opencv3.1实现SVM图像分类算法

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/JennyBi/article/details/80234976

#include "opencv2/opencv.hpp"
using namespace cv;
using namespace cv::ml;




int main(int, char**)
{
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3);  //创建窗口可视化




// 设置训练数据
int labels[10] = { 1, -1, 1, 1,-1,1,-1,1,-1,-1 };
Mat labelsMat(10, 1, CV_32SC1, labels);




float trainingData[10][2] = { { 501, 150 },{ 255, 10 },{ 501, 255 },{ 10, 501 },{ 25, 80 },
{ 150, 300 },{ 77, 200 } ,{ 300, 300 } ,{ 45, 250 } ,{ 200, 200 } };
Mat trainingDataMat(10, 2, CV_32FC1, trainingData);




// 创建分类器并设置参数
Ptr<SVM> model = SVM::create();
model->setType(SVM::C_SVC);
model->setKernel(SVM::LINEAR);  //核函数




//设置训练数据 
Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);




// 训练分类器
model->train(tData);




Vec3b green(0, 255, 0), blue(255, 0, 0);
// Show the decision regions given by the SVM
for (int i = 0; i < image.rows; ++i)
for (int j = 0; j < image.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1, 2) << j, i);  //生成测试数据
float response = model->predict(sampleMat);  //进行预测，返回1或-1




if (response == 1)
image.at<Vec3b>(i, j) = green;
else if (response == -1)
image.at<Vec3b>(i, j) = blue;
}




// 显示训练数据
int thickness = -1;
int lineType = 8;
Scalar c1 = Scalar::all(0); //标记为1的显示成黑点
Scalar c2 = Scalar::all(255); //标记成-1的显示成白点
  //绘图时，先宽后高，对应先列后行
for (int i = 0; i < labelsMat.rows; i++)
{
const float* v = trainingDataMat.ptr<float>(i); //取出每行的头指针
Point pt = Point((int)v[0], (int)v[1]);
if (labels[i] == 1)
circle(image, pt, 5, c1, thickness, lineType);
else
circle(image, pt, 5, c2, thickness, lineType);


}




imshow("SVM Simple Example", image);
waitKey(0);

}

运行结果：