opencv3.1中svm的使用范例_opencv3.1 svm没有成员params-优快云博客

本文链接：https://blog.youkuaiyun.com/xpplearnc/article/details/51909187

本文通过一个简单的示例介绍了如何使用OpenCV库实现支持向量机(SVM)进行分类任务。示例中定义了训练数据和标签，并设置了SVM参数，包括类型为C-SVC和支持向量机核函数为线性。训练完成后，展示了决策区域并标记了支持向量。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui.hpp>
#include <opencv2/ml.hpp>

using namespace cv;
using namespace cv::ml;

int main(int, char**)
{
    // Data for visual representation
    int width = 512, height = 512;
    Mat image = Mat::zeros(height, width, CV_8UC3);

    // Set up training data
    int labels[4] = {1, -1, -1, -1};
    Mat labelsMat(4, 1, CV_32SC1, labels);

    float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
    Mat trainingDataMat(4, 2, CV_32FC1, trainingData);

    // Set up SVM's parameters
    SVM::Params params;
    params.svmType    = SVM::C_SVC;
    params.kernelType = SVM::LINEAR;
    params.termCrit   = TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6);

    // Train the SVM
    Ptr<SVM> svm = StatModel::train<SVM>(trainingDataMat, ROW_SAMPLE, labelsMat, params);

    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);
            float response = svm->predict(sampleMat);

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

    // Show the training data
    int thickness = -1;
    int lineType = 8;
    circle( image, Point(501,  10), 5, Scalar(  0,   0,   0), thickness, lineType );
    circle( image, Point(255,  10), 5, Scalar(255, 255, 255), thickness, lineType );
    circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType );
    circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType );

    // Show support vectors
    thickness = 2;
    lineType  = 8;
    Mat sv = svm->getSupportVectors();

    for (int i = 0; i < sv.rows; ++i)
    {
        const float* v = sv.ptr<float>(i);
        circle( image,  Point( (int) v[0], (int) v[1]),   6,  Scalar(128, 128, 128), thickness, lineType);
    }

    imwrite("result.png", image);        // save the image

    imshow("SVM Simple Example", image); // show it to the user
    waitKey(0);

}