在opencv3中的机器学习算法

2022-11-01 09:29:15

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 我已在另外一篇博文中介绍过：在opencv3中实现机器学习之：利用正态贝叶斯分类

2、K最近邻：k nearest neighbors classifier

3、支持向量机：support vectors machine 请参考我的另外一篇博客：在opencv3中实现机器学习之：利用svm(支持向量机)分类

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.

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();

    svm->setCoef0();

    svm->setNu(0.5);

    svm->setP();

    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, , 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();   //设置最大深度

    dtree->setMinSampleCount();

    dtree->setUseSurrogates(false);

    dtree->setCVFolds(); //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();

    boost->setWeightTrimRate(0.95);

    boost->setMaxDepth();

    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

                         , // weak_count

                         0.1f, // shrinkage

                         1.0f, // subsample_portion

                         , // 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();

    rtrees->setMinSampleCount();

    rtrees->setRegressionAccuracy(.f);

    rtrees->setUseSurrogates(false);

    rtrees->setMaxCategories();

    rtrees->setPriors(Mat());

    rtrees->setCalculateVarImportance(false);

    rtrees->setActiveVarCount();

    rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, , ));

   Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);

   rtrees->train(tData);

   float response = rtrees->predict(testMat);

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, , );

    ann->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, , 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 = ; i < nmodels; i++ )

    {

        const int componentCount = ;

        modelSamples.release();

        for (j = ; j < labelsMat.rows; j++)

        {

            if (labelsMat.at<int>(j,)== 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(, nmodels, CV_64FC1, Scalar(-DBL_MAX));

 for( i = ; i < nmodels; i++ )

            {

                if( !em_models[i].empty() )

                    logLikelihoods.at<double>(i) = em_models[i]->predict2(testMat, noArray())[];

            }

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。

其中一些算法的具体代码练习：在opencv3中的机器学习算法练习：对OCR进行分类

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