0

I want to train my own SVM with features extracted from images,so I extract features from my sequence of images(720x576) wich contain persons. I want to use svm to classify object detected such as a person or an other object. A piece of my features.dat:

    [-0.00011304629, -0.0012236957, 0.00027119479, 0.0012647118, -0.0018265223, -0.018638615, 0.0098637585, 0.020142596, -0.0012514826, -0.0067296866, 0.0043024337, 0.0080097318, 0.0011584486, -0.00077929819, 0.0013905426, 0.00095518644, 0.0017369018, 0.0079498151, 0.0018530694, 0.0085842144, 0.15540655, 0.23714867, 0.15860459, 0.23714867, -0.028113011, 0.25352797, 0.268282, 0.279008, -0.037383422, 0.02249903, 0.042661294, 0.02340897, 0.0013400698, 0.0048319609, 0.0021884302, 0.0056850719, 0.37904194, -0.066282742, 0.41964364, 0.080511056, -0.23055254, 0.010807713, 0.46629098, 0.049539972, -0.041018508, 0.0058587855, 0.045177955, 0.0067843986, -0.00016448426, 0.0008450991, 0.0019716914, 0.0026907444, 0.053080089, -0.0070953579, 0.057075631, 0.0085162139, -0.03202837, 0.0012674005, 0.053649519, 0.0080802822, 9.4343457e-05, 0.00010923775, 0.0083377175, 0.0015355083;
  -0.0024956728, -0.0024603403, 0.0044404925, 0.0040253471, 0.055309348, 0.006694437, 0.058669563, 0.008123802, -0.031681322, -0.0028714964, 0.056370165, 0.0060769469, -0.0010096794, 6.9015719e-06, 0.0067517483, 0.0011420506, 0.001166827, -0.00068250211, 0.0015904121, 0.0022194763, 0.37670693, 0.056508597, 0.40771538, 0.065799005, -0.22081745, -0.017952221, 0.47782964, 0.051509246, -0.037203062, -0.0030379333, 0.038590115, 0.0047737048, 0.0011766918, -0.01367875, 0.001783903, 0.014545292, 0.15056053, -0.25023517, 0.16026139, 0.25364164, -0.065814815, -0.27434519, 0.20718332, 0.29574585, -0.016427297, -0.033821814, 0.023312433, 0.034270149, -4.2133952e-06, 0.0015094492, 0.00026528162, 0.0015094492, 0.0021759982, 0.021547779, 0.0083950981, 0.021547779, -0.0033076329, 0.015553456, 0.0094896331, 0.015718656, -6.9612266e-05, 0.00065918162, 0.0017252946, 0.0017238797;
VICTOR
  • 1,894
  • 5
  • 25
  • 54
para
  • 37
  • 1
  • 9

2 Answers2

1

You need to change the image array into the row matrix first. 

Mat img_mat = imread(imgname,0); // I used 0 for greyscale
int ii = 0; // Current column in training_mat
for (int i = 0; i<img_mat.rows; i++) {
    for (int j = 0; j < img_mat.cols; j++) {
        training_mat.at<float>(file_num,ii++) = img_mat.at<uchar>(i,j);
    }
}


CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::POLY;
params.gamma = 3;
CvSVM svm;
svm.train(training_mat, labels, Mat(), Mat(), params);
svm.predict(img_mat_1d);

Besides, the image scale is quite large (720*576 = 414720). You can have some preprocess before training the model. For example, using PCA to reduce the dimension. For more details,look at using OpenCV and SVM with images

Community
  • 1
  • 1
VICTOR
  • 1,894
  • 5
  • 25
  • 54
  • I want SVM using C++. – para May 25 '16 at 08:49
  • It is the same for opencv in C++. You need to convert your image into 1D array. You may do something like this `CvSVMParams params; params.svm_type = CvSVM::C_SVC; params.kernel_type = CvSVM::POLY; params.gamma = 3; CvSVM svm; svm.train(image, labels, Mat(), Mat(), params);` – VICTOR May 25 '16 at 08:53
  • Should I put the data in features.dat in a Mat,and for labels I don't know what I put.For my case I have to classify an object such a person or not so labels should be like that:labels[4] = { 1, 0}? – para May 25 '16 at 08:57
  • If your training image is a person, then label 1, else -1. label[x], where x is the number of the training image – VICTOR May 25 '16 at 09:03
  • How to use it for testing an image smaple after that? – para May 25 '16 at 09:28
  • `svm.predict(test_img);` It will return a value based on what you set as your labels (1 for person, -1 for not a person) – VICTOR May 25 '16 at 09:30
0

I wrote this program:

 #include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv/cv.h>
#include <iostream>
#include <vector>

#include <stdio.h>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/features2d.hpp>
#include <fstream>




using namespace std;
using namespace cv;



//const float kRescaleFactor = 1.5;

//! main program 
int main(int argc, char* argv[])
{
    ofstream myfile;
    myfile.open("features.dat");
    String folder = "PETS2006/input/*.jpg";
        vector<String> filenames;

        glob(folder, filenames);


    Mat gray;




    int num_files=filenames.size();
    int img_area=720*576;
    Mat training_mat(num_files,img_area,CV_32FC1);
    Mat labels=Mat::ones(num_files,1,CV_32FC1);

    for (size_t i = 0; i <filenames.size(); ++i){

        Mat img=imread(filenames[i]);
        cvtColor(img,gray, CV_RGB2GRAY);



        int ii = 0; // Current column in training_mat
        for (int k = 0; i<gray.rows; i++) {
            for (int j = 0; j < gray.cols; j++) {
            training_mat.at<float>(i,ii++) = gray.at<uchar>(i,j);
            }
        }





    }
        CvSVMParams params;
    params.svm_type = CvSVM::C_SVC;
    params.kernel_type = CvSVM::POLY;
    params.gamma = 3;
    CvSVM svm;
    svm.train(training_mat, labels, Mat(), Mat(), params);
     svm.save("svm_filename");
    return 0;

}

I got this error:

assertion failed DataType<_Tp>one of the arguments values is out of range (The kernel parameter <degree> must be positive in CVSVM::set_params
para
  • 37
  • 1
  • 9