OpenCV機器學習庫中主要實現算法如下： 1）一般貝葉斯分類器（Normal Bayes Classifier）：CvNormalBayesClassifier 2）K近鄰分類（K-nearest Neighbor Classifier）： CvKNearest 3）支持向量機（Support Vector Machine）：CvSVM 4）期望最大化（Expection Maximization）： EM 5）決策樹（Decision Tree）：CvDTree 6）隨機森林（Random Treess Classifier）：CvForestTree 7）超隨機樹分類器（Extremely randomized trees Classifier）： CvERTrees 8）Boost分類器（Boosted tree Classifier）： CvBoost 9）梯度下降Boost分類器（Gradient Boosted Trees）：CvGBTrees 10）神經網絡（Artificial Neural Networks）： CvANN_MLP
#include "opencv2/core/core.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgipt = points.at<Point2f>(i); circle( img, ipt, 2, colorTab[clusterIdx], CV_FILLED, CV_AA ); } imshow("clusters", img); char key = (char)waitKey(); //無限等待 if( key == 27 || key == 'q' || key == 'Q' ) // 'ESC' break; }}#define NTRAINING_SAMPLES 100 // Number of training samples per class#define FRAC_LINEAR_SEP 0.9f // Fraction of samples which compose the linear separable part/************************************************************************//* SVM,support vector machine(支持向量機)； *//************************************************************************/void SVM_(){ // Data for visual representation const int WIDTH = 512, HEIGHT = 512; Mat I = Mat::zeros(HEIGHT, WIDTH, CV_8UC3); //--------------------- 1. Set up training data randomly --------------------------------------- Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1); Mat labels (2*NTRAINING_SAMPLES, 1, CV_32FC1); RNG rng(100); // Random value generation class // Set up the linearly separable part of the training data int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES); // Generate random points for the class 1 Mat trainClass = trainData.rowRange(0, nLinearSamples); // The x coordinate of the points is in [0, 0.4) Mat c = trainClass.colRange(0, 1); rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * WIDTH)); // The y coordinate of the points is in [0, 1) c = trainClass.colRange(1,2); rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT)); // Generate random points for the class 2 trainClass = trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES); // The x coordinate of the points is in [0.6, 1] c = trainClass.colRange(0 , 1); rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH)); // The y coordinate of the points is in [0, 1) c = trainClass.colRange(1,2); rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT)); //------------------ Set up the non-linearly separable part of the training data --------------- // Generate random points for the classes 1 and 2 trainClass = trainData.rowRange( nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples); // The x coordinate of the points is in [0.4, 0.6) c = trainClass.colRange(0,1); rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH)); // The y coordinate of the points is in [0, 1) c = trainClass.colRange(1,2); rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT)); //------------------------- Set up the labels for the classes --------------------------------- labels.rowRange( 0, NTRAINING_SAMPLES).setTo(1); // Class 1 labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2); // Class 2 //------------------------ 2. Set up the support vector machines parameters -------------------- CvSVMParams params; params.svm_type = SVM::C_SVC; params.C = 0.1; params.kernel_type = SVM::LINEAR; params.term_crit = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6); //------------------------ 3. Train the svm ---------------------------------------------------- cout << "Starting training process" << endl; CvSVM svm; /* svm.train(trainData, labels, Mat(), Mat(), params); svm.save("supportVectorMachine.txt"); */ svm.load("supportVectorMachine.txt"); cout << "Finished training process" << endl; //------------------------ 4. Show the decision regions ---------------------------------------- Vec3b green(0,100,0), blue (100,0,0); for (int i = 0; i < I.rows; ++i) for (int j = 0; j < I.cols; ++j) { Mat sampleMat = (Mat_<float>(1,2) << i, j); float response = svm.predict(sampleMat); if (response == 1) I.at<Vec3b>(j, i) = green; else if (response == 2) I.at<Vec3b>(j, i) = blue; } //----------------------- 5. Show the training data -------------------------------------------- int thick = -1; int lineType = 8; float px, py; // Class 1 for (int i = 0; i < NTRAINING_SAMPLES; ++i) { px = trainData.at<float>(i,0); py = trainData.at<float>(i,1); circle(I, Point( (int) px, (int) py ), 3, Scalar(0, 255, 0), thick, lineType); } // Class 2 for (int i = NTRAINING_SAMPLES; i <2*NTRAINING_SAMPLES; ++i) { px = trainData.at<float>(i,0); py = trainData.at<float>(i,1); circle(I, Point( (int) px, (int) py ), 3, Scalar(255, 0, 0), thick, lineType); } //------------------------- 6. Show support vectors -------------------------------------------- thick = 2; lineType = 8; int x = svm.get_support_vector_count(); for (int i = 0; i < x; ++i) { const float* v = svm.get_support_vector(i); circle( I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick, lineType); } imwrite("result.png", I); // save the Image imshow("SVM for Non-Linear Training Data", I); // show it to the user waitKey(0);}/************************************************************************//* bayesian,Normal Bayes Classifier（貝葉斯分類） *//************************************************************************/void NBC() { float trainingData[8][3] = { {6, 180, 12}, {5.92, 190, 11}, {5.58, 170, 12}, {5.92, 165, 10}, {5, 100, 6}, {5.5, 150, 8},{5.42, 130, 7}, {5.75, 150, 9}}; Mat trainingDataMat(8, 3, CV_32FC1, trainingData); float responses[8] = {'M', 'M', 'M', 'M', 'F', 'F', 'F', 'F'}; Mat responsesMat(8, 1, CV_32FC1, responses); NormalBayesClassifier nbc; //NormalBayesClassifier nbc2; /* nbc.train(trainingDataMat, responsesMat); nbc.save("normalBayes.txt"); */ nbc.load("normalBayes.txt"); float myData[3] = {6, 130, 8}; Mat myDataMat(1, 3, CV_32FC1, myData); float r = nbc.predict( myDataMat ); cout<<endl<<"result: "<<(char)r<<endl; system("pause"); } //Gradient Boosted Trees/************************************************************************//* Gradient Boosted Trees （梯度Boost樹算法） *//************************************************************************/void GBT(){ double trainingData[28][2]={{210.4, 3}, {240.0, 3}, {300.0, 4}, {153.4, 3}, {138.0, 3}, {194.0,4}, {189.0, 3}, {126.8, 3}, {132.0, 2}, {260.9, 4}, {176.7,3}, {160.4, 3}, {389.0, 3}, {145.8, 3}, {160.0, 3}, {141.6,2}, {198.5, 4}, {142.7, 3}, {149.4, 3}, {200.0, 3}, {447.8,5}, {230.0, 4}, {123.6, 3}, {303.1, 4}, {188.8, 2}, {196.2,4}, {110.0, 3}, {252.6, 3} }; Mat trainingDataMat(28, 2, CV_32FC1, trainingData); float responses[28] = { 399900, 369000, 539900, 314900, 212000, 239999, 329999, 259900, 299900, 499998, 252900, 242900, 573900, 464500, 329900, 232000, 299900, 198999, 242500, 347000, 699900, 449900, 199900, 599000, 255000, 259900, 249900, 469000}; Mat responsesMat(28, 1, CV_32FC1, responses); //設置參數 CvGBTreesParams params; params.loss_function_type = CvGBTrees::ABSOLUTE_LOSS; params.weak_count = 10; params.shrinkage = 0.01f; params.subsample_portion = 0.8f; params.max_depth = 3; params.use_surrogates = false; CvGBTrees gbt; //訓練樣本 gbt.train(trainingDataMat, CV_ROW_SAMPLE, responsesMat, Mat(), Mat(), Mat(), Mat(),params); double sampleData[2]={185.4, 4}; //待預測樣本 Mat sampleMat(2, 1, CV_32FC1, sampleData); float r = gbt.predict(sampleMat); //預測 cout<<endl<<"result: "<<r<<endl; system("pause");}/************************************************************************//* Extremely randomized trees Classifier（絕對隨機森林算法） *//************************************************************************/void ET(){ double trainingData[28][2]={{210.4, 3}, {240.0, 3}, {300.0, 4}, {153.4, 3}, {138.0, 3}, {194.0,4}, {189.0, 3}, {126.8, 3}, {132.0, 2}, {260.9, 4}, {176.7,3}, {160.4, 3}, {389.0, 3}, {145.8, 3}, {160.0, 3}, {141.6,2}, {198.5, 4}, {142.7, 3}, {149.4, 3}, {200.0, 3}, {447.8,5}, {230.0, 4}, {123.6, 3}, {303.1, 4}, {188.8, 2}, {196.2,4}, {110.0, 3}, {252.6, 3} }; CvMat trainingDataCvMat = cvMat( 28, 2, CV_32FC1, trainingData ); float responses[28] = { 399900, 369000, 539900, 314900, 212000, 239999, 329999, 259900, 299900, 499998, 252900, 242900, 573900, 464500, 329900, 232000, 299900, 198999, 242500, 347000, 699900, 449900, 199900, 599000, 255000, 259900, 249900, 469000}; CvMat responsesCvMat = cvMat( 28, 1, CV_32FC1, responses ); CvRTParams params= CvRTParams(10, 2, 0, false,16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ); CvRTrees rtrees; rtrees.train(&trainingDataCvMat, CV_ROW_SAMPLE, &responsesCvMat, NULL, NULL, NULL, NULL,params); double sampleData[2]={201.5, 3}; Mat sampleMat(2, 1, CV_32FC1, sampleData); float r = rtrees.predict(sampleMat); cout<<endl<<"result: "<<r<<endl; system("pause");}/************************************************************************//* Expectation - Maximization （EM算法） *//************************************************************************/void EM_(){ Mat src = imread("4.jpg"); //讀取圖像 namedWindow( "my daughter", WINDOW_AUTOSIZE ); imshow( "my daughter", src); //顯示原始圖像 waitKey(0); //data為樣本數據，labels為樣本數據的類別標簽 Mat data, labels; //由彩色圖像轉換為EM算法所需的樣本數據 for (int i = 0; i < src.rows; i++) { for (int j = 0; j < src.cols; j++) { Vec3b point = src.at<Vec3b>(i, j); //提取出當前像素的彩色值 //三個顏色強度值轉換為一個樣本數據 Mat tmp = (Mat_<float>(1, 3) << point[0], point[1], point[2]); data.push_back(tmp); //存儲當前樣本 } } int clusters = 4; //表示要分割的數量，即一共分4個類 EM em = EM(clusters); //實例化EM //訓練樣本，得到樣本的類別標簽labels em.train(data, noArray(), labels, noArray()); //不同的類用不同的顏色代替 Vec3b colorTab[] = { Vec3b(0, 0, 255), Vec3b(0, 255, 0), Vec3b(255, 100, 100), Vec3b(255, 0, 255), Vec3b(0, 255, 255) }; int n = 0; //樣本數據的索引 for (int i = 0; i < src.rows; i++) { for (int j = 0; j < src.cols; j++) { int clusterIdx = labels.at<int>(n); //得到當前像素的類別標簽 src.at<Vec3b>(i, j) = colorTab[clusterIdx]; //賦上相應的顏色值 n++; } } namedWindow( "EM", WINDOW_AUTOSIZE ); imshow( "EM", src); //顯示分割結果 waitKey(0);}static const char* var_desc[] ={ "Age (young=Y, middle=M, old=O)", "Salary? (low=L, medium=M, high=H)", "Own_House? (false=N, true=Y)", "Own_Car? (false=N, true=Y)", "Credit_Rating (fair=F, good=G, Excellent=E)", 0};/************************************************************************//* Decision Tree（決策樹）； *//************************************************************************/void DT(){ //19個訓練樣本 float trainingData[19][5]={ {'Y','L','N','N','F'}, {'Y','L','Y','N','G'}, {'Y','M','Y','N','G'}, {'Y','M','Y','Y','G'}, {'Y','H','Y','Y','G'}, {'Y','M','N','Y','G'}, {'M','L','Y','Y','E'}, {'M','H','Y','Y','G'}, {'M','L','N','Y','G'}, {'M','M','Y','Y','F'}, {'M','H','Y','Y','E'}, {'M','M','N','N','G'}, {'O','L','N','N','G'}, {'O','L','Y','Y','E'}, {'O','L','Y','N','E'}, {'O','M','N','Y','G'}, {'O','L','N','N','E'}, {'O','H','N','Y','F'}, {'O','H','Y','Y','E'} }; Mat trainingDataMat(19, 5, CV_32FC1, trainingData); //樣本的矩陣形式 //樣本的分類結果，即響應值 float responses[19] = {'N','N','Y','Y','Y','N','Y','Y','N','N','Y','N','N','Y','Y','N','N','N','Y'}; Mat responsesMat(19, 1, CV_32FC1, responses); //矩陣形式 float priors[5] = {1, 1, 1, 1, 1}; //先驗概率，這里的每個特征屬性的作用都是相同 //定義決策樹的參數 CvDTreeParams params( 15, // 決策樹的最大深度 1, //決策樹葉節點的最小樣本數 0, //回歸精度，這里不需要 false, //是否使用替代分叉屬性，由于沒有缺失的特征屬性，所以這里不需要替代分叉屬性 25, //最大的類數量 0, // 交叉驗證的子集數，由于樣本太少，這里不需要交叉驗證 false, //使用1SE規則，這里不需要 false, //是否真正的去掉被剪切的分支，這里不需要 priors //先驗概率 ); //類形式的掩碼，這里是分類樹，而且5個特征屬性都是類的形式，因此該變量都為1 Mat varTypeMat(6, 1, CV_8U, Scalar::all(1)); CvDTree* dtree = new CvDTree(); //實例化CvDTree類 //訓練樣本，構建決策樹 dtree->train ( trainingDataMat, //訓練樣本 CV_ROW_SAMPLE, //樣本矩陣的行表示樣本，列表示特征屬性 responsesMat, //樣本的響應值矩陣 Mat(), //應用所有的特征屬性 Mat(), //應用所有的訓練樣本 varTypeMat, //類形式的掩碼 Mat(), //沒有缺失任何特征屬性 params //決策樹參數 ); //調用get_var_importance函數 const CvMat* var_importance = dtree->get_var_importance(); //輸出特征屬性重要性程度 for( int i = 0; i < var_importance->cols*var_importance->rows; i++ ) { double val = var_importance->data.db[i]; char buf[100]; int len = (int)(strchr( var_desc[i], '(' ) - var_desc[i] - 1); strncpy( buf, var_desc[i], len ); buf[len] = '/0'; printf( "%s", buf ); printf( ": %g%%/n", val*100. ); } float myData[5] = {'M','H','Y','N','F'}; //預測樣本 Mat myDataMat(5, 1, CV_32FC1, myData); //矩陣形式 double r = dtree->predict( myDataMat, Mat(), false)->value; //得到預測結果 cout<<endl<<"result: "<<(char)r<<endl; //輸出預測結果 system("pause");}/************************************************************************//* Boosted tree classifier （Boost樹算法 ) *//************************************************************************/void BT(){ //訓練樣本 float trainingData[42][2]={ {40, 55},{35, 35},{55, 15},{45, 25},{10, 10},{15, 15},{40, 10}, {30, 15},{30, 50},{100, 20},{45, 65},{20, 35},{80, 20},{90, 5}, {95, 35},{80, 65},{15, 55},{25, 65},{85, 35},{85, 55},{95, 70}, {105, 50},{115, 65},{110, 25},{120, 45},{15, 45}, {55, 30},{60, 65},{95, 60},{25, 40},{75, 45},{105, 35},{65, 10}, {50, 50},{40, 35},{70, 55},{80, 30},{95, 45},{60, 20},{70, 30}, {65, 45},{85, 40} }; Mat trainingDataMat(42, 2, CV_32FC1, trainingData); //訓練樣本的響應值 float responses[42] = {'R','R','R','R','R','R','R','R','R','R','R','R','R','R','R','R', 'R','R','R','R','R','R','R','R','R','R', 'B','B','B','B','B','B','B','B','B','B','B','B','B','B','B','B' }; Mat responsesMat(42, 1, CV_32FC1, responses); float priors[2] = {1, 1}; //先驗概率 CvBoostParams params( CvBoost::REAL, // boost_type 10, // weak_count 0.95, // weight_trim_rate 15, // max_depth false, // use_surrogates priors // priors ); // CvBoost boost; Boost boost; boost.train ( trainingDataMat, CV_ROW_SAMPLE, responsesMat, Mat(), Mat(), Mat(), Mat(), params ); //預測樣本 float myData[2] = {55, 25}; Mat myDataMat(2, 1, CV_32FC1, myData); double r = boost.predict( myDataMat ); cout<<endl<<"result: "<<(char)r<<endl; system("pause");}void main(){ //KNN(); //Kmeans(); //SVM_(); //NBC(); GBT(); //ET(); //EM_(); //DT(); //BT(); //float labels[2][2][3][2] = {0,1,2,3,4,5,6,7,8,9,10,11}; //Mat labelsMat(2, 4, CV_32FC3, labels); //cout<<labelsMat<<endl; system("pause");}新聞熱點
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