im正在学习SVM，因此我制作了一个样本程序，训练SVM来检测符号是否在图像中。所有的图像都是黑白的（符号是黑色的，背景是白色的）。我有12张训练图像，6张阳性（带符号）和6张阴性（不带符号）。Im使用hu moments来获得每个图像的描述符，然后用这些描述符构造训练矩阵。我还有一个Labels矩阵，它包含每个图像的标签：如果是正的，则为1，如果是负的，则是0。但我在训练SVM的线路上出现了错误（有点像分割错误）。这是我的代码：

using namespace cv;
using namespace std;
int main(int argc, char* argv[])
{
    //arrays where the labels and the features will be stored
    float labels[12] ;
    float trainingData[12][7] ;
    Moments moment;
    double hu[7];
  //===============extracting the descriptos for each positive image=========
    for ( int i = 0; i <= 5; i++){
        //the images are called t0.png ... t5.png and are in the folder train
        std::string path("train/t");
        path += std::to_string(i);
        path += ".png";
        Mat input = imread(path, 0); //read the images
        bitwise_not(input, input); //invert black and white
        Mat BinaryInput;
        threshold(input, BinaryInput, 100, 255, cv::THRESH_BINARY); //apply theshold
        moment = moments(BinaryInput, true); //calculate the moments of the current image
        HuMoments(moment, hu); //calculate the hu moments (this will be our descriptor)
        //setting the row i of the training data as the hu moments
        for (int j = 0; j <= 6; j++){
            trainingData[i][j] = (float)hu[j];
        }
        labels[i] = 1; //label=1 because is a positive image
    }
  //===============extracting the descriptos for each negative image=========
    for (int i = 0; i <= 5; i++){
        //the images are called tn0.png ... tn5.png and are in the folder train
        std::string path("train/tn");
        path += std::to_string(i);
        path += ".png";
        Mat input = imread(path, 0); //read the images
        bitwise_not(input, input); //invert black and white
        Mat BinaryInput;
        threshold(input, BinaryInput, 100, 255, cv::THRESH_BINARY); //apply theshold
        moment = moments(BinaryInput, true); //calculate the moments of the current image
        HuMoments(moment, hu); //calculate the hu moments (this will be our descriptor)
        for (int j = 0; j <= 6; j++){
            trainingData[i + 6][j] = (float)hu[j];
        }
         labels[i + 6] = 0;  //label=0 because is a negative image
    }
    
//===========================training the SVM================
    //we convert the labels and trainingData matrixes to Mat objects
    Mat labelsMat(12, 1, CV_32FC1, labels);
    Mat trainingDataMat(12, 7, CV_32FC1, trainingData);
    //create the SVM
    Ptr<ml::SVM> svm = ml::SVM::create();
    
    //set the parameters of the SVM
    svm->setType(ml::SVM::C_SVC);
    svm->setKernel(ml::SVM::LINEAR);
    CvTermCriteria criteria = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
    svm->setTermCriteria(criteria);
     //Train the SVM !!!!!HERE OCCURS THE ERROR!!!!!!
     svm->train(trainingDataMat, ml::ROW_SAMPLE, labelsMat);

    //Testing the SVM...
    Mat test = imread("train/t1.png", 0); //this should be a positive test
    bitwise_not(test, test);
    Mat testBin;
    threshold(test, testBin, 100, 255, cv::THRESH_BINARY);
    Moments momentP = moments(testBin, true); //calculate the moments of the test image
    double huP[7];
    HuMoments(momentP, huP);
    Mat testMat(1, 7, CV_32FC1, huP); //setting the hu moments to the test matrix
    double resp = svm->predict(testMat); //pretiction of the SVM
    printf("%f", resp); //Response
    getchar();
}

我知道程序在那一行之前运行得很好，因为我打印了labelsMat和trainingDataMat，它们里面的值都还可以。即使在控制台中，我也可以看到程序在那行执行之前运行得不错。控制台随后显示以下消息：

OpenCV error: Bad argument (in the case of classification problem the responses must be categorical;  either specify varType when creating TrainDatam or pass integer responses)

我真的不知道这意味着什么。你知道是什么导致了这个问题吗？如果你需要任何其他细节，请告诉我。

编辑

面向未来读者：

问题在于我将CCD_ 5阵列定义为float的阵列并且将LabelsMat定义为CCD_。包含标签的数组内部需要有整数，所以我更改了：

float labels[12];

至

int labels[12];

还改变了

Mat labelsMat(12, 1, CV_32FC1, labels);

至

Mat labelsMat(12, 1, CV_32SC1, labels);

这就解决了错误。感谢