使用 SVM 和 BOW 进行图像分类

我对openCV不太有经验。我有一些图像，我想检查它们是否包含我正在寻找的标志。所以，我想使用我有一些代码的 SVM 技术。我已经理解了代码的大部分部分，但我不知道如何实现这段代码。该代码有三个功能，即 createTrainDataUsingBow()第二个是 int trainSVM和int svmPredict .

问题：我知道首先我必须训练 SVM，然后使用 predict((。但是，我不明白在他们通话期间要传递的论点。我的意思是，如果我创建一个main()那么我应该用什么参数来调用int trainSVM.

整个代码给出如下：

1. 创建训练数据使用弓(( 的代码

void createTrainDataUsingBow(std::vector<char*> files, cv::Mat& train, cv::Mat&        response, int label)
{
    cv::Ptr<cv::DescriptorMatcher> matcher =    cv::DescriptorMatcher::create("FlannBased");
    cv::Ptr<cv::DescriptorExtractor> extractor = new cv::SurfDescriptorExtractor();
    cv::BOWImgDescriptorExtractor dextract( extractor, matcher );
    cv::SurfFeatureDetector detector(500);
    // cluster count
    int cluster = 100;
    // create the object for the vocabulary.
    cv::BOWKMeansTrainer bow( cluster,cv::TermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, FLT_EPSILON), 1, cv::KMEANS_PP_CENTERS );
    // get SURF descriptors and add to BOW each input files
    std::vector<char*>::const_iterator file;
    for( file = files.begin(); file != files.end(); file++)
    {
        cv::Mat img = cv::imread( *file, CV_LOAD_IMAGE_GRAYSCALE );
        std::vector<cv::KeyPoint> keypoints = detector.detect( img, keypoints);
        cv::Mat descriptors;
        extractor->compute( img, keypoints, descriptors);
        if ( !descriptors.empty() ) bow.add( descriptors );
    }
    // Create the vocabulary with KMeans.
    cv::Mat vocabulary;
    vocabulary = bow.cluster();
    for( file = files.begin(); file != files.end(); file++)
    {
        // set training data using BOWImgDescriptorExtractor
        dextract.setVocabulary( vocabulary );
        std::vector<cv::KeyPoint> keypoints;
        cv::Mat img = cv::imread( *file, CV_LOAD_IMAGE_GRAYSCALE );
        detector.detect( img, keypoints);
        cv::Mat desc;
        dextract.compute( img, keypoints, desc );
        if ( !desc.empty() )
        {
            train.push_back( desc );            // update training data
            response.push_back( label );        // update response data
        }
    }
}

2. 训练SVM((的代码

int trainSVM((std::vector<char*> positive, std::vector<char*> negative)
{
    // create training data
    cv::Mat train;
    cv::Mat response;
    createTrainDataUsingBow(positive, train, response, 1.0);
    createTrainDataUsingBow(negative, train, response, -1.0);
    // svm parameters
    CvTermCriteria criteria = cvTermCriteria(CV_TERMCRIT_EPS, 1000, FLT_EPSILON);
    CvSVMParams svm_param = CvSVMParams( CvSVM::C_SVC, CvSVM::RBF, 10.0, 8.0, 1.0, 10.0,     0.5, 0.1, NULL, criteria);
    // train svm
    cv::SVM svm;
    svm.train(train, response, cv::Mat(), cv::Mat(), svm_param);
    svm.save("svm-classifier.xml");
    return 0;
}

3. svmPredict(( 的代码

int svmPredict(const char* classifier, const char* vocaname, const char* query, const char* method)
{
    // load image
    cv::Mat img = cv::imread(query, CV_LOAD_IMAGE_GRAYSCALE);
    // load svm
    cv::SVM svm;
    svm.load(classifier);
    // declare BOWImgDescriptorExtractor
    cv::Ptr<cv::DescriptorMatcher> matcher =     cv::DescriptorMatcher::create("FlannBased");
    cv::Ptr<cv::DescriptorExtractor> extractor = new cv::SurfDescriptorExtractor();
    cv::BOWImgDescriptorExtractor dextract( extractor, matcher );
    // load vocabulary data
    cv::Mat vocabulary;
    cv::FileStorage fs( vocaname, cv::FileStorage::READ);
    fs["vocabulary data"] >> vocabulary;
    fs.release();
    if( vocabulary.empty()  ) return 1;
    // Set the vocabulary
    dextract.setVocabulary( vocabulary );
    std::vector<cv::KeyPoint> keypoints;
    detector.detect( img, keypoints);
    cv::Mat desc_bow;
    dextract.compute( img, keypoints, desc_bow );
    if( desc_bow.empty() )  return 1;
    // svm predict
    float predict = svm.predict(centroids, true);
    std::cout << predict << std::endl;
    return 0;
}