1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
|
#!/usr/bin/env python3
# Copyright (c) 2017-2023 California Institute of Technology ("Caltech"). U.S.
# Government sponsorship acknowledged. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
r'''Validate deriving the input noise from the solve residuals
SYNOPSIS
$ validate-input-noise.py [01].cameramodel
Noise ratios measured/actual. Should be ~ 1.0
observed, by looking at the distribution of residulas: 0.998
predicted, by correcting the above by sqrt(1-Nstates/Nmeasurements_observed): 1.000
'''
import sys
import argparse
import re
import os
def parse_args():
parser = \
argparse.ArgumentParser(description = __doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--observed-pixel-uncertainty',
type = float,
default = 0.3,
help='''How much noise to inject into perfect solves''')
parser.add_argument('models',
type=str,
nargs='+',
help='''The camera models to process. Each is handled
individually''')
return parser.parse_args()
args = parse_args()
# I import the LOCAL mrcal
sys.path[:0] = f"{os.path.dirname(os.path.realpath(__file__))}/..",
import mrcal
import mrcal.model_analysis
import numpy as np
import numpysane as nps
models = [mrcal.cameramodel(f) for f in args.models]
for model in models:
optimization_inputs = model.optimization_inputs()
mrcal.make_perfect_observations(optimization_inputs,
observed_pixel_uncertainty = args.observed_pixel_uncertainty)
mrcal.optimize(**optimization_inputs)
# The ratio of observed noise to what I expected. Should be ~ 1.0
noise_observed_ratio = \
mrcal.model_analysis._observed_pixel_uncertainty_from_inputs(optimization_inputs) / args.observed_pixel_uncertainty
Nstates = mrcal.num_states(**optimization_inputs)
Nmeasurements = mrcal.num_measurements(**optimization_inputs)
# This correction is documented here:
# https://mrcal.secretsauce.net/docs-3.0/formulation.html#estimating-input-noise
# This probably should be added to
# _observed_pixel_uncertainty_from_inputs(). Today (2025/11/15) it has not
# yet been. Because very're usually VERY overdetermined, which can be
# validated by this script. I will add this factor later, if I discover that this is necessary
f = np.sqrt(1 - Nstates/Nmeasurements)
noise_predicted_ratio = noise_observed_ratio/f
print("Noise ratios measured/actual. Should be ~ 1.0")
print(f" observed, by looking at the distribution of residulas: {noise_observed_ratio:.3f}")
print(f" predicted, by correcting the above by sqrt(1-Nstates/Nmeasurements_observed): {noise_predicted_ratio:.3f}")
|
