opencv 机器学习算法汇总

opencv提供了非常多的机器学习算法用于研究。这里对这些算法进行分类学习和研究，以抛砖引玉。这里使用的机器学习算法包括：人工神经网络，boost,决策树，最近邻，逻辑回归，贝叶斯，随机森林，SVM等算法等。

机器学习的过程相同，都要经历1、收集样本数据sampleData2.训练分类器mode3.对测试数据testData进行预测

这里使用一个在别处看到的例子，利用身高体重等原始信息预测男女的概率。通过一些简单的数据学习，用测试数据预测男女概率。

import org.opencv.core.Core;import org.opencv.core.CvType;import org.opencv.core.Mat;import org.opencv.core.TermCriteria;import org.opencv.ml.ANN_MLP;import org.opencv.ml.Boost;import org.opencv.ml.DTrees;import org.opencv.ml.KNearest;import org.opencv.ml.LogisticRegression;import org.opencv.ml.Ml;import org.opencv.ml.NormalBayesClassifier;import org.opencv.ml.RTrees;import org.opencv.ml.SVM;import org.opencv.ml.SVMSGD;import org.opencv.ml.TrainData;public class ML {public static void main(String[] args) {System.loadLibrary(Core.NATIVE_LIBRARY_NAME);// 训练数据，两个维度，表示身高和体重float[] trainingData = { 186, 80, 185, 81, 160, 50, 161, 48 };// 训练标签数据，前两个表示男生0，后两个表示女生1,由于使用了多种机器学习算法，他们的输入有些不一样，所以labelsMat有三种 float[] labels = { 0f, 0f, 0f, 0f, 1f, 1f, 1f, 1f };int[] labels2 = { 0, 0, 1, 1 };float[] labels3 = { 0, 0, 1, 1 };// 测试数据,先男后女float[] test = { 184, 79, 159, 50 };Mat trainingDataMat = new Mat(4, 2, CvType.CV_32FC1);trainingDataMat.put(0, 0, trainingData);Mat labelsMat = new Mat(4, 2, CvType.CV_32FC1);labelsMat.put(0, 0, labels);Mat labelsMat2 = new Mat(4, 1, CvType.CV_32SC1);labelsMat2.put(0, 0, labels2);Mat labelsMat3 = new Mat(4, 1, CvType.CV_32FC1);labelsMat3.put(0, 0, labels3);Mat sampleMat = new Mat(2, 2, CvType.CV_32FC1);sampleMat.put(0, 0, test);MyAnn(trainingDataMat, labelsMat, sampleMat);MyBoost(trainingDataMat, labelsMat2, sampleMat);MyDtrees(trainingDataMat, labelsMat2, sampleMat);MyKnn(trainingDataMat, labelsMat3, sampleMat);MyLogisticRegression(trainingDataMat, labelsMat3, sampleMat);MyNormalBayes(trainingDataMat, labelsMat2, sampleMat);MyRTrees(trainingDataMat, labelsMat2, sampleMat);MySvm(trainingDataMat, labelsMat2, sampleMat);MySvmsgd(trainingDataMat, labelsMat2, sampleMat);}// 人工神经网络public static Mat MyAnn(Mat trainingData, Mat labels, Mat testData) {// train data using aNNTrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);Mat layerSizes = new Mat(1, 4, CvType.CV_32FC1);// 含有两个隐含层的网络结构，输入、输出层各两个节点，每个隐含层含两个节点layerSizes.put(0, 0, new float[] { 2, 2, 2, 2 });ANN_MLP ann = ANN_MLP.create();ann.setLayerSizes(layerSizes);ann.setTrainMethod(ANN_MLP.BACKPROP);ann.setBackpropWeightScale(0.1);ann.setBackpropMomentumScale(0.1);ann.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1);ann.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER + TermCriteria.EPS, 300, 0.0));boolean success = ann.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());System.out.println("Ann training result: " + success);// ann.save("D:/bp.xml");//存储模型// ann.load("D:/bp.xml");//读取模型// 测试数据Mat responseMat = new Mat();ann.predict(testData, responseMat, 0);System.out.println("Ann responseMat:\n" + responseMat.dump());for (int i = 0; i < responseMat.size().height; i++) {if (responseMat.get(i, 0)[0] + responseMat.get(i, i)[0] >= 1)System.out.println("Girl\n");if (responseMat.get(i, 0)[0] + responseMat.get(i, i)[0] < 1)System.out.println("Boy\n");}return responseMat;}// Boostpublic static Mat MyBoost(Mat trainingData, Mat labels, Mat testData) {Boost boost = Boost.create();// boost.setBoostType(Boost.DISCRETE);boost.setBoostType(Boost.GENTLE);boost.setWeakCount(2);boost.setWeightTrimRate(0.95);boost.setMaxDepth(2);boost.setUseSurrogates(false);boost.setPriors(new Mat());TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);boolean success = boost.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());System.out.println("Boost training result: " + success);// boost.save("D:/bp.xml");//存储模型Mat responseMat = new Mat();float response = boost.predict(testData, responseMat, 0);System.out.println("Boost responseMat:\n" + responseMat.dump());for (int i = 0; i < responseMat.height(); i++) {if (responseMat.get(i, 0)[0] == 0)System.out.println("Boy\n");if (responseMat.get(i, 0)[0] == 1)System.out.println("Girl\n");}return responseMat;}// 决策树public static Mat MyDtrees(Mat trainingData, Mat labels, Mat testData) {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);TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);boolean success = dtree.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());System.out.println("Dtrees training result: " + success);// dtree.save("D:/bp.xml");//存储模型Mat responseMat = new Mat();float response = dtree.predict(testData, responseMat, 0);System.out.println("Dtrees responseMat:\n" + responseMat.dump());for (int i = 0; i < responseMat.height(); i++) {if (responseMat.get(i, 0)[0] == 0)System.out.println("Boy\n");if (responseMat.get(i, 0)[0] == 1)System.out.println("Girl\n");}return responseMat;}// K最邻近public static Mat MyKnn(Mat trainingData, Mat labels, Mat testData) {final int K = 2;TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);KNearest knn = KNearest.create();boolean success = knn.train(trainingData, Ml.ROW_SAMPLE, labels);System.out.println("Knn training result: " + success);// knn.save("D:/bp.xml");//存储模型// find the nearest neighbours of test dataMat results = new Mat();Mat neighborResponses = new Mat();Mat dists = new Mat();knn.findNearest(testData, K, results, neighborResponses, dists);System.out.println("results:\n" + results.dump());System.out.println("Knn neighborResponses:\n" + neighborResponses.dump());System.out.println("dists:\n" + dists.dump());for (int i = 0; i < results.height(); i++) {if (results.get(i, 0)[0] == 0)System.out.println("Boy\n");if (results.get(i, 0)[0] == 1)System.out.println("Girl\n");}return results;}// 逻辑回归public static Mat MyLogisticRegression(Mat trainingData, Mat labels, Mat testData) {LogisticRegression lr = LogisticRegression.create();TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);boolean success = lr.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());System.out.println("LogisticRegression training result: " + success);// lr.save("D:/bp.xml");//存储模型Mat responseMat = new Mat();float response = lr.predict(testData, responseMat, 0);System.out.println("LogisticRegression responseMat:\n" + responseMat.dump());for (int i = 0; i < responseMat.height(); i++) {if (responseMat.get(i, 0)[0] == 0)System.out.println("Boy\n");if (responseMat.get(i, 0)[0] == 1)System.out.println("Girl\n");}return responseMat;}// 贝叶斯public static Mat MyNormalBayes(Mat trainingData, Mat labels, Mat testData) {NormalBayesClassifier nb = NormalBayesClassifier.create();TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);boolean success = nb.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());System.out.println("NormalBayes training result: " + success);// nb.save("D:/bp.xml");//存储模型Mat responseMat = new Mat();float response = nb.predict(testData, responseMat, 0);System.out.println("NormalBayes responseMat:\n" + responseMat.dump());for (int i = 0; i < responseMat.height(); i++) {if (responseMat.get(i, 0)[0] == 0)System.out.println("Boy\n");if (responseMat.get(i, 0)[0] == 1)System.out.println("Girl\n");}return responseMat;}// 随机森林public static Mat MyRTrees(Mat trainingData, Mat labels, Mat testData) {RTrees rtrees = RTrees.create();rtrees.setMaxDepth(4);rtrees.setMinSampleCount(2);rtrees.setRegressionAccuracy(0.f);rtrees.setUseSurrogates(false);rtrees.setMaxCategories(16);rtrees.setPriors(new Mat());rtrees.setCalculateVarImportance(false);rtrees.setActiveVarCount(1);rtrees.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER, 5, 0));TrainData tData = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);boolean success = rtrees.train(tData.getSamples(), Ml.ROW_SAMPLE, tData.getResponses());System.out.println("Rtrees training result: " + success);// rtrees.save("D:/bp.xml");//存储模型Mat responseMat = new Mat();rtrees.predict(testData, responseMat, 0);System.out.println("Rtrees responseMat:\n" + responseMat.dump());for (int i = 0; i < responseMat.height(); i++) {if (responseMat.get(i, 0)[0] == 0)System.out.println("Boy\n");if (responseMat.get(i, 0)[0] == 1)System.out.println("Girl\n");}return responseMat;}// 支持向量机public static Mat MySvm(Mat trainingData, Mat labels, Mat testData) {SVM svm = SVM.create();svm.setKernel(SVM.LINEAR);svm.setType(SVM.C_SVC);TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 1000, 0);svm.setTermCriteria(criteria);svm.setGamma(0.5);svm.setNu(0.5);svm.setC(1);TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);boolean success = svm.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());System.out.println("Svm training result: " + success);// svm.save("D:/bp.xml");//存储模型// svm.load("D:/bp.xml");//读取模型Mat responseMat = new Mat();svm.predict(testData, responseMat, 0);System.out.println("SVM responseMat:\n" + responseMat.dump());for (int i = 0; i < responseMat.height(); i++) {if (responseMat.get(i, 0)[0] == 0)System.out.println("Boy\n");if (responseMat.get(i, 0)[0] == 1)System.out.println("Girl\n");}return responseMat;}// SGD支持向量机public static Mat MySvmsgd(Mat trainingData, Mat labels, Mat testData) {SVMSGD Svmsgd = SVMSGD.create();TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 1000, 0);Svmsgd.setTermCriteria(criteria);Svmsgd.setInitialStepSize(2);Svmsgd.setSvmsgdType(SVMSGD.SGD);Svmsgd.setMarginRegularization(0.5f);boolean success = Svmsgd.train(trainingData, Ml.ROW_SAMPLE, labels);System.out.println("SVMSGD training result: " + success);// svm.save("D:/bp.xml");//存储模型// svm.load("D:/bp.xml");//读取模型Mat responseMat = new Mat();Svmsgd.predict(testData, responseMat, 0);System.out.println("SVMSGD responseMat:\n" + responseMat.dump());for (int i = 0; i < responseMat.height(); i++) {if (responseMat.get(i, 0)[0] == 0)System.out.println("Boy\n");if (responseMat.get(i, 0)[0] == 1)System.out.println("Girl\n");}return responseMat;}}

输出结果：

Ann training result: trueAnn responseMat:[0.014712702, 0.01492399; 0.98786205, 0.987822]BoyGirlBoost training result: trueBoost responseMat:[0; 0]BoyBoyDtrees training result: trueDtrees responseMat:[0; 1]BoyGirlKnn training result: trueresults:[0; 1]Knn neighborResponses:[0, 0; 1, 1]dists:[5, 5; 1, 8]BoyGirlLogisticRegression training result: trueLogisticRegression responseMat:[0; 1]BoyGirlNormalBayes training result: trueNormalBayes responseMat:[0; 1]BoyGirlRtrees training result: trueRtrees responseMat:[0; 1]BoyGirlSvm training result: trueSVM responseMat:[0; 1]BoyGirlSVMSGD training result: trueSVMSGD responseMat:[1; 1]GirlGirl

参考：

http://www.cnblogs.com/denny402/p/5032232.html

http://www.cnblogs.com/denny402/p/5032490.html