OpenCV3.4.1 vs2015 自定义过程的图片拼接

OpenCV3.4.1 vs2015 自定义过程的图片拼接

实验环境：win10，vs2015，OpenCV3.4.1。环境配置点击这里可以看到很好的一个参考。
OpenCV有自带的图片拼接函数，样例在opencv3.4.1\opencv\sources\samples\cpp\stitching.cpp，
这里实现的是逐步提取特征、图片配对、拼接的图片拼接代码，是基于opencv3.4.1\opencv\sources\samples\cpp\stitching_detailed.cpp做的，是后续做摄像头实时拼接的一个前期工作。功能就是读入三张图片做拼接（当然也可以是其他张数），然后展示并保存为result.jpg。
直接上代码：

#include <iostream>
#include <fstream>
#include <string>
#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::endl

using 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 = "plane";
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
	Mat framecap[10];
	//读入这三张图片做拼接，然后展示并保存为result.jpg
	framecap[0] = imread("images1/41.jpg");
	framecap[1] = imread("images1/42.jpg");
	framecap[2] = imread("images1/43.jpg");
	img_names.push_back(0);
	img_names.push_back(1);
	img_names.push_back(2);
	//img_names.push_back("images1/41.jpg");
	//img_names.push_back("images1/42.jpg");
	//img_names.push_back("images1/43.jpg");
	//img_names.push_back("images3/2.jpg");
	//img_names.push_back("images3/3.jpg");
	/*img_names.push_back("images0/0.png");
	img_names.push_back("images0/1.png");
	img_names.push_back("images0/2.png");
	img_names.push_back("images0/3.png");
	img_names.push_back("images0/4.png");*/

	// Check if have enough images
	int num_images = 3;
	if (num_images < 2)
	{
		LOGLN("Need more images");
		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<FeaturesFinder> finder;
	if (features_type == "surf")
	{
#ifdef HAVE_OPENCV_XFEATURES2D
		if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
			finder = makePtr<SurfFeaturesFinderGpu>();
		else
#endif
			finder = makePtr<SurfFeaturesFinder>();
	}
	else if (features_type == "orb")
	{
		finder = makePtr<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 = framecap[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, 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<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);
	else
		matcher = makePtr<BestOf2NearestRangeMatcher>(range_width, try_cuda, match_conf);

	(*matcher)(features, pairwise_matches);
	matcher->collectGarbage();

	LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");

	// Check if we should save matches graph

	// 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<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 images
	num_images = static_cast<int>(img_names.size());
	if (num_images < 2)
	{
		LOGLN("Need more images");
		return -1;
	}

	Ptr<Estimator> estimator;
	if (estimator_type == "affine")
		estimator = makePtr<AffineBasedEstimator>();
	else
		estimator = makePtr<HomographyBasedEstimator>();

	vector<CameraParams> cameras;
	if (!(*estimator)(features, pairwise_matches, cameras))
	{
		cout << "Homography estimation failed.\n";
		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;
	if (ba_cost_func == "reproj") adjuster = makePtr<detail::BundleAdjusterReproj>();
	else if (ba_cost_func == "ray") adjuster = makePtr<detail::BundleAdjusterRay>();
	else if (ba_cost_func == "affine") adjuster = makePtr<detail::BundleAdjusterAffinePartial>();
	else if (ba_cost_func == "no") adjuster = makePtr<NoBundleAdjuster>();
	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);
	if (!(*adjuster)(features, pairwise_matches, cameras))
	{
		cout << "Camera parameters adjusting failed.\n";
		return -1;
	}

	// Find median focal length

	vector<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]);
	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];
	}

	LOGLN("Warping images (auxiliary)... ");
#if ENABLE_LOG
	t = getTickCount();
#endif

	vector<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 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;
#ifdef HAVE_OPENCV_CUDAWARPING
	if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
	{
		if (warp_type == "plane")
			warper_creator = makePtr<cv::PlaneWarperGpu>();
		else if (warp_type == "cylindrical")
			warper_creator = makePtr<cv::CylindricalWarperGpu>();
		else if (warp_type == "spherical")
			warper_creator = makePtr<cv::SphericalWarperGpu>();
	}
	else
#endif
	{
		if (warp_type == "plane")
			warper_creator = makePtr<cv::PlaneWarper>();
		else if (warp_type == "affine")
			warper_creator = makePtr<cv::AffineWarper>();
		else if (warp_type == "cylindrical")
			warper_creator = makePtr<cv::CylindricalWarper>();
		else if (warp_type == "spherical")
			warper_creator = makePtr<cv::SphericalWarper>();
		else if (warp_type == "fisheye")
			warper_creator = makePtr<cv::FisheyeWarper>();
		else if (warp_type == "stereographic")
			warper_creator = makePtr<cv::StereographicWarper>();
		else if (warp_type == "compressedPlaneA2B1")
			warper_creator = makePtr<cv::CompressedRectilinearWarper>(2.0f, 1.0f);
		else if (warp_type == "compressedPlaneA1.5B1")
			warper_creator = makePtr<cv::CompressedRectilinearWarper>(1.5f, 1.0f);
		else if (warp_type == "compressedPlanePortraitA2B1")
			warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(2.0f, 1.0f);
		else if (warp_type == "compressedPlanePortraitA1.5B1")
			warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(1.5f, 1.0f);
		else if (warp_type == "paniniA2B1")
			warper_creator = makePtr<cv::PaniniWarper>(2.0f, 1.0f);
		else if (warp_type == "paniniA1.5B1")
			warper_creator = makePtr<cv::PaniniWarper>(1.5f, 1.0f);
		else if (warp_type == "paniniPortraitA2B1")
			warper_creator = makePtr<cv::PaniniPortraitWarper>(2.0f, 1.0f);
		else if (warp_type == "paniniPortraitA1.5B1")
			warper_creator = makePtr<cv::PaniniPortraitWarper>(1.5f, 1.0f);
		else if (warp_type == "mercator")
			warper_creator = makePtr<cv::MercatorWarper>();
		else if (warp_type == "transverseMercator")
			warper_creator = makePtr<cv::TransverseMercatorWarper>();
	}

	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 == "no")
		seam_finder = makePtr<detail::NoSeamFinder>();
	else if (seam_find_type == "voronoi")
		seam_finder = makePtr<detail::VoronoiSeamFinder>();
	else if (seam_find_type == "gc_color")
	{
#ifdef HAVE_OPENCV_CUDALEGACY
		if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
			seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR);
		else
#endif
			seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR);
	}
	else if (seam_find_type == "gc_colorgrad")
	{
#ifdef HAVE_OPENCV_CUDALEGACY
		if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
			seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
		else
#endif
			seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
	}
	else if (seam_find_type == "dp_color")
		seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR);
	else if (seam_find_type == "dp_colorgrad")
		seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR_GRAD);
	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> 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 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<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, 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<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());
			}
			else if (blend_type == Blender::FEATHER)
			{
				FeatherBlender* fb = dynamic_cast<FeatherBlender*>(blender.get());
				fb->setSharpness(1.f / blend_width);
				LOGLN("Feather blender, sharpness: " << fb->sharpness());
			}
			blender->prepare(corners, sizes);
		}
		else if (!timelapser && timelapse)
		{
			timelapser = Timelapser::createDefault(timelapse_type);
			timelapser->initialize(corners, sizes);
		}

		// Blend the current image

		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");
		result.convertTo(result, CV_8UC1);
		imshow("stitch", result);
		imwrite(result_name, result);
		waitKey(0);
	}

	LOGLN("Finished, total time: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " sec");
	return 0;
}