Question

这是我的代码，我用于训练数据集，但每当我运行代码时它再次启动矢量化和特征计数训练等，每次我开始时都需要时间，我希望它应该训练一些而不是一次又一次地花时间

    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);
         }

我只是了解保存训练数据文件的方法，例如train.xml，而不是在预测中使用它，但我不清楚它及其用途

Answer 1

训练结束后你需要保存SVM，它有方法读写，你还需要保存词汇量。

对于使用训练有素的分类器，您需要加载svm和词汇。然后将词汇表设置为描述符提取器。提取描述符。使用SVM方法预测。

如何只训练一次我的数据

1 个答案: