Opencv virtually camera rotating/translating for bird's eye view - opencv

I've a calibrated camera where I exactly know the intrinsic and extrinsic data. Also the height of the camera is known. Now I want to virtually rotate the camera for getting a Bird's eye view, such that I can build the Homography matrix with the three rotation angles and the translation.
I know that 2 points can be transformed from one image to another via Homography as
x=K*(R-t*n/d)K^-1 * x'
there are a few things I'd like to know now:
if I want to bring back the image coordinate in ccs, I have to multiply it with K^-1, right? As Image coordinate I use (x',y',1) ?
Then I need to built a rotation matrix for rotating the ccs...but which convention should I use? And how do I know how to set up my WCS?
The next thing is the normal and the distance. Is it right just to take three points lying on the ground and compute the normal out of them? and is the distance then the camera height?
Also I'd like to know how I can change the height of the virtually looking bird view camera, such that I can say I want to see the ground plane from 3 meters height. How can I use the unit "meter" in the translation and homography Matrix?
So far for now, it would be great if someone could enlighten and help me. And please don't suggest generating the bird view with "getperspective", I ve already tried that but this way is not suitable for me.
Senna

That is the code i would advise (it's one of mine), to my mind it answers a lot of your questions,
If you want the distance, i would precise that it is in the Z matrix, the (4,3) coefficient.
Hope it will help you...
Mat source=imread("Whatyouwant.jpg");
int alpha_=90., beta_=90., gamma_=90.;
int f_ = 500, dist_ = 500;
Mat destination;
string wndname1 = getFormatWindowName("Source: ");
string wndname2 = getFormatWindowName("WarpPerspective: ");
string tbarname1 = "Alpha";
string tbarname2 = "Beta";
string tbarname3 = "Gamma";
string tbarname4 = "f";
string tbarname5 = "Distance";
namedWindow(wndname1, 1);
namedWindow(wndname2, 1);
createTrackbar(tbarname1, wndname2, &alpha_, 180);
createTrackbar(tbarname2, wndname2, &beta_, 180);
createTrackbar(tbarname3, wndname2, &gamma_, 180);
createTrackbar(tbarname4, wndname2, &f_, 2000);
createTrackbar(tbarname5, wndname2, &dist_, 2000);
imshow(wndname1, source);
while(true) {
double f, dist;
double alpha, beta, gamma;
alpha = ((double)alpha_ - 90.)*PI/180;
beta = ((double)beta_ - 90.)*PI/180;
gamma = ((double)gamma_ - 90.)*PI/180;
f = (double) f_;
dist = (double) dist_;
Size taille = source.size();
double w = (double)taille.width, h = (double)taille.height;
// Projection 2D -> 3D matrix
Mat A1 = (Mat_<double>(4,3) <<
1, 0, -w/2,
0, 1, -h/2,
0, 0, 0,
0, 0, 1);
// Rotation matrices around the X,Y,Z axis
Mat RX = (Mat_<double>(4, 4) <<
1, 0, 0, 0,
0, cos(alpha), -sin(alpha), 0,
0, sin(alpha), cos(alpha), 0,
0, 0, 0, 1);
Mat RY = (Mat_<double>(4, 4) <<
cos(beta), 0, -sin(beta), 0,
0, 1, 0, 0,
sin(beta), 0, cos(beta), 0,
0, 0, 0, 1);
Mat RZ = (Mat_<double>(4, 4) <<
cos(gamma), -sin(gamma), 0, 0,
sin(gamma), cos(gamma), 0, 0,
0, 0, 1, 0,
0, 0, 0, 1);
// Composed rotation matrix with (RX,RY,RZ)
Mat R = RX * RY * RZ;
// Translation matrix on the Z axis change dist will change the height
Mat T = (Mat_<double>(4, 4) <<
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, dist,
0, 0, 0, 1);
// Camera Intrisecs matrix 3D -> 2D
Mat A2 = (Mat_<double>(3,4) <<
f, 0, w/2, 0,
0, f, h/2, 0,
0, 0, 1, 0);
// Final and overall transformation matrix
Mat transfo = A2 * (T * (R * A1));
// Apply matrix transformation
warpPerspective(source, destination, transfo, taille, INTER_CUBIC | WARP_INVERSE_MAP);
imshow(wndname2, destination);
waitKey(30);
}

This code works for me but I don't know why the Roll and Pitch angles are exchanged. When I change "alpha", the image is warped in pitch and when I change "beta" the image in warped in roll. So, I changed my rotation matrix, as can be seen below.
Also, the RY has a signal error. You can check Ry at: http://en.wikipedia.org/wiki/Rotation_matrix.
The rotation metrix I use:
Mat RX = (Mat_<double>(4, 4) <<
1, 0, 0, 0,
0, cos(beta), -sin(beta), 0,
0, sin(beta), cos(beta), 0,
0, 0, 0, 1);
Mat RY = (Mat_<double>(4, 4) <<
cos(alpha), 0, sin(alpha), 0,
0, 1, 0, 0,
-sin(alpha), 0, cos(alpha), 0,
0, 0, 0, 1);
Mat RZ = (Mat_<double>(4, 4) <<
cos(gamma), -sin(gamma), 0, 0,
sin(gamma), cos(gamma), 0, 0,
0, 0, 1, 0,
0, 0, 0, 1);
Regards

Related

How compute divergence and gradient of image in OpenCV?

I know that to implement the following
I would use this code:
Mat o_k;
Mat Lapl;
double lambda;
Laplacian(o_k, Lapl, o_k.depth(), 1, 1, 0, BORDER_REFLECT);
Lapl = 1.0 - 2.0*lambda*Lapl;
However, I am trying to implement in OpenCV the following equation:
I know the div, or divergence, term would be like this, right?
int ksize = parser.get<int>("ksize");
int scale = parser.get<int>("scale");
int delta = parser.get<int>("delta");
Sobel(res, sobelx, CV_64F, 1, 0, ksize, scale, delta, BORDER_DEFAULT);
Sobel(res, sobely, CV_64F, 0, 1, ksize, scale, delta, BORDER_DEFAULT);
div = sobelx + sobely;
Where res is the result of the term in parenthesis. But how I get the term in parenthesis?
Or am I doing this wrong? Would div above actually be equal to the gradient of res? If so, then how do I get the divergence?
EDIT:
According to this link, the magnitude can also be computed as mag = abs(x) + abs(y): https://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html#sobel-derivatives
And since the div of a gradient is the Laplacian, would the below code be equivalent to the 2nd equation?
Sobel(res, sobelx, CV_64F, 1, 0, ksize, scale, delta, BORDER_DEFAULT);
Sobel(res, sobely, CV_64F, 0, 1, ksize, scale, delta, BORDER_DEFAULT);
convertScaleAbs( sobelx, abs_grad_x );
convertScaleAbs( sobely, abs_grad_y );
/// Total Gradient (approximate)
Mat mag;
addWeighted( abs_grad_x, 1, abs_grad_y, 1, 0, mag);
Laplacian(o_k, Lapl, o_k.depth(), 1, 1, 0, BORDER_REFLECT);
Mat top;
top = lambda * Lapl;
Mat result;
divide(top, mag, result);

Using warpPerspective to simulate virtual camera issues

Apologies if this seems trivial - relatively new to openCV.
Essentially, I'm trying to create a function that can take in a camera's image, the known world coordinates of that image, and the world coordinates of some other point 2, and then transform the camera's image to what it would look like if the camera was at point 2. From my understanding, the best way to tackle this is using a homography transformation using the warpPerspective tool.
The experiment is being done inside the Unreal Game simulation engine. Right now, I essentially read the data from the camera, and add a set transformation to the image. However, I seem to be doing something wrong as the image is looking something like this (original image first then distorted image):
Original Image
Distorted Image
This is the current code I have. Basically, it reads in the texture from Unreal engine, and then gets the individual pixel values and puts them into the openCV Mat. Then I try and apply my warpPerspective transformation. Interestingly, if I just try a simple warpAffine transformation (rotation), it works fine. I have seen this questions: Opencv virtually camera rotating/translating for bird's eye view, but I cannot figure out what I am doing wrong vs. their solution. I would really appreciate any help or guidance any of you may have. Thanks in advance!
ROSCamTextureRenderTargetRes->ReadPixels(ImageData);
cv::Mat image_data_matrix(TexHeight, TexWidth, CV_8UC3);
cv::Mat warp_dst, warp_rotate_dst;
int currCol = 0;
int currRow = 0;
cv::Vec3b* pixel_left = image_data_matrix.ptr<cv::Vec3b>(currRow);
for (auto color : ImageData)
{
pixel_left[currCol][2] = color.R;
pixel_left[currCol][1] = color.G;
pixel_left[currCol][0] = color.B;
currCol++;
if (currCol == TexWidth)
{
currRow++;
currCol = 0;
pixel_left = image_data_matrix.ptr<cv::Vec3b>(currRow);
}
}
warp_dst = cv::Mat(image_data_matrix.rows, image_data_matrix.cols, image_data_matrix.type());
double rotX = (45 - 90)*PI / 180;
double rotY = (90 - 90)*PI / 180;
double rotZ = (90 - 90)*PI / 180;
cv::Mat A1 = (cv::Mat_<float>(4, 3) <<
1, 0, (-1)*TexWidth / 2,
0, 1, (-1)*TexHeight / 2,
0, 0, 0,
0, 0, 1);
// Rotation matrices Rx, Ry, Rz
cv::Mat RX = (cv::Mat_<float>(4, 4) <<
1, 0, 0, 0,
0, cos(rotX), (-1)*sin(rotX), 0,
0, sin(rotX), cos(rotX), 0,
0, 0, 0, 1);
cv::Mat RY = (cv::Mat_<float>(4, 4) <<
cos(rotY), 0, (-1)*sin(rotY), 0,
0, 1, 0, 0,
sin(rotY), 0, cos(rotY), 0,
0, 0, 0, 1);
cv::Mat RZ = (cv::Mat_<float>(4, 4) <<
cos(rotZ), (-1)*sin(rotZ), 0, 0,
sin(rotZ), cos(rotZ), 0, 0,
0, 0, 1, 0,
0, 0, 0, 1);
// R - rotation matrix
cv::Mat R = RX * RY * RZ;
// T - translation matrix
cv::Mat T = (cv::Mat_<float>(4, 4) <<
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, dist,
0, 0, 0, 1);
// K - intrinsic matrix
cv::Mat K = (cv::Mat_<float>(3, 4) <<
12.5, 0, TexHeight / 2, 0,
0, 12.5, TexWidth / 2, 0,
0, 0, 1, 0
);
cv::Mat warp_mat = K * (T * (R * A1));
//warp_mat = cv::getRotationMatrix2D(srcTri[0], 43.0, 1);
//cv::warpAffine(image_data_matrix, warp_dst, warp_mat, warp_dst.size());
cv::warpPerspective(image_data_matrix, warp_dst, warp_mat, image_data_matrix.size(), CV_INTER_CUBIC | CV_WARP_INVERSE_MAP);
cv::imshow("distort", warp_dst);
cv::imshow("imaage", image_data_matrix)

OpenCV: Why projectPoints() is giving me weird results?

Why is my code snippet giving me weird results for projected points?
//Generate the one 3D Point which i want to project onto 2D plane
vector<Point3d> points_3d;
points_3d.push_back(Point3d(10, 10, 100));
Mat points3d = Mat(points_3d);
//Generate the identity matrix and zero vector for rotation matrix and translation vector
Mat rvec = (Mat_<double>(3, 3) << (1, 0, 0, 0, 1, 0, 0, 0, 1));
Mat tvec = (Mat_<double>(3, 1) << (0, 0, 0));
//Generate a camera intrinsic matrix
Mat K = (Mat_<double>(3,3)
<< (1000, 0, 50,
0, 1000, 50,
0, 0, 1));
//Project the 3D Point onto 2D plane
Mat points_2d;
projectPoints(points_3d, rvec, tvec, K, Mat(), points_2d);
//Output
cout << points_2d;
I get as projected 2D Point
points_2d = (-1.708699427820658e+024, -9.673395654445999e-026)
If i calculate it on a paper on my own, i'm expecting a point points_2d = (150, 150) with that formula:
Add cv::Rodrigues(InputArray src, OutputArray dst, OutputArray jacobian=noArray()). OpenCv uses rotation vector inside calculation instead of rotation matrix. Rodrigues transformation allows you to convert rotation vector to matrix and matrix to vector. Below i attached part of your code with one line added.
//Generate the identity matrix and zero vector for rotation matrix and translation vector
Mat rvec,rMat = (Mat_<double>(3, 3) << (1, 0, 0, 0, 1, 0, 0, 0, 1));
Rodrigues(rMat,rvec); //here
Mat tvec = (Mat_<double>(3, 1) << (0, 0, 0));
And it should work properly. It also will be better to define distortion coefficents as
Mat dist = Mat::zeros(8,1,CV_32f);
EDIT:
One more remark, you have little syntax error in matrix initialization:
cv::Mat rvec,rMat = (cv::Mat_<double>(3, 3) << /* ( */1, 0, 0, 0, 1, 0, 0, 0, 1); //you had error here
cv::Rodrigues(rMat, rvec);
cv::Mat tvec = (cv::Mat_<double>(3, 1) <</* ( */ 0, 0, 0); //and here
It works on my computer after changes.

Kalman filters with four input parameters

I have been studying the operation of the Kalman filter for a couple of days now to improve the performance of my face detection program. From the information I have gathered I have put together a code. The code for the Kalman filter part is as follows.
int Kalman(int X,int faceWidth,int Y,int faceHeight, IplImage *img1){
CvRandState rng;
const float T = 0.1;
// Initialize Kalman filter object, window, number generator, etc
cvRandInit( &rng, 0, 1, -1, CV_RAND_UNI );
//IplImage* img = cvCreateImage( cvSize(500,500), 8, 3 );
CvKalman* kalman = cvCreateKalman( 4, 4, 0 );
// Initializing with random guesses
// state x_k
CvMat* state = cvCreateMat( 4, 1, CV_32FC1 );
cvRandSetRange( &rng, 0, 0.1, 0 );
rng.disttype = CV_RAND_NORMAL;
cvRand( &rng, state );
// Process noise w_k
CvMat* process_noise = cvCreateMat( 4, 1, CV_32FC1 );
// Measurement z_k
CvMat* measurement = cvCreateMat( 4, 1, CV_32FC1 );
cvZero(measurement);
/* create matrix data */
const float A[] = {
1, 0, T, 0,
0, 1, 0, T,
0, 0, 1, 0,
0, 0, 0, 1
};
const float H[] = {
1, 0, 0, 0,
0, 0, 0, 0,
0, 0, 1, 0,
0, 0, 0, 0
};
//Didn't use this matrix in the end as it gave an error:'ambiguous call to overloaded function'
/* const float P[] = {
pow(320,2), pow(320,2)/T, 0, 0,
pow(320,2)/T, pow(320,2)/pow(T,2), 0, 0,
0, 0, pow(240,2), pow(240,2)/T,
0, 0, pow(240,2)/T, pow(240,2)/pow(T,2)
}; */
const float Q[] = {
pow(T,3)/3, pow(T,2)/2, 0, 0,
pow(T,2)/2, T, 0, 0,
0, 0, pow(T,3)/3, pow(T,2)/2,
0, 0, pow(T,2)/2, T
};
const float R[] = {
1, 0, 0, 0,
0, 0, 0, 0,
0, 0, 1, 0,
0, 0, 0, 0
};
//Copy created matrices into kalman structure
memcpy( kalman->transition_matrix->data.fl, A, sizeof(A));
memcpy( kalman->measurement_matrix->data.fl, H, sizeof(H));
memcpy( kalman->process_noise_cov->data.fl, Q, sizeof(Q));
//memcpy( kalman->error_cov_post->data.fl, P, sizeof(P));
memcpy( kalman->measurement_noise_cov->data.fl, R, sizeof(R));
//Initialize other Kalman Filter parameters
//cvSetIdentity( kalman->measurement_matrix, cvRealScalar(1) );
//cvSetIdentity( kalman->process_noise_cov, cvRealScalar(1e-5) );
/*cvSetIdentity( kalman->measurement_noise_cov, cvRealScalar(1e-1) );*/
cvSetIdentity( kalman->error_cov_post, cvRealScalar(1e-5) );
/* choose initial state */
kalman->state_post->data.fl[0]=X;
kalman->state_post->data.fl[1]=faceWidth;
kalman->state_post->data.fl[2]=Y;
kalman->state_post->data.fl[3]=faceHeight;
//cvRand( &rng, kalman->state_post );
/* predict position of point */
const CvMat* prediction=cvKalmanPredict(kalman,0);
//generate measurement (z_k)
cvRandSetRange( &rng, 0, sqrt(kalman->measurement_noise_cov->data.fl[0]), 0 );
cvRand( &rng, measurement );
cvMatMulAdd( kalman->measurement_matrix, state, measurement, measurement );
//Draw rectangles in detected face location
cvRectangle( img1,
cvPoint( kalman->state_post->data.fl[0], kalman->state_post->data.fl[2] ),
cvPoint( kalman->state_post->data.fl[1], kalman->state_post->data.fl[3] ),
CV_RGB( 0, 255, 0 ), 1, 8, 0 );
cvRectangle( img1,
cvPoint( prediction->data.fl[0], prediction->data.fl[2] ),
cvPoint( prediction->data.fl[1], prediction->data.fl[3] ),
CV_RGB( 0, 0, 255 ), 1, 8, 0 );
cvShowImage("Kalman",img1);
//adjust kalman filter state
cvKalmanCorrect(kalman,measurement);
cvMatMulAdd(kalman->transition_matrix, state, process_noise, state);
return 0;
}
In the face detection part(not shown), a box is drawn for the face detected. 'X, Y, faceWidth and faceHeight' are coordinates of the box and the width and the height passed into the Kalman filter. 'img1' is the current frame of a video.
Results:
Although I do get two new rectangles from the 'state_post' and 'prediction' data (as seen in the code), none of them seem to be any more stable than the initial box drawn without the Kalman filter.
Here are my questions:
Are the matrices initialized (transition matrix A, measurement matrix H etc.), correct for this four input case? (eg.4*4 matrices for four inputs?)
Can't we set every matrix to be an identity matrix?
Is the method I followed till plotting the rectangles theoretically correct? I followed the examples in this and the book 'Learning OpenCV' which don't use external inputs.
Any help regarding this would be much appreciated!
H[] should be identity if you measure directly from the image. If you have 4 measurements and you make 0 some values on the diagonal, you are making those expected measurements (x*H) 0, when it is not true. Then the innovation ( z- x*H) on the kalman filter will be high.
R[] should also be diagonal, though the covariance of the error of measurement can be different from one. If you have normalized coordinates ( the width=height=1), R could be something like 0.01. If you are dealing with pixel coordinates, R=diagonal_ones means an error of one pixel, and that's ok. You can try with 2,3,4, etc...
Your transition matrix A[], which is supposed to propagate the state on each frame, looks like a transition matrix for a state composed of x,y, v_x and v_y. You don't mention velocity in your model, you only talk about measurements. Be careful, do not confuse State (describes position of the face) with Measurements (used to update the state). Your state can be position, velocity and acceleration, and your measurements can be n points in the image. Or the x and y position of the face.
Hope this helps.

Translating and Rotating an Image in 3D using OpenCV

Given a 3 x 3 rotation matrix,R, and a 3 x 1 translation matrix,T, I am wondering how to multiply the T and R matrices to an image?
Lets say the Iplimage img is 640 x 480.
What I want to do is R*(T*img).
I was thinking of using cvGemm, but that didn't work.
The function you are searching for is probably warpPerspective() : this is a use case...
// Projection 2D -> 3D matrix
Mat A1 = (Mat_<double>(4,3) <<
1, 0, -w/2,
0, 1, -h/2,
0, 0, 0,
0, 0, 1);
// Rotation matrices around the X axis
Mat R = (Mat_<double>(4, 4) <<
1, 0, 0, 0,
0, cos(alpha), -sin(alpha), 0,
0, sin(alpha), cos(alpha), 0,
0, 0, 0, 1);
// Translation matrix on the Z axis
Mat T = (Mat_<double>(4, 4) <<
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, dist,
0, 0, 0, 1);
// Camera Intrisecs matrix 3D -> 2D
Mat A2 = (Mat_<double>(3,4) <<
f, 0, w/2, 0,
0, f, h/2, 0,
0, 0, 1, 0);
Mat transfo = A2 * (T * (R * A1));
Mat source;
Mat destination;
warpPerspective(source, destination, transfo, source.size(), INTER_CUBIC | WARP_INVERSE_MAP);
I hope it could help you,
Julien
PS : I gave the example with a projection from 2D to 3D but you can use directly transfo = T* R;

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