openCV 放大

    IplImage* img_src0 = cvLoadImage("d:\\1.jpg");
    IplImage* img_src1 = cvLoadImage("d:\\1.jpg");

    CvRect rect0 = cvRect(59, 19, 62, 32);
    CvRect rect1 = cvRect(25, 94, 24, 116);
    //cvRectangleR(img_src, rect0, CV_RGB(255, 0, 0));
    cvSetImageROI(img_src0, rect0);
    double dDiff = ComputeTextureDiff(img_src0, img_src1);
    printf("%f",dDiff);

    cvDilate(img_src0, img_src0);
    cvErode(img_src0, img_src0);
    Mat img1=Mat(img_src0, false);

    //cvSetImageROI(img_src1, rect0);
    CvPoint2D32f center = cvPoint2D32f(img_src1->width/4.5, img_src1->height/1.8);  
    CvMat* mat_rot    = cvCreateMat(2,3,CV_32FC1);
    cv2DRotationMatrix(center, 0, 2.5, mat_rot);
    // do the transformation
    cvWarpAffine(img_src1, img_src1, mat_rot);

    Mat img2=Mat(img_src1, false);

    if (img1.empty() ||img2.empty())
        return -1;

    int minHessian = 1000;

    SurfFeatureDetector detector( minHessian );

    std::vector<KeyPoint> keypoints_1, keypoints_2;
    
    detector.detect( img1, keypoints_1 );
    detector.detect( img2, keypoints_2 );

    //-- Step 2: Calculate descriptors (feature vectors)
    SurfDescriptorExtractor extractor;

    Mat descriptors_1, descriptors_2;

    extractor.compute( img1, keypoints_1, descriptors_1 );
    extractor.compute( img2, keypoints_2, descriptors_2 );

    //-- Step 3: Matching descriptor vectors with a brute force matcher
    FlannBasedMatcher matcher;  
    std::vector< DMatch > matches;  
    matcher.match( descriptors_1, descriptors_2, matches );  

    double max_dist = 0; double min_dist = 100;  

    //-- Quick calculation of max and min distances between keypoints  
    for( int i = 0; i < descriptors_1.rows; i++ )  
    { double dist = matches[i].distance;  
    if( dist < min_dist ) min_dist = dist;  
    if( dist > max_dist ) max_dist = dist;  
    }  

    printf("-- Max dist : %f \n", max_dist );  
    printf("-- Min dist : %f \n", min_dist );  

    //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )  
    //-- PS.- radiusMatch can also be used here.  
    std::vector< DMatch > good_matches;  

    for( int i = 0; i < descriptors_1.rows; i++ )  
    { if( matches[i].distance < 2*min_dist )  
        { good_matches.push_back( matches[i]); }  
    }    

    //-- Draw matches
    Mat img_matches;
    drawMatches( img1, keypoints_1, img2, keypoints_2, matches, img_matches );

    //-- Show detected matches
    imshow("Matches", img_matches );

    waitKey(0);

    return 0;