图像拼接(image stitching)
# OpenCV中stitching的使用
OpenCV提供了高级别的函数封装在Stitcher类中,使用很方便,不用考虑太多的细节。
低级别函数封装在detail命名空间中,展示了OpenCV算法实现的很多步骤和细节,使熟悉如下拼接流水线的用户,方便自己定制。
可见OpenCV图像拼接模块的实现是十分精密和复杂的,拼接的结果很完善,但同时也是费时的,完全不能够实现实时应用。
官方提供的stitching和stitching_detailed使用示例,分别是高级别和低级别封装这两种方式正确地使用示例。两种结果产生的拼接结果相同,后者却可以允许用户,在参数变量初始化时,选择各项算法。
具体算法流程:
- 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,
代码:
- #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/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()
- {
- cout <<
- "Rotation model images stitcher.\n\n"
- "stitching_detailed 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";
- #else
- string features_type = "orb";
- #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;
- 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 = "F:/opencv/build/bin/sample-data/stitching/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
- img_names.push_back("F:/opencv/build/bin/sample-data/stitching/st1.jpg");
- img_names.push_back("F:/opencv/build/bin/sample-data/stitching/st2.jpg");
- img_names.push_back("F:/opencv/build/bin/sample-data/stitching/st3.jpg");
- img_names.push_back("F:/opencv/build/bin/sample-data/stitching/st4.jpg");
- // 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();
- }
- else if (features_type == "sift") {
- finder = xfeatures2d::SIFT::create();
- }
- #endif
- else
- {
- cout << "Unknown 2D features type: '" << features_type << "'.\n";
- return -1;
- }
- cout << "Current 2D features type: '" << features_type << "'.\n";
- 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 from the same panorama");
- 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");
- 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;
- }
结果:
Finding features...
Current 2D features type: 'surf'.
[ INFO:0] Initialize OpenCL runtime...
Features in image #1: 911
Features in image #2: 1085
Features in image #3: 1766
Features in image #4: 2001
Finding features, time: 3.33727 sec
Pairwise matchingPairwise matching, time: 3.2849 sec
Initial camera intrinsics #1:
K:
[4503.939581818162, 0, 285;
0, 4503.939581818162, 210;
0, 0, 1]
R:
[1.0011346, 0.0019526235, -0.0037489906;
0.00011878588, 1.0000151, -0.052518897;
-0.0011389133, 0.021224562, 1]
Initial camera intrinsics #2:
K:
[4503.939581818162, 0, 249;
0, 4503.939581818162, 222;
0, 0, 1]
R:
[1.0023992, 0.0045258515, 0.083801955;
-9.7107059e-06, 1.0006112, -0.049870808;
0.015923418, 0.048128795, 1.0000379]
Initial camera intrinsics #3:
K:
[4503.939581818162, 0, 302.5;
0, 4503.939581818162, 173.5;
0, 0, 1]
R:
[1, 0, 0;
0, 1, 0;
0, 0, 1]
Initial camera intrinsics #4:
K:
[4503.939581818162, 0, 274.5;
0, 4503.939581818162, 194.5;
0, 0, 1]
R:
[1.0004042, 0.00080040237, 0.078620218;
0.00026136645, 1.0005095, -0.0048735617;
0.0061902963, 0.0096427174, 1.0004393]
Camera #1:
K:
[6569.821976030652, 0, 285;
0, 6569.821976030652, 210;
0, 0, 1]
R:
[0.99999672, 0.00038595949, -0.0025201384;
-0.00047636221, 0.99935275, -0.035969362;
0.0025046244, 0.035970442, 0.99934971]
Camera #2:
K:
[6571.327169846625, 0, 249;
0, 6571.327169846625, 222;
0, 0, 1]
R:
[0.99835128, 0.0012797765, 0.057385404;
0.00068109832, 0.99941689, -0.03413773;
-0.05739563, 0.034120534, 0.99776828]
Camera #3:
K:
[6570.486320822205, 0, 302.5;
0, 6570.486320822205, 173.5;
0, 0, 1]
R:
[1, -1.2951205e-09, 0;
-1.2914825e-09, 1, 0;
0, -4.6566129e-10, 1]
Camera #4:
K:
[6571.394840241929, 0, 274.5;
0, 6571.394840241929, 194.5;
0, 0, 1]
R:
[0.99855018, -0.00018820527, 0.053829439;
0.0003683792, 0.99999434, -0.0033372282;
-0.053828511, 0.0033522192, 0.99854457]
Warping images (auxiliary)...
[ INFO:0] Successfully initialized OpenCL cache directory: C:\Users\mzhu\AppData\Local\Temp\opencv\4.1\opencl_cache\
[ INFO:0] Preparing OpenCL cache configuration for context: Intel_R__Corporation--Intel_R__HD_Graphics_620--21_20_16_4574
Warping images, time: 0.0817463 sec
Compensating exposure...
Compensating exposure, time: 0.22982 sec
Finding seams...
Finding seams, time: 1.49795 sec
Compositing...
Compositing image #1
Multi-band blender, number of bands: 5
Compositing image #2
Compositing image #3
Compositing image #4
Compositing, time: 0.705931 sec
Finished, total time: 116.51 sec
OpenPano: Automatic Panorama Stitching From Scratch (https://github.com/ppwwyyxx/OpenPano)
StitchIt: Optimization and Parallelization of Image Stitching (https://github.com/stitchit/StitchIt)
ParaPano: Parallel image stitching using CUDA (https://github.com/zq-chen/ParaPano)
NISwGSP : Natural Image Stitching with the Global Similarity Prior (For Windows:https://github.com/firdauslubis88/NISwGSP,论文阅读笔记:https://zhuanlan.zhihu.com/p/57543736)
推荐:
图像拼接现在还有研究的价值吗?有哪些可以研究的点?现在技术发展如何?
Best Panorama Software for Stitching Images
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