OpenCV3 Java 机器学习使用方法汇总

               原文链接：OpenCV3 Java 机器学习使用方法汇总                              

 前言

          按道理来说，C++版本的OpenCV训练的版本XML文件，在java中可以无缝使用。但要注意OpenCV本身的版本问题。从2.4 到3.x版本出现了很大的改变，XML文件本身的存储格式本身也不同，不能通用。

          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。

实例代码：

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 aNN          TrainData 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;      }        // Boost      public 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 data          Mat 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;      }  } 

备注：作者的代码运行无误，可直接测试。