SVM训练错误（示例类型的断言失败）

Question

Mat response_hist;
Mat histograms;
Mat classes;
ifstream ifs("train.txt");
int total_samples_in_file = 0;
vector<string> classes_names;
vector<string> lines;


for (int i = 1; i <= trainingSetSize; i++){
    cout << "in for loop iteration"<< i << endl;
    _snprintf_s(filepath, 100, "C:/Users/Randal/Desktop/TestCase1Training/train/%d.bmp", i);
    Mat temp = imread(filepath, CV_LOAD_IMAGE_GRAYSCALE);
    Mat tempBW;
    adaptiveThreshold(temp, tempBW, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 11, 2);
    detector->detect(tempBW, keypoints1);
    BOW.compute(tempBW, keypoints1, response_hist);
    response_hist.convertTo(response_hist, CV_32F);
    histograms.push_back(response_hist);
}

    //read from the file - ifs and put into a vector
    std::string line;
    float class_num;
    string imgfilepath;
    for (int j = 1; getline(ifs, line); j++)
        {
            istringstream ss(line);             
            ss >> imgfilepath >> class_num;
            classes.push_back(class_num);

        }

Ptr<ml::SVM> svm = ml::SVM::create();

svm->setType(ml::SVM::C_SVC);
svm->setKernel(ml::SVM::POLY);
svm->setGamma(3);

Mat trainingDataMat(histograms);
Mat trainingDataClass(classes);

trainingDataMat.convertTo(trainingDataMat, CV_32F);
trainingDataMat = trainingDataMat.reshape(trainingDataMat.cols, 1);
trainingDataClass.convertTo(classes, CV_32F);
svm->train(trainingDataMat, ml::ROW_SAMPLE, trainingDataClass); //incorrect types? I think it is a problem with ROW_SAMPLE
Mat res;   // output
svm->predict(output, res);

运行此操作时，我得到错误“Assertion failed(Samples.type()==CV_32F samples.type()==CV_32S)in cv：：ML：：TrainDataImpl：：SetData”。但是，我已经放入了几行代码，将我的类Mat和直方图Mat转换为cv_32f类型。问题是与我的输入有关，还是与SVM->Train中的ROW_SAMPLE有关？非常感谢任何帮助。

谢谢

Answer 1

错误是由于我的输入不正确造成的。我在它的声明中将Mat类的类型更改为CV_32S。我也变了

trainingdatamat_32.reshape（trainingdatamat_32.cols,1）；

以具有正确的通道和行数。

这导致了一个关于我正在使用的内核的新错误（SVM参数“poly”）。我不得不在kernel参数的正下方添加另一行：

SVM->SetDegree(3)；

这修复了错误。我的输出不正确，但这解决了断言失败。