How to find Mat file consisting of hog features

Question

I want to train a HOG based classifier to detect pedestrians in videos, for this I have done computing hog features and storing them in text file with proper labeling. I am turning to use CvSVM utility and there I need a Mat object carrying information of all featureVectors of overall samples (Positive and negative). I found this code that could convert those featureVector file (text file) into Mat but the code

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

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

}

It provides error of assertion. I can see the error is because my featureVector ( per image) is of the size 1*3780 but the code is trying to load featureVector at each new row. my basic source of confusion is "WHATthe proper format of Mat file that needs to be fed to CvSVM load function?". here is given the idea of Mat format but that is for 2 feature vectors i.e. height and width as feature vecotor, should that be same for pedestrian detection as well?

Answer 1

this sample should contain the cases that we have discussed above in the comments.

//dummy readers
std:: vector<float> 
dummyDerReaderForOneDer(const vector<float> &pattern)
{
    int i = std::rand() % pattern.size();
    int j = std::rand() % pattern.size();
    vector<float> patternPulNoise(pattern);
    std::random_shuffle(patternPulNoise.begin()+std::min(i,j),patternPulNoise.begin()+std::max(i,j));
    return patternPulNoise;
};

std:: vector<float> 
dummyDerReaderForManyDers(const vector<float> &posPattern,
                                const vector<float> &negPattern, 
                                    vector<float> labels,
                                        float posLabel, float negLabel)
{

    int i,j;
    vector<float> allPatternsInOneVector;

    for(i=0;i< labels.size(); i++)
    {
        vector<float> patternPulNoise;
        if(labels[i]==posLabel)
        {
            patternPulNoise  = dummyDerReaderForOneDer(posPattern);
        }
        if(labels[i]==negLabel)
        {
            patternPulNoise  = dummyDerReaderForOneDer(negPattern);
        }

        for(j=0;j< patternPulNoise.size(); j++)
        {
            allPatternsInOneVector.push_back(patternPulNoise[j]);
        }
    }
    return allPatternsInOneVector;
}



// main harness entry point for detector test
int main (int argc, const char * argv[])
{

    //dummy variables for example
    int posFiles = 128;
    int negFiles = 128;
    int dims = 3780;

    //setup some dummy data
    vector<float> dummyPosPattern;
    dummyPosPattern.assign(1000,1.f);
    dummyPosPattern.resize(dims );
    random_shuffle(dummyPosPattern.begin(),dummyPosPattern.end());

    vector<float> dummyNegPattern;
    dummyNegPattern.assign(1000,1.f);
    dummyNegPattern.resize(dims );
    random_shuffle(dummyNegPattern.begin(),dummyNegPattern.end());

    // the labels and lables mat
    float posLabel = 1.f;
    float negLabel = 2.f;
    cv::Mat cSvmLabels;

    //the data mat
    cv::Mat cSvmTrainingData;

    //dummy linear svm parmas
    SVMParams cSvmParams;
    cSvmParams.svm_type = cv::SVM::C_SVC;
    cSvmParams.C = 0.0100;
    cSvmParams.kernel_type = cv::SVM::LINEAR;
    cSvmParams.term_crit =  cv::TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000000, FLT_EPSILON);




    cout << "creating training data. please wait" << endl;
    int i;
    for(i=0;i<posFiles;i++)
    {
        //your feature for one box from file
        vector<float> d = dummyDerReaderForOneDer(dummyPosPattern);

        //push back a new mat made from the vectors data, with copy  data flag on
        //this shows the format of the mat for a single example, (1 (row) X dims(col) ), as  training mat has each **row** as an example;
        //the push_back works like vector add adds each example to the bottom of the matrix
        cSvmTrainingData.push_back(cv::Mat(1,dims,CV_32FC1,d.data(),true));

        //push back a pos label to the labels mat
        cSvmLabels.push_back(posLabel);
    }

    //do same with neg files;
    for(i=0;i<negFiles;i++)
    {
        float a =  rand(); 
        vector<float> d = dummyDerReaderForOneDer(dummyNegPattern);
        cSvmTrainingData.push_back(cv::Mat(1,dims,CV_32FC1,d.data(),true));
        cSvmLabels.push_back(negLabel);
    }

    //have a look
    cv::Mat viz;
    cSvmTrainingData.convertTo(viz,CV_8UC3);
    viz = viz*255;
    cv::imshow("svmData", viz);
    cv::waitKey(10);
    cout << "press any key to continue" << endl;
    getchar();

    viz.release();

    //create the svm;
    cout << "training, please wait" << endl;

    CvSVM svm;
    svm.train(cSvmTrainingData,cSvmLabels,cv::Mat(),cv::Mat(),cSvmParams);

    cout << "testing, please wait" << endl;
    //test the svm with a large amount of new unseen fake one at a time
    int totExamples = 10000;
    float TP(0.f), FP(0.f), FN(0.f), TN(0.f);
    for(i=0;i<totExamples; i++)
    {
        vector<float> dPos = dummyDerReaderForOneDer(dummyPosPattern);
        cv::Mat dMatPos(1,dims,CV_32FC1,dPos.data(),true);
        float predLabelPos = svm.predict(dMatPos);

        if(predLabelPos == posLabel) TP++;
        else(FN++);

        vector<float> dNeg = dummyDerReaderForOneDer(dummyNegPattern);
        cv::Mat dMatNeg(1,dims,CV_32FC1,dNeg.data(),true);
        float predLabelNeg = svm.predict(dMatNeg);

        if(predLabelNeg == negLabel) TN++;
        else(FP++);

        if(i%1000==0)
            cout << "testing " << i << "of " <<  totExamples << endl; 
    }

    //http://en.wikipedia.org/wiki/Precision_and_recall
    float sensitivity  = TP / (TP + FN);
    float specificity = TN / (TN + FP);
    float precision = TP / (TP + FP);

    cout << "sensitivity: " <<  sensitivity << endl;
    cout << "specificity: " <<  specificity << endl;
    cout << "precision: " <<  precision << endl;

    cout << "press any key to continue" << endl;
    getchar();

    cout << "creating test data, please wait" << endl;
    //test the svm with a large amount of new unseen fake data all at one time
    TP=FP=FN=TN = 0.f;
    vector<float> testLabels;
    for(i=0;i<int(totExamples/2);i++)
    {
        testLabels.push_back(posLabel);
    }
    for(i;i<totExamples;i++)
    {
        testLabels.push_back(negLabel);
    }


    vector<float> allExamples = dummyDerReaderForManyDers(dummyPosPattern,dummyNegPattern,testLabels,posLabel,negLabel);

    //on big mat
    cv::Mat allExamplesMat(1,allExamples.size(),CV_32FC1,allExamples.data(),true);

    //need reshape to one example per row

    allExamplesMat = allExamplesMat.reshape(0,totExamples);

    //have a look
    allExamplesMat.convertTo(viz,CV_8UC3);
    viz = viz*255;
    cv::imshow("testData", viz);
    cv::waitKey(10);
    cout << "press any key to continue" << endl;
    getchar();
    viz.release();

    //test them all at once ( uses intel tbb :)
    cout << "predict all at once, please wait" << endl;  
    cv::Mat predLabels_mat;
    svm.predict(allExamplesMat,predLabels_mat);

    //evaluate
    for(i=0;i<testLabels.size();i++)
    {
        float testLabel = testLabels.at(i);
        float predLabel = predLabels_mat.at<float>(i);
        if(testLabel==predLabel)
        {
            if(testLabel==posLabel) TP++;
            else TN++;

        }
        else
        {
            if(testLabel==posLabel) FP++;
            else FN++;

        }

    }

    //http://en.wikipedia.org/wiki/Precision_and_recall
    sensitivity  = TP / (TP + FN);
    specificity = TN / (TN + FP);
    precision = TP / (TP + FP);

    cout << "sensitivity: " <<  sensitivity << endl;
    cout << "specificity: " <<  specificity << endl;
    cout << "precision: " <<  precision << endl;

    cout << "press any key to continue" << endl;
    getchar();
    return(0);
}