def __init__(self, cameras, baseline_guesses, estimateLandmarks=False, verbose=False, useBlakeZissermanMest=True):
self.cameras = cameras
self.useBlakeZissermanMest = useBlakeZissermanMest
#create the incremental estimator
self.estimator = ic.IncrementalEstimator(CALIBRATION_GROUP_ID)
self.linearSolverOptions = self.estimator.getLinearSolverOptions()
self.optimizerOptions = self.estimator.getOptimizerOptions()
self.target = CalibrationTarget(cameras[0].ctarget.detector.target(), estimateLandmarks=estimateLandmarks)
self.initializeBaselineDVs(baseline_guesses)
#storage for the used views
self.views = list()
def recoverCovariance(self):
#Covariance ordering (=dv ordering)
#ORDERING: N=num cams
# 1. transformation imu-cam0 --> 6
# 2. camera time2imu --> 1*numCams (only if enabled)
print "Recovering covariance..."
estimator = inc.IncrementalEstimator(CALIBRATION_GROUP_ID)
rval = estimator.addBatch(self.problem, True)
est_stds = np.sqrt(estimator.getSigma2Theta().diagonal())
#split and store the variance
self.std_trafo_ic = np.array(est_stds[0:6])
self.std_times = np.array(est_stds[6:])
