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_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 collapse_labels(model, evidence, reduced_variables, reduced_row_idxs,
reduced_values):
import vtool as vt
#assert np.all(reduced_joint.values.ravel() == reduced_joint.values.flatten())
reduced_ttypes = [model.var2_cpd[var].ttype for var in reduced_variables]
evidence_vars = list(evidence.keys())
evidence_state_idxs = ut.dict_take(evidence, evidence_vars)
evidence_ttypes = [model.var2_cpd[var].ttype for var in evidence_vars]
ttype2_ev_indices = dict(zip(*ut.group_indices(evidence_ttypes)))
ttype2_re_indices = dict(zip(*ut.group_indices(reduced_ttypes)))
# ttype2_ev_indices = ut.group_items(range(len(evidence_vars)), evidence_ttypes)
# ttype2_re_indices = ut.group_items(range(len(reduced_variables)), reduced_ttypes)
# Allow specific types of labels to change
# everything is the same, only the names have changed.
# TODO: allow for multiple different label_ttypes
# for label_ttype in label_ttypes
if 'name' not in model.ttype2_template:
return reduced_row_idxs, reduced_values
label_ttypes = ['name']
for label_ttype in label_ttypes:
ev_colxs = ttype2_ev_indices[label_ttype]
re_colxs = ttype2_re_indices[label_ttype]
ev_state_idxs = ut.take(evidence_state_idxs, ev_colxs)
ev_state_idxs_tile = np.tile(ev_state_idxs, (len(reduced_values), 1)).astype(np.int)
num_ev_ = len(ev_colxs)
aug_colxs = list(range(num_ev_)) + (np.array(re_colxs) + num_ev_).tolist()
aug_state_idxs = np.hstack([ev_state_idxs_tile, reduced_row_idxs])
# Relabel rows based on the knowledge that
# everything is the same, only the names have changed.
num_cols = len(aug_state_idxs.T)
mask = vt.index_to_boolmask(aug_colxs, num_cols)
other_colxs, = np.where(~mask)
relbl_states = aug_state_idxs.compress(mask, axis=1)
other_states = aug_state_idxs.compress(~mask, axis=1)
tmp_relbl_states = np.array(list(map(make_temp_state, relbl_states)))
max_tmp_state = -1
min_tmp_state = tmp_relbl_states.min()
# rebuild original state structure with temp state idxs
tmp_state_cols = [None] * num_cols
for count, colx in enumerate(aug_colxs):
tmp_state_cols[colx] = tmp_relbl_states[:, count:count + 1]
for count, colx in enumerate(other_colxs):
tmp_state_cols[colx] = other_states[:, count:count + 1]
tmp_state_idxs = np.hstack(tmp_state_cols)
data_ids = np.array(
vt.compute_unique_data_ids_(list(map(tuple, tmp_state_idxs))))
unique_ids, groupxs = vt.group_indices(data_ids)
print('Collapsed %r states into %r states' % (
len(data_ids), len(unique_ids),))
# Sum the values in the cpd to marginalize the duplicate probs
new_values = np.array([
g.sum() for g in vt.apply_grouping(reduced_values, groupxs)
])
# Take only the unique rows under this induced labeling
unique_tmp_groupxs = np.array(ut.get_list_column(groupxs, 0))
new_aug_state_idxs = tmp_state_idxs.take(unique_tmp_groupxs, axis=0)
tmp_idx_set = set((-np.arange(-max_tmp_state,
(-min_tmp_state) + 1)).tolist())
true_idx_set = set(range(len(model.ttype2_template[label_ttype].basis)))
# Relabel the rows one more time to agree with initial constraints
for colx, true_idx in enumerate(ev_state_idxs):
tmp_idx = np.unique(new_aug_state_idxs.T[colx])
assert len(tmp_idx) == 1
tmp_idx_set -= {tmp_idx[0]}
true_idx_set -= {true_idx}
new_aug_state_idxs[new_aug_state_idxs == tmp_idx] = true_idx
# Relabel the remaining idxs
remain_tmp_idxs = sorted(list(tmp_idx_set))[::-1]
remain_true_idxs = sorted(list(true_idx_set))
for tmp_idx, true_idx in zip(remain_tmp_idxs, remain_true_idxs):
new_aug_state_idxs[new_aug_state_idxs == tmp_idx] = true_idx
# Remove evidence based augmented labels
new_state_idxs = new_aug_state_idxs.T[num_ev_:].T
return new_state_idxs, new_values
def collapse_labels(model, evidence, reduced_variables, reduced_row_idxs,
reduced_values):
import vtool as vt
# assert np.all(reduced_joint.values.ravel() == reduced_joint.values.flatten())
reduced_ttypes = [model.var2_cpd[var].ttype for var in reduced_variables]
evidence_vars = list(evidence.keys())
evidence_state_idxs = ut.dict_take(evidence, evidence_vars)
evidence_ttypes = [model.var2_cpd[var].ttype for var in evidence_vars]
ttype2_ev_indices = dict(zip(*ut.group_indices(evidence_ttypes)))
ttype2_re_indices = dict(zip(*ut.group_indices(reduced_ttypes)))
# ttype2_ev_indices = ut.group_items(range(len(evidence_vars)), evidence_ttypes)
# ttype2_re_indices = ut.group_items(range(len(reduced_variables)), reduced_ttypes)
# Allow specific types of labels to change
# everything is the same, only the names have changed.
# TODO: allow for multiple different label_ttypes
# for label_ttype in label_ttypes
if NAME_TTYPE not in model.ttype2_template:
return reduced_row_idxs, reduced_values
label_ttypes = [NAME_TTYPE]
for label_ttype in label_ttypes:
ev_colxs = ttype2_ev_indices[label_ttype]
re_colxs = ttype2_re_indices[label_ttype]
ev_state_idxs = ut.take(evidence_state_idxs, ev_colxs)
ev_state_idxs_tile = np.tile(ev_state_idxs,
(len(reduced_values), 1)).astype(np.int)
num_ev_ = len(ev_colxs)
aug_colxs = list(
range(num_ev_)) + (np.array(re_colxs) + num_ev_).tolist()
aug_state_idxs = np.hstack([ev_state_idxs_tile, reduced_row_idxs])
# Relabel rows based on the knowledge that
# everything is the same, only the names have changed.
num_cols = len(aug_state_idxs.T)
mask = vt.index_to_boolmask(aug_colxs, num_cols)
(other_colxs, ) = np.where(~mask)
relbl_states = aug_state_idxs.compress(mask, axis=1)
other_states = aug_state_idxs.compress(~mask, axis=1)
tmp_relbl_states = np.array(list(map(make_temp_state, relbl_states)))
max_tmp_state = -1
min_tmp_state = tmp_relbl_states.min()
# rebuild original state structure with temp state idxs
tmp_state_cols = [None] * num_cols
for count, colx in enumerate(aug_colxs):
tmp_state_cols[colx] = tmp_relbl_states[:, count:count + 1]
for count, colx in enumerate(other_colxs):
tmp_state_cols[colx] = other_states[:, count:count + 1]
tmp_state_idxs = np.hstack(tmp_state_cols)
data_ids = np.array(
vt.compute_unique_data_ids_(list(map(tuple, tmp_state_idxs))))
unique_ids, groupxs = vt.group_indices(data_ids)
logger.info('Collapsed %r states into %r states' % (
len(data_ids),
len(unique_ids),
))
# Sum the values in the cpd to marginalize the duplicate probs
new_values = np.array(
[g.sum() for g in vt.apply_grouping(reduced_values, groupxs)])
# Take only the unique rows under this induced labeling
unique_tmp_groupxs = np.array(ut.get_list_column(groupxs, 0))
new_aug_state_idxs = tmp_state_idxs.take(unique_tmp_groupxs, axis=0)
tmp_idx_set = set((-np.arange(-max_tmp_state,
(-min_tmp_state) + 1)).tolist())
true_idx_set = set(range(len(
model.ttype2_template[label_ttype].basis)))
# Relabel the rows one more time to agree with initial constraints
for colx, true_idx in enumerate(ev_state_idxs):
tmp_idx = np.unique(new_aug_state_idxs.T[colx])
assert len(tmp_idx) == 1
tmp_idx_set -= {tmp_idx[0]}
true_idx_set -= {true_idx}
new_aug_state_idxs[new_aug_state_idxs == tmp_idx] = true_idx
# Relabel the remaining idxs
remain_tmp_idxs = sorted(list(tmp_idx_set))[::-1]
remain_true_idxs = sorted(list(true_idx_set))
for tmp_idx, true_idx in zip(remain_tmp_idxs, remain_true_idxs):
new_aug_state_idxs[new_aug_state_idxs == tmp_idx] = true_idx
# Remove evidence based augmented labels
new_state_idxs = new_aug_state_idxs.T[num_ev_:].T
return new_state_idxs, new_values