OpenCV Tutorials —— Support Vector Machines for Non-Linearly Separable Data
与上一篇类似 ~~ 只不过是非线性数据
#include "stdafx.h" #include <iostream> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/ml/ml.hpp> #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 using namespace cv; using namespace std; int main() { // 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); 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); }
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