opencv中svm支持向量机c++简单例子introduction_to_svm.cpp坐标次序问题

最近在做一些纹理分割方面的东西，需要提取图像特征后进行训练分类。在师兄的指点下了解SVM（支持向量机）可以达到很好的效果。

在opencv（版本）自带OpenCV\samples\cpp\tutorial_code\ml\introduction_to_svm  下找到了简单的introduction_to_svm.cpp

#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/ml/ml.hpp>using namespace cv;int main(){    // Data for visual representation    int width = 512, height = 512;    Mat image = Mat::zeros(height, width, CV_8UC3);    // Set up training data    float labels[4] = {1.0, -1.0, -1.0, -1.0};    Mat labelsMat(4, 1, CV_32FC1, 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    CvSVMParams params;    params.svm_type    = CvSVM::C_SVC;    params.kernel_type = CvSVM::LINEAR;    params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);    // Train the SVM    CvSVM SVM;    SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), 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) << i,j);            float response = SVM.predict(sampleMat);            if (response == 1)                image.at<Vec3b>(j, i)  = green;            else if (response == -1)                 image.at<Vec3b>(j, i)  = 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;    int c     = SVM.get_support_vector_count();    for (int i = 0; i < c; ++i)    {        const float* v = SVM.get_support_vector(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);}

在该段程序中，我们运行后得到结果图如下图所示：

但是如果我们将代码第10行的width值改为1024，则数组越界。

最后发现在代码的39行

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

中，将i，j位置改变后，

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

得到结果

明显结果有问题，由于我们改了坐标35行的Mat sampleMat = (Mat_<float>(1,2) << i,j);
顺序也要调换一下，改为Mat sampleMat = (Mat_<float>(1,2) << j,i);结果图就很正常了，如下图

分析该问题的产生原因，主要是image.at<Vec3b>(j, i)这段代码是先行后列，顺序反了。归根到底opencv中的svm以空间为特征的话，一定记得顺序的对应关系，否则会出现很奇怪的结果。

最终修改后代码如下所示：

#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/ml/ml.hpp>using namespace cv;int main(){// Data for visual representationint width = 1024, height = 512;Mat image = Mat::zeros(height, width, CV_8UC3);// Set up training datafloat labels[4] = {1.0, -1.0, -1.0, -1.0};Mat labelsMat(4, 1, CV_32FC1, labels);float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };Mat trainingDataMat(4, 2, CV_32FC1, trainingData);// Set up SVM's parametersCvSVMParams params;params.svm_type    = CvSVM::C_SVC;params.kernel_type = CvSVM::LINEAR;params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);// Train the SVMCvSVM SVM;SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);Vec3b green(0,255,0), blue (255,0,0);// Show the decision regions given by the SVMfor (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 dataint 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 vectorsthickness = 2;lineType  = 8;int c     = SVM.get_support_vector_count();for (int i = 0; i < c; ++i){const float* v = SVM.get_support_vector(i);circle(image,  Point( (int) v[0], (int) v[1]),   6,  Scalar(128, 128, 128), thickness, lineType);}imwrite("result.png", image);        // save the imageimshow("SVM Simple Example", image); // show it to the userwaitKey(0);}