def spatially_verify(kpts1, kpts2, rchip_size2, fm, fs):
'''1) compute a robust transform from img2 -> img1
2) keep feature matches which are inliers
returns fm_V, fs_V, H '''
# Return if pathological
min_num_inliers = 4
if len(fm) < min_num_inliers:
return __default_sv_return()
# Get homography parameters
xy_thresh = params.__XY_THRESH__
scale_thresh_high = params.__SCALE_THRESH_HIGH__
scale_thresh_low = params.__SCALE_THRESH_LOW__
if params.__USE_CHIP_EXTENT__:
diaglen_sqrd = rchip_size2[0]**2 + rchip_size2[1]**2
else:
x_m = kpts2[fm[:,1],0].T
y_m = kpts2[fm[:,1],1].T
diaglen_sqrd = sv2.calc_diaglen_sqrd(x_m, y_m)
# Try and find a homography
sv_tup = sv2.homography_inliers(kpts1, kpts2, fm, xy_thresh,
scale_thresh_high, scale_thresh_low,
diaglen_sqrd, min_num_inliers)
if sv_tup is None:
return __default_sv_return()
# Return the inliers to the homography
(H, inliers, Aff, aff_inliers) = sv_tup
fm_V = fm[inliers, :]
fs_V = fs[inliers]
return fm_V, fs_V
def __spatially_verify(kpts1, kpts2, fm, fs, min_num_inliers, xy_thresh,
scale_thresh_high, scale_thresh_low, diaglen_sqrd):
'''Work function for spatial verification'''
sv_tup = sv2.homography_inliers(kpts1, kpts2, fm, xy_thresh,
scale_thresh_high, scale_thresh_low,
diaglen_sqrd, min_num_inliers)
if not sv_tup is None:
# Return the inliers to the homography
(H, inliers, Aff, aff_inliers) = sv_tup
fm_V = fm[inliers, :]
fs_V = fs[inliers, :]
else:
fm_V = np.empty((0, 2))
fs_V = np.array((0, 1))
H = np.eye(3)
return fm_V, fs_V, H
def spatial_verification(hs, qcx2_chipmatch, qdat):
sv_cfg = qdat.cfg.sv_cfg
if not sv_cfg.sv_on or sv_cfg.xy_thresh is None:
print('[mf] Step 5) Spatial verification: off')
return qcx2_chipmatch
print('[mf] Step 5) Spatial verification: ' + sv_cfg.get_uid())
prescore_method = sv_cfg.prescore_method
nShortlist = sv_cfg.nShortlist
xy_thresh = sv_cfg.xy_thresh
min_scale = sv_cfg.scale_thresh_low
max_scale = sv_cfg.scale_thresh_high
use_chip_extent = sv_cfg.use_chip_extent
min_nInliers = sv_cfg.min_nInliers
just_affine = sv_cfg.just_affine
cx2_rchip_size = hs.cpaths.cx2_rchip_size
cx2_kpts = hs.feats.cx2_kpts
qcx2_chipmatchSV = {}
#printDBG(qdat._dcxs)
dcxs_ = set(qdat._dcxs)
USE_1_to_2 = True
# Find a transform from chip2 to chip1 (the old way was 1 to 2)
for qcx in qcx2_chipmatch.iterkeys():
#printDBG('[mf] verify qcx=%r' % qcx)
chipmatch = qcx2_chipmatch[qcx]
cx2_prescore = score_chipmatch(hs, qcx, chipmatch, prescore_method,
qdat)
(cx2_fm, cx2_fs, cx2_fk) = chipmatch
topx2_cx = cx2_prescore.argsort(
)[::-1] # Only allow indexed cxs to be in the top results
topx2_cx = [cx for cx in iter(topx2_cx) if cx in dcxs_]
nRerank = min(len(topx2_cx), nShortlist)
# Precompute output container
cx2_fm_V, cx2_fs_V, cx2_fk_V = new_fmfsfk(hs)
# Query Keypoints
kpts1 = cx2_kpts[qcx]
# Check the diaglen sizes before doing the homography
topx2_dlen_sqrd = _precompute_topx2_dlen_sqrd(cx2_rchip_size, cx2_kpts,
cx2_fm, topx2_cx,
nRerank, use_chip_extent,
USE_1_to_2)
# spatially verify the top __NUM_RERANK__ results
for topx in xrange(nRerank):
cx = topx2_cx[topx]
fm = cx2_fm[cx]
#printDBG('[mf] vs topcx=%r, score=%r' % (cx, cx2_prescore[cx]))
#printDBG('[mf] len(fm)=%r' % (len(fm)))
if len(fm) >= min_nInliers:
dlen_sqrd = topx2_dlen_sqrd[topx]
kpts2 = cx2_kpts[cx]
fs = cx2_fs[cx]
fk = cx2_fk[cx]
#printDBG('[mf] computing homog')
sv_tup = sv2.homography_inliers(kpts1, kpts2, fm, xy_thresh,
max_scale, min_scale,
dlen_sqrd, min_nInliers,
just_affine)
#printDBG('[mf] sv_tup = %r' % (sv_tup,))
if sv_tup is None:
print_('o') # sv failure
else:
# Return the inliers to the homography
(H, inliers) = sv_tup
cx2_fm_V[cx] = fm[inliers, :]
cx2_fs_V[cx] = fs[inliers]
cx2_fk_V[cx] = fk[inliers]
print_('.') # verified something
else:
print_('x') # not enough initial matches
# Rebuild the feature match / score arrays to be consistent
chipmatchSV = _fix_fmfsfk(cx2_fm_V, cx2_fs_V, cx2_fk_V)
qcx2_chipmatchSV[qcx] = chipmatchSV
print('')
print('[mf] Finished sv')
return qcx2_chipmatchSV
def spatial_verification(hs, qcx2_chipmatch, qdat):
sv_cfg = qdat.cfg.sv_cfg
if not sv_cfg.sv_on or sv_cfg.xy_thresh is None:
print('[mf] Step 5) Spatial verification: off')
return qcx2_chipmatch
print('[mf] Step 5) Spatial verification: ' + sv_cfg.get_uid())
prescore_method = sv_cfg.prescore_method
nShortlist = sv_cfg.nShortlist
xy_thresh = sv_cfg.xy_thresh
min_scale = sv_cfg.scale_thresh_low
max_scale = sv_cfg.scale_thresh_high
use_chip_extent = sv_cfg.use_chip_extent
min_nInliers = sv_cfg.min_nInliers
just_affine = sv_cfg.just_affine
cx2_rchip_size = hs.cpaths.cx2_rchip_size
cx2_kpts = hs.feats.cx2_kpts
qcx2_chipmatchSV = {}
#printDBG(qdat._dcxs)
dcxs_ = set(qdat._dcxs)
USE_1_to_2 = True
# Find a transform from chip2 to chip1 (the old way was 1 to 2)
for qcx in qcx2_chipmatch.iterkeys():
#printDBG('[mf] verify qcx=%r' % qcx)
chipmatch = qcx2_chipmatch[qcx]
cx2_prescore = score_chipmatch(hs, qcx, chipmatch, prescore_method, qdat)
(cx2_fm, cx2_fs, cx2_fk) = chipmatch
topx2_cx = cx2_prescore.argsort()[::-1] # Only allow indexed cxs to be in the top results
topx2_cx = [cx for cx in iter(topx2_cx) if cx in dcxs_]
nRerank = min(len(topx2_cx), nShortlist)
# Precompute output container
cx2_fm_V, cx2_fs_V, cx2_fk_V = new_fmfsfk(hs)
# Query Keypoints
kpts1 = cx2_kpts[qcx]
# Check the diaglen sizes before doing the homography
topx2_dlen_sqrd = _precompute_topx2_dlen_sqrd(cx2_rchip_size, cx2_kpts,
cx2_fm, topx2_cx, nRerank,
use_chip_extent,
USE_1_to_2)
# spatially verify the top __NUM_RERANK__ results
for topx in xrange(nRerank):
cx = topx2_cx[topx]
fm = cx2_fm[cx]
#printDBG('[mf] vs topcx=%r, score=%r' % (cx, cx2_prescore[cx]))
#printDBG('[mf] len(fm)=%r' % (len(fm)))
if len(fm) >= min_nInliers:
dlen_sqrd = topx2_dlen_sqrd[topx]
kpts2 = cx2_kpts[cx]
fs = cx2_fs[cx]
fk = cx2_fk[cx]
#printDBG('[mf] computing homog')
sv_tup = sv2.homography_inliers(kpts1, kpts2, fm, xy_thresh,
max_scale, min_scale, dlen_sqrd,
min_nInliers, just_affine)
#printDBG('[mf] sv_tup = %r' % (sv_tup,))
if sv_tup is None:
print_('o') # sv failure
else:
# Return the inliers to the homography
(H, inliers) = sv_tup
cx2_fm_V[cx] = fm[inliers, :]
cx2_fs_V[cx] = fs[inliers]
cx2_fk_V[cx] = fk[inliers]
print_('.') # verified something
else:
print_('x') # not enough initial matches
# Rebuild the feature match / score arrays to be consistent
chipmatchSV = _fix_fmfsfk(cx2_fm_V, cx2_fs_V, cx2_fk_V)
qcx2_chipmatchSV[qcx] = chipmatchSV
print('')
print('[mf] Finished sv')
return qcx2_chipmatchSV