图像拼接（十一）：双摄像头实时拼接+stitching_detailed

OpenCV自带的stitching模块在追求拼接质量方面已经做得很好了，但是实时性不够，即使是拼接两幅图像。比如源程序拼接两幅640*480分辨率的图像，拼接时间为4.78″。

对stitching_detailed.cpp源码进行改造，有利于提高实时性的举措有：

• 调节初始化参数。比如使用GPU，这个需要重新编译OPenCV库。

• 减少视频帧的分辨率。减少为320*240。

• 将特征提取、匹配、变换矩阵计算等步骤归为初始化部分。变换矩阵不再逐帧计算。

程序代码：

#include <iostream>#include <fstream>#include <string>#include "opencv2/core/core.hpp"#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/opencv_modules.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/stitching/detail/autocalib.hpp"#include "opencv2/stitching/detail/blenders.hpp"#include "opencv2/stitching/detail/camera.hpp"#include "opencv2/stitching/detail/exposure_compensate.hpp"#include "opencv2/stitching/detail/matchers.hpp"#include "opencv2/stitching/detail/motion_estimators.hpp"#include "opencv2/stitching/detail/seam_finders.hpp"#include "opencv2/stitching/detail/util.hpp"#include "opencv2/stitching/detail/warpers.hpp"#include "opencv2/stitching/warpers.hpp"using namespace cv;using namespace std;using namespace cv::detail;#define ENABLE_LOG 1// Default command line argsvector<string> img_names;bool preview = false;bool try_gpu = true;double work_megapix = 0.6;double seam_megapix = 0.1;double compose_megapix = 1;float conf_thresh = 1.f;string features_type = "surf";string ba_cost_func = "ray";string ba_refine_mask = "xxxxx";bool do_wave_correct = false;WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ;bool save_graph = false;std::string save_graph_to;//string warp_type = "spherical";string warp_type = "cylindrical";//int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;int expos_comp_type = ExposureCompensator::NO;float match_conf = 0.3f;//string seam_find_type = "gc_color";string seam_find_type = "no";int blend_type = Blender::MULTI_BAND;float blend_strength = 3;string result_name = "result.jpg";int main(){    //打开摄像头    VideoCapture cap1(0);    VideoCapture cap2(1);    double rate = 60;    int delay = 1000 / rate;    bool stop(false);    Mat frame1;    Mat frame2;    Mat frame;    int k = 100;    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_FRAME_WIDTH, 320);    cap1.set(CV_CAP_PROP_FRAME_HEIGHT, 240);    cap2.set(CV_CAP_PROP_FRAME_WIDTH, 320);    cap2.set(CV_CAP_PROP_FRAME_HEIGHT, 240);    cap1.set(CV_CAP_PROP_FOCUS, 0);    cap2.set(CV_CAP_PROP_FOCUS, 0);    //获取两幅图像，通过这两幅图像来估计摄像机参数    while (k--)    {        if (cap1.read(frame1) && cap2.read(frame2))        {            imshow("cam1", frame1);            imshow("cam2", frame2);            imwrite("frame1.bmp", frame1);            imwrite("frame2.bmp", frame2);        }    }    //计算相机内参数及旋转矩阵等参数#if ENABLE_LOG    int64 app_start_time = getTickCount();#endif    cv::setBreakOnError(true);    //读入图片    img_names.push_back("frame1.bmp");    img_names.push_back("frame2.bmp");    // Check if have enough images    int num_images = static_cast<int>(img_names.size());    if (num_images < 2)    {        LOGLN("Need more images");        return -1;    }    double work_scale = 1, seam_scale = 1, compose_scale = 0.5;    bool is_work_scale_set = false, is_seam_scale_set = false, is_compose_scale_set = false;    cout << "Finding features..." << endl;#if ENABLE_LOG    int64 t = getTickCount();#endif    Ptr<FeaturesFinder> finder;    if (features_type == "surf")    {#if defined(HAVE_OPENCV_NONFREE) && defined(HAVE_OPENCV_GPU)        if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)            finder = new SurfFeaturesFinderGpu();        else#endif            finder = new SurfFeaturesFinder();    }    else if (features_type == "orb")    {        finder = new OrbFeaturesFinder();    }    else    {        cout << "Unknown 2D features type: '" << features_type << "'.\n";        return -1;    }    Mat full_img, img;    vector<ImageFeatures> features(num_images);    vector<Mat> images(num_images);    vector<Size> full_img_sizes(num_images);    double seam_work_aspect = 1;    for (int i = 0; i < num_images; ++i)    {        full_img = imread(img_names[i]);        full_img_sizes[i] = full_img.size();        if (full_img.empty())        {            LOGLN("Can't open image " << img_names[i]);            return -1;        }        if (work_megapix < 0)        {            img = full_img;            work_scale = 1;            is_work_scale_set = true;        }        else        {            if (!is_work_scale_set)            {                work_scale = min(1.0, sqrt(work_megapix * 1e6 / full_img.size().area()));                is_work_scale_set = true;            }            resize(full_img, img, Size(), work_scale, work_scale);        }        if (!is_seam_scale_set)        {            seam_scale = min(1.0, sqrt(seam_megapix * 1e6 / full_img.size().area()));            seam_work_aspect = seam_scale / work_scale;            is_seam_scale_set = true;        }        (*finder)(img, features[i]);        features[i].img_idx = i;        LOGLN("Features in image #" << i + 1 << ": " << features[i].keypoints.size());        resize(full_img, img, Size(), seam_scale, seam_scale);        images[i] = img.clone();    }    finder->collectGarbage();    full_img.release();    img.release();    cout << "Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec" << endl;    cout << ("Pairwise matching") << endl;#if ENABLE_LOG    t = getTickCount();#endif    vector<MatchesInfo> pairwise_matches;    BestOf2NearestMatcher matcher(try_gpu, match_conf);    matcher(features, pairwise_matches);    matcher.collectGarbage();    cout << ("Pairwise matching, time: ") << ((getTickCount() - t) / getTickFrequency()) << " sec" << endl;    // Check if we should save matches graph    if (save_graph)    {        LOGLN("Saving matches graph...");        ofstream f(save_graph_to.c_str());        f << matchesGraphAsString(img_names, pairwise_matches, conf_thresh);    }    // Leave only images we are sure are from the same panorama    vector<int> indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);    vector<Mat> img_subset;    vector<string> img_names_subset;    vector<Size> full_img_sizes_subset;    for (size_t i = 0; i < indices.size(); ++i)    {        img_names_subset.push_back(img_names[indices[i]]);        img_subset.push_back(images[indices[i]]);        full_img_sizes_subset.push_back(full_img_sizes[indices[i]]);    }    images = img_subset;    img_names = img_names_subset;    full_img_sizes = full_img_sizes_subset;    // Check if we still have enough images    num_images = static_cast<int>(img_names.size());    if (num_images < 2)    {        LOGLN("Need more images");        return -1;    }    HomographyBasedEstimator estimator;    vector<CameraParams> cameras;    estimator(features, pairwise_matches, cameras);    for (size_t i = 0; i < cameras.size(); ++i)    {        Mat R;        cameras[i].R.convertTo(R, CV_32F);        cameras[i].R = R;        cout << ("Initial intrinsics #") << indices[i] + 1 << ":\n" << cameras[i].K() << endl;    }    Ptr<detail::BundleAdjusterBase> adjuster;    if (ba_cost_func == "reproj") adjuster = new detail::BundleAdjusterReproj();    else if (ba_cost_func == "ray") adjuster = new detail::BundleAdjusterRay();    else    {        cout << "Unknown bundle adjustment cost function: '" << ba_cost_func << "'.\n";        return -1;    }    adjuster->setConfThresh(conf_thresh);    Mat_<uchar> refine_mask = Mat::zeros(3, 3, CV_8U);    if (ba_refine_mask[0] == 'x') refine_mask(0, 0) = 1;    if (ba_refine_mask[1] == 'x') refine_mask(0, 1) = 1;    if (ba_refine_mask[2] == 'x') refine_mask(0, 2) = 1;    if (ba_refine_mask[3] == 'x') refine_mask(1, 1) = 1;    if (ba_refine_mask[4] == 'x') refine_mask(1, 2) = 1;    adjuster->setRefinementMask(refine_mask);    (*adjuster)(features, pairwise_matches, cameras);    // Find median focal length    vector<double> focals;    for (size_t i = 0; i < cameras.size(); ++i)    {        cout << ("Camera #") << indices[i] + 1 << ":\n" << cameras[i].K() << endl;        focals.push_back(cameras[i].focal);    }    sort(focals.begin(), focals.end());    float warped_image_scale;    if (focals.size() % 2 == 1)        warped_image_scale = static_cast<float>(focals[focals.size() / 2]);    else        warped_image_scale = static_cast<float>(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f;    if (do_wave_correct)    {        vector<Mat> rmats;        for (size_t i = 0; i < cameras.size(); ++i)            rmats.push_back(cameras[i].R.clone());        waveCorrect(rmats, wave_correct);        for (size_t i = 0; i < cameras.size(); ++i)            cameras[i].R = rmats[i];    }    ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////    cout << ("Warping images (auxiliary)... ") << endl;#if ENABLE_LOG    t = getTickCount();#endif    vector<Point> corners(num_images);    vector<Mat> masks_warped(num_images);    vector<Mat> images_warped(num_images);    vector<Size> sizes(num_images);    vector<Mat> masks(num_images);    // Preapre images masks    for (int i = 0; i < num_images; ++i)    {        masks[i].create(images[i].size(), CV_8U);        masks[i].setTo(Scalar::all(255));    }    // Warp images and their masks    Ptr<WarperCreator> warper_creator;#if defined(HAVE_OPENCV_GPU)    if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)    {        if (warp_type == "plane") warper_creator = new cv::PlaneWarperGpu();        else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarperGpu();        else if (warp_type == "spherical") warper_creator = new cv::SphericalWarperGpu();    }    else#endif    {        if (warp_type == "plane") warper_creator = new cv::PlaneWarper();        else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarper();        else if (warp_type == "spherical") warper_creator = new cv::SphericalWarper();        else if (warp_type == "fisheye") warper_creator = new cv::FisheyeWarper();        else if (warp_type == "stereographic") warper_creator = new cv::StereographicWarper();        else if (warp_type == "compressedPlaneA2B1") warper_creator = new cv::CompressedRectilinearWarper(2, 1);        else if (warp_type == "compressedPlaneA1.5B1") warper_creator = new cv::CompressedRectilinearWarper(1.5, 1);        else if (warp_type == "compressedPlanePortraitA2B1") warper_creator = new cv::CompressedRectilinearPortraitWarper(2, 1);        else if (warp_type == "compressedPlanePortraitA1.5B1") warper_creator = new cv::CompressedRectilinearPortraitWarper(1.5, 1);        else if (warp_type == "paniniA2B1") warper_creator = new cv::PaniniWarper(2, 1);        else if (warp_type == "paniniA1.5B1") warper_creator = new cv::PaniniWarper(1.5, 1);        else if (warp_type == "paniniPortraitA2B1") warper_creator = new cv::PaniniPortraitWarper(2, 1);        else if (warp_type == "paniniPortraitA1.5B1") warper_creator = new cv::PaniniPortraitWarper(1.5, 1);        else if (warp_type == "mercator") warper_creator = new cv::MercatorWarper();        else if (warp_type == "transverseMercator") warper_creator = new cv::TransverseMercatorWarper();    }    if (warper_creator.empty())    {        cout << "Can't create the following warper '" << warp_type << "'\n";        return 1;    }    Ptr<RotationWarper> warper = warper_creator->create(static_cast<float>(warped_image_scale * seam_work_aspect));    for (int i = 0; i < num_images; ++i)    {        Mat_<float> K;        cameras[i].K().convertTo(K, CV_32F);        float swa = (float)seam_work_aspect;        K(0, 0) *= swa; K(0, 2) *= swa;        K(1, 1) *= swa; K(1, 2) *= swa;        corners[i] = warper->warp(images[i], K, cameras[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]);        sizes[i] = images_warped[i].size();        warper->warp(masks[i], K, cameras[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);    }    vector<Mat> images_warped_f(num_images);    for (int i = 0; i < num_images; ++i)        images_warped[i].convertTo(images_warped_f[i], CV_32F);    cout << "Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec" << endl;    ////////////////////////////////////warp end/////////////////////////////////////////////////////////////////////////////////////    Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);    compensator->feed(corners, images_warped, masks_warped);    Ptr<SeamFinder> seam_finder;    if (seam_find_type == "no")        seam_finder = new detail::NoSeamFinder();    else if (seam_find_type == "voronoi")        seam_finder = new detail::VoronoiSeamFinder();    else if (seam_find_type == "gc_color")    {#if defined(HAVE_OPENCV_GPU)        if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)            seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR);        else#endif            seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR);    }    else if (seam_find_type == "gc_colorgrad")    {#if defined(HAVE_OPENCV_GPU)        if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)            seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR_GRAD);        else#endif            seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR_GRAD);    }    else if (seam_find_type == "dp_color")        seam_finder = new detail::DpSeamFinder(DpSeamFinder::COLOR);    else if (seam_find_type == "dp_colorgrad")        seam_finder = new detail::DpSeamFinder(DpSeamFinder::COLOR_GRAD);    if (seam_finder.empty())    {        cout << "Can't create the following seam finder '" << seam_find_type << "'\n";        return 1;    }    seam_finder->find(images_warped_f, corners, masks_warped);    // Release unused memory    images.clear();    images_warped.clear();    images_warped_f.clear();    masks.clear();    ///////////////////////////////////exposure&seam end///////////////////////////////////////////////////////////////////////    //实时拼接    while (!stop)    {        if (cap1.read(frame1) && cap2.read(frame2))        {            imshow("cam1", frame1);            imshow("cam2", frame2);            imwrite("frame1.bmp", frame1);            imwrite("frame2.bmp", frame2);            //彩色帧转灰度            //cvtColor(frame1, frame1, CV_RGB2GRAY);            //cvtColor(frame2, frame2, CV_RGB2GRAY);            //拼接过程            //读入图片            cout << "Compositing..." << endl;#if ENABLE_LOG            t = getTickCount();#endif            Mat img_warped, img_warped_s;            Mat dilated_mask, seam_mask, mask, mask_warped;            Ptr<Blender> blender;            //double compose_seam_aspect = 1;            double compose_work_aspect = 1;            img_names.pop_back();            img_names.pop_back();            img_names.push_back("frame1.bmp");            img_names.push_back("frame2.bmp");            for (int img_idx = 0; img_idx < num_images; ++img_idx)            {                LOGLN("Compositing image #" << indices[img_idx] + 1);                // Read image and resize it if necessary                full_img = imread(img_names[img_idx]);/////////////////!!!!!!!!!!!!!!!!!!!!!!!!!!参数固定，可以试着读取不同图像                if (!is_compose_scale_set)                {                    if (compose_megapix > 0)                        compose_scale = min(1.0, sqrt(compose_megapix * 1e6 / full_img.size().area()));                    is_compose_scale_set = true;                    // Compute relative scales                    //compose_seam_aspect = compose_scale / seam_scale;                    compose_work_aspect = compose_scale / work_scale;                    // Update warped image scale                    warped_image_scale *= static_cast<float>(compose_work_aspect);                    warper = warper_creator->create(warped_image_scale);                    // Update corners and sizes                    for (int i = 0; i < num_images; ++i)                    {                        // Update intrinsics                        cameras[i].focal *= compose_work_aspect;                        cameras[i].ppx *= compose_work_aspect;                        cameras[i].ppy *= compose_work_aspect;                        // Update corner and size                        Size sz = full_img_sizes[i];                        if (std::abs(compose_scale - 1) > 1e-1)                        {                            sz.width = cvRound(full_img_sizes[i].width * compose_scale);                            sz.height = cvRound(full_img_sizes[i].height * compose_scale);                        }                        Mat K;                        cameras[i].K().convertTo(K, CV_32F);                        Rect roi = warper->warpRoi(sz, K, cameras[i].R);                        corners[i] = roi.tl();                        sizes[i] = roi.size();                    }                }                if (abs(compose_scale - 1) > 1e-1)                    resize(full_img, img, Size(), compose_scale, compose_scale);                else                    img = full_img;                full_img.release();                Size img_size = img.size();                Mat K;                cameras[img_idx].K().convertTo(K, CV_32F);                // Warp the current image                warper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);                // Warp the current image mask                mask.create(img_size, CV_8U);                mask.setTo(Scalar::all(255));                warper->warp(mask, K, cameras[img_idx].R, INTER_NEAREST, BORDER_CONSTANT, mask_warped);                // Compensate exposure                compensator->apply(img_idx, corners[img_idx], img_warped, mask_warped);                img_warped.convertTo(img_warped_s, CV_16S);                img_warped.release();                img.release();                mask.release();                dilate(masks_warped[img_idx], dilated_mask, Mat());                resize(dilated_mask, seam_mask, mask_warped.size());                mask_warped = seam_mask & mask_warped;                if (blender.empty())                {                    blender = Blender::createDefault(blend_type, try_gpu);                    Size dst_sz = resultRoi(corners, sizes).size();                    float blend_width = sqrt(static_cast<float>(dst_sz.area())) * blend_strength / 100.f;                    if (blend_width < 1.f)                        blender = Blender::createDefault(Blender::NO, try_gpu);                    else if (blend_type == Blender::MULTI_BAND)                    {                        MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(static_cast<Blender*>(blender));                        mb->setNumBands(static_cast<int>(ceil(log(blend_width) / log(2.)) - 1.));                        cout << "Multi-band blender, number of bands: " << mb->numBands() << endl;                    }                    else if (blend_type == Blender::FEATHER)                    {                        FeatherBlender* fb = dynamic_cast<FeatherBlender*>(static_cast<Blender*>(blender));                        fb->setSharpness(1.f / blend_width);                        LOGLN("Feather blender, sharpness: " << fb->sharpness());                    }                    blender->prepare(corners, sizes);                }                // Blend the current image                blender->feed(img_warped_s, mask_warped, corners[img_idx]);            }            Mat result, result_mask;            blender->blend(result, result_mask);            cout << "Compositing, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec" << endl;            result.convertTo(frame, CV_8UC1);            imshow("stitch", frame);        }        else        {            cout << "----------------------" << endl;            cout << "waitting..." << endl;        }        if (waitKey(1) == 13)        {            stop = true;            cout << "程序结束！" << endl;            cout << "*** ***" << endl;        }    }    return 0;}

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测试结果

调整相机采集图像分辨率为320*240，算法处理间隔为0.05″~0.07″，可以满足实时应用。