---
這邊以相機校正的實作為主
就不談細節了
在做相機校正之前要先準備最好15張以上拍好的棋盤格圖片
如果不想自己照的朋友
可以先用我照好的，取其中15張左右就可以了
全部都使用也可以，只是運行時間當然地會花比較久
下載連結
而使用的照片越多，結果也會越精準
---
其中這個部分
#define n_corner_cols 8 //棋盤點行數
#define n_corner_rows 6 //棋盤點列數
#define corner_size 25 //棋盤格大小 (mm)
棋盤點的行數和列數是要看交界點的數量
而不是看棋盤的格子數
所以下面這張範例圖的棋盤行數和列數是8和6
---
程式碼:
#include "opencv2\opencv.hpp"
#include <windows.h>
#include <iostream>
#include <string>
#include <cstring>
#include <vector>
#include <fstream>
#include <iostream>
#include <direct.h>
using namespace std;
using namespace cv;
#define n_corner_cols 8 //棋盤點行數
#define n_corner_rows 6 //棋盤點列數
#define corner_size 25 //棋盤格大小 (mm)
void fprintMatrix(Mat matrix, string name) //輸出矩陣
{
FILE * fp;
fp = fopen(name.c_str(), "w");
int i, j;
printf("%s size %d %d\n", name.c_str(), matrix.cols, matrix.rows);
for (i = 0; i< matrix.rows; ++i)
{
for (j = 0; j< matrix.cols; ++j)
{
fprintf(fp, "%lf ", matrix.at< double >(i, j));
}
fprintf(fp, "\n");
}
fclose(fp);
}
vector <string> getAllFilesName(string inputFolder) //取得資料夾內所有的檔案
{
vector <string> allFileName;
int n_files = 0;
wstring inputFolder_wchar = wstring(inputFolder.begin(), inputFolder.end());
const wchar_t* lookForFile = inputFolder_wchar.c_str();
WIN32_FIND_DATA findFileFullName;
HANDLE hFind;
hFind = FindFirstFile(lookForFile, &findFileFullName);
if (hFind != INVALID_HANDLE_VALUE)
{
do
{
wstring ws(findFileFullName.cFileName); //利用wstring 將wchar型態的檔案名稱轉成string型態
string findFileFullName_str(ws.begin(), ws.end());
allFileName.push_back(findFileFullName_str);
n_files++;
} while (FindNextFile(hFind, &findFileFullName) == TRUE);
}
cout << endl << "Number of files: " << n_files << endl;
return allFileName;
}
void calibration(string folder_colorImg, string folder_calibration)
{
vector <string> fileNames;
fileNames = getAllFilesName(folder_colorImg + "*.png"); //設定要找的資料為.png檔
vector<vector<Point2f>> corner_all_2D;
vector<vector<Point3f>> corner_all_3D;
Size image_size = imread(folder_colorImg + fileNames[0], CV_LOAD_IMAGE_COLOR).size();
for (int index_file = 0; index_file < fileNames.size(); index_file++)
{
cout << fileNames[index_file] << endl;
//read files
Mat img_gray = imread(folder_colorImg + fileNames[index_file], CV_LOAD_IMAGE_GRAYSCALE);
Mat img_color = imread(folder_colorImg + fileNames[index_file], CV_LOAD_IMAGE_COLOR);
imshow("img gray", img_gray);
//find corners
vector <Point2f> corner_per_2D;
vector <Point3f> corner_per_3D;
bool isFindCorner = findChessboardCorners(img_gray, Size(n_corner_cols, n_corner_rows), corner_per_2D);
if (isFindCorner && (corner_per_2D.size() == n_corner_rows*n_corner_cols))
{
//2D corners
cv::TermCriteria param(cv::TermCriteria::MAX_ITER + cv::TermCriteria::EPS, 30, 0.1);
cornerSubPix(img_gray, corner_per_2D, cv::Size(5, 5), cv::Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
drawChessboardCorners(img_color, Size(n_corner_cols, n_corner_rows), corner_per_2D, true);
imshow("img color show", img_color);
corner_all_2D.push_back(corner_per_2D);
//3D corners
for (int r = 0; r < n_corner_rows; r++)
{
for (int c = 0; c < n_corner_cols; c++)
{
corner_per_3D.push_back(Point3f(c*corner_size, r*corner_size, 0));
}
}
corner_all_3D.push_back(corner_per_3D);
}
else
{
putText(img_color, "NO FOUND", Point(0, img_color.rows / 2), FONT_HERSHEY_DUPLEX, 3, Scalar(0, 0, 255));
imshow("img color show", img_color);
}
//cv::waitKey(0); //把這個註解取消掉可以看單張找到棋盤格的狀況
}
//camera calibration
vector< Mat> rvecs, tvecs;
Mat intrinsic_Matrix(3, 3, CV_64F);
Mat distortion_coeffs(8, 1, CV_64F);
calibrateCamera(corner_all_3D, corner_all_2D, image_size, intrinsic_Matrix, distortion_coeffs, rvecs, tvecs);
cout << "Finished IMAGE Camera calibration" << endl;
//save part
fprintMatrix(intrinsic_Matrix, folder_calibration + "intrinsic.txt");
fprintMatrix(distortion_coeffs, folder_calibration + "distortion.txt");
}
int main()
{
string folder_colorImg = ".//data//color//"; //圖片的資料夾
string folder_calibration = ".//calibration//"; //要放置相機矯正資料的資料夾
_mkdir((folder_calibration).c_str()); //建立相機校正資料的資料夾
calibration(folder_colorImg, folder_calibration);
std::system("pause");
return 0;
}
---
運行之後應該就可以在資料夾 calibration 內找到校正的矩陣資料了
---
以下是使用狀態比較不好的圖片所做的實驗
如圖找不到應有的棋盤點數的話會在圖片上顯示NO FOUND字樣
也不會將該圖片的棋盤點拿去做相機校正
