As if i have two web camera,mark as cam1,cam2.And i want to calibrate them to get the transformation between them.
I used cv::stereoCalibrate() to calibrate.
After i got the transformation from cam1 to cam2,mark as R,T.I want to check the accuracy of the calibration result.
So i used cam1 and cam2 to take a picture of a chessboard,mark as pic1,pic2.I got the cam1's extrinsic parameters by cv::solvePnP().And i drew the cam1's world coordinate system by cv::projectPoints() in pic1.
Then,i think the cam2's rotation matrix=cam1's rotation matrix * R.And the cam2's translation matrix=cam1's translation matrix + T.
I calculated the cam2's extrinsic parameters by the above thought.And also drew the cam2's world coordinate system by cv::projectPoints() in pic2.
But the pic2's origin was not in right position.
Here is part of the code i used.
void check_res(const vector<string> &imgs_nm,const Mat &R,const Mat &T,const Mat &cam_c,const Mat &cam_h,const Mat &dist_c,const Mat &dist_h)
{
int imgs_cnt=imgs_nm.size()/2;
vector<Point3f> obj_pts;
for(int i=0;i<boardDimensions.height;i++)
for(int j=0;j<boardDimensions.width;j++)
obj_pts.push_back(Point3f(i*CHESS_LEN,j*CHESS_LEN,0.f));
for(int i=0;i<imgs_cnt;i++)
{
vector<Point2f> c_cners,h_cners;
Mat imgc_gray,imgh_gray;
Mat imgc=imread(imgs_nm[i*2],1);
Mat imgc_rz=imgc.clone();
bool c_found,h_found;
c_found=HasChessBoard(imgc_rz,imgc_gray,c_cners);
if(c_found)
cv::cornerSubPix(imgc_gray, c_cners, cv::Size(11, 11), cv::Size(-1, -1),cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
Mat imgh=imread(imgs_nm[i*2+1],1);
h_found=HasChessBoard(imgh,imgh_gray,h_cners);
if(h_found)
cv::cornerSubPix(imgh_gray, h_cners, cv::Size(11, 11), cv::Size(-1, -1),cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
Mat rvec_c,rvec_h,tvec_c,tvec_h;
cv::solvePnP(obj_pts,c_cners,cam_c,dist_c,rvec_c,tvec_c);
cv::solvePnP(obj_pts,h_cners,cam_h,dist_h,rvec_h,tvec_h);
Mat rrvec_c,rrvec_h;
cv::Rodrigues(rvec_c,rrvec_c);
cv::Rodrigues(rvec_h,rrvec_h);
Mat r1=rrvec_c*R;
Mat t1=tvec_c+T;
Mat img1=imgh.clone();
draw_chess(imgh,rrvec_h,tvec_h,cam_h,dist_h);
imshow("pic1",imgh);
draw_chess(img1,r1,t1,cam_h,dist_h);
imshow("pic2",img1);
char resc=waitKey(0);
if(resc=='q')
exit(1);
}
}
And below is the result i tested by using the sample in opencv.
I don't think it was low calibration accuracy,because i use the opencv's sample and the cv::stereoCalibrate() return rms less than 1 pixel.
Any advice is appreciated.
Thank you!
The formulas are:
pose for the camera 1 (in homogeneous matrix):
homogeneous transformation from camera 1 to camera 2:
pose for camera 2:
For checking the accuracy of your stereo calibration, I would consider a different approach:
Use the function stereoRectify to get the rectification transformation for the camera. Use the translation and rotation matrices you got from stereoCalibrate.
Perform initUndistortRectifyMap once for each camera. Use
Use the results you got to remap the images from both cameras.
If your calibration went well, the output images should be rectified and undistorted.
Related
I am attempting to convert a greyscale image to and from the frequency domain using the Fourier transform in OpenCV. However, the resulting image in very distorted even though I made no changes to the image while in frequency domain. Could anyone help me with this? I've found several other questions explaining this like the links below and I have followed them exactly, but the result always ends up like this.
Inverse fourier transformation in OpenCV
https://coderedirect.com/questions/165340/inverse-fourier-transformation-in-opencv
//Make grayscale image
cvtColor(src, gray_in, COLOR_BGR2GRAY);
gray_in.convertTo(gray_in, CV_32FC1);
//Create complex output variable
//From https://docs.opencv.org/4.x/d8/d01/tutorial_discrete_fourier_transform.html
Mat planes[] = { Mat_<float>(gray_in), Mat::zeros(gray_in.size(), CV_32F) };
Mat complexI;
merge(planes, 2, complexI);
//Transform
dft(gray_in, complexI, DFT_COMPLEX_OUTPUT);
//Compute inverse transform
dft(complexI, tgt, DFT_SCALE | DFT_INVERSE | DFT_REAL_OUTPUT);
//Save file
tgt.convertTo(tgt, CV_32FC2);
imwrite(outfile, tgt);
//Display image
namedWindow(windowName);
imshow(windowName, tgt);
waitKey(0);
destroyWindow(windowName);
I am trying to perform 3D Reconstruction(Structure From Motion) from Multiple Images of Planar Markers. I very new to MVG and openCV.
As far I have understood I have to do the following steps:
Identify corresponding 2D corner points in the one images.
Calculate the Camera Pose of the first image us cv::solvePNP(assuming the
origin to be center of the marker).
Repeat 1 and 2 for the second image.
Estimate the relative motion of the camera by Rot_relative = R2 - R1,
Trans_relative = T2-T1.
Now assume the first camera to be the origin construct the 3x4 Projection
Matrix for both views, P1 =[I|0]*CameraMatrix(known by Calibration) and P2 =
[Rot_relative |Trans_relative ].
Use the created projection matrices and 2D corner points to triangulate the
3D coordinate using cv::triangulatePoints(P1,P2,point1,point2,OutMat)
The 3D coordinate can be found by dividing the each rows of OutMat by the 4th
row.
I was hoping to keep my "First View" as my origin and iterate
through n views repeating steps from 1-7(I suppose its called Global SFM).
I was hoping to get (n-1)3D points of the corners with "The first View as origin" which we could optimize using Bundle Adjustment.
But the result I get is very disappointing the 3D points calculated are displaced by a huge factor.
These are questions:
1.Is there something wrong with the steps I followed?
2.Should I use cv::findHomography() and cv::decomposeHomographyMat() to find the
relative motion of the camera?
3.Should point1 and point2 in cv::triangulatePoints(P1,P2,point1,point2,OutMat)
be normalized and undistorted? If yes, how should the "Outmat" be interpreted?
Please anyone who has insights towards the topic, can you point out my mistake?
P.S. I have come to above understanding after reading "MultiView Geometry in Computer Vision"
Please find the code snippet below:
cv::Mat Reconstruction::Triangulate(std::vector<cv::Point2f>
ImagePointsFirstView, std::vector<cv::Point2f>ImagePointsSecondView)
{
cv::Mat rVectFirstView, tVecFristView;
cv::Mat rVectSecondView, tVecSecondView;
cv::Mat RotMatFirstView = cv::Mat(3, 3, CV_64F);
cv::Mat RotMatSecondView = cv::Mat(3, 3, CV_64F);
cv::solvePnP(RealWorldPoints, ImagePointsFirstView, cameraMatrix, distortionMatrix, rVectFirstView, tVecFristView);
cv::solvePnP(RealWorldPoints, ImagePointsSecondView, cameraMatrix, distortionMatrix, rVectSecondView, tVecSecondView);
cv::Rodrigues(rVectFirstView, RotMatFirstView);
cv::Rodrigues(rVectSecondView, RotMatSecondView);
cv::Mat RelativeRot = RotMatFirstView-RotMatSecondView ;
cv::Mat RelativeTrans = tVecFristView-tVecSecondView ;
cv::Mat RelativePose;
cv::hconcat(RelativeRot, RelativeTrans, RelativePose);
cv::Mat ProjectionMatrix_0 = cameraMatrix*cv::Mat::eye(3, 4, CV_64F);
cv::Mat ProjectionMatrix_1 = cameraMatrix* RelativePose;
cv::Mat X;
cv::undistortPoints(ImagePointsFirstView, ImagePointsFirstView, cameraMatrix, distortionMatrix, cameraMatrix);
cv::undistortPoints(ImagePointsSecondView, ImagePointsSecondView, cameraMatrix, distortionMatrix, cameraMatrix);
cv::triangulatePoints(ProjectionMatrix_0, ProjectionMatrix_1, ImagePointsFirstView, ImagePointsSecondView, X);
X.row(0) = X.row(0) / X.row(3);
X.row(1) = X.row(1) / X.row(3);
X.row(2) = X.row(2) / X.row(3);
return X;
}
I have taken a photo A using an RGB camera. And I know the position of a point g in photo A. The camera needs to do a camera calibration. Now I want to know the position of point g after calibration. I am using the code as following, but I want to get the point position, not image. How can I do that? Can you give me some advice?
initUndistortRectifyMap(
cameraMatrix,
distCoeffs,
Mat(),
Mat(),
Size(640, 480),
CV_32FC1,
map1, map2);
remap(A, B, map1, map2, cv::INTER_LINEAR);
Point2f g = Point2f(...,...);//i want to get the new position of the point not image B
Just get coordinates using maps:
x,y - coordinates after (not before),as pasbi correctly noticed in comments, mapping.
(map1(y,x),map2(y,x)) - coordinates before mapping
In other words:
map1.at<float>(y,x) contains source x coordinates for each destination point
p(x,y).
map2.at<float>(y,x) contains source y coordinates for each destination point
p(x,y).
See documentation on remap function.
best method i found was to recreate a camera matrix, with inverted parameters. work to a certain extent, with like basic images modifications
undistortPoints() is your need。
// src_pts are points in raw(distort) img, rectify_pt_vec are in rectifyImageL
// RL, PL are from stereoRectify()
cv::undistortPoints(src_pts, rectify_pt_vec, cameraMatrixL, distCoeffL, RL, PL);
how to get point in srcimg from dstimg just like pasbi commented below.
I'm new to openCV using version 2.4.9
I am trying to generate a 3D projection of points from a sequence of images without any knowledge of the camera parameters nor have camera used with me to calibrate. The camera used had a fish eye lens.
I used goodFeaturesToTrack() for detecting feature points followed by LK implementation in openCV to track the feature points in the sequence of images. Using these points I was successfully able to estimate the Fundamental Matrix from findFundamentalMat() and implemented stereoRectifyUncalibrated() to generate rectification homography matrices H1 and H2.Then I have computed Rotation matrix R from H as
R = cameraMatrix^{-1}*H*cameraMatrix
Now I need to undistort my images after rectification. Either by initUndistortRectifyMap() and remap() or directly by undistort(), but both the functions also require "distortion co-efficients" to compute corrected image.
I tried to find various methods to estimate those parameters, neither the documentation of the camera model is made available by the company, nor I could find any other method apart from calibrating camera using chessboards or circles grid.
How do I do it??
Am I doing it right?
Is there any other better method?
Can someone kindly help?
Thanks in Advance.
//Code
//Fundamental Matrix
Mat fundamental_matrix = findFundamentalMat(points[0], points[1], FM_RANSAC, 3, 0.99);
cout<<"F:\n" <<fundamental_matrix<<endl;
//Rectification Homographies
Mat H1, H2,F;
F = fundamental_matrix;
stereoRectifyUncalibrated(points[0],points[1], F, image.size(), H1, H2, 3);
cout<<"H1:\n" <<H1<<endl;
cout<<"H2:\n" <<H2<<endl;
//calculating Rotation matrix from homographic maps
Mat fInv= fundamental_matrix.inv();
R = (fInv)*H1*fundamental_matrix;
// Mat distCoeffs = Mat::zeros(8, 1, CV_64F);
initUndistortRectifyMap(fundamental_matrix, distCoeffs, R, fundamental_matrix, image.size() ,CV_32FC1, map1, map2);
//How to compute distCoeffs without a camera nor prior knowledge.Thank You
I want to use OpenCV's Canny edge detector, such as is outlined in this question. For example:
cv::Canny(image,contours,10,350);
However, I wish to not only get the final thresholded image out, but I also wish to get the detected edge angle at each pixel. Is this possible in OpenCV?
canny doesn't give you this directly.
However, you can calculate the angle from the Sobel transform, which is used internally in canny().
Pseudo code:
cv::Canny(image,contours,10,350);
cv::Sobel(image, dx, CV_64F, 1, 0, 3, 1, 0, cv::BORDER_REPLICATE);
cv::Sobel(image, dy, CV_64F, 0, 1, 3, 1, 0, cv::BORDER_REPLICATE);
cv::Mat angle(image.size(), CV_64F)
foreach (i,j) such that contours[i, j] > 0
{
angle[i, j] = atan2(dy[i,j], dx[i , j])
}
Instead of using for loop you can also provide dx and dy gradients to phase function that returns grayscale image of angles direction, then pass it to applyColorMap function and then mask it with edges, so the background is black.
Here is the workflow:
Get the angles
Mat angles;
phase(dx, dy, angles, true);
true argument idicates that the angles are returned in degrees.
Change the range of angles to 0-255 so you can convert to CV_8U without data loss
angles = angles / 360 * 255;
note that angles is still in CV_64F type as it comes from Sobel function
Convert to CV_8U
angles.convertTo(angles, CV_8U);
Apply color map of your choice
applyColorMap(angles, angles, COLORMAP_HSV);
in this case I choose HSV colormap. See this for more info: https://www.learnopencv.com/applycolormap-for-pseudocoloring-in-opencv-c-python/
Apply the edges mask so the background is black
Mat colored;
angles.copyTo(colored, contours);
Finally display image :D
imshow("Colored angles", colored);
In case your source is a video or webcam, before applying the mask of edges you addtionlly must clear colored image, to prevent aggregation:
colored.release();
angles.copyTo(colored, contours);
Full code here:
Mat angles, colored;
phase(dx, dy, angles, true);
angles = angles / 360 * 255;
angles.convertTo(angles, CV_8U);
applyColorMap(angles, angles, COLORMAP_HSV);
colored.release();
angles.copyTo(colored, contours);
imshow("Colored angles", colored);