OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍

关于OpenCV图像拼接的方法,如果不熟悉的话,可以先看看我整理的如下四篇博客:

  • OpenCV常用图像拼接方法(一):直接拼接(硬拼)

  • OpenCV常用图像拼接方法(二):基于模板匹配拼接

  • OpenCV常用图像拼接方法(三):基于特征匹配拼接

  • OpenCV常用图像拼接方法(四):基于Stitcher类拼接

本篇博客是Stitcher类的扩展介绍,通过例程stitching_detailed.cpp的使用和参数介绍,帮助大家了解Stitcher类拼接的具体步骤和方法,先看看其内部的流程结构图(如下):

这里写图片描述

stitching_detailed.cpp目录如下,可以在自己安装的OpenCV目录下找到,笔者这里使用的OpenCV4.4版本 

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第1张图片

stitching_detailed.cpp具体源码如下: 

// 05_Image_Stitch_Stitching_Detailed.cpp : 此文件包含 "main" 函数。程序执行将在此处开始并结束。
//
#include "pch.h"
#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"

#ifdef HAVE_OPENCV_XFEATURES2D
#include "opencv2/xfeatures2d.hpp"
#include "opencv2/xfeatures2d/nonfree.hpp"
#endif

#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;

static void printUsage(char** argv)
{
	cout <<
		"Rotation model images stitcher.\n\n"
		<< argv[0] << " img1 img2 [...imgN] [flags]\n\n"
		"Flags:\n"
		"  --preview\n"
		"      Run stitching in the preview mode. Works faster than usual mode,\n"
		"      but output image will have lower resolution.\n"
		"  --try_cuda (yes|no)\n"
		"      Try to use CUDA. The default value is 'no'. All default values\n"
		"      are for CPU mode.\n"
		"\nMotion Estimation Flags:\n"
		"  --work_megapix \n"
		"      Resolution for image registration step. The default is 0.6 Mpx.\n"
		"  --features (surf|orb|sift|akaze)\n"
		"      Type of features used for images matching.\n"
		"      The default is surf if available, orb otherwise.\n"
		"  --matcher (homography|affine)\n"
		"      Matcher used for pairwise image matching.\n"
		"  --estimator (homography|affine)\n"
		"      Type of estimator used for transformation estimation.\n"
		"  --match_conf \n"
		"      Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.\n"
		"  --conf_thresh \n"
		"      Threshold for two images are from the same panorama confidence.\n"
		"      The default is 1.0.\n"
		"  --ba (no|reproj|ray|affine)\n"
		"      Bundle adjustment cost function. The default is ray.\n"
		"  --ba_refine_mask (mask)\n"
		"      Set refinement mask for bundle adjustment. It looks like 'x_xxx',\n"
		"      where 'x' means refine respective parameter and '_' means don't\n"
		"      refine one, and has the following format:\n"
		"      . The default mask is 'xxxxx'. If bundle\n"
		"      adjustment doesn't support estimation of selected parameter then\n"
		"      the respective flag is ignored.\n"
		"  --wave_correct (no|horiz|vert)\n"
		"      Perform wave effect correction. The default is 'horiz'.\n"
		"  --save_graph \n"
		"      Save matches graph represented in DOT language to  file.\n"
		"      Labels description: Nm is number of matches, Ni is number of inliers,\n"
		"      C is confidence.\n"
		"\nCompositing Flags:\n"
		"  --warp (affine|plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPlaneA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPortraitA1.5B1|mercator|transverseMercator)\n"
		"      Warp surface type. The default is 'spherical'.\n"
		"  --seam_megapix \n"
		"      Resolution for seam estimation step. The default is 0.1 Mpx.\n"
		"  --seam (no|voronoi|gc_color|gc_colorgrad)\n"
		"      Seam estimation method. The default is 'gc_color'.\n"
		"  --compose_megapix \n"
		"      Resolution for compositing step. Use -1 for original resolution.\n"
		"      The default is -1.\n"
		"  --expos_comp (no|gain|gain_blocks|channels|channels_blocks)\n"
		"      Exposure compensation method. The default is 'gain_blocks'.\n"
		"  --expos_comp_nr_feeds \n"
		"      Number of exposure compensation feed. The default is 1.\n"
		"  --expos_comp_nr_filtering \n"
		"      Number of filtering iterations of the exposure compensation gains.\n"
		"      Only used when using a block exposure compensation method.\n"
		"      The default is 2.\n"
		"  --expos_comp_block_size \n"
		"      BLock size in pixels used by the exposure compensator.\n"
		"      Only used when using a block exposure compensation method.\n"
		"      The default is 32.\n"
		"  --blend (no|feather|multiband)\n"
		"      Blending method. The default is 'multiband'.\n"
		"  --blend_strength \n"
		"      Blending strength from [0,100] range. The default is 5.\n"
		"  --output \n"
		"      The default is 'result.jpg'.\n"
		"  --timelapse (as_is|crop) \n"
		"      Output warped images separately as frames of a time lapse movie, with 'fixed_' prepended to input file names.\n"
		"  --rangewidth \n"
		"      uses range_width to limit number of images to match with.\n";
}


// 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;
#ifdef HAVE_OPENCV_XFEATURES2D
string features_type = "surf";
float match_conf = 0.65f;
#else
string features_type = "orb";
float match_conf = 0.3f;
#endif
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";
int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
int expos_comp_nr_feeds = 1;
int expos_comp_nr_filtering = 2;
int expos_comp_block_size = 32;
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;


static int parseCmdArgs(int argc, char** argv)
{
	if (argc == 1)
	{
		printUsage(argv);
		return -1;
	}
	for (int i = 1; i < argc; ++i)
	{
		if (string(argv[i]) == "--help" || string(argv[i]) == "/?")
		{
			printUsage(argv);
			return -1;
		}
		else if (string(argv[i]) == "--preview")
		{
			preview = true;
		}
		else if (string(argv[i]) == "--try_cuda")
		{
			if (string(argv[i + 1]) == "no")
				try_cuda = false;
			else if (string(argv[i + 1]) == "yes")
				try_cuda = true;
			else
			{
				cout << "Bad --try_cuda flag value\n";
				return -1;
			}
			i++;
		}
		else if (string(argv[i]) == "--work_megapix")
		{
			work_megapix = atof(argv[i + 1]);
			i++;
		}
		else if (string(argv[i]) == "--seam_megapix")
		{
			seam_megapix = atof(argv[i + 1]);
			i++;
		}
		else if (string(argv[i]) == "--compose_megapix")
		{
			compose_megapix = atof(argv[i + 1]);
			i++;
		}
		else if (string(argv[i]) == "--result")
		{
			result_name = argv[i + 1];
			i++;
		}
		else if (string(argv[i]) == "--features")
		{
			features_type = argv[i + 1];
			if (string(features_type) == "orb")
				match_conf = 0.3f;
			i++;
		}
		else if (string(argv[i]) == "--matcher")
		{
			if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine")
				matcher_type = argv[i + 1];
			else
			{
				cout << "Bad --matcher flag value\n";
				return -1;
			}
			i++;
		}
		else if (string(argv[i]) == "--estimator")
		{
			if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine")
				estimator_type = argv[i + 1];
			else
			{
				cout << "Bad --estimator flag value\n";
				return -1;
			}
			i++;
		}
		else if (string(argv[i]) == "--match_conf")
		{
			match_conf = static_cast(atof(argv[i + 1]));
			i++;
		}
		else if (string(argv[i]) == "--conf_thresh")
		{
			conf_thresh = static_cast(atof(argv[i + 1]));
			i++;
		}
		else if (string(argv[i]) == "--ba")
		{
			ba_cost_func = argv[i + 1];
			i++;
		}
		else if (string(argv[i]) == "--ba_refine_mask")
		{
			ba_refine_mask = argv[i + 1];
			if (ba_refine_mask.size() != 5)
			{
				cout << "Incorrect refinement mask length.\n";
				return -1;
			}
			i++;
		}
		else if (string(argv[i]) == "--wave_correct")
		{
			if (string(argv[i + 1]) == "no")
				do_wave_correct = false;
			else if (string(argv[i + 1]) == "horiz")
			{
				do_wave_correct = true;
				wave_correct = detail::WAVE_CORRECT_HORIZ;
			}
			else if (string(argv[i + 1]) == "vert")
			{
				do_wave_correct = true;
				wave_correct = detail::WAVE_CORRECT_VERT;
			}
			else
			{
				cout << "Bad --wave_correct flag value\n";
				return -1;
			}
			i++;
		}
		else if (string(argv[i]) == "--save_graph")
		{
			save_graph = true;
			save_graph_to = argv[i + 1];
			i++;
		}
		else if (string(argv[i]) == "--warp")
		{
			warp_type = string(argv[i + 1]);
			i++;
		}
		else if (string(argv[i]) == "--expos_comp")
		{
			if (string(argv[i + 1]) == "no")
				expos_comp_type = ExposureCompensator::NO;
			else if (string(argv[i + 1]) == "gain")
				expos_comp_type = ExposureCompensator::GAIN;
			else if (string(argv[i + 1]) == "gain_blocks")
				expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
			else if (string(argv[i + 1]) == "channels")
				expos_comp_type = ExposureCompensator::CHANNELS;
			else if (string(argv[i + 1]) == "channels_blocks")
				expos_comp_type = ExposureCompensator::CHANNELS_BLOCKS;
			else
			{
				cout << "Bad exposure compensation method\n";
				return -1;
			}
			i++;
		}
		else if (string(argv[i]) == "--expos_comp_nr_feeds")
		{
			expos_comp_nr_feeds = atoi(argv[i + 1]);
			i++;
		}
		else if (string(argv[i]) == "--expos_comp_nr_filtering")
		{
			expos_comp_nr_filtering = atoi(argv[i + 1]);
			i++;
		}
		else if (string(argv[i]) == "--expos_comp_block_size")
		{
			expos_comp_block_size = atoi(argv[i + 1]);
			i++;
		}
		else if (string(argv[i]) == "--seam")
		{
			if (string(argv[i + 1]) == "no" ||
				string(argv[i + 1]) == "voronoi" ||
				string(argv[i + 1]) == "gc_color" ||
				string(argv[i + 1]) == "gc_colorgrad" ||
				string(argv[i + 1]) == "dp_color" ||
				string(argv[i + 1]) == "dp_colorgrad")
				seam_find_type = argv[i + 1];
			else
			{
				cout << "Bad seam finding method\n";
				return -1;
			}
			i++;
		}
		else if (string(argv[i]) == "--blend")
		{
			if (string(argv[i + 1]) == "no")
				blend_type = Blender::NO;
			else if (string(argv[i + 1]) == "feather")
				blend_type = Blender::FEATHER;
			else if (string(argv[i + 1]) == "multiband")
				blend_type = Blender::MULTI_BAND;
			else
			{
				cout << "Bad blending method\n";
				return -1;
			}
			i++;
		}
		else if (string(argv[i]) == "--timelapse")
		{
			timelapse = true;

			if (string(argv[i + 1]) == "as_is")
				timelapse_type = Timelapser::AS_IS;
			else if (string(argv[i + 1]) == "crop")
				timelapse_type = Timelapser::CROP;
			else
			{
				cout << "Bad timelapse method\n";
				return -1;
			}
			i++;
		}
		else if (string(argv[i]) == "--rangewidth")
		{
			range_width = atoi(argv[i + 1]);
			i++;
		}
		else if (string(argv[i]) == "--blend_strength")
		{
			blend_strength = static_cast(atof(argv[i + 1]));
			i++;
		}
		else if (string(argv[i]) == "--output")
		{
			result_name = argv[i + 1];
			i++;
		}
		else
			img_names.push_back(argv[i]);
	}
	if (preview)
	{
		compose_megapix = 0.6;
	}
	return 0;
}


int main(int argc, char* argv[])
{
#if ENABLE_LOG
	int64 app_start_time = getTickCount();
#endif

#if 0
	cv::setBreakOnError(true);
#endif

	int retval = parseCmdArgs(argc, argv);
	if (retval)
		return retval;

	// Check if have enough images
	int num_images = static_cast(img_names.size());
	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 finder;
	if (features_type == "orb")
	{
		finder = ORB::create();
	}
	else if (features_type == "akaze")
	{
		finder = AKAZE::create();
	}
#ifdef HAVE_OPENCV_XFEATURES2D
	else if (features_type == "surf")
	{
		finder = xfeatures2d::SURF::create();
	}
#endif
	else if (features_type == "sift")
	{
		finder = SIFT::create();
	}
	else
	{
		cout << "Unknown 2D features type: '" << features_type << "'.\n";
		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 = imread(samples::findFile(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;
		}

		computeImageFeatures(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();
	}

	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");

	// 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 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");
		return -1;
	}

	Ptr estimator;
	if (estimator_type == "affine")
		estimator = makePtr();
	else
		estimator = makePtr();

	vector 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 adjuster;
	if (ba_cost_func == "reproj") adjuster = makePtr();
	else if (ba_cost_func == "ray") adjuster = makePtr();
	else if (ba_cost_func == "affine") adjuster = makePtr();
	else if (ba_cost_func == "no") adjuster = makePtr();
	else
	{
		cout << "Unknown bundle adjustment cost function: '" << ba_cost_func << "'.\n";
		return -1;
	}
	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";
		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);

	// Prepare 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)
	{
		if (warp_type == "plane")
			warper_creator = makePtr();
		else if (warp_type == "cylindrical")
			warper_creator = makePtr();
		else if (warp_type == "spherical")
			warper_creator = makePtr();
	}
	else
#endif
	{
		if (warp_type == "plane")
			warper_creator = makePtr();
		else if (warp_type == "affine")
			warper_creator = makePtr();
		else if (warp_type == "cylindrical")
			warper_creator = makePtr();
		else if (warp_type == "spherical")
			warper_creator = makePtr();
		else if (warp_type == "fisheye")
			warper_creator = makePtr();
		else if (warp_type == "stereographic")
			warper_creator = makePtr();
		else if (warp_type == "compressedPlaneA2B1")
			warper_creator = makePtr(2.0f, 1.0f);
		else if (warp_type == "compressedPlaneA1.5B1")
			warper_creator = makePtr(1.5f, 1.0f);
		else if (warp_type == "compressedPlanePortraitA2B1")
			warper_creator = makePtr(2.0f, 1.0f);
		else if (warp_type == "compressedPlanePortraitA1.5B1")
			warper_creator = makePtr(1.5f, 1.0f);
		else if (warp_type == "paniniA2B1")
			warper_creator = makePtr(2.0f, 1.0f);
		else if (warp_type == "paniniA1.5B1")
			warper_creator = makePtr(1.5f, 1.0f);
		else if (warp_type == "paniniPortraitA2B1")
			warper_creator = makePtr(2.0f, 1.0f);
		else if (warp_type == "paniniPortraitA1.5B1")
			warper_creator = makePtr(1.5f, 1.0f);
		else if (warp_type == "mercator")
			warper_creator = makePtr();
		else if (warp_type == "transverseMercator")
			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");

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

	Ptr compensator = ExposureCompensator::createDefault(expos_comp_type);
	if (dynamic_cast(compensator.get()))
	{
		GainCompensator* gcompensator = dynamic_cast(compensator.get());
		gcompensator->setNrFeeds(expos_comp_nr_feeds);
	}

	if (dynamic_cast(compensator.get()))
	{
		ChannelsCompensator* ccompensator = dynamic_cast(compensator.get());
		ccompensator->setNrFeeds(expos_comp_nr_feeds);
	}

	if (dynamic_cast(compensator.get()))
	{
		BlocksCompensator* bcompensator = dynamic_cast(compensator.get());
		bcompensator->setNrFeeds(expos_comp_nr_feeds);
		bcompensator->setNrGainsFilteringIterations(expos_comp_nr_filtering);
		bcompensator->setBlockSize(expos_comp_block_size, expos_comp_block_size);
	}

	compensator->feed(corners, images_warped, masks_warped);

	LOGLN("Compensating exposure, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");

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

	Ptr seam_finder;
	if (seam_find_type == "no")
		seam_finder = makePtr();
	else if (seam_find_type == "voronoi")
		seam_finder = makePtr();
	else 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);
	}
	else if (seam_find_type == "gc_colorgrad")
	{
#ifdef HAVE_OPENCV_CUDALEGACY
		if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
			seam_finder = makePtr(GraphCutSeamFinderBase::COST_COLOR_GRAD);
		else
#endif
			seam_finder = makePtr(GraphCutSeamFinderBase::COST_COLOR_GRAD);
	}
	else if (seam_find_type == "dp_color")
		seam_finder = makePtr(DpSeamFinder::COLOR);
	else if (seam_find_type == "dp_colorgrad")
		seam_finder = makePtr(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);

	LOGLN("Finding seams, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");

	// 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 = imread(samples::findFile(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());
			}
			else if (blend_type == Blender::FEATHER)
			{
				FeatherBlender* fb = dynamic_cast(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
		if (timelapse)
		{
			timelapser->process(img_warped_s, Mat::ones(img_warped_s.size(), CV_8UC1), corners[img_idx]);
			String fixedFileName;
			size_t pos_s = String(img_names[img_idx]).find_last_of("/\\");
			if (pos_s == String::npos)
			{
				fixedFileName = "fixed_" + img_names[img_idx];
			}
			else
			{
				fixedFileName = "fixed_" + String(img_names[img_idx]).substr(pos_s + 1, String(img_names[img_idx]).length() - pos_s);
			}
			imwrite(fixedFileName, timelapser->getDst());
		}
		else
		{
			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");

		imwrite(result_name, result);
	}

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


stitching_detail 程序运行流程

  • 命令行调用程序,输入源图像以及程序的参数      
  • 特征点检测,判断是使用 surf 还是 orb,默认是 surf
  • 对图像的特征点进行匹配,使用最近邻和次近邻方法,将两个最优的匹配的置信度 保存下来
  • 对图像进行排序以及将置信度高的图像保存到同一个集合中,删除置信度比较低的图像间的匹配,得到能正确匹配的图像序列。这样将置信度高于门限的所有匹配合并到一个集合中 
  • 对所有图像进行相机参数粗略估计,然后求出旋转矩阵
  • 使用光束平均法进一步精准的估计出旋转矩阵
  • 波形校正,水平或者垂直
  • 拼接      
  • 融合,多频段融合,光照补偿

stitching_detail 程序接口介绍 

  • img1 img2 img3 输入图像      
  • --preview  以预览模式运行程序,比正常模式要快,但输出图像分辨率低,拼接的分辨 率 compose_megapix 设置为 0.6
  • --try_gpu  (yes|no)  是否使用 CUDA加速,默认为 no,使用CPU模式
  • /* 运动估计参数 */    
  • --work_megapix <--work_megapix > 图像匹配时的分辨率大小,默认为 0.6    
  • --features (surf | orb | sift | akaze) 选择 surf 或者 orb 算法进行特征点匹配,默认为 surf  
  • --matcher (homography | affine) 用于成对图像匹配的匹配器  
  • --estimator (homography | affine) 用于转换估计的估计器类型
  • --match_conf 特征点匹配步骤的匹配置信度,最近邻匹配距离与次近邻匹配距离的比值,surf 默认为 0.65,orb 默认为 0.3    
  • --conf_thresh 两幅图来自同一全景图的置信度,默认为 1.0    
  • --ba (no | reproj | ray | affine) 光束平均法的误差函数选择,默认是 ray 方法    
  • --ba_refine_mask (mask) 光束平均法设置优化掩码
  • --wave_correct (no|horiz|vert) 波形校验水平,垂直或者没有 默认是 horiz(水平)
  • --save_graph 将匹配的图形以点的形式保存到文件中, Nm 代表匹配的数量,NI代表正确匹配的数量,C 表示置信度
  • /*图像融合参数:*/ 
  • --warp (plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPla  neA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPor traitA1.5B1|mercator|transverseMercator)     选择融合的平面,默认是球形    
  • --seam_megapix 拼接缝像素的大小 默认是 0.1
  • --seam (no|voronoi|gc_color|gc_colorgrad) 拼接缝隙估计方法 默认是 gc_color    
  • --compose_megapix 拼接分辨率,默认为-1    
  • --expos_comp (no|gain|gain_blocks) 光照补偿方法,默认是 gain_blocks    
  • --blend (no|feather|multiband) 融合方法,默认是多频段融合    
  • --blend_strength 融合强度,0-100.默认是 5.    
  • --output 输出图像的文件名,默认是 result,jpg     命令使用实例,以及程序运行时的提示: 

未完,待更新。。。

youtube有一个台湾老师对这个例程的简单解读,教新手查看例程和分析结构的步骤可以看看(下面是视频链接):

https://www.youtube.com/watch?v=1Pp8VIqN_ro

另外我这里有一个pdf可以参考,帮助你进一步了解拼接的细节,1积分

https://download.csdn.net/download/stq054188/12956421

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第2张图片

上面使用默认参数,详细输出信息如下:

E:\Practice\OpenCV\Algorithm_Summary\Image_Stitching\x64\Debug>05_Image_Stitch_Stitching_Detailed.exe ./imgs/boat1.jpg ./imgs/boat2.jpg ./imgs/boat3.jpg ./imgs/boat4.jpg ./imgs/boat5.jpg ./imgs/boat6.jpg
Finding features...
[ INFO:0] global C:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\ocl.cpp (891) cv::ocl::haveOpenCL Initialize OpenCL runtime...
Features in image #1: 500
[ INFO:0] global C:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\ocl.cpp (433) cv::ocl::OpenCLBinaryCacheConfigurator::OpenCLBinaryCacheConfigurator Successfully initialized OpenCL cache directory: C:\Users\A4080599\AppData\Local\Temp\opencv\4.4\opencl_cache\
[ INFO:0] global C:\build\master_winpack-build-win64-vc15\opencv\modules\core\src\ocl.cpp (457) cv::ocl::OpenCLBinaryCacheConfigurator::prepareCacheDirectoryForContext Preparing OpenCL cache configuration for context: NVIDIA_Corporation--GeForce_GTX_1070--411_31
Features in image #2: 500
Features in image #3: 500
Features in image #4: 500
Features in image #5: 500
Features in image #6: 500
Finding features, time: 5.46377 sec
Pairwise matchingPairwise matching, time: 3.24159 sec
Initial camera intrinsics #1:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[0.91843718, -0.09762425, -1.1678253;
 0.0034433089, 1.0835428, -0.025021957;
 0.28152198, 0.16100603, 0.91920781]
Initial camera intrinsics #2:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[1.001171, -0.085758291, -0.64530683;
 0.010103324, 1.0520245, -0.030576767;
 0.15743911, 0.12035993, 1]
Initial camera intrinsics #3:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[1, 0, 0;
 0, 1, 0;
 0, 0, 1]
Initial camera intrinsics #4:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[0.8474561, 0.028589081, 0.75133896;
 -0.0014587968, 0.92028928, 0.033205934;
 -0.17483309, 0.018777205, 0.84592116]
Initial camera intrinsics #5:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[0.60283858, 0.069275051, 1.2121853;
 -0.014153662, 0.85474133, 0.014057174;
 -0.29529575, 0.053770453, 0.61932623]
Initial camera intrinsics #6:
K:
[534.6674906996568, 0, 474.5;
 0, 534.6674906996568, 316;
 0, 0, 1]
R:
[0.41477469, 0.075901195, 1.4396564;
 -0.015423983, 0.82344943, 0.0061162044;
 -0.35168326, 0.055747174, 0.42653102]
Camera #1:
K:
[1068.953598931666, 0, 474.5;
 0, 1068.953598931666, 316;
 0, 0, 1]
R:
[0.84266716, -0.010490002, -0.53833258;
 0.004485324, 0.99991232, -0.01246338;
 0.53841609, 0.0080878884, 0.84264034]
Camera #2:
K:
[1064.878323247434, 0, 474.5;
 0, 1064.878323247434, 316;
 0, 0, 1]
R:
[0.95117813, -0.015436338, -0.3082563;
 0.01137107, 0.99982315, -0.014980057;
 0.308433, 0.010743499, 0.95118535]
Camera #3:
K:
[1065.382193682081, 0, 474.5;
 0, 1065.382193682081, 316;
 0, 0, 1]
R:
[1, -1.6298145e-09, 0;
 -1.5716068e-09, 1, 0;
 0, 0, 1]
Camera #4:
K:
[1067.611537959627, 0, 474.5;
 0, 1067.611537959627, 316;
 0, 0, 1]
R:
[0.91316396, -7.9067249e-06, 0.40759254;
 -0.0075879274, 0.99982637, 0.017019274;
 -0.4075219, -0.018634165, 0.91300529]
Camera #5:
K:
[1080.708135180496, 0, 474.5;
 0, 1080.708135180496, 316;
 0, 0, 1]
R:
[0.70923853, 0.0025724203, 0.70496398;
 -0.0098195076, 0.99993235, 0.0062302947;
 -0.70490021, -0.01134116, 0.70921582]
Camera #6:
K:
[1080.90412660159, 0, 474.5;
 0, 1080.90412660159, 316;
 0, 0, 1]
R:
[0.49985889, 3.5938341e-05, 0.86610687;
 -0.00682831, 0.99996907, 0.0038993564;
 -0.86607999, -0.0078631733, 0.49984369]
Warping images (auxiliary)...
Warping images, time: 0.0791121 sec
Compensating exposure...
Compensating exposure, time: 0.72288 sec
Finding seams...
Finding seams, time: 3.09237 sec
Compositing...
Compositing image #1
Multi-band blender, number of bands: 8
Compositing image #2
Compositing image #3
Compositing image #4
Compositing image #5
Compositing image #6
Compositing, time: 13.7766 sec
Finished, total time: 29.4535 sec

输入图像boat1.jpg、boat2.jpg、boat3.jpg、boat4.jpg、boat5.jpg、boat6.jpg如下(可以在OpenCV安装目录下找到D:\OpenCV4.4\opencv_extra-master\testdata\stitching)

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第3张图片

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第4张图片

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第5张图片 

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第6张图片 

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第7张图片 

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第8张图片

结果图:

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第9张图片

参数warp_type 设置为"plane",效果图如下:

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第10张图片

参数warp_type 设置为"fisheye",效果图如下(旋转90°后):

OpenCV图像拼接-Stitcher类-Stitching detailed使用与参数介绍_第11张图片

其他的参数可以根据自己需要修改,如果要自己完成还需要详细了解拼接步骤再优化。

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