堆損壞時，停止顯示圖像的OpenCV

http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html

它的工作原理以及一些獨處的iamge mathching，但是當我把它變成一個功能，並從主函數調用它，有堆腐敗。該程序顯示圖像，然後當我按空間關閉程序時，它會中斷。

int main(int argc, char ** argv) 
{ 

    // Part 2 panorama 
    Mat im1=imread("panorama_image1.jpg", CV_LOAD_IMAGE_GRAYSCALE); 
    Mat im2=imread("panorama_image2.jpg", CV_LOAD_IMAGE_GRAYSCALE); 

    immosaic(im1,im2); 

    return 0; 
}

這是鑲嵌功能，從教程

void immosaic(Mat im_object, Mat im_scene) 
{ 
    //-- Step 1: Detect the keypoints using SURF Detector 
    int minHessian = 400; 
    SurfFeatureDetector detector(minHessian); 
    std::vector<KeyPoint> keypoints_object, keypoints_scene; 

    detector.detect(im_object, keypoints_object); 
    detector.detect(im_scene, keypoints_scene); 

    //-- Step 2: Calculate descriptors (feature vectors) 
    SurfDescriptorExtractor extractor; 
    Mat descriptors_object, descriptors_scene; 
    extractor.compute(im_object, keypoints_object, descriptors_object); 
    extractor.compute(im_scene, keypoints_scene, descriptors_scene); 

    //-- Step 3: Matching descriptor vectors using FLANN matcher 
    FlannBasedMatcher matcher; 
    std::vector<DMatch> matches; 
    matcher.match(descriptors_object, descriptors_scene, matches); 

    double max_dist = 0; double min_dist = 100; 

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

    //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist) 
    std::vector<DMatch> good_matches; 

    for(int i = 0; i < descriptors_object.rows; i++) 
    { 
     if(matches[i].distance < 3*min_dist) 
      good_matches.push_back(matches[i]); 
    } 

    Mat img_matches; 
    drawMatches(im_object, keypoints_object, im_scene, keypoints_scene, 
       good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), 
       vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS); 

    //-- Localize the object 
    std::vector<Point2f> obj; 
    std::vector<Point2f> scene; 

    for(int i = 0; i < good_matches.size(); i++) 
    { 
    //-- Get the keypoints from the good matches 
    obj.push_back(keypoints_object[ good_matches[i].queryIdx ].pt); 
    scene.push_back(keypoints_scene[ good_matches[i].trainIdx ].pt); 
    } 

    Mat H = findHomography(obj, scene, CV_RANSAC); 

    //-- Get the corners from the image_1 (the object to be "detected") 
    std::vector<Point2f> obj_corners(4); 
    obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint(im_object.cols, 0); 
    obj_corners[2] = cvPoint(im_object.cols, im_object.rows); obj_corners[3] = cvPoint(0, im_object.rows); 
    std::vector<Point2f> scene_corners(4); 

    perspectiveTransform(obj_corners, scene_corners, H); 

    //-- Draw lines between the corners (the mapped object in the scene - image_2) 
    line(img_matches, scene_corners[0] + Point2f(im_object.cols, 0), scene_corners[1] + Point2f(im_object.cols, 0), Scalar(0, 255, 0), 4); 
    line(img_matches, scene_corners[1] + Point2f(im_object.cols, 0), scene_corners[2] + Point2f(im_object.cols, 0), Scalar(0, 255, 0), 4); 
    line(img_matches, scene_corners[2] + Point2f(im_object.cols, 0), scene_corners[3] + Point2f(im_object.cols, 0), Scalar(0, 255, 0), 4); 
    line(img_matches, scene_corners[3] + Point2f(im_object.cols, 0), scene_corners[0] + Point2f(im_object.cols, 0), Scalar(0, 255, 0), 4); 

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

    waitKey(0); 
    //return 0; 

}

來源

2013-02-25 Dzung Nguyen