特征检测之特征提取（Detect）

特征检测之特征提取&＃xff08;Detect&＃xff09;

// Detect keypoints in image using the traditional Shi-Thomasi detector
void detKeypointsShiTomasi(vector &keypoints, cv::Mat &img, bool bVis)
{// compute detector parameters based on image sizeint blockSize &＃61; 4; // size of an average block for computing a derivative covariation matrix over each pixel neighborhooddouble maxOverlap &＃61; 0.0; // max. permissible overlap between two features in %double minDistance &＃61; (1.0 - maxOverlap) * blockSize;int maxCorners &＃61; img.rows * img.cols / max(1.0, minDistance); // max. num. of keypointsdouble qualityLevel &＃61; 0.01; // minimal accepted quality of image cornersdouble k &＃61; 0.04;// Apply corner detectiondouble t &＃61; (double)cv::getTickCount();vector corners;cv::goodFeaturesToTrack(img, corners, maxCorners, qualityLevel, minDistance, cv::Mat(), blockSize, false, k);// add corners to result vectorfor (auto it &＃61; corners.begin(); it !&＃61; corners.end(); &＃43;&＃43;it){cv::KeyPoint newKeyPoint;newKeyPoint.pt &＃61; cv::Point2f((*it).x, (*it).y);newKeyPoint.size &＃61; blockSize;keypoints.push_back(newKeyPoint);}t &＃61; ((double)cv::getTickCount() - t) / cv::getTickFrequency();cout <<"Shi-Tomasi detection with n&＃61;" <}//FAST, BRISK, ORB, AKAZE, SIFT
void detKeypointsModern(std::vector &keypoints, cv::Mat &img, std::string detectorType, bool bVis)
{string windowName;if (detectorType.compare("FAST") &＃61;&＃61; 0){int threshold &＃61; 30;bool bNMS &＃61; true;cv::FastFeatureDetector::DetectorType type &＃61; cv::FastFeatureDetector::TYPE_9_16; // TYPE_9_16, TYPE_7_12, TYPE_5_8cv::Ptr detector &＃61; cv::FastFeatureDetector::create(threshold, bNMS, type);double t &＃61; (double)cv::getTickCount();detector->detect(img, keypoints);t &＃61; ((double)cv::getTickCount() - t) / cv::getTickFrequency();cout <<"FAST detection with n&＃61; " < detector &＃61; cv::BRISK::create();double t &＃61; (double)cv::getTickCount();detector->detect(img, keypoints);t &＃61; ((double)cv::getTickCount() - t) / cv::getTickFrequency();cout <<"BRISK detection with n&＃61; " < detector &＃61; cv::ORB::create();double t &＃61; (double)cv::getTickCount();detector->detect(img, keypoints);t &＃61; ((double)cv::getTickCount() - t) / cv::getTickFrequency();cout <<"ORB detection with n&＃61; " < detector &＃61; cv::AKAZE::create();double t &＃61; (double)cv::getTickCount();detector->detect(img, keypoints);t &＃61; ((double)cv::getTickCount() - t) / cv::getTickFrequency();cout <<"AKAZE detection with n&＃61; " < detector &＃61; cv::xfeatures2d::SIFT::create();double t &＃61; (double)cv::getTickCount();detector->detect(img, keypoints);t &＃61; ((double)cv::getTickCount() - t) / cv::getTickFrequency();cout <<"SIFT detection with n&＃61; " <}// Detect keypoints in image using the traditional Harris detector
void detKeypointsHarris(std::vector &keypoints, cv::Mat &img, bool bVis)
{// Detector parametersint blockSize &＃61; 2; // for every pixel, a blockSize × blockSize neighborhood is consideredint apertureSize &＃61; 3; // aperture parameter for Sobel operator (must be odd)int minResponse &＃61; 100; // minimum value for a corner in the 8bit scaled response matrixdouble k &＃61; 0.04; // Harris parameter (see equation for details)// Detect Harris corners and normalize outputdouble t &＃61; (double)cv::getTickCount();cv::Mat dst, dst_norm, dst_norm_scaled;dst &＃61; cv::Mat::zeros(img.size(), CV_32FC1);cv::cornerHarris(img, dst, blockSize, apertureSize, k, cv::BORDER_DEFAULT);cv::normalize(dst, dst_norm, 0, 255, cv::NORM_MINMAX, CV_32FC1, cv::Mat());cv::convertScaleAbs(dst_norm, dst_norm_scaled);double maxOverlap &＃61; 0.0; // max. permissible overlap between two features in %, used during non-maxima suppressionfor (size_t j &＃61; 0; j (j, i);if (response > minResponse){ // only store points above a thresholdcv::KeyPoint newKeyPoint;newKeyPoint.pt &＃61; cv::Point2f(i, j);newKeyPoint.size &＃61; 2 * apertureSize;newKeyPoint.response &＃61; response;// perform non-maximum suppression (NMS) in local neighbourhood around new key pointbool bOverlap &＃61; false;for (auto it &＃61; keypoints.begin(); it !&＃61; keypoints.end(); &＃43;&＃43;it){double kptOverlap &＃61; cv::KeyPoint::overlap(newKeyPoint, *it);if (kptOverlap > maxOverlap){bOverlap &＃61; true;if (newKeyPoint.response > (*it).response){ // if overlap is >t AND response is higher for new kpt*it &＃61; newKeyPoint; // replace old key point with new onebreak; // quit loop over keypoints}}}if (!bOverlap){ // only add new key point if no overlap has been found in previous NMSkeypoints.push_back(newKeyPoint); // store new keypoint in dynamic list}}} // eof loop over cols} // eof loop over rowst &＃61; ((double)cv::getTickCount() - t) / cv::getTickFrequency();cout <<"Harris detection with n&＃61;" <