#!/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'') 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__ = '0.4' __maintainer__ = "David Pagnon" __email__ = "contact@david-pagnon.com" __status__ = "Development" ## FUNCTIONS 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 natural_sort(C_vid)] 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 RT_qca2cv(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.dot(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.dot(r_ax_y).dot(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.dot(rt_h) r = r_ax_h__rt_h[:3,:3] t = r_ax_h__rt_h[:3,3] return r, t def natural_sort(list): ''' Sorts list of strings with numbers in natural order Example: ['item_1', 'item_2', 'item_10'] Taken from: https://stackoverflow.com/a/11150413/12196632 ''' convert = lambda text: int(text) if text.isdigit() else text.lower() alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] return sorted(list, key=alphanum_key) 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'') 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 = [RT_qca2cv(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)