1、原理
前提：摄像头固定由于摄像头一动，内参不变（畸变系数），可是外参（坐标变换）会变。
经过拍摄几张标定板的照片，而后获得畸变系数和相机内外参系数，而后每次读取摄像机图片时，将这些系数带进去，计算以后就能够获得矫正后的图片了。
效果以下：ios
畸变校订前
缓存
畸变校订后
函数
显然上面图片四周直线都是弯曲的，被矫正后，变得效果不错了。ui
2、具体步骤
标定图：
程序在第三部分，具体步骤以下：
一、将第三步的代码复制到工程里
二、 在工程目录下（主函数.cpp相同目录下）创建一个caliberation文件夹，采集10——20张照片（不一样角度，方向，可是要把角点所有显示出来），将照片放入该文件夹下。spa
效果以下：
三、新建一个calibdata.txt文件，将步骤2的图片路径写进去格式以下：3d
./caliberation/1.jpg ./caliberation/2.jpg ./caliberation/3.jpg ./caliberation/4.jpg ./caliberation/5.jpg ./caliberation/6.jpg ./caliberation/7.jpg ./caliberation/8.jpg ./caliberation/9.jpg ./caliberation/10.jpg ./caliberation/11.jpg ./caliberation/12.jpg
四、新建一个chess文件夹（名字随便，记得在程序里改），用于保存畸变校订后的图片。
五、运行程序，会生成一个caliberation_result.txt文件，里面保存了内外参等一些参数。好比畸变系数，旋转矩阵，平移矩阵等。code
3、参数获取程序代码
//2018.6.19：畸变校订
#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;
void main()
{
ifstream fin("calibdata.txt"); /* 标定所用图像文件的路径 */
ofstream fout("caliberation_result.txt"); /* 保存标定结果的文件 */
//读取每一幅图像，从中提取出角点，而后对角点进行亚像素精确化
cout << "开始提取角点………………";
int image_count = 0; /* 图像数量 */
Size image_size; /* 图像的尺寸 */
Size board_size = Size(4, 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; /* 保存检测到的全部角点 */
string filename;
int count = -1;//用于存储角点个数。
while (getline(fin, filename))
{
image_count++;
// 用于观察检验输出
cout << "image_count = " << image_count << endl;
/* 输出检验*/
cout << "-->count = " << count;
Mat imageInput = imread(filename);
if (imageInput.empty())
{
cout << "can not open pic!\n";
exit(-1);
}
if (image_count == 1) //读入第一张图片时获取图像宽高信息
{
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, CV_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
namedWindow("Camera Calibration", 0);//建立窗口
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);
}
//输出全部的角点
cout << " -->" << image_points_seq[ii][0].x;
cout << " -->" << image_points_seq[ii][0].y;
}
cout << "角点提取完成！\n";
//如下是摄像机标定
cout << "开始标定………………";
/*棋盘三维信息*/
Size square_size = Size(10, 10); /* 实际测量获得的标定板上每一个棋盘格的大小 */
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; /* 每幅图像的旋转向量 */
vector<Mat> rvecsMat; /* 每幅图像的平移向量 */
/* 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，假定每幅图像中均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; /* 全部图像的平均偏差的总和 */
double err = 0.0; /* 每幅图像的平均偏差 */
vector<Point2f> image_points2; /* 保存从新计算获得的投影点 */
cout << "\t每幅图像的标定偏差：\n";
fout << "每幅图像的标定偏差：\n";
for (i = 0; i<image_count; i++)
{
vector<Point3f> tempPointSet = object_points[i];
/* 经过获得的摄像机内外参数，对空间的三维点进行从新投影计算，获得新的投影点 */
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偏差*/
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 << "幅图像的平均偏差：" << err << "像素" << endl;
fout << "第" << i + 1 << "幅图像的平均偏差：" << err << "像素" << endl;
}
std::cout << "整体平均偏差：" << total_err / image_count << "像素" << endl;
fout << "整体平均偏差：" << 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 */
fout << "相机内参数矩阵：" << endl;
fout << cameraMatrix << endl << endl;
fout << "畸变系数：\n";
fout << distCoeffs << endl << endl << endl;
for (int i = 0; i<image_count; i++)
{
fout << "第" << i + 1 << "幅图像的旋转向量：" << endl;
fout << tvecsMat[i] << endl;
/* 将旋转向量转换为相对应的旋转矩阵 */
Rodrigues(tvecsMat[i], rotation_matrix);
fout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl;
fout << rotation_matrix << endl;
fout << "第" << i + 1 << "幅图像的平移向量：" << endl;
fout << rvecsMat[i] << endl << endl;
}
std::cout << "完成保存" << endl;
fout << 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();
imageFileName.clear();
string filePath = "chess";
StrStm << i + 1;
StrStm >> imageFileName;
filePath += imageFileName;
filePath += ".jpg";
Mat imageSource = imread("1.jpg"); //读取畸变图片
Mat newimage = imageSource.clone(); //校订后输出图片
//另外一种不须要转换矩阵的方式
// undistort(imageSource,newimage,cameraMatrix,distCoeffs);
remap(imageSource, newimage, mapx, mapy, INTER_LINEAR);
StrStm.clear();
filePath.clear();
StrStm << i + 1;
StrStm >> imageFileName;
imageFileName += "_d.jpg";
imwrite(imageFileName, newimage);
}
std::cout << "保存结束" << endl;
return;
}
4、使用程序
这个部分就是将获得的参数，应用到具体的程序中，不用每次进行标定，只要摄像头位置不变，就能够将畸变参数带进去就能够矫正。orm
void InitMat(Mat& m, float* num)
{
for (int i = 0; i<m.rows; i++)
for (int j = 0; j<m.cols; j++)
m.at<float>(i, j) = *(num + i * m.rows + j);
}
int main()
{
int i = 1000;
int n = 1;
Mat edges;
Mat frame = imread("2.jpg"); //读取畸变图片
Mat R = Mat::eye(3, 3, CV_32F);
Size image_size; /* 图像的尺寸 */
//获取图像大小
image_size.width = 1920;
image_size.height = 1080;
//cameraMatrix为 "相机内参数矩阵：" << endl;
Mat mapx = Mat(image_size, CV_32FC1);
Mat mapy = Mat(image_size, CV_32FC1);
//参数矩阵
float neican_data[] = { 9558.649257742036, 0, 959.3165310990756, 0, 9435.752651759443, 532.7507141910969, 0, 0, 1 };
Mat cameraMatrix(3, 3, CV_32FC1);
InitMat(cameraMatrix, neican_data);
cout << "cameraMatrix= " << endl << " " << cameraMatrix << endl << endl;
//测得的畸变系数
float jibian_data[] = { -6.956561513881647, -68.83902522804168, -0.004834538444671919, 0.01471273691928269, -0.4916103704308509 };
Mat distCoeffs(1, 5, CV_32FC1); /* 摄像机的5个畸变系数：k1,k2,p1,p2,k3 */
InitMat(distCoeffs, jibian_data);
cout << "distCoeffs= " << endl << " " << distCoeffs << endl << endl;
i = 0;
namedWindow("【原始图】", 0);//参数为零，则能够自由拖动
imshow("【原始图】", frame);
/********相机矫正*******************************************************************************/
initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, image_size, CV_32FC1, mapx, mapy);
Mat imageSource = frame; //读取畸变图片
Mat newimage = imageSource.clone(); //校订后输出图片
remap(imageSource, newimage, mapx, mapy, INTER_LINEAR);
namedWindow("畸变校订后的图片", 0);//参数为零，则能够自由拖动
imshow("畸变校订后的图片", newimage);
}
上面只是矫正部分的代码图片