Opencv_SVM训练_识别几何体

#include "opencv2/opencv.hpp" #include "CameraCalibration.h" using namespace cv; using namespace std; Size SampleSize(60,60); //训练正样本尺寸 uchar TrainSample = 200; //每个种类的样本数 uchar Classes = 5; //样本种类 Mat Train_data,TrainClasses; //声明训练数据和响应数据 string str0 = "Hollow Cylinder"; //空心圆柱 string str1 = "Cube"; //正方体 string str2 = "Cone"; //圆锥 string str3 = "Cylindrical"; //圆柱 string str4 = "Cuboid"; //长方体 string Picture_Name; int Picture_NameCounter=1; Mat CalibrationImage; Mat element = getStructuringElement(MORPH_RECT,Size(9,9)); void Get_TrainData(void); void ReadImage_to_SaveBoundingRect(const Mat& Image,vector<Rect>& TemRectangle); Mat Background_separation(const Mat& Image); void main() { Mat GrayImage,BinaryImage,TestImage; CameraCalibration_Init(); namedWindow("【CalibrationImage】",WINDOW_AUTOSIZE); CvSVMParams SVM_Params; SVM_Params.svm_type = CvSVM::C_SVC; //SVM类型（允许用异常值惩罚因子C进行不完全分类） SVM_Params.kernel_type = CvSVM::LINEAR; //SVM的内核类型 SVM_Params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER,10000,1e-6);//指定迭代终止的条件 printf("\t\t\t\t开始获取训练集数据\n"); Get_TrainData(); printf("\t\t\t\t成功获取训练集数据\n"); printf("\t\t\t\tSVM训练样本开始\n"); CvSVM SVM_Classifier; SVM_Classifier.train_auto(Train_data,TrainClasses,Mat(),Mat(),SVM_Params);//自动训练并优化参数 SVM_Classifier.save("svm.xml"); printf("\t\t\t\tSVM训练样本结束\n"); while(1) { ReturnCameraCalibration(CalibrationImage); imshow("【CalibrationImage】",CalibrationImage); cvtColor(CalibrationImage,GrayImage,COLOR_BGR2GRAY); GaussianBlur(GrayImage,GrayImage,Size(7,7),0,0); BinaryImage = Background_separation(CalibrationImage); vector<Rect> TemRectangle; ReadImage_to_SaveBoundingRect(BinaryImage,TemRectangle); for (unsigned int i=0;i<TemRectangle.size();i++) { TestImage = GrayImage(TemRectangle[i]); Mat imageNewSize; resize(TestImage, imageNewSize, SampleSize); //统一摄像头画面里面采集到的轮廓图像的尺寸 TestImage.release(); //把image的矩阵信息释放（清除） TestImage = imageNewSize.reshape(0, 1); //图像深度不变，把图片矩阵转为一行储存 TestImage.convertTo(TestImage, CV_32FC1); int Response = static_cast<int>(SVM_Classifier.predict(TestImage)); switch (Response) { case 0: rectangle(CalibrationImage,TemRectangle[i],Scalar(0,0,255),1,8); putText(CalibrationImage, str0, Point(TemRectangle[i].x + TemRectangle[i].width / 2, TemRectangle[i].y + TemRectangle[i].height / 2), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255), 1, 8); break; case 1: rectangle(CalibrationImage,TemRectangle[i],Scalar(0,0,255),1,8); putText(CalibrationImage, str1, Point(TemRectangle[i].x + TemRectangle[i].width / 2, TemRectangle[i].y + TemRectangle[i].height / 2), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255), 1, 8); break; case 2: rectangle(CalibrationImage,TemRectangle[i],Scalar(0,0,255),1,8); putText(CalibrationImage, str2, Point(TemRectangle[i].x + TemRectangle[i].width / 2, TemRectangle[i].y + TemRectangle[i].height / 2), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255), 1, 8); break; case 3: rectangle(CalibrationImage,TemRectangle[i],Scalar(0,0,255),1,8); putText(CalibrationImage, str3, Point(TemRectangle[i].x + TemRectangle[i].width / 2, TemRectangle[i].y + TemRectangle[i].height / 2), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255), 1, 8); break; case 4: rectangle(CalibrationImage,TemRectangle[i],Scalar(0,0,255),1,8); putText(CalibrationImage, str4, Point(TemRectangle[i].x + TemRectangle[i].width / 2, TemRectangle[i].y + TemRectangle[i].height / 2), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255), 1, 8); break; default: break; } } imshow("【效果图】",CalibrationImage); uchar keyValue = (uchar)waitKey(1); if (keyValue == 27) break; else if(keyValue == 'q') { printf("\t\t已成功保存%d张图片至工程文件夹\n",Picture_NameCounter); Picture_Name = to_string(static_cast<long double>(Picture_NameCounter++))+".jpg"; //to_string:将数值转化为字符串。返回对应的字符串 imwrite(Picture_Name,CalibrationImage); } } } void Get_TrainData(void) { Train_data.create(TrainSample*Classes+2000,SampleSize.height*SampleSize.width,CV_32FC1);//每个种类的样本数为TrainSample，种类为Classes， TrainClasses.create(TrainSample*Classes+2000, 1, CV_32FC1); //创建TrainSample*Classes行、SampleSize.width*SampleSize.height列的矩阵存储信息（将一副图像的信息转为一行储存），训练数据必须是CV_32FC1 Mat TemparyImage,ImageNewSize; char file[255]; for (unsigned int i=0;i<Classes;i++) { for (unsigned int j=1;j<=TrainSample;j++)//训练集标号从1开始 { sprintf(file,"samples/positive/s%d/%d.jpg",i,j); TemparyImage = imread(file,IMREAD_GRAYSCALE); if (TemparyImage.empty()) { printf("\t\t\tError: Cant load image %s\n", file); continue; } resize(TemparyImage,ImageNewSize,SampleSize); //统一训练集尺寸 TemparyImage.release(); //把TemparyImage的矩阵信息释放（清除） TemparyImage = ImageNewSize.reshape(0,1); //图像深度不变，把图片矩阵转为一行储存 TemparyImage.convertTo(Train_data(Range(i*Classes+j-1,i*Classes+j),Range(0,Train_data.cols)),CV_32FC1);//Range为定义ROI的方式之一，表示从起始到终止索引（不包括终止索引）的连续序列 TrainClasses.at<float>(i*Classes+j-1,0) = i; } } for (unsigned int i =1;i<=200;i++) { sprintf(file, "samples/negative/%d.jpg",i); Mat SrcImage = imread(file,IMREAD_GRAYSCALE); if (SrcImage.empty()) { printf("\t\t\tError: Cant load image %s\n", file); continue; } resize(SrcImage,ImageNewSize,SampleSize); //统一训练集尺寸 SrcImage.release(); //把TemparyImage的矩阵信息释放（清除） SrcImage = ImageNewSize.reshape(0,1); //图像深度不变，把图片矩阵转为一行储存 SrcImage.convertTo(Train_data(Range(999+i,999+i+1),Range(0,Train_data.cols)),CV_32FC1);//Range为定义ROI的方式之一，表示从起始到终止索引（不包括终止索引）的连续序列 TrainClasses.at<float>(999+i,0) = -1; } } /*-------背景分离-----*/ Mat Background_separation(const Mat& Image) { Mat HSVimage,BinaryImage; cvtColor(Image,HSVimage,COLOR_BGR2HSV); GaussianBlur(HSVimage,HSVimage,Size(7,7),0,0); inRange(HSVimage,Scalar(0,0,30),Scalar(180,85,255),BinaryImage); morphologyEx(BinaryImage,BinaryImage,MORPH_OPEN,element); morphologyEx(BinaryImage,BinaryImage,MORPH_CLOSE,element); return BinaryImage; } /*-------寻找目标几何体的外包围矩形-----*/ void ReadImage_to_SaveBoundingRect(const Mat& Image,vector<Rect>& TemRectangle) { vector<vector<Point>> contours; vector<Vec4i> hierarchy; findContours(Image,contours,hierarchy,RETR_TREE,CHAIN_APPROX_SIMPLE,Point(0,0)); if(!contours.data()) return; Rect Rectangle; double MINcontoursArea = 500,MAXcontoursArea = 8000; for (unsigned int i=0;i<contours.size();i++) { if (fabs(contourArea(contours[i]))>MINcontoursArea && fabs(contourArea(contours[i]))<MAXcontoursArea) { Rectangle = boundingRect(contours[i]); /*-----防止矩形越界，出现图像崩溃问题-----*/ if(Rectangle.x >= 5) Rectangle.x = Rectangle.x -5; if(Rectangle.y >= 12) Rectangle.y = Rectangle.y -12; if((Rectangle.y+Rectangle.height + 18) >= CalibrationImage.rows) Rectangle.height = Rectangle.height + 18-(Rectangle.y+Rectangle.height + 18- CalibrationImage.rows); else Rectangle.height = Rectangle.height + 18; if((Rectangle.x+Rectangle.width + 10) >= CalibrationImage.cols) Rectangle.width = Rectangle.width + 10-(Rectangle.x+Rectangle.width + 10-Calibr