使用 SVM 和 opencv 3 进行图像训练

Image train using SVM and opencv 3

本文关键字：图像 SVM opencv 使用      更新时间：2023-10-16
#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()
{
Mat img_mat = imread("/home/buddhika/workspace/project/images/t.jpg");
// Load images in the C++ format
Size size(64,124);//the image size,e.g.64x124
resize(img_mat ,img_mat ,size);//resize image
int num_files = 1;//number of images
int img_area = 64*124;//imag size
//initialize the training matrix
//The number of rows in the matrix would be 5, and the number of   columns would be the area of the image, 64*124 = 12
Mat training_mat(num_files,img_area,CV_32FC1);
// "fill in" the rows of training_mat with the data from each image.
cvtColor(img_mat,img_mat, CV_RGB2GRAY);
 imshow("",img_mat);
int ii = 0; // Current column in training_mat
//Do this for every training image
int file_num=0;
for (int i = 0; i<img_mat.rows; i++) {
    for (int j = 0; j < img_mat.cols; j++) {
        training_mat.at<float>(file_num,ii++) = img_mat.at<uchar>(i,j);
    }
}
// training matrix set up properly to pass into the SVM functions
//set up labels for each training image
//1D matrix, where each element in the 1D matrix corresponds to each row in the 2D matrix.
//-1 for non-human and 1 for human
//labels matrix
 float label = 1.0;
 cout << training_mat.rows<< endl;
 cout << training_mat.cols<< endl;
 Mat labels(1,7936 , CV_32SC1, label);
// 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 it based on your data
   svmOld->train(training_mat, ROW_SAMPLE, labels);
    //same svm
  svmOld->save("positive.xml");

    //Initialize SVM object
    Ptr<SVM> svmNew = SVM::create();
    //Load Previously saved SVM from XML
    //can save the trained SVM so you don't have to retrain it every time
    svmNew = SVM::load<SVM>("positive.xml");
//To test your images using the trained SVM, simply read an image, convert it to a 1D matrix, and pass that in to svm
// td.predict( training_mat);//It will return a value based on what you set as your labels
waitKey(0);
return(0);
}

这是我使用 SVM 进行正数据训练的代码。但 

svmOld->train(training_mat, ROW_SAMPLE, labels);

代码崩溃并给出以下错误。我是如何克服的？

OpenCV错误：错误的参数（在分类问题的情况下 答复必须是绝对的;创建时指定 varType 训练数据，或传递整数响应）在训练中，文件 /home/buddhika/Documents/OpenCV/modules/ml/src/svm.cpp，第 1618 行 在抛出"cv：：异常"实例后终止调用

发生这种情况是因为您在将示例写入列时指定示例位于training_mat行上。

创建训练矩阵的代码应如下所示：

Mat training_mat(img_area,num_files,CV_32FC1);
// "fill in" the rows of training_mat with the data from each image.
cvtColor(img_mat,img_mat, CV_RGB2GRAY);
imshow("",img_mat);
int ii = 0; // Current column in training_mat
//Do this for every training image
int file_num=0;
for (int i = 0; i<img_mat.rows; i++) {
    for (int j = 0; j < img_mat.cols; j++) {
        training_mat.at<float>(ii++,file_num) = img_mat.at<uchar>(i,j);
    }
}

你的labels也应该改变：Mat labels(training_mat.rows,1 , CV_32SC1, label);

我不知道

我是否正确，但我想尝试一下。

现在谈谈你的问题。因为问题似乎是 SVM 的实现。我首先建议您先研究 SVM 的 opencv 实现。https://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html

我从 SVM 文档中了解到的是，您必须首先向其提供带有类标签的训练数据集。根据提供的训练数据，它将训练 SVM。此外，您还必须针对正标签和负标签对其进行训练。

你正在做的事情是安静的错误。我已经在我的机器上运行了你的代码并对其进行了一些调试。使用此调试的代码。

Mat img_mat = imread("C:\Users\saurabh chandra\Desktop\52.png");
Mat img_mat1 = imread("C:\Users\saurabh chandra\Desktop\53.png");
Size size(64, 124);                
resize(img_mat, img_mat, size);   
resize(img_mat1, img_mat1, size);
int num_files = 2;             
int img_area = 64 * 124;      

cvtColor(img_mat, img_mat, CV_RGB2GRAY);  
cvtColor(img_mat1, img_mat1, COLOR_BGR2GRAY);
int ii = 0; 
int file_num = 0;
for (int i = 0; i<img_mat.rows; i++) 
{
for (int j = 0; j < img_mat.cols; j++) 
{
    training_mat.at<float>(file_num, ii++) = img_mat.at<uchar>(i, j);
}
 }
ii = 0;
 file_num =file_num+1;
 for (int i = 0; i<img_mat.rows; i++)
  {
     for (int j = 0; j < img_mat.cols; j++)
     {
      training_mat.at<float>(file_num, ii++) = img_mat.at<uchar>(i, j);
     }
  }

 float label[2] = { 1.0,1.0 };
 cout << training_mat.rows << endl;
 cout << training_mat.cols << endl;
 Mat labels(2, 1, CV_32SC1, label);
 Ptr<SVM> svmOld = SVM::create();
 svmOld->setType(SVM::C_SVC);
 svmOld->setKernel(SVM::LINEAR);
 svmOld->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
 svmOld->train(training_mat, ROW_SAMPLE, labels);
 svmOld->save("positive.xml");
 waitKey(0);
 return 0;
}

此代码将 SVM 训练结果保存到 Positive.xml 文件中。我已经为两个图像实现了您的代码。但我建议您使用良好的大量训练数据以获得更好的结果。

为了更好地了解 SVM 的实施，您可以查看此处。

将 OpenCV 和 SVM 与图像一起使用

如果这有帮助，请告诉我。

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