• 关于OpenCV的stitching使用


    配置环境:VS2010+OpenCV2.4.9

    为了使用OpenCV实现图像拼接头痛了好长时间,一直都没时间做,今天下定决心去实现基本的图像拼接。

    首先,看一看使用OpenCV进行拼接的方法

    基本都是用Stitcher类中的stitch方法。下面是网上的代码,同时也是opencvsamplescppstitching.cpp的代码。

    #include <iostream>
    #include <fstream>
    #include "opencv2/highgui/highgui.hpp"
    #include "opencv2/stitching/stitcher.hpp"
    
    using namespace std;
    using namespace cv;
    
    bool try_use_gpu = false;
    vector<Mat> imgs;
    string result_name = "result.jpg";
    
    void printUsage();
    int parseCmdArgs(int argc, char** argv);
    
    int main(int argc, char* argv[])
    {
        int retval = parseCmdArgs(argc, argv);
        if (retval) return -1;
    
        Mat pano;
        Stitcher stitcher = Stitcher::createDefault(try_use_gpu);
        Stitcher::Status status = stitcher.stitch(imgs, pano);
    
        if (status != Stitcher::OK)
        {
            cout << "Can't stitch images, error code = " << int(status) << endl;
            return -1;
        }
    
        imwrite(result_name, pano);
        return 0;
    }
    
    
    void printUsage()
    {
        cout <<
            "Rotation model images stitcher.
    
    "
            "stitching img1 img2 [...imgN]
    
    "
            "Flags:
    "
            "  --try_use_gpu (yes|no)
    "
            "      Try to use GPU. The default value is 'no'. All default values
    "
            "      are for CPU mode.
    "
            "  --output <result_img>
    "
            "      The default is 'result.jpg'.
    ";
    }
    
    
    int parseCmdArgs(int argc, char** argv)
    {
        if (argc == 1)
        {
            printUsage();
            return -1;
        }
        for (int i = 1; i < argc; ++i)
        {
            if (string(argv[i]) == "--help" || string(argv[i]) == "/?")
            {
                printUsage();
                return -1;
            }
            else if (string(argv[i]) == "--try_use_gpu")
            {
                if (string(argv[i + 1]) == "no")
                    try_use_gpu = false;
                else if (string(argv[i + 1]) == "yes")
                    try_use_gpu = true;
                else
                {
                    cout << "Bad --try_use_gpu flag value
    ";
                    return -1;
                }
                i++;
            }
            else if (string(argv[i]) == "--output")
            {
                result_name = argv[i + 1];
                i++;
            }
            else
            {
                Mat img = imread(argv[i]);
                if (img.empty())
                {
                    cout << "Can't read image '" << argv[i] << "'
    ";
                    return -1;
                }
                imgs.push_back(img);
            }
        }
        return 0;
    }
    

      感觉这个说的比较繁琐,我就改写成了下面的代码

    #include <iostream>
    #include <fstream>
    #include <opencv2/core/core.hpp>
    #include "opencv2/highgui/highgui.hpp"
    #include "opencv2/stitching/stitcher.hpp"
    #include<Windows.h>
    
    using namespace std;
    using namespace cv;
    
    bool try_use_gpu = false;
    vector<Mat> imgs;
    string result_name = "result.jpg";
    
    int main()
    {
    	Mat img1=imread("1.jpg");
    	Mat img2=imread("2.jpg");
    	imgs.push_back(img1);
    	imgs.push_back(img2);
    	Mat pano;
    	Stitcher stitcher = Stitcher::createDefault(try_use_gpu);
    	Stitcher::Status status = stitcher.stitch(imgs, pano);
    	if (status != Stitcher::OK)
    	{
    		cout << "Can't stitch images, error code = " << status << endl;
    		return -1;
    	}
    	namedWindow(result_name);
    	imshow(result_name,pano);
    	imwrite(result_name,pano);
    	waitKey();
    	return 0;
    }
    

    下面看一下原图和效果图,(以四张原图为例,分为左上,右上,左下,右下)

    效果图如下:

        

      可以发现代码中最关键的两句就是:

    Stitcher stitcher = Stitcher::createDefault(try_use_gpu);
    Stitcher::Status status = stitcher.stitch(imgs, pano);
    

      Stitcher是OpenCV的一个类,下面看一下这个类的源代码:

    class CV_EXPORTS Stitcher
    {
    public:
        enum { ORIG_RESOL = -1 };
        enum Status { OK, ERR_NEED_MORE_IMGS };
    
        // Creates stitcher with default parameters
        static Stitcher createDefault(bool try_use_gpu = false);
    
        Status estimateTransform(InputArray images);
        Status estimateTransform(InputArray images, const std::vector<std::vector<Rect> > &rois);
    
        Status composePanorama(OutputArray pano);
        Status composePanorama(InputArray images, OutputArray pano);
    
        Status stitch(InputArray images, OutputArray pano);
        Status stitch(InputArray images, const std::vector<std::vector<Rect> > &rois, OutputArray pano);
    
        double registrationResol() const { return registr_resol_; }
        void setRegistrationResol(double resol_mpx) { registr_resol_ = resol_mpx; }
    
        double seamEstimationResol() const { return seam_est_resol_; }
        void setSeamEstimationResol(double resol_mpx) { seam_est_resol_ = resol_mpx; }
    
        double compositingResol() const { return compose_resol_; }
        void setCompositingResol(double resol_mpx) { compose_resol_ = resol_mpx; }
    
        double panoConfidenceThresh() const { return conf_thresh_; }
        void setPanoConfidenceThresh(double conf_thresh) { conf_thresh_ = conf_thresh; }
    
        bool waveCorrection() const { return do_wave_correct_; }
        void setWaveCorrection(bool flag) { do_wave_correct_ = flag; }
    
        detail::WaveCorrectKind waveCorrectKind() const { return wave_correct_kind_; }
        void setWaveCorrectKind(detail::WaveCorrectKind kind) { wave_correct_kind_ = kind; }
    
        Ptr<detail::FeaturesFinder> featuresFinder() { return features_finder_; }
        const Ptr<detail::FeaturesFinder> featuresFinder() const { return features_finder_; }
        void setFeaturesFinder(Ptr<detail::FeaturesFinder> features_finder)
            { features_finder_ = features_finder; }
    
        Ptr<detail::FeaturesMatcher> featuresMatcher() { return features_matcher_; }
        const Ptr<detail::FeaturesMatcher> featuresMatcher() const { return features_matcher_; }
        void setFeaturesMatcher(Ptr<detail::FeaturesMatcher> features_matcher)
            { features_matcher_ = features_matcher; }
    
        const cv::Mat& matchingMask() const { return matching_mask_; }
        void setMatchingMask(const cv::Mat &mask)
        {
            CV_Assert(mask.type() == CV_8U && mask.cols == mask.rows);
            matching_mask_ = mask.clone();
        }
    
        Ptr<detail::BundleAdjusterBase> bundleAdjuster() { return bundle_adjuster_; }
        const Ptr<detail::BundleAdjusterBase> bundleAdjuster() const { return bundle_adjuster_; }
        void setBundleAdjuster(Ptr<detail::BundleAdjusterBase> bundle_adjuster)
            { bundle_adjuster_ = bundle_adjuster; }
    
        Ptr<WarperCreator> warper() { return warper_; }
        const Ptr<WarperCreator> warper() const { return warper_; }
        void setWarper(Ptr<WarperCreator> warper) { warper_ = warper; }
    
        Ptr<detail::ExposureCompensator> exposureCompensator() { return exposure_comp_; }
        const Ptr<detail::ExposureCompensator> exposureCompensator() const { return exposure_comp_; }
        void setExposureCompensator(Ptr<detail::ExposureCompensator> exposure_comp)
            { exposure_comp_ = exposure_comp; }
    
        Ptr<detail::SeamFinder> seamFinder() { return seam_finder_; }
        const Ptr<detail::SeamFinder> seamFinder() const { return seam_finder_; }
        void setSeamFinder(Ptr<detail::SeamFinder> seam_finder) { seam_finder_ = seam_finder; }
    
        Ptr<detail::Blender> blender() { return blender_; }
        const Ptr<detail::Blender> blender() const { return blender_; }
        void setBlender(Ptr<detail::Blender> blender) { blender_ = blender; }
    
    private:
        /* hidden */
    };
    

      

      可以看到Stitcher大致有这些成员函数:createDefault,estimateTransform,composePanorama,stitch等等。

    Stitcher stitcher = Stitcher::createDefault(try_use_gpu);这句话表示使用默认参数创建Stitcher类的对象stitcher,try_use_gpu表示是否打开GPU,默认不打开,即try_use_gpu=false;下面是这个函数的原型:
    C++: Stitcher Stitcher::createDefault(bool try_use_gpu=false)
    参数:Flag indicating whether GPU should be used whenever it’s possible.
    return:Stitcher class instance.(即创建了一个对象)
    Stitcher::Status status = stitcher.stitch(imgs, pano);这句话表示:try to stitch the given images
    C++: Status Stitcher::stitch(InputArray images, OutputArray pano)
    C++: Status Stitcher::stitch(InputArray images, const std::vector<std::vector<Rect>>& rois, OutputArray pano)
    参数:images – Input images.
        rois – Region of interest rectangles.(感兴趣区)
          pano – Final pano.
    return:Status code.(数据成员中枚举数组的一项)
    

      Stitcher::estimateTransform和Stitcher::composePanorama的使用为高级使用,需要清楚Stitching pipeline的过程。

    下面贴出pipeline:

    可以看出这个过程很复杂,需要涉及到很多的算法,比如:特征点的提取、特征点匹配、图像融合等等。这些过程OpenCV都为我们封装在Stitcher类中,不在此细述。

    总结

    虽然用OpenCV中的Stitcher类实现了基本的拼接,但是有一个最大的问题是,运行的效率是极低的,就这个代码中,拼接3张图片差不多用了一分钟,这在需要做实时拼接的时候是根本不可能使用的,所以后面需要做的工作任然是弄清楚Stitching pipeline的详细过程,进一步优化代码,提高拼接运行效率。

    下面贴出参考资料:

    http://docs.opencv.org/2.4.2/modules/stitching/doc/high_level.html

    下面贴出源代码和OpenCV中的stiching.cpp和stitching_detailed.cpp的下载地址:

    http://download.csdn.net/detail/u013637931/8255767

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  • 原文地址:https://www.cnblogs.com/CHLL55/p/4161551.html
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