def euclidean_error_function(arr):
error = 0.0
ctr = 0
buf = []
cbuf = []
for m in matches:
d, q = m['db'], m['query']
feature = map3d[int(d[0]), int(d[1])]
if not feature:
continue
ctr += 1
pz, px = geom.lltom(self.lat + meter * arr[0],
self.lon + meter * arr[1], feature['lat'],
feature['lon'])
py = feature['alt'] # feature['alt'] - arr[2]
x, y, z = geom.camera_transform(px, py, pz, self.pitch,
self.yaw, self.roll)
coord = geom.project2d(x, y, z, self.focal_length)
pixel = geom.center(coord, self.imsize)
error += abs(pixel[0] - q[0])
buf.append((pixel[0], pixel[1]))
cbuf.append((q[0], q[1]))
# for i,j in buf:
# print i,j
# print
# for i,j in cbuf:
# print i,j
if error < best[0]:
best[0] = min(error, best[0])
# print best[0]/ctr
return error / ctr
def euclidean_error_function(arr):
error = 0.0
ctr = 0
buf = []
cbuf = []
for m in matches:
d, q = m['db'], m['query']
feature = map3d[int(d[0]), int(d[1])]
if not feature:
continue
ctr += 1
pz, px = geom.lltom(self.lat + meter*arr[0], self.lon + meter*arr[1], feature['lat'], feature['lon'])
py = feature['alt'] # feature['alt'] - arr[2]
x, y, z = geom.camera_transform(px, py, pz, self.pitch, self.yaw, self.roll)
coord = geom.project2d(x, y, z, self.focal_length)
pixel = geom.center(coord, self.imsize)
error += abs(pixel[0] - q[0])
buf.append((pixel[0], pixel[1]))
cbuf.append((q[0], q[1]))
# for i,j in buf:
# print i,j
# print
# for i,j in cbuf:
# print i,j
if error < best[0]:
best[0] = min(error, best[0])
# print best[0]/ctr
return error/ctr
def solve(C, Q, matches, dbsiftpath, dbimgpath):
# open EarthMine info
info = os.path.join(C.infodir, os.path.basename(dbimgpath)[:-4] + '.info')
em = render_tags.EarthmineImageInfo(dbimgpath, info)
map3d = C.pixelmap.open(dbsiftpath)
# find non-None features
vector = []
for i, m in enumerate(matches):
d = m['db']
# get 3d pt of feature
feature = map3d[int(d[0]), int(d[1])]
if not feature:
continue
# convert from latlon to meters relative to earthmine camera
pz, px = geom.lltom(em.lat, em.lon, feature['lat'], feature['lon'])
py = feature['alt'] - em.alt
vector.append((m['query'][:2], (px, py, -pz)))
print vector[0]
# reference camera matrix
# f 0 0
# 0 f 0
# 0 0 1
A = cv.CreateMat(3, 3, cv.CV_64F)
cv.SetZero(A)
f = 662 # focal len?
cv.Set2D(A, 0, 0, cv.Scalar(f))
cv.Set2D(A, 1, 1, cv.Scalar(f))
cv.Set2D(A, 2, 2, cv.Scalar(1))
# convert vector to to cvMats
objectPoints3d = cv.CreateMat(len(vector), 1, cv.CV_64FC3)
imagePoints2d = cv.CreateMat(len(vector), 1, cv.CV_64FC2)
for i, (p2d, p3d) in enumerate(vector):
cv.Set2D(imagePoints2d, i, 0, cv.Scalar(*p2d))
cv.Set2D(objectPoints3d, i, 0, cv.Scalar(*p3d))
coeff = cv.CreateMat(4, 1, cv.CV_64F)
rvec = cv.CreateMat(3, 1, cv.CV_64F)
tvec = cv.CreateMat(3, 1, cv.CV_64F)
cv.SetZero(coeff)
cv.SetZero(rvec)
cv.SetZero(tvec)
# since rvec, tvec are zero the initial guess is the earthmine camera
ret = cv.FindExtrinsicCameraParams2(objectPoints3d, imagePoints2d, A,
coeff, rvec, tvec, useExtrinsicGuess=False)
np_rvec = np.matrix(rvec)
np_tvec = np.matrix(tvec)
print np_rvec
print np_tvec
return np_rvec, np_tvec
def map_tags_camera(self, elat=0, elon=0, ep=0, ey=0, er=0):
"Returns (tag, (dist, pixel)) pairs using camera transform."
if not elat or not elon:
elat = self.lat
elon = self.lon
tags = []
possible_tags = self.get_frustum()
for tag in possible_tags:
pz, px = geom.lltom(elat, elon, tag.lat, tag.lon)
py = tag.alt - self.alt;
x, y, z = geom.camera_transform(px, py, pz, self.pitch + ep, self.yaw + ey, self.roll + er)
coord = geom.project2d(x, y, z, self.source.focal_length)
pixel = geom.center(coord, self.image.size)
# tags.append((tag, (0, geom.constrain(pixel, self.image.size))))
tags.append((tag, (0, pixel)))
return tags
def estimate_pose(C, Q, dbmatch, gtStatus=None):
# settings
param = C.pose_param
runflag = param['runflag']
np.seterr(all='ignore')
Q.datafile = os.path.join(C.pose_param['resultsdir'],'data_'+Q.name+'.txt')
open(Q.datafile,'w').close()
#####----- PRINT RUN DETAILS -----#####
run_info = os.path.join(param['resultsdir'],param['run_info'])
open(run_info,'w').close()
with open(run_info,'a') as ri:
if runflag == 11: print >>ri, 'Method: Yaw, planes from VPs. Scale computed with homography.'
elif runflag == 10: print >>ri, 'Method: Yaw, planes from VPs. Scale computed after homography.'
if param['cheat']: print >>ri, 'Ground truth yaw and plane used (cheating).'
print >>ri, 'Inlier base error threshold: %.3f' % param['inlier_error']
print >>ri, 'Base iteration scale: %d' % param['ransac_iter']
#####----- PRINT RUN DETAILS -----#####
# get high res db image and sift paths
dbinfo = os.path.join(C.hiresdir, dbmatch + '.info')
dbimg = os.path.join(C.hiresdir,dbmatch+'.jpg')
dbsift = os.path.join(C.hiresdir,dbmatch+'sift.txt')
dbsource = render_tags.EarthmineImageInfo(dbimg, dbinfo)
# Set Kd, wRd, and db position
wx,wy = dbsource.image.size
fov = dbsource.fov
Kd = geom.cameramat(wx, wy, fov)
Kdinv = alg.inv(Kd)
y,p,r = dbsource.yaw, dbsource.pitch, dbsource.roll
wRd = geom.RfromYPR(y,p,r) # db camera orientation (camera to world)
olat,olon,oalt = dbsource.lat,dbsource.lon,dbsource.alt # database location
# get high res query information
qname = Q.name
qimg = os.path.join(C.querydir,'hires',qname+'.jpg')
qsift = os.path.join(C.querydir,'hires',qname+'sift.txt')
qsource = render_tags.QueryImageInfo(Q.datasource)
glat,glon = qsource.lat,qsource.lon
gzx = geom.lltom(olat,olon,glat,glon)
timename = qname[-12:-10]+qname[-9:-7]+qname[-6:-4]#+qname[-3:]
# Set Kq
wx,wy = qsource.image.size
fov = qsource.fov
Kq = geom.cameramat(wx, wy, fov)
Kqinv = alg.inv(Kq)
cyaw,p,r = qsource.yaw, qsource.pitch, qsource.roll # cyaw - cell phone yaw
# get high res sift rematch
matches = highresSift(C, Q, dbmatch)
with open(Q.datafile,'a') as df:
print >>df, 'Number of matches | number of queries | ratio: %.0f | %.0f | %.2f' % (matches['nmat'], matches['numq'], float(matches['nmat'])/matches['numq'])
print >>df, ''
# Get estimated ground truth query location and normal direction
tlat, tlon, tnorm, tyaw = getGTpose(C, Q)
qzx = geom.lltom(olat,olon,tlat,tlon)
# get query yaw and plane yaw from vanishing point anaylsis
yawforvp = tyaw if param['cheat'] else np.nan
vyaw, vnorms = vp_analysis.getQNyaws(C, Q, qimg, dbimg, qsource, yawforvp)
# get dominant planes
dplanes, psizes, planeprms = find_dbplanes(C, Q, dbmatch, Kdinv, wRd)
# match vanishing point planes to database planes
pyaws, planes, pconfs = combine_planes(runflag,vnorms,dplanes,psizes,planeprms)
print 'VP and DB Planes: ' + str(np.int_(pyaws)) + ', ' + str(planes)
with open(Q.datafile,'a') as df:
# print >>df, 'Planes detected with vanishing points:'
for i in range(len(pconfs)):
perr = np.round(np.mod(pyaws[i]-tnorm,360))
perr = perr if perr<180 else 360-perr
print >>df, 'Plane Yaw | DB plane | Confidence | Error : %3.0f | %d | %.2f | %.0f' % (pyaws[i],0 if planes[i]<0 else planes[i],pconfs[i],perr)
yerr = np.round(np.mod(vyaw-tyaw,360))
yerr = yerr if yerr<180 else 360-yerr
print >>df, 'VP Yaw | Confidence | Error : %3.0f | %.2f | %.0f' % (vyaw,np.nan,yerr)
print >>df, 'Cell yaw | True yaw | Plane : %3.0f | %3.0f | %3.0f' % (cyaw,tyaw,tnorm)
print >>df, ''
# Set yaw value to be used
if runflag >= 10: # vanishing point methods
if np.isnan(vyaw): yaw, yawerr = cyaw, 0
else: yaw, yawerr = vyaw, 0
else: yaw, yawerr = cyaw, 0 # set cell phone yaw to use, plane normal
wRq = geom.RfromYPR(yaw,p,r) # camera orientation (camera to world)
### --- THIS IS FOR CHEATING --- ###
if param['cheat']:
if not np.isnan(tnorm):
pyaws, planes, pconfs = np.append(pyaws,tnorm), np.append(planes,-1), np.append(pconfs,1)
yaw, yawerr = tyaw, 0
wRq = geom.RfromYPR(yaw,p,r) # camera orientation (camera to world)
### --- THIS IS FOR CHEATING --- ###
# print pre-homography data to file
vyaw_err = np.round(np.round(np.mod(tyaw-vyaw,360))) if not np.isnan(vyaw) else np.nan
vyaw_err = vyaw_err if vyaw_err<180 else 360-vyaw_err
dbears = np.mod( 180/np.pi*np.arctan2(planeprms[:,0],planeprms[:,2]) , 360 )
print 'Computed / ground truth cell phone yaw: %.0f / %.0f' % (vyaw,tyaw)
with open(os.path.join(param['resultsdir'],param['extras_file']),'a') as extras_file:
print >>extras_file, '\t'.join([timename, '%.0f' % tnorm, '%.0f' % np.round(tyaw), '%.0f' % cyaw, '%.0f' % vyaw, '%.4f'%np.nan, str(vyaw_err)])
print >>extras_file, '\t'.join([ '%.4f' % 0 for vnorm in vnorms ])
print >>extras_file, '\t'.join([ '%.0f' % vnorm for vnorm in vnorms ])
print >>extras_file, '\t'.join([ '%.0f' % plane for plane in planes ])
print >>extras_file, '\t'.join([ '%.0f' % dplane for dplane in dplanes ])
print >>extras_file, '\t'.join([ '%.0f' % dbear for dbear in dbears ])
print >>extras_file, '\t'.join([ '%.3f' % dnerr for dnerr in planeprms[:,4] ])
# Fill out match information
nmat = matches['nmat']
matches['qray'] = geom.normalrows(tp(np.dot(Kqinv,np.append(tp(matches['q2d']),[np.ones(nmat)],0))))
matches['dray'] = geom.normalrows(tp(np.dot(Kdinv,np.append(tp(matches['d2d']),[np.ones(nmat)],0))))
matches = match_info(C, Q, matches, dbmatch, wRd)
matches_start = matches.copy()
# Solve for query pose using constrained image geometry
init_matches = initMatches(matches.copy())
matches['numi'], matches['hconf'] = 0, 0
runflag, ntry, planechose = param['runflag'], 0, 0
parameters = ( wRq, wRd, yaw, np.nan, runflag, param['inlier_error'], param['ransac_iter'], 10, True )
if param['ransac_iter'] == 0:
matches = init_matches
matches['numi'], matches['hconf'] == 0, 0
pose = np.zeros(6)
pose[3:5] = np.nan
elif runflag < 10:
matches, pose = solveGeom(init_matches,parameters,yawerr)
else:
ntry = 1
viter = -np.ones(3)
parameters = ( wRq, wRd, yaw, np.nan, runflag, param['inlier_error'], param['ransac_iter'], 15, True )
matches, pose, planechose = solveNorm(C,Q,dbmatch,pyaws,planes,init_matches,parameters,yawerr)
viter[0] = matches['viter']
if matches['numi'] == 0 or matches['hconf'] == 0:
ntry = 2
parameters = ( wRq, wRd, yaw, np.nan, runflag, 3*param['inlier_error'], param['ransac_iter'], 10, True )
matches, pose, planechose = solveNorm(C,Q,dbmatch,pyaws,planes,init_matches,parameters,yawerr)
viter[1] = matches['viter']
if matches['numi'] == 0 or matches['hconf'] == 0:
ntry, planechose = 3, 0
parameters = ( wRq, wRd, yaw, np.nan, 7, 3*param['inlier_error'], param['ransac_iter'], 10, True )
matches, pose = solveYaw(init_matches,parameters,yawerr)
viter[2] = matches['viter']
if matches['numi'] == 0 or matches['hconf'] == 0:
ntry, planechose = 4, 0
# save matches to disk
matches_file = os.path.join(param['resultsdir'],'matches_'+qname+'.pkl')
matches_out = open(matches_file,'wb')
pickle.dump(matches,matches_out)
matches_out.close()
# extract pose parameters
comp_runflag = matches['runflag']
tray = pose[:3]
comp_yaw = pose[3]
comp_pyaw = pose[4] if runflag>=0 else np.nan
scale = pose[5] if runflag>=0 else np.nan
# Get scaled translation for query location
if np.isnan(scale):
wRq_pr = geom.YPRfromR(wRq)[1:]
comp_wRq = geom.RfromYPR(comp_yaw, wRq_pr[0], wRq_pr[1])
qloc = scalefrom3d(matches, tray, comp_wRq)[0]
else: # scale calculated in RANSAC loop
qloc = scale*tray
# temporarily get yaw error
qyaw_error = np.round(abs(np.mod(tyaw-comp_yaw,360)))
qyaw_error = qyaw_error if qyaw_error<180 else abs(qyaw_error-360)
# compute location errors wrt estimated query locations
loc_err = ( (qloc[0]-qzx[1])**2 + (qloc[2]-qzx[0])**2 )**0.5
gps_err = ( (gzx[1] -qzx[1])**2 + (gzx[0] -qzx[0])**2 )**0.5
# compute the angle difference between T and ground truth translation
tyaw_error = np.round(abs( 180/np.pi * np.arccos( np.abs(qloc[0]*qzx[1]+qloc[2]*qzx[0]) / (alg.norm([qloc[0],qloc[2]])*alg.norm(qzx)) ) ))
# compute the plane normal angle error
nyaw_error = np.nan if np.isnan(comp_pyaw) or np.isnan(tnorm) else np.mod(np.round(abs(comp_pyaw-tnorm)),180)
nyaw_error = nyaw_error if nyaw_error<90 else abs(nyaw_error-180)
# write pose estimation results to file
yaw_err = np.nan
pose_file = os.path.join(param['resultsdir'],param['pose_file'])
with open(pose_file,'a') as pf:
print >>pf, '\t'.join([qname, str(loc_err), str(gps_err), \
str(tyaw_error), str(qyaw_error), str(nyaw_error), str(matches['numi']), \
str(matches['numq']), str(matches['nmat']), str(matches['hconf']), \
str(qloc[0]), str(qloc[2]), str(yaw_err), str(runflag)])
# print post-homography data to file
with open(Q.datafile,'a') as df:
print >>df, ''
print >>df, '------------------'
print >>df, ''
if ntry==1: print >>df, 'Homography solution using low error threshold with restrictions.'
elif ntry==2: print >>df, 'Homography solution using high error threshold with restrictions.'
else: print >>df, 'Solution not found. Setting T=0.'
if planechose==0: print >>df, 'Solution formed with unset plane normal.'
else: 'Solution chosen with plane normal %d chosen.' % planechose
print >>df, 'VP yaw | Computed yaw | Actual Yaw | Error : %3.0f | %3.0f | %3.0f | %3.0f' % (vyaw,comp_yaw,tyaw,qyaw_error)
print >>df, 'Computed Normal | Actual Normal | Error : %3.0f | %3.0f | %3.0f' % (comp_pyaw,tnorm,nyaw_error)
print >>df, 'Translation (x|y|z): %.1f | %.1f | %.1f' % (qloc[0],qloc[1],qloc[2])
print >>df, 'True position (x|-|z): %.1f | - | %.1f' % (qzx[1],qzx[0])
print >>df, 'Angle error | Location error: %.0f | %.1f' % (tyaw_error,loc_err)
print >>df, 'Number of Inliers | Total matches | Ratio: %d | %d | %.2f' % (matches['numi'],matches['nmat'],np.nan if matches['nmat']==0 else float(matches['numi'])/matches['nmat'])
print >>df, 'Reprojection error | Homography confidence: %.3f | %.1f' % (matches['rperr'],matches['hconf'])
print >>df, 'Valid Homographies | Iterations | Ratio: %d | %d | %.3f' % (matches['viter'],matches['niter'],np.nan if matches['niter']==0 else float(matches['viter'])/matches['niter'])
print >>df, ''
print >>df, '------------------'
print >>df, ''
booleans = [ loc_err<5, loc_err<10, not(5<np.mod(vyaw-tyaw,360)<355), \
not(10<np.mod(vyaw-tyaw,360)<350), \
not(5<np.mod(comp_yaw-tyaw,360)<355), ntry==1, ntry!=3, \
planechose!=0, matches['nmat']!=matches_start['nmat'], \
0 if planechose==0 else pconfs[planechose-1]>0, comp_yaw-vyaw ]
print >>df, '|'.join(['%.0f' % (b) for b in booleans])
# draw matches
close = int(loc_err<5) + int(loc_err<10)
yawclose = int(not(5<np.mod(vyaw-tyaw,360)<355)) + int(not(10<np.mod(vyaw-tyaw,360)<350))
imgpath = os.path.join( param['resultsdir'] , qname + ';locerr=%.2f' % (loc_err) + ';locPerf_' + str(close) \
+ ';yawPerf_' + str(yawclose) + ';nplanes_' + str(len(pyaws)) + ';plane_' + str(planes[planechose]) + ';try_' + str(ntry) \
+ ';tAng=%.0f' % (tyaw_error) + ';qAng=%.0f' % (qyaw_error) + ';nAng=%.0f' % (nyaw_error) + ';' + dbmatch + '.jpg')
draw_matches(matches, qimg, dbimg, imgpath)
# imgpath = os.path.join( param['resultsdir'] , 'homography;' + qname + ';' + dbmatch + '.jpg')
# draw_hom(matches, pose, wRq, wRd, Kq, Kd, qimg, dbimg, imgpath)
if C.QUERY == 'oakland1': C.pose_param['draw_tags'] = False
if C.pose_param['draw_tags']: draw_tags(C, Q, matches, pose, dbmatch, olat, olon, Kd, Kq)
print 'Computed yaw / ground truth yaw : %.0f / %.0f' % (comp_yaw,tyaw)
if runflag < 10: print 'Computed normal bearing / ground truth : %.0f / %.0f' % (comp_pyaw,tnorm)
print 'Computed query location relative to db : %.1f, %.1f, %.1f' % tuple(qloc)
print 'Ground truth query location relative to db : %.1f, - , %.1f' % (qzx[1],qzx[0])
input = (wRq, wRd)
return qloc, loc_err, matches, input
def estimate_pose(C, Q, dbmatch, gtStatus=None):
# settings
param = C.pose_param
runflag = param['runflag']
np.seterr(all='ignore')
Q.datafile = os.path.join(C.pose_param['resultsdir'],
'data_' + Q.name + '.txt')
open(Q.datafile, 'w').close()
#####----- PRINT RUN DETAILS -----#####
run_info = os.path.join(param['resultsdir'], param['run_info'])
open(run_info, 'w').close()
with open(run_info, 'a') as ri:
if runflag == 11:
print >> ri, 'Method: Yaw, planes from VPs. Scale computed with homography.'
elif runflag == 10:
print >> ri, 'Method: Yaw, planes from VPs. Scale computed after homography.'
if param['cheat']:
print >> ri, 'Ground truth yaw and plane used (cheating).'
print >> ri, 'Inlier base error threshold: %.3f' % param['inlier_error']
print >> ri, 'Base iteration scale: %d' % param['ransac_iter']
#####----- PRINT RUN DETAILS -----#####
# get high res db image and sift paths
dbinfo = os.path.join(C.hiresdir, dbmatch + '.info')
dbimg = os.path.join(C.hiresdir, dbmatch + '.jpg')
dbsift = os.path.join(C.hiresdir, dbmatch + 'sift.txt')
dbsource = render_tags.EarthmineImageInfo(dbimg, dbinfo)
# Set Kd, wRd, and db position
wx, wy = dbsource.image.size
fov = dbsource.fov
Kd = geom.cameramat(wx, wy, fov)
Kdinv = alg.inv(Kd)
y, p, r = dbsource.yaw, dbsource.pitch, dbsource.roll
wRd = geom.RfromYPR(y, p, r) # db camera orientation (camera to world)
olat, olon, oalt = dbsource.lat, dbsource.lon, dbsource.alt # database location
# get high res query information
qname = Q.name
qimg = os.path.join(C.querydir, 'hires', qname + '.jpg')
qsift = os.path.join(C.querydir, 'hires', qname + 'sift.txt')
qsource = render_tags.QueryImageInfo(Q.datasource)
glat, glon = qsource.lat, qsource.lon
gzx = geom.lltom(olat, olon, glat, glon)
timename = qname[-12:-10] + qname[-9:-7] + qname[-6:-4] #+qname[-3:]
# Set Kq
wx, wy = qsource.image.size
fov = qsource.fov
Kq = geom.cameramat(wx, wy, fov)
Kqinv = alg.inv(Kq)
cyaw, p, r = qsource.yaw, qsource.pitch, qsource.roll # cyaw - cell phone yaw
# get high res sift rematch
matches = highresSift(C, Q, dbmatch)
with open(Q.datafile, 'a') as df:
print >> df, 'Number of matches | number of queries | ratio: %.0f | %.0f | %.2f' % (
matches['nmat'], matches['numq'],
float(matches['nmat']) / matches['numq'])
print >> df, ''
# Get estimated ground truth query location and normal direction
tlat, tlon, tnorm, tyaw = getGTpose(C, Q)
qzx = geom.lltom(olat, olon, tlat, tlon)
# get query yaw and plane yaw from vanishing point anaylsis
yawforvp = tyaw if param['cheat'] else np.nan
vyaw, vnorms = vp_analysis.getQNyaws(C, Q, qimg, dbimg, qsource, yawforvp)
# get dominant planes
dplanes, psizes, planeprms = find_dbplanes(C, Q, dbmatch, Kdinv, wRd)
# match vanishing point planes to database planes
pyaws, planes, pconfs = combine_planes(runflag, vnorms, dplanes, psizes,
planeprms)
print 'VP and DB Planes: ' + str(np.int_(pyaws)) + ', ' + str(planes)
with open(Q.datafile, 'a') as df:
# print >>df, 'Planes detected with vanishing points:'
for i in range(len(pconfs)):
perr = np.round(np.mod(pyaws[i] - tnorm, 360))
perr = perr if perr < 180 else 360 - perr
print >> df, 'Plane Yaw | DB plane | Confidence | Error : %3.0f | %d | %.2f | %.0f' % (
pyaws[i], 0 if planes[i] < 0 else planes[i], pconfs[i], perr)
yerr = np.round(np.mod(vyaw - tyaw, 360))
yerr = yerr if yerr < 180 else 360 - yerr
print >> df, 'VP Yaw | Confidence | Error : %3.0f | %.2f | %.0f' % (
vyaw, np.nan, yerr)
print >> df, 'Cell yaw | True yaw | Plane : %3.0f | %3.0f | %3.0f' % (
cyaw, tyaw, tnorm)
print >> df, ''
# Set yaw value to be used
if runflag >= 10: # vanishing point methods
if np.isnan(vyaw): yaw, yawerr = cyaw, 0
else: yaw, yawerr = vyaw, 0
else: yaw, yawerr = cyaw, 0 # set cell phone yaw to use, plane normal
wRq = geom.RfromYPR(yaw, p, r) # camera orientation (camera to world)
### --- THIS IS FOR CHEATING --- ###
if param['cheat']:
if not np.isnan(tnorm):
pyaws, planes, pconfs = np.append(pyaws, tnorm), np.append(
planes, -1), np.append(pconfs, 1)
yaw, yawerr = tyaw, 0
wRq = geom.RfromYPR(yaw, p, r) # camera orientation (camera to world)
### --- THIS IS FOR CHEATING --- ###
# print pre-homography data to file
vyaw_err = np.round(np.round(np.mod(
tyaw - vyaw, 360))) if not np.isnan(vyaw) else np.nan
vyaw_err = vyaw_err if vyaw_err < 180 else 360 - vyaw_err
dbears = np.mod(180 / np.pi * np.arctan2(planeprms[:, 0], planeprms[:, 2]),
360)
print 'Computed / ground truth cell phone yaw: %.0f / %.0f' % (vyaw, tyaw)
with open(os.path.join(param['resultsdir'], param['extras_file']),
'a') as extras_file:
print >> extras_file, '\t'.join([
timename,
'%.0f' % tnorm,
'%.0f' % np.round(tyaw),
'%.0f' % cyaw,
'%.0f' % vyaw,
'%.4f' % np.nan,
str(vyaw_err)
])
print >> extras_file, '\t'.join(['%.4f' % 0 for vnorm in vnorms])
print >> extras_file, '\t'.join(['%.0f' % vnorm for vnorm in vnorms])
print >> extras_file, '\t'.join(['%.0f' % plane for plane in planes])
print >> extras_file, '\t'.join(
['%.0f' % dplane for dplane in dplanes])
print >> extras_file, '\t'.join(['%.0f' % dbear for dbear in dbears])
print >> extras_file, '\t'.join(
['%.3f' % dnerr for dnerr in planeprms[:, 4]])
# Fill out match information
nmat = matches['nmat']
matches['qray'] = geom.normalrows(
tp(np.dot(Kqinv, np.append(tp(matches['q2d']), [np.ones(nmat)], 0))))
matches['dray'] = geom.normalrows(
tp(np.dot(Kdinv, np.append(tp(matches['d2d']), [np.ones(nmat)], 0))))
matches = match_info(C, Q, matches, dbmatch, wRd)
matches_start = matches.copy()
# Solve for query pose using constrained image geometry
init_matches = initMatches(matches.copy())
matches['numi'], matches['hconf'] = 0, 0
runflag, ntry, planechose = param['runflag'], 0, 0
parameters = (wRq, wRd, yaw, np.nan, runflag, param['inlier_error'],
param['ransac_iter'], 10, True)
if param['ransac_iter'] == 0:
matches = init_matches
matches['numi'], matches['hconf'] == 0, 0
pose = np.zeros(6)
pose[3:5] = np.nan
elif runflag < 10:
matches, pose = solveGeom(init_matches, parameters, yawerr)
else:
ntry = 1
viter = -np.ones(3)
parameters = (wRq, wRd, yaw, np.nan, runflag, param['inlier_error'],
param['ransac_iter'], 15, True)
matches, pose, planechose = solveNorm(C, Q, dbmatch, pyaws, planes,
init_matches, parameters, yawerr)
viter[0] = matches['viter']
if matches['numi'] == 0 or matches['hconf'] == 0:
ntry = 2
parameters = (wRq, wRd, yaw, np.nan, runflag,
3 * param['inlier_error'], param['ransac_iter'], 10,
True)
matches, pose, planechose = solveNorm(C, Q, dbmatch, pyaws, planes,
init_matches, parameters,
yawerr)
viter[1] = matches['viter']
if matches['numi'] == 0 or matches['hconf'] == 0:
ntry, planechose = 3, 0
parameters = (wRq, wRd, yaw, np.nan, 7, 3 * param['inlier_error'],
param['ransac_iter'], 10, True)
matches, pose = solveYaw(init_matches, parameters, yawerr)
viter[2] = matches['viter']
if matches['numi'] == 0 or matches['hconf'] == 0:
ntry, planechose = 4, 0
# save matches to disk
matches_file = os.path.join(param['resultsdir'],
'matches_' + qname + '.pkl')
matches_out = open(matches_file, 'wb')
pickle.dump(matches, matches_out)
matches_out.close()
# extract pose parameters
comp_runflag = matches['runflag']
tray = pose[:3]
comp_yaw = pose[3]
comp_pyaw = pose[4] if runflag >= 0 else np.nan
scale = pose[5] if runflag >= 0 else np.nan
# Get scaled translation for query location
if np.isnan(scale):
wRq_pr = geom.YPRfromR(wRq)[1:]
comp_wRq = geom.RfromYPR(comp_yaw, wRq_pr[0], wRq_pr[1])
qloc = scalefrom3d(matches, tray, comp_wRq)[0]
else: # scale calculated in RANSAC loop
qloc = scale * tray
# temporarily get yaw error
qyaw_error = np.round(abs(np.mod(tyaw - comp_yaw, 360)))
qyaw_error = qyaw_error if qyaw_error < 180 else abs(qyaw_error - 360)
# compute location errors wrt estimated query locations
loc_err = ((qloc[0] - qzx[1])**2 + (qloc[2] - qzx[0])**2)**0.5
gps_err = ((gzx[1] - qzx[1])**2 + (gzx[0] - qzx[0])**2)**0.5
# compute the angle difference between T and ground truth translation
tyaw_error = np.round(
abs(180 / np.pi * np.arccos(
np.abs(qloc[0] * qzx[1] + qloc[2] * qzx[0]) /
(alg.norm([qloc[0], qloc[2]]) * alg.norm(qzx)))))
# compute the plane normal angle error
nyaw_error = np.nan if np.isnan(comp_pyaw) or np.isnan(tnorm) else np.mod(
np.round(abs(comp_pyaw - tnorm)), 180)
nyaw_error = nyaw_error if nyaw_error < 90 else abs(nyaw_error - 180)
# write pose estimation results to file
yaw_err = np.nan
pose_file = os.path.join(param['resultsdir'], param['pose_file'])
with open(pose_file, 'a') as pf:
print >>pf, '\t'.join([qname, str(loc_err), str(gps_err), \
str(tyaw_error), str(qyaw_error), str(nyaw_error), str(matches['numi']), \
str(matches['numq']), str(matches['nmat']), str(matches['hconf']), \
str(qloc[0]), str(qloc[2]), str(yaw_err), str(runflag)])
# print post-homography data to file
with open(Q.datafile, 'a') as df:
print >> df, ''
print >> df, '------------------'
print >> df, ''
if ntry == 1:
print >> df, 'Homography solution using low error threshold with restrictions.'
elif ntry == 2:
print >> df, 'Homography solution using high error threshold with restrictions.'
else:
print >> df, 'Solution not found. Setting T=0.'
if planechose == 0:
print >> df, 'Solution formed with unset plane normal.'
else:
'Solution chosen with plane normal %d chosen.' % planechose
print >> df, 'VP yaw | Computed yaw | Actual Yaw | Error : %3.0f | %3.0f | %3.0f | %3.0f' % (
vyaw, comp_yaw, tyaw, qyaw_error)
print >> df, 'Computed Normal | Actual Normal | Error : %3.0f | %3.0f | %3.0f' % (
comp_pyaw, tnorm, nyaw_error)
print >> df, 'Translation (x|y|z): %.1f | %.1f | %.1f' % (
qloc[0], qloc[1], qloc[2])
print >> df, 'True position (x|-|z): %.1f | - | %.1f' % (qzx[1],
qzx[0])
print >> df, 'Angle error | Location error: %.0f | %.1f' % (tyaw_error,
loc_err)
print >> df, 'Number of Inliers | Total matches | Ratio: %d | %d | %.2f' % (
matches['numi'], matches['nmat'], np.nan if matches['nmat'] == 0
else float(matches['numi']) / matches['nmat'])
print >> df, 'Reprojection error | Homography confidence: %.3f | %.1f' % (
matches['rperr'], matches['hconf'])
print >> df, 'Valid Homographies | Iterations | Ratio: %d | %d | %.3f' % (
matches['viter'], matches['niter'], np.nan if matches['niter'] == 0
else float(matches['viter']) / matches['niter'])
print >> df, ''
print >> df, '------------------'
print >> df, ''
booleans = [ loc_err<5, loc_err<10, not(5<np.mod(vyaw-tyaw,360)<355), \
not(10<np.mod(vyaw-tyaw,360)<350), \
not(5<np.mod(comp_yaw-tyaw,360)<355), ntry==1, ntry!=3, \
planechose!=0, matches['nmat']!=matches_start['nmat'], \
0 if planechose==0 else pconfs[planechose-1]>0, comp_yaw-vyaw ]
print >> df, '|'.join(['%.0f' % (b) for b in booleans])
# draw matches
close = int(loc_err < 5) + int(loc_err < 10)
yawclose = int(not (5 < np.mod(vyaw - tyaw, 360) < 355)) + int(not (
10 < np.mod(vyaw - tyaw, 360) < 350))
imgpath = os.path.join( param['resultsdir'] , qname + ';locerr=%.2f' % (loc_err) + ';locPerf_' + str(close) \
+ ';yawPerf_' + str(yawclose) + ';nplanes_' + str(len(pyaws)) + ';plane_' + str(planes[planechose]) + ';try_' + str(ntry) \
+ ';tAng=%.0f' % (tyaw_error) + ';qAng=%.0f' % (qyaw_error) + ';nAng=%.0f' % (nyaw_error) + ';' + dbmatch + '.jpg')
draw_matches(matches, qimg, dbimg, imgpath)
# imgpath = os.path.join( param['resultsdir'] , 'homography;' + qname + ';' + dbmatch + '.jpg')
# draw_hom(matches, pose, wRq, wRd, Kq, Kd, qimg, dbimg, imgpath)
if C.QUERY == 'oakland1': C.pose_param['draw_tags'] = False
if C.pose_param['draw_tags']:
draw_tags(C, Q, matches, pose, dbmatch, olat, olon, Kd, Kq)
print 'Computed yaw / ground truth yaw : %.0f / %.0f' % (comp_yaw,
tyaw)
if runflag < 10:
print 'Computed normal bearing / ground truth : %.0f / %.0f' % (
comp_pyaw, tnorm)
print 'Computed query location relative to db : %.1f, %.1f, %.1f' % tuple(
qloc)
print 'Ground truth query location relative to db : %.1f, - , %.1f' % (
qzx[1], qzx[0])
input = (wRq, wRd)
return qloc, loc_err, matches, input
def qsolve(C, Q, matches, dbsiftpath, dbimgpath):
# Get image information.
info = os.path.join(C.infodir, os.path.basename(dbimgpath)[:-4] + '.info')
qsource = render_tags.QueryImageInfo(Q.datasource)
dbsource = render_tags.EarthmineImageInfo(dbimgpath, info)
map3d = C.pixelmap.open(dbsiftpath)
# Get 2d pixels and 3d locations of feature inliers
matches = [(m['query'][0],m['query'][1],m['db'][0],m['db'][1]) for m in matches]
matches = list(set(matches))
q2d = [arr([m[0],m[1]]) for m in matches]
db2d = [arr([m[2],m[3]]) for m in matches]
db3d = [map3d[int(d[0]),int(d[1])] for d in db2d]
i = 0
while i < len(db3d):
if db3d[i] is None:
q2d.pop(i)
db2d.pop(i)
db3d.pop(i)
else:
i = i+1
olat,olon,oalt = dbsource.lat,dbsource.lon,dbsource.alt
qlat,qlon = qsource.lat,qsource.lon
qzx = geom.lltom(olat,olon,qlat,qlon)
zx = [geom.lltom(olat,olon,d['lat'],d['lon']) for d in db3d]
y = [dbsource.alt-d['alt'] for d in db3d]
xyz = [[zx[i][1],y[i],zx[i][0]] for i in range(len(y))]
print len(xyz)
# Set K, Rhat
wx,wy = qsource.pgmsize[0], qsource.pgmsize[1]
tx,ty = qsource.view_angle[0], qsource.view_angle[1]
f1, f2 = (wx-1)/(2*np.tan(tx/2)), (wy-1)/(2*np.tan(ty/2))
f = (f1+f2) / 2
K = arr([[f,0,(wx-1)/2.0],
[0,f,(wy-1)/2.0],
[0,0,1]])
y,p,r = qsource.yaw, qsource.pitch, qsource.roll
print [180*y/np.pi,180*p/np.pi,180*r/np.pi]
Ry = arr([[np.cos(y),0,np.sin(y)],
[0,1,0],
[-np.sin(y),0,np.cos(y)]])
Rx = arr([[1,0,0],
[0,np.cos(p),-np.sin(p)],
[0,np.sin(p),np.cos(p)]])
Rz = arr([[np.cos(r),-np.sin(r),0],
[np.sin(r),np.cos(r),0],
[0,0,1]])
Rhat = dot(Ry,dot(Rx,Rz)) # camera orientation (camera to world)
tRhat = tp(Rhat)
# reference camera matrix
# f 0 cx
# 0 f cy
# 0 0 1
A = cv.CreateMat(3, 3, cv.CV_64F)
cv.SetZero(A)
cv.Set2D(A, 0, 0, cv.Scalar(f))
cv.Set2D(A, 1, 1, cv.Scalar(f))
cv.Set2D(A, 2, 2, cv.Scalar(1))
cv.Set2D(A, 0, 2, cv.Scalar((wx-1)/2.0))
cv.Set2D(A, 1, 2, cv.Scalar((wy-1)/2.0))
# convert 2d, 3d points to cvMats
objectPoints3d = cv.CreateMat(len(xyz), 1, cv.CV_64FC3)
imagePoints2d = cv.CreateMat(len(xyz), 1, cv.CV_64FC2)
for i in range(len(xyz)):
cv.Set2D(imagePoints2d, i, 0, cv.Scalar(*q2d[i]))
cv.Set2D(objectPoints3d, i, 0, cv.Scalar(*xyz[i]))
# set initial rotation and translation vectors, distortion coefficients
coeff = cv.CreateMat(4, 1, cv.CV_64F)
cv.SetZero(coeff)
rmat = cv.CreateMat(3, 3, cv.CV_64F)
cv.Set2D(rmat, 0, 0, cv.Scalar(tRhat[0,0]))
cv.Set2D(rmat, 0, 1, cv.Scalar(tRhat[0,1]))
cv.Set2D(rmat, 0, 2, cv.Scalar(tRhat[0,2]))
cv.Set2D(rmat, 1, 0, cv.Scalar(tRhat[1,0]))
cv.Set2D(rmat, 1, 1, cv.Scalar(tRhat[1,1]))
cv.Set2D(rmat, 1, 2, cv.Scalar(tRhat[1,2]))
cv.Set2D(rmat, 2, 0, cv.Scalar(tRhat[2,0]))
cv.Set2D(rmat, 2, 1, cv.Scalar(tRhat[2,1]))
cv.Set2D(rmat, 2, 2, cv.Scalar(tRhat[2,2]))
print 'YPR init for PnP'
print 180*np.array(geom.ypr_fromR(Rhat))/np.pi
rvec = cv.CreateMat(3, 1, cv.CV_64F)
cv.SetZero(rvec)
cv.Rodrigues2(rmat,rvec) # convert from rotation matrix to Rodrigues vector
tvec = cv.CreateMat(3, 1, cv.CV_64F)
cv.SetZero(tvec)
# solvepnp
ret = cv.FindExtrinsicCameraParams2(objectPoints3d, imagePoints2d, A,
coeff, rvec, tvec, useExtrinsicGuess=False)
np_rvec = np.matrix(rvec).A
cv.Rodrigues2(rvec,rmat)
np_rmat = np.transpose(np.matrix(rmat)).A
np_tvec = np.dot(np_rmat,np.squeeze(np.matrix(tvec).A))
print 'Rq from PnP'
print np_rmat
print 'YPR from PnP'
print 180*np.array(geom.ypr_fromR(np_rmat))/np.pi
return np_rmat, np_tvec
def dbsolve(C, Rhat, matches, dbsiftpath, dbimgpath):
# Get image information.
info = os.path.join(C.infodir, os.path.basename(dbimgpath)[:-4] + '.info')
dbsource = render_tags.EarthmineImageInfo(dbimgpath, info)
map3d = C.pixelmap.open(dbsiftpath)
# Get 2d pixels and 3d locations of feature inliers
matches = [(m['query'][0],m['query'][1],m['db'][0],m['db'][1]) for m in matches]
matches = list(set(matches))
db2d = [arr([m[2],m[3]]) for m in matches]
db3d = [map3d[int(d[0]),int(d[1])] for d in db2d]
i = 0
while i < len(db3d):
if db3d[i] is None:
db2d.pop(i)
db3d.pop(i)
else:
i = i+1
olat,olon,oalt = dbsource.lat,dbsource.lon,dbsource.alt
zx = [geom.lltom(olat,olon,d['lat'],d['lon']) for d in db3d]
y = [dbsource.alt-d['alt'] for d in db3d]
xyz = [[zx[i][1],y[i],zx[i][0]] for i in range(len(y))]
# reference camera matrix Kd
wx,wy = dbsource.image.size
fov = dbsource.fov
f = (wx-1)/(2*np.tan(fov/2))
# f 0 cx
# 0 f cy
# 0 0 1
A = cv.CreateMat(3, 3, cv.CV_64F)
cv.SetZero(A)
cv.Set2D(A, 0, 0, cv.Scalar(f))
cv.Set2D(A, 1, 1, cv.Scalar(f))
cv.Set2D(A, 2, 2, cv.Scalar(1))
cv.Set2D(A, 0, 2, cv.Scalar((wx-1)/2.0))
cv.Set2D(A, 1, 2, cv.Scalar((wy-1)/2.0))
# convert 2d, 3d points to cvMats
objectPoints3d = cv.CreateMat(len(xyz), 1, cv.CV_64FC3)
imagePoints2d = cv.CreateMat(len(xyz), 1, cv.CV_64FC2)
for i in range(len(xyz)):
cv.Set2D(imagePoints2d, i, 0, cv.Scalar(*db2d[i]))
cv.Set2D(objectPoints3d, i, 0, cv.Scalar(*xyz[i]))
# set initial rotation and translation vectors, distortion coefficients
coeff = cv.CreateMat(4, 1, cv.CV_64F)
cv.SetZero(coeff)
rmat = cv.CreateMat(3, 3, cv.CV_64F)
tRhat = tp(Rhat)
cv.Set2D(rmat, 0, 0, cv.Scalar(tRhat[0,0]))
cv.Set2D(rmat, 0, 1, cv.Scalar(tRhat[0,1]))
cv.Set2D(rmat, 0, 2, cv.Scalar(tRhat[0,2]))
cv.Set2D(rmat, 1, 0, cv.Scalar(tRhat[1,0]))
cv.Set2D(rmat, 1, 1, cv.Scalar(tRhat[1,1]))
cv.Set2D(rmat, 1, 2, cv.Scalar(tRhat[1,2]))
cv.Set2D(rmat, 2, 0, cv.Scalar(tRhat[2,0]))
cv.Set2D(rmat, 2, 1, cv.Scalar(tRhat[2,1]))
cv.Set2D(rmat, 2, 2, cv.Scalar(tRhat[2,2]))
rvec = cv.CreateMat(3, 1, cv.CV_64F)
cv.SetZero(rvec)
cv.Rodrigues2(rmat,rvec) # convert from rotation matrix to Rodrigues vector
tvec = cv.CreateMat(3, 1, cv.CV_64F)
cv.SetZero(tvec)
#print Rhat
# solvepnp
ret = cv.FindExtrinsicCameraParams2(objectPoints3d, imagePoints2d, A,
coeff, rvec, tvec, useExtrinsicGuess=False)
np_rvec = np.matrix(rvec).A
cv.Rodrigues2(rvec,rmat)
np_rmat = np.transpose(np.matrix(rmat)).A
np_tvec = np.dot(np_rmat,np.squeeze(np.matrix(tvec).A))
return np_rmat, np_tvec
