与Opencv1相比，OpenCV 2.4非常慢

我有这个非常奇怪的问题，我想我可能做错了什么，但我有一个opencv1实现金字塔卢卡斯Kanade和一个opencv2实现。不同之处在于，opencv2比opencv1需要更长的时间来运行(特别是goodFeaturesToTrack函数)。此外，在opencv1实现中包含opencv2库和头文件也会导致该实现变得非常慢(我们谈论的是每两个图像0.002秒，而不是每两个图像1秒)。我做错了什么吗?

Windows 7, 64位。这是运行非常慢的opencv2代码，大约每秒1帧。正如我所说，采用openv1实现并切换库版本会导致相同的速度降低10倍或更多。我觉得这很奇怪，谷歌没有找到任何信息!谢谢! !

#include <opencv2/opencv.hpp>
#include <iostream>
#include <vector>
#include <cmath>
using namespace cv;
using namespace std; 
int64 now, then;
double elapsed_seconds, tickspersecond=cvGetTickFrequency() * 1.0e6;
int main(int argc, char** argv)
{
    // Load two images and allocate other structures
    Mat imgA = imread("0000.png", CV_LOAD_IMAGE_GRAYSCALE);
    Mat imgB = imread("0001.png", CV_LOAD_IMAGE_GRAYSCALE); 
    Size img_sz = imgA.size();
    Mat imgC(img_sz,1);
    int win_size = 15;
    int maxCorners = 100; 
    double qualityLevel = 0.05; 
    double minDistance = 2.0; 
    int blockSize = 3; 
    double k = 0.04; 
    std::vector<cv::Point2f> cornersA; 
    cornersA.reserve(maxCorners); 
    std::vector<cv::Point2f> cornersB; 
    cornersB.reserve(maxCorners);
 then = cvGetTickCount();
    goodFeaturesToTrack( imgA,cornersA,maxCorners,qualityLevel,minDistance,cv::Mat(),blockSize,true);
    goodFeaturesToTrack( imgB,cornersB,maxCorners,qualityLevel,minDistance,cv::Mat(),blockSize,true);
now = cvGetTickCount();
cout << (double)(now - then) / tickspersecond;

    cornerSubPix( imgA, cornersA, Size( win_size, win_size ), Size( -1, -1 ), 
                  TermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03 ) );
    cornerSubPix( imgB, cornersB, Size( win_size, win_size ), Size( -1, -1 ), 
                  TermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03 ) );
    // Call Lucas Kanade algorithm
    CvSize pyr_sz = Size( img_sz.width+8, img_sz.height/3 );
    std::vector<uchar> features_found; 
    features_found.reserve(maxCorners);
    std::vector<float> feature_errors; 
    feature_errors.reserve(maxCorners);
    calcOpticalFlowPyrLK( imgA, imgB, cornersA, cornersB, features_found, feature_errors ,
        Size( win_size, win_size ), 5,
         cvTermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.3 ), 0 );
    // Make an image of the results
    for( int i=0; i < features_found.size(); i++ ){
        //  cout<<"Error is "<<feature_errors[i]<<endl;
            //continue;
        //cout<<"Got it"<<endl;
        Point p0( ceil( cornersA[i].x ), ceil( cornersA[i].y ) );
        Point p1( ceil( cornersB[i].x ), ceil( cornersB[i].y ) );
        line( imgC, p0, p1, CV_RGB(255,255,255), 2 );
    }
    namedWindow( "ImageA", 0 );
    namedWindow( "ImageB", 0 );
    namedWindow( "LKpyr_OpticalFlow", 0 );
    imshow( "ImageA", imgA );
    imshow( "ImageB", imgB );
    imshow( "LKpyr_OpticalFlow", imgC );
    cvWaitKey(0);
    return 0;
}