查找图像

Question

我试图写一个C++程序来寻找图片给出图像（标志）标志，我已经使用的代码从这里：http://docs.opencv.org/2.4/doc/tutorials/features2d/feature_homography/feature_homography.html

所以，我有两个图像 - 一个是一个标志，另一个包含它（或不）。 徽标可以旋转或缩放或部分覆盖。但现在我试图在任何情况下取得令人满意的结果，但比较两张相同的图像的情况。到目前为止，我的成绩已经不亚于可怕。 我有一个宝马标志和图像，其中包含的标志和一些抽象绘画。匹配似乎是绝望的随机。 我很感激任何关于如何使这项工作更好的想法/建议。 我运行代码：

#include <stdio.h> 
#include <iostream> 
#include <stdio.h> 
#include <iostream> 
#include "opencv2/core.hpp" 
#include "opencv2/features2d.hpp" 
#include "opencv2/imgcodecs.hpp" 
#include "opencv2/highgui.hpp" 
#include "opencv2/xfeatures2d.hpp" 
#include <opencv2/imgproc/imgproc.hpp> 
#include <opencv2/calib3d.hpp> 


using namespace std; 
using namespace cv; 
using namespace cv::xfeatures2d; 


int main(){ 

    Mat img_object = imread("bmw_logo.jpg", CV_LOAD_IMAGE_GRAYSCALE); 
    Mat img_scene = imread("bmw_search.jpg", CV_LOAD_IMAGE_GRAYSCALE); 
    resize(img_object, img_object, Size(img_object.cols/2, img_object.rows/2)); 
    resize(img_scene, img_scene, Size(img_scene.cols/2, img_scene.rows/2)); 


    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; 

    Ptr<SURF> detector = SURF::create(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) 
    Ptr<SURF> extractor = SURF::create(minHessian); 

    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; 
} 

Answer 1

我会建议你使用Cascade Classification，如果你只关心找出是否有一个标志或没有。与对象特征做匹配是不够的，以达到你想要的效果。

您将需要收集正面图像 - 徽标 - 以及其他不包含徽标的图像，并让分类器为您完成工作。当然，你可以阅读关于级联分类器以了解它是如何工作的;）