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I'm trying to get opencv camera calibration working but having trouble getting it to output valid data. I have an uncalibrated camera that I would like to calibrate, but to test my code I am using an Azure Kinect camera (the color camera), since the SDK supplies the correct intrinsics for it and I can verify them. I've collected 30 images of a chessboard from slightly different angles, which I understand should be sufficient, and run the calibration function, but no matter what flags I pass in I get values for fx and fy that are pretty different from the correct fx and fy, and distortion coefficients that are WILDLY different. Am I doing something wrong? Do I need more or better data?
A sample of the images I'm using can be found here: https://www.dropbox.com/sh/9pa94uedoe5mlxz/AABisSvgWwBT-bY65lfzp2N3a?dl=0
Save them in c:\calibration_test to run the code below.
#include <filesystem>
#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
using namespace std;
namespace fs = experimental::filesystem;
static bool extractCorners(cv::Mat colorImage, vector<cv::Point3f>& corners3d, vector<cv::Point2f>& corners)
{
// Each square is 20x20mm
const float kSquareSize = 0.020f;
const cv::Size boardSize(7, 9);
const cv::Point3f kCenterOffset((float)(boardSize.width - 1) * kSquareSize, (float)(boardSize.height - 1) * kSquareSize, 0.f);
cv::Mat image;
cv::cvtColor(colorImage, image, cv::COLOR_BGRA2GRAY);
int chessBoardFlags = cv::CALIB_CB_ADAPTIVE_THRESH | cv::CALIB_CB_NORMALIZE_IMAGE;
if (!cv::findChessboardCorners(image, boardSize, corners, chessBoardFlags))
{
return false;
}
cv::cornerSubPix(image, corners, cv::Size(11, 11), cv::Size(-1, -1),
cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 30, 0.1));
// Construct the corners
for (int i = 0; i < boardSize.height; ++i)
for (int j = 0; j < boardSize.width; ++j)
corners3d.push_back(cv::Point3f(j * kSquareSize, i * kSquareSize, 0) - kCenterOffset);
return true;
}
int main()
{
vector<cv::Mat> frames;
for (const auto& p : fs::directory_iterator("c:\\calibration_test\\"))
{
frames.push_back(cv::imread(p.path().string()));
}
int numFrames = (int)frames.size();
vector<vector<cv::Point2f>> corners(numFrames);
vector<vector<cv::Point3f>> corners3d(numFrames);
int framesWithCorners = 0;
for (int i = 0; i < numFrames; ++i)
{
if (extractCorners(frames[i], corners3d[framesWithCorners], corners[framesWithCorners]))
{
++framesWithCorners;
}
}
numFrames = framesWithCorners;
corners.resize(numFrames);
corners3d.resize(numFrames);
// Camera intrinsics come from the Azure Kinect API
cv::Matx33d cameraMatrix(
914.111755f, 0.f, 960.887390f,
0.f, 913.880615f, 551.566528f,
0.f, 0.f, 1.f);
vector<float> distCoeffs = { 0.576340079f, -2.71203661f, 0.000563957903f, -0.000239689150f, 1.54344523f, 0.454746544f, -2.53860712f, 1.47272563f };
cv::Size imageSize = frames[0].size();
vector<cv::Point3d> rotations;
vector<cv::Point3d> translations;
int flags = cv::CALIB_USE_INTRINSIC_GUESS | cv::CALIB_FIX_PRINCIPAL_POINT | cv::CALIB_RATIONAL_MODEL;
double result = cv::calibrateCamera(corners3d, corners, imageSize, cameraMatrix, distCoeffs, rotations, translations,
flags);
// After this call, cameraMatrix has different values for fx and fy, and WILDLY different distortion coefficients.
cout << "fx: " << cameraMatrix(0, 0) << endl;
cout << "fy: " << cameraMatrix(1, 1) << endl;
cout << "cx: " << cameraMatrix(0, 2) << endl;
cout << "cy: " << cameraMatrix(1, 2) << endl;
for (size_t i = 0; i < distCoeffs.size(); ++i)
{
cout << "d" << i << ": " << distCoeffs[i] << endl;
}
return 0;
}
Some sample output is:
fx: 913.143
fy: 917.965
cx: 960.887
cy: 551.567
d0: 0.327596
d1: -73.1837
d2: -0.00125972
d3: 0.002805
d4: -7.93086
d5: 0.295437
d6: -73.481
d7: -3.25043
d8: 0
d9: 0
d10: 0
d11: 0
d12: 0
d13: 0
Any idea what I'm doing wrong?
Bonus question: Why do I get 14 distortion coefficients back instead of 8? If I leave off CALIB_RATIONAL_MODEL then I only get 5 (three radial and two tangential).
You need to take images from the whole field of view of the camera to correctly capture the lens distortion characteristics. The images you provide only show the chessboad in one position, slightly angled.
Ideally you should have images of the chessboard evenly distributed over the x and y axis of the image plane, right up to the edges of the image. Make sure sufficient white boarder around the board is always visible though for detection robustness.
You should also try to capture images where the chessboard is nearer to the camera and farther away, not just a uniform distance. The different angles you provide look good on the other hand.
You can find an extensive guide how to ensure good calibration results in this answer: How to verify the correctness of calibration of a webcam?
Comparing your camera matrix to the one coming from Azure Kinect API it doesn't look so bad. The principle point is pretty spot on and the focal length is in a reasonable range. If you improve the quality of the input with my tips and the SO answer I have provided the results should be even closer. Comparing sets of distortion coefficients by their distance doesn't really work that well, the error function is not convex so you can have lots of local minima that produce relatively good results but they are far from the global minimum that would yield the best results. If that explanation makes sense to you.
Regarding your bonus question: I only see 8 values filled in in the output you return, the rest is 0 so doesn't have any influence. I'm not sure if the output is expected to be different from that function.
My goal is to use an SVM w/ HOG features to classify vehicles in traffic under sedans and SUVs.
I've used various kernels (RBF, LINEAR, POLY) and each give different results, but they give the same results no matter the parameters changed. For example, if I am using a POLY kernel and the degree is greater than or equal to .65 it will classify everything as an SUV, if its less than .65 then it will classify all my testing images as sedans.
With a LINEAR kernel, the only parameter changed is C. No matter what the parameter C is, I always get 8/10 images classified as sedans and the same 2 classified as SUVs.
Now I only have about 70 training images and 10 testing images, I haven't been able to find a good dataset of vehicles from the rear and up like from a bridge that I will be using this for. Could the problem be due to this small dataset, or the parameters, or something else? Also, I see that my support vectors are usually very high, like 58 out of the 70 training images, so that may be a problem with the dataset? Is there a way for me to visualize the training points somehow--in the SVM examples they always have a nice 2D plot of points and draw a line through it, but is there a way to plot those points with images so I can see if my data is linearly separable and make adjustments accordingly? Are my HOG parameters accurate for a 150x200 image of a car?
Also note that when I use testing images that are the same as training images, then the SVM model predicts perfectly, but obviously that's cheating.
The following image shows the result, and an example of a testing image
Here is my code, I didn't include most of it because I'm not sure the code is the problem. First I take the positive images, extract HOG features, then load them into the training Mat, and then do the same for the negative images in the same way that I do for the included testing part.
//Set SVM Parameters (not sure about these values, but just wanna see something)
Ptr<SVM> svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::POLY);
svm->setC(50);
svm->setGamma(100);
svm->setDegree(.65);
//svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
cout << "Parameters Set..." << endl;
svm->train(HOGFeat_train, ROW_SAMPLE, labels_mat);
Mat SV = svm->getSupportVectors();
Mat USV = svm->getUncompressedSupportVectors();
cout << "Support Vectors: " << SV.rows << endl;
cout << "Uncompressed Support Vectors: " << USV.rows << endl;
cout << "Training Successful" << endl;
waitKey(0);
//TESTING PORTION
cout << "Begin Testing..." << endl;
int num_test_images = 10;
Mat HOGFeat_test(1, derSize, CV_32FC1); //Creates a 1 x descriptorSize Mat to house the HoG features from the test image
for (int file_count = 1; file_count < (num_test_images + 1); file_count++)
{
test << nameTest << file_count << type; //'Test_1.jpg' ... 'Test_2.jpg' ... etc ...
string filenameTest = test.str();
test.str("");
Mat test_image = imread(filenameTest, 0); //Read the file folder
HOGDescriptor hog_test;// (Size(64, 64), Size(32, 32), Size(16, 16), Size(32, 32), 9, 1, -1, 0, .2, 1, 64, false);
vector<float> descriptors_test;
vector<Point> locations_test;
hog_test.compute(test_image, descriptors_test, Size(64, 64), Size(0, 0), locations_test);
for (int i = 0; i < descriptors_test.size(); i++)
HOGFeat_test.at<float>(0, i) = descriptors_test.at(i);
namedWindow("Test Image", CV_WINDOW_NORMAL);
imshow("Test Image", test_image);
//Should return a 1 if its an SUV, or a -1 if its a sedan
float result = svm->predict(HOGFeat_test);
if (result <= 0)
cout << "Sedan" << endl;
else
cout << "SUV" << endl;
cout << "Result: " << result << endl;
waitKey(0);
}
Two things solved this issue:
1) I got a larger dataset of vehicles. I used about 400 SUV images and 400 sedan images for the training portion and then another 50 images for the testing portion.
2) In: Mat HOGFeat_test(1, derSize, CV_32FC1), I had the wrong derSize by about an order of magnitude larger. The actual size was 15120, but I had the Mat have 113400 columns. Thus, I filled only about 10% of the testing mat with useful feature data, so it was much harder for the SVM to tell any difference between SUVs and Sedans.
Now it works great with both the linear and poly kernel (C = 10), and my accuracy is better than I expected at a whopping 96%.
I am trying to get the pose of the camera with the help of solvePNP() from OpenCV.
After running my program I get the following errors:
OpenCV Error: Assertion failed (npoints >= 0 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F))) in solvePnP, file /opt/local/var/macports/build/_opt_local_var_macports_sources_rsync.macports.org_release_tarballs_ports_graphics_opencv/opencv/work/OpenCV-2.4.2/modules/calib3d/src/solvepnp.cpp, line 55
libc++abi.dylib: terminate called throwing an exception
I tried to search how to solve these errors, but I couldn't resolve it unfortunately!
Here is my code, all comment/help is much appreciated:
enum Pattern { NOT_EXISTING, CHESSBOARD, CIRCLES_GRID, ASYMMETRIC_CIRCLES_GRID };
void calcBoardCornerPositions(Size boardSize, float squareSize, vector<Point3f>& corners,
Pattern patternType)
{
corners.clear();
switch(patternType)
{
case CHESSBOARD:
case 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 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;
}
}
int main(int argc, char* argv[])
{
float squareSize = 50.f;
Pattern calibrationPattern = CHESSBOARD;
//vector<Point2f> boardCorners;
vector<vector<Point2f> > imagePoints(1);
vector<vector<Point3f> > boardPoints(1);
Size boardSize;
boardSize.width = 9;
boardSize.height = 6;
vector<Mat> intrinsics, distortion;
string filename = "out_camera_xml.xml";
FileStorage fs(filename, FileStorage::READ);
fs["camera_matrix"] >> intrinsics;
fs["distortion_coefficients"] >> distortion;
fs.release();
vector<Mat> rvec, tvec;
Mat img = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE); // at kell adnom egy kepet
bool found = findChessboardCorners(img, boardSize, imagePoints[0], CV_CALIB_CB_ADAPTIVE_THRESH);
calcBoardCornerPositions(boardSize, squareSize, boardPoints[0], calibrationPattern);
boardPoints.resize(imagePoints.size(),boardPoints[0]);
//***Debug start***
cout << imagePoints.size() << endl << boardPoints.size() << endl << intrinsics.size() << endl << distortion.size() << endl;
//***Debug end***
solvePnP(Mat(boardPoints), Mat(imagePoints), intrinsics, distortion, rvec, tvec);
for(int i=0; i<rvec.size(); i++) {
cout << rvec[i] << endl;
}
return 0;
}
EDIT (some debug info):
I debugged it row by row. I stepped into all of the functions. I am getting the Assertion failed in SolvePNP(...). You can see below what I see when I step into the solvePNP function. First it jumps over the first if statement /if(vec.empty())/, and goes into the second if statement /if( !copyData )/, there when it executes the last line /*datalimit = dataend = datastart + rows*step[0]*/ jumps back to the first if statement and returns => than I get the Assertion failed error.
template<typename _Tp> inline Mat::Mat(const vector<_Tp>& vec, bool copyData)
: flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG),
dims(2), rows((int)vec.size()), cols(1), data(0), refcount(0),
datastart(0), dataend(0), allocator(0), size(&rows)
{
if(vec.empty())
return;
if( !copyData )
{
step[0] = step[1] = sizeof(_Tp);
data = datastart = (uchar*)&vec[0];
datalimit = dataend = datastart + rows*step[0];
}
else
Mat((int)vec.size(), 1, DataType<_Tp>::type, (uchar*)&vec[0]).copyTo(*this);
}
Step into the function in a debugger and see exactly which assertion is failing. ( Probably it requires values in double (CV_64F) rather than float. )
OpenCVs new "inputarray" wrapper issuppsoed to allow you to call functions with any shape of mat, vector of points, etc - and it will sort it out. But a lot of functions assume a particular inut format or have obsolete assertions enforcing a particular format.
The stereo/calibration systems are the worst for requiring a specific layout, and frequently succesive operations require a different layout.
The types don't seem right, at least in the code that worked for me I used different types(as mentioned in the documentation).
objectPoints – Array of object points in the object coordinate space, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector can be also passed here.
imagePoints – Array of corresponding image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points.
vector can be also passed here.
cameraMatrix – Input camera matrix A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1} .
distCoeffs – Input
vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4,
k_5, k_6]]) of 4, 5, or 8 elements. If the vector is NULL/empty, the
zero distortion coefficients are assumed.
rvec – Output rotation vector (see Rodrigues() ) that, together with tvec , brings points from the model coordinate system to the
camera coordinate system.
tvec – Output translation vector.
useExtrinsicGuess – If true (1), the function uses the provided rvec and tvec values as initial
approximations of the rotation and translation vectors, respectively,
and further optimizes them.
Documentation from here.
vector<Mat> rvec, tvec should be Mat rvec, tvec instead.
vector<vector<Point2f> > imagePoints(1) should be vector<Point2f> imagePoints(1) instead.
vector<vector<Point3f> > boardPoints(1) should be
vector<Point3f> boardPoints(1) instead.
Note: I encountered the exact same problem, and this worked for me(It is a little bit confusing since calibrateCamera use vectors). Haven't tried it for imagePoints or boardPoints though.(but as it is documented in the link above, vector,vector should work, I thought I'd better mention it), but for rvec,trec I tried it myself.
I run in exactly the same problem with solvePnP and opencv3. I tried to isolate the problem in a single test case. I seams passing a std::vector to cv::InputArray does not what is expected. The following small test works with opencv 2.4.9 but not with 3.2.
And this is exactly the problem when passing a std::vector of points to solvePnP and causes the assert at line 63 in solvepnp.cpp to fail !
Generating a cv::mat out of the vector list before passing to solvePnP works.
//create list with 3 points
std::vector<cv::Point3f> vectorList;
vectorList.push_back(cv::Point3f(1.0, 1.0, 1.0));
vectorList.push_back(cv::Point3f(1.0, 1.0, 1.0));
vectorList.push_back(cv::Point3f(1.0, 1.0, 1.0));
//to input array
cv::InputArray inputArray(vectorList);
cv::Mat mat = inputArray.getMat();
cv::Mat matDirect = cv::Mat(vectorList);
LOG_INFO("Size vector: %d mat: %d matDirect: %d", vectorList.size(), mat.checkVector(3, CV_32F), matDirect.checkVector(3, CV_32F));
QVERIFY(vectorList.size() == mat.checkVector(3, CV_32F));
Result opencv 2.4.9 macos:
TestObject: OpenCV
Size vector: 3 mat: 3 matDirect: 3
Result opencv 3.2 win64:
TestObject: OpenCV
Size vector: 3 mat: 9740 matDirect: 3
I faced the same issue. In my case, (in python) converted the input array type as float.
It worked fine afterwards.
I'm trying to perform an RGB Color mixing operation in opencv. I have the image contained in an MxNx3 Mat. I would like to multiple this with a 3x3 matrix. In Matlab I do the following:
*Flatten the image from MxNx3 to a MNx3
*multiply the MNx3 matrix by the 3x3 color mixing matrix
*reshape back to a MxNx3
In Opencv I would like to do the following:
void RGBMixing::mixColors(Mat &imData, Mat &rgbMixData)
{
float rgbmix[] = {1.4237, -0.12364, -0.30003, -0.65221, 2.1936, -0.54141, -0.38854, -0.47458, 1.8631};
Mat rgbMixMat(3, 3, CV_32F, rgbmix);
// Scale the coefficents
multiply(rgbMixMat, 1, rgbMixMat, 256);
Mat temp = imData.reshape(0, 1);
temp = temp.t();
multiply(temp, rgbMixMat, rgbMixData);
}
This compiles but generates an exception:
OpenCV Error: Sizes of input arguments do not match (The operation is
neither 'a rray op array' (where arrays have the same size and the
same number of channels) , nor 'array op scalar', nor 'scalar op
array') in arithm_op, file C:/slave/WinI
nstallerMegaPack/src/opencv/modules/core/src/arithm.cpp, line 1253
terminate called after throwing an instance of 'cv::Exception'
what():
C:/slave/WinInstallerMegaPack/src/opencv/modules/core/src/arithm.cpp:
1253: error: (-209) The operation is neither 'array op array' (where
arrays have the same size and the same number of channels), nor
'array op scalar', nor 'sca lar op array' in function arithm_op
This application has requested the Runtime to terminate it in an
unusual way. Please contact the application's support team for more
information.
Update 1:
This is code that appears to work:
void RGBMixing::mixColors(Mat &imData, Mat&rgbMixData)
{
Size tempSize;
uint32_t channels;
float rgbmix[] = {1.4237, -0.12364, -0.30003, -0.65221, 2.1936, -0.54141, -0.38854, -0.47458, 1.8631};
Mat rgbMixMat(3, 3, CV_32F, rgbmix);
Mat flatImage = imData.reshape(1, 3);
tempSize = flatImage.size();
channels = flatImage.channels();
cout << "temp channels: " << channels << " Size: " << tempSize.width << " x " << tempSize.height << endl;
Mat flatFloatImage;
flatImage.convertTo(flatFloatImage, CV_32F);
Mat mixedImage = flatFloatImage.t() * rgbMixMat;
mixedImage = mixedImage.t();
rgbMixData = mixedImage.reshape(3, 1944);
channels = rgbMixData.channels();
tempSize = rgbMixData.size();
cout << "temp channels: " << channels << " Size: " << tempSize.width << " x " << tempSize.height << endl;
}
But the resulting image is distorted. If I skip the multiplication of the two matrices and just assign
mixedImage = flatFloatImage
The resulting image looks fine (just not color mixed). So I must be doing something wrong, but am getting close.
I see a couple of things here:
For scaling the coefficients, OpenCV supports multiplication by scalar so instead of multiply(rgbMixMat, 1, rgbMixMat, 256); you should do directly rgbMixMat = 256 * rgbMixMat;.
If that is all your code, you don't properly initialize or assign values to imData, so the line Mat temp = imData.reshape(0, 1); is probably going to crash.
Assuming that imData is a MxNx3 (3-channel Mat), you want to reshape that into a MNx3 (1-channel). According to the documentation, when you write Mat temp = imData.reshape(0, 1); you are saying that you want the number of channels to remain the same, and the row, should be 1. Instead, it should be:
Mat myData = Mat::ones(100, 100, CV_32FC3); // 100x100x3 matrix
Mat myDataReshaped = myData.reshape(1, myData.rows*myData.cols); // 10000x3 matrix
Why do you take the transpose temp = temp.t(); ?
When you write multiply(temp, rgbMixMat, mixData);, this is the per-element product. You want the matrix product, so you just have to do mixData = myDataReshaped * rgbMixMat; (and then reshape that).
Edit: It crashes if you don't use the transpose, because you do imData.reshape(1, 3); instead of imData.reshape(1, imData.rows);
Try
void RGBMixing::mixColors(Mat &imData, Mat&rgbMixData)
{
Size tempSize;
uint32_t channels;
float rgbmix[] = {1.4237, -0.12364, -0.30003, -0.65221, 2.1936, -0.54141, -0.38854, -0.47458, 1.8631};
Mat rgbMixMat(3, 3, CV_32F, rgbmix);
Mat flatImage = imData.reshape(1, imData.rows*imData.cols);
Mat flatFloatImage;
flatImage.convertTo(flatFloatImage, CV_32F);
Mat mixedImage = flatFloatImage * rgbMixMat;
rgbMixData = mixedImage.reshape(3, imData.rows);
}
My questions are:
How do I figure out if my fundamental matrix is correct?
Is the code I posted below a good effort toward that?
My end goal is to do some sort of 3D reconstruction. Right now I'm trying to calculate the fundamental matrix so that I can estimate the difference between the two cameras. I'm doing this within openFrameworks, using the ofxCv addon, but for the most part it's just pure OpenCV. It's difficult to post code which isolates the problem since ofxCv is also in development.
My code basically reads in two 640x480 frames taken by my webcam from slightly different positions (basically just sliding the laptop a little bit horizontally). I already have a calibration matrix for it, obtained from ofxCv's calibration code, which uses findChessboardCorners. The undistortion example code seems to indicate that the calibration matrix is accurate. It calculates the optical flow between the pictures (either calcOpticalFlowPyrLK or calcOpticalFlowFarneback), and feeds those point pairs to findFundamentalMatrix.
To test if the fundamental matrix is valid, I decomposed it to a rotation and translation matrix. I then multiplied the rotation matrix by the points of the second image, to see what the rotation difference between the cameras was. I figured that any difference should be small, but I'm getting big differences.
Here's the fundamental and rotation matrix of my last code, if it helps:
fund: [-8.413948689969405e-07, -0.0001918870646474247, 0.06783422344973795;
0.0001877654679452431, 8.522397812179886e-06, 0.311671691674232;
-0.06780237856576941, -0.3177275967586101, 1]
R: [0.8081771697692786, -0.1096128431920695, -0.5786490187247098;
-0.1062963539438068, -0.9935398408215166, 0.03974506055610323;
-0.5792674230456705, 0.02938723035105822, -0.8146076621848839]
t: [0, 0.3019063882496216, -0.05799044915951077;
-0.3019063882496216, 0, -0.9515721940769112;
0.05799044915951077, 0.9515721940769112, 0]
Here's my portion of the code, which occurs after the second picture is taken:
const ofImage& image1 = images[images.size() - 2];
const ofImage& image2 = images[images.size() - 1];
std::vector<cv::Point2f> points1 = flow->getPointsPrev();
std::vector<cv::Point2f> points2 = flow->getPointsNext();
std::vector<cv::KeyPoint> keyPoints1 = convertFrom(points1);
std::vector<cv::KeyPoint> keyPoints2 = convertFrom(points2);
std::cout << "points1: " << points1.size() << std::endl;
std::cout << "points2: " << points2.size() << std::endl;
fundamentalMatrix = (cv::Mat)cv::findFundamentalMat(points1, points2);
cv::Mat cameraMatrix = (cv::Mat)calibration.getDistortedIntrinsics().getCameraMatrix();
cv::Mat cameraMatrixInv = cameraMatrix.inv();
std::cout << "fund: " << fundamentalMatrix << std::endl;
essentialMatrix = cameraMatrix.t() * fundamentalMatrix * cameraMatrix;
cv::SVD svd(essentialMatrix);
Matx33d W(0,-1,0, //HZ 9.13
1,0,0,
0,0,1);
cv::Mat_<double> R = svd.u * Mat(W).inv() * svd.vt; //HZ 9.19
std::cout << "R: " << (cv::Mat)R << std::endl;
Matx33d Z(0, -1, 0,
1, 0, 0,
0, 0, 0);
cv::Mat_<double> t = svd.vt.t() * Mat(Z) * svd.vt;
std::cout << "t: " << (cv::Mat)t << std::endl;
Vec3d tVec = Vec3d(t(1,2), t(2,0), t(0,1));
Matx34d P1 = Matx34d(R(0,0), R(0,1), R(0,2), tVec(0),
R(1,0), R(1,1), R(1,2), tVec(1),
R(2,0), R(2,1), R(2,2), tVec(2));
ofMatrix4x4 ofR(R(0,0), R(0,1), R(0,2), 0,
R(1,0), R(1,1), R(1,2), 0,
R(2,0), R(2,1), R(2,2), 0,
0, 0, 0, 1);
ofRs.push_back(ofR);
cv::Matx34d P(1,0,0,0,
0,1,0,0,
0,0,1,0);
for (int y = 0; y < image1.height; y += 10) {
for (int x = 0; x < image1.width; x += 10) {
Vec3d vec(x, y, 0);
Point3d point1(vec.val[0], vec.val[1], vec.val[2]);
Vec3d result = (cv::Mat)((cv::Mat)R * (cv::Mat)vec);
Point3d point2 = result;
mesh.addColor(image1.getColor(x, y));
mesh.addVertex(ofVec3f(point1.x, point1.y, point1.z));
mesh.addColor(image2.getColor(x, y));
mesh.addVertex(ofVec3f(point2.x, point2.y, point2.z));
}
}
Any ideas? Does my fundamental matrix look correct, or do I have the wrong idea in testing it?
If you want to find out if your Fundamental Matrix is correct, you should compute error.
Using the epipolar constraint equation, you can check how close the detected features in one image lie on the epipolar lines of the other image. Ideally, these dot products should sum to 0, and thus, the calibration error is computed as the sum of absolute distances (SAD). The mean of the SAD is reported as stereo calibration error. Basically, you are computing SAD of the computed features in image_left (could be chessboard corners) from the corresponding epipolar lines. This error is measured in pixel^2, anything below 1 is acceptable.
OpenCV has code examples, look at the Stereo Calibrate cpp file, it shows you how to compute this error.
https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/c/stereo_calib.cpp?rev=2614
Look at "avgErr" Lines 260-269
Ankur
i think that you did not remove matches which are incorrect before you use then to calculate F.
Also i have an idea on how to validate F ,from x'Fx=0,you can replace several x' and x in the formula.
KyleFan
I wrote a python function to do this:
def Ferror(F,pts1,pts2): # pts are Nx3 array of homogenous coordinates.
# how well F satisfies the equation pt1 * F * pt2 == 0
vals = pts1.dot(F).dot(pts2.T)
err = np.abs(vals)
print("avg Ferror:",np.mean(err))
return np.mean(err)