图像拼接（九）：双摄像头实时视频拼接（单应变换模型）

89 阅读 0 评论 59 点赞

我是靠谱客的博主愉快大山，最近开发中收集的这篇文章主要介绍图像拼接（九）：双摄像头实时视频拼接（单应变换模型），觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

单应变换相比平移变换，具有更广泛的场景适应性，但同时稳定性会有一定程度下降。

设计到的技术细节有：

特征检测与描述
特征匹配与单应矩阵估计
opencv采集视频
渐入渐出图像融合

这个解决方案的硬件条件包括：有两个USB接口的计算机，两个合理放置的USB摄像头。

合理放置是指：两个摄像头分隔一定夹角，相机中心相距接近，所拍摄场景有足够的重叠部分。以上保证了单应变换的可用性。

代码实现：

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

using namespace cv;
using namespace std;

int main()
{
    VideoCapture cap1(0);
    VideoCapture cap2(1);

    double rate = 60;
    int delay = 1000 / rate;
    bool stop(false);
    Mat img1;
    Mat img2;
    Mat result;
    int d = 200;//渐入渐出融合宽度
    Mat homography;
    int k = 0;

    namedWindow("cam1", CV_WINDOW_AUTOSIZE);
    namedWindow("cam2", CV_WINDOW_AUTOSIZE);
    namedWindow("stitch", CV_WINDOW_AUTOSIZE);

    if (cap1.isOpened() && cap2.isOpened())
    {
        cout << "*** ***" << endl;
        cout << "摄像头已启动！" << endl;
    }
    else
    {
        cout << "*** ***" << endl;
        cout << "警告：请检查摄像头是否安装好!" << endl;
        cout << "程序结束！" << endl << "*** ***" << endl;
        return -1;
    }

    cap1.set(CV_CAP_PROP_FOCUS, 0);
    cap2.set(CV_CAP_PROP_FOCUS, 0);

    while (!stop)
    {
        if (cap1.read(img1) && cap2.read(img2))
        {
            imshow("cam1", img1);
            imshow("cam2", img2);

            //彩色帧转灰度
            //cvtColor(img1, img1, CV_RGB2GRAY);
            //cvtColor(img2, img2, CV_RGB2GRAY);

            //计算单应矩阵
            if (k < 1 || waitKey(delay) == 13)
            {
                cout << "正在匹配..." << endl;
                
                vector<KeyPoint> keypoints1, keypoints2;
                //构造检测器
                //Ptr<FeatureDetector> detector = new ORB(120);
                Ptr<FeatureDetector> detector = new SIFT(80);
                detector->detect(img1, keypoints1);
                detector->detect(img2, keypoints2);
                //构造描述子提取器
                Ptr<DescriptorExtractor> descriptor = detector;
                //提取描述子
                Mat descriptors1, descriptors2;

                descriptor->compute(img1, keypoints1, descriptors1);
                descriptor->compute(img2, keypoints2, descriptors2);
                //构造匹配器
                BFMatcher matcher(NORM_L2, true);
                //匹配描述子
                vector<DMatch> matches;
                matcher.match(descriptors1, descriptors2, matches);

                vector<Point2f> selPoints1, selPoints2;
                vector<int> pointIndexes1, pointIndexes2;
                for (vector<DMatch>::const_iterator it = matches.begin(); it != matches.end(); ++it)
                {
                    selPoints1.push_back(keypoints1.at(it->queryIdx).pt);
                    selPoints2.push_back(keypoints2.at(it->trainIdx).pt);
                }

                vector<uchar> inliers(selPoints1.size(), 0);
                homography = findHomography(selPoints1, selPoints2, inliers, CV_FM_RANSAC, 1.0);

                //根据RANSAC重新筛选匹配
                vector<DMatch> outMatches;
                vector<uchar>::const_iterator itIn = inliers.begin();
                vector<DMatch>::const_iterator itM = matches.begin();
                for (; itIn != inliers.end(); ++itIn, ++itM)
                {
                    if (*itIn)
                    {
                        outMatches.push_back(*itM);
                    }

                }
                k++;

                //画出匹配结果
                //Mat matchImage;
                //drawMatches(img1, keypoints1, img2, keypoints2, outMatches, matchImage, 255, 255);
                //imshow("match", matchImage);
                ///

            }
            //拼接
            double t = getTickCount();
            warpPerspective(img1, result, homography, Size(2 * img1.cols-d, img1.rows));//Size设置结果图像宽度，宽度裁去一部分，d可调

            Mat half(result, Rect(0, 0, img2.cols - d, img2.rows));
            img2(Range::all(), Range(0, img2.cols - d)).copyTo(half);
            for (int i = 0; i < d; i++)
            {
                result.col(img2.cols - d + i) = (d - i) / (float)d*img2.col(img2.cols - d + i) + i / (float)d*result.col(img2.cols - d + i);
            }

            imshow("stitch", result);
            t = ((double)getTickCount() - t) / getTickFrequency();
            //cout << t << endl;

        }
        else
        {
            cout << "----------------------" << endl;
            cout << "waitting..." << endl;
        }

        if (waitKey(1) == 27)
        {
            stop = true;
            cout << "程序结束！" << endl;
            cout << "*** ***" << endl;
        }
    }
    return 0;
}