# 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，水平或者垂直融合，多频段融合，光照补偿，#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::endlusing namespace std;using namespace cv;using namespace cv::detail;static void printUsage() cout "Rotation model images stitcher. "stitching_detailed img1 img2 [...imgN] [flags] "Flags: " --preview " Run stitching in the preview mode. Works faster than usual mode, " but output image will have lower resolution. " --try_cuda (yes|no) " Try to use CUDA. The default value is 'no'. All default values " are for CPU mode.Motion Estimation Flags: " --work_megapix float " Resolution for image registration step. The default is 0.6 Mpx. " --features (surf|orb|sift|akaze) " Type of features used for images matching. " The default is surf if available, orb otherwise. " --matcher (homography|affine) " Matcher used for pairwise image matching. " --estimator (homography|affine) " Type of estimator used for transformation estimation. " --match_conf float " Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb. " --conf_thresh float " Threshold for two images are from the same panorama confidence. " The default is 1.0. " --ba (no|reproj|ray|affine) " Bundle adjustment cost function. The default is ray. " --ba_refine_mask (mask) " Set refinement mask for bundle adjustment. It looks like 'x_xxx', " where 'x' means refine respective parameter and '_' means don't " refine one, and has the following format: " fx skew ppx aspect ppy . The default mask is 'xxxxx'. If bundle " adjustment doesn't support estimation of selected parameter then " the respective flag is ignored. " --wave_correct (no|horiz|vert) " Perform wave effect correction. The default is 'horiz'. " --save_graph file_name " Save matches graph represented in DOT language to file_name file. " Labels description: Nm is number of matches, Ni is number of inliers, " C is confidence.Compositing Flags: " --warp (affine|plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPlaneA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPortraitA1.5B1|mercator|transverseMercator) " Warp surface type. The default is 'spherical'. " --seam_megapix float " Resolution for seam estimation step. The default is 0.1 Mpx. " --seam (no|voronoi|gc_color|gc_colorgrad) " Seam estimation method. The default is 'gc_color'. " --compose_megapix float " Resolution for compositing step. Use -1 for original resolution. " The default is -1. " --expos_comp (no|gain|gain_blocks|channels|channels_blocks) " Exposure compensation method. The default is 'gain_blocks'. " --expos_comp_nr_feeds int " Number of exposure compensation feed. The default is 1. " --expos_comp_nr_filtering int " Number of filtering iterations of the exposure compensation gains. " Only used when using a block exposure compensation method. " The default is 2. " --expos_comp_block_size int " BLock size in pixels used by the exposure compensator. " Only used when using a block exposure compensation method. " The default is 32. " --blend (no|feather|multiband) " Blending method. The default is 'multiband'. " --blend_strength float " Blending strength from [0,100] range. The default is 5. " --output result_img " The default is 'result.jpg'. " --timelapse (as_is|crop) " Output warped images separately as frames of a time lapse movie, with 'fixed_' prepended to input file names. " --rangewidth int " uses range_width to limit number of images to match with.
float blend_strength = 5;string result_name = "F:/opencv/build/bin/sample-data/stitching/result.jpg";bool timelapse = false;int range_width = -1;
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 "'. return -1; cout "Current 2D features type: '" features_type "'. 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. 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 ":" cameras[i].K() "" 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 "'. 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. return -1; // Find median focal length vector double focals; for (size_t i = 0; i cameras.size(); ++i) LOGLN("Camera #" indices[i] + 1 ":" cameras[i].K() "" 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 "' 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 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 "' 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;}
Finding features, time: 3.33727 secPairwise matchingPairwise matching, time: 3.2849 secInitial camera intrinsics #1:[4503.939581818162, 0, 285; 0, 4503.939581818162, 210; 0, 0, 1][1.0011346, 0.0019526235, -0.0037489906; 0.00011878588, 1.0000151, -0.052518897; -0.0011389133, 0.021224562, 1]Initial camera intrinsics #2:[4503.939581818162, 0, 249; 0, 4503.939581818162, 222; 0, 0, 1][1.0023992, 0.0045258515, 0.083801955; -9.7107059e-06, 1.0006112, -0.049870808; 0.015923418, 0.048128795, 1.0000379]Initial camera intrinsics #3:[4503.939581818162, 0, 302.5; 0, 4503.939581818162, 173.5; 0, 0, 1][1, 0, 0; 0, 1, 0; 0, 0, 1]Initial camera intrinsics #4:[4503.939581818162, 0, 274.5; 0, 4503.939581818162, 194.5; 0, 0, 1][1.0004042, 0.00080040237, 0.078620218; 0.00026136645, 1.0005095, -0.0048735617; 0.0061902963, 0.0096427174, 1.0004393]Camera #1:[6569.821976030652, 0, 285; 0, 6569.821976030652, 210; 0, 0, 1][0.99999672, 0.00038595949, -0.0025201384; -0.00047636221, 0.99935275, -0.035969362; 0.0025046244, 0.035970442, 0.99934971]Camera #2:[6571.327169846625, 0, 249; 0, 6571.327169846625, 222; 0, 0, 1][0.99835128, 0.0012797765, 0.057385404; 0.00068109832, 0.99941689, -0.03413773; -0.05739563, 0.034120534, 0.99776828]Camera #3:[6570.486320822205, 0, 302.5; 0, 6570.486320822205, 173.5; 0, 0, 1][1, -1.2951205e-09, 0; -1.2914825e-09, 1, 0; 0, -4.6566129e-10, 1]Camera #4:[6571.394840241929, 0, 274.5; 0, 6571.394840241929, 194.5; 0, 0, 1][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:UsersmzhuAppDataLocalTempopencv4.1opencl_cache[ INFO:0] Preparing OpenCL cache configuration for context: Intel_R__Corporation--Intel_R__HD_Graphics_620--21_20_16_4574Warping images, time: 0.0817463 secCompensating exposure...Compensating exposure, time: 0.22982 secFinding seams...Finding seams, time: 1.49795 secCompositing...Compositing image #1Multi-band blender, number of bands: 5Compositing image #2Compositing image #3Compositing image #4Compositing, time: 0.705931 secFinished, total time: 116.51 sec