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asked 2013-08-08 08:10:44 -0600

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Predict the train data output from video

This is my code for training the dataset , but i want it to predict the data from video , like i train it on vehicle , so if it found vehicle in the video , it count it as 1

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
        count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
for(j=1;j<=4;j++)
    for(i=1;i<=60;i++){


                    sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
                    const char* imageName = ch;
                    img2 = cvLoadImage(imageName,0);

                    detector.detect(img2, keypoint1);


                        bowDE.compute(img2, keypoint1, bowDescriptor1);

                        trainingData.push_back(bowDescriptor1);

                        labels.push_back((float) j);
    }



    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
     k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
    for(i=1;i<=60;i++){


                    sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
                    const char* imageName = ch;
                    img2 = cvLoadImage(imageName,0);

                    detector.detect(img2, keypoint2);
                        bowDE.compute(img2, keypoint2, bowDescriptor2);

                        evalData.push_back(bowDescriptor2);
                        groundTruth.push_back((float) j);
                        float response = svm.predict(bowDescriptor2);
                        results.push_back(response);
    }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

Predict the train data output from video

This is my code for training the dataset of for example vehicles , but when it train fully , i want it to predict the data data(vehicle) from video video(.avi) , like i train it on vehicle , so if when it found vehicle in the video , it count it as 1

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
        count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
 for(j=1;j<=4;j++)
    for(i=1;i<=60;i++){


      for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);

      cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
               bowDE.compute(img2, keypoint1, bowDescriptor1);
           trainingData.push_back(bowDescriptor1);
           labels.push_back((float) j);
     }
     //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
     k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
    for(i=1;i<=60;i++){


           for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
            const char* imageName = ch;
            img2 = cvLoadImage(imageName,0);
            detector.detect(img2, keypoint2);
            bowDE.compute(img2, keypoint2, bowDescriptor2);
            evalData.push_back(bowDescriptor2);
            groundTruth.push_back((float) j);
            float response = svm.predict(bowDescriptor2);
            results.push_back(response);
     }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

Predict the train data output from video

This is my code for training the dataset of for example vehicles , when it train fully , i want it to predict the data(vehicle) from video(.avi) , like i train it on vehicle , so when it found vehicle in the video , it count it as 1

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
       count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
   for(j=1;j<=4;j++)
    for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
              bowDE.compute(img2, keypoint1, bowDescriptor1);
           trainingData.push_back(bowDescriptor1);
           labels.push_back((float) j);
             }
    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
      for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);
           detector.detect(img2, keypoint2);
           bowDE.compute(img2, keypoint2, bowDescriptor2);
           evalData.push_back(bowDescriptor2);
           groundTruth.push_back((float) j);
           float response = svm.predict(bowDescriptor2);
           results.push_back(response);
         }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

Predict the train trained data output from video

This is my code for training the dataset of for example vehicles , when it train fully , i want it to predict the data(vehicle) from video(.avi) but don't know how , like i train it on vehicle , so when it found vehicle in the video , it count it as 1

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
       count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
   for(j=1;j<=4;j++)
    for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
              const char* imageName = ch;
              img2 = cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
              bowDE.compute(img2, keypoint1, bowDescriptor1);
              trainingData.push_back(bowDescriptor1);
              labels.push_back((float) j);
             }
    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
      for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);
           detector.detect(img2, keypoint2);
           bowDE.compute(img2, keypoint2, bowDescriptor2);
           evalData.push_back(bowDescriptor2);
           groundTruth.push_back((float) j);
           float response = svm.predict(bowDescriptor2);
           results.push_back(response);
         }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

Predict the trained data output How to get the detect object from videovideo using SVM

This is my code for training the dataset of for example vehicles , when it train fully , i want it to predict the data(vehicle) from video(.avi) but don't know , how to do it , i want that when the vehicle is shown in the video it count it as 1 and cout that the object is detected and if second vehicle come it increment the count as 2 , like i train it on vehicle , so when it found vehicle in the video , it count it as 1

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
       count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
   for(j=1;j<=4;j++)
    for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
              const char* imageName = ch;
              img2 = cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
              bowDE.compute(img2, keypoint1, bowDescriptor1);
              trainingData.push_back(bowDescriptor1);
              labels.push_back((float) j);
             }
    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
      for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);
           detector.detect(img2, keypoint2);
           bowDE.compute(img2, keypoint2, bowDescriptor2);
           evalData.push_back(bowDescriptor2);
           groundTruth.push_back((float) j);
           float response = svm.predict(bowDescriptor2);
           results.push_back(response);
         }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

How to get the detect object from video using SVM

This is my code for training the dataset of for example vehicles , when it train fully , i want it to predict the data(vehicle) from video(.avi) , how to do it , i want that when the vehicle is shown in the video it count it as 1 and cout that the object is detected and if second vehicle come it increment the count as 2 , like i train it on vehicle , so when it found vehicle in the video , it count it as 1

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
       count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
   for(j=1;j<=4;j++)
    for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
              const char* imageName = ch;
              img2 = cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
              bowDE.compute(img2, keypoint1, bowDescriptor1);
              trainingData.push_back(bowDescriptor1);
              labels.push_back((float) j);
             }
    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
      for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);
           detector.detect(img2, keypoint2);
           bowDE.compute(img2, keypoint2, bowDescriptor2);
           evalData.push_back(bowDescriptor2);
           groundTruth.push_back((float) j);
           float response = svm.predict(bowDescriptor2);
           results.push_back(response);
         }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

How to detect object from video using SVM

This is my code for training the dataset of for example vehicles , when it train fully , i want it to predict the data(vehicle) from video(.avi) , how to do it , i want that when the vehicle is shown in the video it count it as 1 and cout that the object is detected and if second vehicle come it increment the count as 2 , like i train it on vehicle , so when it found vehicle in the video , it count it as 1

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
       count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
   for(j=1;j<=4;j++)
    for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
              const char* imageName = ch;
              img2 = cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
              bowDE.compute(img2, keypoint1, bowDescriptor1);
              trainingData.push_back(bowDescriptor1);
              labels.push_back((float) j);
             }
    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
      for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);
           detector.detect(img2, keypoint2);
           bowDE.compute(img2, keypoint2, bowDescriptor2);
           evalData.push_back(bowDescriptor2);
           groundTruth.push_back((float) j);
           float response = svm.predict(bowDescriptor2);
           results.push_back(response);
         }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

For those who didn't understand the question :

I want to play a movie in above code

VideoCapture cap("movie.avi");

Suppose i have a trained data which contain vehicle's , and "movie.avi" contain 5 vehicles , so it should detect that vehicles from the movie.avi and give me 5 as output

How to do this part in the above code

click to hide/show revision 8
No.8 Revision

updated 2013-08-12 09:20:41 -0600

StevenPuttemans gravatar image

How to detect object from video using SVM

This is my code for training the dataset of for example vehicles , when it train fully , i want it to predict the data(vehicle) from video(.avi) , how to do it , i want that when the vehicle is shown in the video it count it as 1 and cout that the object is detected and if second vehicle come it increment the count as 2 , like i train it on vehicle , so when it found vehicle in the video , it count it as 1

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
       count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
   for(j=1;j<=4;j++)
    for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
              const char* imageName = ch;
              img2 = cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
              bowDE.compute(img2, keypoint1, bowDescriptor1);
              trainingData.push_back(bowDescriptor1);
              labels.push_back((float) j);
             }
    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
      for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);
           detector.detect(img2, keypoint2);
           bowDE.compute(img2, keypoint2, bowDescriptor2);
           evalData.push_back(bowDescriptor2);
           groundTruth.push_back((float) j);
           float response = svm.predict(bowDescriptor2);
           results.push_back(response);
         }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

For those who didn't understand the question :

I want to play a movie in above code

VideoCapture cap("movie.avi");

Suppose i have a trained data which contain vehicle's , and "movie.avi" contain 5 vehicles , so it should detect that vehicles from the movie.avi and give me 5 as output

How to do this part in the above code

How to detect object from video using SVM

This is my code for training the dataset of for example vehicles , when it train fully , i want it to predict the data(vehicle) from video(.avi) , how to do it , i want that when the vehicle is shown in the video it count it as 1 and cout that the object is detected and if second vehicle come it increment the count as 2 , like i train it on vehicle , so when it found vehicle in the video , it count it as 1

char ch[30];

//--------Using SURF as feature extractor and FlannBased for assigning a new point to the nearest one in the dictionary
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();
SurfFeatureDetector detector(500);
//---dictionary size=number of cluster's centroids
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bowTrainer(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor bowDE(extractor, matcher);



void collectclasscentroids() {
    IplImage *img;
    int i,j;
    for(j=1;j<=4;j++)
    for(i=1;i<=60;i++){
        sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
                    const char* imageName = ch;
                    img = cvLoadImage(imageName,0);
                    vector<KeyPoint> keypoint;
                    detector.detect(img, keypoint);
                    Mat features;
                    extractor->compute(img, keypoint, features);
                    bowTrainer.add(features);
    }
return;
}



   int _tmain(int argc, _TCHAR* argv[])
  {

   int i,j;
   IplImage *img2;

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
       count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
   for(j=1;j<=4;j++)
    for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
              const char* imageName = ch;
              img2 = cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
              bowDE.compute(img2, keypoint1, bowDescriptor1);
              trainingData.push_back(bowDescriptor1);
              labels.push_back((float) j);
             }
    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
      for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);
           detector.detect(img2, keypoint2);
           bowDE.compute(img2, keypoint2, bowDescriptor2);
           evalData.push_back(bowDescriptor2);
           groundTruth.push_back((float) j);
           float response = svm.predict(bowDescriptor2);
           results.push_back(response);
         }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

For those who didn't understand the question :

I want to play a movie in above code

VideoCapture cap("movie.avi");

Suppose i have a trained data which contain vehicle's , and "movie.avi" contain 5 vehicles , so it should detect that vehicles from the movie.avi and give me 5 as output

How to do this part in the above code

How to detect object from video using SVM

This is my code for training the dataset of for example vehicles , when it train fully , i want it to predict the data(vehicle) from video(.avi) , how to do it , i want that when the vehicle is shown in the video it count it as 1 and cout that the object is detected and if second vehicle come it increment the count as 2 , like i train it on vehicle , so when it found vehicle in the video , it count it as 1

char ch[30];

//--------Using SURF as feature extractor and FlannBased for assigning a new point to the nearest one in the dictionary
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();
SurfFeatureDetector detector(500);
//---dictionary size=number of cluster's centroids
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bowTrainer(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor bowDE(extractor, matcher);



void collectclasscentroids() {
    IplImage *img;
    int i,j;
    for(j=1;j<=4;j++)
    for(i=1;i<=60;i++){
        sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
                    const char* imageName = ch;
                    img = cvLoadImage(imageName,0);
                    vector<KeyPoint> keypoint;
                    detector.detect(img, keypoint);
                    Mat features;
                    extractor->compute(img, keypoint, features);
                    bowTrainer.add(features);
    }
return;
}



   int _tmain(int argc, _TCHAR* argv[])
  {

   int i,j;
   IplImage *img2;

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
       count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
   for(j=1;j<=4;j++)
    for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
              const char* imageName = ch;
              img2 = cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
              bowDE.compute(img2, keypoint1, bowDescriptor1);
              trainingData.push_back(bowDescriptor1);
              labels.push_back((float) j);
             }
    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
      for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);
           detector.detect(img2, keypoint2);
           bowDE.compute(img2, keypoint2, bowDescriptor2);
           evalData.push_back(bowDescriptor2);
           groundTruth.push_back((float) j);
           float response = svm.predict(bowDescriptor2);
           results.push_back(response);
         }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

For those who didn't understand the question :

I want to play a movie in above code

VideoCapture cap("movie.avi");

Suppose i have a trained data which contain vehicle's , and "movie.avi" contain 5 vehicles , so it should detect that vehicles from the movie.avi and give me 5 as output

How to do this part in the above code

How to detect object from video using SVM

This is my code for training the dataset of for example vehicles , when it train fully , i want it to predict the data(vehicle) from video(.avi) , how to do it , i want that when the vehicle is shown in the video it count it as 1 and cout that the object is detected and if second vehicle come it increment the count as 2 , like i train it on vehicle , so when it found vehicle in the video , it count it as 1

char ch[30];

//--------Using SURF as feature extractor and FlannBased for assigning a new point to the nearest one in the dictionary
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();
SurfFeatureDetector detector(500);
//---dictionary size=number of cluster's centroids
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bowTrainer(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor bowDE(extractor, matcher);



void collectclasscentroids() {
    IplImage *img;
    int i,j;
    for(j=1;j<=4;j++)
    for(i=1;i<=60;i++){
        sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
                    const char* imageName = ch;
                    img = cvLoadImage(imageName,0);
                    vector<KeyPoint> keypoint;
                    detector.detect(img, keypoint);
                    Mat features;
                    extractor->compute(img, keypoint, features);
                    bowTrainer.add(features);
    }
return;
}



   int _tmain(int argc, _TCHAR* argv[])
  {

   int i,j;
   IplImage *img2;

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
       count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
   for(j=1;j<=4;j++)
    for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
              const char* imageName = ch;
              img2 = cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
              bowDE.compute(img2, keypoint1, bowDescriptor1);
              trainingData.push_back(bowDescriptor1);
              labels.push_back((float) j);
             }
    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.gamma=0.50625000000000009;
    params.C=312.50000000000000;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
      for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);
           detector.detect(img2, keypoint2);
           bowDE.compute(img2, keypoint2, bowDescriptor2);
           evalData.push_back(bowDescriptor2);
           groundTruth.push_back((float) j);
           float response = svm.predict(bowDescriptor2);
           results.push_back(response);
         }



    //calculate the number of unmatched classes 
    double errorRate = (double) countNonZero(groundTruth- results) / evalData.rows;

For those who didn't understand the question :

I want to play a movie in above code

VideoCapture cap("movie.avi");

Suppose i have a trained data which contain vehicle's , and "movie.avi" contain 5 vehicles , so it should detect that vehicles from the movie.avi and give me 5 as output

How to do this part in the above code

How to detect object from video using SVM

This is my code for training the dataset of for example vehicles , when it train fully , i want it to predict the data(vehicle) from video(.avi) , how to do it , i want that when the vehicle is shown in the video it count it as 1 and cout that the object is detected and if second vehicle come it increment the count as 2 , like i train it on vehicle , so when it found vehicle in the video , it count it as 1

 int _tmain(int argc, _TCHAR* argv[])
  {

   int i,j;
   IplImage *img2;

    cout<<"Vector quantization..."<<endl;
    collectclasscentroids();
    vector<Mat> descriptors = bowTrainer.getDescriptors();
    int count=0;
    for(vector<Mat>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
    {
       count+=iter->rows;
    }
    cout<<"Clustering "<<count<<" features"<<endl;
    //choosing cluster's centroids as dictionary's words
    Mat dictionary = bowTrainer.cluster();
    bowDE.setVocabulary(dictionary);
    cout<<"extracting histograms in the form of BOW for each image "<<endl;
    Mat labels(0, 1, CV_32FC1);
    Mat trainingData(0, dictionarySize, CV_32FC1);
    int k=0;
    vector<KeyPoint> keypoint1;
    Mat bowDescriptor1;
    //extracting histogram in the form of bow for each image 
   for(j=1;j<=4;j++)
    for(i=1;i<=60;i++)
            {
              sprintf( ch,"%s%d%s%d%s","train/",j," (",i,").jpg");
              const char* imageName = ch;
              img2 = cvLoadImage(imageName,0); 
              detector.detect(img2, keypoint1);
              bowDE.compute(img2, keypoint1, bowDescriptor1);
              trainingData.push_back(bowDescriptor1);
              labels.push_back((float) j);
             }
    //Setting up SVM parameters
    CvSVMParams params;
    params.kernel_type=CvSVM::RBF;
    params.svm_type=CvSVM::C_SVC;
    params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,100,0.000001);
    CvSVM svm;



    printf("%s\n","Training SVM classifier");

    bool res=svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);

    cout<<"Processing evaluation data..."<<endl;


    Mat groundTruth(0, 1, CV_32FC1);
    Mat evalData(0, dictionarySize, CV_32FC1);
    k=0;
    vector<KeyPoint> keypoint2;
    Mat bowDescriptor2;


    Mat results(0, 1, CV_32FC1);;
    for(j=1;j<=4;j++)
      for(i=1;i<=60;i++)
         {
           sprintf( ch,"%s%d%s%d%s","eval/",j," (",i,").jpg");
           const char* imageName = ch;
           img2 = cvLoadImage(imageName,0);
           detector.detect(img2, keypoint2);
           bowDE.compute(img2, keypoint2, bowDescriptor2);
           evalData.push_back(bowDescriptor2);
           groundTruth.push_back((float) j);
           float response = svm.predict(bowDescriptor2);
           results.push_back(response);
         }

For those who didn't understand the question :

I want to play a movie in above code

VideoCapture cap("movie.avi");

Suppose i have a trained data which contain vehicle's , and "movie.avi" contain 5 vehicles , so it should detect that vehicles from the movie.avi and give me 5 as output

How to do this part in the above code

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