给定一组2D点，我如何应用undistortPoints的反面？
我有相机内在函数和distCoeffs，并希望（例如）创建一个正方形，并扭曲它，好像相机已通过镜头观看它。
我发现了一个＆＃39;扭曲＆＃39;在这里修补：http://code.opencv.org/issues/1387但它似乎只适用于图像，我想处理稀疏点。
答案 0 :(得分：6)
这个问题相当陈旧，但由于我在谷歌搜索结束时没有看到一个简洁的答案，我决定回答它。
有一个名为projectPoints的函数就是这样做的。在使用calibrateCamera和stereoCalibrate
编辑：
要使用2D点作为输入，我们可以使用convertPointsToHomogeneous将所有z坐标设置为1，并使用projectPoints而无需旋转且无翻译。
cv::Mat points2d = ...;
cv::Mat points3d;
cv::Mat distorted_points2d;
convertPointsToHomogeneous(points2d, points3d);
projectPoints(points3d, cv::Vec3f(0,0,0), cv::Vec3f(0,0,0), camera_matrix, dist_coeffs, distorted_points2d);
答案 1 :(得分：5)
一个简单的解决方案是使用initUndistortRectifyMap从未失真的坐标到失真的坐标获取地图：
cv::Mat K = ...; // 3x3 intrinsic parameters
cv::Mat D = ...; // 4x1 or similar distortion parameters
int W = 640; // image width
int H = 480; // image height
cv::Mat mapx, mapy;
cv::initUndistortRectifyMap(K, D, cv::Mat(), K, cv::Size(W, H),
CV_32F, mapx, mapy);
float distorted_x = mapx.at<float>(y, x);
float distorted_y = mapy.at<float>(y, x);
我编辑以澄清代码是否正确：
我引用了initUndistortRectifyMap的文档：
目的地中的每个像素（u，v）（已纠正并纠正） 图像，该函数计算相应的坐标 源图像（即来自相机的原始图像。
map_x（u，v）= x''f_x + c_x
map_y（u，v）= y''f_y + c_y
答案 2 :(得分：1)
我有完全相同的需求。 这是一个可能的解决方案：
void MyDistortPoints(const std::vector<cv::Point2d> & src, std::vector<cv::Point2d> & dst,
const cv::Mat & cameraMatrix, const cv::Mat & distorsionMatrix)
{
dst.clear();
double fx = cameraMatrix.at<double>(0,0);
double fy = cameraMatrix.at<double>(1,1);
double ux = cameraMatrix.at<double>(0,2);
double uy = cameraMatrix.at<double>(1,2);
double k1 = distorsionMatrix.at<double>(0, 0);
double k2 = distorsionMatrix.at<double>(0, 1);
double p1 = distorsionMatrix.at<double>(0, 2);
double p2 = distorsionMatrix.at<double>(0, 3);
double k3 = distorsionMatrix.at<double>(0, 4);
//BOOST_FOREACH(const cv::Point2d &p, src)
for (unsigned int i = 0; i < src.size(); i++)
{
const cv::Point2d &p = src[i];
double x = p.x;
double y = p.y;
double xCorrected, yCorrected;
//Step 1 : correct distorsion
{
double r2 = x*x + y*y;
//radial distorsion
xCorrected = x * (1. + k1 * r2 + k2 * r2 * r2 + k3 * r2 * r2 * r2);
yCorrected = y * (1. + k1 * r2 + k2 * r2 * r2 + k3 * r2 * r2 * r2);
//tangential distorsion
//The "Learning OpenCV" book is wrong here !!!
//False equations from the "Learning OpenCv" book
//xCorrected = xCorrected + (2. * p1 * y + p2 * (r2 + 2. * x * x));
//yCorrected = yCorrected + (p1 * (r2 + 2. * y * y) + 2. * p2 * x);
//Correct formulae found at : http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/parameters.html
xCorrected = xCorrected + (2. * p1 * x * y + p2 * (r2 + 2. * x * x));
yCorrected = yCorrected + (p1 * (r2 + 2. * y * y) + 2. * p2 * x * y);
}
//Step 2 : ideal coordinates => actual coordinates
{
xCorrected = xCorrected * fx + ux;
yCorrected = yCorrected * fy + uy;
}
dst.push_back(cv::Point2d(xCorrected, yCorrected));
}
}
void MyDistortPoints(const std::vector<cv::Point2d> & src, std::vector<cv::Point2d> & dst,
const cv::Matx33d & cameraMatrix, const cv::Matx<double, 1, 5> & distorsionMatrix)
{
cv::Mat cameraMatrix2(cameraMatrix);
cv::Mat distorsionMatrix2(distorsionMatrix);
return MyDistortPoints(src, dst, cameraMatrix2, distorsionMatrix2);
}
void TestDistort()
{
cv::Matx33d cameraMatrix = 0.;
{
//cameraMatrix Init
double fx = 1000., fy = 950.;
double ux = 324., uy = 249.;
cameraMatrix(0, 0) = fx;
cameraMatrix(1, 1) = fy;
cameraMatrix(0, 2) = ux;
cameraMatrix(1, 2) = uy;
cameraMatrix(2, 2) = 1.;
}
cv::Matx<double, 1, 5> distorsionMatrix;
{
//distorsion Init
const double k1 = 0.5, k2 = -0.5, k3 = 0.000005, p1 = 0.07, p2 = -0.05;
distorsionMatrix(0, 0) = k1;
distorsionMatrix(0, 1) = k2;
distorsionMatrix(0, 2) = p1;
distorsionMatrix(0, 3) = p2;
distorsionMatrix(0, 4) = k3;
}
std::vector<cv::Point2d> distortedPoints;
std::vector<cv::Point2d> undistortedPoints;
std::vector<cv::Point2d> redistortedPoints;
distortedPoints.push_back(cv::Point2d(324., 249.));// equals to optical center
distortedPoints.push_back(cv::Point2d(340., 200));
distortedPoints.push_back(cv::Point2d(785., 345.));
distortedPoints.push_back(cv::Point2d(0., 0.));
cv::undistortPoints(distortedPoints, undistortedPoints, cameraMatrix, distorsionMatrix);
MyDistortPoints(undistortedPoints, redistortedPoints, cameraMatrix, distorsionMatrix);
cv::undistortPoints(redistortedPoints, undistortedPoints, cameraMatrix, distorsionMatrix);
//Poor man's unit test ensuring we have an accuracy that is better than 0.001 pixel
for (unsigned int i = 0; i < undistortedPoints.size(); i++)
{
cv::Point2d dist = redistortedPoints[i] - distortedPoints[i];
double norm = sqrt(dist.dot(dist));
std::cout << "norm = " << norm << std::endl;
assert(norm < 1E-3);
}
}
答案 3 :(得分：0)
undistortPoint是项目点的简单反向版本
在我的情况下，我想这样做：
无瑕点：
int undisortPoints(const vector<cv::Point2f> &uv, vector<cv::Point2f> &xy, const cv::Mat &M, const cv::Mat &d)
{
cv::undistortPoints(uv, xy, M, d, cv::Mat(), M);
return 0;
}
这将使点与原始图像的坐标非常相似，但不会失真。这是cv :: undistort（）函数的默认行为。
redistort积分：
int distortPoints(const vector<cv::Point2f> &xy, vector<cv::Point2f> &uv, const cv::Mat &M, const cv::Mat &d)
{
vector<cv::Point2f> xy2;
vector<cv::Point3f> xyz;
cv::undistortPoints(xy, xy2, M, cv::Mat());
for (cv::Point2f p : xy2)xyz.push_back(cv::Point3f(p.x, p.y, 1));
cv::Mat rvec = cv::Mat::zeros(3, 1, CV_64FC1);
cv::Mat tvec = cv::Mat::zeros(3, 1, CV_64FC1);
cv::projectPoints(xyz, rvec, tvec, M, d, uv);
return 0;
}
这里有点棘手的事情是首先使用线性相机模型将点投影到z = 1平面。之后，使用原始相机型号进行投影。
我觉得这些很有用，我希望它也适合你。
答案 4 :(得分：0)
这是main.cpp。它是自给自足的，除了opencv不需要其他任何东西。我不记得我在哪里找到它了，它可以在我的项目中使用。该程序将吃掉一组标准的棋盘图像，并生成带有相机所有变形的json / xml文件。
#include <iostream>
#include <sstream>
#include <time.h>
#include <stdio.h>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/highgui/highgui.hpp>
#ifndef _CRT_SECURE_NO_WARNINGS
# define _CRT_SECURE_NO_WARNINGS
#endif
using namespace cv;
using namespace std;
static void help()
{
cout << "This is a camera calibration sample." << endl
<< "Usage: calibration configurationFile" << endl
<< "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." << endl;
}
class Settings
{
public:
Settings() : goodInput(false) {}
enum Pattern { NOT_EXISTING, CHESSBOARD, CIRCLES_GRID, ASYMMETRIC_CIRCLES_GRID };
enum InputType {INVALID, CAMERA, VIDEO_FILE, IMAGE_LIST};
void write(FileStorage& fs) const //Write serialization for this class
{
fs << "{" << "BoardSize_Width" << boardSize.width
<< "BoardSize_Height" << boardSize.height
<< "Square_Size" << squareSize
<< "Calibrate_Pattern" << patternToUse
<< "Calibrate_NrOfFrameToUse" << nrFrames
<< "Calibrate_FixAspectRatio" << aspectRatio
<< "Calibrate_AssumeZeroTangentialDistortion" << calibZeroTangentDist
<< "Calibrate_FixPrincipalPointAtTheCenter" << calibFixPrincipalPoint
<< "Write_DetectedFeaturePoints" << bwritePoints
<< "Write_extrinsicParameters" << bwriteExtrinsics
<< "Write_outputFileName" << outputFileName
<< "Show_UndistortedImage" << showUndistorsed
<< "Input_FlipAroundHorizontalAxis" << flipVertical
<< "Input_Delay" << delay
<< "Input" << input
<< "}";
}
void read(const FileNode& node) //Read serialization for this class
{
node["BoardSize_Width" ] >> boardSize.width;
node["BoardSize_Height"] >> boardSize.height;
node["Calibrate_Pattern"] >> patternToUse;
node["Square_Size"] >> squareSize;
node["Calibrate_NrOfFrameToUse"] >> nrFrames;
node["Calibrate_FixAspectRatio"] >> aspectRatio;
node["Write_DetectedFeaturePoints"] >> bwritePoints;
node["Write_extrinsicParameters"] >> bwriteExtrinsics;
node["Write_outputFileName"] >> outputFileName;
node["Calibrate_AssumeZeroTangentialDistortion"] >> calibZeroTangentDist;
node["Calibrate_FixPrincipalPointAtTheCenter"] >> calibFixPrincipalPoint;
node["Input_FlipAroundHorizontalAxis"] >> flipVertical;
node["Show_UndistortedImage"] >> showUndistorsed;
node["Input"] >> input;
node["Input_Delay"] >> delay;
interprate();
}
void interprate()
{
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 (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 << " Inexistent input: " << input << endl;
goodInput = false;
}
flag = 0;
if(calibFixPrincipalPoint) flag |= CV_CALIB_FIX_PRINCIPAL_POINT;
if(calibZeroTangentDist) flag |= CV_CALIB_ZERO_TANGENT_DIST;
if(aspectRatio) flag |= CV_CALIB_FIX_ASPECT_RATIO;
calibrationPattern = NOT_EXISTING;
if (!patternToUse.compare("CHESSBOARD")) calibrationPattern = CHESSBOARD;
if (!patternToUse.compare("CIRCLES_GRID")) calibrationPattern = CIRCLES_GRID;
if (!patternToUse.compare("ASYMMETRIC_CIRCLES_GRID")) calibrationPattern = ASYMMETRIC_CIRCLES_GRID;
if (calibrationPattern == NOT_EXISTING)
{
cerr << " Inexistent camera calibration mode: " << patternToUse << endl;
goodInput = false;
}
atImageList = 0;
}
Mat nextImage()
{
Mat result;
if( inputCapture.isOpened() )
{
Mat view0;
inputCapture >> view0;
view0.copyTo(result);
}
else if( atImageList < (int)imageList.size() )
result = imread(imageList[atImageList++], CV_LOAD_IMAGE_COLOR);
return result;
}
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;
}
public:
Size boardSize; // The size of the board -> Number of items by width and height
Pattern calibrationPattern;// One of the Chessboard, circles, or asymmetric circle pattern
float squareSize; // The size of a square in your defined unit (point, millimeter,etc).
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 bwritePoints; // Write detected feature points
bool bwriteExtrinsics; // Write extrinsic parameters
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 showUndistorsed; // Show undistorted images after calibration
string input; // The input ->
int cameraID;
vector<string> imageList;
int atImageList;
VideoCapture inputCapture;
InputType inputType;
bool goodInput;
int flag;
private:
string patternToUse;
};
static 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 );
int main(int argc, char* argv[])
{
// help();
Settings s;
const string inputSettingsFile = argc > 1 ? argv[1] : "default.xml";
FileStorage fs(inputSettingsFile, FileStorage::READ); // Read the settings
if (!fs.isOpened())
{
cout << "Could not open the configuration file: \"" << inputSettingsFile << "\"" << endl;
return -1;
}
fs["Settings"] >> s;
fs.release(); // close Settings file
if (!s.goodInput)
{
cout << "Invalid input detected. Application stopping. " << endl;
return -1;
}
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;
for(int i = 0;;++i)
{
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() >= (unsigned)s.nrFrames )
{
if( runCalibrationAndSave(s, imageSize, cameraMatrix, distCoeffs, imagePoints))
mode = CALIBRATED;
else
mode = DETECTION;
}
if(view.empty()) // If no more images then run calibration, save and stop loop.
{
if( imagePoints.size() > 0 )
runCalibrationAndSave(s, imageSize, cameraMatrix, distCoeffs, imagePoints);
break;
}
imageSize = view.size(); // Format input image.
if( s.flipVertical ) flip( view, view, 0 );
vector<Point2f> pointBuf;
bool found;
switch( s.calibrationPattern ) // Find feature points on the input format
{
case Settings::CHESSBOARD:
found = findChessboardCorners( view, s.boardSize, pointBuf,
CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FAST_CHECK | CV_CALIB_CB_NORMALIZE_IMAGE);
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;
}
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(11,11),
Size(-1,-1), TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 30, 0.1 ));
}
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.
drawChessboardCorners( view, s.boardSize, Mat(pointBuf), found );
}
//----------------------------- 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.showUndistorsed)
msg = format( "%d/%d Undist", (int)imagePoints.size(), s.nrFrames );
else
msg = format( "%d/%d", (int)imagePoints.size(), s.nrFrames );
}
putText( view, msg, textOrigin, 1, 1, mode == CALIBRATED ? GREEN : RED);
if( blinkOutput )
bitwise_not(view, view);
//------------------------- Video capture output undistorted ------------------------------
if( mode == CALIBRATED && s.showUndistorsed )
{
Mat temp = view.clone();
undistort(temp, view, cameraMatrix, distCoeffs);
}
//------------------------------ Show image and check for input commands -------------------
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.showUndistorsed = !s.showUndistorsed;
if( s.inputCapture.isOpened() && key == 'g' )
{
mode = CAPTURING;
imagePoints.clear();
}
}
// -----------------------Show the undistorted image for the image list ------------------------
if( s.inputType == Settings::IMAGE_LIST && s.showUndistorsed )
{
Mat view, rview, map1, map2;
initUndistortRectifyMap(cameraMatrix, distCoeffs, Mat(),
getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, 1, imageSize, 0),
imageSize, CV_16SC2, map1, map2);
for(int i = 0; i < (int)s.imageList.size(); i++ )
{
view = imread(s.imageList[i], 1);
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;
}
}
return 0;
}
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)
{
vector<Point2f> imagePoints2;
int i, totalPoints = 0;
double totalErr = 0, err;
perViewErrors.resize(objectPoints.size());
for( i = 0; i < (int)objectPoints.size(); ++i )
{
projectPoints( Mat(objectPoints[i]), rvecs[i], tvecs[i], cameraMatrix,
distCoeffs, imagePoints2);
err = norm(Mat(imagePoints[i]), Mat(imagePoints2), CV_L2);
int n = (int)objectPoints[i].size();
perViewErrors[i] = (float) std::sqrt(err*err/n);
totalErr += err*err;
totalPoints += n;
}
return std::sqrt(totalErr/totalPoints);
}
static void calcBoardCornerPositions(Size boardSize, float squareSize, vector<Point3f>& corners,
Settings::Pattern patternType /*= Settings::CHESSBOARD*/)
{
corners.clear();
switch(patternType)
{
case Settings::CHESSBOARD:
case Settings::CIRCLES_GRID:
for( int i = 0; i < boardSize.height; ++i )
for( int j = 0; j < boardSize.width; ++j )
corners.push_back(Point3f(float( j*squareSize ), float( i*squareSize ), 0));
break;
case Settings::ASYMMETRIC_CIRCLES_GRID:
for( int i = 0; i < boardSize.height; i++ )
for( int j = 0; j < boardSize.width; j++ )
corners.push_back(Point3f(float((2*j + i % 2)*squareSize), float(i*squareSize), 0));
break;
default:
break;
}
}
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)
{
cameraMatrix = Mat::eye(3, 3, CV_64F);
if( s.flag & CV_CALIB_FIX_ASPECT_RATIO )
cameraMatrix.at<double>(0,0) = 1.0;
distCoeffs = Mat::zeros(8, 1, CV_64F);
vector<vector<Point3f> > objectPoints(1);
calcBoardCornerPositions(s.boardSize, s.squareSize, objectPoints[0], s.calibrationPattern);
objectPoints.resize(imagePoints.size(),objectPoints[0]);
//Find intrinsic and extrinsic camera parameters
double rms = calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix,
distCoeffs, rvecs, tvecs, s.flag|CV_CALIB_FIX_K4|CV_CALIB_FIX_K5);
cout << "Re-projection error reported by calibrateCamera: "<< rms << endl;
bool ok = checkRange(cameraMatrix) && checkRange(distCoeffs);
totalAvgErr = computeReprojectionErrors(objectPoints, imagePoints,
rvecs, tvecs, cameraMatrix, distCoeffs, reprojErrs);
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 )
{
FileStorage fs( s.outputFileName, FileStorage::WRITE );
time_t tm;
time( &tm );
struct tm *t2 = localtime( &tm );
char buf[1024];
strftime( buf, sizeof(buf)-1, "%c", t2 );
fs << "calibration_Time" << buf;
if( !rvecs.empty() || !reprojErrs.empty() )
fs << "nrOfFrames" << (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;
if( s.flag & CV_CALIB_FIX_ASPECT_RATIO )
fs << "FixAspectRatio" << s.aspectRatio;
if( s.flag )
{
sprintf( buf, "flags: %s%s%s%s",
s.flag & CV_CALIB_USE_INTRINSIC_GUESS ? " +use_intrinsic_guess" : "",
s.flag & CV_CALIB_FIX_ASPECT_RATIO ? " +fix_aspectRatio" : "",
s.flag & CV_CALIB_FIX_PRINCIPAL_POINT ? " +fix_principal_point" : "",
s.flag & CV_CALIB_ZERO_TANGENT_DIST ? " +zero_tangent_dist" : "" );
cvWriteComment( *fs, buf, 0 );
}
fs << "flagValue" << s.flag;
fs << "Camera_Matrix" << cameraMatrix;
fs << "Distortion_Coefficients" << distCoeffs;
fs << "Avg_Reprojection_Error" << totalAvgErr;
if( !reprojErrs.empty() )
fs << "Per_View_Reprojection_Errors" << Mat(reprojErrs);
if( !rvecs.empty() && !tvecs.empty() )
{
CV_Assert(rvecs[0].type() == tvecs[0].type());
Mat bigmat((int)rvecs.size(), 6, rvecs[0].type());
for( int i = 0; i < (int)rvecs.size(); i++ )
{
Mat r = bigmat(Range(i, i+1), Range(0,3));
Mat t = bigmat(Range(i, i+1), Range(3,6));
CV_Assert(rvecs[i].rows == 3 && rvecs[i].cols == 1);
CV_Assert(tvecs[i].rows == 3 && tvecs[i].cols == 1);
//*.t() is MatExpr (not Mat) so we can use assignment operator
r = rvecs[i].t();
t = tvecs[i].t();
}
cvWriteComment( *fs, "a set of 6-tuples (rotation vector + translation vector) for each view", 0 );
fs << "Extrinsic_Parameters" << bigmat;
}
if( !imagePoints.empty() )
{
Mat imagePtMat((int)imagePoints.size(), (int)imagePoints[0].size(), CV_32FC2);
for( int i = 0; i < (int)imagePoints.size(); i++ )
{
Mat r = imagePtMat.row(i).reshape(2, imagePtMat.cols);
Mat imgpti(imagePoints[i]);
imgpti.copyTo(r);
}
fs << "Image_points" << imagePtMat;
}
}
bool runCalibrationAndSave(Settings& s, Size imageSize, Mat& cameraMatrix, Mat& distCoeffs,vector<vector<Point2f> > imagePoints )
{
vector<Mat> rvecs, tvecs;
vector<float> reprojErrs;
double totalAvgErr = 0;
bool ok = runCalibration(s,imageSize, cameraMatrix, distCoeffs, imagePoints, rvecs, tvecs,
reprojErrs, totalAvgErr);
cout << (ok ? "Calibration succeeded" : "Calibration failed")
<< ". avg re projection error = " << totalAvgErr ;
if( ok )
saveCameraParams( s, imageSize, cameraMatrix, distCoeffs, rvecs ,tvecs, reprojErrs,
imagePoints, totalAvgErr);
return ok;
}
答案 5 :(得分：0)
对于那些仍在搜索的人，这是一个简单的python函数，会将点扭曲回去：
def distortPoints(undistortedPoints, k, d):
undistorted = np.float32(undistortedPoints[:, np.newaxis, :])
kInv = np.linalg.inv(k)
for i in range(len(undistorted)):
srcv = np.array([undistorted[i][0][0], undistorted[i][0][1], 1])
dstv = kInv.dot(srcv)
undistorted[i][0][0] = dstv[0]
undistorted[i][0][1] = dstv[1]
distorted = cv2.fisheye.distortPoints(undistorted, k, d)
return distorted
示例：
undistorted = np.array([(639.64, 362.09), (234, 567)])
distorted = distortPoints(undistorted, camK, camD)
print(distorted)