Pass cv::Mat to labview - c++

Question

i try to build an DLL with the OpenCV PCA included to make it usable under Labview.
I have defined the the function:
extern "C" __declspec(dllexport) int __cdecl doPCA(float *input,int32_t input_rows,int32_t input_cols,double maxComponents,float *output);
And wrote it like:
int __cdecl doPCA(float *input,int32_t input_rows, int32_t input_cols,double maxComponents,float *output)
{
Mat pcaset = Mat(input_rows,input_cols, CV_32FC1, &input); //CV_32FC1 is for float valued pixel
PCA pca(pcaset, // pass the data
Mat(), // we do not have a pre-computed mean vector, // so let the PCA engine to compute it
CV_PCA_DATA_AS_ROW, // indicate that the vectors// are stored as matrix rows// (use PCA::DATA_AS_COL if the vectors are // the matrix columns)
2 // specify, how many principal components to retain
);
int i, j;
for(i = 0; i < input_rows; i++)
{
for(j = 0; j < input_cols; j++)
{
output[(i * input_cols) + j] = pca.eigenvectors.data[(i * input_cols) + j]; // Write Values to 1D output array
}
}
if(pca.eigenvectors.empty()){return 0;} // is empty
if(!pca.eigenvectors.empty()){return 1;} // is not empty
}
At Labview side I access the function by the compiled DLL:
But I can´t figure it out, how to pass value the from pca.eigenvectors cv::Mat to the 1D float output array.
int i, j;
for(i = 0; i < input_rows; i++)
{
for(j = 0; j < input_cols; j++)
{
output[(i * input_cols) + j] = pca.eigenvectors.data[(i * input_cols) + j]; // Write Values to 1D output array
}
}
Could anyone give a hint?

Answer 1

I learn how to do PCA from the page that Miki gives.
This is my code to do the similar thing.
///! Convert pointer to cv::Mat, do PCA, and convert back.
///! 2017.10.05 10:28:25 CST
int doPCA(float* data, int rows, int cols, int maxC, float* eigenvecs ) {
// convert pointer to Mat, CV_32FC1 is for float valued pixel.
Mat pcaset = Mat(rows,cols, CV_32FC1, data);
// let opencv compute the eigenvectors, and treat data as row, extract the first 2 principle components.
// pca.means : eigenvalues as row matrix
// pca.eigenvectors: eigenvectors as row matrix
maxC = (maxC >0 && maxC <= rows)?maxC:rows;
PCA pca(pcaset, Mat(), CV_PCA_DATA_AS_ROW,maxC);
cout << "Eigen values:\n"<< pca.mean <<endl;
cout << "Eigen vectors:\n"<<pca.eigenvectors<<endl;
if(pca.eigenvectors.empty()) {
return 0; // is empty
}
float *pvec = eigenvecs;
// get eigenvector in revered order
for(int i=maxC-1; i>=0; --i){
for(int j=0; j<cols; ++j){
*pvec++ = pca.eigenvectors.at<float>(i,j);
}
}
return 1;
}
int testPCA(){
// row first
float data[4] = {1.0,2.0,2.0,5.0};
int cols = 2;
int rows = 2;
// alloc two eigenvectors length: 2x2=4
float eigenvecs[4]={0};
// max components nums
int maxC = 2;
int res = doPCA(data, rows, cols, maxC, eigenvecs);
Mat eigenvalues(Size(cols, rows), CV_32FC1, eigenvecs);
cout << "Flag:\n" << res << endl;
cout << "Principle Components:\n"<< eigenvalues<<endl;
return 0;
}
Result:
Eigen values:
[1.5, 3.5]
Eigen vectors:
[0.31622776, 0.94868332;
0.94868332, -0.31622776]
Flag:
1
Principle Components:
[0.94868332, -0.31622776;
0.31622776, 0.94868332]