带旋转的模板匹配的原理及算法实现(c++)

#include<iostream> #include<opencv2/opencv.hpp> //#include<cv.h> #include<ctime> using namespace cv; using namespace std; //ROI区域的位置信息 #define xx 100//300// //横坐标 #define yy 100//80//240// //纵坐标 #define zz 300//400// //正方形ROI区域的边长 #define PI 3.14159267989 #define ModelPATH "..\\ConsoleApplication1\\picture\\my Logo.bmp"//模板图像路径 #define SearchPATH "..\\ConsoleApplication1\\picture\\my Logo.bmp"//待检测图像路径 int nThresh = 80;//二值化处理的控制阈值，最大值为255 //图片旋转函数 void imrotate(Mat &img, Mat &newIm, double angle)//img:输入图片；newIm：输出图片；angle：旋转角度(°) { //int len = max((img.cols), (img.rows)); //Point2f pt(len / 2.0, len / 2.0); //Mat r = getRotationMatrix2D(pt, angle, 1.0); //warpAffine(img, newIm, r, Size(len, len)); //better performance ： Point2f pt(img.cols / 2., img.rows / 2.); Mat r = getRotationMatrix2D(pt, angle, 1.0); warpAffine(img, newIm, r, img.size()); } //取圆形ROI区域函数：具体实现功能为输入原图，取原图最大可能的原型区域输出 Mat circle_tr(Mat src) { Mat dst = Mat::zeros(src.size(), src.type()); Mat mask = Mat::zeros(src.size(), CV_8U); Point circleCenter(mask.cols / 2, mask.rows / 2); int radius = min(mask.cols, mask.rows) / 2; circle(mask, circleCenter, radius, Scalar(255), -1); src.copyTo(dst, mask); return dst; } //旋转匹配函数（输入参数分别为匹配区域的左上角坐标、匹配角度、模板图像的ROI区域、待匹配图像） void RatationMatch(Point &location, double &angle, Mat modelpicture, Mat searchpicture) { Mat modeltmp, searchtmp; //对模板ROI区域和待检测图像分别进行三层图像金字塔下采样 pyrDown(modelpicture, modeltmp, Size(modelpicture.cols / 2, modelpicture.rows / 2)); pyrDown(searchpicture, searchtmp, Size(searchpicture.cols / 2, searchpicture.rows / 2)); pyrDown(modeltmp, modeltmp, Size(modeltmp.cols / 2, modeltmp.rows / 2)); pyrDown(searchtmp, searchtmp, Size(searchtmp.cols / 2, searchtmp.rows / 2)); pyrDown(modeltmp, modeltmp, Size(modeltmp.cols / 2, modeltmp.rows / 2)); pyrDown(searchtmp, searchtmp, Size(searchtmp.cols / 2, searchtmp.rows / 2)); Mat newIm;//临时储存每个旋转角度对应的图片 //定义图片匹配所需要的参数 int result_cols = searchtmp.cols - modeltmp.cols; int result_rows = searchtmp.rows - modeltmp.rows; Mat result = Mat(result_cols, result_rows, CV_32FC1); double minVal, maxVal, temp;//minVal：当前匹配计算的最小值 Point minLoc, maxLoc, matchLoc1;//minLoc：当前最佳匹配区域的左上角坐标 //在没有旋转的情况下进行第一次匹配计算 newIm = circle_tr(modeltmp); matchTemplate(searchtmp, newIm, result, CV_TM_SQDIFF); minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, Mat()); location = minLoc; temp = minVal; angle = 0;//当前旋转角度记录为0 //以步长为5进行第一次粗循环匹配 for (int i = -20; i < 21; i = i + 5) { double start = (double)clock(); imrotate(modeltmp, newIm, i); newIm = circle_tr(newIm); matchTemplate(searchtmp, newIm, result, CV_TM_SQDIFF); minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, Mat()); double finish = (double)clock(); if (minVal < temp) { location = minLoc; temp = minVal; angle = i; } cout << minVal << " " << temp << " " << finish - start << endl; } //在当前最优匹配角度周围10的区间以1为步长循环进行循环匹配计算 for (int j = angle - 5; j < angle + 6; j++) { imrotate(modeltmp, newIm, j); newIm = circle_tr(newIm); matchTemplate(searchtmp, newIm, result, CV_TM_SQDIFF); minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, Mat()); if (minVal < temp) { location = minLoc; temp = minVal; angle = j; } cout << minVal << " " << temp << endl; } //在当前最优匹配角度周围2的区间以0.1为步长进行循环匹配计算 double k_angle = angle - 0.9; for (int k = 0; k < 19; k = k + 1) { k_angle = k_angle + 0.1; imrotate(modeltmp, newIm, k_angle); newIm = circle_tr(newIm); matchTemplate(searchtmp, newIm, result, CV_TM_SQDIFF); minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, Mat()); if (minVal < temp) { location = minLoc; temp = minVal; angle = k_angle;; } cout << minVal << " " << temp << endl; } //用下采样前的图片来进行精匹配计算 result_cols = searchpicture.cols - modelpicture.cols; result_rows = searchpicture.rows - modelpicture.rows; result = Mat(result_cols, result_rows, CV_32FC1); k_angle = angle - 0.1; imrotate(modelpicture, newIm, k_angle); newIm = circle_tr(newIm); matchTemplate(searchpicture, newIm, result, CV_TM_SQDIFF); minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, Mat()); location = minLoc; temp = minVal; angle = k_angle; for (int k = 1; k < 3; k = k + 1) { k_angle = k_angle + 0.1; imrotate(modelpicture, newIm, k_angle); newIm = circle_tr(newIm); matchTemplate(searchpicture, newIm, result, CV_TM_SQDIFF); minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, Mat()); if (minVal < temp) { location = minLoc; temp = minVal; angle = k_angle;; } cout << minVal << " " << temp << endl; } location.x = location.x+50; location.y = location.y+50; //前面得到的是在待检测图片中的匹配区间的左上角坐标，下面将这个坐标换算到待检测图片旋转后的坐标中 int tmpx, tmpy; tmpx = (zz / 2)*(1 - cos(angle*PI / 180) - sin(angle*PI / 180)); tmpy = (zz / 2)*(1 - cos(angle*PI / 180) + sin(angle*PI / 180)); location.x = location.x + tmpx; location.y = location.y + tmpy; //前面得到的旋转角度是匹配时模板图像旋转的角度，后面需要的角度值是待检测图像应该旋转的角度值，故需要做相反数变换 angle = -angle; } int main() { //读入模板图像 Mat ModelImage = imread(ModelPATH); //将图片缩放到合适的大小（这一步是因为笔记本屏幕比较小才进行的） //resize(ModelImage, ModelImage, Size((ModelImage.cols) / 2, (ModelImage.rows) / 2)); //显示模板图像 cout << "显示模板图像" << endl; imshow("模板图像", ModelImage); waitKey(0); //定义ROI区域(起始点横坐标，纵坐标，长度，宽度） Mat ROIPart = ModelImage(Rect(xx, yy, zz, zz)); //将ROI区域标记为蓝色 for (int i = 0; i < ROIPart.rows; i++) { for (int j = 0; j < ROIPart.cols; j++) { ROIPart.at<Vec3b>(i, j)[0] = 255; } } cout << "选择ROI区域" << endl; imshow("模板图像", ModelImage); waitKey(0); //重新读入模板图像并进行边缘提取处理 ModelImage = imread(ModelPATH); //resize(ModelImage, ModelImage, Size((ModelImage.cols) / 2, (ModelImage.rows) / 2));//将图片缩放到合适的比例 //高斯滤波 GaussianBlur(ModelImage, ModelImage, Size(5, 5), 0, 0, BORDER_DEFAULT); //边缘提取 Mat ModelImage_edge = Mat(ModelImage); Canny(ModelImage, ModelImage_edge, 15, 100, 3); //定义边缘提取后的模板图像上的ROI区域 ROIPart = ModelImage_edge(Rect(xx, yy, zz, zz));//定义ROI区域(起始点横坐标，纵坐标，长度，宽度） //显示形状模板 destroyWindow("模板图像"); cout << "形状模板" << endl; imshow("形状模板", ROIPart); waitKey(0); //读入待检测图像 Mat SearchImage = imread(SearchPATH); //resize(SearchImage, SearchImage, Size((SearchImage.cols) / 2, (SearchImage.rows) / 2));//将图片缩放到合适的比例 //高斯滤波 GaussianBlur(SearchImage, SearchImage, Size(5, 5), 0, 0, BORDER_DEFAULT); //显示待检测图片 destroyWindow("形状模板"); cout << "检测图像" << endl; imshow("检测图像", SearchImage); waitKey(0); //待检测图像做边缘提取 Mat SearchImage_edge = Mat(SearchImage); Canny(SearchImage, SearchImage_edge, 10, 100, 3); //取待检测图像边缘的一部分来进行匹配以加快匹配速度 Mat serch_ROIPart = SearchImage_edge(Rect(50, 50, SearchImage_edge.cols - 100, SearchImage_edge.rows-100)); //显示待检测图片边缘提取后的结果 waitKey(0); //用ROI区域做匹配，找到在检测图像中的匹配坐标location double angle; Point location; double start = (double)clock(); RatationMatch(location, angle,ROIPart,serch_ROIPart ); double finish = (double)clock(); cout<<"匹配所花时间为："<< finish - start << endl; cout << "匹配的最优区域的起点坐标为：（" << location.x <<