/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // // //M*/ #include #include #include #include "opencv2/opencv_modules.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/stitching/detail/autocalib.hpp" #include "opencv2/stitching/detail/blenders.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/util.hpp" #include "opencv2/stitching/detail/warpers.hpp" #include "opencv2/stitching/warpers.hpp" 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_gpu (yes|no)\n" " Try to use GPU. 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)\n" " Type of features used for images matching. The default is surf.\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 (reproj|ray)\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 (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)\n" " Exposure compensation method. The default is 'gain_blocks'.\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"; } // Default command line args vector img_names; bool preview = false; bool try_gpu = false; double work_megapix = 0.6; double seam_megapix = 0.1; double compose_megapix = -1; float conf_thresh = 1.f; string features_type = "surf"; string 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; float match_conf = 0.3f; string seam_find_type = "gc_color"; int blend_type = Blender::MULTI_BAND; float blend_strength = 5; string result_name = "result.jpg"; static int parseCmdArgs(int argc, char** argv) { if (argc == 1) { printUsage(); return -1; } for (int i = 1; i < argc; ++i) { if (string(argv[i]) == "--help" || string(argv[i]) == "/?") { printUsage(); return -1; } else if (string(argv[i]) == "--preview") { preview = true; } else if (string(argv[i]) == "--try_gpu") { if (string(argv[i + 1]) == "no") try_gpu = false; else if (string(argv[i + 1]) == "yes") try_gpu = true; else { cout << "Bad --try_gpu 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 (features_type == "orb") match_conf = 0.3f; 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 { cout << "Bad exposure compensation method\n"; return -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]) == "--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[]) { int64 app_start_time = getTickCount(); cv::setBreakOnError(true); 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..."); int64 t = getTickCount(); Ptr finder; if (features_type == "surf") { #ifdef HAVE_OPENCV_GPU if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0) finder = new SurfFeaturesFinderGpu(); else #endif finder = new SurfFeaturesFinder(); } else if (features_type == "orb") { finder = new OrbFeaturesFinder(); } 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(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); } if (!is_seam_scale_set) { seam_scale = min(1.0, sqrt(seam_megapix * 1e6 / full_img.size().area())); seam_work_aspect = seam_scale / work_scale; is_seam_scale_set = true; } (*finder)(img, features[i]); features[i].img_idx = i; LOGLN("Features in image #" << i+1 << ": " << features[i].keypoints.size()); resize(full_img, img, Size(), seam_scale, seam_scale); images[i] = img.clone(); } finder->collectGarbage(); full_img.release(); img.release(); LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec"); LOG("Pairwise matching"); t = getTickCount(); vector pairwise_matches; BestOf2NearestMatcher matcher(try_gpu, 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; } HomographyBasedEstimator estimator; vector cameras; estimator(features, pairwise_matches, cameras); for (size_t i = 0; i < cameras.size(); ++i) { Mat R; cameras[i].R.convertTo(R, CV_32F); cameras[i].R = R; LOGLN("Initial intrinsics #" << indices[i]+1 << ":\n" << cameras[i].K()); } Ptr adjuster; if (ba_cost_func == "reproj") adjuster = new detail::BundleAdjusterReproj(); else if (ba_cost_func == "ray") adjuster = new detail::BundleAdjusterRay(); 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); (*adjuster)(features, pairwise_matches, cameras); // Find median focal length vector focals; for (size_t i = 0; i < cameras.size(); ++i) { LOGLN("Camera #" << indices[i]+1 << ":\n" << cameras[i].K()); 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); waveCorrect(rmats, wave_correct); for (size_t i = 0; i < cameras.size(); ++i) cameras[i].R = rmats[i]; } LOGLN("Warping images (auxiliary)... "); t = getTickCount(); vector corners(num_images); vector masks_warped(num_images); vector images_warped(num_images); vector sizes(num_images); vector 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 warper_creator; #ifdef HAVE_OPENCV_GPU if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0) { if (warp_type == "plane") warper_creator = new cv::PlaneWarperGpu(); else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarperGpu(); else if (warp_type == "spherical") warper_creator = new cv::SphericalWarperGpu(); } else #endif { if (warp_type == "plane") warper_creator = new cv::PlaneWarper(); else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarper(); else if (warp_type == "spherical") warper_creator = new cv::SphericalWarper(); else if (warp_type == "fisheye") warper_creator = new cv::FisheyeWarper(); else if (warp_type == "stereographic") warper_creator = new cv::StereographicWarper(); else if (warp_type == "compressedPlaneA2B1") warper_creator = new cv::CompressedRectilinearWarper(2, 1); else if (warp_type == "compressedPlaneA1.5B1") warper_creator = new cv::CompressedRectilinearWarper(1.5, 1); else if (warp_type == "compressedPlanePortraitA2B1") warper_creator = new cv::CompressedRectilinearPortraitWarper(2, 1); else if (warp_type == "compressedPlanePortraitA1.5B1") warper_creator = new cv::CompressedRectilinearPortraitWarper(1.5, 1); else if (warp_type == "paniniA2B1") warper_creator = new cv::PaniniWarper(2, 1); else if (warp_type == "paniniA1.5B1") warper_creator = new cv::PaniniWarper(1.5, 1); else if (warp_type == "paniniPortraitA2B1") warper_creator = new cv::PaniniPortraitWarper(2, 1); else if (warp_type == "paniniPortraitA1.5B1") warper_creator = new cv::PaniniPortraitWarper(1.5, 1); else if (warp_type == "mercator") warper_creator = new cv::MercatorWarper(); else if (warp_type == "transverseMercator") warper_creator = new cv::TransverseMercatorWarper(); } if (warper_creator.empty()) { 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"); Ptr compensator = ExposureCompensator::createDefault(expos_comp_type); compensator->feed(corners, images_warped, masks_warped); Ptr seam_finder; if (seam_find_type == "no") seam_finder = new detail::NoSeamFinder(); else if (seam_find_type == "voronoi") seam_finder = new detail::VoronoiSeamFinder(); else if (seam_find_type == "gc_color") { #ifdef HAVE_OPENCV_GPU if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0) seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR); else #endif seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR); } else if (seam_find_type == "gc_colorgrad") { #ifdef HAVE_OPENCV_GPU if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0) seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR_GRAD); else #endif seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR_GRAD); } else if (seam_find_type == "dp_color") seam_finder = new detail::DpSeamFinder(DpSeamFinder::COLOR); else if (seam_find_type == "dp_colorgrad") seam_finder = new detail::DpSeamFinder(DpSeamFinder::COLOR_GRAD); if (seam_finder.empty()) { cout << "Can't create the following seam finder '" << seam_find_type << "'\n"; return 1; } seam_finder->find(images_warped_f, corners, masks_warped); // Release unused memory images.clear(); images_warped.clear(); images_warped_f.clear(); masks.clear(); LOGLN("Compositing..."); t = getTickCount(); Mat img_warped, img_warped_s; Mat dilated_mask, seam_mask, mask, mask_warped; Ptr blender; //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(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); 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()); mask_warped = seam_mask & mask_warped; if (blender.empty()) { blender = Blender::createDefault(blend_type, try_gpu); 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_gpu); else if (blend_type == Blender::MULTI_BAND) { MultiBandBlender* mb = dynamic_cast(static_cast(blender)); 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(static_cast(blender)); fb->setSharpness(1.f/blend_width); LOGLN("Feather blender, sharpness: " << fb->sharpness()); } blender->prepare(corners, sizes); } // Blend the current image blender->feed(img_warped_s, mask_warped, corners[img_idx]); } 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; }