SVM训练错误（样本类型的断言失败）

Question

I am trying to use SVM to match a query image with its appropriate class. Right now the classes are just 1 or 0. I extract the class from a .txt file and store it into a Mat. I use BoW to compute a histogram for each image in the training set, and also store it into a Mat.

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);

        }

The Mats class_num and histograms are used in training the SVM. Each row in "histograms" represents a sample (a histogram of an image in the training set). "class_num" is one row with each column being the class (1 or 0) of a corresponding image in the training set.

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);

When I run this I get the error "Assertion failed (samples.type() == CV_32F || samples.type() == CV_32S) in cv::ml::TrainDataImpl::setData". However, I have placed lines of code in to convert both my class Mat and my histogram Mat to type CV_32F. Is the issue with my inputs or does it have something to do with ROW_SAMPLE in svm->train? Any help is greatly appreciated.

Thanks

Answer 1

The error was caused by my input being incorrect. I changed the type of Mat classesMat to CV_32S in its declaration. I also changed

trainingDataMat_32.reshape(trainingDataMat_32.cols, 1);

to have the correct number of channels and rows.

trainingDataMat_32.reshape(1, trainingDataMat_32.rows);

TrainingDataMat.cols was not the correct value at all. It needed 1 channel (first parameter) and the same number of rows as my histogram input.

This caused a new error regarding the kernel I am using (SVM parameter "POLY"). I had to add another line right below the kernel parameter:

svm->setDegree(3);

This fixed the error. My output is not correct, but this solved the assertion failure.