An example using PCA for dimensionality reduction while maintaining an amount of variance
#include <iostream>
#include <fstream>
#include <sstream>
#include <opencv2/core.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui.hpp>
static void read_imgList(const string& filename, vector<Mat>& images) {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
}
while (getline(file, line)) {
images.push_back(
imread(line, 0));
}
}
static Mat formatImagesForPCA(
const vector<Mat> &data)
{
Mat dst(static_cast<int>(data.size()), data[0].rows*data[0].cols,
CV_32F);
for(unsigned int i = 0; i < data.size(); i++)
{
Mat image_row = data[i].clone().reshape(1,1);
}
return dst;
}
}
return dst;
}
struct params
{
int ch;
int rows;
string winName;
};
static void onTrackbar(int pos, void* ptr)
{
cout << "Retained Variance = " << pos << "% ";
cout << "re-calculating PCA..." << std::flush;
double var = pos / 100.0;
struct params *p = (struct params *)ptr;
Mat point = p->pca.project(p->data.row(0));
Mat reconstruction = p->pca.backProject(point);
reconstruction = reconstruction.
reshape(p->ch, p->rows);
reconstruction = toGrayscale(reconstruction);
imshow(p->winName, reconstruction);
cout << "done! # of principal components: " << p->pca.eigenvectors.rows << endl;
}
int main(int argc, char** argv)
{
{
exit(0);
}
string imgList = parser.
get<
string>(
"@input");
if (imgList.empty())
{
exit(1);
}
vector<Mat> images;
try {
read_imgList(imgList, images);
cerr <<
"Error opening file \"" << imgList <<
"\". Reason: " << e.
msg << endl;
exit(1);
}
if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
}
Mat data = formatImagesForPCA(images);
reconstruction = reconstruction.
reshape(images[0].channels(), images[0].rows);
reconstruction = toGrayscale(reconstruction);
string winName = "Reconstruction | press 'q' to quit";
params p;
p.data = data;
p.ch = images[0].channels();
p.rows = images[0].rows;
p.pca = pca;
p.winName = winName;
int pos = 95;
createTrackbar(
"Retained Variance (%)", winName, &pos, 100, onTrackbar, (
void*)&p);
imshow(winName, reconstruction);
char key = 0;
while(key != 'q')
return 0;
}