OpenCV无法设置SVM参数 [英] OpenCV unable to set up SVM Parameters

问题描述

我刚刚开始使用C ++ OpenCV对SVM进行学习，并引用了SVM文档此处。我想尝试从链接的示例源代码，以先熟悉它，但我不能运行示例源代码。它返回错误：

I have just started my learning on SVM using C++ OpenCV and was referring to the SVM documentation here. I wanted to try out the sample source code from the link to get familiar with it first but I couldn't run the sample source code. It returns the error :

错误1错误C2065：'CvSVMParams'：undeclared identifier

Error 1 error C2065: 'CvSVMParams' : undeclared identifier

我使用OpenCV 3.0.0的Visual Studio 2012。

I'm using Visual Studio 2012 with OpenCV 3.0.0. The setup process should be correct as all other codes are working well except this.

推荐答案

很多事情都发生了改变从OpenCV 2.4到OpenCV 3.0 。其中，机器学习模块，不向后兼容。

A lot of things changed from OpenCV 2.4 to OpenCV 3.0. Among others, the machine learning module, which isn't backward compatible.

这是OpenCV SVM教程代码，更新OpenCV 3.0：

This is the OpenCV tutorial code for the SVM, update for OpenCV 3.0:

#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
    Ptr<SVM> svm = SVM::create();
    svm->setType(SVM::C_SVC);
    svm->setKernel(SVM::LINEAR);
    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));

    // Train the SVM with given parameters
    Ptr<TrainData> td = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
    svm->train(td);

    // Or train the SVM with optimal parameters
    //svm->trainAuto(td);

    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);

}

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