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采用鼠标事件，手动选择样本点，包括目标样本和背景样本。组成训练数据进行训练
1、主函数
#include "stdafx.h" #include "opencv2/opencv.hpp" using namespace cv; using namespace cv::ml; 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 = "d:/peppers.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; }
首先输入图像，调用setMouseCallback函数进行鼠标取点
2、鼠标事件
//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); //画圆，在图上显示点击的点 } 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); } }
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、svm分类
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 // 设置参数 Ptr<SVM> svm = SVM::create(); svm->setType(SVM::C_SVC); svm->setKernel(SVM::LINEAR); // 训练分类器 Ptr<TrainData> tData = TrainData::create(train_data, ROW_SAMPLE, train_label); svm->train(tData); Vec3b color(0, 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) { 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float if ((int)response != 1) image.at<Vec3b>(i, j) = color; //将背景点设为黑色 } imshow("SVM Simple Example", image); // show it to the user waitKey(0); }
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。
4、完整程序
// svm.cpp : 定义控制台应用程序的入口点。 // #include "stdafx.h" #include "opencv2/opencv.hpp" using namespace cv; using namespace cv::ml; 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 = "d:/peppers.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 // 设置参数 Ptr<SVM> svm = SVM::create(); svm->setType(SVM::C_SVC); svm->setKernel(SVM::LINEAR); // 训练分类器 Ptr<TrainData> tData = TrainData::create(train_data, ROW_SAMPLE, train_label); svm->train(tData); Vec3b color(0, 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) { 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float if ((int)response != 1) image.at<Vec3b>(i, j) = color; //将背景设置为黑色 } imshow("SVM Simple Example", image); waitKey(0); }
输入原图像：
程序运行后显示：
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原文地址：http://www.cnblogs.com/denny402/p/5020551.html
