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：
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)：
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
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 <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"
#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 <float>\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 <float>\n"
" Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.\n"
" --conf_thresh <float>\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"
" <fx><skew><ppx><aspect><ppy>. 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 <file_name>\n"
" Save matches graph represented in DOT language to <file_name> 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 <float>\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 <float>\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 <int>\n"
" Number of exposure compensation feed. The default is 1.\n"
" --expos_comp_nr_filtering <int>\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 <int>\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 <float>\n"
" Blending strength from [0,100] range. The default is 5.\n"
" --output <result_img>\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 <int>\n"
" uses range_width to limit number of images to match with.\n";
}
// Default command line args
vector<String> 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<float>(atof(argv[i + 1]));
i++;
}
else if (string(argv[i]) == "--conf_thresh")
{
conf_thresh = static_cast<float>(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<float>(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<int>(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<Feature2D> 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<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(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<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
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;
}
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);
// 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<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");
LOGLN("Compensating exposure...");
#if ENABLE_LOG
t = getTickCount();
#endif
Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);
if (dynamic_cast<GainCompensator*>(compensator.get()))
{
GainCompensator* gcompensator = dynamic_cast<GainCompensator*>(compensator.get());
gcompensator->setNrFeeds(expos_comp_nr_feeds);
}
if (dynamic_cast<ChannelsCompensator*>(compensator.get()))
{
ChannelsCompensator* ccompensator = dynamic_cast<ChannelsCompensator*>(compensator.get());
ccompensator->setNrFeeds(expos_comp_nr_feeds);
}
if (dynamic_cast<BlocksCompensator*>(compensator.get()))
{
BlocksCompensator* bcompensator = dynamic_cast<BlocksCompensator*>(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<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);
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> 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 = 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<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
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 <float>> 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 <float> 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 <float> 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 <file_name> 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 <float> 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 <float> 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 <float> 融合强度，0-100.默认是 5.
- --output <result_img> 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
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)
结果图：
参数warp_type 设置为"plane"，效果图如下：
参数warp_type 设置为"fisheye"，效果图如下(旋转90°后)：
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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