在opencv3.0中，提供了一个ml.cpp的文件，这里面全是机器学习的算法，共提供了这么几种：
1、正态贝叶斯：normal Bayessian classifier
2、K最近邻：k nearest neighbors classifier
3、支持向量机：support vectors machine
4、决策树： decision tree
5、ADA Boost：adaboost
6、梯度提升决策树：gradient boosted trees
7、随机森林：random forest
8、人工神经网络：artificial neural networks
9、EM算法：expectation-maximization
这些算法在任何一本机器学习书本上都可以介绍过，他们大致的分类过程都很相似，主要分为三个环节：
一、收集样本数据sampleData
二、训练分类器mode
三、对测试数据testData进行预测
不同的地方就是在OpenCV中的参数设定，假设训练数据为trainingDataMat,且已经标注好labelsMat。待测数据为testMat
这些算法在任何一本机器学习书本上都可以介绍过，他们大致的分类过程都很相似，主要分为三个环节：
一、收集样本数据sampleData
二、训练分类器mode
三、对测试数据testData进行预测
不同的地方就是在OpenCV中的参数设定，假设训练数据为trainingDataMat,且已经标注好labelsMat。待测数据为testMat.
1、正态贝叶斯
// 创建贝叶斯分类器
Ptr<NormalBayesClassifier> model=NormalBayesClassifier::create();
// 设置训练数据
Ptr<TrainData> tData =TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
//训练分类器
model->train(tData);
//预测数据
float response = model->predict(testMat);
2、K最近邻
Ptr<KNearest> knn = KNearest::create(); //创建knn分类器
knn->setDefaultK(K); //设定k值
knn->setIsClassifier(true);
// 设置训练数据
Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
knn->train(tData);
float response = knn->predict(testMat);
3、支持向量机
Ptr<SVM> svm = SVM::create(); //创建一个分类器
svm->setType(SVM::C_SVC); //设置svm类型
svm->setKernel(SVM::POLY); //设置核函数;
svm->setDegree(0.5);
svm->setGamma(1);
svm->setCoef0(1);
svm->setNu(0.5);
svm->setP(0);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01));
svm->setC(C);
Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
svm->train(tData);
float response = svm->predict(testMat);
4、决策树： decision tree
Ptr<DTrees> dtree = DTrees::create(); //创建分类器
dtree->setMaxDepth(8); //设置最大深度
dtree->setMinSampleCount(2);
dtree->setUseSurrogates(false);
dtree->setCVFolds(0); //交叉验证
dtree->setUse1SERule(false);
dtree->setTruncatePrunedTree(false);
Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
dtree->train(tData);
float response = dtree->predict(testMat);
5、ADA Boost：adaboost
Ptr<Boost> boost = Boost::create();
boost->setBoostType(Boost::DISCRETE);
boost->setWeakCount(100);
boost->setWeightTrimRate(0.95);
boost->setMaxDepth(2);
boost->setUseSurrogates(false);
boost->setPriors(Mat());
Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
boost->train(tData);
float response = boost->predict(testMat);
6、梯度提升决策树：gradient boosted trees
此算法在opencv3.0中被注释掉了，原因未知，因此此处提供一个老版本的算法。
GBTrees::Params params( GBTrees::DEVIANCE_LOSS, // loss_function_type
100, // weak_count
0.1f, // shrinkage
1.0f, // subsample_portion
2, // max_depth
false // use_surrogates )
);
Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
Ptr<GBTrees> gbtrees = StatModel::train<GBTrees>(tData, params);
float response = gbtrees->predict(testMat);
7、随机森林：random forest
Ptr<RTrees> rtrees = RTrees::create();
rtrees->setMaxDepth(4);
rtrees->setMinSampleCount(2);
rtrees->setRegressionAccuracy(0.f);
rtrees->setUseSurrogates(false);
rtrees->setMaxCategories(16);
rtrees->setPriors(Mat());
rtrees->setCalculateVarImportance(false);
rtrees->setActiveVarCount(1);
rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 5, 0));
Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
rtrees->train(tData);
float response = rtrees->predict(testMat);
-
#include <opencv2\opencv.hpp> -
using namespace cv; -
using namespace std; -
int main() -
{ -
{ -
auto rtrees = cv::ml::RTrees::create(); -
rtrees->setMaxDepth(10); -
rtrees->setMinSampleCount(2); -
rtrees->setRegressionAccuracy(0); -
rtrees->setUseSurrogates(false); -
rtrees->setMaxCategories(16); -
rtrees->setPriors(cv::Mat()); -
rtrees->setCalculateVarImportance(false); -
rtrees->setActiveVarCount(0); -
rtrees->setTermCriteria({ cv::TermCriteria::MAX_ITER, 100, 0 }); -
// Some dummy stuff here... -
Mat1f feat(1, 5, 0.f); -
Mat1f labels = (Mat1f(1, 5) << 1, 0, 1, 0, 1); -
rtrees->train(feat, cv::ml::ROW_SAMPLE, labels); -
rtrees->write(cv::FileStorage("smoke_classifier.xml", -
cv::FileStorage::WRITE)); -
} -
{ -
auto rtrees2 = cv::ml::RTrees::create(); -
cv::FileStorage read("smoke_classifier.xml", cv::FileStorage::READ); -
rtrees2->read(read.root()); -
int a = rtrees2->getMinSampleCount(); -
} -
return 0; -
}
8、人工神经网络：artificial neural networks
Ptr<ANN_MLP> ann = ANN_MLP::create();
ann->setLayerSizes(layer_sizes);
ann->setActivationFunction(ANN_MLP::SIGMOID_SYM, 1, 1);
ann->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, FLT_EPSILON));
ann->setTrainMethod(ANN_MLP::BACKPROP, 0.001);
Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
ann->train(tData);
float response = ann->predict(testMat);
9、EM算法：expectation-maximization
EM算法与前面的稍微有点不同，它需要创建很多个model,将trainingDataMat分成很多个modelSamples，每个modelSamples训练出一个model
训练核心代码为：
int nmodels = (int)labelsMat.size();
vector<Ptr<EM> > em_models(nmodels);
Mat modelSamples;
for( i = 0; i < nmodels; i++ )
{
const int componentCount = 3;
modelSamples.release();
for (j = 0; j < labelsMat.rows; j++)
{
if (labelsMat.at<int>(j,0)== i)
modelSamples.push_back(trainingDataMat.row(j));
}
// learn models
if( !modelSamples.empty() )
{
Ptr<EM> em = EM::create();
em->setClustersNumber(componentCount);
em->setCovarianceMatrixType(EM::COV_MAT_DIAGONAL);
em->trainEM(modelSamples, noArray(), noArray(), noArray());
em_models[i] = em;
}
}
预测：
Mat logLikelihoods(1, nmodels, CV_64FC1, Scalar(-DBL_MAX));
for( i = 0; i < nmodels; i++ )
{
if( !em_models[i].empty() )
logLikelihoods.at<double>(i) = em_models[i]->predict2(testMat, noArray())[0];
}
这么多的机器学习算法，在实际用途中照我的理解其实只需要掌握svm算法就可以了。
ANN算法在opencv中也叫多层感知机，因此在训练的时候，需要分多层。
EM算法需要为每一类创建一个model。