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#!/usr/bin/env python3
r'''Basic camera-calibration test
I observe, with noise, a number of chessboards from various angles with several
cameras. And I make sure that I can more or less compute the camera intrinsics
and extrinsics
'''
import sys
import numpy as np
import numpysane as nps
import os
testdir = os.path.dirname(os.path.realpath(__file__))
# I import the LOCAL mrcal since that's what I'm testing
sys.path[:0] = f"{testdir}/..",
import mrcal
import testutils
from test_calibration_helpers import sample_dqref
import copy
# I want the RNG to be deterministic
np.random.seed(0)
############# Set up my world, and compute all the perfect positions, pixel
############# observations of everything
models_ref = ( mrcal.cameramodel(f"{testdir}/data/cam0.opencv8.cameramodel"),
mrcal.cameramodel(f"{testdir}/data/cam0.opencv8.cameramodel"),
mrcal.cameramodel(f"{testdir}/data/cam1.opencv8.cameramodel"),
mrcal.cameramodel(f"{testdir}/data/cam1.opencv8.cameramodel") )
imagersizes = nps.cat( *[m.imagersize() for m in models_ref] )
lensmodel = models_ref[0].intrinsics()[0]
# I have opencv8 models_ref, but let me truncate to opencv4 models_ref to keep this
# simple and fast
lensmodel = 'LENSMODEL_OPENCV4'
for m in models_ref:
m.intrinsics( intrinsics = (lensmodel, m.intrinsics()[1][:8]))
Nintrinsics = mrcal.lensmodel_num_params(lensmodel)
Ncameras = len(models_ref)
Nframes = 50
models_ref[0].rt_cam_ref(np.zeros((6,), dtype=float))
models_ref[1].rt_cam_ref(np.array((0.08,0.2,0.02, 1., 0.9,0.1)))
models_ref[2].rt_cam_ref(np.array((0.01,0.07,0.2, 2.1,0.4,0.2)))
models_ref[3].rt_cam_ref(np.array((-0.1,0.08,0.08, 4.4,0.2,0.1)))
pixel_uncertainty_stdev = 1.5
object_spacing = 0.1
object_width_n = 10
object_height_n = 9
calobject_warp_ref = np.array((0.002, -0.005))
# shapes (Nframes, Ncameras, Nh, Nw, 2),
# (Nframes, 4,3)
q_ref,Rt_ref_board_ref = \
mrcal.synthesize_board_observations(models_ref,
object_width_n = object_width_n,
object_height_n = object_height_n,
object_spacing = object_spacing,
calobject_warp = calobject_warp_ref,
rt_ref_boardcenter = np.array((0., 0., 0., -2, 0, 4.0)),
rt_ref_boardcenter__noiseradius = np.array((np.pi/180.*30., np.pi/180.*30., np.pi/180.*20., 2.5, 2.5, 2.0)),
Nframes = Nframes)
frames_ref = mrcal.rt_from_Rt(Rt_ref_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 make some of the observations bogus, and mark them as input outliers.
# The solve should be robust to that, but any code that uses the bogus data
# DESPITE it being marked as bogus will generate a test failure
#
# Let's pretend the center of the chessboard has an apriltag, so all those
# observations are bogus. I block out a 5x5 chunk in the center
i0 = object_height_n//2
j0 = object_width_n//2
observations[..., i0-2:i0+3,j0-2:j0+3, 2] = -1. # weight<=0: outlier
observations[..., i0-2:i0+3,j0-2:j0+3,:2] = -100.0 # all the values are bogus
############# 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_camintrinsics_camextrinsics = \
nps.glue(indices_frame_camera,
indices_frame_camera[:,(1,)]-1,
axis=-1)
intrinsics_data,rt_cam_ref,rt_ref_frame = \
mrcal.seed_stereographic(imagersizes = imagersizes,
focal_estimate = 1500,
indices_frame_camera = indices_frame_camera,
observations = observations,
object_spacing = object_spacing)
# I have a stereographic intrinsics estimate. Mount it into a full distortiony
# model, seeded with random numbers
intrinsics = np.zeros((Ncameras,Nintrinsics), dtype=float)
intrinsics[:,:4] = intrinsics_data
intrinsics[:,4:] = np.random.random( (Ncameras, intrinsics.shape[1]-4) ) * 1e-6
optimization_inputs = \
dict( intrinsics = intrinsics,
rt_cam_ref = rt_cam_ref,
rt_ref_frame = rt_ref_frame,
points = None,
observations_board = observations,
indices_frame_camintrinsics_camextrinsics = indices_frame_camintrinsics_camextrinsics,
observations_point = None,
indices_point_camintrinsics_camextrinsics = None,
lensmodel = lensmodel,
calobject_warp = None,
imagersizes = imagersizes,
calibration_object_spacing = object_spacing,
verbose = False,
do_apply_regularization = True)
# Solve this thing incrementally
optimization_inputs['do_optimize_intrinsics_core'] = False
optimization_inputs['do_optimize_intrinsics_distortions'] = False
optimization_inputs['do_optimize_extrinsics'] = True
optimization_inputs['do_optimize_frames'] = True
optimization_inputs['do_optimize_calobject_warp'] = False
mrcal.optimize(**optimization_inputs,
do_apply_outlier_rejection = True)
optimization_inputs['do_optimize_intrinsics_core'] = True
optimization_inputs['do_optimize_intrinsics_distortions'] = False
optimization_inputs['do_optimize_extrinsics'] = True
optimization_inputs['do_optimize_frames'] = True
optimization_inputs['do_optimize_calobject_warp'] = False
mrcal.optimize(**optimization_inputs,
do_apply_outlier_rejection = True)
testutils.confirm_equal( mrcal.num_states(**optimization_inputs),
4*Ncameras + 6*(Ncameras-1) + 6*Nframes,
msg="num_states()")
testutils.confirm_equal( mrcal.num_states_intrinsics(**optimization_inputs),
4*Ncameras,
msg="num_states_intrinsics()")
testutils.confirm_equal( mrcal.num_intrinsics_optimization_params(**optimization_inputs),
4,
msg="num_intrinsics_optimization_params()")
testutils.confirm_equal( mrcal.num_states_extrinsics(**optimization_inputs),
6*(Ncameras-1),
msg="num_states_extrinsics()")
testutils.confirm_equal( mrcal.num_states_frames(**optimization_inputs),
6*Nframes,
msg="num_states_frames()")
testutils.confirm_equal( mrcal.num_states_points(**optimization_inputs),
0,
msg="num_states_points()")
testutils.confirm_equal( mrcal.num_states_calobject_warp(**optimization_inputs),
0,
msg="num_states_calobject_warp()")
testutils.confirm_equal( mrcal.num_measurements_boards(**optimization_inputs),
object_width_n*object_height_n*2*Nframes*Ncameras,
msg="num_measurements_boards()")
testutils.confirm_equal( mrcal.num_measurements_points(**optimization_inputs),
0,
msg="num_measurements_points()")
testutils.confirm_equal( mrcal.num_measurements_regularization(**optimization_inputs),
Ncameras * 2,
msg="num_measurements_regularization()")
optimization_inputs['do_optimize_intrinsics_core'] = True
optimization_inputs['do_optimize_intrinsics_distortions'] = True
optimization_inputs['do_optimize_extrinsics'] = True
optimization_inputs['do_optimize_frames'] = True
optimization_inputs['do_optimize_calobject_warp'] = True
optimization_inputs['calobject_warp'] = np.array((0.001, 0.001))
stats = mrcal.optimize(**optimization_inputs,
do_apply_outlier_rejection = True)
rmserr = stats['rms_reproj_error__pixels']
testutils.confirm_equal( mrcal.state_index_intrinsics(2, **optimization_inputs),
8*2,
msg="state_index_intrinsics()")
testutils.confirm_equal( mrcal.state_index_extrinsics(2, **optimization_inputs),
8*Ncameras + 6*2,
msg="state_index_extrinsics()")
testutils.confirm_equal( mrcal.state_index_frames(2, **optimization_inputs),
8*Ncameras + 6*(Ncameras-1) + 6*2,
msg="state_index_frames()")
testutils.confirm_equal( mrcal.state_index_calobject_warp(**optimization_inputs),
8*Ncameras + 6*(Ncameras-1) + 6*Nframes,
msg="state_index_calobject_warp()")
testutils.confirm_equal( mrcal.measurement_index_boards(2, **optimization_inputs),
object_width_n*object_height_n*2* 2,
msg="measurement_index_boards()")
testutils.confirm_equal( mrcal.measurement_index_regularization(**optimization_inputs),
object_width_n*object_height_n*2*Nframes*Ncameras,
msg="measurement_index_regularization()")
############# Calibration computed. Now I see how well I did
models_solved = \
[ mrcal.cameramodel( optimization_inputs = optimization_inputs,
icam_intrinsics = i )
for i in range(Ncameras)]
if False:
for i in range(0,Ncameras):
f = f'/tmp/tst{i}.cameramodel'
models_solved[i].write(f)
print(f"Wrote '{f}'")
testutils.confirm_equal(rmserr, 0,
eps = 2.5,
msg = "Converged to a low RMS error")
testutils.confirm_equal( optimization_inputs['calobject_warp'],
calobject_warp_ref,
eps = 2e-3,
msg = "Recovered the calibration object shape" )
testutils.confirm_equal( np.std( mrcal.measurements_board(optimization_inputs,
x = stats['x'])),
pixel_uncertainty_stdev,
eps = pixel_uncertainty_stdev*0.1,
msg = "Residual have the expected distribution" )
# Checking the extrinsics. These aren't defined absolutely: each solve is free
# to put the observed frames anywhere it likes. The projection-diff code
# computes a transformation to address this. Here I simply look at the relative
# transformations between cameras, which would cancel out any extra
# transformations, AND since camera0 is fixed at the identity transformation, I
# can simply look at each extrinsics transformation.
for icam in range(1,len(models_ref)):
Rt_extrinsics_err = \
mrcal.compose_Rt( models_solved[icam].Rt_cam_ref(),
models_ref [icam].Rt_ref_cam() )
testutils.confirm_equal( nps.mag(Rt_extrinsics_err[3,:]),
0.0,
eps = 0.05,
msg = f"Recovered extrinsic translation for camera {icam}")
testutils.confirm_equal( (np.trace(Rt_extrinsics_err[:3,:]) - 1) / 2.,
1.0,
eps = np.cos(1. * np.pi/180.0), # 1 deg
msg = f"Recovered extrinsic rotation for camera {icam}")
Rt_frame_err = \
mrcal.compose_Rt( mrcal.Rt_from_rt(optimization_inputs['rt_ref_frame']),
mrcal.invert_Rt(Rt_ref_board_ref) )
testutils.confirm_equal( np.max(nps.mag(Rt_frame_err[..., 3,:])),
0.0,
eps = 0.08,
msg = "Recovered frame translation")
testutils.confirm_equal( np.min( (nps.trace(Rt_frame_err[..., :3,:]) - 1)/2. ),
1.0,
eps = np.cos(1. * np.pi/180.0), # 1 deg
msg = "Recovered frame rotation")
# Checking the intrinsics. Each intrinsics vector encodes an implicit
# transformation. I compute and apply this transformation when making my
# intrinsics comparisons. I make sure that within some distance of the pixel
# center, the projections match up to within some number of pixels
Nw = 60
def projection_diff(models_ref, max_dist_from_center):
lensmodels = [model.intrinsics()[0] for model in models_ref]
intrinsics_data = [model.intrinsics()[1] for model in models_ref]
# v shape (...,Ncameras,Nheight,Nwidth,...)
# q0 shape (..., Nheight,Nwidth,...)
v,q0 = \
mrcal.sample_imager_unproject(Nw,None,
*imagersizes[0],
lensmodels, intrinsics_data,
normalize = True)
W,H = imagersizes[0]
focus_center = None
focus_radius = -1
if focus_center is None: focus_center = ((W-1.)/2., (H-1.)/2.)
if focus_radius < 0: focus_radius = min(W,H)/6.
implied_Rt10 = \
mrcal.implied_Rt10__from_unprojections(q0,
v[0,...], v[1,...],
focus_center = focus_center,
focus_radius = focus_radius)
q1 = mrcal.project( mrcal.transform_point_Rt(implied_Rt10,
v[0,...]),
lensmodels[1], intrinsics_data[1])
diff = nps.mag(q1 - q0)
# zero-out everything too far from the center
center = (imagersizes[0] - 1.) / 2.
diff[ nps.norm2(q0 - center) > max_dist_from_center*max_dist_from_center ] = 0
# gp.plot(diff,
# ascii = True,
# using = mrcal.imagergrid_using(imagersizes[0], Nw),
# square=1, _with='image', tuplesize=3, hardcopy='/tmp/yes.gp', cbmax=3)
return diff
for icam in range(len(models_ref)):
diff = projection_diff( (models_ref[icam], models_solved[icam]), 800)
testutils.confirm_equal(diff, 0,
worstcase = True,
eps = 6.,
msg = f"Recovered intrinsics for camera {icam}")
# It would be nice to check the outlier detections, but this is iffy. Here I'm
# generating 1% outliers (hard-coded in sample_dqref()), but the outlier
# rejection is overly aggressive. I'm currently seeing 4.4%:
#
# np.count_nonzero(observations[...,2]<=0) / observations[...,0].ravel().size
#
# The outlier rejection scheme just cuts out 3sigma residuals and above, so it's
# not great. I'm not entirely sure why it's over-reporting the outliers here,
# but I should investigate that at the same time as I overhaul the outlier
# rejection scheme (presumably to use one of my flavors of Cook's D factor)
# I test make_perfect_observations(). Doing it here is easy; doing it elsewhere
# it much more work
if True:
optimization_inputs_perfect = copy.deepcopy(optimization_inputs)
mrcal.make_perfect_observations(optimization_inputs_perfect,
observed_pixel_uncertainty=0)
x = mrcal.optimizer_callback(**optimization_inputs_perfect,
no_jacobian = True,
no_factorization = True)[1]
Nmeas = mrcal.num_measurements_boards(**optimization_inputs_perfect)
if Nmeas > 0:
i_meas0 = mrcal.measurement_index_boards(0, **optimization_inputs_perfect)
testutils.confirm_equal( x[i_meas0:i_meas0+Nmeas],
0,
worstcase = True,
eps = 1e-8,
msg = f"make_perfect_observations() works for boards")
else:
testutils.confirm( False,
msg = f"Nmeasurements_boards <= 0")
testutils.finish()
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