How To Visualize Extracted Features - c++

Question

I extracted four features of regions in an image using opencv249 and Visual Studio 2013. Here is the code:
vector<double> calculateFeatures(Mat src, Mat mask, Rect Rect){
vector<double> sonuc;
double Feature1, Feature2, Feature3, Feature4;
Mat bolum(src, Rect);
Scalar mean_number = mean(src, mask);
double num = mean_number.val[0];
double minVal;
double maxVal = 0;
Point minLoc;
Point maxLoc = 0;
minMaxLoc(bolum, &minVal, &maxVal, &minLoc, &maxLoc);
double fark = num - minVal;
Feature1 = fark;
double thresh = num*0.95;
int sayi = countNonZero(bolum < thresh);
int alan = countNonZero(mask);
double pixelSayisi = sayi / alan;
Feature2 = pixelSayisi;
Mat dst, smoothed;
GaussianBlur(bolum, smoothed, Size(25, 25), 4, 4);
Laplacian(smoothed, dst, CV_8UC1, 25, 1, 0, BORDER_DEFAULT);
cv::Scalar s = cv::sum(dst);
double toplam = s.val[0];
Feature3 = toplam;
cout << "s: " << s << endl;
Mat dst2;
cornerHarris(bolum, dst2, 2, 25, 0.04, BORDER_DEFAULT);
Scalar top = sum(dst2);
double top2 = top.val[0];
Feature4 = top2 / alan;
cout << "Feature4: " << Feature4 << endl;
sonuc.push_back(Feature1);
sonuc.push_back(Feature2);
sonuc.push_back(Feature3);
sonuc.push_back(Feature4);
return sonuc;
}
I would like to see representative feature images of that regions in order to evaluate features whether they are useful or not. How can I implement?

Answer 1

Here I computed the features on patches 32x32 on this image:
You can create an image for each dimension of your feature, and the value of the pixels is given by the feature value at that location.
If you have at most 4 features, like in this case, you can combine all images into a 4 channel image:
Full code for reference:
#include <opencv2/opencv.hpp>
#include <vector>
using namespace cv;
using namespace std;
vector<double> calculateFeatures(Mat src, Mat mask, Rect Rect){
vector<double> sonuc;
double Feature1, Feature2, Feature3, Feature4;
Mat bolum(src, Rect);
Scalar mean_number = mean(src, mask);
double num = mean_number.val[0];
double minVal;
double maxVal = 0;
Point minLoc;
Point maxLoc = 0;
minMaxLoc(bolum, &minVal, &maxVal, &minLoc, &maxLoc);
double fark = num - minVal;
Feature1 = fark;
double thresh = num*0.95;
int sayi = countNonZero(bolum < thresh);
int alan = countNonZero(mask);
double pixelSayisi = sayi / alan;
Feature2 = pixelSayisi;
Mat dst, smoothed;
GaussianBlur(bolum, smoothed, Size(25, 25), 4, 4);
Laplacian(smoothed, dst, CV_8UC1, 25, 1, 0, BORDER_DEFAULT);
cv::Scalar s = cv::sum(dst);
double toplam = s.val[0];
Feature3 = toplam;
cout << "s: " << s << endl;
Mat dst2;
cornerHarris(bolum, dst2, 2, 25, 0.04, BORDER_DEFAULT);
Scalar top = sum(dst2);
double top2 = top.val[0];
Feature4 = top2 / alan;
cout << "Feature4: " << Feature4 << endl;
sonuc.push_back(Feature1);
sonuc.push_back(Feature2);
sonuc.push_back(Feature3);
sonuc.push_back(Feature4);
return sonuc;
}
int main()
{
Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);
int featuresSize = 4;
int patch_size = 32;
int right = patch_size - (img.cols % patch_size);
int bottom = patch_size - (img.rows % patch_size);
copyMakeBorder(img, img, 0, bottom, 0, right, BORDER_REFLECT101);
vector<Mat1d> visual;
for (int i = 0; i < featuresSize; ++i)
{
Mat1d v(img.rows, img.cols);
v.setTo(0);
visual.push_back(v.clone());
}
for (int r = 0; r < img.rows; r += patch_size)
{
for (int c = 0; c < img.cols; c += patch_size)
{
Rect roi(c, r, patch_size, patch_size);
Mat1b mask(img.rows, img.cols, uchar(0));
mask(roi) = uchar(255);
vector<double> features = calculateFeatures(img, mask, roi);
for (int i = 0; i < featuresSize; ++i)
{
visual[i](roi) = features[i];
}
}
}
for (int i = 0; i < featuresSize; ++i)
{
normalize(visual[i], visual[i], 0.0, 1.0, NORM_MINMAX);
Mat1b b;
visual[i].convertTo(b, CV_8U, 255.0);
imshow("Feature " + to_string(i), b);
}
// Makes sense only if nFeatures <= 4
Mat all;
merge(visual, all);
Mat allb;
all.convertTo(allb, CV_8U, 255.0);
imshow("All", all);
waitKey(0);
return 0;
}