opencv 3(C ++)自动训练的SVM加载问题 [英] opencv 3 (C++) auto trained SVM loading issue

问题描述

我正在使用SVM进行分类，我在一个项目中进行培训，在另一个项目中进行测试，以便仅进行一次培训.

I'm using SVM to do classification, and I do the training in one project, testing in another, in order to train only once.

培训部分如下:

classifier->trainAuto(trainData);
string svmDir = "/File/Dir/";
string svmFile = "svmClassifier.xml";
classifier->save(svmDir+svmFile);

测试部分是:

string svmDir = "/File/Dir/";
string svmFile = "svmClassifier.xml";
Ptr<ml::SVM> classifier = ml::SVM::load<ml::SVM>(svmDir+svmFile);
...
float response = classifier->predict(tDescriptor);

预测给出全0(全为负).但是，当我在培训项目中进行SVM培训后立即进行预测时，预测是正确的(我在预测"之前使用了断点，传递给预测的tDescriptor在两个项目中都是相同的.) 因此，我认为保存和加载过程可能有问题.

The prediction gives all 0s (all negative). But when I do the prediction right after the SVM training in the Training Project, the predictions are correct (I used breakpoint before "predict", the tDescriptor passed to predict are the same in both projects.) So I think there might be something wrong with saving and loading process..

可以保存和加载经过自动训练的SVM吗?还是必须在statModel中?

Can the auto trained SVM be saved and loaded? Or it must be in the statModel?

感谢帮助！

推荐答案

从

The flowing code was taken and modified from Introduction to Support Vector Machines for open CV. I saved the SVM parameters from the object svmOld "trainedSVM.xml". Then loaded the XML file and used them to create the object svmNew.

#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;
using namespace std;

int main()
{



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

    // Set up training data
    int Lable[] = { 1, -1, -1, -1 };
    Mat labelsMat(4, 1, CV_32S, Lable);


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

    // Train the SVM with given parameters
    Ptr<TrainData> td = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
    svmOld->train(td);
    //same svm 
    svmOld->save("trainedSVM.xml");


    //Initialize SVM object   
    Ptr<SVM> svmNew = SVM::create();
    //Load Previously saved SVM from XML 

    svmNew = SVM::load<SVM>("trainedSVM.xml");



    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 = svmNew->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 = svmNew->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);
    return(0);
}

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