利用SVM进行图像目标检测和分类

#include <iostream>    #include <opencv2/opencv.hpp>  #include <opencv2/core/core.hpp>    #include <opencv2/highgui/highgui.hpp>   #include <opencv2/imgproc/imgproc.hpp>   #include<opencv2/ml/ml.hpp>using namespace cv;using namespace std;Mat img, image;Mat targetData, backData;bool flag = true;string wdname = "image";void on_mouse(int event, int x, int y, int flags, void* ustc); //鼠标取样本点void getTrainData(Mat &train_data, Mat &train_label);  //生成训练数据 void svm(); //svm分类int main(int argc, char** argv){string path = "1.png";img = imread(path);img.copyTo(image);if (img.empty()){cout << "Image load error";return 0;}namedWindow(wdname);setMouseCallback(wdname, on_mouse, 0);for (;;){imshow("image", img);int c = waitKey(0);if ((c & 255) == 27){cout << "Exiting ...\n";break;}if ((char)c == 'c'){flag = false;}if ((char)c == 'q'){destroyAllWindows();break;}}svm();return 0;}//鼠标在图像上取样本点，按q键退出void on_mouse(int event, int x, int y, int flags, void* ustc){if (event == CV_EVENT_LBUTTONDOWN){Point pt = Point(x, y);Vec3b point = img.at<Vec3b>(y, x);  //取出该坐标处的像素值，注意x,y的顺序Mat tmp = (Mat_<float>(1, 3) << point[0], point[1], point[2]);if (flag){targetData.push_back(tmp); //加入正样本矩阵circle(img, pt, 2, Scalar(0, 255, 255), -1, 8); //画出点击的点 }else{backData.push_back(tmp); //加入负样本矩阵circle(img, pt, 2, Scalar(255, 0, 0), -1, 8);}imshow(wdname, img);}}void getTrainData(Mat &train_data, Mat &train_label){int m = targetData.rows;int n = backData.rows;cout << "正样本数：:" << m << endl;cout << "负样本数：" << n << endl;vconcat(targetData, backData, train_data); //合并所有的样本点，作为训练数据train_label = Mat(m + n, 1, CV_32S, Scalar::all(1)); //初始化标注for (int i = m; i < m + n; i++)train_label.at<int>(i, 0) = -1;}void svm(){Mat train_data, train_label;getTrainData(train_data, train_label); //获取鼠标选择的样本训练数据CvSVMParams params;params.svm_type = CvSVM::C_SVC;params.kernel_type = CvSVM::LINEAR;params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);// 对SVM进行训练CvSVM SVM;SVM.train(train_data, train_label, Mat(), Mat(), params);Vec3b color(0, 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){Vec3b point = img.at<Vec3b>(i, j);  //取出该坐标处的像素值Mat sampleMat = (Mat_<float>(1, 3) << point[0], point[1], point[2]);float response = SVM.predict(sampleMat);  //进行预测，返回1或-1,返回类型为floatif ((int)response != 1)image.at<Vec3b>(i, j) = color;  //将背景设置为黑色}imshow("SVM Simple Example", image);waitKey(0);}

代码如上：

1.首先输入图像，调用setMouseCallback函数进行鼠标取点

2.用鼠标在图像上点击，取出当前点的红绿蓝像素值进行训练。先选择任意个目标样本，然后按"c“键后选择任意个背景样本。样本数可以自己随意决定。样本选择完后，按”q"键完成样本选择。

3.将正负样本矩阵，用vconcat合并成一个矩阵，用作训练分类器，并对相应的样本进行标注。最后将识别出的目标保留，将背景部分调成黑色。

原图：

效果图：