关于Opencv单双目相机标定与三维重建

单双目相机标定

上述为本人初学双目标定与三维重建时开发的一款相机标定的软件，最终生成的点云可通过OpenGL或者PCL进行三维重建，利用opencv与mfc的基本编程知识。

单目标定

首先在某宝上购买标定板及双目相机，标定板圆形标定板与棋盘格均可，然后拍摄若干张标定图片。10-12张左右比较合适，然后对相机进行单目标定，单目标定主要采用张正友标定法。

单目标定代码

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
using namespace std;
using namespace cv;
  int main()
 {
	 ifstream inImgPath("calibdata.txt");    //标定所用图像文件的路径
	vector<string> imgList;
	vector<string>::iterator p;
	string temp;
	if (!inImgPath.is_open())
	{
		cout << "没有找到文件" << endl;
	}
	//读取文件中保存的图片文件路径，并存放在数组中
	while (getline(inImgPath, temp))
	{
		imgList.push_back(temp);
	 }
	ofstream fout("caliberation_result.txt");   //保存标定结果的文件
	cout << "开始提取角点......" << endl;
	cv::Size image_size;//保存图片大小
	cv::Size pattern_size = cv::Size(8, 16);//标定板上每行、每列的角点数；测试图片中的标定板上内角点数为4*6
	vector<cv::Point2f> corner_points_buf;//建一个数组缓存检测到的角点，通常采用Point2f形式
	vector<cv::Point2f>::iterator corner_points_buf_ptr;
	vector<vector<cv::Point2f>> corner_points_of_all_imgs;
	int image_num = 0;
	string filename;
	while (image_num < imgList.size()-1)
	{
		filename = imgList[image_num++];
		cout << "image_num = " << image_num << endl;
		cout << filename.c_str() << endl;
		cv::Mat imageInput = cv::imread(filename.c_str());
		if (image_num == 1)
		{
			image_size.width = imageInput.cols;
			image_size.height = imageInput.rows;
			cout << "image_size.width = " << image_size.width << endl;
			cout << "image_size.height = " << image_size.height << endl;
		}
		if (findChessboardCorners(imageInput, pattern_size, corner_points_buf) == 0)
		{
			cout << "can not find chessboard corners!\n";   //找不到角点
			exit(1);
		}
		else
		{
			cv::Mat gray;
			cv::cvtColor(imageInput, gray, CV_RGB2GRAY);
			cv::find4QuadCornerSubpix(gray, corner_points_buf, cv::Size(5, 5));
			corner_points_of_all_imgs.push_back(corner_points_buf);
			cv::drawChessboardCorners(imageInput, pattern_size, corner_points_buf, true);
			//namedWindow("camera calibration",0);
			//cv::imshow("camera calibration",imageInput);
			//cv::waitKey(0);
		}
	}
	int total = corner_points_of_all_imgs.size();
	cout << "total=" << total << endl;
	int cornerNum = pattern_size.width * pattern_size.height;//每张图片上的总的角点数
	for (int i = 0; i < total; i++)
	{
		cout << "--> 第" << i + 1 << "幅图片的数据 -->:" << endl;
		for (int j = 0; j < cornerNum; j++)
		{
			cout << "-->" << corner_points_of_all_imgs[i][j].x;
			cout << "-->" << corner_points_of_all_imgs[i][j].y;
			if ((j + 1) % 3 == 0)
			{
				cout << endl;
			}
			else
			{
				cout.width(10);
			}
		}
		cout << endl;
	}
	cout << endl << "角点提取完成" << endl;
	//摄像机标定
	cout << "开始标定………………" << endl;
	cv::Mat cameraMatrix = cv::Mat(3, 3, CV_32FC1, cv::Scalar::all(0));//内外参矩阵，H——单应性矩阵
	cv::Mat distCoefficients = cv::Mat(1, 5, CV_32FC1, cv::Scalar::all(0));//摄像机的5个畸变系数：k1,k2,p1,p2,k3
	vector<cv::Mat> tvecsMat;//每幅图像的平移向量，t
	vector<cv::Mat> rvecsMat;//每幅图像的旋转向量（罗德里格旋转向量）
	vector<vector<cv::Point3f>> objectPoints;//保存所有图片的角点的三维坐标
											 //初始化每一张图片中标定板上角点的三维坐标
	int i, j, k;
	for (k = 0; k < image_num; k++)//遍历每一张图片
	{
		vector<cv::Point3f> tempCornerPoints;//每一幅图片对应的角点数组
		//遍历所有的角点
		for (i = 0; i < pattern_size.height; i++)
		{
			for (j = 0; j < pattern_size.width; j++)
			{
				cv::Point3f singleRealPoint;//一个角点的坐标
				singleRealPoint.x = i * 30;
				singleRealPoint.y = j * 30;
				singleRealPoint.z = 0;//假设z=0
				tempCornerPoints.push_back(singleRealPoint);
			}
		}
		objectPoints.push_back(tempCornerPoints);
	}
	cv::calibrateCamera(objectPoints, corner_points_of_all_imgs, image_size, cameraMatrix, distCoefficients, rvecsMat, tvecsMat, 0);
	cout << "标定完成" << endl;
	//开始保存标定结果
	cout << "开始保存标定结果" << endl;
	cout << endl << "相机相关参数：" << endl;
	fout << "相机相关参数：" << endl;
	cout << "1.内外参数矩阵:" << endl;
	fout << "1.内外参数矩阵:" << endl;
	cout << "大小：" << cameraMatrix.size() << endl;
	fout << "大小：" << cameraMatrix.size() << endl;
	cout << cameraMatrix << endl;
	fout << cameraMatrix << endl;
	cout << "2.畸变系数：" << endl;
	fout << "2.畸变系数：" << endl;
	cout << "大小：" << distCoefficients.size() << endl;
	fout << "大小：" << distCoefficients.size() << endl;
	cout << distCoefficients << endl;
	fout << distCoefficients << endl;
	cout << endl << "图像相关参数：" << endl;
	fout << endl << "图像相关参数：" << endl;
	cv::Mat rotation_Matrix = cv::Mat(3, 3, CV_32FC1, cv::Scalar::all(0));//旋转矩阵
	for (i = 0; i < image_num; i++)
	{
		cout << "第" << i + 1 << "幅图像的旋转向量：" << endl;
		fout << "第" << i + 1 << "幅图像的旋转向量：" << endl;
		cout << rvecsMat[i] << endl;
		fout << rvecsMat[i] << endl;
		cout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl;
		fout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl;
		cv::Rodrigues(rvecsMat[i], rotation_Matrix);//将旋转向量转换为相对应的旋转矩阵
		cout << rotation_Matrix << endl;
		fout << rotation_Matrix << endl;
		cout << "第" << i + 1 << "幅图像的平移向量：" << endl;
		fout << "第" << i + 1 << "幅图像的平移向量：" << endl;
		cout << tvecsMat[i] << endl;
		fout << tvecsMat[i] << endl;
	}
	cout << "结果保存完毕" << endl;
	//对标定结果进行评价
	cout << "开始评价标定结果......" << endl;
	//计算每幅图像中的角点数量，假设全部角点都检测到了
	int corner_points_counts;
	corner_points_counts = pattern_size.width * pattern_size.height;
	cout << "每幅图像的标定误差：" << endl;
	fout << "每幅图像的标定误差：" << endl;
	double err = 0;//单张图像的误差
	double total_err = 0;//所有图像的平均误差
	for (i = 0; i < image_num; i++)
	{
		vector<cv::Point2f> image_points_calculated;//存放新计算出的投影点的坐标
		vector<cv::Point3f> tempPointSet = objectPoints[i];
		cv::projectPoints(tempPointSet, rvecsMat[i], tvecsMat[i], cameraMatrix, distCoefficients,image_points_calculated);
		//计算新的投影点与旧的投影点之间的误差
		vector<cv::Point2f> image_points_old = corner_points_of_all_imgs[i];
		//将两组数据换成Mat格式
		cv::Mat image_points_calculated_mat = cv::Mat(1, image_points_calculated.size(), CV_32FC2);
		cv::Mat image_points_old_mat = cv::Mat(1, image_points_old.size(), CV_32FC2);
		for (j = 0; j < tempPointSet.size(); j++)
		{
			image_points_calculated_mat.at<cv::Vec2f>(0, j) = cv::Vec2f(image_points_calculated[j].x, image_points_calculated[j].y);
			image_points_old_mat.at<cv::Vec2f>(0, j) = cv::Vec2f(image_points_old[j].x, image_points_old[j].y);
		}
		err = cv::norm(image_points_calculated_mat, image_points_old_mat, cv::NORM_L2);
		err /= corner_points_counts;
		total_err += err;
		cout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
		fout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
	}
	cout << "总体平均误差：" << total_err / image_num << "像素" << endl;
	fout << "总体平均误差：" << total_err / image_num << "像素" << endl;
	cout << "评价完成" << endl;
	fout.close();
	cv::Mat mapx = cv::Mat(image_size, CV_32FC1);
	cv::Mat mapy = cv::Mat(image_size, CV_32FC1);
	cv::Mat R = cv::Mat::eye(3, 3, CV_32F);
	cout << "保存矫正图像" << endl;
	string imageFileName;
	std::stringstream StrStm;
	for (int i = 0; i < image_num; i++)
	{
		cout << "Frame #" << i + 1 << endl;
		cv::initUndistortRectifyMap(cameraMatrix, distCoefficients, R, cameraMatrix, image_size, CV_32FC1, mapx, mapy);
		cv::Mat src_image = cv::imread(imgList[i].c_str(), 1);
		cv::Mat new_image = src_image.clone();
		cv::remap(src_image, new_image, mapx, mapy, cv::INTER_LINEAR);
		imshow("原始图像", src_image);
		imshow("矫正后图像", new_image);
		StrStm.clear();
		imageFileName.clear();
		StrStm << i + 1;
		StrStm >> imageFileName;
		imageFileName += "_d.jpg";
		cv::imwrite(imageFileName, new_image);
		cv::waitKey(200);
	}
	cout << "保存结束" << endl;
	cv::waitKey(0);
	return 0;
}

双目标定

在对左右相机进行内参数标定后，对两相机的相对位置，也就是外参数完成标定。求得平移矩阵T与旋转矩阵R。双目标定的内容有点长，后续会继续做相关介绍。