3
- 的OpenCV => 3.2
- 操作系統/平臺座標=>視窗64位
- 編譯=>的Visual Studio 2015
我目前正致力於我的項目，涉及車輛檢測和跟蹤以及車輛周圍的長方體的估算和優化。爲此，我完成了對車輛的檢測和跟蹤，並且我需要找到車輛邊界框邊緣圖像點的三維世界座標，然後估計長方體邊緣和項目的世界座標它回到圖像來顯示它。轉化2D圖像座標到3D世界其中z = 0
所以，我是計算機視覺和OpenCV的新手，但據我所知，我只需要4個點的圖像，需要知道這4個點的世界座標，並在OpenCV中使用solvePNP來獲得旋轉和平移向量（我已經有相機矩陣和失真係數）。然後，我需要使用Rodrigues將旋轉矢量轉換爲旋轉矩陣，然後將其與平移矢量連接以獲得我的外部矩陣，然後將外部矩陣與相機矩陣相乘以得到我的投影矩陣。由於我的z座標是零，所以我需要從投影矩陣中取出第三列，該矩陣給出了將二維圖像點轉換爲三維世界點的單應矩陣。現在，我找到了單應矩陣的逆矩陣，它給出了3D世界點與2D圖像點之間的單應矩陣。之後，我將圖像點[x，y，1] t與逆單應矩陣相乘以得到[wX，wY，w] t，並將整個向量除以標量w得到[X，Y，1]給我世界座標的X和Y值。
我的代碼如下所示：
#include "opencv2/opencv.hpp"
#include <stdio.h>
#include <iostream>
#include <sstream>
#include <math.h>
#include <conio.h>
using namespace cv;
using namespace std;
Mat cameraMatrix, distCoeffs, rotationVector, rotationMatrix,
translationVector,extrinsicMatrix, projectionMatrix, homographyMatrix,
inverseHomographyMatrix;
Point point;
vector<Point2d> image_points;
vector<Point3d> world_points;
int main()
{
FileStorage fs1("intrinsics.yml", FileStorage::READ);
fs1["camera_matrix"] >> cameraMatrix;
cout << "Camera Matrix: " << cameraMatrix << endl << endl;
fs1["distortion_coefficients"] >> distCoeffs;
cout << "Distortion Coefficients: " << distCoeffs << endl << endl;
image_points.push_back(Point2d(275, 204));
image_points.push_back(Point2d(331, 204));
image_points.push_back(Point2d(331, 308));
image_points.push_back(Point2d(275, 308));
cout << "Image Points: " << image_points << endl << endl;
world_points.push_back(Point3d(0.0, 0.0, 0.0));
world_points.push_back(Point3d(1.775, 0.0, 0.0));
world_points.push_back(Point3d(1.775, 4.620, 0.0));
world_points.push_back(Point3d(0.0, 4.620, 0.0));
cout << "World Points: " << world_points << endl << endl;
solvePnP(world_points, image_points, cameraMatrix, distCoeffs, rotationVector, translationVector);
cout << "Rotation Vector: " << endl << rotationVector << endl << endl;
cout << "Translation Vector: " << endl << translationVector << endl << endl;
Rodrigues(rotationVector, rotationMatrix);
cout << "Rotation Matrix: " << endl << rotationMatrix << endl << endl;
hconcat(rotationMatrix, translationVector, extrinsicMatrix);
cout << "Extrinsic Matrix: " << endl << extrinsicMatrix << endl << endl;
projectionMatrix = cameraMatrix * extrinsicMatrix;
cout << "Projection Matrix: " << endl << projectionMatrix << endl << endl;
double p11 = projectionMatrix.at<double>(0, 0),
p12 = projectionMatrix.at<double>(0, 1),
p14 = projectionMatrix.at<double>(0, 3),
p21 = projectionMatrix.at<double>(1, 0),
p22 = projectionMatrix.at<double>(1, 1),
p24 = projectionMatrix.at<double>(1, 3),
p31 = projectionMatrix.at<double>(2, 0),
p32 = projectionMatrix.at<double>(2, 1),
p34 = projectionMatrix.at<double>(2, 3);
homographyMatrix = (Mat_<double>(3, 3) << p11, p12, p14, p21, p22, p24, p31, p32, p34);
cout << "Homography Matrix: " << endl << homographyMatrix << endl << endl;
inverseHomographyMatrix = homographyMatrix.inv();
cout << "Inverse Homography Matrix: " << endl << inverseHomographyMatrix << endl << endl;
Mat point2D = (Mat_<double>(3, 1) << image_points[0].x, image_points[0].y, 1);
cout << "First Image Point" << point2D << endl << endl;
Mat point3Dw = inverseHomographyMatrix*point2D;
cout << "Point 3D-W : " << point3Dw << endl << endl;
double w = point3Dw.at<double>(2, 0);
cout << "W: " << w << endl << endl;
Mat matPoint3D;
divide(w, point3Dw, matPoint3D);
cout << "Point 3D: " << matPoint3D << endl << endl;
_getch();
return 0;
我有四個世界知名點的圖像座標和硬編碼它簡化。 image_points包含四點的圖像座標和world_points包含四點的世界座標。我正在將第一個世界點視爲世界軸上的原點（0,0,0），並使用已知距離計算其他四個點的座標。現在計算逆單應矩陣後，我將其與[世界座標（0,0,0）相關的[image_points [0] .x，image_points [0] .y，1] t相乘。然後我將結果除以第三個分量w得到[X，Y，1]。但是在打印出X和Y的值後，結果分別不是0,0。什麼做錯了？
我的代碼的輸出是這樣的：
Camera Matrix: [517.0036881709533, 0, 320;
0, 517.0036881709533, 212;
0, 0, 1]
Distortion Coefficients: [0.1128663679798094;
-1.487790079922432;
0;
0;
2.300571896761067]
Image Points: [275, 204;
331, 204;
331, 308;
275, 308]
World Points: [0, 0, 0;
1.775, 0, 0;
1.775, 4.62, 0;
0, 4.62, 0]
Rotation Vector:
[0.661476468596541;
-0.02794460022559267;
0.01206996342819649]
Translation Vector:
[-1.394495345140898;
-0.2454153722672731;
15.47126945512652]
Rotation Matrix:
[0.9995533907649279, -0.02011656447351923, -0.02209848058392758;
0.002297501163799448, 0.7890323093017149, -0.6143474069013439;
0.02979497438726573, 0.6140222623910194, 0.7887261380159]
Extrinsic Matrix:
[0.9995533907649279, -0.02011656447351923, -0.02209848058392758,
-1.394495345140898;
0.002297501163799448, 0.7890323093017149, -0.6143474069013439,
-0.2454153722672731;
0.02979497438726573, 0.6140222623910194, 0.7887261380159,
15.47126945512652]
Projection Matrix:
[526.3071813531748, 186.086785938988, 240.9673682002232, 4229.846989065414;
7.504351145361707, 538.1053336219271, -150.4099339268854, 3153.028471890794;
0.02979497438726573, 0.6140222623910194, 0.7887261380159, 15.47126945512652]
Homography Matrix:
[526.3071813531748, 186.086785938988, 4229.846989065414;
7.504351145361707, 538.1053336219271, 3153.028471890794;
0.02979497438726573, 0.6140222623910194, 15.47126945512652]
Inverse Homography Matrix:
[0.001930136511648154, -8.512427241879318e-05, -0.5103513244724983;
-6.693679705844383e-06, 0.00242178892313387, -0.4917279870709287
-3.451449134581896e-06, -9.595179260534558e-05, 0.08513443835773901]
First Image Point[275;
204;
1]
Point 3D-W : [0.003070864657310213;
0.0004761913292736786;
0.06461112415423849]
W: 0.0646111
Point 3D: [21.04004290792539;
135.683117651025;
1]
如果您知道3D點位於一個平面上，則可以使用光線和平面之間的交點來計算相機框架中的3D點。然後，使用相機姿態將相機框中的3D點轉換爲對象框。 – Catree