标定系列——基于OpenCV实现相机标定（五）
说明
该程序可以实现多种标定板的相机标定工作
代码解析
VID5.xml
<?xml version="1.0"?>
<!-- 相机拍摄的标定板图像路径名 -->
<opencv_storage>
<images>
images/CameraCalibration/VID5/xx1.jpg
images/CameraCalibration/VID5/xx2.jpg
images/CameraCalibration/VID5/xx3.jpg
images/CameraCalibration/VID5/xx4.jpg
images/CameraCalibration/VID5/xx5.jpg
images/CameraCalibration/VID5/xx6.jpg
images/CameraCalibration/VID5/xx7.jpg
images/CameraCalibration/VID5/xx8.jpg
</images>
</opencv_storage>
in_VID5.xml
<?xml version="1.0"?>
<opencv_storage>
<Settings>
<!-- 标定板尺寸. (可以是正方形、圆形) -->
<BoardSize_Width>9</BoardSize_Width>
<BoardSize_Height>6</BoardSize_Height>
<!-- 用户定义的方格的尺寸 (像素,毫米)-->
<Square_Size>50</Square_Size>
<Marker_Size>25</Marker_Size>
<!-- 相机标定所使用的标定板类型. 可以是CHESSBOARD CHARUCOBOARD CIRCLES_GRID ASYMMETRIC_CIRCLES_GRID -->
<Calibrate_Pattern>"CHESSBOARD"</Calibrate_Pattern>
<ArUco_Dict_Name>DICT_4X4_50</ArUco_Dict_Name>
<ArUco_Dict_File_Name></ArUco_Dict_File_Name>
<!-- 用于标定的输入来源。
使用输入摄像头 -> 提供摄像头的ID，例如 "1"
使用输入视频 -> 提供输入视频的路径，例如 "/tmp/x.avi"
使用图像列表 -> 提供含有图像列表的XML或YAML文件的路径，例如 "/tmp/circles_list.xml"
-->
<Input>"images/CameraCalibration/VID5/VID5.xml"</Input>
<!-- 如果为真（非零），则沿水平轴翻转输入图像 -->
<Input_FlipAroundHorizontalAxis>0</Input_FlipAroundHorizontalAxis>
<!-- 摄像头的帧之间的时间延迟 -->
<Input_Delay>100</Input_Delay>
<!-- 用于标定的帧数量 -->
<Calibrate_NrOfFrameToUse>25</Calibrate_NrOfFrameToUse>
<!-- 只考虑fy作为自由参数，比率fx/fy与输入cameraMatrix中的相同 -->
<Calibrate_FixAspectRatio> 1 </Calibrate_FixAspectRatio>
<!-- 如果为真（非零），切向畸变系数将被设置为零并保持为零 -->
<Calibrate_AssumeZeroTangentialDistortion>1</Calibrate_AssumeZeroTangentialDistortion>
<!-- 如果为真（非零），在全局优化过程中主点不会改变 -->
<Calibrate_FixPrincipalPointAtTheCenter> 1 </Calibrate_FixPrincipalPointAtTheCenter>
<!-- 输出日志文件名 -->
<Write_outputFileName>"out_camera_data.xml"</Write_outputFileName>
<!-- 如果为真（非零），将检测到的特征点写入输出文件 -->
<Write_DetectedFeaturePoints>1</Write_DetectedFeaturePoints>
<!-- 如果为真（非零），我们将外部相机参数写入输出文件 -->
<Write_extrinsicParameters>1</Write_extrinsicParameters>
<!-- 如果为真（非零），我们将优化后的3D目标网格点写入输出文件 -->
<Write_gridPoints>1</Write_gridPoints>
<!-- 如果为真（非零），校准后我们显示无畸变的图像 -->
<Show_UndistortedImage>1</Show_UndistortedImage>
<!-- 如果为真（非零），将使用鱼眼相机模型进行标定 -->
<Calibrate_UseFisheyeModel>0</Calibrate_UseFisheyeModel>
<!-- 如果为真（非零），畸变系数k1将等于零 -->
<Fix_K1>0</Fix_K1>
<!-- 如果为真（非零），畸变系数k2将等于零 -->
<Fix_K2>0</Fix_K2>
<!-- 如果为真（非零），畸变系数k3将等于零 -->
<Fix_K3>0</Fix_K3>
<!-- 如果为真（非零），畸变系数k4将等于零 -->
<Fix_K4>1</Fix_K4>
<!-- 如果为真（非零），畸变系数k5将等于零 -->
<Fix_K5>1</Fix_K5>
</Settings>
</opencv_storage>
camera_calibration.cpp
核心代码就是camera_calibration.cpp，主要通过多张标定板图像进行相机的内参和畸变参数的计算，大体看了一下，里面的逻辑很清晰，就不做过多注解了
#include <iostream>
#include <sstream>
#include <string>
#include <ctime>
#include <cstdio>
#include <opencv2/core.hpp>
#include <opencv2/core/utility.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/calib3d.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/highgui.hpp>
#include "opencv2/objdetect/charuco_detector.hpp"
using namespace cv;
using namespace std;
class Settings
{
public:
Settings() : goodInput(false) {}
enum Pattern { NOT_EXISTING, CHESSBOARD, CHARUCOBOARD, CIRCLES_GRID, ASYMMETRIC_CIRCLES_GRID };
enum InputType { INVALID, CAMERA, VIDEO_FILE, IMAGE_LIST };
void write(FileStorage& fs) const //将数据写入文件
{
fs << "{"
<< "BoardSize_Width" << boardSize.width
<< "BoardSize_Height" << boardSize.height
<< "Square_Size" << squareSize
<< "Marker_Size" << markerSize
<< "Calibrate_Pattern" << patternToUse
<< "ArUco_Dict_Name" << arucoDictName
<< "ArUco_Dict_File_Name" << arucoDictFileName
<< "Calibrate_NrOfFrameToUse" << nrFrames
<< "Calibrate_FixAspectRatio" << aspectRatio
<< "Calibrate_AssumeZeroTangentialDistortion" << calibZeroTangentDist
<< "Calibrate_FixPrincipalPointAtTheCenter" << calibFixPrincipalPoint
<< "Write_DetectedFeaturePoints" << writePoints
<< "Write_extrinsicParameters" << writeExtrinsics
<< "Write_gridPoints" << writeGrid
<< "Write_outputFileName" << outputFileName
<< "Show_UndistortedImage" << showUndistorted
<< "Input_FlipAroundHorizontalAxis" << flipVertical
<< "Input_Delay" << delay
<< "Input" << input
<< "}";
}
void read(const FileNode& node) //从文件中读
{
node["BoardSize_Width"] >> boardSize.width;
node["BoardSize_Height"] >> boardSize.height;
node["Calibrate_Pattern"] >> patternToUse;
node["ArUco_Dict_Name"] >> arucoDictName;
node["ArUco_Dict_File_Name"] >> arucoDictFileName;
node["Square_Size"] >> squareSize;
node["Marker_Size"] >> markerSize;
node["Calibrate_NrOfFrameToUse"] >> nrFrames;
node["Calibrate_FixAspectRatio"] >> aspectRatio;
node["Write_DetectedFeaturePoints"] >> writePoints;
node["Write_extrinsicParameters"] >> writeExtrinsics;
node["Write_gridPoints"] >> writeGrid;
node["Write_outputFileName"] >> outputFileName;
node["Calibrate_AssumeZeroTangentialDistortion"] >> calibZeroTangentDist;
node["Calibrate_FixPrincipalPointAtTheCenter"] >> calibFixPrincipalPoint;
node["Calibrate_UseFisheyeModel"] >> useFisheye;
node["Input_FlipAroundHorizontalAxis"] >> flipVertical;
node["Show_UndistortedImage"] >> showUndistorted;
node["Input"] >> input;
node["Input_Delay"] >> delay;
node["Fix_K1"] >> fixK1;
node["Fix_K2"] >> fixK2;
node["Fix_K3"] >> fixK3;
node["Fix_K4"] >> fixK4;
node["Fix_K5"] >> fixK5;
validate();
}
// 输入值验证
void validate()
{
goodInput = true;
if (boardSize.width <= 0 || boardSize.height <= 0)
{
cerr << "Invalid Board size: " << boardSize.width << " " << boardSize.height << endl;
goodInput = false;
}
if (squareSize <= 10e-6)
{
cerr << "Invalid square size " << squareSize << endl;
goodInput = false;
}
if (nrFrames <= 0)
{
cerr << "Invalid number of frames " << nrFrames << endl;
goodInput = false;
}
if (input.empty()) // Check for valid input
inputType = INVALID;
else
{
if (input[0] >= '0' && input[0] <= '9')
{
stringstream ss(input);
ss >> cameraID;
inputType = CAMERA;
}
else
{
if (isListOfImages(input) && readStringList(input, imageList))
{
inputType = IMAGE_LIST;
nrFrames = (nrFrames < (int)imageList.size()) ? nrFrames : (int)imageList.size();
}
else
inputType = VIDEO_FILE;
}
if (inputType == CAMERA)
inputCapture.open(cameraID);
if (inputType == VIDEO_FILE)
inputCapture.open(input);
if (inputType != IMAGE_LIST && !inputCapture.isOpened())
inputType = INVALID;
}
if (inputType == INVALID)
{
cerr << " Input does not exist: " << input;
goodInput = false;
}
flag = 0;
if(calibFixPrincipalPoint) flag |= CALIB_FIX_PRINCIPAL_POINT;
if(calibZeroTangentDist) flag |= CALIB_ZERO_TANGENT_DIST;
if(aspectRatio) flag |= CALIB_FIX_ASPECT_RATIO;
if(fixK1) flag |= CALIB_FIX_K1;
if(fixK2) flag |= CALIB_FIX_K2;
if(fixK3) flag |= CALIB_FIX_K3;
if(fixK4) flag |= CALIB_FIX_K4;
if(fixK5) flag |= CALIB_FIX_K5;
if (useFisheye) {
// the fisheye model has its own enum, so overwrite the flags
flag = fisheye::CALIB_FIX_SKEW | fisheye::CALIB_RECOMPUTE_EXTRINSIC;
if(fixK1) flag |= fisheye::CALIB_FIX_K1;
if(fixK2) flag |= fisheye::CALIB_FIX_K2;
if(fixK3) flag |= fisheye::CALIB_FIX_K3;
if(fixK4) flag |= fisheye::CALIB_FIX_K4;
if (calibFixPrincipalPoint) flag |= fisheye::CALIB_FIX_PRINCIPAL_POINT;
}
calibrationPattern = NOT_EXISTING;
if (!patternToUse.compare("CHESSBOARD")) calibrationPattern = CHESSBOARD;
if (!patternToUse.compare("CHARUCOBOARD")) calibrationPattern = CHARUCOBOARD;
if (!patternToUse.compare("CIRCLES_GRID")) calibrationPattern = CIRCLES_GRID;
if (!patternToUse.compare("ASYMMETRIC_CIRCLES_GRID")) calibrationPattern = ASYMMETRIC_CIRCLES_GRID;
if (calibrationPattern == NOT_EXISTING)
{
cerr << " Camera calibration mode does not exist: " << patternToUse << endl;
goodInput = false;
}
atImageList = 0;
}
// 获取图像
Mat nextImage()
{
Mat result;
if( inputCapture.isOpened() )
{
Mat view0;
inputCapture >> view0;
view0.copyTo(result);
}
else if( atImageList < imageList.size() )
result = imread(imageList[atImageList++], IMREAD_COLOR);
return result;
}
//读取图像名，保存在vector
static bool readStringList( const string& filename, vector<string>& l )
{
l.clear();
FileStorage fs(filename, FileStorage::READ);
if( !fs.isOpened() )
return false;
FileNode n = fs.getFirstTopLevelNode();
if( n.type() != FileNode::SEQ )
return false;
FileNodeIterator it = n.begin(), it_end = n.end();
for( ; it != it_end; ++it )
l.push_back((string)*it);
return true;
}
static bool isListOfImages( const string& filename)
{
string s(filename);
// Look for file extension
if( s.find(".xml") == string::npos && s.find(".yaml") == string::npos && s.find(".yml") == string::npos )
return false;
else
return true;
}
public:
Size boardSize; // The size of the board -> Number of items by width and height
Pattern calibrationPattern; // One of the Chessboard, ChArUco board, circles, or asymmetric circle pattern
float squareSize; // The size of a square in your defined unit (point, millimeter,etc).
float markerSize; // The size of a marker in your defined unit (point, millimeter,etc).
string arucoDictName; // The Name of ArUco dictionary which you use in ChArUco pattern
string arucoDictFileName; // The Name of file which contains ArUco dictionary for ChArUco pattern
int nrFrames; // The number of frames to use from the input for calibration
float aspectRatio; // The aspect ratio
int delay; // In case of a video input
bool writePoints; // Write detected feature points
bool writeExtrinsics; // Write extrinsic parameters
bool writeGrid; // Write refined 3D target grid points
bool calibZeroTangentDist; // Assume zero tangential distortion
bool calibFixPrincipalPoint; // Fix the principal point at the center
bool flipVertical; // Flip the captured images around the horizontal axis
string outputFileName; // The name of the file where to write
bool showUndistorted; // Show undistorted images after calibration
string input; // The input ->
bool useFisheye; // use fisheye camera model for calibration
bool fixK1; // fix K1 distortion coefficient
bool fixK2; // fix K2 distortion coefficient
bool fixK3; // fix K3 distortion coefficient
bool fixK4; // fix K4 distortion coefficient
bool fixK5; // fix K5 distortion coefficient
int cameraID;
vector<string> imageList;
size_t atImageList;
VideoCapture inputCapture;
InputType inputType;
bool goodInput;
int flag;
private:
string patternToUse;
};
static inline void read(const FileNode& node, Settings& x, const Settings& default_value = Settings())
{
if(node.empty())
x = default_value;
else
x.read(node);
}
enum { DETECTION = 0, CAPTURING = 1, CALIBRATED = 2 };
bool runCalibrationAndSave(Settings& s, Size imageSize, Mat& cameraMatrix, Mat& distCoeffs,
vector<vector<Point2f> > imagePoints, float grid_width, bool release_object);
int main(int argc, char* argv[])
{
const String keys
= "{help h usage ? | | print this message }"
"{@settings |default.xml| input setting file }"
"{d | | actual distance between top-left and top-right corners of "
"the calibration grid }"
"{winSize | 11 | Half of search window for cornerSubPix }";
CommandLineParser parser(argc, argv, keys);
parser.about("This is a camera calibration sample.\n"
"Usage: camera_calibration [configuration_file -- default ./default.xml]\n"
"Near the sample file you'll find the configuration file, which has detailed help of "
"how to edit it. It may be any OpenCV supported file format XML/YAML.");
if (!parser.check()) {
parser.printErrors();
return 0;
}
if (parser.has("help")) {
parser.printMessage();
return 0;
}
//! [file_read]
Settings s;
const string inputSettingsFile = parser.get<string>(0);
FileStorage fs(inputSettingsFile, FileStorage::READ); // Read the settings
if (!fs.isOpened())
{
cout << "Could not open the configuration file: \"" << inputSettingsFile << "\"" << endl;
parser.printMessage();
return -1;
}
fs["Settings"] >> s;
fs.release(); // close Settings file
//! [file_read]
if (!s.goodInput)
{
cout << "Invalid input detected. Application stopping. " << endl;
return -1;
}
int winSize = parser.get<int>("winSize"); // 获取角点搜索窗口大小的一半
float grid_width = s.squareSize * (s.boardSize.width - 1);
if (s.calibrationPattern == Settings::Pattern::CHARUCOBOARD) {
grid_width = s.squareSize * (s.boardSize.width - 2);
}
bool release_object = false;
if (parser.has("d")) {
grid_width = parser.get<float>("d");
release_object = true;
}
// 创建CharucoBoard棋盘对象
cv::aruco::Dictionary dictionary;
// 如果标定模式为CHARUCOBOARD，创建相应的字典
if (s.calibrationPattern == Settings::CHARUCOBOARD) {
if (s.arucoDictFileName == "") {
cv::aruco::PredefinedDictionaryType arucoDict;
if (s.arucoDictName == "DICT_4X4_50") { arucoDict = cv::aruco::DICT_4X4_50; }
else if (s.arucoDictName == "DICT_4X4_100") { arucoDict = cv::aruco::DICT_4X4_100; }
else if (s.arucoDictName == "DICT_4X4_250") { arucoDict = cv::aruco::DICT_4X4_250; }
else if (s.arucoDictName == "DICT_4X4_1000") { arucoDict = cv::aruco::DICT_4X4_1000; }
else if (s.arucoDictName == "DICT_5X5_50") { arucoDict = cv::aruco::DICT_5X5_50; }
else if (s.arucoDictName == "DICT_5X5_100") { arucoDict = cv::aruco::DICT_5X5_100; }
else if (s.arucoDictName == "DICT_5X5_250") { arucoDict = cv::aruco::DICT_5X5_250; }
else if (s.arucoDictName == "DICT_5X5_1000") { arucoDict = cv::aruco::DICT_5X5_1000; }
else if (s.arucoDictName == "DICT_6X6_50") { arucoDict = cv::aruco::DICT_6X6_50; }
else if (s.arucoDictName == "DICT_6X6_100") { arucoDict = cv::aruco::DICT_6X6_100; }
else if (s.arucoDictName == "DICT_6X6_250") { arucoDict = cv::aruco::DICT_6X6_250; }
else if (s.arucoDictName == "DICT_6X6_1000") { arucoDict = cv::aruco::DICT_6X6_1000; }
else if (s.arucoDictName == "DICT_7X7_50") { arucoDict = cv::aruco::DICT_7X7_50; }
else if (s.arucoDictName == "DICT_7X7_100") { arucoDict = cv::aruco::DICT_7X7_100; }
else if (s.arucoDictName == "DICT_7X7_250") { arucoDict = cv::aruco::DICT_7X7_250; }
else if (s.arucoDictName == "DICT_7X7_1000") { arucoDict = cv::aruco::DICT_7X7_1000; }
else if (s.arucoDictName == "DICT_ARUCO_ORIGINAL") { arucoDict = cv::aruco::DICT_ARUCO_ORIGINAL; }
else if (s.arucoDictName == "DICT_APRILTAG_16h5") { arucoDict = cv::aruco::DICT_APRILTAG_16h5; }
else if (s.arucoDictName == "DICT_APRILTAG_25h9") { arucoDict = cv::aruco::DICT_APRILTAG_25h9; }
else if (s.arucoDictName == "DICT_APRILTAG_36h10") { arucoDict = cv::aruco::DICT_APRILTAG_36h10; }
else if (s.arucoDictName == "DICT_APRILTAG_36h11") { arucoDict = cv::aruco::DICT_APRILTAG_36h11; }
else {
cout << "incorrect name of aruco dictionary \n";
return 1;
}
dictionary = cv::aruco::getPredefinedDictionary(arucoDict);
}
else {
cv::FileStorage dict_file(s.arucoDictFileName, cv::FileStorage::Mode::READ);
cv::FileNode fn(dict_file.root());
dictionary.readDictionary(fn);
}
}
else {
// default dictionary
dictionary = cv::aruco::getPredefinedDictionary(0);
}
// 创建CharucoBoard对象和检测器
cv::aruco::CharucoBoard ch_board({s.boardSize.width, s.boardSize.height}, s.squareSize, s.markerSize, dictionary);
cv::aruco::CharucoDetector ch_detector(ch_board);
std::vector<int> markerIds;
vector<vector<Point2f> > imagePoints;
Mat cameraMatrix, distCoeffs;
Size imageSize;
int mode = s.inputType == Settings::IMAGE_LIST ? CAPTURING : DETECTION;
clock_t prevTimestamp = 0;
const Scalar RED(0,0,255), GREEN(0,255,0);
const char ESC_KEY = 27;
//! [get_input]
// 循环处理图像
for(;;)
{
Mat view;
bool blinkOutput = false;
view = s.nextImage();
//----- If no more image, or got enough, then stop calibration and show result -------------
if( mode == CAPTURING && imagePoints.size() >= (size_t)s.nrFrames )
{
// 调用标定函数，成功则切换到CALIBRATED模式，否则回到DETECTION模式
if(runCalibrationAndSave(s, imageSize, cameraMatrix, distCoeffs, imagePoints, grid_width,
release_object))
mode = CALIBRATED;
else
mode = DETECTION;
}
if(view.empty()) // If there are no more images stop the loop
{
// if calibration threshold was not reached yet, calibrate now
if( mode != CALIBRATED && !imagePoints.empty() )
runCalibrationAndSave(s, imageSize, cameraMatrix, distCoeffs, imagePoints, grid_width,
release_object);
break;
}
//! [get_input]
imageSize = view.size(); // Format input image.
if( s.flipVertical ) flip( view, view, 0 );
//! [find_pattern]
vector<Point2f> pointBuf;
bool found;
int chessBoardFlags = CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE;
if(!s.useFisheye) {
// fast check erroneously fails with high distortions like fisheye
chessBoardFlags |= CALIB_CB_FAST_CHECK;
}
switch( s.calibrationPattern ) // Find feature points on the input format
{
case Settings::CHESSBOARD:
found = findChessboardCorners( view, s.boardSize, pointBuf, chessBoardFlags);
break;
case Settings::CHARUCOBOARD:
ch_detector.detectBoard( view, pointBuf, markerIds);
found = pointBuf.size() == (size_t)((s.boardSize.height - 1)*(s.boardSize.width - 1));
break;
case Settings::CIRCLES_GRID:
found = findCirclesGrid( view, s.boardSize, pointBuf );
break;
case Settings::ASYMMETRIC_CIRCLES_GRID:
found = findCirclesGrid( view, s.boardSize, pointBuf, CALIB_CB_ASYMMETRIC_GRID );
break;
default:
found = false;
break;
}
//! [find_pattern]
//! [pattern_found]
if (found) // If done with success,
{
// improve the found corners' coordinate accuracy for chessboard
if( s.calibrationPattern == Settings::CHESSBOARD)
{
Mat viewGray;
cvtColor(view, viewGray, COLOR_BGR2GRAY);
cornerSubPix( viewGray, pointBuf, Size(winSize,winSize),
Size(-1,-1), TermCriteria( TermCriteria::EPS+TermCriteria::COUNT, 30, 0.0001 ));
}
if( mode == CAPTURING && // For camera only take new samples after delay time
(!s.inputCapture.isOpened() || clock() - prevTimestamp > s.delay*1e-3*CLOCKS_PER_SEC) )
{
imagePoints.push_back(pointBuf);
prevTimestamp = clock();
blinkOutput = s.inputCapture.isOpened();
}
// Draw the corners.
if(s.calibrationPattern == Settings::CHARUCOBOARD)
drawChessboardCorners( view, cv::Size(s.boardSize.width-1, s.boardSize.height-1), Mat(pointBuf), found );
else
drawChessboardCorners( view, s.boardSize, Mat(pointBuf), found );
}
//! [pattern_found]
//----------------------------- Output Text ------------------------------------------------
//! [output_text]
string msg = (mode == CAPTURING) ? "100/100" :
mode == CALIBRATED ? "Calibrated" : "Press 'g' to start";
int baseLine = 0;
Size textSize = getTextSize(msg, 1, 1, 1, &baseLine);
Point textOrigin(view.cols - 2*textSize.width - 10, view.rows - 2*baseLine - 10);
if( mode == CAPTURING )
{
if(s.showUndistorted)
msg = cv::format( "%d/%d Undist", (int)imagePoints.size(), s.nrFrames );
else
msg = cv::format( "%d/%d", (int)imagePoints.size(), s.nrFrames );
}
putText( view, msg, textOrigin, 1, 1, mode == CALIBRATED ? GREEN : RED);
if( blinkOutput )
bitwise_not(view, view);
//! [output_text]
//------------------------- Video capture output undistorted ------------------------------
//! [output_undistorted]
if( mode == CALIBRATED && s.showUndistorted )
{
Mat temp = view.clone();
if (s.useFisheye)
{
Mat newCamMat;
fisheye::estimateNewCameraMatrixForUndistortRectify(cameraMatrix, distCoeffs, imageSize,
Matx33d::eye(), newCamMat, 1);
cv::fisheye::undistortImage(temp, view, cameraMatrix, distCoeffs, newCamMat);
}
else
undistort(temp, view, cameraMatrix, distCoeffs);
}
//! [output_undistorted]
//------------------------------ Show image and check for input commands -------------------
//! [await_input]
imshow("Image View", view);
char key = (char)waitKey(s.inputCapture.isOpened() ? 50 : s.delay);
if( key == ESC_KEY )
break;
if( key == 'u' && mode == CALIBRATED )
s.showUndistorted = !s.showUndistorted;
if( s.inputCapture.isOpened() && key == 'g' )
{
mode = CAPTURING;
imagePoints.clear();
}
//! [await_input]
}
// -----------------------Show the undistorted image for the image list ------------------------
//! [show_results]
if( s.inputType == Settings::IMAGE_LIST && s.showUndistorted && !cameraMatrix.empty())
{
Mat view, rview, map1, map2;
if (s.useFisheye) // 如果使用鱼眼镜头模型进行畸变矫正
{
Mat newCamMat; // 定义新的相机矩阵
// 估计畸变校正和矩形映射所需的新相机矩阵
fisheye::estimateNewCameraMatrixForUndistortRectify(cameraMatrix, distCoeffs, imageSize,
Matx33d::eye(), newCamMat, 1);
// 初始化畸变矫正和矩形映射
fisheye::initUndistortRectifyMap(cameraMatrix, distCoeffs, Matx33d::eye(), newCamMat, imageSize,
CV_16SC2, map1, map2);
}
else
{
initUndistortRectifyMap(
cameraMatrix, distCoeffs, Mat(),
getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, 1, imageSize, 0), imageSize,
CV_16SC2, map1, map2);
}
for(size_t i = 0; i < s.imageList.size(); i++ )
{
view = imread(s.imageList[i], IMREAD_COLOR);
if(view.empty())
continue;
remap(view, rview, map1, map2, INTER_LINEAR);
imshow("Image View", rview);
char c = (char)waitKey();
if( c == ESC_KEY || c == 'q' || c == 'Q' )
break;
}
}
//! [show_results]
return 0;
}
//! [compute_errors] 计算重映射误差的函数
static double computeReprojectionErrors( const vector<vector<Point3f> >& objectPoints,
const vector<vector<Point2f> >& imagePoints,
const vector<Mat>& rvecs, const vector<Mat>& tvecs,
const Mat& cameraMatrix , const Mat& distCoeffs,
vector<float>& perViewErrors, bool fisheye)
{
vector<Point2f> imagePoints2;
size_t totalPoints = 0;
double totalErr = 0, err;
perViewErrors.resize(objectPoints.size());
for(size_t i = 0; i < objectPoints.size(); ++i )
{
if (fisheye) // 如果是鱼眼镜头模型，使用fisheye命名空间的函数来投影点
{
fisheye::projectPoints(objectPoints[i], imagePoints2, rvecs[i], tvecs[i], cameraMatrix,
distCoeffs);
}
else
{
projectPoints(objectPoints[i], rvecs[i], tvecs[i], cameraMatrix, distCoeffs, imagePoints2);
}
err = norm(imagePoints[i], imagePoints2, NORM_L2);
size_t n = objectPoints[i].size();
perViewErrors[i] = (float) std::sqrt(err*err/n);
totalErr += err*err;
totalPoints += n;
}
return std::sqrt(totalErr/totalPoints);
}
//! [compute_errors]
//! [board_corners]计算棋盘格角点位置
static void calcBoardCornerPositions(Size boardSize, float squareSize, vector<Point3f>& corners,
Settings::Pattern patternType /*= Settings::CHESSBOARD*/)
{
corners.clear();
switch(patternType)
{
case Settings::CHESSBOARD: // 标准棋盘格
case Settings::CIRCLES_GRID: // 圆形网格棋盘
for (int i = 0; i < boardSize.height; ++i) {
for (int j = 0; j < boardSize.width; ++j) {
corners.push_back(Point3f(j*squareSize, i*squareSize, 0));
}
}
break;
case Settings::CHARUCOBOARD: // CHARUCO棋盘
for (int i = 0; i < boardSize.height - 1; ++i) {
for (int j = 0; j < boardSize.width - 1; ++j) {
corners.push_back(Point3f(j*squareSize, i*squareSize, 0));
}
}
break;
case Settings::ASYMMETRIC_CIRCLES_GRID: // 非对称圆形网格
for (int i = 0; i < boardSize.height; i++) {
for (int j = 0; j < boardSize.width; j++) {
corners.push_back(Point3f((2 * j + i % 2)*squareSize, i*squareSize, 0));
}
}
break;
default:
break;
}
}
//! [board_corners]
static bool runCalibration( Settings& s, Size& imageSize, Mat& cameraMatrix, Mat& distCoeffs,
vector<vector<Point2f> > imagePoints, vector<Mat>& rvecs, vector<Mat>& tvecs,
vector<float>& reprojErrs, double& totalAvgErr, vector<Point3f>& newObjPoints,
float grid_width, bool release_object)
{
//! [fixed_aspect]
cameraMatrix = Mat::eye(3, 3, CV_64F);
if( !s.useFisheye && s.flag & CALIB_FIX_ASPECT_RATIO )
cameraMatrix.at<double>(0,0) = s.aspectRatio;
//! [fixed_aspect]
if (s.useFisheye) {
distCoeffs = Mat::zeros(4, 1, CV_64F);
} else {
distCoeffs = Mat::zeros(8, 1, CV_64F);
}
vector<vector<Point3f> > objectPoints(1);
calcBoardCornerPositions(s.boardSize, s.squareSize, objectPoints[0], s.calibrationPattern);
if (s.calibrationPattern == Settings::Pattern::CHARUCOBOARD) {
objectPoints[0][s.boardSize.width - 2].x = objectPoints[0][0].x + grid_width;
}
else {
objectPoints[0][s.boardSize.width - 1].x = objectPoints[0][0].x + grid_width;
}
newObjPoints = objectPoints[0];
objectPoints.resize(imagePoints.size(),objectPoints[0]);
//Find intrinsic and extrinsic camera parameters
double rms;
if (s.useFisheye) {
Mat _rvecs, _tvecs;
rms = fisheye::calibrate(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, _rvecs,
_tvecs, s.flag);
rvecs.reserve(_rvecs.rows);
tvecs.reserve(_tvecs.rows);
for(int i = 0; i < int(objectPoints.size()); i++){
rvecs.push_back(_rvecs.row(i));
tvecs.push_back(_tvecs.row(i));
}
} else {
int iFixedPoint = -1;
if (release_object)
iFixedPoint = s.boardSize.width - 1;
rms = calibrateCameraRO(objectPoints, imagePoints, imageSize, iFixedPoint,
cameraMatrix, distCoeffs, rvecs, tvecs, newObjPoints,
s.flag | CALIB_USE_LU);
}
if (release_object) {
cout << "New board corners: " << endl;
cout << newObjPoints[0] << endl;
cout << newObjPoints[s.boardSize.width - 1] << endl;
cout << newObjPoints[s.boardSize.width * (s.boardSize.height - 1)] << endl;
cout << newObjPoints.back() << endl;
}
cout << "Re-projection error reported by calibrateCamera: "<< rms << endl;
bool ok = checkRange(cameraMatrix) && checkRange(distCoeffs);
objectPoints.clear();
objectPoints.resize(imagePoints.size(), newObjPoints);
totalAvgErr = computeReprojectionErrors(objectPoints, imagePoints, rvecs, tvecs, cameraMatrix,
distCoeffs, reprojErrs, s.useFisheye);
return ok;
}
// Print camera parameters to the output file
static void saveCameraParams( Settings& s, Size& imageSize, Mat& cameraMatrix, Mat& distCoeffs,
const vector<Mat>& rvecs, const vector<Mat>& tvecs,
const vector<float>& reprojErrs, const vector<vector<Point2f> >& imagePoints,
double totalAvgErr, const vector<Point3f>& newObjPoints )
{
FileStorage fs( s.outputFileName, FileStorage::WRITE );
time_t tm;
time( &tm );
struct tm *t2 = localtime( &tm );
char buf[1024];
strftime( buf, sizeof(buf), "%c", t2 );
fs << "calibration_time" << buf;
if( !rvecs.empty() || !reprojErrs.empty() )
fs << "nr_of_frames" << (int)std::max(rvecs.size(), reprojErrs.size());
fs << "image_width" << imageSize.width;
fs << "image_height" << imageSize.height;
fs << "board_width" << s.boardSize.width;
fs << "board_height" << s.boardSize.height;
fs << "square_size" << s.squareSize;
fs << "marker_size" << s.markerSize;
if( !s.useFisheye && s.flag & CALIB_FIX_ASPECT_RATIO )
fs << "fix_aspect_ratio" << s.aspectRatio;
if (s.flag)
{
std::stringstream flagsStringStream;
if (s.useFisheye)
{
flagsStringStream << "flags:"
<< (s.flag & fisheye::CALIB_FIX_SKEW ? " +fix_skew" : "")
<< (s.flag & fisheye::CALIB_FIX_K1 ? " +fix_k1" : "")
<< (s.flag & fisheye::CALIB_FIX_K2 ? " +fix_k2" : "")
<< (s.flag & fisheye::CALIB_FIX_K3 ? " +fix_k3" : "")
<< (s.flag & fisheye::CALIB_FIX_K4 ? " +fix_k4" : "")
<< (s.flag & fisheye::CALIB_RECOMPUTE_EXTRINSIC ? " +recompute_extrinsic" : "");
}
else
{
flagsStringStream << "flags:"
<< (s.flag & CALIB_USE_INTRINSIC_GUESS ? " +use_intrinsic_guess" : "")
<< (s.flag & CALIB_FIX_ASPECT_RATIO ? " +fix_aspectRatio" : "")
<< (s.flag & CALIB_FIX_PRINCIPAL_POINT ? " +fix_principal_point" : "")
<< (s.flag & CALIB_ZERO_TANGENT_DIST ? " +zero_tangent_dist" : "")
<< (s.flag & CALIB_FIX_K1 ? " +fix_k1" : "")
<< (s.flag & CALIB_FIX_K2 ? " +fix_k2" : "")
<< (s.flag & CALIB_FIX_K3 ? " +fix_k3" : "")
<< (s.flag & CALIB_FIX_K4 ? " +fix_k4" : "")
<< (s.flag & CALIB_FIX_K5 ? " +fix_k5" : "");
}
fs.writeComment(flagsStringStream.str());
}
fs << "flags" << s.flag;
fs << "fisheye_model" << s.useFisheye;
fs << "camera_matrix" << cameraMatrix;
fs << "distortion_coefficients" << distCoeffs;
fs << "avg_reprojection_error" << totalAvgErr;
if (s.writeExtrinsics && !reprojErrs.empty())
fs << "per_view_reprojection_errors" << Mat(reprojErrs);
if(s.writeExtrinsics && !rvecs.empty() && !tvecs.empty() )
{
CV_Assert(rvecs[0].type() == tvecs[0].type());
Mat bigmat((int)rvecs.size(), 6, CV_MAKETYPE(rvecs[0].type(), 1));
bool needReshapeR = rvecs[0].depth() != 1 ? true : false;
bool needReshapeT = tvecs[0].depth() != 1 ? true : false;
for( size_t i = 0; i < rvecs.size(); i++ )
{
Mat r = bigmat(Range(int(i), int(i+1)), Range(0,3));
Mat t = bigmat(Range(int(i), int(i+1)), Range(3,6));
if(needReshapeR)
rvecs[i].reshape(1, 1).copyTo(r);
else
{
//*.t() is MatExpr (not Mat) so we can use assignment operator
CV_Assert(rvecs[i].rows == 3 && rvecs[i].cols == 1);
r = rvecs[i].t();
}
if(needReshapeT)
tvecs[i].reshape(1, 1).copyTo(t);
else
{
CV_Assert(tvecs[i].rows == 3 && tvecs[i].cols == 1);
t = tvecs[i].t();
}
}
fs.writeComment("a set of 6-tuples (rotation vector + translation vector) for each view");
fs << "extrinsic_parameters" << bigmat;
}
if(s.writePoints && !imagePoints.empty() )
{
Mat imagePtMat((int)imagePoints.size(), (int)imagePoints[0].size(), CV_32FC2);
for( size_t i = 0; i < imagePoints.size(); i++ )
{
Mat r = imagePtMat.row(int(i)).reshape(2, imagePtMat.cols);
Mat imgpti(imagePoints[i]);
imgpti.copyTo(r);
}
fs << "image_points" << imagePtMat;
}
if( s.writeGrid && !newObjPoints.empty() )
{
fs << "grid_points" << newObjPoints;
}
}
//! [run_and_save]
bool runCalibrationAndSave(Settings& s, Size imageSize, Mat& cameraMatrix, Mat& distCoeffs,
vector<vector<Point2f> > imagePoints, float grid_width, bool release_object)
{
vector<Mat> rvecs, tvecs;
vector<float> reprojErrs;
double totalAvgErr = 0;
vector<Point3f> newObjPoints;
bool ok = runCalibration(s, imageSize, cameraMatrix, distCoeffs, imagePoints, rvecs, tvecs, reprojErrs,
totalAvgErr, newObjPoints, grid_width, release_object);
cout << (ok ? "Calibration succeeded" : "Calibration failed")
<< ". avg re projection error = " << totalAvgErr << endl;
if (ok)
saveCameraParams(s, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, reprojErrs, imagePoints,
totalAvgErr, newObjPoints);
return ok;
}
//! [run_and_save]
注：该标定例程为OpenCV自带，可自行查找，也可从我的博客下载https://download.csdn.net/download/jppdss/89046059
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