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calib_qca_to_toml.py
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calib_qca_to_toml.py
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#!/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__ = "contact@david-pagnon.com"
__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)