245 lines
6.7 KiB
Python
245 lines
6.7 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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###########################################################################
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## OTHER SHARED UTILITIES ##
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###########################################################################
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Functions shared between modules, and other utilities
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'''
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## INIT
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import toml
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import numpy as np
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import re
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import cv2
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import matplotlib as mpl
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mpl.use('qt5agg')
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mpl.rc('figure', max_open_warning=0)
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from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
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from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
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from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QTabWidget, QVBoxLayout
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import sys
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## AUTHORSHIP INFORMATION
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__author__ = "David Pagnon"
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__copyright__ = "Copyright 2021, Maya-Mocap"
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__credits__ = ["David Pagnon"]
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__license__ = "BSD 3-Clause License"
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__version__ = '0.4'
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__maintainer__ = "David Pagnon"
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__email__ = "contact@david-pagnon.com"
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__status__ = "Development"
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## FUNCTIONS
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def computeP(calib_file):
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'''
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Compute projection matrices from toml calibration file.
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INPUT:
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- calib_file: calibration .toml file.
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OUTPUT:
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- P: projection matrix as list of arrays
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'''
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K, R, T, Kh, H = [], [], [], [], []
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P = []
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calib = toml.load(calib_file)
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for cam in list(calib.keys()):
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if cam != 'metadata':
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K = np.array(calib[cam]['matrix'])
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Kh = np.block([K, np.zeros(3).reshape(3,1)])
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R, _ = cv2.Rodrigues(np.array(calib[cam]['rotation']))
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T = np.array(calib[cam]['translation'])
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H = np.block([[R,T.reshape(3,1)], [np.zeros(3), 1 ]])
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P.append(Kh.dot(H))
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return P
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def weighted_triangulation(P_all,x_all,y_all,likelihood_all):
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'''
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Triangulation with direct linear transform,
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weighted with likelihood of joint pose estimation.
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INPUTS:
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- P_all: list of arrays. Projection matrices of all cameras
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- x_all,y_all: x, y 2D coordinates to triangulate
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- likelihood_all: likelihood of joint pose estimation
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OUTPUT:
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- Q: array of triangulated point (x,y,z,1.)
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'''
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A = np.empty((0,4))
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for c in range(len(x_all)):
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P_cam = P_all[c]
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A = np.vstack((A, (P_cam[0] - x_all[c]*P_cam[2]) * likelihood_all[c] ))
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A = np.vstack((A, (P_cam[1] - y_all[c]*P_cam[2]) * likelihood_all[c] ))
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if np.shape(A)[0] >= 4:
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S, U, Vt = cv2.SVDecomp(A)
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V = Vt.T
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Q = np.array([V[0][3]/V[3][3], V[1][3]/V[3][3], V[2][3]/V[3][3], 1])
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else:
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Q = np.array([0.,0.,0.,1])
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return Q
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def reprojection(P_all, Q):
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'''
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Reprojects 3D point on all cameras.
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INPUTS:
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- P_all: list of arrays. Projection matrix for all cameras
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- Q: array of triangulated point (x,y,z,1.)
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OUTPUTS:
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- x_calc, y_calc: list of coordinates of point reprojected on all cameras
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'''
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x_calc, y_calc = [], []
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for c in range(len(P_all)):
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P_cam = P_all[c]
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x_calc.append(P_cam[0].dot(Q) / P_cam[2].dot(Q))
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y_calc.append(P_cam[1].dot(Q) / P_cam[2].dot(Q))
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return x_calc, y_calc
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def euclidean_distance(q1, q2):
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'''
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Euclidean distance between 2 points (N-dim).
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INPUTS:
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- q1: list of N_dimensional coordinates of point
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- q2: idem
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OUTPUTS:
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- euc_dist: float. Euclidian distance between q1 and q2
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'''
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q1 = np.array(q1)
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q2 = np.array(q2)
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dist = q2 - q1
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euc_dist = np.sqrt(np.sum( [d**2 for d in dist]))
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return euc_dist
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def RT_qca2cv(r, t):
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'''
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Converts rotation R and translation T
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from Qualisys object centered perspective
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to OpenCV camera centered perspective
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and inversely.
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Qc = RQ+T --> Q = R-1.Qc - R-1.T
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'''
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r = r.T
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t = - r.dot(t)
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return r, t
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def rotate_cam(r, t, ang_x=0, ang_y=0, ang_z=0):
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'''
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Apply rotations around x, y, z in cameras coordinates
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Angle in radians
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'''
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r,t = np.array(r), np.array(t)
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if r.shape == (3,3):
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rt_h = np.block([[r,t.reshape(3,1)], [np.zeros(3), 1 ]])
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elif r.shape == (3,):
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rt_h = np.block([[cv2.Rodrigues(r)[0],t.reshape(3,1)], [np.zeros(3), 1 ]])
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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)
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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)
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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)
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r_ax = r_ax_z.dot(r_ax_y).dot(r_ax_x)
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r_ax_h = np.block([[r_ax,np.zeros(3).reshape(3,1)], [np.zeros(3), 1]])
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r_ax_h__rt_h = r_ax_h.dot(rt_h)
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r = r_ax_h__rt_h[:3,:3]
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t = r_ax_h__rt_h[:3,3]
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return r, t
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def natural_sort(list):
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'''
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Sorts list of strings with numbers in natural order
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Example: ['item_1', 'item_2', 'item_10']
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Taken from: https://stackoverflow.com/a/11150413/12196632
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'''
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convert = lambda text: int(text) if text.isdigit() else text.lower()
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alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
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return sorted(list, key=alphanum_key)
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## CLASSES
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class plotWindow():
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'''
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Display several figures in tabs
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Taken from https://github.com/superjax/plotWindow/blob/master/plotWindow.py
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USAGE:
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pw = plotWindow()
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f = plt.figure()
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plt.plot(x1, y1)
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pw.addPlot("1", f)
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f = plt.figure()
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plt.plot(x2, y2)
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pw.addPlot("2", f)
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'''
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def __init__(self, parent=None):
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self.app = QApplication(sys.argv)
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self.MainWindow = QMainWindow()
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self.MainWindow.__init__()
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self.MainWindow.setWindowTitle("Multitabs figure")
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self.canvases = []
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self.figure_handles = []
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self.toolbar_handles = []
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self.tab_handles = []
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self.current_window = -1
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self.tabs = QTabWidget()
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self.MainWindow.setCentralWidget(self.tabs)
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self.MainWindow.resize(1280, 720)
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self.MainWindow.show()
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def addPlot(self, title, figure):
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new_tab = QWidget()
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layout = QVBoxLayout()
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new_tab.setLayout(layout)
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figure.subplots_adjust(left=0.1, right=0.99, bottom=0.1, top=0.91, wspace=0.2, hspace=0.2)
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new_canvas = FigureCanvas(figure)
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new_toolbar = NavigationToolbar(new_canvas, new_tab)
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layout.addWidget(new_canvas)
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layout.addWidget(new_toolbar)
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self.tabs.addTab(new_tab, title)
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self.toolbar_handles.append(new_toolbar)
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self.canvases.append(new_canvas)
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self.figure_handles.append(figure)
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self.tab_handles.append(new_tab)
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def show(self):
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self.app.exec_() |