I'm trying to use cvPerspectiveTransform to transform four 2D points. I got the transformation matrix (3x3) already through cvFindHomography. I can't figure out what kind of structure to provide to not run into some error.
Would anybody be so kind to show me how to do it with these points?
x:y
0:0
640:0
0:480
640:480
I'm using OpenCV 2.4.0 on Win.
This is one way to initialize your matrices correctly. It's probably not the most elegant, but it works:
CvMat* input = cvCreateMat(1, 4, CV_32FC2);
CvMat* output = cvCreateMat(1, 4, CV_32FC2);
float data[8] = {0,0,0,640,480,0,640,480};
for (int i =0; i < 8; i++)
{
input->data.fl[i] = data[i];
}
cvPerspectiveTransform(input, output, matrix_from_cvFindHomography);
The C++ API offers a more intuitive implementation. Many OpenCV functions, like perspectiveTransform, accept vectors of points as inputs, which can be initialized in this manner:
std::vector<cv::Point2f> inputs;
std::vector<cv::Point2f> outputs;
inputs.push_back(cv::Point2f(0,0));
inputs.push_back(cv::Point2f(640,0));
inputs.push_back(cv::Point2f(0,480));
inputs.push_back(cv::Point2f(640,480));
cv::perspectiveTransform(inputs, outputs, matrix_from_findHomography);
assuming you have a 3x3 cv::Mat of floats, you can convert that to (if you want double change all the f's to d's)
cv::Matx33f transform(your_cv_Mat);
cv::Matx31f pt1(0,0,1);
cv::Matx31f pt2(640,0,1);
...
pt1 = transform*pt1;
pt2 = transform*pt2;
...
make sure you normalize by the third coordinate, read up on homogenous coordinates if that does not make sense
pt1 *= 1/pt1(2);
pt2 *= 1/pt2(2);
...
cv::Point2f final_pt1(pt1(0),pt1(1));
cv::Point2f final_pt2(pt2(0),pt2(1));
You do not need to do this with Matx, it will work with cv::Mat just as well. Personally I like Matx for working with transforms because its size and type is easier to keep track of and its contents can be more easily viewed in the debugger.
Related
I am doing a 6-dof transformation with the RANSAC given in OpenCV and I now want to convert two matrices of cv::Mat to an Isometry3d of Eigen but I didn't find good examples about this problem.
e.g.
cv::Mat rot;
cv::Mat trsl;
// the rot is 3-by-3 and trsl is 3-by-1 vector.
Eigen::Isometry3d trsf;
trsf.rotation = rot;
trsf.translation = trsl; // I know trsf has two members but it seems not the correct way to do a concatenation.
Anyone give me a hand? Thanks.
Essentially, you need an Eigen::Map to read the opencv data and store it to parts of your trsf:
typedef Eigen::Matrix<double, 3, 3, Eigen::RowMajor> RMatrix3d;
Eigen::Isometry3d trsf;
trsf.linear() = RMatrix3d::Map(reinterpret_cast<const double*>(rot.data));
trsf.translation() = Eigen::Vector3d::Map(reinterpret_cast<const double*>(trsl.data));
You need to be sure that rot and trsl indeed hold double data (perhaps consider using cv::Mat_<double> instead).
I want to mutiply 2 with each element of vec3 in opencv as we do in Matlab simplt by ".*". I searched alot but didn't find any command is their any command for this or not in opencv? thanks in advance for any help.
This answer would suggest you can just use the * assignment operator in C++.
If you are using Java I don't think this is possible, you can only multiply a Mat by another Mat.
So you would need to create a new Mat instance of the same size and type, initialised with the scalar value you want to multiply by.
You can easily create a funcion to do this:
public Mat multiplyScalar(Mat m, double i)
{
return m = m.mul(new Mat((int)m.size().height, (int)m.size().width, m.type(), new Scalar(i)));
}
Then x = multiplyScalar(x, 5); will multiply each element by 5.
I am trying to port code that uses FFTW to use KissFFT.
The code uses fftwf_plan_r2r_2d() with FFTW_REDFT01.
What would be the equivalent call in KissFFT?
If this call (with FFTW_REDFT01) is equivalent to a DCT, could I just use a direct DCT transform instead, e.g. such as OpenCV cv::dct?
Is there some input data modification I'd need to do, like reflections and symmetrizations?
Answering my own question...
With the help of these two references, I ended up not using DFT at all, but using OpenCV's cv::dct() and cv::idct() instead.
To answer the question, fftwf_plan_r2r_2d(...,FFTW_REDFT10, FFTW_REDFT10,...) can be replaced by this OpenCV code with the additional scaling:
cv::dct(img, resFFT); // fwd dct. This is like Matlab's dct2()
resFFT *= (4 * sqrt(float(img.rows/2)) * sqrt(float(img.cols/2)));
resFFT.row(0) *= sqrt(2.f);
resFFT.col(0) *= sqrt(2.f);
The inverse with FFTW_REDFT01 can be done like so:
// First re-scale the data for idct():
resFFT /= (4 * sqrt(float(img.rows/2)) * sqrt(float(img.cols/2)));
resFFT.row(0) /= sqrt(2.f);
resFFT.col(0) /= sqrt(2.f);
cv::idct(resFFT, outImg); // this will return the input exactly
// However, the transforms computed by FFTW are unnormalized, exactly like the corresponding,
// so computing a transform followed by its inverse yields the original array scaled by N, where N is the logical DFT size.
// The logical DFT size: Logical N=2*n for each axis, this is th implicit symmetrization
// of the image: reflect right and then reflect both halves down.
int logicalSizeN = (2*img.rows) * (2*img.cols);
outImg *= logicalSizeN; // scale to be like FFTW result
More helpful links here and here.
Note that OpenCV supports only images with an even number of rows and columns.
can any one help me about how to get the absolute value of a complex matrix.the matrix contains real value in one channel and imaginary value in another one channel.please help me
if s possible means give me some example.
Thanks in advance
Arangarajan
Let's assume you have 2 components: X and Y, two matrices of the same size and type. In your case it can be real/im values.
// n rows, m cols, type float; we assume the following matrices are filled
cv::Mat X(n,m,CV_32F);
cv::Mat Y(n,m,CV_32F);
You can compute the absolute value of each complex number like this:
// create a new matrix for storage
cv::Mat A(n,m,CV_32F,cv::Scalar(0.0));
for(int i=0;i<n;i++){
// pointer to row(i) values
const float* rowi_x = X.ptr<float>(i);
const float* rowi_y = Y.ptr<float>(i);
float* rowi_a = A.ptr<float>(i);
for(int j=0;j<=m;j++){
rowi_a[j] = sqrt(rowi_x[j]*rowi_x[j]+rowi_y[j]*rowi_y[j]);
}
}
If you look in the OpenCV phasecorr.cpp module, there's a function called magSpectrums that does this already and will handle conjugate symmetry-packed DFT results too. I don't think it's exposed by the header file, but it's easy enough to copy it. If you care about speed, make sure you compile with any available SIMD options turned on too because they can make a big difference with this calculation.
Currently, I'm working on a project in medical engineering. I have a big image with several sub-images of the cell, so my first task is to divide the image.
I thought about the next thing:
Convert the image into binary
doing a projection of the brightness pixels into the x-axis so I can see where there are gaps between brightnesses values and then divide the image.
The problem comes when I try to reach the second part. My idea is using a vector as the projection and sum all the brightnesses values all along one column, so the position number 0 of the vector is the sum of all the brightnesses values that are in the first column of the image, the same until I reach the last column, so at the end I have the projection.
This is how I have tried:
void calculo(cv::Mat &result,cv::Mat &binary){ //result=the sum,binary the imag.
int i,j;
for (i=0;i<=binary.rows;i++){
for(j=0;j<=binary.cols;j++){
cv::Scalar intensity= binaria.at<uchar>(j,i);
result.at<uchar>(i,i)=result.at<uchar>(i,i)+intensity.val[0];
}
cv::Scalar intensity2= result.at<uchar>(i,i);
cout<< "content" "\n"<< intensity2.val[0] << endl;
}
}
When executing this code, I have a violation error. Another problem is that I cannot create a matrix with one unique row, so...I don't know what could I do.
Any ideas?! Thanks!
At the end, it does not work, I need to sum all the pixels in one COLUMN. I did:
cv::Mat suma(cv::Mat& matrix){
int i;
cv::Mat output(1,matrix.cols,CV_64F);
for (i=0;i<=matrix.cols;i++){
output.at<double>(0,i)=norm(matrix.col(i),1);
}
return output;
}
but It gave me a mistake:
Assertion failed (0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols) in Mat, file /home/usuario/OpenCV-2.2.0/modules/core/src/matrix.cpp, line 276
I dont know, any idea would be helpful, anyway many thanks mevatron, you really left me in the way.
If you just want the sum of the binary image, you could simply take the L1-norm. Like so:
Mat binaryVectorSum(const Mat& binary)
{
Mat output(1, binary.rows, CV_64F);
for(int i = 0; i < binary.rows; i++)
{
output.at<double>(0, i) = norm(binary.row(i), NORM_L1);
}
return output;
}
I'm at work, so I can't test it out, but that should get you close.
EDIT : Got home. Tested it. It works. :) One caveat...this function works if your binary matrix is truly binary (i.e., 0's and 1's). You may need to scale the norm output with the maximum value if the binary matrix is say 0's and 255's.
EDIT : If you don't have using namespace cv; in your .cpp file, then you'll need to declare the namespace to use NORM_L1 like this cv::NORM_L1.
Have you considered transposing the matrix before you call the function? Like this:
sumCols = binaryVectorSum(binary.t());
vs.
sumRows = binaryVectorSum(binary);
EDIT : A bug with my code :)
I changed:
Mat output(1, binary.cols, CV_64F);
to
Mat output(1, binary.rows, CV_64F);
My test case was a square matrix, so that bug didn't get found...
Hope that is helpful!