if show_func is None:
show_func = pt.imshow
lbl = ut.get_funcname(test_func)
cfgresult_list = [
test_func(*args, **cfgdict)
for cfgdict in ut.ProgressIter(cfgdict_list, lbl=lbl)
]
onclick_func = None
ut.interact_gridsearch_result_images(
show_func, cfgdict_list, cfglbl_list,
cfgresult_list, fnum=fnum,
figtitle=lbl, unpack=False,
max_plots=25, onclick_func=onclick_func)
pt.iup()
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 ishow_chip(ibs, aid, fnum=2, fx=None, dodraw=True, config2_=None,
ischild=False, **kwargs):
r"""
# TODO:
split into two interactions
interact chip and interact chip features
Args:
ibs (IBEISController): ibeis controller object
aid (int): annotation id
fnum (int): figure number
fx (None):
CommandLine:
python -m ibeis.viz.interact.interact_chip --test-ishow_chip --show
python -m ibeis.viz.interact.interact_chip --test-ishow_chip --show --aid 2
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.interact.interact_chip import * # NOQA
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> aid = ut.get_argval('--aid', type_=int, default=1)
>>> fnum = 2
>>> fx = None
>>> # execute function
>>> dodraw = ut.show_was_requested()
>>> result = ishow_chip(ibs, aid, fnum, fx, dodraw)
>>> # verify results
>>> pt.show_if_requested()
>>> print(result)
"""
fnum = pt.ensure_fnum(fnum)
vh.ibsfuncs.assert_valid_aids(ibs, (aid,))
# TODO: Reconcile this with interact keypoints.
# Preferably this will call that but it will set some fancy callbacks
if not ischild:
fig = ih.begin_interaction('chip', fnum)
else:
fig = pt.gcf()
#fig = pt.figure(fnum=fnum, pnum=pnum)
# Get chip info (make sure get_chips is called first)
#mode_ptr = [1]
mode_ptr = [0]
def _select_fxth_kpt(fx):
# Get the fx-th keypiont
chip = ibs.get_annot_chips(aid, config2_=config2_)
kp = ibs.get_annot_kpts(aid, config2_=config2_)[fx]
sift = ibs.get_annot_vecs(aid, config2_=config2_)[fx]
# Draw chip + keypoints + highlighted plots
_chip_view(pnum=(2, 1, 1), sel_fx=fx)
#ishow_chip(ibs, aid, fnum=None, fx=fx, config2_=config2_, **kwargs)
# Draw the selected feature plots
nRows, nCols, px = (2, 3, 3)
draw_feat_row(chip, fx, kp, sift, fnum, nRows, nCols, px, None)
def _chip_view(mode=0, pnum=(1, 1, 1), **kwargs):
print('... _chip_view mode=%r' % mode_ptr[0])
kwargs['ell'] = mode_ptr[0] == 1
kwargs['pts'] = mode_ptr[0] == 2
if not ischild:
df2.figure(fnum=fnum, pnum=pnum, docla=True, doclf=True)
# Toggle no keypoints view
viz.show_chip(ibs, aid, fnum=fnum, pnum=pnum, config2_=config2_,
**kwargs)
df2.set_figtitle('Chip View')
def _on_chip_click(event):
print('[inter] clicked chip')
ax, x, y = event.inaxes, event.xdata, event.ydata
if ih.clicked_outside_axis(event):
if not ischild:
print('... out of axis')
mode_ptr[0] = (mode_ptr[0] + 1) % 3
_chip_view(**kwargs)
else:
if event.button == 3: # right-click
import guitool
#from ibeis.viz.interact import interact_chip
height = fig.canvas.geometry().height()
qpoint = guitool.newQPoint(event.x, height - event.y)
refresh_func = partial(_chip_view, **kwargs)
callback_list = build_annot_context_options(
ibs, aid, refresh_func=refresh_func,
with_interact_chip=False,
config2_=config2_)
qwin = fig.canvas
guitool.popup_menu(qwin, qpoint, callback_list)
#interact_chip.show_annot_context_menu(
# ibs, aid, fig.canvas, qpoint, refresh_func=refresh_func,
# with_interact_chip=False, config2_=config2_)
else:
viztype = vh.get_ibsdat(ax, 'viztype')
print('[ic] viztype=%r' % viztype)
if viztype == 'chip' and event.key == 'shift':
_chip_view(**kwargs)
ih.disconnect_callback(fig, 'button_press_event')
elif viztype == 'chip':
kpts = ibs.get_annot_kpts(aid, config2_=config2_)
if len(kpts) > 0:
fx = vt.nearest_point(
x, y, kpts, conflict_mode='next')[0]
print('... clicked fx=%r' % fx)
_select_fxth_kpt(fx)
else:
print('... len(kpts) == 0')
elif viztype in ['warped', 'unwarped']:
fx = vh.get_ibsdat(ax, 'fx')
if fx is not None and viztype == 'warped':
viz.show_keypoint_gradient_orientations(
ibs, aid, fx, fnum=df2.next_fnum())
else:
print('...Unknown viztype: %r' % viztype)
viz.draw()
# Draw without keypoints the first time
if fx is not None:
_select_fxth_kpt(fx)
else:
_chip_view(**kwargs)
if dodraw:
viz.draw()
if not ischild:
ih.connect_callback(fig, 'button_press_event', _on_chip_click)
def gridsearch_chipextract():
r"""
CommandLine:
python -m vtool.chip --test-gridsearch_chipextract --show
Example:
>>> # GRIDSEARCH
>>> from vtool.chip import * # NOQA
>>> gridsearch_chipextract()
>>> ut.show_if_requested()
"""
import cv2
test_func = extract_chip_from_img
if False:
gpath = ut.grab_test_imgpath('carl.jpg')
bbox = (100, 3, 100, 100)
theta = 0.0
new_size = (58, 34)
else:
gpath = '/media/raid/work/GZ_Master1/_ibsdb/images/1524525d-2131-8770-d27c-3a5f9922e9e9.jpg'
bbox = (450, 373, 2062, 1124)
theta = 0.0
old_size = bbox[2:4]
#target_area = 700 ** 2
target_area = 1200 ** 2
new_size = get_scaled_sizes_with_area(target_area, [old_size])[0]
print('old_size = %r' % (old_size,))
print('new_size = %r' % (new_size,))
#new_size = (677, 369)
imgBGR = gtool.imread(gpath)
args = (imgBGR, bbox, theta, new_size)
param_info = ut.ParamInfoList('extract_params', [
ut.ParamInfo('interpolation', cv2.INTER_LANCZOS4,
varyvals=[
cv2.INTER_LANCZOS4,
cv2.INTER_CUBIC,
cv2.INTER_LINEAR,
cv2.INTER_NEAREST,
#cv2.INTER_AREA
],)
])
show_func = None
# Generalize
import plottool as pt
pt.imshow(imgBGR) # HACK
cfgdict_list, cfglbl_list = param_info.get_gridsearch_input(defaultslice=slice(0, 10))
fnum = pt.ensure_fnum(None)
if show_func is None:
show_func = pt.imshow
lbl = ut.get_funcname(test_func)
cfgresult_list = [
test_func(*args, **cfgdict)
for cfgdict in ut.ProgressIter(cfgdict_list, lbl=lbl)
]
onclick_func = None
ut.interact_gridsearch_result_images(
show_func, cfgdict_list, cfglbl_list,
cfgresult_list, fnum=fnum,
figtitle=lbl, unpack=False,
max_plots=25, onclick_func=onclick_func)
pt.iup()
def show_model(model, evidence={}, soft_evidence={}, **kwargs):
"""
References:
http://stackoverflow.com/questions/22207802/pygraphviz-networkx-set-node-level-or-layer
Ignore:
pkg-config --libs-only-L libcgraph
sudo apt-get install libgraphviz-dev -y
sudo apt-get install libgraphviz4 -y
# sudo apt-get install pkg-config
sudo apt-get install libgraphviz-dev
# pip install git+git://github.com/pygraphviz/pygraphviz.git
pip install pygraphviz
python -c "import pygraphviz; print(pygraphviz.__file__)"
sudo pip3 install pygraphviz --install-option="--include-path=/usr/include/graphviz" --install-option="--library-path=/usr/lib/graphviz/"
python3 -c "import pygraphviz; print(pygraphviz.__file__)"
"""
if ut.get_argval('--hackmarkov') or ut.get_argval('--hackjunc'):
draw_tree_model(model, **kwargs)
return
import plottool as pt
import networkx as netx
import matplotlib as mpl
fnum = pt.ensure_fnum(None)
fig = pt.figure(fnum=fnum, pnum=(3, 1, (slice(0, 2), 0)), doclf=True) # NOQA
#fig = pt.figure(fnum=fnum, pnum=(3, 2, (1, slice(1, 2))), doclf=True) # NOQA
ax = pt.gca()
var2_post = {f.variables[0]: f for f in kwargs.get('factor_list', [])}
netx_graph = (model)
#netx_graph.graph.setdefault('graph', {})['size'] = '"10,5"'
#netx_graph.graph.setdefault('graph', {})['rankdir'] = 'LR'
pos = get_hacked_pos(netx_graph)
#netx.pygraphviz_layout(netx_graph)
#pos = netx.pydot_layout(netx_graph, prog='dot')
#pos = netx.graphviz_layout(netx_graph)
drawkw = dict(pos=pos, ax=ax, with_labels=True, node_size=1500)
if evidence is not None:
node_colors = [
# (pt.TRUE_BLUE
(pt.WHITE
if node not in soft_evidence else
pt.LIGHT_PINK)
if node not in evidence
else pt.FALSE_RED
for node in netx_graph.nodes()]
for node in netx_graph.nodes():
cpd = model.var2_cpd[node]
if cpd.ttype == 'score':
pass
drawkw['node_color'] = node_colors
netx.draw(netx_graph, **drawkw)
show_probs = True
if show_probs:
textprops = {
'family': 'monospace',
'horizontalalignment': 'left',
#'horizontalalignment': 'center',
#'size': 12,
'size': 8,
}
textkw = dict(
xycoords='data', boxcoords='offset points', pad=0.25,
frameon=True, arrowprops=dict(arrowstyle='->'),
#bboxprops=dict(fc=node_attr['fillcolor']),
)
netx_nodes = model.nodes(data=True)
node_key_list = ut.get_list_column(netx_nodes, 0)
pos_list = ut.dict_take(pos, node_key_list)
artist_list = []
offset_box_list = []
for pos_, node in zip(pos_list, netx_nodes):
x, y = pos_
variable = node[0]
cpd = model.var2_cpd[variable]
prior_marg = (cpd if cpd.evidence is None else
cpd.marginalize(cpd.evidence, inplace=False))
prior_text = None
text = None
if variable in evidence:
text = cpd.variable_statenames[evidence[variable]]
elif variable in var2_post:
post_marg = var2_post[variable]
text = pgm_ext.make_factor_text(post_marg, 'post')
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
else:
if len(evidence) == 0 and len(soft_evidence) == 0:
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
show_post = kwargs.get('show_post', False)
show_prior = kwargs.get('show_prior', False)
show_prior = True
show_post = True
show_ev = (evidence is not None and variable in evidence)
if (show_post or show_ev) and text is not None:
offset_box = mpl.offsetbox.TextArea(text, textprops)
artist = mpl.offsetbox.AnnotationBbox(
# offset_box, (x + 5, y), xybox=(20., 5.),
offset_box, (x, y + 5), xybox=(4., 20.),
#box_alignment=(0, 0),
box_alignment=(.5, 0),
**textkw)
offset_box_list.append(offset_box)
artist_list.append(artist)
if show_prior and prior_text is not None:
offset_box2 = mpl.offsetbox.TextArea(prior_text, textprops)
artist2 = mpl.offsetbox.AnnotationBbox(
# offset_box2, (x - 5, y), xybox=(-20., -15.),
# offset_box2, (x, y - 5), xybox=(-15., -20.),
offset_box2, (x, y - 5), xybox=(-4, -20.),
#box_alignment=(1, 1),
box_alignment=(.5, 1),
**textkw)
offset_box_list.append(offset_box2)
artist_list.append(artist2)
for artist in artist_list:
ax.add_artist(artist)
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
num_annots = len(model.ttype2_cpds['name'])
if num_annots > 4:
ax.set_xlim((xmin - 40, xmax + 40))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(30, 7)
else:
ax.set_xlim((xmin - 42, xmax + 42))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(23, 7)
fig = pt.gcf()
title = 'num_names=%r, num_annots=%r' % (model.num_names, num_annots,)
map_assign = kwargs.get('map_assign', None)
#max_marginal_list = []
#for name, marginal in marginalized_joints.items():
# states = list(ut.iprod(*marginal.statenames))
# vals = marginal.values.ravel()
# x = vals.argmax()
# max_marginal_list += ['P(' + ', '.join(states[x]) + ') = ' + str(vals[x])]
# title += str(marginal)
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
map_assign, map_prob = top_assignments[0]
if map_assign is not None:
# title += '\nMAP=' + ut.repr2(map_assign, strvals=True)
title += '\nMAP: ' + map_assign + ' @' + '%.2f%%' % (100 * map_prob,)
if kwargs.get('show_title', True):
pt.set_figtitle(title, size=14)
#pt.set_xlabel()
def hack_fix_centeralign():
if textprops['horizontalalignment'] == 'center':
print('Fixing centeralign')
fig = pt.gcf()
fig.canvas.draw()
# Superhack for centered text. Fix bug in
# /usr/local/lib/python2.7/dist-packages/matplotlib/offsetbox.py
# /usr/local/lib/python2.7/dist-packages/matplotlib/text.py
for offset_box in offset_box_list:
offset_box.set_offset
z = offset_box._text.get_window_extent()
(z.x1 - z.x0) / 2
offset_box._text
T = offset_box._text.get_transform()
A = mpl.transforms.Affine2D()
A.clear()
A.translate((z.x1 - z.x0) / 2, 0)
offset_box._text.set_transform(T + A)
hack_fix_centeralign()
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(bin_labels, bin_vals, fnum=fnum, pnum=(3, 8, (2, slice(4, None))),
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob', xlabel='assignment')
pt.set_title('Assignment probabilities')
def show_multiple_chips(ibs, aid_list, in_image=True, fnum=0, sel_aids=[],
subtitle='', annote=False, **kwargs):
"""
CommandLine:
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=6435,9861,137,6563,9167,12547,9332,12598,13285 --no-inimage --notitle
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=137,6563,12547,9332,12598,13285 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=6563,9332,13285,12598 --no-inimage --notitle --adjust=.05 --rc=1,4
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db PZ_Master0 --aids=1288 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db PZ_Master0 --aids=4020,4839 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --db NNP_Master3 --aids=6524,6540,6571,6751 --no-inimage --notitle --adjust=.05 --diskshow
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST -a default:index=0:4 --show
--aids=1 --doboth --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --rc=2,1 --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --rc=2,1 --show --notitle --trydrawline --no-draw_lbls
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1,2 --doboth --show --notitle --trydrawline
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1,2,3,4,5 --doboth --rc=2,5 --show --chrlbl --trydrawline --qualtitle --no-figtitle --notitle
--doboth
--doboth --show
python -m ibeis.viz.viz_name --test-show_multiple_chips --db NNP_Master3 --aids=15419 --doboth --rc=2,1 --show --notitle --trydrawline --no-draw_lbls
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_name import * # NOQA
>>> import ibeis
>>> ibs, aid_list, in_image = testdata_multichips()
>>> fnum = 0
>>> sel_aids = []
>>> subtitle = ''
>>> annote = False
>>> fig = show_multiple_chips(ibs, aid_list, in_image, fnum, sel_aids, subtitle, annote)
>>> ut.quit_if_noshow()
>>> fig.canvas.draw()
>>> ut.show_if_requested()
"""
fnum = pt.ensure_fnum(fnum)
nAids = len(aid_list)
if nAids == 0:
fig = df2.figure(fnum=fnum, pnum=(1, 1, 1), **kwargs)
df2.imshow_null(fnum=fnum, **kwargs)
return fig
# Trigger computation of all chips in parallel
ibsfuncs.ensure_annotation_data(ibs, aid_list, chips=(not in_image or annote), feats=annote)
print('[viz_name] * annot_vuuid=%r' % ((ibs.get_annot_visual_uuids(aid_list),)))
print('[viz_name] * aid_list=%r' % ((aid_list,)))
DOBOTH = ut.get_argflag('--doboth')
rc = ut.get_argval('--rc', type_=list, default=None)
if rc is None:
nRows, nCols = ph.get_square_row_cols(nAids * (2 if DOBOTH else 1))
else:
nRows, nCols = rc
notitle = ut.get_argflag('--notitle')
draw_lbls = not ut.get_argflag('--no-draw_lbls')
show_chip_kw = dict(annote=annote, in_image=in_image, notitle=notitle, draw_lbls=draw_lbls)
#print('[viz_name] * r=%r, c=%r' % (nRows, nCols))
#gs2 = gridspec.GridSpec(nRows, nCols)
pnum_ = df2.get_pnum_func(nRows, nCols)
fig = df2.figure(fnum=fnum, pnum=pnum_(0), **kwargs)
fig.clf()
ax_list1 = []
for px, aid in enumerate(aid_list):
print('px = %r' % (px,))
_fig, _ax1 = viz_chip.show_chip(ibs, aid=aid, pnum=pnum_(px), **show_chip_kw)
print('other_aids = %r' % (ibs.get_annot_contact_aids(aid),))
ax = df2.gca()
ax_list1.append(_ax1)
if aid in sel_aids:
df2.draw_border(ax, df2.GREEN, 4)
if ut.get_argflag('--chrlbl') and not DOBOTH:
ax.set_xlabel('(' + chr(ord('a') - 1 + px) + ')')
elif ut.get_argflag('--numlbl') and not DOBOTH:
ax.set_xlabel('(' + str(px + 1) + ')')
#plot_aid3(ibs, aid)
# HACK to show in image and not in image
if DOBOTH:
#ut.embed()
#ph.get_plotdat_dict(ax_list1[1])
#ph.get_plotdat_dict(ax_list2[1])
ax_list2 = []
show_chip_kw['in_image'] = not show_chip_kw['in_image']
start = px + 1
for px, aid in enumerate(aid_list, start=start):
_fig, _ax2 = viz_chip.show_chip(ibs, aid=aid, pnum=pnum_(px), **show_chip_kw)
ax = df2.gca()
ax_list2.append(_ax2)
if ut.get_argflag('--chrlbl'):
ax.set_xlabel('(' + chr(ord('a') - start + px) + ')')
elif ut.get_argflag('--numlbl'):
ax.set_xlabel('(' + str(px - start + 1) + ')')
if ut.get_argflag('--qualtitle'):
qualtext = ibs.get_annot_quality_texts(aid)
ax.set_title(qualtext)
if aid in sel_aids:
df2.draw_border(ax, df2.GREEN, 4)
if in_image:
ax_list1, ax_list2 = ax_list2, ax_list1
if ut.get_argflag('--trydrawline'):
# Unfinished
#ut.embed()
# Draw lines between corresponding axes
# References:
# http://stackoverflow.com/questions/17543359/drawing-lines-between-two-plots-in-matplotlib
import matplotlib as mpl
import vtool as vt
# !!!
#http://matplotlib.org/users/transforms_tutorial.html
#invTransFigure_fn1 = fig.transFigure.inverted().transform
#invTransFigure_fn2 = fig.transFigure.inverted().transform
#print(ax_list1)
#print(ax_list2)
assert len(ax_list1) == len(ax_list2)
for ax1, ax2 in zip(ax_list1, ax_list2):
#_ = ax1.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
#bbox1 = (0, 0, _.width * fig.dpi, _.height * fig.dpi)
# returns in figure coordinates
#bbox1 = df2.get_axis_bbox(ax=ax1)
#if bbox1[-1] < 0:
# # Weird bug
# bbox1 = bbox1[1]
print('--')
print('ax1 = %r' % (ax1,))
print('ax2 = %r' % (ax2,))
chipshape = ph.get_plotdat(ax1, 'chipshape')
#_bbox1 = ax1.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
#bbox1 = (0, 0, _bbox1.width * fig.dpi, _bbox1.height * fig.dpi)
bbox1 = (0, 0, chipshape[1], chipshape[0])
aid_ = ph.get_plotdat(ax2, 'aid')
aid_list_ = ph.get_plotdat(ax2, 'aid_list')
index = aid_list_.index(aid_)
annotation_bbox_list = ph.get_plotdat(ax2, 'annotation_bbox_list')
bbox2 = annotation_bbox_list[index]
print('bbox1 = %r' % (bbox1,))
print('bbox2 = %r' % (bbox2,))
vert_list1 = np.array(vt.verts_from_bbox(bbox1))
vert_list2 = np.array(vt.verts_from_bbox(bbox2))
print('vert_list1 = %r' % (vert_list1,))
print('vert_list2 = %r' % (vert_list2,))
#for vx in [0, 1, 2, 3]:
for vx in [0, 1]:
vert1 = vert_list1[vx].tolist()
vert2 = vert_list2[vx].tolist()
print(' ***')
print(' * vert1 = %r' % (vert1,))
print(' * vert2 = %r' % (vert2,))
coordsA = coordsB = 'data'
#coords = 'axes points'
#'axes fraction'
#'axes pixels'
#coordsA = 'axes pixels'
#coordsB = 'data'
#'figure fraction'
#'figure pixels'
#'figure pixels'
#'figure points'
#'polar'
#'offset points'
con = mpl.patches.ConnectionPatch(
xyA=vert1, xyB=vert2, coordsA=coordsA,
coordsB=coordsB,
axesA=ax1, axesB=ax2,
linewidth=1, color='k')
#, arrowstyle="-")
#ut.embed()
#con.set_zorder(None)
ax1.add_artist(con)
#ax2.add_artist(con)
#ut.embed()
#verts2.T[1] -= bbox2[-1]
#bottom_left1, bottom_right1 = verts1[1:3].tolist()
#bottom_left2, bottom_right2 = verts2[1:3].tolist()
##transAxes1 = ax1.transData.inverted()
#transAxes1_fn = ax1.transData.transform
#transAxes2_fn = ax2.transData.transform
#transAxes1_fn = ut.identity
#transAxes2_fn = ut.identity
#coord_bl1 = transFigure.transform(transAxes1.transform(bottom_left1))
#coord_br1 = transFigure.transform(transAxes1.transform(bottom_right1))
#coord_bl1 = invTransFigure_fn1(transAxes1_fn(bottom_left1))
#print('bottom_left2 = %r' % (bottom_left2,))
#coord_bl1 = (5, 5)
#coord_bl2 = invTransFigure_fn2(transAxes2_fn(bottom_left2))
#print('coord_bl2 = %r' % (coord_bl2,))
#coord_br1 = invTransFigure_fn1(transAxes1_fn(bottom_right1))
#coord_br2 = invTransFigure_fn2(transAxes2_fn(bottom_right2))
##print('coord_bl1 = %r' % (coord_bl1,))
#line_coords1 = np.vstack([coord_bl1, coord_bl2])
#line_coords2 = np.vstack([coord_br1, coord_br2])
#print('line_coords1 = %r' % (line_coords1,))
#line1 = mpl.lines.Line2D((line_coords1[0]), (line_coords1[1]), transform=fig.transFigure)
#line2 = mpl.lines.Line2D((line_coords2[0]), (line_coords2[1]), transform=fig.transFigure)
#xs1, ys1 = line_coords1.T
#xs2, ys2 = line_coords2.T
#linekw = dict(transform=fig.transFigure)
#linekw = dict()
#print('xs1 = %r' % (xs1,))
#print('ys1 = %r' % (ys1,))
#line1 = mpl.lines.Line2D(xs1, ys1, **linekw)
#line2 = mpl.lines.Line2D(xs2, ys2, **linekw) # NOQA
#shrinkA=5, shrinkB=5, mutation_scale=20, fc="w")
#ax2.add_artist(con)
#fig.lines.append(line1)
#fig.lines.append(line2)
pass
return fig
def ishow_chip(ibs, aid, fnum=2, fx=None, dodraw=True, config2_=None,
ischild=False, **kwargs):
r"""
# TODO:
split into two interactions
interact chip and interact chip features
Args:
ibs (IBEISController): ibeis controller object
aid (int): annotation id
fnum (int): figure number
fx (None):
CommandLine:
python -m ibeis.viz.interact.interact_chip --test-ishow_chip --show
python -m ibeis.viz.interact.interact_chip --test-ishow_chip --show --aid 2
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.interact.interact_chip import * # NOQA
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> aid = ut.get_argval('--aid', type_=int, default=1)
>>> fnum = 2
>>> fx = None
>>> # execute function
>>> dodraw = ut.show_was_requested()
>>> result = ishow_chip(ibs, aid, fnum, fx, dodraw)
>>> # verify results
>>> pt.show_if_requested()
>>> print(result)
"""
fnum = pt.ensure_fnum(fnum)
vh.ibsfuncs.assert_valid_aids(ibs, (aid,))
# TODO: Reconcile this with interact keypoints.
# Preferably this will call that but it will set some fancy callbacks
if not ischild:
fig = ih.begin_interaction('chip', fnum)
else:
fig = pt.gcf()
#fig = pt.figure(fnum=fnum, pnum=pnum)
# Get chip info (make sure get_chips is called first)
#mode_ptr = [1]
mode_ptr = [0]
def _select_fxth_kpt(fx):
# Get the fx-th keypiont
chip = ibs.get_annot_chips(aid, config2_=config2_)
kp = ibs.get_annot_kpts(aid, config2_=config2_)[fx]
sift = ibs.get_annot_vecs(aid, config2_=config2_)[fx]
# Draw chip + keypoints + highlighted plots
_chip_view(pnum=(2, 1, 1), sel_fx=fx)
#ishow_chip(ibs, aid, fnum=None, fx=fx, config2_=config2_, **kwargs)
# Draw the selected feature plots
nRows, nCols, px = (2, 3, 3)
draw_feat_row(chip, fx, kp, sift, fnum, nRows, nCols, px, None)
def _chip_view(mode=0, pnum=(1, 1, 1), **kwargs):
print('... _chip_view mode=%r' % mode_ptr[0])
kwargs['ell'] = mode_ptr[0] == 1
kwargs['pts'] = mode_ptr[0] == 2
if not ischild:
df2.figure(fnum=fnum, pnum=pnum, docla=True, doclf=True)
# Toggle no keypoints view
viz.show_chip(ibs, aid, fnum=fnum, pnum=pnum, config2_=config2_,
**kwargs)
df2.set_figtitle('Chip View')
def _on_chip_click(event):
print('[inter] clicked chip')
ax, x, y = event.inaxes, event.xdata, event.ydata
if ih.clicked_outside_axis(event):
if not ischild:
print('... out of axis')
mode_ptr[0] = (mode_ptr[0] + 1) % 3
_chip_view(**kwargs)
else:
if event.button == 3: # right-click
import guitool
#from ibeis.viz.interact import interact_chip
height = fig.canvas.geometry().height()
qpoint = guitool.newQPoint(event.x, height - event.y)
refresh_func = partial(_chip_view, **kwargs)
callback_list = build_annot_context_options(
ibs, aid, refresh_func=refresh_func,
with_interact_chip=False,
config2_=config2_)
qwin = fig.canvas
guitool.popup_menu(qwin, qpoint, callback_list)
#interact_chip.show_annot_context_menu(
# ibs, aid, fig.canvas, qpoint, refresh_func=refresh_func,
# with_interact_chip=False, config2_=config2_)
else:
viztype = vh.get_ibsdat(ax, 'viztype')
print('[ic] viztype=%r' % viztype)
if viztype == 'chip' and event.key == 'shift':
_chip_view(**kwargs)
ih.disconnect_callback(fig, 'button_press_event')
elif viztype == 'chip':
kpts = ibs.get_annot_kpts(aid, config2_=config2_)
if len(kpts) > 0:
fx = vt.nearest_point(
x, y, kpts, conflict_mode='next')[0]
print('... clicked fx=%r' % fx)
_select_fxth_kpt(fx)
else:
print('... len(kpts) == 0')
elif viztype in ['warped', 'unwarped']:
fx = vh.get_ibsdat(ax, 'fx')
if fx is not None and viztype == 'warped':
viz.show_keypoint_gradient_orientations(
ibs, aid, fx, fnum=df2.next_fnum())
else:
print('...Unknown viztype: %r' % viztype)
viz.draw()
# Draw without keypoints the first time
if fx is not None:
_select_fxth_kpt(fx)
else:
_chip_view(**kwargs)
if dodraw:
viz.draw()
if not ischild:
ih.connect_callback(fig, 'button_press_event', _on_chip_click)
def 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 show_model(model, evidence={}, soft_evidence={}, **kwargs):
"""
References:
http://stackoverflow.com/questions/22207802/pygraphviz-networkx-set-node-level-or-layer
Ignore:
pkg-config --libs-only-L libcgraph
sudo apt-get install libgraphviz-dev -y
sudo apt-get install libgraphviz4 -y
# sudo apt-get install pkg-config
sudo apt-get install libgraphviz-dev
# pip install git+git://github.com/pygraphviz/pygraphviz.git
pip install pygraphviz
python -c "import pygraphviz; print(pygraphviz.__file__)"
sudo pip3 install pygraphviz --install-option="--include-path=/usr/include/graphviz" --install-option="--library-path=/usr/lib/graphviz/"
python3 -c "import pygraphviz; print(pygraphviz.__file__)"
"""
if ut.get_argval('--hackmarkov') or ut.get_argval('--hackjunc'):
draw_tree_model(model, **kwargs)
return
import plottool as pt
import networkx as netx
import matplotlib as mpl
fnum = pt.ensure_fnum(None)
fig = pt.figure(fnum=fnum, pnum=(3, 1, (slice(0, 2), 0)),
doclf=True) # NOQA
#fig = pt.figure(fnum=fnum, pnum=(3, 2, (1, slice(1, 2))), doclf=True) # NOQA
ax = pt.gca()
var2_post = {f.variables[0]: f for f in kwargs.get('factor_list', [])}
netx_graph = (model)
#netx_graph.graph.setdefault('graph', {})['size'] = '"10,5"'
#netx_graph.graph.setdefault('graph', {})['rankdir'] = 'LR'
pos = get_hacked_pos(netx_graph)
#netx.nx_agraph.pygraphviz_layout(netx_graph)
#pos = netx.nx_agraph.pydot_layout(netx_graph, prog='dot')
#pos = netx.nx_agraph.graphviz_layout(netx_graph)
drawkw = dict(pos=pos, ax=ax, with_labels=True, node_size=1500)
if evidence is not None:
node_colors = [
# (pt.TRUE_BLUE
(pt.WHITE if node not in soft_evidence else pt.LIGHT_PINK)
if node not in evidence else pt.FALSE_RED
for node in netx_graph.nodes()
]
for node in netx_graph.nodes():
cpd = model.var2_cpd[node]
if cpd.ttype == 'score':
pass
drawkw['node_color'] = node_colors
netx.draw(netx_graph, **drawkw)
show_probs = True
if show_probs:
textprops = {
'family': 'monospace',
'horizontalalignment': 'left',
#'horizontalalignment': 'center',
#'size': 12,
'size': 8,
}
textkw = dict(
xycoords='data',
boxcoords='offset points',
pad=0.25,
framewidth=True,
arrowprops=dict(arrowstyle='->'),
#bboxprops=dict(fc=node_attr['fillcolor']),
)
netx_nodes = model.nodes(data=True)
node_key_list = ut.get_list_column(netx_nodes, 0)
pos_list = ut.dict_take(pos, node_key_list)
artist_list = []
offset_box_list = []
for pos_, node in zip(pos_list, netx_nodes):
x, y = pos_
variable = node[0]
cpd = model.var2_cpd[variable]
prior_marg = (cpd if cpd.evidence is None else cpd.marginalize(
cpd.evidence, inplace=False))
prior_text = None
text = None
if variable in evidence:
text = cpd.variable_statenames[evidence[variable]]
elif variable in var2_post:
post_marg = var2_post[variable]
text = pgm_ext.make_factor_text(post_marg, 'post')
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
else:
if len(evidence) == 0 and len(soft_evidence) == 0:
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
show_post = kwargs.get('show_post', False)
show_prior = kwargs.get('show_prior', False)
show_prior = True
show_post = True
show_ev = (evidence is not None and variable in evidence)
if (show_post or show_ev) and text is not None:
offset_box = mpl.offsetbox.TextArea(text, textprops)
artist = mpl.offsetbox.AnnotationBbox(
# offset_box, (x + 5, y), xybox=(20., 5.),
offset_box,
(x, y + 5),
xybox=(4., 20.),
#box_alignment=(0, 0),
box_alignment=(.5, 0),
**textkw)
offset_box_list.append(offset_box)
artist_list.append(artist)
if show_prior and prior_text is not None:
offset_box2 = mpl.offsetbox.TextArea(prior_text, textprops)
artist2 = mpl.offsetbox.AnnotationBbox(
# offset_box2, (x - 5, y), xybox=(-20., -15.),
# offset_box2, (x, y - 5), xybox=(-15., -20.),
offset_box2,
(x, y - 5),
xybox=(-4, -20.),
#box_alignment=(1, 1),
box_alignment=(.5, 1),
**textkw)
offset_box_list.append(offset_box2)
artist_list.append(artist2)
for artist in artist_list:
ax.add_artist(artist)
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
num_annots = len(model.ttype2_cpds['name'])
if num_annots > 4:
ax.set_xlim((xmin - 40, xmax + 40))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(30, 7)
else:
ax.set_xlim((xmin - 42, xmax + 42))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(23, 7)
fig = pt.gcf()
title = 'num_names=%r, num_annots=%r' % (
model.num_names,
num_annots,
)
map_assign = kwargs.get('map_assign', None)
#max_marginal_list = []
#for name, marginal in marginalized_joints.items():
# states = list(ut.iprod(*marginal.statenames))
# vals = marginal.values.ravel()
# x = vals.argmax()
# max_marginal_list += ['P(' + ', '.join(states[x]) + ') = ' + str(vals[x])]
# title += str(marginal)
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
map_assign, map_prob = top_assignments[0]
if map_assign is not None:
# title += '\nMAP=' + ut.repr2(map_assign, strvals=True)
title += '\nMAP: ' + map_assign + ' @' + '%.2f%%' % (
100 * map_prob, )
if kwargs.get('show_title', True):
pt.set_figtitle(title, size=14)
#pt.set_xlabel()
def hack_fix_centeralign():
if textprops['horizontalalignment'] == 'center':
print('Fixing centeralign')
fig = pt.gcf()
fig.canvas.draw()
# Superhack for centered text. Fix bug in
# /usr/local/lib/python2.7/dist-packages/matplotlib/offsetbox.py
# /usr/local/lib/python2.7/dist-packages/matplotlib/text.py
for offset_box in offset_box_list:
offset_box.set_offset
z = offset_box._text.get_window_extent()
(z.x1 - z.x0) / 2
offset_box._text
T = offset_box._text.get_transform()
A = mpl.transforms.Affine2D()
A.clear()
A.translate((z.x1 - z.x0) / 2, 0)
offset_box._text.set_transform(T + A)
hack_fix_centeralign()
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(
bin_labels,
bin_vals,
fnum=fnum,
pnum=(3, 8, (2, slice(4, None))),
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob',
xlabel='assignment')
pt.set_title('Assignment probabilities')
def show_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 hackshow_names(ibs, aid_list, fnum=None):
r"""
Args:
ibs (IBEISController): ibeis controller object
aid_list (list):
CommandLine:
python -m ibeis.other.dbinfo --exec-hackshow_names --show
python -m ibeis.other.dbinfo --exec-hackshow_names --show --db PZ_Master1
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.other.dbinfo import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='PZ_MTEST')
>>> aid_list = ibs.get_valid_aids()
>>> result = hackshow_names(ibs, aid_list)
>>> print(result)
>>> ut.show_if_requested()
"""
import plottool as pt
import vtool as vt
grouped_aids, nid_list = ibs.group_annots_by_name(aid_list)
grouped_aids = [aids for aids in grouped_aids if len(aids) > 1]
unixtimes_list = ibs.unflat_map(ibs.get_annot_image_unixtimes_asfloat, grouped_aids)
yaws_list = ibs.unflat_map(ibs.get_annot_yaws, grouped_aids)
#markers_list = [[(1, 2, yaw * 360 / (np.pi * 2)) for yaw in yaws] for yaws in yaws_list]
unixtime_list = ut.flatten(unixtimes_list)
timemax = np.nanmax(unixtime_list)
timemin = np.nanmin(unixtime_list)
timerange = timemax - timemin
unixtimes_list = [((unixtimes[:] - timemin) / timerange) for unixtimes in unixtimes_list]
for unixtimes in unixtimes_list:
num_nan = sum(np.isnan(unixtimes))
unixtimes[np.isnan(unixtimes)] = np.linspace(-1, -.5, num_nan)
#ydata_list = [np.arange(len(aids)) for aids in grouped_aids]
sortx_list = vt.argsort_groups(unixtimes_list, reverse=False)
#markers_list = ut.list_ziptake(markers_list, sortx_list)
yaws_list = ut.list_ziptake(yaws_list, sortx_list)
ydatas_list = vt.ziptake(unixtimes_list, sortx_list)
#ydatas_list = sortx_list
#ydatas_list = vt.argsort_groups(unixtimes_list, reverse=False)
# Sort by num members
#ydatas_list = ut.take(ydatas_list, np.argsort(list(map(len, ydatas_list))))
xdatas_list = [np.zeros(len(ydatas)) + count for count, ydatas in enumerate(ydatas_list)]
#markers = ut.flatten(markers_list)
#yaws = np.array(ut.flatten(yaws_list))
y_data = np.array(ut.flatten(ydatas_list))
x_data = np.array(ut.flatten(xdatas_list))
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum)
ax = pt.gca()
#unique_yaws, groupxs = vt.group_indices(yaws)
ax.scatter(x_data, y_data, color=[1, 0, 0], s=1, marker='.')
#pt.draw_stems(x_data, y_data, marker=markers, setlims=True, linestyle='')
pt.dark_background()
ax = pt.gca()
ax.set_xlim(min(x_data) - .1, max(x_data) + .1)
ax.set_ylim(min(y_data) - .1, max(y_data) + .1)
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 __init__(self,
ibs,
cm,
aid2=None,
fnum=None,
figtitle='Match Interaction',
same_fig=True,
qreq_=None,
**kwargs):
self.qres = cm
self.ibs = ibs
self.cm = cm
self.qreq_ = qreq_
self.fnum = pt.ensure_fnum(fnum)
# Unpack Args
if aid2 is None:
index = 0
# FIXME: no sortself
cm.sortself()
self.rank = index
else:
index = cm.daid2_idx.get(aid2, None)
# TODO: rank?
self.rank = None
if index is not None:
self.qaid = self.cm.qaid
self.daid = self.cm.daid_list[index]
self.fm = self.cm.fm_list[index]
self.fk = self.cm.fk_list[index]
self.fsv = self.cm.fsv_list[index]
if self.cm.fs_list is None:
fs_list = self.cm.get_fsv_prod_list()
else:
fs_list = self.cm.fs_list
self.fs = None if fs_list is None else fs_list[index]
self.score = None if self.cm.score_list is None else self.cm.score_list[
index]
self.H1 = None if self.cm.H_list is None else cm.H_list[index]
else:
self.qaid = self.cm.qaid
self.daid = aid2
self.fm = np.empty((0, 2), dtype=hstypes.FM_DTYPE)
self.fk = np.empty(0, dtype=hstypes.FK_DTYPE)
self.fsv = np.empty((0, 2), dtype=hstypes.FS_DTYPE)
self.fs = np.empty(0, dtype=hstypes.FS_DTYPE)
self.score = None
self.H1 = None
# Read properties
self.query_config2_ = (None if self.qreq_ is None else
self.qreq_.get_external_query_config2())
self.data_config2_ = (None if self.qreq_ is None else
self.qreq_.get_external_data_config2())
self.rchip1 = vh.get_chips(ibs, [self.qaid],
config2_=self.query_config2_)[0]
self.rchip2 = vh.get_chips(ibs, [self.daid],
config2_=self.data_config2_)[0]
# Begin Interaction
# call doclf docla and make figure
self.fig = ih.begin_interaction('matches', self.fnum)
self.xywh2_ptr = [None]
self.mode = kwargs.pop('mode', 0)
# New state vars
self.same_fig = same_fig
self.use_homog = False
self.vert = kwargs.pop('vert', None)
self.mx = kwargs.pop('mx', None)
self.last_fx = 0
self.fnum2 = pt.next_fnum()
self.figtitle = figtitle
self.kwargs = kwargs
abstract_interaction.register_interaction(self)
ut.inject_func_as_method(self,
AbstractInteraction.append_button.im_func)
ut.inject_func_as_method(self,
AbstractInteraction.show_popup_menu.im_func)
self.scope = []
if not kwargs.get('nobegin', False):
dodraw = kwargs.get('dodraw', True)
self.begin(dodraw=dodraw)
def show_multiple_chips(ibs,
aid_list,
in_image=True,
fnum=0,
sel_aids=[],
subtitle='',
annote=False,
**kwargs):
"""
CommandLine:
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=6435,9861,137,6563,9167,12547,9332,12598,13285 --no-inimage --notitle
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=137,6563,12547,9332,12598,13285 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=6563,9332,13285,12598 --no-inimage --notitle --adjust=.05 --rc=1,4
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db PZ_Master0 --aids=1288 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db PZ_Master0 --aids=4020,4839 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --db NNP_Master3 --aids=6524,6540,6571,6751 --no-inimage --notitle --adjust=.05 --diskshow
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST -a default:index=0:4 --show
--aids=1 --doboth --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --rc=2,1 --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --rc=2,1 --show --notitle --trydrawline --no-draw_lbls
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1,2 --doboth --show --notitle --trydrawline
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1,2,3,4,5 --doboth --rc=2,5 --show --chrlbl --trydrawline --qualtitle --no-figtitle --notitle
--doboth
--doboth --show
python -m ibeis.viz.viz_name --test-show_multiple_chips --db NNP_Master3 --aids=15419 --doboth --rc=2,1 --show --notitle --trydrawline --no-draw_lbls
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_name import * # NOQA
>>> import ibeis
>>> ibs, aid_list, in_image = testdata_multichips()
>>> if True:
>>> import matplotlib as mpl
>>> from ibeis.scripts.thesis import TMP_RC
>>> mpl.rcParams.update(TMP_RC)
>>> fnum = 0
>>> sel_aids = []
>>> subtitle = ''
>>> annote = False
>>> fig = show_multiple_chips(ibs, aid_list, in_image, fnum, sel_aids, subtitle, annote)
>>> ut.quit_if_noshow()
>>> fig.canvas.draw()
>>> ut.show_if_requested()
"""
fnum = pt.ensure_fnum(fnum)
nAids = len(aid_list)
if nAids == 0:
fig = df2.figure(fnum=fnum, pnum=(1, 1, 1), **kwargs)
df2.imshow_null(fnum=fnum, **kwargs)
return fig
# Trigger computation of all chips in parallel
ibsfuncs.ensure_annotation_data(ibs,
aid_list,
chips=(not in_image or annote),
feats=annote)
print('[viz_name] * annot_vuuid=%r' %
((ibs.get_annot_visual_uuids(aid_list), )))
print('[viz_name] * aid_list=%r' % ((aid_list, )))
DOBOTH = ut.get_argflag('--doboth')
rc = ut.get_argval('--rc', type_=list, default=None)
if rc is None:
nRows, nCols = ph.get_square_row_cols(nAids * (2 if DOBOTH else 1))
else:
nRows, nCols = rc
notitle = ut.get_argflag('--notitle')
draw_lbls = not ut.get_argflag('--no-draw_lbls')
show_chip_kw = dict(annote=annote,
in_image=in_image,
notitle=notitle,
draw_lbls=draw_lbls)
#print('[viz_name] * r=%r, c=%r' % (nRows, nCols))
#gs2 = gridspec.GridSpec(nRows, nCols)
pnum_ = df2.get_pnum_func(nRows, nCols)
fig = df2.figure(fnum=fnum, pnum=pnum_(0), **kwargs)
fig.clf()
ax_list1 = []
for px, aid in enumerate(aid_list):
print('px = %r' % (px, ))
_fig, _ax1 = viz_chip.show_chip(ibs,
aid=aid,
pnum=pnum_(px),
**show_chip_kw)
print('other_aids = %r' % (ibs.get_annot_contact_aids(aid), ))
ax = df2.gca()
ax_list1.append(_ax1)
if aid in sel_aids:
df2.draw_border(ax, df2.GREEN, 4)
if ut.get_argflag('--chrlbl') and not DOBOTH:
ax.set_xlabel('(' + chr(ord('a') - 1 + px) + ')')
elif ut.get_argflag('--numlbl') and not DOBOTH:
ax.set_xlabel('(' + str(px + 1) + ')')
#plot_aid3(ibs, aid)
# HACK to show in image and not in image
if DOBOTH:
#ut.embed()
#ph.get_plotdat_dict(ax_list1[1])
#ph.get_plotdat_dict(ax_list2[1])
ax_list2 = []
show_chip_kw['in_image'] = not show_chip_kw['in_image']
start = px + 1
for px, aid in enumerate(aid_list, start=start):
_fig, _ax2 = viz_chip.show_chip(ibs,
aid=aid,
pnum=pnum_(px),
**show_chip_kw)
ax = df2.gca()
ax_list2.append(_ax2)
if ut.get_argflag('--chrlbl'):
ax.set_xlabel('(' + chr(ord('a') - start + px) + ')')
elif ut.get_argflag('--numlbl'):
ax.set_xlabel('(' + str(px - start + 1) + ')')
if ut.get_argflag('--qualtitle'):
qualtext = ibs.get_annot_quality_texts(aid)
ax.set_title(qualtext)
if aid in sel_aids:
df2.draw_border(ax, df2.GREEN, 4)
if in_image:
ax_list1, ax_list2 = ax_list2, ax_list1
if ut.get_argflag('--trydrawline'):
# Unfinished
#ut.embed()
# Draw lines between corresponding axes
# References:
# http://stackoverflow.com/questions/17543359/drawing-lines-between-two-plots-in-matplotlib
import matplotlib as mpl
import vtool as vt
# !!!
#http://matplotlib.org/users/transforms_tutorial.html
#invTransFigure_fn1 = fig.transFigure.inverted().transform
#invTransFigure_fn2 = fig.transFigure.inverted().transform
#print(ax_list1)
#print(ax_list2)
assert len(ax_list1) == len(ax_list2)
for ax1, ax2 in zip(ax_list1, ax_list2):
#_ = ax1.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
#bbox1 = (0, 0, _.width * fig.dpi, _.height * fig.dpi)
# returns in figure coordinates
#bbox1 = df2.get_axis_bbox(ax=ax1)
#if bbox1[-1] < 0:
# # Weird bug
# bbox1 = bbox1[1]
print('--')
print('ax1 = %r' % (ax1, ))
print('ax2 = %r' % (ax2, ))
chipshape = ph.get_plotdat(ax1, 'chipshape')
#_bbox1 = ax1.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
#bbox1 = (0, 0, _bbox1.width * fig.dpi, _bbox1.height * fig.dpi)
bbox1 = (0, 0, chipshape[1], chipshape[0])
aid_ = ph.get_plotdat(ax2, 'aid')
aid_list_ = ph.get_plotdat(ax2, 'aid_list')
index = aid_list_.index(aid_)
annotation_bbox_list = ph.get_plotdat(ax2,
'annotation_bbox_list')
bbox2 = annotation_bbox_list[index]
print('bbox1 = %r' % (bbox1, ))
print('bbox2 = %r' % (bbox2, ))
vert_list1 = np.array(vt.verts_from_bbox(bbox1))
vert_list2 = np.array(vt.verts_from_bbox(bbox2))
print('vert_list1 = %r' % (vert_list1, ))
print('vert_list2 = %r' % (vert_list2, ))
#for vx in [0, 1, 2, 3]:
for vx in [0, 1]:
vert1 = vert_list1[vx].tolist()
vert2 = vert_list2[vx].tolist()
print(' ***')
print(' * vert1 = %r' % (vert1, ))
print(' * vert2 = %r' % (vert2, ))
coordsA = coordsB = 'data'
#coords = 'axes points'
#'axes fraction'
#'axes pixels'
#coordsA = 'axes pixels'
#coordsB = 'data'
#'figure fraction'
#'figure pixels'
#'figure pixels'
#'figure points'
#'polar'
#'offset points'
con = mpl.patches.ConnectionPatch(xyA=vert1,
xyB=vert2,
coordsA=coordsA,
coordsB=coordsB,
axesA=ax1,
axesB=ax2,
linewidth=1,
color='k')
#, arrowstyle="-")
#ut.embed()
#con.set_zorder(None)
ax1.add_artist(con)
#ax2.add_artist(con)
#ut.embed()
#verts2.T[1] -= bbox2[-1]
#bottom_left1, bottom_right1 = verts1[1:3].tolist()
#bottom_left2, bottom_right2 = verts2[1:3].tolist()
##transAxes1 = ax1.transData.inverted()
#transAxes1_fn = ax1.transData.transform
#transAxes2_fn = ax2.transData.transform
#transAxes1_fn = ut.identity
#transAxes2_fn = ut.identity
#coord_bl1 = transFigure.transform(transAxes1.transform(bottom_left1))
#coord_br1 = transFigure.transform(transAxes1.transform(bottom_right1))
#coord_bl1 = invTransFigure_fn1(transAxes1_fn(bottom_left1))
#print('bottom_left2 = %r' % (bottom_left2,))
#coord_bl1 = (5, 5)
#coord_bl2 = invTransFigure_fn2(transAxes2_fn(bottom_left2))
#print('coord_bl2 = %r' % (coord_bl2,))
#coord_br1 = invTransFigure_fn1(transAxes1_fn(bottom_right1))
#coord_br2 = invTransFigure_fn2(transAxes2_fn(bottom_right2))
##print('coord_bl1 = %r' % (coord_bl1,))
#line_coords1 = np.vstack([coord_bl1, coord_bl2])
#line_coords2 = np.vstack([coord_br1, coord_br2])
#print('line_coords1 = %r' % (line_coords1,))
#line1 = mpl.lines.Line2D((line_coords1[0]), (line_coords1[1]), transform=fig.transFigure)
#line2 = mpl.lines.Line2D((line_coords2[0]), (line_coords2[1]), transform=fig.transFigure)
#xs1, ys1 = line_coords1.T
#xs2, ys2 = line_coords2.T
#linekw = dict(transform=fig.transFigure)
#linekw = dict()
#print('xs1 = %r' % (xs1,))
#print('ys1 = %r' % (ys1,))
#line1 = mpl.lines.Line2D(xs1, ys1, **linekw)
#line2 = mpl.lines.Line2D(xs2, ys2, **linekw) # NOQA
#shrinkA=5, shrinkB=5, mutation_scale=20, fc="w")
#ax2.add_artist(con)
#fig.lines.append(line1)
#fig.lines.append(line2)
pass
return fig
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 gridsearch_chipextract():
r"""
CommandLine:
python -m vtool.chip --test-gridsearch_chipextract --show
Example:
>>> # GRIDSEARCH
>>> from vtool.chip import * # NOQA
>>> gridsearch_chipextract()
>>> ut.show_if_requested()
"""
import cv2
test_func = extract_chip_from_img
if False:
gpath = ut.grab_test_imgpath('carl.jpg')
bbox = (100, 3, 100, 100)
theta = 0.0
new_size = (58, 34)
else:
gpath = '/media/raid/work/GZ_Master1/_ibsdb/images/1524525d-2131-8770-d27c-3a5f9922e9e9.jpg'
bbox = (450, 373, 2062, 1124)
theta = 0.0
old_size = bbox[2:4]
#target_area = 700 ** 2
target_area = 1200**2
new_size = get_scaled_sizes_with_area(target_area, [old_size])[0]
print('old_size = %r' % (old_size, ))
print('new_size = %r' % (new_size, ))
#new_size = (677, 369)
imgBGR = gtool.imread(gpath)
args = (imgBGR, bbox, theta, new_size)
param_info = ut.ParamInfoList(
'extract_params',
[
ut.ParamInfo(
'interpolation',
cv2.INTER_LANCZOS4,
varyvals=[
cv2.INTER_LANCZOS4,
cv2.INTER_CUBIC,
cv2.INTER_LINEAR,
cv2.INTER_NEAREST,
#cv2.INTER_AREA
],
)
])
show_func = None
# Generalize
import plottool as pt
pt.imshow(imgBGR) # HACK
cfgdict_list, cfglbl_list = param_info.get_gridsearch_input(
defaultslice=slice(0, 10))
fnum = pt.ensure_fnum(None)
if show_func is None:
show_func = pt.imshow
lbl = ut.get_funcname(test_func)
cfgresult_list = [
test_func(*args, **cfgdict)
for cfgdict in ut.ProgressIter(cfgdict_list, lbl=lbl)
]
onclick_func = None
ut.interact_gridsearch_result_images(show_func,
cfgdict_list,
cfglbl_list,
cfgresult_list,
fnum=fnum,
figtitle=lbl,
unpack=False,
max_plots=25,
onclick_func=onclick_func)
pt.iup()
def show_nx(graph, with_labels=True, fnum=None, pnum=None, layout='agraph',
ax=None, pos=None, img_dict=None, title=None, layoutkw=None,
verbose=None, **kwargs):
r"""
Args:
graph (networkx.Graph):
with_labels (bool): (default = True)
node_size (int): (default = 1100)
fnum (int): figure number(default = None)
pnum (tuple): plot number(default = None)
CommandLine:
python -m plottool.nx_helpers --exec-show_nx --show
python -m dtool --tf DependencyCache.make_graph --show
python -m ibeis.scripts.specialdraw double_depcache_graph --show --testmode
python -m vtool.clustering2 unsupervised_multicut_labeling --show
Example:
>>> # DISABLE_DOCTEST
>>> from plottool.nx_helpers import * # NOQA
>>> import networkx as nx
>>> graph = nx.DiGraph()
>>> graph.add_nodes_from(['a', 'b', 'c', 'd'])
>>> graph.add_edges_from({'a': 'b', 'b': 'c', 'b': 'd', 'c': 'd'}.items())
>>> nx.set_node_attributes(graph, 'shape', 'rect')
>>> nx.set_node_attributes(graph, 'image', {'a': ut.grab_test_imgpath('carl.jpg')})
>>> nx.set_node_attributes(graph, 'image', {'d': ut.grab_test_imgpath('lena.png')})
>>> #nx.set_node_attributes(graph, 'height', 100)
>>> with_labels = True
>>> fnum = None
>>> pnum = None
>>> e = show_nx(graph, with_labels, fnum, pnum, layout='agraph')
>>> ut.show_if_requested()
"""
import plottool as pt
import networkx as nx
if ax is None:
fnum = pt.ensure_fnum(fnum)
pt.figure(fnum=fnum, pnum=pnum)
ax = pt.gca()
if img_dict is None:
img_dict = nx.get_node_attributes(graph, 'image')
if verbose is None:
verbose = ut.VERBOSE
use_image = kwargs.get('use_image', True)
if verbose:
print('Getting layout')
layout_info = get_nx_layout(graph, layout, layoutkw=layoutkw,
verbose=verbose)
if verbose:
print('Drawing graph')
# zoom = kwargs.pop('zoom', .4)
framewidth = kwargs.pop('framewidth', 1.0)
patch_dict = draw_network2(graph, layout_info, ax, verbose=verbose, **kwargs)
layout_info.update(patch_dict)
if kwargs.get('modify_ax', True):
ax.grid(False)
pt.plt.axis('equal')
ax.axesPatch.set_facecolor('white')
ax.autoscale()
ax.autoscale_view(True, True, True)
#axes.facecolor
node_size = layout_info['node']['size']
node_pos = layout_info['node']['pos']
if node_size is not None:
half_size_arr = np.array(ut.take(node_size, graph.nodes())) / 2.
pos_arr = np.array(ut.take(node_pos, graph.nodes()))
# autoscale does not seem to work
#ul_pos = pos_arr - half_size_arr
#br_pos = pos_arr + half_size_arr
# hack because edges are cut off.
# need to take into account extent of edges as well
ul_pos = pos_arr - half_size_arr * 1.5
br_pos = pos_arr + half_size_arr * 1.5
xmin, ymin = ul_pos.min(axis=0)
xmax, ymax = br_pos.max(axis=0)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
#pt.plt.axis('off')
ax.set_xticks([])
ax.set_yticks([])
if use_image and img_dict is not None and len(img_dict) > 0:
if verbose:
print('Drawing images')
node_list = sorted(img_dict.keys())
pos_list = ut.dict_take(node_pos, node_list)
img_list = ut.dict_take(img_dict, node_list)
size_list = ut.dict_take(node_size, node_list)
#color_list = ut.dict_take(nx.get_node_attributes(graph, 'color'), node_list, None)
color_list = ut.dict_take(nx.get_node_attributes(graph, 'framecolor'), node_list, None)
framewidth_list = ut.dict_take(nx.get_node_attributes(graph, 'framewidth'),
node_list, framewidth)
# TODO; frames without images
imgdat = pt.netx_draw_images_at_positions(img_list, pos_list,
size_list, color_list,
framewidth_list=framewidth_list)
imgdat['node_list'] = node_list
layout_info['imgdat'] = imgdat
else:
if verbose:
print('Not drawing images')
if title is not None:
pt.set_title(title)
return layout_info
def __init__(self, ibs, cm, aid2=None, fnum=None,
figtitle='Match Interaction', same_fig=True,
qreq_=None, **kwargs):
self.qres = cm
self.ibs = ibs
self.cm = cm
self.qreq_ = qreq_
self.fnum = pt.ensure_fnum(fnum)
# Unpack Args
if aid2 is None:
index = 0
# FIXME: no sortself
cm.sortself()
self.rank = index
else:
index = cm.daid2_idx.get(aid2, None)
# TODO: rank?
self.rank = None
if index is not None:
self.qaid = self.cm.qaid
self.daid = self.cm.daid_list[index]
self.fm = self.cm.fm_list[index]
self.fk = self.cm.fk_list[index]
self.fsv = self.cm.fsv_list[index]
if self.cm.fs_list is None:
fs_list = self.cm.get_fsv_prod_list()
else:
fs_list = self.cm.fs_list
self.fs = None if fs_list is None else fs_list[index]
self.score = None if self.cm.score_list is None else self.cm.score_list[index]
self.H1 = None if self.cm.H_list is None else cm.H_list[index]
else:
self.qaid = self.cm.qaid
self.daid = aid2
self.fm = np.empty((0, 2), dtype=hstypes.FM_DTYPE)
self.fk = np.empty(0, dtype=hstypes.FK_DTYPE)
self.fsv = np.empty((0, 2), dtype=hstypes.FS_DTYPE)
self.fs = np.empty(0, dtype=hstypes.FS_DTYPE)
self.score = None
self.H1 = None
# Read properties
self.query_config2_ = (None if self.qreq_ is None else
self.qreq_.get_external_query_config2())
self.data_config2_ = (None if self.qreq_ is None else
self.qreq_.get_external_data_config2())
self.rchip1 = vh.get_chips(ibs, [self.qaid], config2_=self.query_config2_)[0]
self.rchip2 = vh.get_chips(ibs, [self.daid], config2_=self.data_config2_)[0]
# Begin Interaction
# call doclf docla and make figure
self.fig = ih.begin_interaction('matches', self.fnum)
self.xywh2_ptr = [None]
self.mode = kwargs.pop('mode', 0)
# New state vars
self.same_fig = same_fig
self.use_homog = False
self.vert = kwargs.pop('vert', None)
self.mx = kwargs.pop('mx', None)
self.last_fx = 0
self.fnum2 = pt.next_fnum()
self.figtitle = figtitle
self.kwargs = kwargs
abstract_interaction.register_interaction(self)
ut.inject_func_as_method(self, AbstractInteraction.append_button.im_func)
ut.inject_func_as_method(self, AbstractInteraction.show_popup_menu.im_func)
self.scope = []
if not kwargs.get('nobegin', False):
dodraw = kwargs.get('dodraw', True)
self.begin(dodraw=dodraw)
def 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 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 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 show_model(model, evidence={}, soft_evidence={}, **kwargs):
"""
References:
http://stackoverflow.com/questions/22207802/pygraphviz-networkx-set-node-level-or-layer
Ignore:
pkg-config --libs-only-L libcgraph
sudo apt-get install libgraphviz-dev -y
sudo apt-get install libgraphviz4 -y
# sudo apt-get install pkg-config
sudo apt-get install libgraphviz-dev
# pip install git+git://github.com/pygraphviz/pygraphviz.git
pip install pygraphviz
python -c "import pygraphviz; print(pygraphviz.__file__)"
sudo pip3 install pygraphviz --install-option="--include-path=/usr/include/graphviz" --install-option="--library-path=/usr/lib/graphviz/"
python3 -c "import pygraphviz; print(pygraphviz.__file__)"
CommandLine:
python -m ibeis.algo.hots.bayes --exec-show_model --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.bayes import * # NOQA
>>> model = '?'
>>> evidence = {}
>>> soft_evidence = {}
>>> result = show_model(model, evidence, soft_evidence)
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
if ut.get_argval('--hackmarkov') or ut.get_argval('--hackjunc'):
draw_tree_model(model, **kwargs)
return
import plottool as pt
import networkx as netx
fnum = pt.ensure_fnum(None)
netx_graph = (model)
#netx_graph.graph.setdefault('graph', {})['size'] = '"10,5"'
#netx_graph.graph.setdefault('graph', {})['rankdir'] = 'LR'
pos_dict = get_hacked_pos(netx_graph)
#pos_dict = netx.nx_agraph.pygraphviz_layout(netx_graph)
#pos = netx.nx_agraph.nx_pydot.pydot_layout(netx_graph, prog='dot')
#pos_dict = netx.nx_agraph.graphviz_layout(netx_graph)
textprops = {
'family': 'monospace',
'horizontalalignment': 'left',
#'horizontalalignment': 'center',
#'size': 12,
'size': 8,
}
netx_nodes = model.nodes(data=True)
node_key_list = ut.get_list_column(netx_nodes, 0)
pos_list = ut.dict_take(pos_dict, node_key_list)
var2_post = {f.variables[0]: f for f in kwargs.get('factor_list', [])}
prior_text = None
post_text = None
evidence_tas = []
post_tas = []
prior_tas = []
node_color = []
has_inferred = evidence or var2_post
if has_inferred:
ignore_prior_with_ttype = [SCORE_TTYPE, MATCH_TTYPE]
show_prior = False
else:
ignore_prior_with_ttype = []
#show_prior = True
show_prior = False
dpy = 5
dbx, dby = (20, 20)
takw1 = {
'bbox_align': (.5, 0),
'pos_offset': [0, dpy],
'bbox_offset': [dbx, dby]
}
takw2 = {
'bbox_align': (.5, 1),
'pos_offset': [0, -dpy],
'bbox_offset': [-dbx, -dby]
}
name_colors = pt.distinct_colors(max(model.num_names, 10))
name_colors = name_colors[:model.num_names]
#cmap_ = 'hot' #mx = 0.65 #mn = 0.15
cmap_, mn, mx = 'plasma', 0.15, 1.0
_cmap = pt.plt.get_cmap(cmap_)
def cmap(x):
return _cmap((x * mx) + mn)
for node, pos in zip(netx_nodes, pos_list):
variable = node[0]
cpd = model.var2_cpd[variable]
prior_marg = (cpd if cpd.evidence is None else cpd.marginalize(
cpd.evidence, inplace=False))
show_evidence = variable in evidence
show_prior = cpd.ttype not in ignore_prior_with_ttype
show_post = variable in var2_post
show_prior |= cpd.ttype not in ignore_prior_with_ttype
post_marg = None
if show_post:
post_marg = var2_post[variable]
def get_name_color(phi):
order = phi.values.argsort()[::-1]
if len(order) < 2:
dist_next = phi.values[order[0]]
else:
dist_next = phi.values[order[0]] - phi.values[order[1]]
dist_total = (phi.values[order[0]])
confidence = (dist_total * dist_next)**(2.5 / 4)
#print('confidence = %r' % (confidence,))
color = name_colors[order[0]]
color = pt.color_funcs.desaturate_rgb(color, 1 - confidence)
color = np.array(color)
return color
if variable in evidence:
if cpd.ttype == SCORE_TTYPE:
cmap_index = evidence[variable] / (cpd.variable_card - 1)
color = cmap(cmap_index)
color = pt.lighten_rgb(color, .4)
color = np.array(color)
node_color.append(color)
elif cpd.ttype == NAME_TTYPE:
color = name_colors[evidence[variable]]
color = np.array(color)
node_color.append(color)
else:
color = pt.FALSE_RED
node_color.append(color)
#elif variable in soft_evidence:
# color = pt.LIGHT_PINK
# show_prior = True
# color = get_name_color(prior_marg)
# node_color.append(color)
else:
if cpd.ttype == NAME_TTYPE and post_marg is not None:
color = get_name_color(post_marg)
node_color.append(color)
elif cpd.ttype == MATCH_TTYPE and post_marg is not None:
color = cmap(post_marg.values[1])
color = pt.lighten_rgb(color, .4)
color = np.array(color)
node_color.append(color)
else:
#color = pt.WHITE
color = pt.NEUTRAL
node_color.append(color)
if show_prior:
if variable in soft_evidence:
prior_color = pt.LIGHT_PINK
else:
prior_color = None
prior_text = pgm_ext.make_factor_text(prior_marg, 'prior')
prior_tas.append(
dict(text=prior_text, pos=pos, color=prior_color, **takw2))
if show_evidence:
_takw1 = takw1
if cpd.ttype == SCORE_TTYPE:
_takw1 = takw2
evidence_text = cpd.variable_statenames[evidence[variable]]
if isinstance(evidence_text, int):
evidence_text = '%d/%d' % (evidence_text + 1,
cpd.variable_card)
evidence_tas.append(
dict(text=evidence_text, pos=pos, color=color, **_takw1))
if show_post:
_takw1 = takw1
if cpd.ttype == MATCH_TTYPE:
_takw1 = takw2
post_text = pgm_ext.make_factor_text(post_marg, 'post')
post_tas.append(dict(text=post_text, pos=pos, color=None,
**_takw1))
def trnps_(dict_list):
""" tranpose dict list """
list_dict = ut.ddict(list)
for dict_ in dict_list:
for key, val in dict_.items():
list_dict[key + '_list'].append(val)
return list_dict
takw1_ = trnps_(post_tas + evidence_tas)
takw2_ = trnps_(prior_tas)
# Draw graph
if has_inferred:
pnum1 = (3, 1, (slice(0, 2), 0))
else:
pnum1 = None
fig = pt.figure(fnum=fnum, pnum=pnum1, doclf=True) # NOQA
ax = pt.gca()
#print('node_color = %s' % (ut.repr3(node_color),))
drawkw = dict(pos=pos_dict,
ax=ax,
with_labels=True,
node_size=1500,
node_color=node_color)
netx.draw(netx_graph, **drawkw)
hacks = []
if len(post_tas + evidence_tas):
hacks.append(pt.draw_text_annotations(textprops=textprops, **takw1_))
if prior_tas:
hacks.append(pt.draw_text_annotations(textprops=textprops, **takw2_))
xmin, ymin = np.array(pos_list).min(axis=0)
xmax, ymax = np.array(pos_list).max(axis=0)
num_annots = len(model.ttype2_cpds[NAME_TTYPE])
if num_annots > 4:
ax.set_xlim((xmin - 40, xmax + 40))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(30, 7)
else:
ax.set_xlim((xmin - 42, xmax + 42))
ax.set_ylim((ymin - 50, ymax + 50))
fig.set_size_inches(23, 7)
fig = pt.gcf()
title = 'num_names=%r, num_annots=%r' % (
model.num_names,
num_annots,
)
map_assign = kwargs.get('map_assign', None)
top_assignments = kwargs.get('top_assignments', None)
if top_assignments is not None:
map_assign, map_prob = top_assignments[0]
if map_assign is not None:
def word_insert(text):
return '' if len(text) == 0 else text + ' '
title += '\n%sMAP: ' % (word_insert(kwargs.get('method', '')))
title += map_assign + ' @' + '%.2f%%' % (100 * map_prob, )
if kwargs.get('show_title', True):
pt.set_figtitle(title, size=14)
for hack in hacks:
hack()
# Hack in colorbars
if has_inferred:
pt.colorbar(np.linspace(0, 1, len(name_colors)),
name_colors,
lbl=NAME_TTYPE,
ticklabels=model.ttype2_template[NAME_TTYPE].basis,
ticklocation='left')
basis = model.ttype2_template[SCORE_TTYPE].basis
scalars = np.linspace(0, 1, len(basis))
scalars = np.linspace(0, 1, 100)
colors = pt.scores_to_color(scalars,
cmap_=cmap_,
reverse_cmap=False,
cmap_range=(mn, mx))
colors = [pt.lighten_rgb(c, .4) for c in colors]
if ut.list_type(basis) is int:
pt.colorbar(scalars,
colors,
lbl=SCORE_TTYPE,
ticklabels=np.array(basis) + 1)
else:
pt.colorbar(scalars, colors, lbl=SCORE_TTYPE, ticklabels=basis)
#print('basis = %r' % (basis,))
# Draw probability hist
if has_inferred and top_assignments is not None:
bin_labels = ut.get_list_column(top_assignments, 0)
bin_vals = ut.get_list_column(top_assignments, 1)
# bin_labels = ['\n'.join(ut.textwrap.wrap(_lbl, width=30)) for _lbl in bin_labels]
pt.draw_histogram(
bin_labels,
bin_vals,
fnum=fnum,
pnum=(3, 8, (2, slice(4, None))),
transpose=True,
use_darkbackground=False,
#xtick_rotation=-10,
ylabel='Prob',
xlabel='assignment')
pt.set_title('Assignment probabilities')
def show_multi_images(ibs, gid_list, fnum=None, **kwargs):
r"""
Args:
ibs (IBEISController): ibeis controller object
gid_list (list):
fnum (int): figure number(default = None)
CommandLine:
python -m ibeis.viz.viz_image --test-show_multi_images --db NNP_Master3 --gids=7409,7448,4670,7497,7496,7464,7446,7442 --show
python -m ibeis.viz.viz_image --test-show_multi_images --db NNP_Master3 --gids=1,2,3 --show
Ignore:
>>> # print to 8 gids sorted by num aids
>>> import ibeis
>>> ibs = ibeis.opendb('NNP_Master3')
>>> gid_list = ibs.get_valid_gids()
>>> aids_list = ibs.get_image_aids(gid_list)
>>> index_list = ut.list_argsort(list(map(len, aids_list)))[::-1]
>>> gid_list = ut.take(gid_list, index_list[0:8])
>>> print(','.join(map(str, gid_list)))
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_image import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> gid_list = ut.get_argval('--gids', list, default=[1, 2])
>>> fnum = None
>>> result = show_multi_images(ibs, gid_list, fnum, draw_lbls=False, notitle=True, sel_aids='all')
>>> print(result)
>>> ut.show_if_requested()
"""
fnum = pt.ensure_fnum(fnum)
nGids = len(gid_list)
if nGids == 0:
fig = pt.figure(fnum=fnum, pnum=(1, 1, 1), **kwargs)
pt.imshow_null(fnum=fnum, **kwargs)
return fig
# Trigger computation of all chips in parallel
#ibsfuncs.ensure_annotation_data(ibs, aid_list, chips=(not in_image or annote), feats=annote)
rc = ut.get_argval('--rc', type_=list, default=None)
if rc is None:
nRows, nCols = ph.get_square_row_cols(nGids)
else:
nRows, nCols = rc
#notitle = ut.get_argflag('--notitle')
#draw_lbls = not ut.get_argflag('--no-draw_lbls')
#show_chip_kw = dict(annote=annote, in_image=in_image, notitle=notitle, draw_lbls=draw_lbls)
#print('[viz_name] * r=%r, c=%r' % (nRows, nCols))
#gs2 = gridspec.GridSpec(nRows, nCols)
pnum_ = pt.get_pnum_func(nRows, nCols)
fig = pt.figure(fnum=fnum, pnum=pnum_(0), **kwargs)
fig.clf()
for px, gid in enumerate(gid_list):
print(pnum_(px))
_fig, _ax1 = show_image(ibs, gid, fnum=fnum, pnum=pnum_(px), **kwargs)
#ax = pt.gca()
#if aid in sel_aids:
# pt.draw_border(ax, pt.GREEN, 4)
#if ut.get_argflag('--numlbl') and not DOBOTH:
# ax.set_xlabel('(' + str(px + 1) + ')')
#plot_aid3(ibs, aid)
pass
def 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')
