1、原理
前提：攝像頭固定由於攝像頭一動，內參不變（畸變係數），可是外參（座標變換）會變。
經過拍攝幾張標定板的照片，而後獲得畸變係數和相機內外參係數，而後每次讀取攝像機圖片時，將這些係數帶進去，計算以後就能夠獲得矯正後的圖片了。
效果以下：ios
畸變校訂前
緩存
畸變校訂後
函數
顯然上面圖片四周直線都是彎曲的，被矯正後，變得效果不錯了。ui
2、具體步驟
標定圖：
程序在第三部分，具體步驟以下：
一、將第三步的代碼複製到工程裏
二、 在工程目錄下（主函數.cpp相同目錄下）創建一個caliberation文件夾，採集10——20張照片（不一樣角度，方向，可是要把角點所有顯示出來），將照片放入該文件夾下。spa
效果以下：
三、新建一個calibdata.txt文件，將步驟2的圖片路徑寫進去格式以下：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、新建一個chess文件夾（名字隨便，記得在程序裏改），用於保存畸變校訂後的圖片。
五、運行程序，會生成一個caliberation_result.txt文件，裏面保存了內外參等一些參數。好比畸變係數，旋轉矩陣，平移矩陣等。code
3、參數獲取程序代碼
//2018.6.19：畸變校訂
#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"); /* 標定所用圖像文件的路徑 */
ofstream fout("caliberation_result.txt"); /* 保存標定結果的文件 */
//讀取每一幅圖像，從中提取出角點，而後對角點進行亞像素精確化
cout << "開始提取角點………………";
int image_count = 0; /* 圖像數量 */
Size image_size; /* 圖像的尺寸 */
Size board_size = Size(4, 6); /* 標定板上每行、列的角點數 */
vector<Point2f> image_points_buf; /* 緩存每幅圖像上檢測到的角點 */
vector<vector<Point2f>> image_points_seq; /* 保存檢測到的全部角點 */
string filename;
int count = -1;//用於存儲角點個數。
while (getline(fin, filename))
{
image_count++;
// 用於觀察檢驗輸出
cout << "image_count = " << image_count << endl;
/* 輸出檢驗*/
cout << "-->count = " << count;
Mat imageInput = imread(filename);
if (imageInput.empty())
{
cout << "can not open pic!\n";
exit(-1);
}
if (image_count == 1) //讀入第一張圖片時獲取圖像寬高信息
{
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;
}
/* 提取角點 */
if (0 == findChessboardCorners(imageInput, board_size, image_points_buf))
{
cout << "can not find chessboard corners!\n"; //找不到角點
exit(1);
}
else
{
Mat view_gray;
cvtColor(imageInput, view_gray, CV_RGB2GRAY);
/* 亞像素精確化 */
find4QuadCornerSubpix(view_gray, image_points_buf, Size(5, 5)); //對粗提取的角點進行精確化
//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); //保存亞像素角點
/* 在圖像上顯示角點位置 */
drawChessboardCorners(view_gray, board_size, image_points_buf, false); //用於在圖片中標記角點
namedWindow("Camera Calibration", 0);//建立窗口
imshow("Camera Calibration", view_gray);//顯示圖片
waitKey(500);//暫停0.5S
}
}
int total = image_points_seq.size();
cout << "total = " << total << endl;
int CornerNum = board_size.width*board_size.height; //每張圖片上總的角點數
for (int ii = 0; ii<total; ii++)
{
if (0 == ii % CornerNum)// 24 是每幅圖片的角點個數。此判斷語句是爲了輸出 圖片號，便於控制檯觀看
{
int i = -1;
i = ii / CornerNum;
int j = i + 1;
cout << "--> 第 " << j << "圖片的數據 --> : " << endl;
}
if (0 == ii % 3) // 此判斷語句，格式化輸出，便於控制檯查看
{
cout << endl;
}
else
{
cout.width(10);
}
//輸出全部的角點
cout << " -->" << image_points_seq[ii][0].x;
cout << " -->" << image_points_seq[ii][0].y;
}
cout << "角點提取完成！\n";
//如下是攝像機標定
cout << "開始標定………………";
/*棋盤三維信息*/
Size square_size = Size(10, 10); /* 實際測量獲得的標定板上每一個棋盤格的大小 */
vector<vector<Point3f>> object_points; /* 保存標定板上角點的三維座標 */
/*內外參數*/
Mat cameraMatrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 攝像機內參數矩陣 */
vector<int> point_counts; // 每幅圖像中角點的數量
Mat distCoeffs = Mat(1, 5, CV_32FC1, Scalar::all(0)); /* 攝像機的5個畸變係數：k1,k2,p1,p2,k3 */
vector<Mat> tvecsMat; /* 每幅圖像的旋轉向量 */
vector<Mat> rvecsMat; /* 每幅圖像的平移向量 */
/* 初始化標定板上角點的三維座標 */
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;
/* 假設標定板放在世界座標系中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);
}
/* 初始化每幅圖像中的角點數量，假定每幅圖像中均可以看到完整的標定板 */
for (i = 0; i<image_count; i++)
{
point_counts.push_back(board_size.width*board_size.height);
}
/* 開始標定 */
calibrateCamera(object_points, image_points_seq, image_size, cameraMatrix, distCoeffs, rvecsMat, tvecsMat, 0);
cout << "標定完成！\n";
//對標定結果進行評價
cout << "開始評價標定結果………………\n";
double total_err = 0.0; /* 全部圖像的平均偏差的總和 */
double err = 0.0; /* 每幅圖像的平均偏差 */
vector<Point2f> image_points2; /* 保存從新計算獲得的投影點 */
cout << "\t每幅圖像的標定偏差：\n";
fout << "每幅圖像的標定偏差：\n";
for (i = 0; i<image_count; i++)
{
vector<Point3f> tempPointSet = object_points[i];
/* 經過獲得的攝像機內外參數，對空間的三維點進行從新投影計算，獲得新的投影點 */
projectPoints(tempPointSet, rvecsMat[i], tvecsMat[i], cameraMatrix, distCoeffs, image_points2);
/* 計算新的投影點和舊的投影點之間的偏差*/
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 << "幅圖像的平均偏差：" << err << "像素" << endl;
fout << "第" << i + 1 << "幅圖像的平均偏差：" << err << "像素" << endl;
}
std::cout << "整體平均偏差：" << total_err / image_count << "像素" << endl;
fout << "整體平均偏差：" << total_err / image_count << "像素" << endl << endl;
std::cout << "評價完成！" << endl;
//保存定標結果
std::cout << "開始保存定標結果………………" << endl;
Mat rotation_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 保存每幅圖像的旋轉矩陣 */
fout << "相機內參數矩陣：" << endl;
fout << cameraMatrix << endl << endl;
fout << "畸變係數：\n";
fout << distCoeffs << endl << endl << endl;
for (int i = 0; i<image_count; i++)
{
fout << "第" << i + 1 << "幅圖像的旋轉向量：" << endl;
fout << tvecsMat[i] << endl;
/* 將旋轉向量轉換爲相對應的旋轉矩陣 */
Rodrigues(tvecsMat[i], rotation_matrix);
fout << "第" << i + 1 << "幅圖像的旋轉矩陣：" << endl;
fout << rotation_matrix << endl;
fout << "第" << i + 1 << "幅圖像的平移向量：" << endl;
fout << rvecsMat[i] << endl << endl;
}
std::cout << "完成保存" << endl;
fout << endl;
/************************************************************************
顯示定標結果
*************************************************************************/
Mat mapx = Mat(image_size, CV_32FC1);
Mat mapy = Mat(image_size, CV_32FC1);
Mat R = Mat::eye(3, 3, CV_32F);
std::cout << "保存矯正圖像" << 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"); //讀取畸變圖片
Mat newimage = imageSource.clone(); //校訂後輸出圖片
//另外一種不須要轉換矩陣的方式
// 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 << "保存結束" << endl;
return;
}
4、使用程序
這個部分就是將獲得的參數，應用到具體的程序中，不用每次進行標定，只要攝像頭位置不變，就能夠將畸變參數帶進去就能夠矯正。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"); //讀取畸變圖片
Mat R = Mat::eye(3, 3, CV_32F);
Size image_size; /* 圖像的尺寸 */
//獲取圖像大小
image_size.width = 1920;
image_size.height = 1080;
//cameraMatrix爲 "相機內參數矩陣：" << endl;
Mat mapx = Mat(image_size, CV_32FC1);
Mat mapy = Mat(image_size, CV_32FC1);
//參數矩陣
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;
//測得的畸變係數
float jibian_data[] = { -6.956561513881647, -68.83902522804168, -0.004834538444671919, 0.01471273691928269, -0.4916103704308509 };
Mat distCoeffs(1, 5, CV_32FC1); /* 攝像機的5個畸變係數：k1,k2,p1,p2,k3 */
InitMat(distCoeffs, jibian_data);
cout << "distCoeffs= " << endl << " " << distCoeffs << endl << endl;
i = 0;
namedWindow("【原始圖】", 0);//參數爲零，則能夠自由拖動
imshow("【原始圖】", frame);
/********相機矯正*******************************************************************************/
initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, image_size, CV_32FC1, mapx, mapy);
Mat imageSource = frame; //讀取畸變圖片
Mat newimage = imageSource.clone(); //校訂後輸出圖片
remap(imageSource, newimage, mapx, mapy, INTER_LINEAR);
namedWindow("畸變校訂後的圖片", 0);//參數爲零，則能夠自由拖動
imshow("畸變校訂後的圖片", newimage);
}
上面只是矯正部分的代碼圖片