def plotoptions_measurement_boundaries(**optimization_inputs):
r'''Return the 'set' plot options for gnuplotlib to show the measurement boundaries
SYNOPSIS
import numpy as np
import gnuplotlib as gp
import mrcal
model = mrcal.cameramodel('xxx.cameramodel')
optimization_inputs = model.optimization_inputs()
x = mrcal.optimizer_callback(**optimization_inputs)[1]
gp.plot( np.abs(x),
_set = mrcal.plotoptions_measurement_boundaries(**optimization_inputs) )
# a plot pops up showing the magnitude of each measurement, with boundaries
# between the different measurements denoted
When plotting the measurement vector (or anything relating to it, such as
columns of the Jacobian), it is usually very useful to infer at a glance the
meaning of each part of the plot. This function returns a list of 'set'
directives passable to gnuplotlib that show the boundaries inside the
measurement vector.
ARGUMENTS
**optimization_inputs: a dict() of arguments passable to mrcal.optimize() and
mrcal.optimizer_callback(). These define the full optimization problem, and can
be obtained from the optimization_inputs() method of mrcal.cameramodel
RETURNED VALUE
A list of 'set' directives passable as plot options to gnuplotlib
'''
imeas0 = []
try:
imeas0.append(mrcal.measurement_index_boards(0, **optimization_inputs))
except:
pass
try:
imeas0.append(mrcal.measurement_index_points(0, **optimization_inputs))
except:
pass
try:
imeas0.append(
mrcal.measurement_index_regularization(0, **optimization_inputs))
except:
pass
return [f"arrow nohead from {x},graph 0 to {x},graph 1" for x in imeas0]
8 * 2, "state_index_intrinsics()")
testutils.confirm_equal(mrcal.state_index_extrinsics(2, **optimization_inputs),
8 * Ncameras + 6 * 2, "state_index_extrinsics()")
testutils.confirm_equal(mrcal.state_index_frames(2, **optimization_inputs),
8 * Ncameras + 6 * (Ncameras - 1) + 6 * 2,
"state_index_frames()")
testutils.confirm_equal(
mrcal.state_index_calobject_warp(**optimization_inputs),
8 * Ncameras + 6 * (Ncameras - 1) + 6 * Nframes,
"state_index_calobject_warp()")
testutils.confirm_equal(
mrcal.measurement_index_boards(2, **optimization_inputs),
object_width_n * object_height_n * 2 * 2, "measurement_index_boards()")
testutils.confirm_equal(
mrcal.measurement_index_regularization(**optimization_inputs),
object_width_n * object_height_n * 2 * Nframes * Ncameras,
"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 0:
# for i in range(1,Ncameras):
# models_solved[i].write(f'/tmp/tst{i}.cameramodel')
testutils.confirm_equal(rmserr, 0, eps=2.5, msg="Converged to a low RMS error")
8*2,
"state_index_intrinsics()")
testutils.confirm_equal( mrcal.state_index_extrinsics(2, **optimization_inputs),
8*Ncameras + 6*2,
"state_index_extrinsics()")
testutils.confirm_equal( mrcal.state_index_frames(2, **optimization_inputs),
8*Ncameras + 6*(Ncameras-1) + 6*2,
"state_index_frames()")
testutils.confirm_equal( mrcal.state_index_calobject_warp(**optimization_inputs),
8*Ncameras + 6*(Ncameras-1) + 6*Nframes,
"state_index_calobject_warp()")
testutils.confirm_equal( mrcal.measurement_index_boards(2, **optimization_inputs),
object_width_n*object_height_n*2* 2,
"measurement_index_boards()")
testutils.confirm_equal( mrcal.measurement_index_regularization(**optimization_inputs),
object_width_n*object_height_n*2*Nframes*Ncameras,
"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 0:
# for i in range(1,Ncameras):
# models_solved[i].write(f'/tmp/tst{i}.cameramodel')