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.
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 create a 1-D array of exactly 100 values and at each index store the index of another array. If I am to use std::vector<int16_t> someVector, how do I ensure that someVector has only 100 values and maybe add the first value at location 48 like someVector[48] = 29322, and so on.
As an alternative I tried creating a 1-D mat of Mat someArray(1,100,CV_16UC1,Scalar(9999)). Now when I try to retrieve the value at index 48, by using int retrievedValue = someArray.row(0).col(48), it says cannot convert from Mat to int.
I realize I'm doing something crazy for something very simple, but please help.
When you initialize vector you can set its size:
std::vector<int16_t> someVector(100);
This way it will be created with as array of 100 elements. But don't forget that size of vector may be changed later.
As for Mat, operators like row() or col() give you sub-matrix of initial matrix. So the code you created will return you a 1x1 matrix, not a short. If you want to access an element in matrix it should be:
int retrievedValue = someArray.at<ushort>(0,48);
I'm having an issue trying to perform a two dimensional transform on an array of floats using cuFFT. I've had a look at the documentation, but some of the information is contradictory/not clear; so I have a few questions:
My data is 480 rows, with 640 columns (e.g. float data[480][640] but in a single dimension so float data[480*640])
If we say my input dimensions (of real data) are N1 = 480 and N2 = 640. Are the dimensions (after a real to complex transform) N1=480, N2=321?
Can I cudaMemcpy the data directly into a cufftReal array of the same size? Or must it be acufftComplex array?
If it must be acufftComplex array, I am assuming the elements need to be in the place of the real components?
What is the correct structure of a call to cufftPlan2d, cufftExecR2C and cufftC2R given the above values.
I think that's all for now...
Many thanks in advance
EDIT: So, I've implemented the Forward and Inverse transforms as suggested by JackOLantern. However my results are not what I am expecting (an identical Result after FFT as Before it). I have an image gallery here showing two sets of examples. The first is from my room, the second from my University Project.
In the cuFFT Documentation, there is ambiguity in the use of cufftPlan2d (hence why I asked). In the documentation, for a two dimensional array, the data should be input as above (float data[480][640] == float data[NY][NX]) So NY represents the rows. However in the function listing for cufftPlan2d, it states that nx (the parameter) is for the rows...
Swapping the values of NX and NY in the function call gives the result as in the project image (correct orientation, but split into three partially overlapping images at 1/4 the normal size) however, using the parameters as JackOLantern states in his answer gives a slanted/skewed result.
Am I doing something wrong here? Or does the cuFFT library have issues with this type of thing.
ALSO: I have undone a couple of the edits made by JackOLantern to this question as my issues MAY stem from the fact my data is coming from OpenCV.
EDIT: I've recently found out that I was the one who made a mistake in the way I used the function.
Originally I though the function definition referred to the size of the data being passed into it.
However, it appears that the parameters actually refer directly to the size of the REAL part.
This means that the parameters refer to:
The size of the input data when using R2C (Real to Complex)
The size of the output data when using C2R (Complex to Real)
So it appears that the cuFFT documentation and the library itself do not correspond.
When performing an R2C followed by a C2R (real to complex, complex to real respectively), the documentation states that for a Real input of NX x NY dimensions, the Complex output is NX x (floor(NY/2) +1); and vice versa.
However the actual output is of dimensions NX x NY and the actual input is of dimensions NX x NY. This is (half) mentioned on the very first page as
C2R - Symmetric complex input to real output
Implying that the complex data must be Symmetric, i.e. must also have the redundant data in addition to the non-redundant data.
There are a number of other contradictions within the documentation as well which I won't go into.
Needless to say, the problem has been solved.
I have included a MWE below. Near the top are a couple of lines with #define NUM_C2 and appropriate comments. Changing this changes whether the documentation format is followed, or my "fix".
The output is
The Input Real data
The Intermediate Complex data
The output Real data
The ratio of the output data to the input data (there are minor FFT errors, ~1 indicates correct)
Feel free to change the parameters (NUM_R and NUM_C) and feel free to comment if you think I have made a mistake somewhere.
#include <iostream>
#include <math.h>
#include <cufft.h>
// e.g. float data[NUM_R][NUM_C]
#define NUM_R 12
#define NUM_C 16
// Documentation Version
//#define NUM_C2 (1+NUM_C/2)
// "Correct" Version
#define NUM_C2 NUM_C
using namespace std;
int main(int argc, char** argv)
{
cufftReal *in_h, *out_h, *in_d, *out_d;
cufftComplex *mid_d, *mid_h;
cufftHandle pF, pI;
int r, c;
in_h = (cufftReal*) malloc(NUM_R * NUM_C * sizeof(cufftReal));
out_h= (cufftReal*) malloc(NUM_R * NUM_C * sizeof(cufftReal));
mid_h= (cufftComplex*)malloc(NUM_C2*NUM_R*sizeof(cufftComplex));
cudaMalloc((void**) &in_d, NUM_R * NUM_C * sizeof(cufftReal));
cudaMalloc((void**)&out_d, NUM_R * NUM_C * sizeof(cufftReal));
cudaMalloc((void**)&mid_d, NUM_C2 * NUM_R * sizeof(cufftComplex));
cufftPlan2d(&pF, NUM_R, NUM_C, CUFFT_R2C);
cufftPlan2d(&pI, NUM_R,NUM_C2, CUFFT_C2R);
cout<<endl<<"------"<<endl;
for(r=0; r<NUM_R; r++)
{
for(c=0; c<NUM_C; c++)
{
in_h[c + NUM_C * r] = cos(2.0*M_PI*(c*7.0/NUM_C+r*3.0/NUM_R));
out_h[c+ NUM_C * r] = 0.f;
cout<<in_h[c+NUM_C*r];
if(c<(NUM_C-1)) cout<<", ";
else cout<<endl;
}
}
cudaMemcpy((cufftReal*)in_d, (cufftReal*)in_h, NUM_R * NUM_C * sizeof(cufftReal),cudaMemcpyHostToDevice);
cufftExecR2C(pF, (cufftReal*)in_d, (cufftComplex*)mid_d);
cudaMemcpy((cufftComplex*)mid_h, (cufftComplex*)mid_d, NUM_C2*NUM_R*sizeof(cufftComplex), cudaMemcpyDeviceToHost);
cout<<endl<<"------"<<endl;
for(r=0; r<NUM_R; r++)
{
for(c=0; c<NUM_C2; c++)
{
cout<<mid_h[c+(NUM_C2)*r].x<<"|"<<mid_h[c+(NUM_C2)*r].y;
if(c<(NUM_C2-1)) cout<<", ";
else cout<<endl;
}
}
cufftExecC2R(pI, (cufftComplex*)mid_d, (cufftReal*)out_d);
cudaMemcpy((cufftReal*)out_h, (cufftReal*)out_d, NUM_R*NUM_C*sizeof(cufftReal), cudaMemcpyDeviceToHost);
cout<<endl<<"------"<<endl;
for(r=0; r<NUM_R; r++)
{
for(c=0; c<NUM_C; c++)
{
cout<<out_h[c+NUM_C*r]/(NUM_R*NUM_C);
if(c<(NUM_C-1)) cout<<", ";
else cout<<endl;
}
}
cout<<endl<<"------"<<endl;
for(r=0; r<NUM_R; r++)
{
for(c=0; c<NUM_C; c++)
{
cout<<(out_h[c+NUM_C*r]/(NUM_R*NUM_C))/in_h[c+NUM_C*r];
if(c<(NUM_C-1)) cout<<", ";
else cout<<endl;
}
}
free(in_h);
free(out_h);
free(mid_h);
cudaFree(in_d);
cudaFree(out_h);
cudaFree(mid_d);
return 0;
}
1) If we say my input dimensions (of real data) are N1 = 480 and N2 = 640. Are the dimensions (after a real to complex transform) N1=480, N2=321?
The output of cufftExecR2C is a NX*(NY/2+1) cufftComplex matrix. So in your case, you will have a 480x321 float2 matrix as output.
2) Can I cudaMemcpy the data directly into a cufftReal array of the same size? Or must it be a cufftComplex array?
If it must be a cufftComplex array, I am assuming the elements need to be in the place of the real components?
Yes, you can copy the data to a cufftReal array and the N1xN2 data.
3) What is the correct structure of a call to cufftPlan2d, cufftExecR2C and cufftC2R given the above values.
cufftPlan2d(&plan, N1, N2, CUFFT_R2C);
cufftExecR2C(plan, (cufftReal*)idata, (cufftComplex*) odata);
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.
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!