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

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：https://pan.baidu.com/s/1Cu0e2Fcto88AyRR7GFrXCg
提取码：hze9

复制这段内容后打开百度网盘手机App，操作更方便哦
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   
#include "myhead.h"
#include   
#include  

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 time
	AAtime = getTickCount(); //check processing time

	Stitcher::Status status = stitcher.stitch(vImg, rImg);

	BBtime = getTickCount(); //check processing time 
	printf("Time consuming: %.2lf sec \n", (BBtime - AAtime) / getTickFrequency()); //check processing time

	if (Stitcher::OK == status)
		imshow("Stitching Result", rImg);
	else
		printf("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 
#include 
#include 
#include 
#include "opencv2/opencv_modules.hpp"
#include 
#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::endl

using namespace std;
using namespace cv;
using namespace cv::detail;

// Default command line args
vector 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_LOG
	int64 app_start_time = getTickCount();
#endif

#if 0
	cv::setBreakOnError(true);
#endif
	VideoCapture 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 images
	int num_images = static_cast(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_LOG
	int64 t = getTickCount();
#endif

	Ptr finder;
	if (features_type == "surf")
	{
#ifdef HAVE_OPENCV_XFEATURES2D
		if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
			finder = makePtr();
		else
#endif
			finder = makePtr();
	}
	else
	{
		cout << "Unknown 2D features type: '" << features_type << "'.\n";
		getchar();
		return -1;
	}

	Mat full_img, img;
	vector features(num_images);
	vector images(num_images);
	vector 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_LOG
	t = getTickCount();
#endif
	vector pairwise_matches;
	Ptr matcher;
	if (matcher_type == "affine")
		matcher = makePtr(false, try_cuda, match_conf);
	else if (range_width == -1)
		matcher = makePtr(try_cuda, match_conf);
	else
		matcher = makePtr(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 panorama
	vector indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);
	vector img_subset;
	vector img_names_subset;
	vector 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(img_names.size());
	if (num_images < 2)
	{
		LOGLN("Need more images 2");
		getchar();
		return -1;
	}

	Ptr estimator = makePtr();
	vector 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 adjuster = makePtr();
	adjuster->setConfThresh(conf_thresh);
	Mat_ 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 length

	vector 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(focals[focals.size() / 2]);
	else
		warped_image_scale = static_cast(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f;

	if (do_wave_correct)
	{
		vector 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_LOG
	t = getTickCount();
#endif

	vector corners(num_images);
	vector masks_warped(num_images);
	vector images_warped(num_images);
	vector sizes(num_images);
	vector 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 warper_creator;
#ifdef HAVE_OPENCV_CUDAWARPING
	if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
	{
		warper_creator = makePtr();
	}
	else
#endif
	{
		warper_creator = makePtr();
	}

	if (!warper_creator)
	{
		cout << "Can't create the following warper '" << warp_type << "'\n";
		return 1;
	}

	Ptr warper = warper_creator->create(static_cast(warped_image_scale * seam_work_aspect));
	for (int i = 0; i < num_images; ++i)
	{
		Mat_ 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 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 compensator = ExposureCompensator::createDefault(expos_comp_type);
	compensator->feed(corners, images_warped, masks_warped);

	Ptr seam_finder;
	if (seam_find_type == "gc_color")
	{
#ifdef HAVE_OPENCV_CUDALEGACY
		if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
			seam_finder = makePtr(GraphCutSeamFinderBase::COST_COLOR);
		else
#endif
			seam_finder = makePtr(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 memory
	images.clear();
	images_warped.clear();
	images_warped_f.clear();
	masks.clear();

	LOGLN("Compositing...");
#if ENABLE_LOG
	t = getTickCount();
#endif

	Mat img_warped, img_warped_s;
	Mat dilated_mask, seam_mask, mask, mask_warped;
	Ptr blender;
	Ptr 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 necessary
		full_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 scale
			warped_image_scale *= static_cast(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, INTER_LINEAR_EXACT);
		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(), 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(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(blender.get());
				mb->setNumBands(static_cast(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;

		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);
			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(), 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(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！