Question

我使用opencv网站提供的以下代码来查找比例和旋转不变模板匹配，但我想计算源图像中模板图像的精确像素坐标，尤其是旋转图像。

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/nonfree.hpp"

using namespace cv;

void readme();

/** @function main */
int main( int argc, char** argv )
{
    if( argc != 3 )
    { 
        readme();
        return -1;
    }

    Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
    Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );

    if( !img_object.data || !img_scene.data )
    {
        std::cout<< " --(!) Error reading images " << std::endl;
        return -1; 
    }

    //-- Step 1: Detect the keypoints using SURF Detector
    int minHessian = 400;

    SurfFeatureDetector detector( minHessian );

    std::vector<KeyPoint> keypoints_object, keypoints_scene;

    detector.detect( img_object, keypoints_object );
    detector.detect( img_scene, keypoints_scene );

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

    Mat descriptors_object, descriptors_scene;

    extractor.compute( img_object, keypoints_object, descriptors_object );
    extractor.compute( img_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;
    }

    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 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( img_object, keypoints_object, img_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( img_object.cols, 0 );
    obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); 
    obj_corners[3] = cvPoint( 0, img_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( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
    line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
    line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
    line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );

    //-- Show detected matches
    imshow( "Good Matches & Object detection", img_matches );

    waitKey(0);
    return 0;
}

/** @function readme */
void readme()
{ 
    std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; 
}

Answer 1

在计算透视变换后，您已在scene_corners中拥有这些坐标。 scene_corners中的四个点组成了一个矩形，即场景中找到的模板匹配，因此这四个角是您正在寻找的。

此外，如果您希望从场景中的模板中找到任何点的经过转换的坐标，您只需要通过perspectiveTransform函数将相同的单应性应用于点。

如何在opencv中使用surf算法在源图像中找到模板图像的确切角点

1 个答案: