frames_ref = mrcal.rt_from_Rt(Rt_cam0_board_ref)
############# I have perfect observations in q_ref. I corrupt them by noise
# weight has shape (Nframes, Ncameras, Nh, Nw),
weight01 = (np.random.rand(*q_ref.shape[:-1]) + 1.) / 2. # in [0,1]
weight0 = 0.2
weight1 = 1.0
weight = weight0 + (weight1 - weight0) * weight01
# I want observations of shape (Nframes*Ncameras, Nh, Nw, 3) where each row is
# (x,y,weight)
observations_ref = nps.clump(nps.glue(q_ref, nps.dummy(weight, -1), axis=-1),
n=2)
q_noise, observations = sample_dqref(observations_ref,
pixel_uncertainty_stdev,
make_outliers=True)
############# Now I pretend that the noisy observations are all I got, and I run
############# a calibration from those
# Dense observations. All the cameras see all the boards
indices_frame_camera = np.zeros((Nframes * Ncameras, 2), dtype=np.int32)
indices_frame = indices_frame_camera[:, 0].reshape(Nframes, Ncameras)
indices_frame.setfield(nps.outer(np.arange(Nframes, dtype=np.int32),
np.ones((Ncameras, ), dtype=np.int32)),
dtype=np.int32)
indices_camera = indices_frame_camera[:, 1].reshape(Nframes, Ncameras)
indices_camera.setfield(nps.outer(np.ones((Nframes, ), dtype=np.int32),
np.arange(Ncameras, dtype=np.int32)),
dtype=np.int32)
indices_frame = indices_frame_camera[:, 0].reshape(Nframes, Ncameras)
indices_frame.setfield(nps.outer(np.arange(Nframes, dtype=np.int32),
np.ones((Ncameras, ), dtype=np.int32)),
dtype=np.int32)
indices_camera = indices_frame_camera[:, 1].reshape(Nframes, Ncameras)
indices_camera.setfield(nps.outer(np.ones((Nframes, ), dtype=np.int32),
np.arange(Ncameras, dtype=np.int32)),
dtype=np.int32)
indices_frame_camintrinsics_camextrinsics = \
nps.glue(indices_frame_camera,
indices_frame_camera[:,(1,)] - 1,
axis=-1)
# Add a bit of noise to make my baseline not perfect
_, observations_baseline = sample_dqref(observations_ref,
pixel_uncertainty_stdev)
baseline = \
dict(intrinsics = intrinsics_ref,
extrinsics_rt_fromref = extrinsics_ref,
frames_rt_toref = frames_ref,
points = None,
observations_board = observations_baseline,
indices_frame_camintrinsics_camextrinsics = indices_frame_camintrinsics_camextrinsics,
observations_point = None,
indices_point_camintrinsics_camextrinsics = None,
lensmodel = lensmodel,
do_optimize_calobject_warp = True,
calobject_warp = calobject_warp_ref,
do_optimize_intrinsics_core = True,
do_optimize_intrinsics_distortions = True,
do_optimize_extrinsics = True,
testutils.finish()
sys.exit()
intrinsics_sampled = np.zeros((args.Nsamples, args.Ncameras, Nintrinsics),
dtype=float)
extrinsics_sampled_mounted = np.zeros((args.Nsamples, args.Ncameras, 6),
dtype=float)
frames_sampled = np.zeros((args.Nsamples, args.Nframes, 6), dtype=float)
calobject_warp_sampled = np.zeros((args.Nsamples, 2), dtype=float)
for isample in range(args.Nsamples):
print(f"Sampling {isample+1}/{args.Nsamples}")
optimization_inputs = copy.deepcopy(optimization_inputs_baseline)
optimization_inputs['observations_board'] = \
sample_dqref(observations_true, pixel_uncertainty_stdev)[1]
mrcal.optimize(**optimization_inputs)
intrinsics_sampled[isample, ...] = optimization_inputs['intrinsics']
frames_sampled[isample, ...] = optimization_inputs['frames_rt_toref']
calobject_warp_sampled[isample,
...] = optimization_inputs['calobject_warp']
if fixedframes:
extrinsics_sampled_mounted[
isample, ...] = optimization_inputs['extrinsics_rt_fromref']
else:
# the remaining row is already 0
extrinsics_sampled_mounted[
isample, 1:, ...] = optimization_inputs['extrinsics_rt_fromref']