opencv 机器学习算法汇总

其他 2018-09-29 11:38:27 阅读次数: 0

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 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;
	}
}

输出结果： 

Ann training result: true
Ann responseMat:
[0.014712702, 0.01492399;
 0.98786205, 0.987822]
Boy

Girl

Boost training result: true
Boost responseMat:
[0;
 0]
Boy

Boy

Dtrees training result: true
Dtrees responseMat:
[0;
 1]
Boy

Girl

Knn training result: true
results:
[0;
 1]
Knn neighborResponses:
[0, 0;
 1, 1]
dists:
[5, 5;
 1, 8]
Boy

Girl

LogisticRegression training result: true
LogisticRegression responseMat:
[0;
 1]
Boy

Girl

NormalBayes training result: true
NormalBayes responseMat:
[0;
 1]
Boy

Girl

Rtrees training result: true
Rtrees responseMat:
[0;
 1]
Boy

Girl

Svm training result: true
SVM responseMat:
[0;
 1]
Boy

Girl

SVMSGD training result: true
SVMSGD responseMat:
[1;
 1]
Girl

Girl


参考：

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

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


猜你喜欢

转载自blog.csdn.net/blogercn/article/details/78005680
今日推荐
《美国对全球网络空间安全与发展的威胁和破坏》报告发布
火速冲上 GitHub 热榜 —— 开源编程语言、框架哪有这么可爱？
北京人形机器人创新中心发布全球首个纯电驱拟人奔跑的全尺寸人形机器人“天工”
LFOSSA 源来如此公开课 | 掌握云原生未来：CNCF 认证全面攻略与备考秘籍
周排行
循环神经网络（rnn）讲解
Tigao教程四：单独的关节运动
金蝶K3WISE15.0-注册套打教程
如何在Mac上配置Kubernetes
Android应用结束自身进程的方法
SpringMVC学习 十三 拦截器栈
中国驻洛杉矶总领馆举行新春招待会
HttpClient get post 发送
11 - three.js 笔记 - 绘制三维字体模型
Mysql递归获取某个父节点下面的所有子节点和子节点上的所有父节点
每日归档
更多
2024-05-01(4)
2024-04-30(1)
2024-04-29(40)
2024-04-28(0)
2024-04-27(56)
2024-04-26(39)
2024-04-25(22)
2024-04-24(36)
2024-04-23(26)
2024-04-22(39)

代码天地 © 2018 codetd.com

境外服务器   最新电视剧2022