学习OpenCV——通过KeyPoints进行目标定位

目前的AR应用基本都是基于marker的比较多，但是不依靠marker首先要完成的定位工作。这篇文章主要描述，使用特征点进行检测和定位的问题（其中包含一些matching优化方式，具体请参考：学习OpenCV——KeyPoint Matching 优化方式）。 
步骤： 
1. 目标图特征点检测和描述子计算； 
2. 打开camera采集图像，并计算特征点及描述子； 
3. 进行特征点匹配，采用KNN或Cross-check方式； 
4. 寻找单应性变换矩阵，并通过单应性变换矩阵进行进一步优化，去除伪匹配； 
5. 通过单应性变换矩阵进行目标定位。 

#include <opencv/cv.h>#include <opencv/highgui.h>#include <vector>#include <iostream>using namespace cv;using namespace std;Mat src,frameImg;int width;int height;vector<Point> srcCorner(4);vector<Point> dstCorner(4);static bool createDetectorDescriptorMatcher( const string& detectorType, const string& descriptorType, const string& matcherType,Ptr<FeatureDetector>& featureDetector,Ptr<DescriptorExtractor>& descriptorExtractor,Ptr<DescriptorMatcher>& descriptorMatcher ){cout << "< Creating feature detector, descriptor extractor and descriptor matcher ..." << endl;featureDetector = FeatureDetector::create( detectorType );descriptorExtractor = DescriptorExtractor::create( descriptorType );descriptorMatcher = DescriptorMatcher::create( matcherType );cout << ">" << endl;bool isCreated = !( featureDetector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty() );if( !isCreated )cout << "Can not create feature detector or descriptor extractor or descriptor matcher of given types." << endl << ">" << endl;return isCreated;}bool refineMatchesWithHomography(const std::vector<cv::KeyPoint>& queryKeypoints,   const std::vector<cv::KeyPoint>& trainKeypoints,    float reprojectionThreshold,   std::vector<cv::DMatch>& matches,   cv::Mat& homography  ){const int minNumberMatchesAllowed = 4;  if (matches.size() < minNumberMatchesAllowed)  return false;  // Prepare data for cv::findHomography  std::vector<cv::Point2f> queryPoints(matches.size());  std::vector<cv::Point2f> trainPoints(matches.size());  for (size_t i = 0; i < matches.size(); i++)  {  queryPoints[i] = queryKeypoints[matches[i].queryIdx].pt;  trainPoints[i] = trainKeypoints[matches[i].trainIdx].pt;  }  // Find homography matrix and get inliers mask  std::vector<unsigned char> inliersMask(matches.size());  homography = cv::findHomography(queryPoints,   trainPoints,   CV_FM_RANSAC,   reprojectionThreshold,   inliersMask);  std::vector<cv::DMatch> inliers;  for (size_t i=0; i<inliersMask.size(); i++)  {  if (inliersMask[i])  inliers.push_back(matches[i]);  }  matches.swap(inliers);Mat homoShow;drawMatches(src,queryKeypoints,frameImg,trainKeypoints,matches,homoShow,Scalar::all(-1),CV_RGB(255,255,255),Mat(),2);     imshow("homoShow",homoShow); return matches.size() > minNumberMatchesAllowed; }bool matchingDescriptor(const vector<KeyPoint>& queryKeyPoints,const vector<KeyPoint>& trainKeyPoints,const Mat& queryDescriptors,const Mat& trainDescriptors, Ptr<DescriptorMatcher>& descriptorMatcher,bool enableRatioTest = true){vector<vector<DMatch>> m_knnMatches;vector<DMatch>m_Matches;if (enableRatioTest){cout<<"KNN Matching"<<endl;const float minRatio = 1.f / 1.5f;descriptorMatcher->knnMatch(queryDescriptors,trainDescriptors,m_knnMatches,2);for (size_t i=0; i<m_knnMatches.size(); i++){const cv::DMatch& bestMatch = m_knnMatches[i][0];const cv::DMatch& betterMatch = m_knnMatches[i][1];float distanceRatio = bestMatch.distance / betterMatch.distance;if (distanceRatio < minRatio){m_Matches.push_back(bestMatch);}}}else{cout<<"Cross-Check"<<endl;Ptr<cv::DescriptorMatcher> BFMatcher(new cv::BFMatcher(cv::NORM_HAMMING, true));BFMatcher->match(queryDescriptors,trainDescriptors, m_Matches );}Mat homo;float homographyReprojectionThreshold = 1.0;bool homographyFound = refineMatchesWithHomography(queryKeyPoints,trainKeyPoints,homographyReprojectionThreshold,m_Matches,homo);if (!homographyFound)return false;else{double h[9];bool isFound=true;h[0]=homo.at<double>(0,0);  h[1]=homo.at<double>(0,1);  h[2]=homo.at<double>(0,2);   h[3]=homo.at<double>(1,0);  h[4]=homo.at<double>(1,1);  h[5]=homo.at<double>(1,2);  h[6]=homo.at<double>(2,0);  h[7]=homo.at<double>(2,1);  h[8]=homo.at<double>(2,2);printf("homo\n"  "%f %f %f\n"  "%f %f %f\n"  "%f %f %f\n",   homo.at<double>(0,0), homo.at<double>(0,1), homo.at<double>(0,2),   homo.at<double>(1,0), homo.at<double>(1,1), homo.at<double>(1,2),   homo.at<double>(2,0), homo.at<double>(2,1), homo.at<double>(2,2));   for (int i=0;i<4;i++){float x = (float)srcCorner[i].x;float y = (float)srcCorner[i].y;  float Z = (float)1./(h[6]*x + h[7]*y + h[8]);  float X = (float)(h[0]*x + h[1]*y + h[2])*Z;  float Y = (float)(h[3]*x + h[4]*y + h[5])*Z; //if(X>=0&&X<width&&Y>=0&&Y<height){dstCorner[i]=Point(int(X),int(Y));}//else{//isFound &= false;}}if (isFound){line(frameImg,dstCorner[0],dstCorner[1],CV_RGB(255,0,0),2);line(frameImg,dstCorner[1],dstCorner[2],CV_RGB(255,0,0),2);line(frameImg,dstCorner[2],dstCorner[3],CV_RGB(255,0,0),2);line(frameImg,dstCorner[3],dstCorner[0],CV_RGB(255,0,0),2);return true;}return true;}}int main(){string filename = "D:\\Img\\Heads\\03.jpg";src = imread(filename,0);width = src.cols;height = src.rows;string detectorType = "ORB";string descriptorType = "ORB";string matcherType = "BruteForce";Ptr<FeatureDetector> featureDetector;Ptr<DescriptorExtractor> descriptorExtractor;Ptr<DescriptorMatcher> descriptorMatcher;if( !createDetectorDescriptorMatcher( detectorType, descriptorType, matcherType, featureDetector, descriptorExtractor, descriptorMatcher ) ){cout<<"Creat Detector Descriptor Matcher False!"<<endl;return -1;}//Intial: read the pattern img keyPoint vector<KeyPoint> queryKeypoints; Mat queryDescriptor; featureDetector->detect(src,queryKeypoints); descriptorExtractor->compute(src,queryKeypoints,queryDescriptor); VideoCapture cap(0); // open the default camera if(!cap.isOpened())  // check if we succeeded { cout<<"Can't Open Camera!"<<endl; return -1; } srcCorner[0] = Point(0,0); srcCorner[1] = Point(width,0); srcCorner[2] = Point(width,height); srcCorner[3] = Point(0,height); vector<KeyPoint> trainKeypoints; Mat trainDescriptor; Mat frame,grayFrame; char key=0; //frame = imread("D:\\Img\\Heads\\00.jpg");  while (key!=27) { cap>>frame; frame.copyTo(frameImg); cvtColor(frame,grayFrame,CV_BGR2GRAY); trainKeypoints.clear();// if (!trainDescriptor.empty())trainDescriptor.setTo(0); featureDetector->detect(grayFrame,trainKeypoints); if(trainKeypoints.size()!=0) { descriptorExtractor->compute(grayFrame,trainKeypoints,trainDescriptor); bool isFound = matchingDescriptor(queryKeypoints,trainKeypoints,queryDescriptor,trainDescriptor,descriptorMatcher);  imshow("foundImg",frameImg);  } key = waitKey(1);  } }