基于Qt和OpenCV进行相机参数标定（内含code）

初始化

在Qt Creator编译器编写完成。
这个部分包括了一些参数的定义和文件的导入。
ps：以下代码需要提前 using namespace cv;

void A(QStringList files, Size square_size, Size board_size){
	int image_count = 0;//图像数量
	Size image_size;//图像尺寸
	vector<Point2f> image_points_buf;//缓存每幅图上检测的角点
	vector<vector<Point2f>> image_points_seq;//保存检测到的所有角点
	//读文件，检测所有角点，再进行subpixel精确化
	for(int m = 0; m < 文件数量; m++){
		image_count ++;
		QString fileName = files[m];
		Mat imageInput = imread(fileName.toStdString());
		//初始化
		if(image_count == 1){
			image_size.width = imageInput.cols;
			image_size.weight = imageInput.rows;
		}
		if(0 == findCheseboardCorners(imageInput, board_size, image_points_buf)){
			exit(1);
		}
		else{
			Mat view_gray;
			cvtColor(imageInput, view_gray, CV_RGB2GRAY);
			cornerSubpix(view_gray, image_points_buf, Size(5,5), Size(-1,-1),
						TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 20, 0.1));
			image_points_seq.pushback(image_points_buf);
			drawCheseboardCorners(view_gray, board_size, image_points_buf, false);
		}
	}
}

相机参数定义

这个部分包括了角点和相机参数的定义：

	int cornerNum = board_size.width * board_size.height;//角点个数
	vector<vector<Point3f>> object_points;//保存标定板上角点上的三维坐标
	Mat cameraMatrix = Mat(3, 3, CV_32FC1, Scalar::all(0));//K
	vector<int> point_counts;
	Mat distCoeffs = Mat(1, 5, CV_32FC1, Scalar::all(0));//畸变系数
	vector<Mat> tvecsMat;
	vector<Mat> rvecsMat;

初始化三维坐标

	int i, j, t;
	{
		for(t = 0; t < image_count; t++){
			vector<Point3f> tempPointSet;
			for(i = 0; i < board_size.height; i++){
				for(j = 0; j < board_size.width; j++){
					Point3f realPoint;
					realPoint.x = i * square_size.width;
					realPoint.y = j * square_size.height;
					realPoint.z = 0;
					tempPointSet.push_back(realPoint);
					object_points.push_back(tempPointSet);
				}
			}
		}
	}

初始化每幅图中角点数量，假设每一张图片都可以看到完整的标定板

		for(i = 0; i < image_count; i++){
		point_counts.push_back(board_size.width * board_size.height);
		}
		calibrateCamera(object_points, image_points+seq, image_size, cameraMatrix, 
						distCoeffs, rvecsMat, tvecsMat, 0);

评价

		double total_err = 0.0;
		double err = 0.0;
		vector<Point2f> image_points2;//保存重投影的点
		for(i = 0; i < image_count; i++){
			vector<Point3f> tempPointSet = object_points[i];
			projectPoints(tempPointSet, rvecsMat[i], tvecsMat[i], cameraMatrix, distCoeffs, image_points2);
			vector<Point2f> tempImagePoint = image_points_seq[i];
			Mat tempImagePointMat = Mat(1, tempImagePoint.size(), CV_32FC2);
			Mat image_points2Mat = Mat(1, tempImagePoint2.size(), CV_32FC2);
			for(j = 0; j < tempImagePoint.size(); j++){
				image_points2Mat.at<Vec2f>(0, j) = Vec2f(image_points2[j].x, image_points2[j].y);
				tempImagePointMat.at<Vec2f>(0, j) = Vec2f(tempImagePoint[j].x, tempImagePoint[j].y);
			}
			err = norm(image_points2Mat, tempImagePointMat, NORM_L2);
			total_err += err /= point_counts[i];
		}