def make_plot(icam, report_center_points=True, **kwargs):
q_sampled_mean = np.mean(q_sampled[:, icam, :], axis=-2)
def make_tuple(*args):
return args
data_tuples = \
make_tuple((q_sampled[:,icam,0], q_sampled[:,icam,1],
dict(_with = 'points pt 6 ps 0.5',
tuplesize = 2)),
*get_point_cov_plot_args(q_sampled[:,icam,:], "Observed uncertainty"),
*get_cov_plot_args(q_sampled_mean, Var_dq[icam], "Predicted uncertainty"),)
if report_center_points:
data_tuples += \
( (q0_baseline,
dict(tuplesize = -2,
_with = 'points pt 3 ps 3',
legend = 'Baseline center point')),
(q0_true[args.distances[0]][icam],
dict(tuplesize = -2,
_with = 'points pt 3 ps 3',
legend = 'True center point')),
(q_sampled_mean,
dict(tuplesize = -2,
_with = 'points pt 3 ps 3',
legend = 'Sampled mean')))
plot_options = \
dict(square=1,
_xrange=(q0_baseline[0]-2,q0_baseline[0]+2),
_yrange=(q0_baseline[1]-2,q0_baseline[1]+2),
title=f'Uncertainty reprojection distribution for camera {icam}',
**kwargs)
gp.add_plot_option(plot_options, 'set', ('xtics 1', 'ytics 1'))
return data_tuples, plot_options
def makeplots(dohardcopy, processoptions_base):
processoptions = copy.deepcopy(processoptions_base)
gp.add_plot_option(processoptions, 'set', ('xtics 0.01', 'ytics 0.01'))
# The key should use smaller text than the rest of the plot, if possible
if 'terminal' in processoptions:
m = re.search('font ",([0-9]+)"', processoptions['terminal'])
if m is not None:
s = int(m.group(1))
gp.add_plot_option(
processoptions, 'set',
('xtics 0.01', 'ytics 0.01', f'key font ",{int(s*0.8)}"'))
if dohardcopy:
processoptions['hardcopy'] = \
f'{args.make_documentation_plots_path}--ellipses.{extension}'
processoptions['terminal'] = shorter_terminal(
processoptions['terminal'])
# Hardcopies do things a little differently, to be nicer for docs
gp.plot(*subplots,
multiplot=f'layout 1,2',
unset='grid',
**processoptions)
else:
# Interactive plotting, so no multiplots. Interactive plots
for p in subplots:
gp.plot(*p[:-1], **p[-1], **processoptions)
processoptions = copy.deepcopy(processoptions_base)
if dohardcopy:
processoptions['hardcopy'] = \
f'{args.make_documentation_plots_path}--p0-p1-magnitude-covariance.{extension}'
processoptions['title'] = title_covariance
gp.plotimage(
np.abs(Var_p_joint.reshape(Npoints * 3, Npoints * 3)),
square=True,
xlabel='Variable index (left point x,y,z; right point x,y,z)',
ylabel='Variable index (left point x,y,z; right point x,y,z)',
**processoptions)
processoptions = copy.deepcopy(processoptions_base)
binwidth = np.sqrt(Var_ranges_joint[0]) / 4.
equation_range0_observed_gaussian = \
mrcal.fitted_gaussian_equation(x = ranges_sampled[0],
binwidth = binwidth,
legend = "Idealized gaussian fit to data")
equation_range0_predicted_joint_gaussian = \
mrcal.fitted_gaussian_equation(mean = ranges[0],
sigma = np.sqrt(Var_ranges_joint[0]),
N = len(ranges_sampled[0]),
binwidth = binwidth,
legend = "Predicted-joint")
equation_range0_predicted_calibration_gaussian = \
mrcal.fitted_gaussian_equation(mean = ranges[0],
sigma = np.sqrt(Var_ranges_calibration[0]),
N = len(ranges_sampled[0]),
binwidth = binwidth,
legend = "Predicted-calibration")
equation_range0_predicted_observations_gaussian = \
mrcal.fitted_gaussian_equation(mean = ranges[0],
sigma = np.sqrt(Var_ranges_observations[0]),
N = len(ranges_sampled[0]),
binwidth = binwidth,
legend = "Predicted-observations")
if dohardcopy:
processoptions['hardcopy'] = \
f'{args.make_documentation_plots_path}--range-to-p0.{extension}'
processoptions['title'] = title_range0
gp.add_plot_option(processoptions, 'set', 'samples 1000')
gp.plot(
ranges_sampled[0],
histogram=True,
binwidth=binwidth,
equation_above=(equation_range0_predicted_joint_gaussian,
equation_range0_predicted_calibration_gaussian,
equation_range0_predicted_observations_gaussian,
equation_range0_observed_gaussian),
xlabel="Range to the left triangulated point (m)",
ylabel="Frequency",
**processoptions)
processoptions = copy.deepcopy(processoptions_base)
binwidth = np.sqrt(Var_distance) / 4.
equation_distance_observed_gaussian = \
mrcal.fitted_gaussian_equation(x = distance_sampled,
binwidth = binwidth,
legend = "Idealized gaussian fit to data")
equation_distance_predicted_gaussian = \
mrcal.fitted_gaussian_equation(mean = distance,
sigma = np.sqrt(Var_distance),
N = len(distance_sampled),
binwidth = binwidth,
legend = "Predicted")
if dohardcopy:
processoptions['hardcopy'] = \
f'{args.make_documentation_plots_path}--distance-p1-p0.{extension}'
processoptions['title'] = title_distance
gp.add_plot_option(processoptions, 'set', 'samples 1000')
gp.plot(distance_sampled,
histogram=True,
binwidth=binwidth,
equation_above=(equation_distance_predicted_gaussian,
equation_distance_observed_gaussian),
xlabel="Distance between triangulated points (m)",
ylabel="Frequency",
**processoptions)
# to evaluate a different world point for each camera
q0_baseline = imagersizes[0] / 3.
if args.make_documentation_plots is not None:
import gnuplotlib as gp
if args.make_documentation_plots:
for extension in ('pdf', 'svg', 'png', 'gp'):
processoptions_output = dict(
wait=False,
terminal=terminal[extension],
_set=extraset[extension],
hardcopy=
f'{args.make_documentation_plots}--simulated-geometry.{extension}'
)
gp.add_plot_option(processoptions_output, 'set',
'xyplane relative 0')
mrcal.show_geometry(models_baseline,
unset='key',
**processoptions_output)
else:
processoptions_output = dict(wait=True)
gp.add_plot_option(processoptions_output, 'set', 'xyplane relative 0')
mrcal.show_geometry(models_baseline,
unset='key',
**processoptions_output)
def observed_points(icam):
obs_cam = observations_true[
indices_frame_camintrinsics_camextrinsics[:, 1] == icam,
..., :2].ravel()
def makeplots(dohardcopy, processoptions_base):
processoptions = copy.deepcopy(processoptions_base)
if dohardcopy:
processoptions['hardcopy'] = \
f'{args.make_documentation_plots_filename}--ellipses.{extension}'
processoptions['terminal'] = shorter_terminal(processoptions['terminal'])
gp.plot( *subplots,
multiplot = f'title "{title_triangulation}" layout 1,2',
**processoptions )
processoptions = copy.deepcopy(processoptions_base)
if dohardcopy:
processoptions['hardcopy'] = \
f'{args.make_documentation_plots_filename}--p0-p1-magnitude-covariance.{extension}'
processoptions['title'] = title_covariance
gp.plotimage( np.abs(Var_p0p1_triangulated),
square = True,
xlabel = 'Variable index (left point x,y,z; right point x,y,z)',
ylabel = 'Variable index (left point x,y,z; right point x,y,z)',
**processoptions)
processoptions = copy.deepcopy(processoptions_base)
binwidth = np.sqrt(Var_range0) / 4.
equation_range0_observed_gaussian = \
mrcal.fitted_gaussian_equation(x = range0_sampled,
binwidth = binwidth,
legend = "Idealized gaussian fit to data")
equation_range0_predicted_gaussian = \
mrcal.fitted_gaussian_equation(mean = range0,
sigma = np.sqrt(Var_range0),
N = len(range0_sampled),
binwidth = binwidth,
legend = "Predicted")
if dohardcopy:
processoptions['hardcopy'] = \
f'{args.make_documentation_plots_filename}--range-to-p0.{extension}'
processoptions['title'] = title_range0
gp.add_plot_option(processoptions, 'set', 'samples 1000')
gp.plot(range0_sampled,
histogram = True,
binwidth = binwidth,
equation_above = (equation_range0_predicted_gaussian,
equation_range0_observed_gaussian),
xlabel = "Range to the left triangulated point (m)",
ylabel = "Frequency",
**processoptions)
processoptions = copy.deepcopy(processoptions_base)
binwidth = np.sqrt(Var_distance) / 4.
equation_distance_observed_gaussian = \
mrcal.fitted_gaussian_equation(x = distance_sampled,
binwidth = binwidth,
legend = "Idealized gaussian fit to data")
equation_distance_predicted_gaussian = \
mrcal.fitted_gaussian_equation(mean = distance,
sigma = np.sqrt(Var_distance),
N = len(distance_sampled),
binwidth = binwidth,
legend = "Predicted")
if dohardcopy:
processoptions['hardcopy'] = \
f'{args.make_documentation_plots_filename}--distance-p1-p0.{extension}'
processoptions['title'] = title_distance
gp.add_plot_option(processoptions, 'set', 'samples 1000')
gp.plot(distance_sampled,
histogram = True,
binwidth = binwidth,
equation_above = (equation_distance_predicted_gaussian,
equation_distance_observed_gaussian),
xlabel = "Distance between triangulated points (m)",
ylabel = "Frequency",
**processoptions)