贝叶斯和svm分类器在opencv3中的实现

Question

我想在Bayes和SVM分类器中实现OpenCV 3.3。我为SVM编写了以下代码

int main()
{
// Load data

FileStorage fs("newStorageFile.yml", FileStorage::READ);

// Read data

Mat test_data, test_labels, enrol_data, enrol_labels;
Mat train_labels = Mat::zeros(650, 1, CV_32S);
Mat train_data = Mat::zeros(650, 600, CV_32S);

fs["train_data"] >> train_data;
fs["train_labels"] >> train_labels;
fs["test_data"] >> test_data;
fs["test_labels"] >> test_labels;
fs["enrol_data"] >> enrol_data;
fs["enrol_labels"] >> enrol_labels;


Ptr<ml::SVM> SVM_Model = ml::SVM::create();
SVM_Model->setType(ml::SVM::C_SVC);
SVM_Model->setKernel(ml::SVM::RBF);

Ptr<ml::TrainData> trainingData = ml::TrainData::create(train_data, ml::SampleTypes::ROW_SAMPLE, train_labels);
SVM_Model->trainAuto(trainingData);

return 0;
}

但是我在SVM_Model->trainAuto(trainingData)上有下面的异常错误。

Unhandled exception at 0x755B5608 in SVMimplemantation.exe: Microsoft C++ exception: cv::Exception at memory location 0x00D8DF58.

还有关于Bayes分类器，我编写了以下代码：

int main()
{
// Load data

FileStorage fs("newStorageFile.yml", FileStorage::READ);

// Read data

Mat test_data, test_labels, enrol_data, enrol_labels;
Mat train_labels = Mat::zeros(650, 1, CV_32F);
Mat train_data = Mat::zeros(650, 600, CV_32F);

fs["train_data"] >> train_data;
fs["train_labels"] >> train_labels;
fs["test_data"] >> test_data;
fs["test_labels"] >> test_labels;
fs["enrol_data"] >> enrol_data;
fs["enrol_labels"] >> enrol_labels;

Ptr<ml::NormalBayesClassifier> bayes = ml::NormalBayesClassifier::create();
Ptr<ml::TrainData> trainData = ml::TrainData::create(train_data, ml::SampleTypes::ROW_SAMPLE, train_labels);
bayes->train(trainData);

Mat output, outputProb;

bayes->predictProb(test_data, output, outputProb);

return 0;
}

关于这个案例，我在bayes->train(trainData)上也有下面的例外。

Unhandled exception at 0x755B5608 in BayesImplementation.exe: Microsoft C++ exception: cv::Exception at memory location 0x00A5D79C.

为了能够编译这个项目，我上传了我的数据集这里。有什么问题，如何解决？

Answer 1

问题是train_label的类型。试着做以下几点：

Mat train_labels32S = Mat::zeros(train_labels.rows, 1, CV_32S);

for (int i = 0; i < train_labels.rows; i++)
    train_labels32S.at<int>(i, 0) = train_labels.at<int>(i, 0);
//some code
    Ptr<ml::TrainData> trainingData = ml::TrainData::create(train_data, ml::SampleTypes::ROW_SAMPLE, train_labels32S);