训练SVM时出现内存异常

Question

训练SVM分类器后，在准备TraingMatData和LabelsMat后，我使用了从不同图像中提取的特征（每个图像具有特征数量=！其他图像，因为我使用SLIC superpixeland从每个区域中提取了20个特征） 3}}发出通知：Microsoft C ++异常：内存位置0x000000E5176E2740的cv :: Exception。我不明白我的代码有什么问题，这是我的代码，我在等待您的帮助。

int main()
{
    /// training 
    cv::Mat image;
    //load the features from files 

    Mat trainingDataMat(47,27000, CV_32FC1);

    float features[1350][20];
    float labels[47] = {-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,... i delet the rest of them};

    vector<int> size;
    for (int i = 1; i < 48; i++) {
        vector<float> numbers;
        //Mat features(1200, 20, CV_32FC1);

        string filename = "D:\\mémoire\\training\\trainingData\\featuers";
        filename = filename + std::to_string(i);
        filename = filename + ".txt";
        int size_e = 0;
        //read(numbers, filename);
        ifstream in(filename);
        float number;
        string aa;

        while (in.good()) {

            getline(in, aa, ',');
            number = strtod(aa.c_str(), NULL);
            numbers.push_back(number);
            size_e = size_e + 1;
            //features.push_back(number);
        }

        in.close();

        size.push_back(size_e);
        int pp = numbers.size()/ 20;

        int d = 0;
        for (int p = 0; p < 1350; p++) {
            for (int p1 = 0; p1 < 20; p1++) {
                if (pp <= d) {
                    features[p][p1] = 0;
                }
                else {
                    //features.at<uchar>(p, p1) = numbers[d];
                    features[p][p1] = numbers[d];
                    d++;
                }
            }
        }
        cv::Mat A(1350, 20, CV_32FC1, features);
        trainingDataMat.push_back(A.reshape(1, 1));
    }

        Mat labelsMat(47, 1, CV_32FC1, labels);
        //////////////////////////////////////////////////////////////////////////////////////////////////////
        //seting the SVM

        Ptr<ml::SVM> svm = ml::SVM::create();
        // edit: the params struct got removed,

        svm->setType(ml::SVM::C_SVC);
        svm->setKernel(ml::SVM::RBF);
        svm->setGamma(100);

        //Mat trainData; // one row per feature

        svm->train(trainingDataMat, ml::ROW_SAMPLE, labelsMat);
        // ...