def solveFullBatch(cameras, baseline_guesses, graph):
############################################
## solve the bundle adjustment
############################################
problem = aopt.OptimizationProblem()
#add camera dvs
for cam in cameras:
cam.setDvActiveStatus(True, True, False)
problem.addDesignVariable(cam.dv.distortionDesignVariable())
problem.addDesignVariable(cam.dv.projectionDesignVariable())
problem.addDesignVariable(cam.dv.shutterDesignVariable())
baseline_dvs = list()
for baseline_idx in range(0, len(cameras) - 1):
baseline_dv = aopt.TransformationDv(baseline_guesses[baseline_idx])
for i in range(0, baseline_dv.numDesignVariables()):
problem.addDesignVariable(baseline_dv.getDesignVariable(i))
baseline_dvs.append(baseline_dv)
#corner uncertainty
cornerUncertainty = 1.0
R = np.eye(2) * cornerUncertainty * cornerUncertainty
invR = np.linalg.inv(R)
#get the target
target = cameras[0].ctarget.detector.target()
#Add calibration target reprojection error terms for all camera in chain
target_pose_dvs = list()
#shuffle the views
reprojectionErrors = []
timestamps = graph.obs_db.getAllViewTimestamps()
for view_id, timestamp in enumerate(timestamps):
#get all observations for all cams at this time
obs_tuple = graph.obs_db.getAllObsAtTimestamp(timestamp)
#create a target pose dv for all target views (= T_cam0_w)
T0 = graph.getTargetPoseGuess(timestamp, cameras, baseline_guesses)
target_pose_dv = addPoseDesignVariable(problem, T0)
target_pose_dvs.append(target_pose_dv)
for cidx, obs in obs_tuple:
cam = cameras[cidx]
#calibration target coords to camera X coords
T_cam0_calib = target_pose_dv.toExpression().inverse()
#build pose chain (target->cam0->baselines->camN)
T_camN_calib = T_cam0_calib
for idx in range(0, cidx):
T_camN_calib = baseline_dvs[idx].toExpression() * T_camN_calib
## add error terms
for i in range(0, target.size()):
p_target = aopt.HomogeneousExpression(
sm.toHomogeneous(target.point(i)))
valid, y = obs.imagePoint(i)
if valid:
rerr = cameras[cidx].model.reprojectionError(
y, invR, T_camN_calib * p_target, cameras[cidx].dv)
problem.addErrorTerm(rerr)
reprojectionErrors.append(rerr)
sm.logDebug("solveFullBatch: added {0} camera error terms".format(
len(reprojectionErrors)))
############################################
## solve
############################################
options = aopt.Optimizer2Options()
options.verbose = True if sm.getLoggingLevel(
) == sm.LoggingLevel.Debug else False
options.nThreads = 4
options.convergenceDeltaX = 1e-3
options.convergenceDeltaJ = 1
options.maxIterations = 250
options.trustRegionPolicy = aopt.LevenbergMarquardtTrustRegionPolicy(10)
optimizer = aopt.Optimizer2(options)
optimizer.setProblem(problem)
#verbose output
if sm.getLoggingLevel() == sm.LoggingLevel.Debug:
sm.logDebug("Before optimization:")
e2 = np.array([e.evaluateError() for e in reprojectionErrors])
sm.logDebug(
" Reprojection error squarred (camL): mean {0}, median {1}, std: {2}"
.format(np.mean(e2), np.median(e2), np.std(e2)))
#run intrinsic calibration
try:
retval = optimizer.optimize()
if retval.linearSolverFailure:
sm.logError("calibrateIntrinsics: Optimization failed!")
success = not retval.linearSolverFailure
except:
sm.logError("calibrateIntrinsics: Optimization failed!")
success = False
baselines = list()
for baseline_dv in baseline_dvs:
baselines.append(sm.Transformation(baseline_dv.T()))
return success, baselines
def calibrateIntrinsics(cam_geometry,
obslist,
distortionActive=True,
intrinsicsActive=True):
#verbose output
if sm.getLoggingLevel() == sm.LoggingLevel.Debug:
d = cam_geometry.geometry.projection().distortion().getParameters(
).flatten()
p = cam_geometry.geometry.projection().getParameters().flatten()
sm.logDebug("calibrateIntrinsics: intrinsics guess: {0}".format(p))
sm.logDebug("calibrateIntrinsics: distortion guess: {0}".format(d))
############################################
## solve the bundle adjustment
############################################
problem = aopt.OptimizationProblem()
#add camera dvs
cam_geometry.setDvActiveStatus(intrinsicsActive, distortionActive, False)
problem.addDesignVariable(cam_geometry.dv.distortionDesignVariable())
problem.addDesignVariable(cam_geometry.dv.projectionDesignVariable())
problem.addDesignVariable(cam_geometry.dv.shutterDesignVariable())
#corner uncertainty
cornerUncertainty = 1.0
R = np.eye(2) * cornerUncertainty * cornerUncertainty
invR = np.linalg.inv(R)
#get the image and target points corresponding to the frame
target = cam_geometry.ctarget.detector.target()
#target pose dv for all target views (=T_camL_w)
reprojectionErrors = []
sm.logDebug(
"calibrateIntrinsics: adding camera error terms for {0} calibration targets"
.format(len(obslist)))
target_pose_dvs = list()
for obs in obslist:
success, T_t_c = cam_geometry.geometry.estimateTransformation(obs)
target_pose_dv = addPoseDesignVariable(problem, T_t_c)
target_pose_dvs.append(target_pose_dv)
T_cam_w = target_pose_dv.toExpression().inverse()
## add error terms
for i in range(0, target.size()):
p_target = aopt.HomogeneousExpression(
sm.toHomogeneous(target.point(i)))
valid, y = obs.imagePoint(i)
if valid:
rerr = cam_geometry.model.reprojectionError(
y, invR, T_cam_w * p_target, cam_geometry.dv)
problem.addErrorTerm(rerr)
reprojectionErrors.append(rerr)
sm.logDebug("calibrateIntrinsics: added {0} camera error terms".format(
len(reprojectionErrors)))
############################################
## solve
############################################
options = aopt.Optimizer2Options()
options.verbose = True if sm.getLoggingLevel(
) == sm.LoggingLevel.Debug else False
options.nThreads = 4
options.convergenceDeltaX = 1e-3
options.convergenceDeltaJ = 1
options.maxIterations = 200
options.trustRegionPolicy = aopt.LevenbergMarquardtTrustRegionPolicy(10)
optimizer = aopt.Optimizer2(options)
optimizer.setProblem(problem)
#verbose output
if sm.getLoggingLevel() == sm.LoggingLevel.Debug:
sm.logDebug("Before optimization:")
e2 = np.array([e.evaluateError() for e in reprojectionErrors])
sm.logDebug(
" Reprojection error squarred (camL): mean {0}, median {1}, std: {2}"
.format(np.mean(e2), np.median(e2), np.std(e2)))
#run intrinsic calibration
try:
retval = optimizer.optimize()
if retval.linearSolverFailure:
sm.logError("calibrateIntrinsics: Optimization failed!")
success = not retval.linearSolverFailure
except:
sm.logError("calibrateIntrinsics: Optimization failed!")
success = False
#verbose output
if sm.getLoggingLevel() == sm.LoggingLevel.Debug:
d = cam_geometry.geometry.projection().distortion().getParameters(
).flatten()
p = cam_geometry.geometry.projection().getParameters().flatten()
sm.logDebug(
"calibrateIntrinsics: guess for intrinsics cam: {0}".format(p))
sm.logDebug(
"calibrateIntrinsics: guess for distortion cam: {0}".format(d))
return success
def stereoCalibrate(camL_geometry,
camH_geometry,
obslist,
distortionActive=False,
baseline=None):
#####################################################
## find initial guess as median of all pnp solutions
#####################################################
if baseline is None:
r = []
t = []
for obsL, obsH in obslist:
#if we have observations for both camss
if obsL is not None and obsH is not None:
success, T_L = camL_geometry.geometry.estimateTransformation(
obsL)
success, T_H = camH_geometry.geometry.estimateTransformation(
obsH)
baseline = T_H.inverse() * T_L
t.append(baseline.t())
rv = sm.RotationVector()
r.append(rv.rotationMatrixToParameters(baseline.C()))
r_median = np.median(np.asmatrix(r), axis=0).flatten().T
R_median = rv.parametersToRotationMatrix(r_median)
t_median = np.median(np.asmatrix(t), axis=0).flatten().T
baseline_HL = sm.Transformation(sm.rt2Transform(R_median, t_median))
else:
baseline_HL = baseline
#verbose output
if sm.getLoggingLevel() == sm.LoggingLevel.Debug:
dL = camL_geometry.geometry.projection().distortion().getParameters(
).flatten()
pL = camL_geometry.geometry.projection().getParameters().flatten()
dH = camH_geometry.geometry.projection().distortion().getParameters(
).flatten()
pH = camH_geometry.geometry.projection().getParameters().flatten()
sm.logDebug("initial guess for stereo calib: {0}".format(
baseline_HL.T()))
sm.logDebug("initial guess for intrinsics camL: {0}".format(pL))
sm.logDebug("initial guess for intrinsics camH: {0}".format(pH))
sm.logDebug("initial guess for distortion camL: {0}".format(dL))
sm.logDebug("initial guess for distortion camH: {0}".format(dH))
############################################
## solve the bundle adjustment
############################################
problem = aopt.OptimizationProblem()
#baseline design variable
baseline_dv = addPoseDesignVariable(problem, baseline_HL)
#target pose dv for all target views (=T_camL_w)
target_pose_dvs = list()
for obsL, obsH in obslist:
if obsL is not None: #use camL if we have an obs for this one
success, T_t_cL = camL_geometry.geometry.estimateTransformation(
obsL)
else:
success, T_t_cH = camH_geometry.geometry.estimateTransformation(
obsH)
T_t_cL = T_t_cH * baseline_HL #apply baseline for the second camera
target_pose_dv = addPoseDesignVariable(problem, T_t_cL)
target_pose_dvs.append(target_pose_dv)
#add camera dvs
camL_geometry.setDvActiveStatus(True, distortionActive, False)
camH_geometry.setDvActiveStatus(True, distortionActive, False)
problem.addDesignVariable(camL_geometry.dv.distortionDesignVariable())
problem.addDesignVariable(camL_geometry.dv.projectionDesignVariable())
problem.addDesignVariable(camL_geometry.dv.shutterDesignVariable())
problem.addDesignVariable(camH_geometry.dv.distortionDesignVariable())
problem.addDesignVariable(camH_geometry.dv.projectionDesignVariable())
problem.addDesignVariable(camH_geometry.dv.shutterDesignVariable())
############################################
## add error terms
############################################
#corner uncertainty
# \todo pass in the detector uncertainty somehow.
cornerUncertainty = 1.0
R = np.eye(2) * cornerUncertainty * cornerUncertainty
invR = np.linalg.inv(R)
#Add reprojection error terms for both cameras
reprojectionErrors0 = []
reprojectionErrors1 = []
for cidx, cam in enumerate([camL_geometry, camH_geometry]):
sm.logDebug(
"stereoCalibration: adding camera error terms for {0} calibration targets"
.format(len(obslist)))
#get the image and target points corresponding to the frame
target = cam.ctarget.detector.target()
#add error terms for all observations
for view_id, obstuple in enumerate(obslist):
#add error terms if we have an observation for this cam
obs = obstuple[cidx]
if obs is not None:
T_cam_w = target_pose_dvs[view_id].toExpression().inverse()
#add the baseline for the second camera
if cidx != 0:
T_cam_w = baseline_dv.toExpression() * T_cam_w
for i in range(0, target.size()):
p_target = aopt.HomogeneousExpression(
sm.toHomogeneous(target.point(i)))
valid, y = obs.imagePoint(i)
if valid:
# Create an error term.
rerr = cam.model.reprojectionError(
y, invR, T_cam_w * p_target, cam.dv)
rerr.idx = i
problem.addErrorTerm(rerr)
if cidx == 0:
reprojectionErrors0.append(rerr)
else:
reprojectionErrors1.append(rerr)
sm.logDebug("stereoCalibrate: added {0} camera error terms".format(
len(reprojectionErrors0) + len(reprojectionErrors1)))
############################################
## solve
############################################
options = aopt.Optimizer2Options()
options.verbose = True if sm.getLoggingLevel(
) == sm.LoggingLevel.Debug else False
options.nThreads = 4
options.convergenceDeltaX = 1e-3
options.convergenceDeltaJ = 1
options.maxIterations = 200
options.trustRegionPolicy = aopt.LevenbergMarquardtTrustRegionPolicy(10)
optimizer = aopt.Optimizer2(options)
optimizer.setProblem(problem)
#verbose output
if sm.getLoggingLevel() == sm.LoggingLevel.Debug:
sm.logDebug("Before optimization:")
e2 = np.array([e.evaluateError() for e in reprojectionErrors0])
sm.logDebug(
" Reprojection error squarred (camL): mean {0}, median {1}, std: {2}"
.format(np.mean(e2), np.median(e2), np.std(e2)))
e2 = np.array([e.evaluateError() for e in reprojectionErrors1])
sm.logDebug(
" Reprojection error squarred (camH): mean {0}, median {1}, std: {2}"
.format(np.mean(e2), np.median(e2), np.std(e2)))
sm.logDebug("baseline={0}".format(
baseline_dv.toTransformationMatrix()))
try:
retval = optimizer.optimize()
if retval.linearSolverFailure:
sm.logError("stereoCalibrate: Optimization failed!")
success = not retval.linearSolverFailure
except:
sm.logError("stereoCalibrate: Optimization failed!")
success = False
if sm.getLoggingLevel() == sm.LoggingLevel.Debug:
sm.logDebug("After optimization:")
e2 = np.array([e.evaluateError() for e in reprojectionErrors0])
sm.logDebug(
" Reprojection error squarred (camL): mean {0}, median {1}, std: {2}"
.format(np.mean(e2), np.median(e2), np.std(e2)))
e2 = np.array([e.evaluateError() for e in reprojectionErrors1])
sm.logDebug(
" Reprojection error squarred (camH): mean {0}, median {1}, std: {2}"
.format(np.mean(e2), np.median(e2), np.std(e2)))
#verbose output
if sm.getLoggingLevel() == sm.LoggingLevel.Debug:
dL = camL_geometry.geometry.projection().distortion().getParameters(
).flatten()
pL = camL_geometry.geometry.projection().getParameters().flatten()
dH = camH_geometry.geometry.projection().distortion().getParameters(
).flatten()
pH = camH_geometry.geometry.projection().getParameters().flatten()
sm.logDebug("guess for intrinsics camL: {0}".format(pL))
sm.logDebug("guess for intrinsics camH: {0}".format(pH))
sm.logDebug("guess for distortion camL: {0}".format(dL))
sm.logDebug("guess for distortion camH: {0}".format(dH))
if success:
baseline_HL = sm.Transformation(baseline_dv.toTransformationMatrix())
return success, baseline_HL
else:
#return the initial guess if we fail
return success, baseline_HL
def solveFullBatch(cameras, baseline_guesses, graph):
############################################
## solve the bundle adjustment
############################################
problem = aopt.OptimizationProblem()
#add camera dvs
for cam in cameras:
cam.setDvActiveStatus(True, True, False)
problem.addDesignVariable(cam.dv.distortionDesignVariable())
problem.addDesignVariable(cam.dv.projectionDesignVariable())
problem.addDesignVariable(cam.dv.shutterDesignVariable())
baseline_dvs = list()
for baseline_idx in range(0, len(cameras)-1):
baseline_dv = aopt.TransformationDv(baseline_guesses[baseline_idx])
for i in range(0, baseline_dv.numDesignVariables()):
problem.addDesignVariable(baseline_dv.getDesignVariable(i))
baseline_dvs.append( baseline_dv )
#corner uncertainty
cornerUncertainty = 1.0
R = np.eye(2) * cornerUncertainty * cornerUncertainty
invR = np.linalg.inv(R)
#get the target
target = cameras[0].ctarget.detector.target()
#Add calibration target reprojection error terms for all camera in chain
target_pose_dvs = list()
#shuffle the views
reprojectionErrors = [];
timestamps = graph.obs_db.getAllViewTimestamps()
for view_id, timestamp in enumerate(timestamps):
#get all observations for all cams at this time
obs_tuple = graph.obs_db.getAllObsAtTimestamp(timestamp)
#create a target pose dv for all target views (= T_cam0_w)
T0 = graph.getTargetPoseGuess(timestamp, cameras, baseline_guesses)
target_pose_dv = addPoseDesignVariable(problem, T0)
target_pose_dvs.append(target_pose_dv)
for cidx, obs in obs_tuple:
cam = cameras[cidx]
#calibration target coords to camera X coords
T_cam0_calib = target_pose_dv.toExpression().inverse()
#build pose chain (target->cam0->baselines->camN)
T_camN_calib = T_cam0_calib
for idx in range(0, cidx):
T_camN_calib = baseline_dvs[idx].toExpression() * T_camN_calib
## add error terms
for i in range(0, target.size()):
p_target = aopt.HomogeneousExpression(sm.toHomogeneous(target.point(i)));
valid, y = obs.imagePoint(i)
if valid:
rerr = cameras[cidx].model.reprojectionError(y, invR, T_camN_calib * p_target, cameras[cidx].dv)
problem.addErrorTerm(rerr)
reprojectionErrors.append(rerr)
sm.logDebug("solveFullBatch: added {0} camera error terms".format(len(reprojectionErrors)))
############################################
## solve
############################################
options = aopt.Optimizer2Options()
options.verbose = True if sm.getLoggingLevel()==sm.LoggingLevel.Debug else False
options.nThreads = 4
options.convergenceDeltaX = 1e-3
options.convergenceDeltaJ = 1
options.maxIterations = 250
options.trustRegionPolicy = aopt.LevenbergMarquardtTrustRegionPolicy(10)
optimizer = aopt.Optimizer2(options)
optimizer.setProblem(problem)
#verbose output
if sm.getLoggingLevel()==sm.LoggingLevel.Debug:
sm.logDebug("Before optimization:")
e2 = np.array([ e.evaluateError() for e in reprojectionErrors ])
sm.logDebug( " Reprojection error squarred (camL): mean {0}, median {1}, std: {2}".format(np.mean(e2), np.median(e2), np.std(e2) ) )
#run intrinsic calibration
try:
retval = optimizer.optimize()
if retval.linearSolverFailure:
sm.logError("calibrateIntrinsics: Optimization failed!")
success = not retval.linearSolverFailure
except:
sm.logError("calibrateIntrinsics: Optimization failed!")
success = False
baselines=list()
for baseline_dv in baseline_dvs:
baselines.append( sm.Transformation(baseline_dv.T()) )
return success, baselines
def calibrateIntrinsics(cam_geometry, obslist, distortionActive=True, intrinsicsActive=True):
#verbose output
if sm.getLoggingLevel()==sm.LoggingLevel.Debug:
d = cam_geometry.geometry.projection().distortion().getParameters().flatten()
p = cam_geometry.geometry.projection().getParameters().flatten()
sm.logDebug("calibrateIntrinsics: intrinsics guess: {0}".format(p))
sm.logDebug("calibrateIntrinsics: distortion guess: {0}".format(d))
############################################
## solve the bundle adjustment
############################################
problem = aopt.OptimizationProblem()
#add camera dvs
cam_geometry.setDvActiveStatus(intrinsicsActive, distortionActive, False)
problem.addDesignVariable(cam_geometry.dv.distortionDesignVariable())
problem.addDesignVariable(cam_geometry.dv.projectionDesignVariable())
problem.addDesignVariable(cam_geometry.dv.shutterDesignVariable())
#corner uncertainty
cornerUncertainty = 1.0
R = np.eye(2) * cornerUncertainty * cornerUncertainty
invR = np.linalg.inv(R)
#get the image and target points corresponding to the frame
target = cam_geometry.ctarget.detector.target()
#target pose dv for all target views (=T_camL_w)
reprojectionErrors = [];
sm.logDebug("calibrateIntrinsics: adding camera error terms for {0} calibration targets".format(len(obslist)))
target_pose_dvs=list()
for obs in obslist:
success, T_t_c = cam_geometry.geometry.estimateTransformation(obs)
target_pose_dv = addPoseDesignVariable(problem, T_t_c)
target_pose_dvs.append(target_pose_dv)
T_cam_w = target_pose_dv.toExpression().inverse()
## add error terms
for i in range(0, target.size()):
p_target = aopt.HomogeneousExpression(sm.toHomogeneous(target.point(i)));
valid, y = obs.imagePoint(i)
if valid:
rerr = cam_geometry.model.reprojectionError(y, invR, T_cam_w * p_target, cam_geometry.dv)
problem.addErrorTerm(rerr)
reprojectionErrors.append(rerr)
sm.logDebug("calibrateIntrinsics: added {0} camera error terms".format(len(reprojectionErrors)))
############################################
## solve
############################################
options = aopt.Optimizer2Options()
options.verbose = True if sm.getLoggingLevel()==sm.LoggingLevel.Debug else False
options.nThreads = 4
options.convergenceDeltaX = 1e-3
options.convergenceDeltaJ = 1
options.maxIterations = 200
options.trustRegionPolicy = aopt.LevenbergMarquardtTrustRegionPolicy(10)
optimizer = aopt.Optimizer2(options)
optimizer.setProblem(problem)
#verbose output
if sm.getLoggingLevel()==sm.LoggingLevel.Debug:
sm.logDebug("Before optimization:")
e2 = np.array([ e.evaluateError() for e in reprojectionErrors ])
sm.logDebug( " Reprojection error squarred (camL): mean {0}, median {1}, std: {2}".format(np.mean(e2), np.median(e2), np.std(e2) ) )
#run intrinsic calibration
try:
retval = optimizer.optimize()
if retval.linearSolverFailure:
sm.logError("calibrateIntrinsics: Optimization failed!")
success = not retval.linearSolverFailure
except:
sm.logError("calibrateIntrinsics: Optimization failed!")
success = False
#verbose output
if sm.getLoggingLevel()==sm.LoggingLevel.Debug:
d = cam_geometry.geometry.projection().distortion().getParameters().flatten()
p = cam_geometry.geometry.projection().getParameters().flatten()
sm.logDebug("calibrateIntrinsics: guess for intrinsics cam: {0}".format(p))
sm.logDebug("calibrateIntrinsics: guess for distortion cam: {0}".format(d))
return success
def stereoCalibrate(camL_geometry, camH_geometry, obslist, distortionActive=False, baseline=None):
#####################################################
## find initial guess as median of all pnp solutions
#####################################################
if baseline is None:
r=[]; t=[]
for obsL, obsH in obslist:
#if we have observations for both camss
if obsL is not None and obsH is not None:
success, T_L = camL_geometry.geometry.estimateTransformation(obsL)
success, T_H = camH_geometry.geometry.estimateTransformation(obsH)
baseline = T_H.inverse()*T_L
t.append(baseline.t())
rv=sm.RotationVector()
r.append(rv.rotationMatrixToParameters( baseline.C() ))
r_median = np.median(np.asmatrix(r), axis=0).flatten().T
R_median = rv.parametersToRotationMatrix(r_median)
t_median = np.median(np.asmatrix(t), axis=0).flatten().T
baseline_HL = sm.Transformation( sm.rt2Transform(R_median, t_median) )
else:
baseline_HL = baseline
#verbose output
if sm.getLoggingLevel()==sm.LoggingLevel.Debug:
dL = camL_geometry.geometry.projection().distortion().getParameters().flatten()
pL = camL_geometry.geometry.projection().getParameters().flatten()
dH = camH_geometry.geometry.projection().distortion().getParameters().flatten()
pH = camH_geometry.geometry.projection().getParameters().flatten()
sm.logDebug("initial guess for stereo calib: {0}".format(baseline_HL.T()))
sm.logDebug("initial guess for intrinsics camL: {0}".format(pL))
sm.logDebug("initial guess for intrinsics camH: {0}".format(pH))
sm.logDebug("initial guess for distortion camL: {0}".format(dL))
sm.logDebug("initial guess for distortion camH: {0}".format(dH))
############################################
## solve the bundle adjustment
############################################
problem = aopt.OptimizationProblem()
#baseline design variable
baseline_dv = addPoseDesignVariable(problem, baseline_HL)
#target pose dv for all target views (=T_camL_w)
target_pose_dvs = list()
for obsL, obsH in obslist:
if obsL is not None: #use camL if we have an obs for this one
success, T_t_cL = camL_geometry.geometry.estimateTransformation(obsL)
else:
success, T_t_cH = camH_geometry.geometry.estimateTransformation(obsH)
T_t_cL = T_t_cH*baseline_HL #apply baseline for the second camera
target_pose_dv = addPoseDesignVariable(problem, T_t_cL)
target_pose_dvs.append(target_pose_dv)
#add camera dvs
camL_geometry.setDvActiveStatus(True, distortionActive, False)
camH_geometry.setDvActiveStatus(True, distortionActive, False)
problem.addDesignVariable(camL_geometry.dv.distortionDesignVariable())
problem.addDesignVariable(camL_geometry.dv.projectionDesignVariable())
problem.addDesignVariable(camL_geometry.dv.shutterDesignVariable())
problem.addDesignVariable(camH_geometry.dv.distortionDesignVariable())
problem.addDesignVariable(camH_geometry.dv.projectionDesignVariable())
problem.addDesignVariable(camH_geometry.dv.shutterDesignVariable())
############################################
## add error terms
############################################
#corner uncertainty
# \todo pass in the detector uncertainty somehow.
cornerUncertainty = 1.0
R = np.eye(2) * cornerUncertainty * cornerUncertainty
invR = np.linalg.inv(R)
#Add reprojection error terms for both cameras
reprojectionErrors0 = []; reprojectionErrors1 = []
for cidx, cam in enumerate([camL_geometry, camH_geometry]):
sm.logDebug("stereoCalibration: adding camera error terms for {0} calibration targets".format(len(obslist)))
#get the image and target points corresponding to the frame
target = cam.ctarget.detector.target()
#add error terms for all observations
for view_id, obstuple in enumerate(obslist):
#add error terms if we have an observation for this cam
obs=obstuple[cidx]
if obs is not None:
T_cam_w = target_pose_dvs[view_id].toExpression().inverse()
#add the baseline for the second camera
if cidx!=0:
T_cam_w = baseline_dv.toExpression() * T_cam_w
for i in range(0, target.size()):
p_target = aopt.HomogeneousExpression(sm.toHomogeneous(target.point(i)));
valid, y = obs.imagePoint(i)
if valid:
# Create an error term.
rerr = cam.model.reprojectionError(y, invR, T_cam_w * p_target, cam.dv)
rerr.idx = i
problem.addErrorTerm(rerr)
if cidx==0:
reprojectionErrors0.append(rerr)
else:
reprojectionErrors1.append(rerr)
sm.logDebug("stereoCalibrate: added {0} camera error terms".format( len(reprojectionErrors0)+len(reprojectionErrors1) ))
############################################
## solve
############################################
options = aopt.Optimizer2Options()
options.verbose = True if sm.getLoggingLevel()==sm.LoggingLevel.Debug else False
options.nThreads = 4
options.convergenceDeltaX = 1e-3
options.convergenceDeltaJ = 1
options.maxIterations = 200
options.trustRegionPolicy = aopt.LevenbergMarquardtTrustRegionPolicy(10)
optimizer = aopt.Optimizer2(options)
optimizer.setProblem(problem)
#verbose output
if sm.getLoggingLevel()==sm.LoggingLevel.Debug:
sm.logDebug("Before optimization:")
e2 = np.array([ e.evaluateError() for e in reprojectionErrors0 ])
sm.logDebug( " Reprojection error squarred (camL): mean {0}, median {1}, std: {2}".format(np.mean(e2), np.median(e2), np.std(e2) ) )
e2 = np.array([ e.evaluateError() for e in reprojectionErrors1 ])
sm.logDebug( " Reprojection error squarred (camH): mean {0}, median {1}, std: {2}".format(np.mean(e2), np.median(e2), np.std(e2) ) )
sm.logDebug("baseline={0}".format(baseline_dv.toTransformationMatrix()))
try:
retval = optimizer.optimize()
if retval.linearSolverFailure:
sm.logError("stereoCalibrate: Optimization failed!")
success = not retval.linearSolverFailure
except:
sm.logError("stereoCalibrate: Optimization failed!")
success = False
if sm.getLoggingLevel()==sm.LoggingLevel.Debug:
sm.logDebug("After optimization:")
e2 = np.array([ e.evaluateError() for e in reprojectionErrors0 ])
sm.logDebug( " Reprojection error squarred (camL): mean {0}, median {1}, std: {2}".format(np.mean(e2), np.median(e2), np.std(e2) ) )
e2 = np.array([ e.evaluateError() for e in reprojectionErrors1 ])
sm.logDebug( " Reprojection error squarred (camH): mean {0}, median {1}, std: {2}".format(np.mean(e2), np.median(e2), np.std(e2) ) )
#verbose output
if sm.getLoggingLevel()==sm.LoggingLevel.Debug:
dL = camL_geometry.geometry.projection().distortion().getParameters().flatten()
pL = camL_geometry.geometry.projection().getParameters().flatten()
dH = camH_geometry.geometry.projection().distortion().getParameters().flatten()
pH = camH_geometry.geometry.projection().getParameters().flatten()
sm.logDebug("guess for intrinsics camL: {0}".format(pL))
sm.logDebug("guess for intrinsics camH: {0}".format(pH))
sm.logDebug("guess for distortion camL: {0}".format(dL))
sm.logDebug("guess for distortion camH: {0}".format(dH))
if success:
baseline_HL = sm.Transformation(baseline_dv.toTransformationMatrix())
return success, baseline_HL
else:
#return the intiial guess if we fail
return success, baseline_HL
