#!/usr/bin/env python3# -*- coding: utf-8 -*-"""Created on Wed May 16 10:50:29 2018@author: dag"""import sympyimport numpy as npimport mathfrom matplotlib.pyplot import plotfrom matplotlib.pyplot import showimport matplotlib.pyplot as pltimport matplotlib#解决无法显示中文问题，fname是加载字体路径，根据自身pc实际确定，具体请百度zhfont1 = matplotlib.font_manager.FontProperties(fname='/System/Library/Fonts/Hiragino Sans GB W3.ttc') #随机产生3个参考节点坐标maxy = 1000maxx = 1000cx = maxx*np.random.rand(3)cy = maxy*np.random.rand(3)dot1 = plot(cx,cy,'k^') #生成盲节点，以及其与参考节点欧式距离mtx = maxx*np.random.rand()mty = maxy*np.random.rand()plt.hold('on')dot2 = plot(mtx,mty,'go')da = math.sqrt(np.square(mtx-cx[0])+np.square(mty-cy[0]))db = math.sqrt(np.square(mtx-cx[1])+np.square(mty-cy[1])) dc = math.sqrt(np.square(mtx-cx[2])+np.square(mty-cy[2])) #计算定位坐标  def triposition(xa,ya,da,xb,yb,db,xc,yc,dc):     x,y = sympy.symbols('x y')    f1 = 2*x*(xa-xc)+np.square(xc)-np.square(xa)+2*y*(ya-yc)+np.square(yc)-np.square(ya)-(np.square(dc)-np.square(da))    f2 = 2*x*(xb-xc)+np.square(xc)-np.square(xb)+2*y*(yb-yc)+np.square(yc)-np.square(yb)-(np.square(dc)-np.square(db))    result = sympy.solve([f1,f2],[x,y])    locx,locy = result[x],result[y]    return [locx,locy]    #解算得到定位节点坐标[locx,locy] = triposition(cx[0],cy[0],da,cx[1],cy[1],db,cx[2],cy[2],dc)plt.hold('on')dot3 = plot(locx,locy,'r*') #显示脚注x = [[locx,cx[0]],[locx,cx[1]],[locx,cx[2]]]y = [[locy,cy[0]],[locy,cy[1]],[locy,cy[2]]]for i in range(len(x)):    plt.plot(x[i],y[i],linestyle = '--',color ='g' )plt.title('三边测量法的定位',fontproperties=zhfont1)  plt.legend(['参考节点','盲节点','定位节点'], loc='lower right',prop=zhfont1)show() derror = math.sqrt(np.square(locx-mtx) + np.square(locy-mty)) print(derror) 

import cv2import numpy as npimport scipy.io as scioif __name__ == '__main__':    print("main function.")    #验证点    point = np.array([1.0 ,2.0, 3.0])    #获取相机参数    cams_data = scio.loadmat('/data1/dy/SuperSMPL/data/AMAfMvS_Dataset/cameras_I_crane.mat')    Pmats = cams_data['Pmats']  # Pmats(8, 3, 4) 投影矩阵     P1 = Pmats[0,::]    P3 = Pmats[2,::]    #通过投影矩阵将点从世界坐标投到像素坐标    pj1 = np.dot(P1, np.vstack([point.reshape(3,1),np.array([1])]))    pj3 = np.dot(P3, np.vstack([point.reshape(3,1),np.array([1])]))    point1 = pj1[:2,:]/pj1[2,:]#两行一列，齐次坐标转化    point3 = pj3[:2,:]/pj3[2,:]    #利用投影矩阵以及对应像素点，进行三角测量    points = cv2.triangulatePoints(P1,P3,point1,point3)    #齐次坐标转化并输出    print(points[0:3,:]/points[3,:])