【OpenCV】全景拼接_多张图像拼接(stitching_detailed)

#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" #include "opencv2/xfeatures2d/nonfree.hpp" using namespace std; using namespace cv; using namespace cv::detail; // Default command line args 默认命令行参数 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 = "cylindrical"; 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 = "result.jpg"; bool timelapse = false; int range_width = -1; double seam_work_aspect = 1; int main(int argc, char* argv[]) { 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; //获取文件夹图片的路径 cv::String filepath = "C:\\Users\\Lenovo-Gu\\Desktop\\photo\\*.jpg"; //！！！更改为图片所在文件夹路径！！！ glob(filepath, img_names, false); size_t num_images = img_names.size(); Ptr<Feature2D> finder = xfeatures2d::SIFT::create(); //用数组存储所有的图片以及图片的特征点、尺寸 Mat full_img, img; vector<ImageFeatures> features(num_images); //声明一个初始大小为num_images的ImageFeatures vector<Mat> images(num_images); //num_images个图像组成的数组 vector<Size> full_img_sizes(num_images); //num_images个图像的尺寸组成的数组 for (int i = 0; i < num_images; ++i) { full_img = imread(samples::findFile(img_names[i])); //cv::samples::findFile(const cv::String & relative_path, bool required = true, bool silentMode = false) full_img_sizes[i] = full_img.size(); //double work_scale = min(1.0, sqrt(0.6 * 1e6 / full_img.size().area())); //resize(full_img, img, Size(), work_scale, work_scale, INTER_LINEAR_EXACT); img = full_img; computeImageFeatures(finder, full_img, features[i]); //计算图像特征 features[i].img_idx = i; cout << "Features in image #" << i + 1 << ": " << features[i].keypoints.size() << endl; images[i] = img.clone(); } full_img.release(); img.release(); for (int i = 0; i < num_images; ++i) { cout << "image #" << i + 1 << "size：" << full_img_sizes[i] << endl; } //两两匹配 //vector<MatchesInfo> pairwise_matches; //表示特征匹配信息变量 //BestOf2NearestMatcher matcher(false, 0.3f); //定义特征匹配器，2NN方法 //matcher(features, pairwise_matches); //进行特征匹配 //matcher->collectGarbage(); // vector<MatchesInfo> pairwise_matches; Ptr<FeaturesMatcher> matcher = makePtr<BestOf2NearestMatcher>(false, 0.3f, 6, 6); // BestOf2NearestMatcher matcher; (*matcher)(features, pairwise_matches); matcher->collectGarbage(); ofstream f(save_graph_to.c_str()); //ofstream：c++ 写操作 f << matchesGraphAsString(img_names, pairwise_matches, conf_thresh); vector<int> indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh); vector<Mat> img_subset; //图像的子集 vector<String> img_names_subset; //图像名字的子集 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; //确信来自同一全景图的图像 重新组成 images img_names = img_names_subset; //确信来自同一全景图的图像名字 重新组成img_names full_img_sizes = full_img_sizes_subset; //新的尺寸集合 // 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; //相机参数 if (!(*estimator)(features, pairwise_matches, cameras)) { cout << "Homography estimation failed.\n"; //单应性估计失败了。 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; //光束平差法，精确相机参数 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"; //相机参数调整失败。 return -1; } //························································· // Find median focal length 求中焦距 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) //波形矫正 { 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)... "); // 扭曲的图像(辅助) #if ENABLE_L