def _blob_matches_secondary(blobs, aed_match):
""" Generate known matches for AED (bottom) and paper-specific triangle groups.
"""
from dimensions import feature_g_landscape_ltr, point_G_landscape_ltr, feature_f_landscape_ltr, point_F_landscape_ltr
from dimensions import feature_g_portrait_ltr, point_G_portrait_ltr, feature_f_portrait_ltr, point_F_portrait_ltr
from dimensions import feature_g_landscape_a4, point_G_landscape_a4, feature_f_landscape_a4, point_F_landscape_a4
from dimensions import feature_g_portrait_a4, point_G_portrait_a4, feature_f_portrait_a4, point_F_portrait_a4
from dimensions import feature_g_landscape_a3, point_G_landscape_a3, feature_f_landscape_a3, point_F_landscape_a3
from dimensions import feature_g_portrait_a3, point_G_portrait_a3, feature_f_portrait_a3, point_F_portrait_a3
from dimensions import min_size, theta_tol, ratio_tol
features_fg = (
(feature_g_landscape_ltr, point_G_landscape_ltr, feature_f_landscape_ltr, point_F_landscape_ltr),
(feature_g_portrait_ltr, point_G_portrait_ltr, feature_f_portrait_ltr, point_F_portrait_ltr),
(feature_g_landscape_a4, point_G_landscape_a4, feature_f_landscape_a4, point_F_landscape_a4),
(feature_g_portrait_a4, point_G_portrait_a4, feature_f_portrait_a4, point_F_portrait_a4),
(feature_g_landscape_a3, point_G_landscape_a3, feature_f_landscape_a3, point_F_landscape_a3),
(feature_g_portrait_a3, point_G_portrait_a3, feature_f_portrait_a3, point_F_portrait_a3)
)
for (feature_g, point_G, feature_f, point_F) in features_fg:
g_theta, g_ratio = feature_g.theta, feature_g.ratio
g_bounds = theta_ratio_bounds(g_theta, theta_tol, g_ratio, ratio_tol)
g_matches = blobs2feats_fitted(aed_match.s1, aed_match.s2, blobs, *g_bounds)
for g_tuple in g_matches:
i0, j0, k0 = g_tuple[0:3]
g_match = MatchedFeature(feature_g, blobs[i0], blobs[j0], blobs[k0])
if not g_match.fits(aed_match):
continue
aed_blobs = (aed_match.s1, aed_match.s2)
if g_match.s1 in aed_blobs and g_match.s2 in aed_blobs:
blob_G = g_match.s3
elif g_match.s1 in aed_blobs and g_match.s3 in aed_blobs:
blob_G = g_match.s2
elif g_match.s2 in aed_blobs and g_match.s3 in aed_blobs:
blob_G = g_match.s1
else:
raise Exception('what?')
#print >> stderr, ' Found a match for point G -', (i0, j0, k0)
#
# We think we have a match for point G, now check for point F.
#
f_theta, f_ratio = feature_f.theta, feature_f.ratio
f_bounds = theta_ratio_bounds(f_theta, theta_tol, f_ratio, ratio_tol)
f_matches = blobs2feats_fitted(blob_G, aed_match.s2, blobs, *f_bounds)
for f_tuple in f_matches:
i1, j1, k1 = f_tuple[0:3]
f_match = MatchedFeature(feature_f, blobs[i1], blobs[j1], blobs[k1])
if not f_match.fits(g_match):
continue
#print >> stderr, ' Found a match for point F -', (i1, j1, k1), point_F
#
# Based on the identity of point_F, we can find paper size and orientation.
#
yield f_match, point_F
def _blob_matches_secondary(blobs, aed_match):
""" Generate known matches for AED (bottom) and paper-specific triangle groups.
"""
from dimensions import feature_g_landscape_ltr, point_G_landscape_ltr, feature_f_landscape_ltr, point_F_landscape_ltr
from dimensions import feature_g_portrait_ltr, point_G_portrait_ltr, feature_f_portrait_ltr, point_F_portrait_ltr
from dimensions import feature_g_landscape_a4, point_G_landscape_a4, feature_f_landscape_a4, point_F_landscape_a4
from dimensions import feature_g_portrait_a4, point_G_portrait_a4, feature_f_portrait_a4, point_F_portrait_a4
from dimensions import feature_g_landscape_a3, point_G_landscape_a3, feature_f_landscape_a3, point_F_landscape_a3
from dimensions import feature_g_portrait_a3, point_G_portrait_a3, feature_f_portrait_a3, point_F_portrait_a3
from dimensions import min_size, theta_tol, ratio_tol
features_fg = ((feature_g_landscape_ltr, point_G_landscape_ltr,
feature_f_landscape_ltr, point_F_landscape_ltr),
(feature_g_portrait_ltr, point_G_portrait_ltr,
feature_f_portrait_ltr, point_F_portrait_ltr),
(feature_g_landscape_a4, point_G_landscape_a4,
feature_f_landscape_a4, point_F_landscape_a4),
(feature_g_portrait_a4, point_G_portrait_a4,
feature_f_portrait_a4, point_F_portrait_a4),
(feature_g_landscape_a3, point_G_landscape_a3,
feature_f_landscape_a3, point_F_landscape_a3),
(feature_g_portrait_a3, point_G_portrait_a3,
feature_f_portrait_a3, point_F_portrait_a3))
for (feature_g, point_G, feature_f, point_F) in features_fg:
g_theta, g_ratio = feature_g.theta, feature_g.ratio
g_bounds = theta_ratio_bounds(g_theta, theta_tol, g_ratio, ratio_tol)
g_matches = blobs2feats_fitted(aed_match.s1, aed_match.s2, blobs,
*g_bounds)
for g_tuple in g_matches:
i0, j0, k0 = g_tuple[0:3]
g_match = MatchedFeature(feature_g, blobs[i0], blobs[j0],
blobs[k0])
if not g_match.fits(aed_match):
continue
aed_blobs = (aed_match.s1, aed_match.s2)
if g_match.s1 in aed_blobs and g_match.s2 in aed_blobs:
blob_G = g_match.s3
elif g_match.s1 in aed_blobs and g_match.s3 in aed_blobs:
blob_G = g_match.s2
elif g_match.s2 in aed_blobs and g_match.s3 in aed_blobs:
blob_G = g_match.s1
else:
raise Exception('what?')
#print ' Found a match for point G -', (i0, j0, k0)
#
# We think we have a match for point G, now check for point F.
#
f_theta, f_ratio = feature_f.theta, feature_f.ratio
f_bounds = theta_ratio_bounds(f_theta, theta_tol, f_ratio,
ratio_tol)
f_matches = blobs2feats_fitted(blob_G, aed_match.s2, blobs,
*f_bounds)
for f_tuple in f_matches:
i1, j1, k1 = f_tuple[0:3]
f_match = MatchedFeature(feature_f, blobs[i1], blobs[j1],
blobs[k1])
if not f_match.fits(g_match):
continue
#print ' Found a match for point F -', (i1, j1, k1), point_F
#
# Based on the identity of point_F, we can find paper size and orientation.
#
yield f_match, point_F
def _blob_matches_primary(blobs):
""" Generate known matches for DBC (top) and AED (bottom) triangle pairs.
"""
from dimensions import feature_dbc, feature_dab, feature_aed, feature_eac
from dimensions import min_size, theta_tol, ratio_tol
dbc_theta, dbc_ratio = feature_dbc.theta, feature_dbc.ratio
dab_theta, dab_ratio = feature_dab.theta, feature_dab.ratio
aed_theta, aed_ratio = feature_aed.theta, feature_aed.ratio
eac_theta, eac_ratio = feature_eac.theta, feature_eac.ratio
dbc_matches = blobs2features(blobs, min_size, *theta_ratio_bounds(dbc_theta, theta_tol, dbc_ratio, ratio_tol))
seen_groups, max_skipped, skipped_groups = set(), 100, 0
for dbc_tuple in dbc_matches:
i0, j0, k0 = dbc_tuple[0:3]
dbc_match = MatchedFeature(feature_dbc, blobs[i0], blobs[j0], blobs[k0])
#print >> stderr, 'Found a match for DBC -', (i0, j0, k0)
dab_matches = blobs2feats_limited([blobs[i0]], blobs, [blobs[j0]], *theta_ratio_bounds(dab_theta, theta_tol, dab_ratio, ratio_tol))
for dab_tuple in dab_matches:
i1, j1, k1 = i0, dab_tuple[1], j0
dab_match = MatchedFeature(feature_dab, blobs[i1], blobs[j1], blobs[k1])
if not dab_match.fits(dbc_match):
continue
#print >> stderr, ' Found a match for DAB -', (i1, j1, k1)
#
# We think we have a match for points A-D, now check for point E.
#
aed_matches = blobs2feats_limited([blobs[j1]], blobs, [blobs[i1]], *theta_ratio_bounds(aed_theta, theta_tol, aed_ratio, ratio_tol))
for aed_tuple in aed_matches:
i2, j2, k2 = j1, aed_tuple[1], i1
aed_match = MatchedFeature(feature_aed, blobs[i2], blobs[j2], blobs[k2])
if not aed_match.fits(dbc_match):
continue
if not aed_match.fits(dab_match):
continue
#print >> stderr, ' Found a match for AED -', (i2, j2, k2)
#
# We now know we have a three-triangle match; try a fourth to verify.
# Use the very small set of blobs from the current set of matches.
#
_blobs = [blobs[n] for n in set((i0, j0, k0) + (i1, j1, k1) + (i2, j2, k2))]
eac_matches = blobs2features(_blobs, min_size, *theta_ratio_bounds(eac_theta, theta_tol, eac_ratio, ratio_tol))
for eac_tuple in eac_matches:
i3, j3, k3 = eac_tuple[0:3]
eac_match = MatchedFeature(feature_eac, _blobs[i3], _blobs[j3], _blobs[k3])
if not eac_match.fits(dbc_match):
continue
if not eac_match.fits(dab_match):
continue
if not eac_match.fits(aed_match):
continue
#print >> stderr, ' Confirmed match with EAC -', (i3, j3, k3)
yield dbc_match, aed_match
def _blob_matches_primary(blobs):
""" Generate known matches for DBC (top) and AED (bottom) triangle pairs.
"""
from dimensions import feature_dbc, feature_dab, feature_aed, feature_eac
from dimensions import min_size, theta_tol, ratio_tol
dbc_theta, dbc_ratio = feature_dbc.theta, feature_dbc.ratio
dab_theta, dab_ratio = feature_dab.theta, feature_dab.ratio
aed_theta, aed_ratio = feature_aed.theta, feature_aed.ratio
eac_theta, eac_ratio = feature_eac.theta, feature_eac.ratio
dbc_matches = blobs2features(
blobs, min_size,
*theta_ratio_bounds(dbc_theta, theta_tol, dbc_ratio, ratio_tol))
seen_groups, max_skipped, skipped_groups = set(), 100, 0
for dbc_tuple in dbc_matches:
i0, j0, k0 = dbc_tuple[0:3]
dbc_match = MatchedFeature(feature_dbc, blobs[i0], blobs[j0],
blobs[k0])
#print 'Found a match for DBC -', (i0, j0, k0)
dab_matches = blobs2feats_limited([blobs[i0]], blobs, [blobs[j0]],
*theta_ratio_bounds(
dab_theta, theta_tol, dab_ratio,
ratio_tol))
for dab_tuple in dab_matches:
i1, j1, k1 = i0, dab_tuple[1], j0
dab_match = MatchedFeature(feature_dab, blobs[i1], blobs[j1],
blobs[k1])
if not dab_match.fits(dbc_match):
continue
#print ' Found a match for DAB -', (i1, j1, k1)
#
# We think we have a match for points A-D, now check for point E.
#
aed_matches = blobs2feats_limited([blobs[j1]], blobs, [blobs[i1]],
*theta_ratio_bounds(
aed_theta, theta_tol,
aed_ratio, ratio_tol))
for aed_tuple in aed_matches:
i2, j2, k2 = j1, aed_tuple[1], i1
aed_match = MatchedFeature(feature_aed, blobs[i2], blobs[j2],
blobs[k2])
if not aed_match.fits(dbc_match):
continue
if not aed_match.fits(dab_match):
continue
#print ' Found a match for AED -', (i2, j2, k2)
#
# We now know we have a three-triangle match; try a fourth to verify.
# Use the very small set of blobs from the current set of matches.
#
_blobs = [
blobs[n]
for n in set((i0, j0, k0) + (i1, j1, k1) + (i2, j2, k2))
]
eac_matches = blobs2features(
_blobs, min_size,
*theta_ratio_bounds(eac_theta, theta_tol, eac_ratio,
ratio_tol))
for eac_tuple in eac_matches:
i3, j3, k3 = eac_tuple[0:3]
eac_match = MatchedFeature(feature_eac, _blobs[i3],
_blobs[j3], _blobs[k3])
if not eac_match.fits(dbc_match):
continue
if not eac_match.fits(dab_match):
continue
if not eac_match.fits(aed_match):
continue
#print ' Confirmed match with EAC -', (i3, j3, k3)
yield dbc_match, aed_match