-
Notifications
You must be signed in to change notification settings - Fork 56
/
Copy pathcalib_qca_to_toml.py
237 lines (187 loc) · 8 KB
/
calib_qca_to_toml.py
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
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
##################################################
## QCA CALIBRATION TO TOML CALIBRATION ##
##################################################
Convert a Qualisys .qca.txt calibration file
to an OpenCV .toml calibration file
Usage:
from Pose2Sim.Utilities import calib_qca_to_toml; calib_qca_to_toml.calib_qca_to_toml_func(r'<input_qca_file>')
OR python -m calib_qca_to_toml -i input_qca_file
OR python -m calib_qca_to_toml -i input_qca_file --binning_factor 2 -o output_toml_file
'''
## INIT
import os
import argparse
import re
import numpy as np
from lxml import etree
import cv2
## AUTHORSHIP INFORMATION
__author__ = "David Pagnon"
__copyright__ = "Copyright 2021, Pose2Sim"
__credits__ = ["David Pagnon"]
__license__ = "BSD 3-Clause License"
__version__ = "0.9.4"
__maintainer__ = "David Pagnon"
__email__ = "[email protected]"
__status__ = "Development"
## FUNCTIONS
def natural_sort_key(s):
'''
Key for natural sorting of strings containing numbers.
'''
return [int(c) if c.isdigit() else c.lower() for c in re.split(r'(\d+)', s)]
def read_qca(qca_path, binning_factor):
'''
Read a Qualisys .qca.txt calibration file
Returns 5 lists of size N (N=number of cameras):
- ret: residual reprojection error in _mm_: list of floats
- C (camera name),
- S (image size),
- D (distorsion),
- K (intrinsic parameters),
- R (extrinsic rotation),
- T (extrinsic translation)
'''
root = etree.parse(qca_path).getroot()
ret, C, S, D, K, R, T = [], [], [], [], [], [], []
vid_id = []
# Camera name
for i, tag in enumerate(root.findall('cameras/camera')):
ret += [float(tag.attrib.get('avg-residual'))/1000]
C += [tag.attrib.get('serial')]
if tag.attrib.get('model') in ('Miqus Video', 'Miqus Video UnderWater', 'none'):
vid_id += [i]
# Image size
for tag in root.findall('cameras/camera/fov_video'):
w = (float(tag.attrib.get('right')) - float(tag.attrib.get('left'))) /binning_factor
h = (float(tag.attrib.get('bottom')) - float(tag.attrib.get('top'))) /binning_factor
S += [[w, h]]
# Intrinsic parameters: distorsion and intrinsic matrix
for i, tag in enumerate(root.findall('cameras/camera/intrinsic')):
k1 = float(tag.get('radialDistortion1'))/64/binning_factor
k2 = float(tag.get('radialDistortion2'))/64/binning_factor
p1 = float(tag.get('tangentalDistortion1'))/64/binning_factor
p2 = float(tag.get('tangentalDistortion2'))/64/binning_factor
D+= [np.array([k1, k2, p1, p2])]
fu = float(tag.get('focalLengthU'))/64/binning_factor
fv = float(tag.get('focalLengthV'))/64/binning_factor
cu = float(tag.get('centerPointU'))/64/binning_factor \
- float(root.findall('cameras/camera/fov_video')[i].attrib.get('left'))
cv = float(tag.get('centerPointV'))/64/binning_factor \
- float(root.findall('cameras/camera/fov_video')[i].attrib.get('top'))
K += [np.array([fu, 0., cu, 0., fv, cv, 0., 0., 1.]).reshape(3,3)]
# Extrinsic parameters: rotation matrix and translation vector
for tag in root.findall('cameras/camera/transform'):
tx = float(tag.get('x'))/1000
ty = float(tag.get('y'))/1000
tz = float(tag.get('z'))/1000
r11 = float(tag.get('r11'))
r12 = float(tag.get('r12'))
r13 = float(tag.get('r13'))
r21 = float(tag.get('r21'))
r22 = float(tag.get('r22'))
r23 = float(tag.get('r23'))
r31 = float(tag.get('r31'))
r32 = float(tag.get('r32'))
r33 = float(tag.get('r33'))
# Rotation (by-column to by-line)
R += [np.array([r11, r21, r31, r12, r22, r32, r13, r23, r33]).reshape(3,3)]
T += [np.array([tx, ty, tz])]
# Cameras names by natural order
C_vid = [C[v] for v in vid_id]
C_vid_id = [C_vid.index(c) for c in sorted(C_vid, key=natural_sort_key)]
C_id = [vid_id[c] for c in C_vid_id]
C = [C[c] for c in C_id]
ret = [ret[c] for c in C_id]
S = [S[c] for c in C_id]
D = [D[c] for c in C_id]
K = [K[c] for c in C_id]
R = [R[c] for c in C_id]
T = [T[c] for c in C_id]
return C, S, D, K, R, T
def world_to_camera_persp(r, t):
'''
Converts rotation R and translation T
from Qualisys object centered perspective
to OpenCV camera centered perspective
and inversely.
Qc = RQ+T --> Q = R-1.Qc - R-1.T
'''
r = r.T
t = - r @ t
return r, t
def rotate_cam(r, t, ang_x=np.pi, ang_y=0, ang_z=0):
'''
Apply rotations around x, y, z in cameras coordinates
'''
rt_h = np.block([[r,t.reshape(3,1)], [np.zeros(3), 1 ]])
r_ax_x = np.array([1,0,0, 0,np.cos(ang_x),-np.sin(ang_x), 0,np.sin(ang_x),np.cos(ang_x)]).reshape(3,3)
r_ax_y = np.array([np.cos(ang_y),0,np.sin(ang_y), 0,1,0, -np.sin(ang_y),0,np.cos(ang_y)]).reshape(3,3)
r_ax_z = np.array([np.cos(ang_z),-np.sin(ang_z),0, np.sin(ang_z),np.cos(ang_z),0, 0,0,1]).reshape(3,3)
r_ax = r_ax_z @ r_ax_y @ r_ax_x
r_ax_h = np.block([[r_ax,np.zeros(3).reshape(3,1)], [np.zeros(3), 1]])
r_ax_h__rt_h = r_ax_h @ rt_h
r = r_ax_h__rt_h[:3,:3]
t = r_ax_h__rt_h[:3,3]
return r, t
def toml_write(toml_path, C, S, D, K, R, T):
'''
Writes calibration parameters to a .toml file.
'''
with open(os.path.join(toml_path), 'w+') as cal_f:
for c in range(len(C)):
cam=f'[cam_{c+1}]\n'
name = f'name = "{C[c]}"\n'
size = f'size = [ {S[c][0]}, {S[c][1]},]\n'
mat = f'matrix = [ [ {K[c][0,0]}, 0.0, {K[c][0,2]},], [ 0.0, {K[c][1,1]}, {K[c][1,2]},], [ 0.0, 0.0, 1.0,],]\n'
dist = f'distortions = [ {D[c][0]}, {D[c][1]}, {D[c][2]}, {D[c][3]},]\n'
rot = f'rotation = [ {R[c][0]}, {R[c][1]}, {R[c][2]},]\n'
tran = f'translation = [ {T[c][0]}, {T[c][1]}, {T[c][2]},]\n'
fish = f'fisheye = false\n\n'
cal_f.write(cam + name + size + mat + dist + rot + tran + fish)
meta = '[metadata]\nadjusted = false\nerror = 0.0\n'
cal_f.write(meta)
def calib_qca_to_toml_func(*args):
'''
Convert a Qualisys .qca.txt calibration file
to an OpenCV .toml calibration file
Usage:
import calib_qca_to_toml; calib_qca_to_toml.calib_qca_to_toml_func(r'<input_qca_file>')
OR calib_qca_to_toml -i input_qca_file
OR calib_qca_to_toml -i input_qca_file --binning_factor 2 -o output_toml_file
'''
try:
qca_path = args[0].get('input_file') # invoked with argparse
binning_factor = int(args[0]['binning_factor'])
if args[0]['output_file'] == None:
toml_path = qca_path.replace('.qca.txt', '.toml')
else:
toml_path = args[0]['output_file']
except:
qca_path = args[0] # invoked as a function
toml_path = qca_path.replace('.qca.txt', '.toml')
try:
binning_factor = int(args[1])
except:
binning_factor = 1
C, S, D, K, R, T = read_qca(qca_path, binning_factor)
RT = [world_to_camera_persp(r,t) for r, t in zip(R, T)]
R = [rt[0] for rt in RT]
T = [rt[1] for rt in RT]
RT = [rotate_cam(r, t, ang_x=np.pi, ang_y=0, ang_z=0) for r, t in zip(R, T)]
R = [rt[0] for rt in RT]
T = [rt[1] for rt in RT]
R = [np.array(cv2.Rodrigues(r)[0]).flatten() for r in R]
T = np.array(T)/1000
toml_write(toml_path, C, S, D, K, R, T)
print('Calibration file generated.\n')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input_file', required = True, help='Qualisys .qca.txt input calibration file')
parser.add_argument('-b', '--binning_factor', required = False, default = 1, help='Binning factor if applied')
parser.add_argument('-o', '--output_file', required=False, help='OpenCV .toml output calibration file')
args = vars(parser.parse_args())
calib_qca_to_toml_func(args)