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#!/usr/bin/env python3
r'''Study the precision and accuracy of the various triangulation routines'''
import sys
import argparse
import re
import os
def parse_args():
parser = \
argparse.ArgumentParser(description = __doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--Nsamples',
type=int,
default=100000,
help='''How many random samples to evaluate. 100000 by
default''')
group = parser.add_mutually_exclusive_group(required = True)
group.add_argument('--ellipses',
action='store_true',
help='''Display the ellipses and samples in the xy plane''')
group.add_argument('--ranges',
action='store_true',
help='''Display the distribution of the range''')
parser.add_argument('--samples',
action='store_true',
help='''If --ellipses, plot the samples ALSO. Usually
this doesn't clarify anything, so the default is to omit
them''')
parser.add_argument('--cache',
type=str,
choices=('read','write'),
help=f'''A cache file stores the recalibration results;
computing these can take a long time. This option allows
us to or write the cache instead of sampling. The cache
file is hardcoded to a cache file (in /tmp). By default,
we do neither: we don't read the cache (we sample
instead), and we do not write it to disk when we're
done. This option is useful for tests where we reprocess
the same scenario repeatedly''')
parser.add_argument('--observed-point',
type = float,
nargs = 3,
default = ( 5000., 300., 2000.),
help='''The camera0 coordinate of the observed point.
Default is ( 5000., 300., 2000.)''')
parser.add_argument('--title',
type=str,
default = None,
help='''Title string for the plot. Overrides the default
title. Exclusive with --extratitle''')
parser.add_argument('--extratitle',
type=str,
default = None,
help='''Additional string for the plot to append to the
default title. Exclusive with --title''')
parser.add_argument('--hardcopy',
type=str,
help='''Write the output to disk, instead of an interactive plot''')
parser.add_argument('--terminal',
type=str,
help=r'''gnuplotlib terminal. The default is good almost always, so most people don't
need this option''')
parser.add_argument('--set',
type=str,
action='append',
help='''Extra 'set' directives to gnuplotlib. Can be given multiple times''')
parser.add_argument('--unset',
type=str,
action='append',
help='''Extra 'unset' directives to gnuplotlib. Can be given multiple times''')
args = parser.parse_args()
if args.title is not None and \
args.extratitle is not None:
print("--title and --extratitle are exclusive", file=sys.stderr)
sys.exit(1)
return args
args = parse_args()
import numpy as np
import numpysane as nps
import gnuplotlib as gp
import pickle
import os.path
# I import the LOCAL mrcal
scriptdir = os.path.dirname(os.path.realpath(__file__))
sys.path[:0] = f"{scriptdir}/../..",
import mrcal
############ bias visualization
#
# I simulate pixel noise, and see what that does to the triangulation. Play with
# the geometric details to get a sense of how these behave
model0 = mrcal.cameramodel( intrinsics = ('LENSMODEL_PINHOLE',
np.array((1000., 1000., 500., 500.))),
imagersize = np.array((1000,1000)) )
model1 = mrcal.cameramodel( intrinsics = ('LENSMODEL_PINHOLE',
np.array((1100., 1100., 500., 500.))),
imagersize = np.array((1000,1000)) )
# square camera layout
t01 = np.array(( 1., 0.1, -0.2))
R01 = mrcal.R_from_r(np.array((0.001, -0.002, -0.003)))
Rt01 = nps.glue(R01, t01, axis=-2)
p = np.array(args.observed_point)
q0 = mrcal.project(p, *model0.intrinsics())
sigma = 0.1
cache_file = "/tmp/triangulation-study-cache.pickle"
if args.cache is None or args.cache == 'write':
v0local_noisy, v1local_noisy,v0_noisy,v1_noisy,_,_,_,_ = \
mrcal.synthetic_data. \
_noisy_observation_vectors_for_triangulation(p,Rt01,
model0.intrinsics(),
model1.intrinsics(),
args.Nsamples,
sigma = sigma)
p_sampled_geometric = mrcal.triangulate_geometric( v0_noisy, v1_noisy, t01 )
p_sampled_lindstrom = mrcal.triangulate_lindstrom( v0local_noisy, v1local_noisy, Rt01 )
p_sampled_leecivera_l1 = mrcal.triangulate_leecivera_l1( v0_noisy, v1_noisy, t01 )
p_sampled_leecivera_linf = mrcal.triangulate_leecivera_linf( v0_noisy, v1_noisy, t01 )
p_sampled_leecivera_mid2 = mrcal.triangulate_leecivera_mid2( v0_noisy, v1_noisy, t01 )
p_sampled_leecivera_wmid2 = mrcal.triangulate_leecivera_wmid2(v0_noisy, v1_noisy, t01 )
q0_sampled_geometric = mrcal.project(p_sampled_geometric, *model0.intrinsics())
q0_sampled_lindstrom = mrcal.project(p_sampled_lindstrom, *model0.intrinsics())
q0_sampled_leecivera_l1 = mrcal.project(p_sampled_leecivera_l1, *model0.intrinsics())
q0_sampled_leecivera_linf = mrcal.project(p_sampled_leecivera_linf, *model0.intrinsics())
q0_sampled_leecivera_mid2 = mrcal.project(p_sampled_leecivera_mid2, *model0.intrinsics())
q0_sampled_leecivera_wmid2 = mrcal.project(p_sampled_leecivera_wmid2, *model0.intrinsics())
range_sampled_geometric = nps.mag(p_sampled_geometric)
range_sampled_lindstrom = nps.mag(p_sampled_lindstrom)
range_sampled_leecivera_l1 = nps.mag(p_sampled_leecivera_l1)
range_sampled_leecivera_linf = nps.mag(p_sampled_leecivera_linf)
range_sampled_leecivera_mid2 = nps.mag(p_sampled_leecivera_mid2)
range_sampled_leecivera_wmid2 = nps.mag(p_sampled_leecivera_wmid2)
if args.cache is not None:
with open(cache_file,"wb") as f:
pickle.dump((v0local_noisy,
v1local_noisy,
v0_noisy,
v1_noisy,
p_sampled_geometric,
p_sampled_lindstrom,
p_sampled_leecivera_l1,
p_sampled_leecivera_linf,
p_sampled_leecivera_mid2,
p_sampled_leecivera_wmid2,
q0_sampled_geometric,
q0_sampled_lindstrom,
q0_sampled_leecivera_l1,
q0_sampled_leecivera_linf,
q0_sampled_leecivera_mid2,
q0_sampled_leecivera_wmid2,
range_sampled_geometric,
range_sampled_lindstrom,
range_sampled_leecivera_l1,
range_sampled_leecivera_linf,
range_sampled_leecivera_mid2,
range_sampled_leecivera_wmid2),
f)
print(f"Wrote cache to {cache_file}")
else:
with open(cache_file,"rb") as f:
(v0local_noisy,
v1local_noisy,
v0_noisy,
v1_noisy,
p_sampled_geometric,
p_sampled_lindstrom,
p_sampled_leecivera_l1,
p_sampled_leecivera_linf,
p_sampled_leecivera_mid2,
p_sampled_leecivera_wmid2,
q0_sampled_geometric,
q0_sampled_lindstrom,
q0_sampled_leecivera_l1,
q0_sampled_leecivera_linf,
q0_sampled_leecivera_mid2,
q0_sampled_leecivera_wmid2,
range_sampled_geometric,
range_sampled_lindstrom,
range_sampled_leecivera_l1,
range_sampled_leecivera_linf,
range_sampled_leecivera_mid2,
range_sampled_leecivera_wmid2) = \
pickle.load(f)
plot_options = {}
if args.set is not None:
plot_options['set'] = args.set
if args.unset is not None:
plot_options['unset'] = args.unset
if args.hardcopy is not None:
plot_options['hardcopy'] = args.hardcopy
if args.terminal is not None:
plot_options['terminal'] = args.terminal
if args.ellipses:
# Plot the reprojected pixels and the fitted ellipses
data_tuples = \
[ *mrcal.utils._plot_args_points_and_covariance_ellipse( q0_sampled_geometric, 'geometric' ),
*mrcal.utils._plot_args_points_and_covariance_ellipse( q0_sampled_lindstrom, 'lindstrom' ),
*mrcal.utils._plot_args_points_and_covariance_ellipse( q0_sampled_leecivera_l1, 'lee-civera-l1' ),
*mrcal.utils._plot_args_points_and_covariance_ellipse( q0_sampled_leecivera_linf, 'lee-civera-linf' ),
*mrcal.utils._plot_args_points_and_covariance_ellipse( q0_sampled_leecivera_mid2, 'lee-civera-mid2' ),
*mrcal.utils._plot_args_points_and_covariance_ellipse( q0_sampled_leecivera_wmid2,'lee-civera-wmid2' ), ]
if not args.samples:
# Not plotting samples. Get rid of all the "dots" I'm plotting
data_tuples = [ t for t in data_tuples if \
not (isinstance(t[-1], dict) and \
'_with' in t[-1] and \
t[-1]['_with'] == 'dots') ]
if args.title is not None:
title = args.title
else:
title = 'Reprojected triangulated point'
if args.extratitle is not None:
title += ': ' + args.extratitle
gp.plot( *data_tuples,
( q0,
dict(_with = 'points pt 3 ps 2',
tuplesize = -2,
legend = 'Ground truth')),
square = True,
wait = 'hardcopy' not in plot_options,
title = title,
**plot_options)
elif args.ranges:
# Plot the range distribution
range_ref = nps.mag(p)
if args.title is not None:
title = args.title
else:
title = "Range distribution"
if args.extratitle is not None:
title += ': ' + args.extratitle
gp.plot( nps.cat( range_sampled_geometric,
range_sampled_lindstrom,
range_sampled_leecivera_l1,
range_sampled_leecivera_linf,
range_sampled_leecivera_mid2,
range_sampled_leecivera_wmid2 ),
legend = np.array(( 'range_sampled_geometric',
'range_sampled_lindstrom',
'range_sampled_leecivera_l1',
'range_sampled_leecivera_linf',
'range_sampled_leecivera_mid2',
'range_sampled_leecivera_wmid2' )),
histogram=True,
binwidth=200,
_with='lines',
_set = f'arrow from {range_ref},graph 0 to {range_ref},graph 1 nohead lw 5',
wait = 'hardcopy' not in plot_options,
title = title,
**plot_options)
else:
raise Exception("Getting here is a bug")
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