程序片段----随机森林 opencv

cv::Mat trainData;cv::Mat ClassData;// ... push_back trainData and ClassData, list features as row sample; set type 0-9 for example as a class value// traincv::Ptr<cv::ml::RTrees> rtrees = cv::ml::RTrees::create();rtrees->setMaxDepth(4);rtrees->setMinSampleCount(2);rtrees->setRegressionAccuracy(0.f);rtrees->setUseSurrogates(false);rtrees->setMaxCategories(16);rtrees->setPriors(cv::Mat());rtrees->setCalculateVarImportance(false);rtrees->setActiveVarCount(1);rtrees->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER, 5, 0));cv::Ptr<cv::ml::TrainData> tData = cv::ml::TrainData::create(trainData, cv::ml::ROW_SAMPLE, ClassData);rtrees->train(tData);rtrees->save("trainingFile.xml");// testcv::Ptr<cv::ml::RTrees> prtrees2 = cv::ml::RTrees::load<cv::ml::RTrees>("trainingFile.xml");cv::Mat testMat;// ... row samplefloat response = prtrees2->predict(testMat);// ... compare the testMat with Class, to know the correct rate