SIFT和SURF算法实现目标检测

SIFT和SURF算法实现目标检测 不会编程的老王 2023-12-12 09:58:16 682

一、SIFT和SURF算子实现特征点检测

概述

在OpenCV的features2d中实现了SIFT和SURF算法,可以用于图像特征点的自动检测。具体实现是采用SiftFeatureDetector/SurfFeatureDetector类的detect函数检测SIFT/SURF特征的关键点,并保存在vector容器中,最后使用drawKeypoints函数绘制出特征点。

实验所用环境是opencv2.4.9+vs2013+win7

SIFT特征点检测
实验代码如下。这里需要注意SiftFeatureDetector是包含在opencv2/nonfree/features2d.hpp中,所以应该include这个头文件,并且在“项目属性->链接器->输入->附加依赖项”中加入库文件:opencv_nonfree249d.lib。

 #include <stdio.h>  
    #include <iostream>  
    #include "opencv2/core/core.hpp"  
    #include "opencv2/highgui/highgui.hpp"  
    #include "opencv2/nonfree/features2d.hpp"   //SurfFeatureDetector实际在该头文件中  
    using namespace cv;  
    using namespace std;  

    int main(int argc, char** argv)  
    {  
        Mat src = imread("image1.jpg", 0);  

        if (!src.data)  
        {  
            cout << " --(!) Error reading images " << endl;  
            return -1;  
        }  

        //1--初始化SIFT检测算子  
        int minHessian = 400;  
        SiftFeatureDetector detector(minHessian);  

        //2--使用SIFT算子检测特征点  
        vector<KeyPoint> keypoints;  
        detector.detect(src, keypoints);  

        //3--绘制特征点  
        Mat keypointImg;  
        drawKeypoints(src, keypoints, keypointImg, Scalar::all(-1), DrawMatchesFlags::DEFAULT);  
        imshow("SIFT keypoints", keypointImg);  
        cout << "keypoint number: " << keypoints.size() << endl;  

        waitKey(0);  
        return 0;  
    }



SURF特征点检测
同样的,使用SURF特征描述子进行特征点检测的过程类似,只不过换成了SurfFeatureDetector类,实验代码如下:

    #include <stdio.h>  
    #include <iostream>  
    #include "opencv2/core/core.hpp"  
    #include "opencv2/highgui/highgui.hpp"  
    #include "opencv2/nonfree/features2d.hpp"   //SurfFeatureDetector实际在该头文件中  
    using namespace cv;  
    using namespace std;  

    int main(int argc, char** argv)  
    {  
        Mat src = imread("image1.jpg", 0);  

        if (!src.data)  
        {  
            cout << " --(!) Error reading images " << endl;  
            return -1;  
        }  

        //1--初始化SIFT检测算子  
        int minHessian = 400;  
        SurfFeatureDetector  detector(minHessian);  

        //2--使用SIFT算子检测特征点  
        vector<KeyPoint> keypoints;  
        detector.detect(src, keypoints);  

        //3--绘制特征点  
        Mat keypointImg;  
        drawKeypoints(src, keypoints, keypointImg, Scalar::all(-1), DrawMatchesFlags::DEFAULT);  
        imshow("SIFT keypoints", keypointImg);  
        cout << "keypoint number: " << keypoints.size() << endl;  

        waitKey(0);  
        return 0;  
    }


从检测结果可以看出,SURF算子检测到的特征点远远多于SIFT算子,至于检测的精确度如何,后面试试利用SIFT和SURF算子进行特征点匹配来区分。

二、SIFT和SURF算子实现特征点提取与匹配

前面SIFT和SURF算子实现特征点检测简单地讲了利用SIFT和SURF算子检测特征点,在检测的基础上可以使用SIFT和SURF算子对特征点进行特征提取并使用匹配函数进行特征点的匹配。具体实现是首先采用SurfFeatureDetector检测特征点,再使用SurfDescriptorExtractor计算特征点的特征向量,最后采用BruteForceMatcher暴力匹配法或者FlannBasedMatcher选择性匹配法(二者的不同)来进行特征点匹配。
实验所用环境是opencv2.4.9+vs2013+win7,需要注意opencv2.4.X版本中SurfFeatureDetector是包含在opencv2/nonfree/features2d.hpp中,BruteForceMatcher是包含在opencv2/legacy/legacy.hpp中,FlannBasedMatcher是包含在opencv2/features2d/features2d.hpp中。

BruteForce匹配法
首先使用BruteForceMatcher暴力匹配法,代码如下:

    #include <stdio.h>    
    #include <iostream>    
    #include "opencv2/core/core.hpp"    
    #include "opencv2/nonfree/features2d.hpp"   //SurfFeatureDetector实际在该头文件中    
    #include "opencv2/legacy/legacy.hpp"    //BruteForceMatcher实际在该头文件中    
    //#include "opencv2/features2d/features2d.hpp"  //FlannBasedMatcher实际在该头文件中    
    #include "opencv2/highgui/highgui.hpp"    
    using namespace cv;  
    using namespace std;  

    int main(int argc, char** argv)  
    {  
        Mat src_1 = imread("image1.jpg", CV_LOAD_IMAGE_GRAYSCALE);  
        Mat src_2 = imread("image2.jpg", CV_LOAD_IMAGE_GRAYSCALE);  

        if (!src_1.data || !src_2.data)  
        {  
            cout << " --(!) Error reading images " << endl;  
            return -1;  
        }  

        //-- Step 1: 使用SURF算子检测特征点    
        int minHessian = 400;  
        SurfFeatureDetector detector(minHessian);  
        vector<KeyPoint> keypoints_1, keypoints_2;  
        detector.detect(src_1, keypoints_1);  
        detector.detect(src_2, keypoints_2);  
        cout << "img1--number of keypoints: " << keypoints_1.size() << endl;  
        cout << "img2--number of keypoints: " << keypoints_2.size() << endl;  

        //-- Step 2: 使用SURF算子提取特征(计算特征向量)    
        SurfDescriptorExtractor extractor;  
        Mat descriptors_1, descriptors_2;  
        extractor.compute(src_1, keypoints_1, descriptors_1);  
        extractor.compute(src_2, keypoints_2, descriptors_2);  

        //-- Step 3: 使用BruteForce法进行暴力匹配    
        BruteForceMatcher< L2<float> > matcher;  
        vector< DMatch > matches;  
        matcher.match(descriptors_1, descriptors_2, matches);  
        cout << "number of matches: " << matches.size() << endl;  

        //-- 显示匹配结果    
        Mat matchImg;  
        drawMatches(src_1, keypoints_1, src_2, keypoints_2, matches, matchImg);  
        imshow("matching result", matchImg);  
        waitKey(0);  

        return 0;  
    }

实验结果:

FLANN匹配法
使用暴力匹配的结果不怎么好,下面使用FlannBasedMatcher进行特征匹配,只保留好的特征匹配点,代码如下:

   #include <stdio.h>  
    #include <iostream>  
    #include "opencv2/core/core.hpp"  
    #include "opencv2/nonfree/features2d.hpp"   //SurfFeatureDetector实际在该头文件中  
    //#include "opencv2/legacy/legacy.hpp"  //BruteForceMatcher实际在该头文件中  
    #include "opencv2/features2d/features2d.hpp"    //FlannBasedMatcher实际在该头文件中  
    #include "opencv2/highgui/highgui.hpp"  
    using namespace cv;  
    using namespace std;  

    int main(int argc, char** argv)  
    {  
        Mat src_1 = imread("image1.jpg", CV_LOAD_IMAGE_GRAYSCALE);  
        Mat src_2 = imread("image2.jpg", CV_LOAD_IMAGE_GRAYSCALE);  

        if (!src_1.data || !src_2.data)  
        {  
            cout << " --(!) Error reading images " << endl;  
            return -1;  
        }  

        //-- Step 1: 使用SURF算子检测特征点  
        int minHessian = 400;  
        SurfFeatureDetector detector(minHessian);  
        vector<KeyPoint> keypoints_1, keypoints_2;  
        detector.detect(src_1, keypoints_1);  
        detector.detect(src_2, keypoints_2);  
        cout << "img1--number of keypoints: " << keypoints_1.size() << endl;  
        cout << "img2--number of keypoints: " << keypoints_2.size() << endl;  

        //-- Step 2: 使用SURF算子提取特征(计算特征向量)  
        SurfDescriptorExtractor extractor;  
        Mat descriptors_1, descriptors_2;  
        extractor.compute(src_1, keypoints_1, descriptors_1);  
        extractor.compute(src_2, keypoints_2, descriptors_2);  

        //-- Step 3: 使用FLANN法进行匹配  
        FlannBasedMatcher matcher;  
        vector< DMatch > allMatches;  
        matcher.match(descriptors_1, descriptors_2, allMatches);  
        cout << "number of matches before filtering: " << allMatches.size() << endl;  

        //-- 计算关键点间的最大最小距离  
        double maxDist = 0;  
        double minDist = 100;  
        for (int i = 0; i < descriptors_1.rows; i++)  
        {  
            double dist = allMatches[i].distance;  
            if (dist < minDist)  
                minDist = dist;  
            if (dist > maxDist)  
                maxDist = dist;  
        }  
        printf("    max dist : %f \n", maxDist);  
        printf("    min dist : %f \n", minDist);  

        //-- 过滤匹配点,保留好的匹配点(这里采用的标准:distance<2*minDist)  
        vector< DMatch > goodMatches;  
        for (int i = 0; i < descriptors_1.rows; i++)  
        {  
            if (allMatches[i].distance < 2 * minDist)  
                goodMatches.push_back(allMatches[i]);  
        }  
        cout << "number of matches after filtering: " << goodMatches.size() << endl;  

        //-- 显示匹配结果  
        Mat matchImg;  
        drawMatches(src_1, keypoints_1, src_2, keypoints_2,  
            goodMatches, matchImg, Scalar::all(-1), Scalar::all(-1),  
            vector<char>(),  
            DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS //不显示未匹配的点  
            );  
        imshow("matching result", matchImg);  
        //-- 输出匹配点的对应关系  
        for (int i = 0; i < goodMatches.size(); i++)  
            printf("    good match %d: keypoints_1 [%d]  -- keypoints_2 [%d]\n", i,  
            goodMatches[i].queryIdx, goodMatches[i].trainIdx);  

        waitKey(0);  
        return 0;  
    }

实验结果:

从第二个实验结果可以看出,经过过滤之后特征点数目从154减少到25,匹配的准确度有所上升。当然也可以使用SIFT算子进行上述两种匹配实验,只需要将SurfFeatureDetector换成SiftFeatureDetector,将SurfDescriptorExtractor换成SiftDescriptorExtractor即可。

区别
二者的区别在于BFMatcher总是尝试所有可能的匹配,从而使得它总能够找到最 佳匹配,这也是Brute Force(暴力法)的原始含义。而FlannBasedMatcher中FLANN的含义是Fast Library forApproximate Nearest Neighbors,从字面意思可知它是一种近似法,算法更快但是找到的是最近邻近似匹配,所以当我们需要找到一个相对好的匹配但是不需要最 佳匹配的时候往往使用FlannBasedMatcher。当然也可以通过调整FlannBasedMatcher的参数来提高匹配的精度或者提高算法速度,但是相应地算法速度或者算法精度会受到影响。
此外,使用特征提取过程得到的特征描述符(descriptor)数据类型有的是float类型的,比如说SurfDescriptorExtractor,SiftDescriptorExtractor,有的是uchar类型的,比如说有ORB,BriefDescriptorExtractor。对应float类型的匹配方式有:FlannBasedMatcher,BruteForce>,BruteForce>,BruteForce>。对应uchar类型的匹配方式有:BruteForce,BruteForce。所以ORB和BRIEF特征描述子只能使用BruteForce匹配法。

三、SIFT和SURF算法实现目标检测

概述
之前SIFT和SURF算子实现特征点检测和SURF算子实现特征点提取与匹配简单地讲了利用SIFT和SURF算子检测特征点,并且对特征点进行特征提取得到特征描述符(descriptors),在此基础上还可以进一步利用透视变换和空间映射找出已知物体(目标检测)。这里具体的实现是首先采用SURF/SIFT特征点检测与特征提取,然后采用FLANN匹配法保留好的匹配点,再利用findHomography找出相应的透视变换,最后采用perspectiveTransform函数映射点群,在场景中获取目标的位置。
实验所用环境是opencv2.4.9+vs2013+win7,需要注意opencv2.4.X版本中SurfFeatureDetector/SiftFeatureDetector是包含在opencv2/nonfree/features2d.hpp中,FlannBasedMatcher是包含在opencv2/features2d/features2d.hpp中。

SURF算子
首先使用SURF算子进行目标检测,代码如下:

  /** 
    * @概述: 采用SURF算子在场景中进行已知目标检测 
    * @类和函数: SurfFeatureDetector + SurfDescriptorExtractor + FlannBasedMatcher + findHomography + perspectiveTransform 
    * @实现步骤: 
    *       Step 1: 在图像中使用SURF算法SurfFeatureDetector检测关键点 
    *       Step 2: 对检测到的每一个关键点使用SurfDescriptorExtractor计算其特征向量(也称描述子) 
    *       Step 3: 使用FlannBasedMatcher通过特征向量对关键点进行匹配,使用阈值剔除不好的匹配 
    *       Step 4: 利用findHomography基于匹配的关键点找出相应的透视变换 
    *       Step 5: 利用perspectiveTransform函数映射点群,在场景中获取目标的位置 
    */  

    #include <ctime>  
    #include <iostream>  
    #include "opencv2/core/core.hpp"      
    #include "opencv2/highgui/highgui.hpp"  
    #include "opencv2/nonfree/features2d.hpp"   //SurfFeatureDetector实际在该头文件中  
    #include "opencv2/features2d/features2d.hpp"    //FlannBasedMatcher实际在该头文件中  
    #include "opencv2/calib3d/calib3d.hpp"  //findHomography所需头文件  
    using namespace cv;  
    using namespace std;  

    int main(int argc, char** argv)  
    {  
        Mat imgObject = imread("image1.jpg", CV_LOAD_IMAGE_GRAYSCALE);  
        Mat imgScene = imread("image2.jpg", CV_LOAD_IMAGE_GRAYSCALE);  

        if (!imgObject.data || !imgScene.data)  
        {  
            cout << " --(!) Error reading images " << endl;  
            return -1;  
        }  

        double begin = clock();  

        ///-- Step 1: 使用SURF算子检测特征点  
        int minHessian = 400;  
        SurfFeatureDetector detector(minHessian);  
        vector<KeyPoint> keypointsObject, keypointsScene;  
        detector.detect(imgObject, keypointsObject);  
        detector.detect(imgScene, keypointsScene);  
        cout << "object--number of keypoints: " << keypointsObject.size() << endl;  
        cout << "scene--number of keypoints: " << keypointsScene.size() << endl;  

        ///-- Step 2: 使用SURF算子提取特征(计算特征向量)  
        SurfDescriptorExtractor extractor;  
        Mat descriptorsObject, descriptorsScene;  
        extractor.compute(imgObject, keypointsObject, descriptorsObject);  
        extractor.compute(imgScene, keypointsScene, descriptorsScene);  

        ///-- Step 3: 使用FLANN法进行匹配  
        FlannBasedMatcher matcher;  
        vector< DMatch > allMatches;  
        matcher.match(descriptorsObject, descriptorsScene, allMatches);  
        cout << "number of matches before filtering: " << allMatches.size() << endl;  

        //-- 计算关键点间的最大最小距离  
        double maxDist = 0;  
        double minDist = 100;  
        for (int i = 0; i < descriptorsObject.rows; i++)  
        {  
            double dist = allMatches[i].distance;  
            if (dist < minDist)  
                minDist = dist;  
            if (dist > maxDist)  
                maxDist = dist;  
        }  
        printf("    max dist : %f \n", maxDist);  
        printf("    min dist : %f \n", minDist);  

        //-- 过滤匹配点,保留好的匹配点(这里采用的标准:distance<3*minDist)  
        vector< DMatch > goodMatches;  
        for (int i = 0; i < descriptorsObject.rows; i++)  
        {  
            if (allMatches[i].distance < 2 * minDist)  
                goodMatches.push_back(allMatches[i]);  
        }  
        cout << "number of matches after filtering: " << goodMatches.size() << endl;  

        //-- 显示匹配结果  
        Mat resultImg;  
        drawMatches(imgObject, keypointsObject, imgScene, keypointsScene,  
            goodMatches, resultImg, Scalar::all(-1), Scalar::all(-1), vector<char>(),  
            DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS //不显示未匹配的点  
            );  
        //-- 输出匹配点的对应关系  
        for (int i = 0; i < goodMatches.size(); i++)  
            printf("    good match %d: keypointsObject [%d]  -- keypointsScene [%d]\n", i,  
            goodMatches[i].queryIdx, goodMatches[i].trainIdx);  

        ///-- Step 4: 使用findHomography找出相应的透视变换  
        vector<Point2f> object;  
        vector<Point2f> scene;  
        for (size_t i = 0; i < goodMatches.size(); i++)  
        {  
            //-- 从好的匹配中获取关键点: 匹配关系是关键点间具有的一 一对应关系,可以从匹配关系获得关键点的索引  
            //-- e.g. 这里的goodMatches[i].queryIdx和goodMatches[i].trainIdx是匹配中一对关键点的索引  
            object.push_back(keypointsObject[goodMatches[i].queryIdx].pt);  
            scene.push_back(keypointsScene[goodMatches[i].trainIdx].pt);  
        }  
        Mat H = findHomography(object, scene, CV_RANSAC);  

        ///-- Step 5: 使用perspectiveTransform映射点群,在场景中获取目标位置  
        std::vector<Point2f> objCorners(4);  
        objCorners[0] = cvPoint(0, 0);  
        objCorners[1] = cvPoint(imgObject.cols, 0);  
        objCorners[2] = cvPoint(imgObject.cols, imgObject.rows);  
        objCorners[3] = cvPoint(0, imgObject.rows);  
        std::vector<Point2f> sceneCorners(4);  
        perspectiveTransform(objCorners, sceneCorners, H);  

        //-- 在被检测到的目标四个角之间划线  
        line(resultImg, sceneCorners[0] + Point2f(imgObject.cols, 0), sceneCorners[1] + Point2f(imgObject.cols, 0), Scalar(0, 255, 0), 4);  
        line(resultImg, sceneCorners[1] + Point2f(imgObject.cols, 0), sceneCorners[2] + Point2f(imgObject.cols, 0), Scalar(0, 255, 0), 4);  
        line(resultImg, sceneCorners[2] + Point2f(imgObject.cols, 0), sceneCorners[3] + Point2f(imgObject.cols, 0), Scalar(0, 255, 0), 4);  
        line(resultImg, sceneCorners[3] + Point2f(imgObject.cols, 0), sceneCorners[0] + Point2f(imgObject.cols, 0), Scalar(0, 255, 0), 4);  

        //-- 显示检测结果  
        imshow("detection result", resultImg);  

        double end = clock();  
        cout << "\nSURF--elapsed time: " << (end - begin) / CLOCKS_PER_SEC * 1000 << " ms\n";  

        waitKey(0);  
        return 0;  
    }


SIFT算子
作为对比,再使用SIFT算子进行目标检测,只需要将SurfFeatureDetector换成SiftFeatureDetector,将SurfDescriptorExtractor换成SiftDescriptorExtractor即可。代码如下:

  /** 
    * @概述: 采用SIFT算子在场景中进行已知目标检测 
    * @类和函数: SiftFeatureDetector + SiftDescriptorExtractor + FlannBasedMatcher + findHomography + perspectiveTransform 
    * @实现步骤: 
    *       Step 1: 在图像中使用SIFT算法SiftFeatureDetector检测关键点 
    *       Step 2: 对检测到的每一个关键点使用SiftDescriptorExtractor计算其特征向量(也称描述子) 
    *       Step 3: 使用FlannBasedMatcher通过特征向量对关键点进行匹配,使用阈值剔除不好的匹配 
    *       Step 4: 利用findHomography基于匹配的关键点找出相应的透视变换 
    *       Step 5: 利用perspectiveTransform函数映射点群,在场景中获取目标的位置 
    */  

    #include <ctime>  
    #include <iostream>  
    #include "opencv2/core/core.hpp"      
    #include "opencv2/highgui/highgui.hpp"  
    #include "opencv2/nonfree/features2d.hpp"   //SiftFeatureDetector实际在该头文件中  
    #include "opencv2/features2d/features2d.hpp"    //FlannBasedMatcher实际在该头文件中  
    #include "opencv2/calib3d/calib3d.hpp"  //findHomography所需头文件  
    using namespace cv;  
    using namespace std;  

    int main(int argc, char** argv)  
    {  
        Mat imgObject = imread("image1.jpg", CV_LOAD_IMAGE_GRAYSCALE);  
        Mat imgScene = imread("image2.jpg", CV_LOAD_IMAGE_GRAYSCALE);  

        if (!imgObject.data || !imgScene.data)  
        {  
            cout << " --(!) Error reading images " << endl;  
            return -1;  
        }  

        double begin = clock();  

        ///-- Step 1: 使用SIFT算子检测特征点  
        //int minHessian = 400;  
        SiftFeatureDetector detector;//( minHessian );  
        vector<KeyPoint> keypointsObject, keypointsScene;  
        detector.detect(imgObject, keypointsObject);  
        detector.detect(imgScene, keypointsScene);  
        cout << "object--number of keypoints: " << keypointsObject.size() << endl;  
        cout << "scene--number of keypoints: " << keypointsScene.size() << endl;  

        ///-- Step 2: 使用SIFT算子提取特征(计算特征向量)  
        SiftDescriptorExtractor extractor;  
        Mat descriptorsObject, descriptorsScene;  
        extractor.compute(imgObject, keypointsObject, descriptorsObject);  
        extractor.compute(imgScene, keypointsScene, descriptorsScene);  

        ///-- Step 3: 使用FLANN法进行匹配  
        FlannBasedMatcher matcher;  
        vector< DMatch > allMatches;  
        matcher.match(descriptorsObject, descriptorsScene, allMatches);  
        cout << "number of matches before filtering: " << allMatches.size() << endl;  

        //-- 计算关键点间的最大最小距离  
        double maxDist = 0;  
        double minDist = 100;  
        for (int i = 0; i < descriptorsObject.rows; i++)  
        {  
            double dist = allMatches[i].distance;  
            if (dist < minDist)  
                minDist = dist;  
            if (dist > maxDist)  
                maxDist = dist;  
        }  
        printf("    max dist : %f \n", maxDist);  
        printf("    min dist : %f \n", minDist);  

        //-- 过滤匹配点,保留好的匹配点(这里采用的标准:distance<3*minDist)  
        vector< DMatch > goodMatches;  
        for (int i = 0; i < descriptorsObject.rows; i++)  
        {  
            if (allMatches[i].distance < 2 * minDist)  
                goodMatches.push_back(allMatches[i]);  
        }  
        cout << "number of matches after filtering: " << goodMatches.size() << endl;  

        //-- 显示匹配结果  
        Mat resultImg;  
        drawMatches(imgObject, keypointsObject, imgScene, keypointsScene,  
            goodMatches, resultImg, Scalar::all(-1), Scalar::all(-1), vector<char>(),  
            DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS //不显示未匹配的点  
            );  
        //-- 输出匹配点的对应关系  
        for (int i = 0; i < goodMatches.size(); i++)  
            printf("    good match %d: keypointsObject [%d]  -- keypointsScene [%d]\n", i,  
            goodMatches[i].queryIdx, goodMatches[i].trainIdx);  

        ///-- Step 4: 使用findHomography找出相应的透视变换  
        vector<Point2f> object;  
        vector<Point2f> scene;  
        for (size_t i = 0; i < goodMatches.size(); i++)  
        {  
            //-- 从好的匹配中获取关键点: 匹配关系是关键点间具有的一 一对应关系,可以从匹配关系获得关键点的索引  
            //-- e.g. 这里的goodMatches[i].queryIdx和goodMatches[i].trainIdx是匹配中一对关键点的索引  
            object.push_back(keypointsObject[goodMatches[i].queryIdx].pt);  
            scene.push_back(keypointsScene[goodMatches[i].trainIdx].pt);  
        }  
        Mat H = findHomography(object, scene, CV_RANSAC);  

        ///-- Step 5: 使用perspectiveTransform映射点群,在场景中获取目标位置  
        std::vector<Point2f> objCorners(4);  
        objCorners[0] = cvPoint(0, 0);  
        objCorners[1] = cvPoint(imgObject.cols, 0);  
        objCorners[2] = cvPoint(imgObject.cols, imgObject.rows);  
        objCorners[3] = cvPoint(0, imgObject.rows);  
        std::vector<Point2f> sceneCorners(4);  
        perspectiveTransform(objCorners, sceneCorners, H);  

        //-- 在被检测到的目标四个角之间划线  
        line(resultImg, sceneCorners[0] + Point2f(imgObject.cols, 0), sceneCorners[1] + Point2f(imgObject.cols, 0), Scalar(0, 255, 0), 4);  
        line(resultImg, sceneCorners[1] + Point2f(imgObject.cols, 0), sceneCorners[2] + Point2f(imgObject.cols, 0), Scalar(0, 255, 0), 4);  
        line(resultImg, sceneCorners[2] + Point2f(imgObject.cols, 0), sceneCorners[3] + Point2f(imgObject.cols, 0), Scalar(0, 255, 0), 4);  
        line(resultImg, sceneCorners[3] + Point2f(imgObject.cols, 0), sceneCorners[0] + Point2f(imgObject.cols, 0), Scalar(0, 255, 0), 4);  

        //-- 显示检测结果  
        imshow("detection result", resultImg);  

        double end = clock();  
        cout << "\nSIFT--elapsed time: " << (end - begin) / CLOCKS_PER_SEC * 1000 << " ms\n";  

        waitKey(0);  
        return 0;  
    }

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