在图片

Question

我试图编写一个C ++程序来查找图片中的给定图像（徽标），并且我已经使用了此处的代码：http://docs.opencv.org/2.4/doc/tutorials/features2d/feature_homography/feature_homography.html

所以，我有两张图片 - 一张是徽标，另一张是（或不包含）。 徽标可以旋转或缩放或部分覆盖。但是现在我试图在任何情况下都能获得满意的结果，但比较两个相同的图像。到目前为止，我的结果简直太糟糕了。 我有一个宝马标志和一个包含徽标和一些抽象绘画的图像。匹配似乎是无可救药的随机。 我很感激有关如何使这项工作更好的任何想法/建议。 代码我正在运行：

#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

如果您只关心是否有徽标，我建议您使用Cascade Classification。与对象特征匹配是不够想要的。

您需要收集正面图像 - 用于徽标 - 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，你可以阅读级联分类器，了解它的工作原理;）