如题，基于OpenCV3.4.13+VS2015做了个双摄像头实时拼接的代码，是一个大项目的一个baseline的一部分。下面先说配环境再给代码。

环境配置

关于OpenCV+VS的环境配置网上已经有很多了，因为这份代码用到了OpenCV_Contrib里面的一些东西，所以这里赘述一下，更详细的过程可以参考这篇博客。
使用OpenCV_Contrib就得编译，编译就很麻烦，比配置还麻烦，因此我做了个资源集合，有需要的兄弟可以自取。

链接（2020.12.15更新）：https://pan.baidu.com/s/14_EKEYvEQVmS5Bls8lcnxw
提取码：bj29

假设你把我这个名为opencv的资源集合放在了$dir$下，$dir$的具体位置根据你的选择而定。则配置过程为：

1. 系统环境变量

此电脑->右键属性->高级系统设置->环境变量->系统变量->Path->新建，写入：

$dir$\opencv\build\x64\vc14\bin

然后一路确定。

2. VS配置

VS2015，菜单栏->视图->其他窗口->属性管理器，对Debug|X64进行配置，右键Microsoft.Cpp.x64.user，点击属性，然后

VC++->包含目录，加入：

$dir$\opencv\build\include
$dir$\opencv\build\include\opencv
$dir$\opencv\build\include\opencv2

VC++->库目录，加入：

$dir$\opencv\build\x64\vc14\lib

注意这里的vc14是跟我的vs2015对应的，如果你不是vs2015就别选vc14目录，找你的vs对应的vc目录。

连接器->输入->附加依赖项，加入：

opencv_aruco341d.lib
opencv_bgsegm341d.lib
opencv_bioinspired341d.lib
opencv_calib3d341d.lib
opencv_ccalib341d.lib
opencv_core341d.lib
opencv_datasets341d.lib
opencv_dnn341d.lib
opencv_dnn_objdetect341d.lib
opencv_dpm341d.lib
opencv_face341d.lib
opencv_features2d341d.lib
opencv_flann341d.lib
opencv_fuzzy341d.lib
opencv_hdf341d.lib
opencv_hfs341d.lib
opencv_highgui341d.lib
opencv_imgcodecs341d.lib
opencv_imgproc341d.lib
opencv_img_hash341d.lib
opencv_line_descriptor341d.lib
opencv_ml341d.lib
opencv_objdetect341d.lib
opencv_optflow341d.lib
opencv_phase_unwrapping341d.lib
opencv_photo341d.lib
opencv_plot341d.lib
opencv_reg341d.lib
opencv_rgbd341d.lib
opencv_saliency341d.lib
opencv_shape341d.lib
opencv_stereo341d.lib
opencv_stitching341d.lib
opencv_structured_light341d.lib
opencv_superres341d.lib
opencv_surface_matching341d.lib
opencv_text341d.lib
opencv_tracking341d.lib
opencv_video341d.lib
opencv_videoio341d.lib
opencv_videostab341d.lib
opencv_viz341d.lib
opencv_xfeatures2d341d.lib
opencv_ximgproc341d.lib
opencv_xobjdetect341d.lib
opencv_xphoto341d.lib

这里一共有46个.lib文件，有的是OpenCV3.4.13带的，有的是我用cmake和opencv_contrib编译出来的，而且都是对应于3.4.13版本的，如果你不是3.4.13版本你就写你对应的版本和.lib名称。而且我当时没有编译world集合lib，所以这里有46个。

调试->选项->常规勾选启动源服务器支持。

注意你调试的时候也要选Debug，x64.

3. 库文件添加到系统目录下

将上述46个名称对应的 .dll 文件全都放到C:\Windows\System32下。注意是.dll文件，位置在：

$dir$\opencv\build\x64\vc14\bin

同样的，vc14是对应我的vs2015的，如果你不是vs2015就别选这个目录。

4. 简单的测试一下环境

这里给出一个简单用Stitcher类做拼接的代码测试一下opencv+contrib的环境：

#include <stdio.h>
#include "myhead.h"
#include <opencv2\opencv.hpp>
#include <opencv2\stitching.hpp> using namespace std;
using namespace cv;int main()
{vector< Mat > vImg;Mat rImg;vImg.push_back(imread("10.jpg"));vImg.push_back(imread("11.jpg"));Stitcher stitcher = Stitcher::createDefault();unsigned long AAtime = 0, BBtime = 0; //check processing timeAAtime = getTickCount(); //check processing timeStitcher::Status status = stitcher.stitch(vImg, rImg);BBtime = getTickCount(); //check processing time printf("Time consuming: %.2lf sec \n", (BBtime - AAtime) / getTickFrequency()); //check processing timeif (Stitcher::OK == status)imshow("Stitching Result", rImg);elseprintf("Stitching fail.");imwrite("test.jpg", rImg);waitKey(0);
}

10.jpg和11.jpg分别如下

如果得到以下显示，那么恭喜你，环境配好了。

双摄像头实时拼接

直接上代码。

#include <iostream>
#include <fstream>
#include <string>
#include <time.h>
#include "opencv2/opencv_modules.hpp"
#include <opencv2/core/utility.hpp>
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/stitching/detail/autocalib.hpp"
#include "opencv2/stitching/detail/blenders.hpp"
#include "opencv2/stitching/detail/timelapsers.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/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"#define ENABLE_LOG 1
#define LOG(msg) std::cout << msg
#define LOGLN(msg) std::cout << msg << std::endlusing namespace std;
using namespace cv;
using namespace cv::detail;// Default command line args
vector<int> img_names;
bool preview = false;
bool try_cuda = false;
double work_megapix = 0.6;
double seam_megapix = 0.1;
double compose_megapix = -1;
float conf_thresh = 1.f;
string features_type = "surf";
string matcher_type = "homography";
string estimator_type = "homography";
string ba_cost_func = "ray";
string ba_refine_mask = "xxxxx";
bool do_wave_correct = true;
WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ;
bool save_graph = false;
std::string save_graph_to;string warp_type = "spherical"; //plane spherical
int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
float match_conf = 0.3f;
string seam_find_type = "gc_color";
int blend_type = Blender::MULTI_BAND;
int timelapse_type = Timelapser::AS_IS;
float blend_strength = 5;
string result_name = "result.jpg";
bool timelapse = false;
int range_width = -1;int main(int argc, char* argv[])
{
#if ENABLE_LOGint64 app_start_time = getTickCount();
#endif#if 0cv::setBreakOnError(true);
#endifVideoCapture cap0(0), cap1(1);Mat framecap[10];if (cap0.isOpened() && cap1.isOpened()){cout << "*** ***" << endl;cout << "摄像头已启动！" << endl;}cap0.set(CV_CAP_PROP_FOCUS, 0);cap1.set(CV_CAP_PROP_FOCUS, 0);int k = 50;while (k--){if (cap0.read(framecap[0]) && cap1.read(framecap[1])){imwrite("frame0.bmp", framecap[0]);imwrite("frame1.bmp", framecap[1]);}}img_names.push_back(0);img_names.push_back(1);// Check if have enough imagesint num_images = static_cast<int>(img_names.size());if (num_images < 2){LOGLN("Need more images 1");getchar();return -1;}double work_scale = 1, seam_scale = 1, compose_scale = 1;bool is_work_scale_set = false, is_seam_scale_set = false, is_compose_scale_set = false;LOGLN("Finding features...");
#if ENABLE_LOGint64 t = getTickCount();
#endifPtr<FeaturesFinder> finder;if (features_type == "surf"){
#ifdef HAVE_OPENCV_XFEATURES2Dif (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)finder = makePtr<SurfFeaturesFinderGpu>();else
#endiffinder = makePtr<SurfFeaturesFinder>();}else{cout << "Unknown 2D features type: '" << features_type << "'.\n";getchar();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 = framecap[i];full_img_sizes[i] = full_img.size();if (full_img.empty()){LOGLN("Can't open image " << img_names[i]);getchar();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, INTER_LINEAR_EXACT);}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, INTER_LINEAR_EXACT);images[i] = img.clone();}finder->collectGarbage();full_img.release();img.release();LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");LOG("Pairwise matching");
#if ENABLE_LOGt = getTickCount();
#endifvector<MatchesInfo> pairwise_matches;Ptr<FeaturesMatcher> matcher;if (matcher_type == "affine")matcher = makePtr<AffineBestOf2NearestMatcher>(false, try_cuda, match_conf);else if (range_width == -1)matcher = makePtr<BestOf2NearestMatcher>(try_cuda, match_conf);elsematcher = makePtr<BestOf2NearestRangeMatcher>(range_width, try_cuda, match_conf);(*matcher)(features, pairwise_matches);matcher->collectGarbage();LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");// Leave only images we are sure are from the same panoramavector<int> indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);vector<Mat> img_subset;vector<int> 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 imagesnum_images = static_cast<int>(img_names.size());if (num_images < 2){LOGLN("Need more images 2");getchar();return -1;}Ptr<Estimator> estimator = makePtr<HomographyBasedEstimator>();vector<CameraParams> cameras;if (!(*estimator)(features, pairwise_matches, cameras)){cout << "Homography estimation failed.\n";getchar();return -1;}for (size_t i = 0; i < cameras.size(); ++i){Mat R;cameras[i].R.convertTo(R, CV_32F);cameras[i].R = R;LOGLN("Initial camera intrinsics #" << indices[i] + 1 << ":\nK:\n" << cameras[i].K() << "\nR:\n" << cameras[i].R);}Ptr<detail::BundleAdjusterBase> adjuster = makePtr<detail::BundleAdjusterRay>();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);if (!(*adjuster)(features, pairwise_matches, cameras)){cout << "Camera parameters adjusting failed.\n";getchar();return -1;}// Find median focal lengthvector<double> focals;for (size_t i = 0; i < cameras.size(); ++i){LOGLN("Camera #" << indices[i] + 1 << ":\nK:\n" << cameras[i].K() << "\nR:\n" << cameras[i].R);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]);elsewarped_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];}LOGLN("Warping images (auxiliary)... ");
#if ENABLE_LOGt = getTickCount();
#endifvector<Point> corners(num_images);vector<UMat> masks_warped(num_images);vector<UMat> images_warped(num_images);vector<Size> sizes(num_images);vector<UMat> masks(num_images);// Preapre images masksfor (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 masksPtr<WarperCreator> warper_creator;
#ifdef HAVE_OPENCV_CUDAWARPINGif (try_cuda && cuda::getCudaEnabledDeviceCount() > 0){warper_creator = makePtr<cv::PlaneWarperGpu>();}else
#endif{warper_creator = makePtr<cv::PlaneWarper>();}if (!warper_creator){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<UMat> images_warped_f(num_images);for (int i = 0; i < num_images; ++i)images_warped[i].convertTo(images_warped_f[i], CV_32F);LOGLN("Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);compensator->feed(corners, images_warped, masks_warped);Ptr<SeamFinder> seam_finder;if (seam_find_type == "gc_color"){
#ifdef HAVE_OPENCV_CUDALEGACYif (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR);else
#endifseam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR);}if (!seam_finder){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 memoryimages.clear();images_warped.clear();images_warped_f.clear();masks.clear();LOGLN("Compositing...");
#if ENABLE_LOGt = getTickCount();
#endifMat img_warped, img_warped_s;Mat dilated_mask, seam_mask, mask, mask_warped;Ptr<Blender> blender;Ptr<Timelapser> timelapser;//double compose_seam_aspect = 1;double compose_work_aspect = 1;for (int img_idx = 0; img_idx < num_images; ++img_idx){LOGLN("Compositing image #" << indices[img_idx] + 1);// Read image and resize it if necessaryfull_img = framecap[img_names[img_idx]];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 scalewarped_image_scale *= static_cast<float>(compose_work_aspect);warper = warper_creator->create(warped_image_scale);// Update corners and sizesfor (int i = 0; i < num_images; ++i){// Update intrinsicscameras[i].focal *= compose_work_aspect;cameras[i].ppx *= compose_work_aspect;cameras[i].ppy *= compose_work_aspect;// Update corner and sizeSize 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, INTER_LINEAR_EXACT);elseimg = full_img;full_img.release();Size img_size = img.size();Mat K;cameras[img_idx].K().convertTo(K, CV_32F);// Warp the current imagewarper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);// Warp the current image maskmask.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 exposurecompensator->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(), 0, 0, INTER_LINEAR_EXACT);mask_warped = seam_mask & mask_warped;if (!blender && !timelapse){blender = Blender::createDefault(blend_type, try_cuda);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_cuda);else if (blend_type == Blender::MULTI_BAND){MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(blender.get());mb->setNumBands(static_cast<int>(ceil(log(blend_width) / log(2.)) - 1.));LOGLN("Multi-band blender, number of bands: " << mb->numBands());}blender->prepare(corners, sizes);}else if (!timelapser && timelapse){timelapser = Timelapser::createDefault(timelapse_type);timelapser->initialize(corners, sizes);}blender->feed(img_warped_s, mask_warped, corners[img_idx]);}if (!timelapse){Mat result, result_mask;blender->blend(result, result_mask);LOGLN("Compositing, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");}LOGLN("Finished preparing, total time: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " sec");images.clear();images_warped.clear();images_warped_f.clear();masks.clear();int numberofframe = 0;time_t beforerun = time(0);while (cap0.read(framecap[0]) && cap1.read(framecap[1])){//imshow("cap0", framecap[0]);//imshow("cap1", framecap[1]);//Mat img_warped, img_warped_s;//Mat dilated_mask, seam_mask, mask, mask_warped;Ptr<Blender> blender;for (int img_idx = 0; img_idx < num_images; ++img_idx){full_img = framecap[img_idx];if (abs(compose_scale - 1) > 1e-1)resize(full_img, img, Size(), compose_scale, compose_scale, INTER_LINEAR_EXACT);elseimg = full_img;full_img.release();Size img_size = img.size();Mat K;cameras[img_idx].K().convertTo(K, CV_32F);// Warp the current imagewarper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);// Warp the current image maskmask.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 exposurecompensator->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(), 0, 0, INTER_LINEAR_EXACT);mask_warped = seam_mask & mask_warped;if (!blender && !timelapse){blender = Blender::createDefault(blend_type, try_cuda);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_cuda);blender->prepare(corners, sizes);}blender->feed(img_warped_s, mask_warped, corners[img_idx]);}Mat result, result_mask;blender->blend(result, result_mask);Mat frame;result.convertTo(frame, CV_8UC1);imshow("stitch", frame);numberofframe++;if (cvWaitKey(10) == 27)  break;}time_t afterrun = time(0);cout << "帧率约为" << numberofframe / (afterrun - beforerun) << "帧/s" << endl;getchar();
}

这份代码是从opencv examples里面的stitching_detailed.cpp改过来的，在CPU的基础上基本做到极限了，可优化的空间我感觉是不大。基于GPU加速还可以更快一点，这是后续工作了。也可以很容易地改为3摄像头或者更多摄像头的实时拼接。
代码整体应该没有大的bug，如果提示Need more images 2并且卡住的话表面双摄像头采集到的结果无法拼接起来，你需要调整一下两个摄像头的位置重新拼接试试。
如有问题可以随时与我联系！

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OpenCV3.4.13+OpenCV_contrib 双摄像头实时拼接环境配置

环境配置

1. 系统环境变量

2. VS配置

3. 库文件添加到系统目录下

4. 简单的测试一下环境

双摄像头实时拼接

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OpenCV3.4.13+OpenCV_contrib 双摄像头实时拼接 环境配置

环境配置

1. 系统环境变量

2. VS配置

3. 库文件添加到系统目录下

4. 简单的测试一下环境

双摄像头实时拼接

OpenCV3.4.13+OpenCV_contrib 双摄像头实时拼接 环境配置相关推荐

最新文章

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OpenCV3.4.13+OpenCV_contrib 双摄像头实时拼接环境配置

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