Question

I’m working on a face recognition project and I am having problems when projecting on PCA subspace.

When I pass a mat vector to my funcion with the resized images, I project them, and then I reconstruct them to verify it’s working well, but all I have in “Cam” window is a grey image (all same color).

I don’t know what I am doing bad.

This is the function:

void doPCA (const vector<Mat>& images)
{
int nEigens = images.size()-1;
Mat data (images.size(), images[0].rows*images[0].cols, images[0].type() );
for (int i = 0; i < images.size(); i++)
{
Mat aux = data.row(i);
images[i].reshape(1,1).copyTo(aux);
}
PCA pca(data,Mat(),CV_PCA_DATA_AS_ROW,nEigens);

//Project images
Mat dataprojected(data.rows, nEigens, CV_32FC1) ;
for(int i=0; i<images.size(); i++)
{
pca.project(data.row(i), dataprojected.row(i));
}

//Backproject to reconstruct images
Mat datareconstructed (data.rows, data.cols, data.type());
for(int i=0; i<images.size(); i++)
{
pca.backProject (dataprojected.row(i), datareconstructed.row(i) );
}
for(int i=0; i<images.size(); i++)
{
imshow ("Cam", datareconstructed.row(i).reshape(1,images[0].rows) );
waitKey();
}
}

Answer 1

I think this post is a duplicate of:

• PCA + SVM using C++ Syntax in OpenCV 2.2

Ah, I have found the error in your code. When you create the data matrix you do:

images[i].reshape(1,1).copyTo(aux);

You have to use convertTo to convert the data into the correct type and copy it to your data matrix:

images[i].reshape(1,1).convertTo(aux, CV_32FC1, 1/255.);

Then the normalized eigenvectors should be ok. And don’t forget to to normalize the values between 0 and 255 before displaying them, you can use cv::normalize to do this, here’s a simple function for turning it into grayscale:

Mat toGrayscale(const Mat& src) {
    Mat srcnorm;
    cv::normalize(src, srcnorm, 0, 255, NORM_MINMAX, CV_8UC1);
    return srcnorm;
}

You may want to look at the example in my blog:

• http://bytefish.de/blog/pca_in_opencv#simple_example