opencv 机器学习算法汇总

2024年5月29日 141次阅读

转载自http://blog.csdn.net/cnbloger/article/details/78005680

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。

[java]
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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：

[plain]
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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