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カメラ ストリーミングでオブジェクトを検出するために、SURF アルゴリズムを適用しました。しかし、ストリーミングが少し遅いことに気づきました。Windows APIGetTickCount()を使用したとき、これらの 2 つの命令を発見しました。 

detector.detect( image, kp_image );
extractor.compute( image, kp_image, des_image );

フレームごとに約 1200 ミリ秒かかります。

そのような問題の解決策はありますか？前もって感謝します

コード全体は次のとおりです。

#include "stdafx.h"
#include <windows.h>
#include <stdio.h>
#include <iostream>
#include <fstream>
#include <string>
#include "opencv2/core/core.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/features2d/features2d.hpp"
//#include "opencv2/legacy/legacy.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"



using namespace cv;
using namespace std;

int main()
{             
    //reference image
    Mat object = imread( "jus.png", CV_LOAD_IMAGE_GRAYSCALE );
    if( !object.data )
    {
        std::cout<< "Error reading object " << std::endl;
        return -1;
    }

    char key = 'a';
    int framecount = 0;

    SurfFeatureDetector detector( 400 );
    SurfDescriptorExtractor extractor;
    FlannBasedMatcher matcher;

    Mat frame, des_object, image;
    Mat des_image, img_matches, H;

    std::vector<KeyPoint> kp_object;
    std::vector<Point2f> obj_corners(4);
    std::vector<KeyPoint> kp_image;
    std::vector<vector<DMatch > > matches;
    std::vector<DMatch > good_matches;
    std::vector<Point2f> obj;
    std::vector<Point2f> scene;
    std::vector<Point2f> scene_corners(4);

    //compute detectors and descriptors of reference image
    detector.detect( object, kp_object );
    extractor.compute( object, kp_object, des_object );   
    //cout<<"Info de lobjet: "<<object.dims<<" des_object, "<<des_object.dims<<" and kp_object: "<<kp_object.size()<<endl;

    //create video capture object
    VideoCapture cap(1);

    //Get the corners from the object
    obj_corners[0] = cvPoint(0,0);
    obj_corners[1] = cvPoint( object.cols, 0 );
    obj_corners[2] = cvPoint( object.cols, object.rows );
    obj_corners[3] = cvPoint( 0, object.rows );

    int before, after;
    //wile loop for real time detection
    while (1)
    {
        //capture one frame from video and store it into image object name 'frame'
        cap >> frame;
         if (framecount < 5)
        {
            framecount++;
            continue;
        }  

        //converting captured frame into gray scale
        cvtColor(frame, image, CV_RGB2GRAY);

        //extract detectors and descriptors of captured frame
        before = GetTickCount();
        detector.detect( image, kp_image );
        extractor.compute( image, kp_image, des_image );
        after = GetTickCount();

        cout<<"Time of detection and extraction is: "<< after-before<<endl;
        //cout<<"Info de limage: "<<image.dims<<" des_image, "<<des_image.dims<<" and kp_image: "<<kp_image.size()<<endl;

        //find matching descriptors of reference and captured image
        matcher.knnMatch(des_object, des_image, matches, 2);

        //finding matching keypoints with Euclidean distance 0.6 times the distance of next keypoint
        //used to find right matches
        for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++)
        {
            if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
            {
                good_matches.push_back(matches[i][0]);
            }
        }    

        //drawKeypoints(object, kp_object, object);

        //Draw only "good" matches
        //drawMatches( object, kp_object, frame, kp_image, good_matches, img_matches,
            //Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

        //3 good matches are enough to describe an object as a right match.
        if (good_matches.size() >= 4)
        {                                     
            for( int i = 0; i < good_matches.size(); i++ )
            {
                //Get the keypoints from the good matches
                obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
                scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
            }
            try
            {
                H = findHomography( obj, scene, CV_RANSAC );
            }
            catch(Exception e){}

            perspectiveTransform( obj_corners, scene_corners, H);

            //Draw lines between the corners (the mapped object in the scene image )
            line( frame, scene_corners[0] /*+ Point2f( object.cols, 0)*/, scene_corners[1] /*+ Point2f( object.cols, 0)*/, Scalar(100, 0, 0), 4 );
            line( frame, scene_corners[1] /*+ Point2f( object.cols, 0)*/, scene_corners[2] /*+ Point2f( object.cols, 0)*/, Scalar( 100, 0, 0), 4 );
            line( frame, scene_corners[2] /*+ Point2f( object.cols, 0)*/, scene_corners[3] /*+ Point2f( object.cols, 0)*/, Scalar( 100, 0, 0), 4 );
            line( frame, scene_corners[3] /*+ Point2f( object.cols, 0)*/, scene_corners[0] /*+ Point2f( object.cols, 0)*/, Scalar( 100, 0, 0), 4 );
        }

        //Show detected matches
        imshow( "Good Matches", frame );

        //clear array
        good_matches.clear();

        key = waitKey(33);
    }
    return 0;
}
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1 に答える 1

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  1. 特徴検出を呼び出す前に、フレームのサイズを小さいサイズに変更してください。たとえば、画像を各次元で 0.5 倍にスケーリングすると、関数の実行速度が 4 倍になります。
  2. SURF 検出器にはいくつかのオプションの引数があることに注意してください: http://docs.opencv.org/modules/nonfree/doc/feature_detection.html#surf-surf。オクターブの数とオクターブ内のレイヤーの数を減らして速度を上げることができますが、オブジェクト検出のパフォーマンスをトレードオフする必要がある場合があります。
于 2013-03-02T09:26:48.470 に答える