def assign_spatially_constrained_matches(chip2_dlen_sqrd, kpts1, kpts2, H,
fx2_to_fx1, fx2_to_dist, match_xy_thresh,
norm_xy_bounds=(0.0, 1.0)):
"""
Args:
chip2_dlen_sqrd (dict):
kpts1 (ndarray[float32_t, ndim=2]): keypoints
kpts2 (ndarray[float32_t, ndim=2]): keypoints
H (ndarray[float64_t, ndim=2]): homography/perspective matrix that maps image1 space into image2 space
fx2_to_fx1 (ndarray): image2s nearest feature indices in image1
fx2_to_dist (ndarray):
match_xy_thresh (float):
norm_xy_bounds (tuple):
Returns:
tuple: assigntup(
fx2_match, - matching feature indices in image 2
fx1_match, - matching feature indices in image 1
fx1_norm, - normmalizing indices in image 1
match_dist, - descriptor distances between fx2_match and fx1_match
norm_dist, - descriptor distances between fx2_match and fx1_norm
)
CommandLine:
python -m vtool.matching --test-assign_spatially_constrained_matches
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.matching import * # NOQA
>>> kpts1 = np.array([[ 6., 4., 15.84, 4.66, 7.24, 0. ],
... [ 9., 3., 20.09, 5.76, 6.2 , 0. ],
... [ 1., 1., 12.96, 1.73, 8.77, 0. ],])
>>> kpts2 = np.array([[ 2., 1., 12.11, 0.38, 8.04, 0. ],
... [ 5., 1., 22.4 , 1.31, 5.04, 0. ],
... [ 6., 1., 19.25, 1.74, 4.72, 0. ],])
>>> match_xy_thresh = .37
>>> chip2_dlen_sqrd = 1400
>>> norm_xy_bounds = (0.0, 1.0)
>>> H = np.array([[ 2, 0, 0],
>>> [ 0, 1, 0],
>>> [ 0, 0, 1]])
>>> fx2_to_fx1 = np.array([[2, 1, 0],
>>> [0, 1, 2],
>>> [2, 0, 1]], dtype=np.int32)
>>> fx2_to_dist = np.array([[.40, .80, .85],
>>> [.30, .50, .60],
>>> [.80, .90, .91]], dtype=np.float32)
>>> # verify results
>>> assigntup = assign_spatially_constrained_matches(chip2_dlen_sqrd, kpts1, kpts2, H, fx2_to_fx1, fx2_to_dist, match_xy_thresh, norm_xy_bounds)
>>> fx2_match, fx1_match, fx1_norm, match_dist, norm_dist = assigntup
>>> result = ut.list_str(assigntup, precision=3)
>>> print(result)
(
np.array([0, 1, 2], dtype=np.int32),
np.array([2, 0, 2], dtype=np.int32),
np.array([1, 1, 0], dtype=np.int32),
np.array([ 0.4, 0.3, 0.8], dtype=np.float32),
np.array([ 0.8, 0.5, 0.9], dtype=np.float32),
)
Example:
assigns spatially constrained vsone match using results of nearest
neighbors.
"""
import vtool as vt
index_dtype = fx2_to_fx1.dtype
# Find spatial errors of keypoints under current homography (kpts1 mapped into image2 space)
fx2_to_xyerr_sqrd = ktool.get_match_spatial_squared_error(kpts1, kpts2, H, fx2_to_fx1)
fx2_to_xyerr = np.sqrt(fx2_to_xyerr_sqrd)
fx2_to_xyerr_norm = np.divide(fx2_to_xyerr, np.sqrt(chip2_dlen_sqrd))
# Find matches and normalizers that satisfy spatial constraints
fx2_to_valid_match = ut.inbounds(fx2_to_xyerr_norm, 0.0, match_xy_thresh, eq=True)
fx2_to_valid_normalizer = ut.inbounds(fx2_to_xyerr_norm, *norm_xy_bounds, eq=True)
fx2_to_fx1_match_col = vt.find_first_true_indices(fx2_to_valid_match)
fx2_to_fx1_norm_col = vt.find_next_true_indices(fx2_to_valid_normalizer, fx2_to_fx1_match_col)
assert fx2_to_fx1_match_col != fx2_to_fx1_norm_col, 'normlizers are matches!'
fx2_to_hasmatch = [pos is not None for pos in fx2_to_fx1_norm_col]
# IMAGE 2 Matching Features
fx2_match = np.where(fx2_to_hasmatch)[0].astype(index_dtype)
match_col_list = np.array(ut.take(fx2_to_fx1_match_col, fx2_match), dtype=fx2_match.dtype)
norm_col_list = np.array(ut.take(fx2_to_fx1_norm_col, fx2_match), dtype=fx2_match.dtype)
# We now have 2d coordinates into fx2_to_fx1
# Covnert into 1d coordinates for flat indexing into fx2_to_fx1
_match_index_2d = np.vstack((fx2_match, match_col_list))
_norm_index_2d = np.vstack((fx2_match, norm_col_list))
_shape2d = fx2_to_fx1.shape
match_index_1d = np.ravel_multi_index(_match_index_2d, _shape2d)
norm_index_1d = np.ravel_multi_index(_norm_index_2d, _shape2d)
# Find initial matches
# IMAGE 1 Matching Features
fx1_match = fx2_to_fx1.take(match_index_1d)
fx1_norm = fx2_to_fx1.take(norm_index_1d)
# compute constrained ratio score
match_dist = fx2_to_dist.take(match_index_1d)
norm_dist = fx2_to_dist.take(norm_index_1d)
# package and return
assigntup = fx2_match, fx1_match, fx1_norm, match_dist, norm_dist
return assigntup
def plot_scores(hs, qcx_list, fnum=1):
print('[dev] plot_scores(fnum=%r)' % fnum)
topN_gt = 1
topN_ranks = 3
qcx2_res = get_qcx2_res(hs, qcx_list)
data_scores = [] # matching scores
data_qpairs = [] # query info (qcx, cx)
data_gtranks = [] # query info (gtrank)
# Append all scores to a giant list
for res in qcx2_res.itervalues():
# Get gt scores first
qcx = res.qcx
top_cxs = np.array(res.topN_cxs(hs, N=topN_ranks, only_gt=False))
gt_cxs = np.array(res.topN_cxs(hs, N=topN_gt, only_gt=True))
top_scores = res.cx2_score[top_cxs]
np.intersect1d(top_cxs, gt_cxs)
# list of ranks if score is ground truth otherwise -1
isgt_ranks = [tx if cx in set(gt_cxs) else -1 for (tx, cx) in enumerate(top_cxs)]
qcx_pairs = [(hs.cx2_cid(qcx), hs.cx2_cid(cx)) for cx in top_cxs]
# Append all scores
data_scores.extend(top_scores.tolist())
data_qpairs.extend(qcx_pairs)
data_gtranks.extend(isgt_ranks)
data_scores = np.array(data_scores)
data_qpairs = np.array(data_qpairs)
data_gtranks = np.array(data_gtranks)
data_sortx = data_scores.argsort()
sorted_scores = data_scores[data_sortx]
# Draw and info
rank_colorbounds = [
((-1, 0), df2.GRAY),
((0, 1), df2.TRUE_GREEN),
((1, 5), df2.UNKNOWN_PURP),
((5, None), df2.FALSE_RED),
]
print('[dev] matching chipscore stats: ' + utool.stats_str(data_scores))
df2.figure(fnum=fnum, doclf=True, docla=True)
# Finds the knee
df2.plot(sorted_scores, color=df2.ORANGE, label='all scores')
# Plot results with ranks within (low, high) bounds
colorbounds_iter = reversed(list(enumerate(rank_colorbounds)))
for count, ((low, high), rankX_color) in colorbounds_iter:
datarank_flag = utool.inbounds(data_gtranks, low, high)
rankX_xs = np.where(datarank_flag[data_sortx])[0]
rankX_ys = sorted_scores[rankX_xs]
if high is None:
rankX_label = '%d <= gt rank' % low
else:
rankX_label = '%d <= gt rank < %d' % (low, high)
if len(rankX_ys) > 0:
df2.plot(rankX_xs, rankX_ys, 'o', color=rankX_color, label=rankX_label, alpha=.5)
rowid = qcx2_res.itervalues().next().rowid
df2.set_logyscale_from_data(data_scores)
df2.set_xlabel('chipmatch index')
df2.dark_background()
df2.set_figtitle('matching scores\n' + rowid)
df2.legend(loc='upper left')
df2.iup()
fnum += 1
score_table = np.vstack((data_scores, data_gtranks, data_qpairs.T)).T
score_table = score_table[data_sortx[::-1]]
column_labels = ['score', 'gtrank', 'qcid', 'cid']
header = 'score_table\nuid=%r' % rowid
column_type = [float, int, int, int]
csv_txt = utool.util_csv.numpy_to_csv(score_table, column_labels, header, column_type)
print(csv_txt)
#utool.embed()
return fnum
def assign_spatially_constrained_matches(chip2_dlen_sqrd,
kpts1,
kpts2,
H,
fx2_to_fx1,
fx2_to_dist,
match_xy_thresh,
norm_xy_bounds=(0.0, 1.0)):
"""
Args:
chip2_dlen_sqrd (dict):
kpts1 (ndarray[float32_t, ndim=2]): keypoints
kpts2 (ndarray[float32_t, ndim=2]): keypoints
H (ndarray[float64_t, ndim=2]): homography/perspective matrix that maps image1 space into image2 space
fx2_to_fx1 (ndarray): image2s nearest feature indices in image1
fx2_to_dist (ndarray):
match_xy_thresh (float):
norm_xy_bounds (tuple):
Returns:
tuple: assigntup(
fx2_match, - matching feature indices in image 2
fx1_match, - matching feature indices in image 1
fx1_norm, - normmalizing indices in image 1
match_dist, - descriptor distances between fx2_match and fx1_match
norm_dist, - descriptor distances between fx2_match and fx1_norm
)
CommandLine:
python -m vtool.matching --test-assign_spatially_constrained_matches
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.matching import * # NOQA
>>> kpts1 = np.array([[ 6., 4., 15.84, 4.66, 7.24, 0. ],
... [ 9., 3., 20.09, 5.76, 6.2 , 0. ],
... [ 1., 1., 12.96, 1.73, 8.77, 0. ],])
>>> kpts2 = np.array([[ 2., 1., 12.11, 0.38, 8.04, 0. ],
... [ 5., 1., 22.4 , 1.31, 5.04, 0. ],
... [ 6., 1., 19.25, 1.74, 4.72, 0. ],])
>>> match_xy_thresh = .37
>>> chip2_dlen_sqrd = 1400
>>> norm_xy_bounds = (0.0, 1.0)
>>> H = np.array([[ 2, 0, 0],
>>> [ 0, 1, 0],
>>> [ 0, 0, 1]])
>>> fx2_to_fx1 = np.array([[2, 1, 0],
>>> [0, 1, 2],
>>> [2, 0, 1]], dtype=np.int32)
>>> fx2_to_dist = np.array([[.40, .80, .85],
>>> [.30, .50, .60],
>>> [.80, .90, .91]], dtype=np.float32)
>>> # verify results
>>> assigntup = assign_spatially_constrained_matches(chip2_dlen_sqrd, kpts1, kpts2, H, fx2_to_fx1, fx2_to_dist, match_xy_thresh, norm_xy_bounds)
>>> fx2_match, fx1_match, fx1_norm, match_dist, norm_dist = assigntup
>>> result = ut.list_str(assigntup, precision=3)
>>> print(result)
(
np.array([0, 1, 2], dtype=np.int32),
np.array([2, 0, 2], dtype=np.int32),
np.array([1, 1, 0], dtype=np.int32),
np.array([ 0.4, 0.3, 0.8], dtype=np.float32),
np.array([ 0.8, 0.5, 0.9], dtype=np.float32),
)
Example:
assigns spatially constrained vsone match using results of nearest
neighbors.
"""
import vtool as vt
index_dtype = fx2_to_fx1.dtype
# Find spatial errors of keypoints under current homography (kpts1 mapped into image2 space)
fx2_to_xyerr_sqrd = ktool.get_match_spatial_squared_error(
kpts1, kpts2, H, fx2_to_fx1)
fx2_to_xyerr = np.sqrt(fx2_to_xyerr_sqrd)
fx2_to_xyerr_norm = np.divide(fx2_to_xyerr, np.sqrt(chip2_dlen_sqrd))
# Find matches and normalizers that satisfy spatial constraints
fx2_to_valid_match = ut.inbounds(fx2_to_xyerr_norm,
0.0,
match_xy_thresh,
eq=True)
fx2_to_valid_normalizer = ut.inbounds(fx2_to_xyerr_norm,
*norm_xy_bounds,
eq=True)
fx2_to_fx1_match_col = vt.find_first_true_indices(fx2_to_valid_match)
fx2_to_fx1_norm_col = vt.find_next_true_indices(fx2_to_valid_normalizer,
fx2_to_fx1_match_col)
assert fx2_to_fx1_match_col != fx2_to_fx1_norm_col, 'normlizers are matches!'
fx2_to_hasmatch = [pos is not None for pos in fx2_to_fx1_norm_col]
# IMAGE 2 Matching Features
fx2_match = np.where(fx2_to_hasmatch)[0].astype(index_dtype)
match_col_list = np.array(ut.take(fx2_to_fx1_match_col, fx2_match),
dtype=fx2_match.dtype)
norm_col_list = np.array(ut.take(fx2_to_fx1_norm_col, fx2_match),
dtype=fx2_match.dtype)
# We now have 2d coordinates into fx2_to_fx1
# Covnert into 1d coordinates for flat indexing into fx2_to_fx1
_match_index_2d = np.vstack((fx2_match, match_col_list))
_norm_index_2d = np.vstack((fx2_match, norm_col_list))
_shape2d = fx2_to_fx1.shape
match_index_1d = np.ravel_multi_index(_match_index_2d, _shape2d)
norm_index_1d = np.ravel_multi_index(_norm_index_2d, _shape2d)
# Find initial matches
# IMAGE 1 Matching Features
fx1_match = fx2_to_fx1.take(match_index_1d)
fx1_norm = fx2_to_fx1.take(norm_index_1d)
# compute constrained ratio score
match_dist = fx2_to_dist.take(match_index_1d)
norm_dist = fx2_to_dist.take(norm_index_1d)
# package and return
assigntup = fx2_match, fx1_match, fx1_norm, match_dist, norm_dist
return assigntup
