北京企业建网站公司,新变更营业执照注册号查了发现之前有备案过网站了,两当网站建设,制作和淘宝商城一样网站Opencv 之ORB特征提取与匹配API简介及使用例程
ORB因其速度较快常被用于视觉SLAM中的位姿估计、视觉里程、图像处理中的特征提取与匹配及图像拼接等领域本文将详细给出使用例程及实现效果展示
1. API 简介
创建
static PtrORB cv::ORB::create (int nfeatures 500…Opencv 之ORB特征提取与匹配API简介及使用例程
ORB因其速度较快常被用于视觉SLAM中的位姿估计、视觉里程、图像处理中的特征提取与匹配及图像拼接等领域本文将详细给出使用例程及实现效果展示
1. API 简介
创建
static Ptrlt;ORBgt; cv::ORB::create (int nfeatures 500, //nfeatures 最终输出最大特征点数目float scaleFactor 1.2f, // scaleFactor 金字塔上采样比率int nlevels 8, // nlevels 金字塔层数int edgeThreshold 31, // edgeThreshold 边缘阈值int firstLevel 0,int WTA_K 2, // WTA_K这个是跟BRIEF描述子用的ORB::ScoreType scoreType ORB::HARRIS_SCORE, // scoreType 对所有的特征点进行排名用的方法int patchSize 31,int fastThreshold 20
)检测
void cv::Feature2D::detect ( InputArray image, //输入图像std::vector KeyPoint keypoints, //待搜索特征点InputArray mask noArray() //操作掩码) 计算
void cv::Feature2D::compute ( InputArrayOfArrays images, //输入图像std::vector std::vector KeyPoint keypoints,OutputArrayOfArrays descriptors //描述子) 检测与计算 void cv::Feature2D::detectAndCompute ( InputArray image,InputArray mask,std::vector KeyPoint keypoints,OutputArray descriptors,bool useProvidedKeypoints false ) 绘制特征点
void cv::drawMatches ( InputArray img1,const std::vector KeyPoint keypoints1,InputArray img2,const std::vector KeyPoint keypoints2,const std::vector DMatch matches1to2,InputOutputArray outImg,const Scalar matchColor Scalar::all(-1),const Scalar singlePointColor Scalar::all(-1),const std::vector char matchesMask std::vector char (),DrawMatchesFlags flags DrawMatchesFlags::DEFAULT ) 绘制匹配点对
void cv::drawMatches ( InputArray img1,const std::vector KeyPoint keypoints1,InputArray img2,const std::vector KeyPoint keypoints2,const std::vector DMatch matches1to2,InputOutputArray outImg,const Scalar matchColor Scalar::all(-1),const Scalar singlePointColor Scalar::all(-1),const std::vector char matchesMask std::vector char (),DrawMatchesFlags flags DrawMatchesFlags::DEFAULT ) 2. 特征提取
讲述特征点提取与描述子计算实现如下
int main()
{Mat img imread(./data/test3/lena.png);if (!img.data || img.empty()){cout 图像读取错误 endl;return -1;}//创建ORB关键点PtrORB orb ORB::create(500, 1.2f);double t1 getTickCount();vectorKeyPointKeypoints;Mat descriptions;
#if 0//计算ORB关键点orb-detect(img, Keypoints);//计算ORB描述子orb-compute(img, Keypoints, descriptions);
#elseorb-detectAndCompute(img, cv::Mat(), Keypoints, descriptions);
#endif // 0double t2 (getTickCount() - t1) / getTickFrequency() * 1000;cout img.size img.size() , cost time t2 ms\n;//绘制特征点Mat imgAngle;img.copyTo(imgAngle);//绘制不含角度和大小的结果drawKeypoints(img, Keypoints, img, Scalar(255, 255, 255));//绘制含有角度和大小的结果drawKeypoints(img, Keypoints, imgAngle, Scalar(255, 255, 255), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);//显示结果string wname1 不含角度和大小的结果, wname2 含角度和大小的结果;namedWindow(wname1, WINDOW_NORMAL);namedWindow(wname2, 0);imshow(wname1, img);imshow(wname2, imgAngle);waitKey(0);return 1;
}3. 特征匹配
暴力匹配实现
#includeiostream
#includeopencv2/opencv.hpp
#includevectorusing namespace std;
using namespace cv;int main()
{//灰度格式读取Mat img1, img2;img1 imread(./data/test3/1.jpg,IMREAD_GRAYSCALE);img2 imread(./data/test3/2.jpg,0);if (img1.empty() || img2.empty()){cout img.empty!!!\n;return -1;}//提取orb特征点vectorKeyPointKeypoints1, Keypoints2;Mat descriptions1, descriptions2;//计算特征点orb_features(img1, Keypoints1, descriptions1);orb_features(img2, Keypoints2, descriptions2);//特征点匹配vectorDMatchmatches; BFMatcher matcher(NORM_HAMMING); //定义特征点匹配的类使用汉明距离matcher.match(descriptions1, descriptions2, matches);cout matches matches.size() endl;//通过汉明距离筛选匹配结果double min_dist 10000, max_dist 0;for (int i 0; i matches.size(); i){double dist matches[i].distance;min_dist min_dist dist ? min_dist : dist;max_dist max_dist dist ? max_dist : dist;}//输出计算的最大、最小距离cout min_dist min_dist endl;cout max_dist max_dist endl;//通过距离提出误差大的点vectorDMatchgoodmatches;for (int i 0; i matches.size(); i){if (matches[i].distance MAX(1.8 * min_dist, 30)) //需调参{goodmatches.push_back(matches[i]);}}cout good_min goodmatches.size() endl;//绘制结果Mat outimg, outimg1;drawMatches(img1, Keypoints1, img2, Keypoints2, matches, outimg);drawMatches(img1, Keypoints1, img2, Keypoints2, goodmatches, outimg1);string wname1 未筛选结果, wname2 最小汉明距离筛选;namedWindow(wname1, WINDOW_NORMAL);namedWindow(wname2, 0);imshow(wname1, outimg);imshow(wname2, outimg1);waitKey(0);return 1;
}其效果如下 最近邻匹配实现如下
if (descriptions1.type() ! CV_32F){descriptions1.convertTo(descriptions1, CV_32F);descriptions2.convertTo(descriptions2, CV_32F);}//特征点匹配vectorDMatchmatches;FlannBasedMatcher matcher; //定义特征点匹配的类使用汉明距离matcher.match(descriptions1, descriptions2, matches);cout matches matches.size() endl;//通过汉明距离筛选匹配结果double min_dist 10000, max_dist 0;for (int i 0; i matches.size(); i){double dist matches[i].distance;min_dist min_dist dist ? min_dist : dist;max_dist max_dist dist ? max_dist : dist;}//输出计算的最大、最小距离cout min_dist min_dist endl;cout max_dist max_dist endl;//通过距离提出误差大的点vectorDMatchgoodmatches;for (int i 0; i matches.size(); i){if (matches[i].distance 0.6 * max_dist) //需调参{goodmatches.push_back(matches[i]);}}cout good_min goodmatches.size() endl;其效果如下
RANSAC优化匹配
void ransac(vectorDMatchmatches, vectorKeyPointqueryKeyPoint,vectorKeyPointtrainKeyPoint, vectorDMatch matches_ransac)
{//定义保存匹配点对坐标vectorPoint2fsrcPoints(matches.size()), dstPoints(matches.size());//保存从关键点中提取到的匹配点对坐标for (int i 0; i matches.size(); i){srcPoints[i] queryKeyPoint[matches[i].queryIdx].pt;dstPoints[i] trainKeyPoint[matches[i].trainIdx].pt;}//匹配点对RANSAC过滤vectorintinlierMask(srcPoints.size());findHomography(srcPoints, dstPoints, RANSAC, 5, inlierMask);//手动保留RANSAC过滤后的匹配点对for (int i 0; i inlierMask.size(); i){if (inlierMask[i])matches_ransac.push_back(matches[i]);}
}//*************************RANSAC*******************************************
//main函数中放在暴力匹配代码后//特征点匹配vectorDMatchmatches; BFMatcher matcher(NORM_HAMMING); //定义特征点匹配的类使用汉明距离matcher.match(descriptions1, descriptions2, matches);cout matches matches.size() endl;//通过汉明距离筛选匹配结果double min_dist 10000, max_dist 0;for (int i 0; i matches.size(); i){double dist matches[i].distance;min_dist min_dist dist ? min_dist : dist;max_dist max_dist dist ? max_dist : dist;}//输出计算的最大、最小距离cout min_dist min_dist endl;cout max_dist max_dist endl;//通过距离提出误差大的点vectorDMatchgoodmatches;for (int i 0; i matches.size(); i){if (matches[i].distance MAX(1.8 * min_dist, 30)) //需调参{goodmatches.push_back(matches[i]);}}cout good_min goodmatches.size() endl;//RANSAC优化vectorDMatchgood_ransac;ransac(goodmatches, Keypoints1, Keypoints2, good_ransac);cout good_ransac good_ransac.size() endl;Mat output_;drawMatches(img1, Keypoints1, img2, Keypoints2, good_ransac, output_);namedWindow(ransac, 0);imshow(ransac, output_);
