首页 > 代码库 > 任意n张图像拼接_效果很好_计算机视觉大作业1终版
任意n张图像拼接_效果很好_计算机视觉大作业1终版
#include <iostream> #include <fstream> #include <string> #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" #include<time.h> using namespace std; using namespace cv; using namespace cv::detail; //定义参数 vector<string> img_names; bool try_gpu = false; double work_megapix = 0.6;//图像匹配的分辨率大小,图像的面积尺寸变为work_megapix*100000 double seam_megapix = 0.1;//拼接缝像素的大小 double compose_megapix =0.6;//拼接分辨率 float conf_thresh = 1.f;//两幅图来自同一全景图的置信度 WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ;//波形校验,水平 int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;//光照补偿方法,默认是gain_blocks float match_conf = 0.3f;//特征点检测置信等级,最近邻匹配距离与次近邻匹配距离的比值,surf默认为0.65 int blend_type = Blender::MULTI_BAND;//融合方法,默认是多频段融合 float blend_strength = 5;//融合强度,0 - 100.默认是5. string result_name = "result.jpg";//输出图像的文件名 int main() { clock_t start,finish; double totaltime; start=clock(); int argc = 10; char* argv[] = {"1.jpg", "2.jpg", "3.jpg", "4.jpg", "5.jpg", "6.jpg", "7.jpg", "8.jpg", "9.jpg", "10.jpg" }; for (int i = 0; i < argc; ++i) img_names.push_back(argv[i]); int num_images = static_cast<int>(img_names.size()); double work_scale = 1, seam_scale = 1, compose_scale = 1; //特征点检测以及对图像进行预处理(尺寸缩放),然后计算每幅图形的特征点,以及特征点描述子 cout<<"Finding features..."<<endl; Ptr<FeaturesFinder> finder; finder = new SurfFeaturesFinder();///采用Surf特征点检测 Mat full_img1,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_img1 = imread(img_names[i]); resize(full_img1,full_img, Size(400,300)); full_img_sizes[i] = full_img.size(); //计算work_scale,将图像resize到面积在work_megapix*10^6以下 work_scale = min(1.0, sqrt(work_megapix * 1e6 / full_img.size().area())); resize(full_img, img, Size(), work_scale, work_scale); //将图像resize到面积在work_megapix*10^6以下 seam_scale = min(1.0, sqrt(seam_megapix * 1e6 / full_img.size().area())); seam_work_aspect = seam_scale / work_scale; // 计算图像特征点,以及计算特征点描述子,并将img_idx设置为i (*finder)(img, features[i]); features[i].img_idx = i; cout<<"Features in image #" << i+1 << ": " << features[i].keypoints.size()<<endl; //将源图像resize到seam_megapix*10^6,并存入image[]中 resize(full_img, img, Size(), seam_scale, seam_scale); images[i] = img.clone(); } finder->collectGarbage(); full_img.release(); img.release(); //对图像进行两两匹配 cout<<"Pairwise matching"<<endl; //使用最近邻和次近邻匹配,对任意两幅图进行特征点匹配 vector<MatchesInfo> pairwise_matches; BestOf2NearestMatcher matcher(try_gpu, match_conf);//最近邻和次近邻法 matcher(features, pairwise_matches); //对每两个图片进行匹配 matcher.collectGarbage(); //将置信度高于门限的所有匹配合并到一个集合中 ///只留下确定是来自同一全景图的图片 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; // 检查图片数量是否依旧满足要求 num_images = static_cast<int>(img_names.size()); if (num_images < 2) { cout<<"Need more images"<<endl; return -1; } HomographyBasedEstimator estimator;//基于单应性的估计量 vector<CameraParams> cameras;//相机参数 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; cout<<"Initial intrinsics #" << indices[i]+1 << ":\n" << cameras[i].K()<<endl; } Ptr<detail::BundleAdjusterBase> adjuster;//光束调整器参数 adjuster = new detail::BundleAdjusterRay();//使用Bundle Adjustment(光束法平差)方法对所有图片进行相机参数校正 adjuster->setConfThresh(conf_thresh);//设置配置阈值 Mat_<uchar> refine_mask = Mat::zeros(3, 3, CV_8U); refine_mask(0,0) = 1; refine_mask(0,1) = 1; refine_mask(0,2) = 1; refine_mask(1,1) = 1; refine_mask(1,2) = 1; adjuster->setRefinementMask(refine_mask); (*adjuster)(features, pairwise_matches, cameras);//进行矫正 // 求出的焦距取中值和所有图片的焦距并构建camera参数,将矩阵写入camera vector<double> focals; for (size_t i = 0; i < cameras.size(); ++i) { cout<<"Camera #" << indices[i]+1 << ":\n" << cameras[i].K()<<endl; 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; ///波形矫正 vector<Mat> rmats; for (size_t i = 0; i < cameras.size(); ++i) rmats.push_back(cameras[i].R); waveCorrect(rmats, wave_correct);////波形矫正 for (size_t i = 0; i < cameras.size(); ++i) cameras[i].R = rmats[i]; cout<<"Warping images ... "<<endl; vector<Point> corners(num_images);//统一坐标后的顶点 vector<Mat> masks_warped(num_images); vector<Mat> images_warped(num_images); vector<Size> sizes(num_images); vector<Mat> masks(num_images);//融合掩码 // 准备图像融合掩码 for (int i = 0; i < num_images; ++i) { masks[i].create(images[i].size(), CV_8U); masks[i].setTo(Scalar::all(255)); } //弯曲图像和融合掩码 Ptr<WarperCreator> warper_creator; warper_creator = new cv::SphericalWarper(); 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]);//计算统一后坐标顶点 sizes[i] = images_warped[i].size(); warper->warp(masks[i], K, cameras[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);//弯曲当前图像 } vector<Mat> images_warped_f(num_images); for (int i = 0; i < num_images; ++i) images_warped[i].convertTo(images_warped_f[i], CV_32F); Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);//建立补偿器以进行关照补偿,补偿方法是gain_blocks compensator->feed(corners, images_warped, masks_warped); //查找接缝 Ptr<SeamFinder> seam_finder; seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR); seam_finder->find(images_warped_f, corners, masks_warped); // 释放未使用的内存 images.clear(); images_warped.clear(); images_warped_f.clear(); masks.clear(); //////图像融合 cout<<"Compositing..."<<endl; Mat img_warped, img_warped_s; Mat dilated_mask, seam_mask, mask, mask_warped; Ptr<Blender> blender; double compose_work_aspect = 1; for (int img_idx = 0; img_idx < num_images; ++img_idx) { cout<<"Compositing image #" << indices[img_idx]+1<<endl; //由于以前进行处理的图片都是以work_scale进行缩放的,所以图像的内参 //corner(统一坐标后的顶点),mask(融合的掩码)都需要重新计算 // 读取图像和做必要的调整 full_img1 = imread(img_names[img_idx]); resize(full_img1,full_img, Size(400,300)); compose_scale = min(1.0, sqrt(compose_megapix * 1e6 / full_img.size().area())); compose_work_aspect = compose_scale / work_scale; // 更新弯曲图像比例 warped_image_scale *= static_cast<float>(compose_work_aspect); warper = warper_creator->create(warped_image_scale); // 更新corners和sizes for (int i = 0; i < num_images; ++i) { // 更新相机以下特性 cameras[i].focal *= compose_work_aspect; cameras[i].ppx *= compose_work_aspect; cameras[i].ppy *= compose_work_aspect; // 更新corners和sizes 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); // 扭曲当前图像 warper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped); // 扭曲当前图像掩模 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); // 曝光补偿 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; //初始化blender if (blender.empty()) { blender = Blender::createDefault(blend_type, try_gpu); 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_gpu); else { MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(static_cast<Blender*>(blender)); mb->setNumBands(static_cast<int>(ceil(log(blend_width)/log(2.)) - 1.)); cout<<"Multi-band blender, number of bands: " << mb->numBands()<<endl; } //根据corners顶点和图像的大小确定最终全景图的尺寸 blender->prepare(corners, sizes); } // // 融合当前图像 blender->feed(img_warped_s, mask_warped, corners[img_idx]); } Mat result, result_mask; blender->blend(result, result_mask); imwrite(result_name, result); finish=clock(); totaltime=(double)(finish-start)/CLOCKS_PER_SEC; cout<<"\n此程序的运行时间为"<<totaltime<<"秒!"<<endl; return 0; }
任意n张图像拼接_效果很好_计算机视觉大作业1终版
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