computer vision - Extracting HoG Features using OpenCV

Question

I am trying to extract features using OpenCV's HoG API, however I can't seem to find the API that allow me to do that.

What I am trying to do is to extract features using HoG from all my dataset (a set number of positive and negative images), then train my own SVM.

I peeked into HoG.cpp under OpenCV, and it didn't help. All the codes are buried within complexities and the need to cater for different hardwares (e.g. Intel's IPP)

My question is:

1. Is there any API from OpenCV that I can use to extract all those features / descriptors to be fed into a SVM ? If there's how can I use it to train my own SVM ?
2. If there isn't, are there any existing libraries out there, which could accomplish the same thing ?

So far, I am actually porting an existing library (http://hogprocessing.altervista.org/) from Processing (Java) to C++, but it's still very slow, with detection taking around at least 16 seconds

Has anyone else successfully to extract HoG features, how did you go around it ? And do you have any open source codes which I could use ?

Thanks in advance

question from:https://stackoverflow.com/questions/11626140/extracting-hog-features-using-opencv

Answer 1

You can use hog class in opencv as follows

HOGDescriptor hog;
vector<float> ders;
vector<Point> locs;

This function computes the hog features for you

hog.compute(grayImg, ders, Size(32, 32), Size(0, 0), locs);

The HOG features computed for grayImg are stored in ders vector to make it into a matrix, which can be used later for training.

Mat Hogfeat(ders.size(), 1, CV_32FC1);

for(int i=0;i<ders.size();i++)
    Hogfeat.at<float>(i,0)=ders.at(i);

Now your HOG features are stored in Hogfeat matrix.

You can also set the window size, cell size and block size by using object hog as follows:

hog.blockSize = 16;
hog.cellSize = 4;
hog.blockStride = 8;

// This is for comparing the HOG features of two images without using any SVM 
// (It is not an efficient way but useful when you want to compare only few or two images)
// Simple distance
// Consider you have two HOG feature vectors for two images Hogfeat1 and Hogfeat2 and those are same size.

double distance = 0;
for(int i = 0; i < Hogfeat.rows; i++)
    distance += abs(Hogfeat.at<float>(i, 0) - Hogfeat.at<float>(i, 0));

if (distance < Threshold)
    cout<<"Two images are of same class"<<endl;
else
    cout<<"Two images are of different class"<<endl;

Hope it is useful :)