基于SVM+HOG的花生品种识别

早在大二时，导师做过一个花生品种识别的程序，但当时用于品种识别的特征是自定义特征；而我的当初的本科毕业设计则是基于SVM + HOG的人脸识别；在后来的工作学习中，又用到了HOG特征，想着是不是之前的花生品种的识别也能用Hog特征进行识别；正所谓学以致用，也能巩固下刚刚所学的知识；

所谓的Hog特征与SVM网上资料一大堆，而且讲解得非常详细，在这里不在做过多的讲解；

本程序的开发环境，主要是依赖于VS2013 + OpenCV2.4.9，开发环境，请自行配置，那么，下面贴出代码，不足之处，请各位大侠不吝批评指正：

特征提取功能的实现：

CvMat* dataMat = NULL;Book* book = xlCreateBook();Sheet* sheet = book->addSheet("Sheet1");for (int j = 1; j <= 13; ++ j){CString strLoadPath = "SubImage\\";CString strFile;strFile.Format("s%d", j);strLoadPath = strLoadPath + strFile + "\\";for (int i = 1; i <= 45; ++i){CString loadPath = strLoadPath;CString stri;stri.Format("%d.jpg", i);loadPath = loadPath + stri;IplImage* srcImage = cvLoadImage(loadPath);IplImage* grayImage = cvCreateImage(cvGetSize(srcImage), IPL_DEPTH_8U, 1);IplImage* hogImage = cvCreateImage(cvSize(96, 64), IPL_DEPTH_8U, 1);//用于提取Hog特征的图像cvCvtColor(srcImage, grayImage, CV_RGB2GRAY);cvResize(grayImage, hogImage, CV_INTER_LINEAR);   //线性插值std::vector<float> vfDescriptors;//vfDescriptors.resize(hogImage->width * hogImage->height);cv::Ptr<cv::HOGDescriptor> hog = new cv::HOGDescriptor(cvSize(hogImage->width, hogImage->height), cvSize(16, 16), cvSize(8, 8), cvSize(8, 8), 9);hog->compute(hogImage, vfDescriptors, cv::Size(1, 1), cv::Size(0, 0));//使用计算函数进行计算//CvMat* mat = cvCreateMat(45 * 13, vfDescriptors.size(), CV_32FC1);//cvZero(mat);int cols = 0;for (auto it = vfDescriptors.begin(); it != vfDescriptors.end(); ++it){if (sheet){sheet->writeNum(45 * (j-1) + (i - 1), cols++, *it);    //把Hog数据存储到Excel 中}}//释放内存cvReleaseImage(&srcImage);srcImage = NULL;cvReleaseImage(&grayImage);grayImage = NULL;cvReleaseImage(&hogImage);hogImage = NULL;}// 内层 for 循环}book->save("Hog.xls");book->release();

SVM训练的实现代码：

Book* book = xlCreateBook();if (book->load("Hog.xls")){Sheet* sheet = book->getSheet(0);if (sheet){CvMat* dataMat = cvCreateMat(sheet->lastRow(), sheet->lastCol(), CV_32FC1);//存储导入的数据for (int i = 0; i < sheet->lastRow(); ++ i){for (int j = 0; j < sheet->lastCol(); ++ j){double temp = sheet->readNum(i, j);cvSetReal2D(dataMat, i, j, temp);}}MessageBox("数据导入完成");CvMat* lableMat = cvCreateMat(sheet->lastRow(), 1, CV_32FC1);cvZero(lableMat);for (int i = 0; i < 13; ++ i){for (int j = 0; j < 45; ++ j){cvSetReal2D(lableMat, i * 45 + j, 0, i + 1);}}CvSVM svm;CvSVMParams svmParams;//向量机参数CvTermCriteria  svmCriteria;//迭代中止条件svmCriteria = cvTermCriteria(CV_TERMCRIT_EPS, 1000, FLT_EPSILON);svmParams = CvSVMParams(CvSVM::C_SVC, CvSVM::RBF, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, NULL, svmCriteria);//利用训练数据和确定的学习参数，进行SVM学习svm.train(dataMat, lableMat, NULL, NULL, svmParams);svm.save("svm.xml");//以下代码用于SVM识别CvMat* svmResult = cvCreateMat(sheet->lastRow(), 1, CV_32FC1);CvMat* svmRow = NULL;for (int i = 0; i < sheet->lastRow(); ++ i){svmRow = cvCreateMat(1, sheet->lastCol(), CV_32FC1);for (int j = 0; j < sheet->lastCol(); ++ j){float temp = cvGetReal2D(dataMat, i, j);cvSetReal2D(svmRow, 0, j, temp);}unsigned int ret = 0;ret = svm.predict(svmRow);cvSetReal2D(svmResult, i, 0, ret);cvReleaseMat(&svmRow);svmRow = NULL;}int nCount = 0;for (int i = 0; i < 13; ++i){for (int j = 0; j < 45; ++ j){int ret = cvGetReal2D(svmResult, i * 45 + j, 0);if (ret == (i + 1)){++nCount;}}}float recognize = 100 * nCount / 13 / 45;CString str;str.Format("SVM 识别率为： %f", recognize);str = str + "%";MessageBox(str);}}

测试功能的实现代码：

CvSVM svm;svm.load("svm.xml");CFileDialog dlg(TRUE, NULL, NULL, 0, "图片文件(*.jpg)|*.jpg||");if (dlg.DoModal() == IDOK){IplImage* testImage = cvLoadImage(dlg.GetPathName());IplImage* grayImage = cvCreateImage(cvGetSize(testImage), IPL_DEPTH_8U, 1);IplImage* hogImage = cvCreateImage(cvSize(96, 64), IPL_DEPTH_8U, 1);cvCvtColor(testImage, grayImage, CV_RGB2GRAY);cvResize(grayImage, hogImage, CV_INTER_LINEAR);std::vector<float> vfDescriptors;//vfDescriptors.resize(hogImage->width * hogImage->height);cv::Ptr<cv::HOGDescriptor> hog = new cv::HOGDescriptor(cvSize(hogImage->width, hogImage->height), cvSize(16, 16), cvSize(8, 8), cvSize(8, 8), 9);hog->compute(hogImage, vfDescriptors, cv::Size(1, 1), cv::Size(0, 0));//使用计算函数进行计算CvMat* mat = cvCreateMat(1, 256, CV_32FC1);int cols = 0;for (auto it = 0; it < 256; ++it){cvSetReal2D(mat, 0, it, vfDescriptors[it]);}int  ret = svm.predict(mat);CString str;switch (ret){case 1:str = "品种1：P12";break;case 2:str = "品种2：矮2";break;case 3:str = "品种3：花玉22";break;case 4:str = "品种4：花玉25";break;case 5:str = "品种5：冀花2号";break;case 6:str = "品种6：冀花4号";break;case 7:str = "品种7：冀花5号";break;case 8:str = "品种8：鲁花9号";break;case 9:str = "品种9：青花6号";break;case 10:str = "品种10：天府3号";break;case 11:str = "品种11：维花8号";break;case 12:str = "品种12：小白沙";break;case 13:str = "品种13：中农108";break;}AfxMessageBox(str);//cvReleaseMat(&mat);}//end if

相关的程序代码，用于测试的图像样本及程序中用到的操作 Excel 的类，已经打包上传到CSDN，请自行下载：http://download.csdn.net/detail/lingtianyulong/8377461