特征检测之特征提取（Detect）

快来打我* 2024-04-17 16:52 68阅读 0赞

特征检测之特征提取（Detect）

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// Detect keypoints in image using the traditional Shi-Thomasi detector
    void detKeypointsShiTomasi(vector<cv::KeyPoint> &keypoints, cv::Mat &img, bool bVis)
    {
        // compute detector parameters based on image size
        int blockSize = 4;       //  size of an average block for computing a derivative covariation matrix over each pixel neighborhood
        double maxOverlap = 0.0; // max. permissible overlap between two features in %
        double minDistance = (1.0 - maxOverlap) * blockSize;
        int maxCorners = img.rows * img.cols / max(1.0, minDistance); // max. num. of keypoints
    
        double qualityLevel = 0.01; // minimal accepted quality of image corners
        double k = 0.04;
    
        // Apply corner detection
        double t = (double)cv::getTickCount();
        vector<cv::Point2f> corners;
        cv::goodFeaturesToTrack(img, corners, maxCorners, qualityLevel, minDistance, cv::Mat(), blockSize, false, k);
    
        // add corners to result vector
        for (auto it = corners.begin(); it != corners.end(); ++it)
        {
    
            cv::KeyPoint newKeyPoint;
            newKeyPoint.pt = cv::Point2f((*it).x, (*it).y);
            newKeyPoint.size = blockSize;
            keypoints.push_back(newKeyPoint);
        }
        t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
        cout << "Shi-Tomasi detection with n=" << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
    
        // visualize results
        if (bVis)
        {
            cv::Mat visImage = img.clone();
            cv::drawKeypoints(img, keypoints, visImage, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
            string windowName = "Shi-Tomasi Corner Detector Results";
            cv::namedWindow(windowName, 6);
            imshow(windowName, visImage);
            cv::waitKey(0);
        }
    }
    
    //FAST, BRISK, ORB, AKAZE, SIFT
    void detKeypointsModern(std::vector<cv::KeyPoint> &keypoints, cv::Mat &img, std::string detectorType, bool bVis)
    {
        string windowName;
        if (detectorType.compare("FAST") == 0)
        {
            int threshold = 30;
            bool bNMS = true;
            cv::FastFeatureDetector::DetectorType type = cv::FastFeatureDetector::TYPE_9_16; // TYPE_9_16, TYPE_7_12, TYPE_5_8
            cv::Ptr<cv::FeatureDetector> detector = cv::FastFeatureDetector::create(threshold, bNMS, type);
            double t = (double)cv::getTickCount();
            detector->detect(img, keypoints);
            t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
            cout << "FAST detection with n= " << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
            windowName = "FAST  Detector Results";
        }  
        else if (detectorType.compare("BRISK") == 0)
        {
            cv::Ptr<cv::FeatureDetector> detector = cv::BRISK::create();
            double t = (double)cv::getTickCount();
            detector->detect(img, keypoints);
            t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
            cout << "BRISK detection with n= " << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
            windowName = "BRISK  Detector Results";
        }
        else if (detectorType.compare("ORB") == 0)
        {
            cv::Ptr<cv::FeatureDetector> detector = cv::ORB::create();
            double t = (double)cv::getTickCount();
            detector->detect(img, keypoints);
            t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
            cout << "ORB detection with n= " << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
            windowName = "ORB  Detector Results";
        }  
        else if (detectorType.compare("AKAZE") == 0)
        {
            cv::Ptr<cv::FeatureDetector> detector = cv::AKAZE::create();
            double t = (double)cv::getTickCount();
            detector->detect(img, keypoints);
            t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
            cout << "AKAZE detection with n= " << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
            windowName = "AKAZE  Detector Results";
        }  
        else if (detectorType.compare("SIFT") == 0)
        {
            cv::Ptr<cv::FeatureDetector> detector = cv::xfeatures2d::SIFT::create();
            double t = (double)cv::getTickCount();
            detector->detect(img, keypoints);
            t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
            cout << "SIFT detection with n= " << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
             windowName = "SIFT  Detector Results";
        }  
        // visualize results
        if (bVis)
        {
            cv::Mat visImage = img.clone();
            cv::drawKeypoints(img, keypoints, visImage, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
            cv::namedWindow(windowName, 6);
            imshow(windowName, visImage);
            cv::waitKey(0);
        }
    }
    
    // Detect keypoints in image using the traditional Harris detector
    void detKeypointsHarris(std::vector<cv::KeyPoint> &keypoints, cv::Mat &img, bool bVis)
    {
       // Detector parameters
        int blockSize = 2;     // for every pixel, a blockSize × blockSize neighborhood is considered
        int apertureSize = 3;  // aperture parameter for Sobel operator (must be odd)
        int minResponse = 100; // minimum value for a corner in the 8bit scaled response matrix
        double k = 0.04;       // Harris parameter (see equation for details)
    
        // Detect Harris corners and normalize output
        double t = (double)cv::getTickCount();
        cv::Mat dst, dst_norm, dst_norm_scaled;
        dst = 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 = 0.0; // max. permissible overlap between two features in %, used during non-maxima suppression
        for (size_t j = 0; j < dst_norm.rows; j++)
        {
            for (size_t i = 0; i < dst_norm.cols; i++)
            {
                int response = (int)dst_norm.at<float>(j, i);
                if (response > minResponse)
                { // only store points above a threshold
    
                    cv::KeyPoint newKeyPoint;
                    newKeyPoint.pt = cv::Point2f(i, j);
                    newKeyPoint.size = 2 * apertureSize;
                    newKeyPoint.response = response;
    
                    // perform non-maximum suppression (NMS) in local neighbourhood around new key point
                    bool bOverlap = false;
                    for (auto it = keypoints.begin(); it != keypoints.end(); ++it)
                    {
                        double kptOverlap = cv::KeyPoint::overlap(newKeyPoint, *it);
                        if (kptOverlap > maxOverlap)
                        {
                            bOverlap = true;
                            if (newKeyPoint.response > (*it).response)
                            {                      // if overlap is >t AND response is higher for new kpt
                                *it = newKeyPoint; // replace old key point with new one
                                break;             // quit loop over keypoints
                            }
                        }
                    }
                    if (!bOverlap)
                    {                                     // only add new key point if no overlap has been found in previous NMS
                        keypoints.push_back(newKeyPoint); // store new keypoint in dynamic list
                    }
                }
            } // eof loop over cols
        }     // eof loop over rows
      
        t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
        cout << "Harris detection with n=" << keypoints.size() << " keypoints in " << 1000 * t / 1.0 << " ms" << endl;
     
        // visualize results
        if (bVis)
        {
            cv::Mat visImage = img.clone();
            cv::drawKeypoints(img, keypoints, visImage, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
            string windowName = "Harris Corner Detector Results";
            cv::namedWindow(windowName, 6);
            imshow(windowName, visImage);
            cv::waitKey(0);
        }
      
    }

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