文章内容：
- 读取棋盘格图片进行标定
- 生成棋盘格图片
- 保存标定后的内容
// // 生成棋盘格(demo)
// void CreateGridironPattern()
// {
// // 单位转换
// int dot_per_inch = 108;
// /*
// * 这里以我惠普 光影精灵9的参数计算如下：
// * 公式： DPI = 1920 / sqrt(15.6 ^ 2 + (1920 / 1080 * 15.6)^2)
// * sqrt(15.6 ^ 2 + (1920 / 1080 * 15.6)^2) ≈ 17.76
// */
// double cm_to_inch = 0.3937; // 1cm = 0.3937inch
// double inch_to_cm = 2.54; // 1inch = 2.54cm( 1 英寸 = 2.54 厘米 是一个国际公认的单位)
// double inch_per_dot = 1.0 / 96.0;
// // 自定义标定板
// double blockSize_cm = 1.5; // 方格尺寸： 边长1.5cm的正方形
// // 设置横列方框数目
// int blockcol = 10;
// int blockrow = 8;
// int blockSize = (int)(blockSize_cm / inch_to_cm * dot_per_inch);
// cout << "标定板尺寸: " << blockSize << endl;
// int imageSizeCol = blockSize * blockrow;
// int imageSizeRow = blockSize * blockcol;
// Mat chessBoard(imageSizeCol, imageSizeRow, CV_8UC3, Scalar::all(0));
// unsigned char color = 0;
// for (int i = 0; i < imageSizeRow; i = i + blockSize)
// {
// color = ~color; // 将颜色值取反,如果开始为0，取反后为255(即黑白互换)
// for (int j = 0; j < imageSizeCol; j = j + blockSize)
// {
// Mat ROI = chessBoard(Rect(i, j, blockSize, blockSize));
// ROI.setTo(Scalar::all(color));
// color = ~color;
// }
// }
// imshow("chess board", chessBoard);
// imwrite("chessBard.jpg", chessBoard);
// waitKey(0);
// return;
// }
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <fstream>
using namespace cv;
using namespace std;
int main()
{
// 读取文件
std::vector<cv::String> images;
std::string path = "./images/*.jpg";
cv::glob(path, images);
if(images.size() == 0)
{
cout << "path is error" << endl;
return 0;
}
// 设置变量
int image_count = 0; // 图像数量
Size image_size; // 图像的尺寸
Size board_size = Size(9, 6); // 标定板上每行、列的角点数
vector<Point2f> image_points_buf; // 缓存每幅图像上检测到的角点
vector<vector<Point2f>> image_points_seq; // 保存检测到的所有角点
// 读取文件并进行操作
for (int i = 0; i < images.size(); i++)
{
image_count++;
cout << "image_count: " << image_count << endl;
Mat imageInput = cv::imread(images[i]);
if(imageInput.empty())
{
cout << "read error" << endl;
return 0;
}
//读入第一张图片时获取图像宽高信息
if (image_count == 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;
}
// 提取角点
if (0 == findChessboardCorners(imageInput, board_size, image_points_buf))
{
cout << "can not find chessboard corners!\n"; //找不到角点
exit(1);
}
else
{
Mat view_gray;
cvtColor(imageInput, view_gray, COLOR_RGB2GRAY);
// 亚像素精确化
find4QuadCornerSubpix(view_gray, image_points_buf, Size(5, 5)); //对粗提取的角点进行精确化
//cornerSubPix(view_gray,image_points_buf,Size(5,5),Size(-1,-1),TermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,0.1));
image_points_seq.push_back(image_points_buf); //保存亚像素角点
// 在图像上显示角点位置
drawChessboardCorners(view_gray, board_size, image_points_buf, false); //用于在图片中标记角点
imshow("Camera Calibration", view_gray); //显示图片
waitKey(500);//暂停0.5S
}
}
int total = image_points_seq.size();
cout << "total = " << total << endl;
int CornerNum = board_size.width * board_size.height; //每张图片上总的角点数
for (int ii = 0 ; ii < total ; ii++)
{
if (0 == ii % CornerNum) // 24 是每幅图片的角点个数。此判断语句是为了输出 图片号，便于控制台观看
{
int i = -1;
i = ii / CornerNum;
int j = i + 1;
cout << "--> 第 " << j << "图片的数据 --> : " << endl;
}
if (0 == ii % 3) // 此判断语句，格式化输出，便于控制台查看
{
cout << endl;
}
else
{
cout.width(10);
}
//输出所有的角点
cout << " -->" << image_points_seq[ii][0].x;
cout << " -->" << image_points_seq[ii][0].y;
}
cout << "角点提取完成！\n";
//以下是摄像机标定
cout << "开始标定………………";
/*棋盘三维信息*/
Size square_size = Size(10, 10); /* 实际测量得到的标定板上每个棋盘格的大小 */
vector<vector<Point3f>> object_points; /* 保存标定板上角点的三维坐标 */
/*内外参数*/
Mat cameraMatrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 摄像机内参数矩阵 */
vector<int> point_counts; // 每幅图像中角点的数量
Mat distCoeffs = Mat(1, 5, CV_32FC1, Scalar::all(0)); /* 摄像机的5个畸变系数：k1,k2,p1,p2,k3 */
vector<Mat> tvecsMat; /* 每幅图像的平移向量 */
vector<Mat> rvecsMat; /* 每幅图像的旋转向量 */
/* 初始化标定板上角点的三维坐标 */
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;
/* 假设标定板放在世界坐标系中z=0的平面上 */
realPoint.x = i * square_size.width;
realPoint.y = j * square_size.height;
realPoint.z = 0;
tempPointSet.push_back(realPoint);
}
}
object_points.push_back(tempPointSet);
}
/* 初始化每幅图像中的角点数量，假定每幅图像中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);
}
/* 开始标定 */
calibrateCamera(object_points, image_points_seq, image_size, cameraMatrix, distCoeffs, rvecsMat, tvecsMat, 0);
cout << "标定完成！\n";
//对标定结果进行评价
cout << "开始评价标定结果………………\n";
double total_err = 0.0; /* 所有图像的平均误差的总和 */
double err = 0.0; /* 每幅图像的平均误差 */
vector<Point2f> image_points2; /* 保存重新计算得到的投影点 */
cout << "\t每幅图像的标定误差：\n";
cout << "每幅图像的标定误差：\n";
for (i = 0; i < image_count; i++)
{
vector<Point3f> tempPointSet = object_points[i];
/* 通过得到的摄像机内外参数，对空间的三维点进行重新投影计算，得到新的投影点 */
projectPoints(tempPointSet, rvecsMat[i], tvecsMat[i], cameraMatrix, distCoeffs, image_points2);
/* 计算新的投影点和旧的投影点之间的误差*/
vector<Point2f> tempImagePoint = image_points_seq[i];
Mat tempImagePointMat = Mat(1, tempImagePoint.size(), CV_32FC2);
Mat image_points2Mat = Mat(1, image_points2.size(), CV_32FC2);
for (int 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];
std::cout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
cout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
}
std::cout << "总体平均误差：" << total_err / image_count << "像素" << endl;
cout << "总体平均误差：" << total_err / image_count << "像素" << endl << endl;
std::cout << "评价完成！" << endl;
//保存定标结果
std::cout << "开始保存定标结果………………" << endl;
Mat rotation_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 保存每幅图像的旋转矩阵 */
cout << "相机内参数矩阵：" << endl;
cout << cameraMatrix << endl << endl;
cout << "畸变系数：\n";
cout << distCoeffs << endl << endl << endl;
for (int i = 0; i < image_count; i++)
{
cout << "第" << i + 1 << "幅图像的旋转向量：" << endl;
cout << rvecsMat[i] << endl;
/* 将旋转向量转换为相对应的旋转矩阵 */
Rodrigues(rvecsMat[i], rotation_matrix);
cout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl;
cout << rotation_matrix << endl;
cout << "第" << i + 1 << "幅图像的平移向量：" << endl;
cout << tvecsMat[i] << endl << endl;
}
std::cout << "完成保存" << endl;
cout << endl;
/************************************************************************
显示定标结果
*************************************************************************/
Mat mapx = Mat(image_size, CV_32FC1);
Mat mapy = Mat(image_size, CV_32FC1);
Mat R = Mat::eye(3, 3, CV_32F);
std::cout << "保存矫正图像" << endl;
string imageFileName;
std::stringstream StrStm;
for (int i = 0 ; i < image_count ; i++)
{
std::cout << "Frame #" << i + 1 << "..." << endl;
initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, image_size, CV_32FC1, mapx, mapy);
StrStm.clear();
cout << images[i] << endl;
Mat imageSource = imread(images[i]);
Mat newimage = imageSource.clone();
//另一种不需要转换矩阵的方式
//undistort(imageSource,newimage,cameraMatrix,distCoeffs);
remap(imageSource, newimage, mapx, mapy, INTER_LINEAR);
StrStm.clear();
StrStm << i + 1;
StrStm >> imageFileName;
imageFileName += "_d.jpg";
imwrite(imageFileName, newimage);
}
std::cout << "保存结束" << endl;
}