之前找到一些代码都是2.X版本的，很多都不能运行，我大致修改了一下，全部能跑动了，2.x版本都注释了，下面替换为新的，可以对照修改。
/*******************************
** 作者： 周小小
** 描述：
*******************************/
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#include <opencv2/opencv.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);//Float 32bits
Mat labels(2 * NTRAINING_SAMPLES, 1, CV_32SC1);//Signed 32bits 有符号的
//为了方便起见，将这个函数的代码的其他部分删除了。
//首先解释一下：samples就是训练的数据。response就是标签。
//通过上面，我们知道再来用Mat的时候，只能用CV_32F和CV_32S。
//由于labels是正负一所以为有符号的。
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 --------------------
cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create();
//CvSVMParams params;
svm->setType(cv::ml::SVM::Types::C_SVC);//类型
//params.svm_type = SVM::C_SVC;
//params.C = 0.1;
svm->setC(0.1);
//params.kernel_type = SVM::LINEAR;
svm->setKernel(cv::ml::SVM::KernelTypes::LINEAR);//核函数类型
//params.term_crit = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);
svm->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER, (int)1e7, 1e-6));//算法终止条件
//------------------------ 3. Train the svm ----------------------------------------------------
cout << "Starting training process" << endl;
/*for (int i = 0; i<trainData.rows; i++) {
for (int j = 0; j<trainData.cols; j++) {
cout << (int)trainData.at<float>(i, j) << endl;
}
}*/
//CvSVM svm;
//svm.train(trainData, labels, Mat(), Mat(), params);
svm->train(trainData, cv::ml::SampleTypes::ROW_SAMPLE, labels);
//svm->train(trainData, cv::ml::SampleTypes::ROW_SAMPLE, labels);
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();
cv::Mat sv = svm->getSupportVectors();
for (int i = 0; i <sv.rows; ++i)
{
const float* v = sv.ptr<float>(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("Two classes of linear nonseparable problems", I); // show it to the user
waitKey(0);
}
