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c++ - 如何在 C++ 中使用 OpenCV 检测多个对象？

问题描述

我从这个答案here中得到灵感，这是一个Python实现，但我需要C++，这个答案非常有效，我的想法是：detectAndCompute获取keypoints，使用kmeans将它们分割成集群，然后为每个集群做每个集群matcher->knnMatch，descriptors然后做其他的东西，比如常见的单一检测方法。主要的问题是，如何descriptors为每个集群提供matcher->knnMatch进程？我认为我们应该将另一个keypoints对应的值设置descriptor为 0（无用），对吗？在我的尝试中遇到了一些问题：

  1. 如何估计集群数量kmeans？
  2. 为什么可以为这样的集群创建 Mat 数组Mat descriptors_scene_clusters[3] = { Mat(descriptors_scene.rows, descriptors_scene.cols, CV_8U, Scalar(0)) };？

非常感谢任何帮助，请！

输出

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

using namespace cv;
using namespace cv::xfeatures2d;

#define MIN_MATCH_COUNT 10

int main()
{
    Mat img_object = imread("./2.PNG", IMREAD_GRAYSCALE);
    Mat img_scene = imread("./1.PNG", IMREAD_GRAYSCALE);

    Ptr<ORB> detector = ORB::create();
    std::vector<KeyPoint> keypoints_object, keypoints_scene;
    Mat descriptors_object, descriptors_scene;
    detector->detectAndCompute(img_object, cv::Mat(), keypoints_object, descriptors_object);
    detector->detectAndCompute(img_scene, cv::Mat(), keypoints_scene, descriptors_scene);


    std::cout << descriptors_scene.row(0) << "\n";
    std::cout << descriptors_scene.cols << "\n";


    std::vector<Point2f> keypoints_scene_points_;
    for (int i=0; i<keypoints_scene.size(); i++) {
        keypoints_scene_points_.push_back(keypoints_scene[i].pt);
    }
    Mat keypoints_scene_points(keypoints_scene_points_);

    Mat labels;
    int estimate_cluster_count = 3; // estimated ??????????
    kmeans(keypoints_scene_points, estimate_cluster_count, labels, TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 1.0), 3, KMEANS_RANDOM_CENTERS);

    std::cout << "==================================111111\n";

    Mat descriptors_scene_clusters[3] = { Mat(descriptors_scene.rows, descriptors_scene.cols, CV_8U, Scalar(0)) };

    std::cout << "==================================111111------\n";

    for (int i=0; i<labels.rows; i++) {
        int clusterIndex = labels.at<int>(i);
        Point2f pt = keypoints_scene_points.at<Point2f>(i);
        descriptors_scene_clusters[clusterIndex].at<uchar>(pt) = descriptors_scene.at<uchar>(pt);  // ?????? error
    }

    std::cout << descriptors_scene_clusters[0] << "\n";
    std::cout << "==================================22222222\n";
    // return 0;

    Mat img_matches = img_scene;
    std::vector<DMatch> all_good_matches;
    for (int i=0; i<estimate_cluster_count; i++) {
        std::cout << "==================================33333\n";

        Ptr<flann::IndexParams> indexParams = makePtr<flann::KDTreeIndexParams>(5);
        Ptr<flann::SearchParams> searchParams = makePtr<flann::SearchParams>(50);
        Ptr<FlannBasedMatcher> matcher = makePtr<FlannBasedMatcher>(indexParams, searchParams);
        // BFMatcher matcher;
        std::vector<std::vector<DMatch>> matches;

        std::cout << "==================================444444\n";
        matcher->knnMatch(descriptors_object, descriptors_scene_clusters[i], matches, 2);
        std::cout << "==================================555555\n";
        std::vector<DMatch> good_matches;

        for (auto &match : matches) {
            if (match[0].distance < 0.7 * match[1].distance) {
                good_matches.push_back(match[0]);
            }
        }

        all_good_matches.insert(all_good_matches.end(), good_matches.begin(), good_matches.end());

        std::cout << "==================================66666\n";

        if (good_matches.size() > MIN_MATCH_COUNT) {

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

            for (auto &match : good_matches) {
                //-- Get the keypoints from the good matches
                obj.push_back(keypoints_object[match.queryIdx].pt);
                scene.push_back(keypoints_scene[match.trainIdx].pt);
            }

            Mat H = findHomography(obj, scene, 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);

            print(scene_corners);
        }
    }

    drawMatches(img_object, keypoints_object, img_scene, keypoints_scene,
                    all_good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
                    std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);


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

    waitKey(0);
    return 0;
}

标签： c++opencvk-meansopencv3.0mean-shift

解决方案

我不知道您的问题的解决方案，但以下内容可能有助于回答您提出的问题。

在评论中它说你可能需要一个meanshiftopencv 已经有的实现。这里是一个示例，这里是带有教程的文档。

  1. clusterCountforkmeans是您要创建链接的集群数。我不知道如何估计您要创建的数字，但我想您应该知道。

  2. descriptors_scene_clusters您仅使用一个元素进行初始化：

Mat descriptors_scene_clusters[3] = { Mat(descriptors_scene.rows, descriptors_scene.cols, CV_8U, Scalar(0)) };

当你迭代它时：

for (int i=0; i<labels.rows; i++) {
    int clusterIndex = labels.at<int>(i);
    Point2f pt = keypoints_scene_points.at<Point2f>(i);
    descriptors_scene_clusters[clusterIndex].at<uchar>(pt) = descriptors_scene.at<uchar>(pt);  // ?????? error
}

clusterIndex是 2 并且您访问数组中未初始化的元素，这会导致EXEC_BAD_ACCESS error.

我希望这有助于进一步调查！

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