学习template算法(template matching)以及改进（二）

为了实现跨越在不同两张图片内的模板和该两张搜索图片的模板匹配，现在先尝试使用图像拼接的方法，获得两个图像的拼接后的全景图像，然后再看看在全景图像中使用原来的matchTemplate算法是否可以匹配出其中模板图像的位置。

参考文章https://blog.csdn.net/dcrmg/article/details/52629856

Sift和Surf算法实现两幅图像拼接的过程是一样的，主要分为4大部分：

对每幅图进行特征点提取
特征点配对，找到两幅图像中匹配点的位置
通过配对点，生成变换矩阵，并对图像1应用变换矩阵生成对图像2的映射图像
图像2拼接到映射图像上，完成拼接
对重叠边界进行特殊处理

一、基于stitching的图像拼接+模板匹配

参考https://blog.csdn.net/czl389/article/details/60767654 ，
代码stitching.cpp参考上文。
得到如下结果：

现在尝试模板匹配：
代码如下：

#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"using namespace std;
using namespace cv;
using namespace cv::detail;//
#define ENABLE_LOG 1// Default command line args
vector<string> img_names;
bool preview = false;
bool try_gpu = true;
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";int main(int argc, char* argv[])
{//读入图像double ttt = getTickCount();img_names.push_back("ref0.jpg");img_names.push_back("ref1.jpg");#if ENABLE_LOGint64 app_start_time = getTickCount();
#endifcv::setBreakOnError(true);/*int retval = parseCmdArgs(argc, argv);if (retval)return retval;*/// Check if have enough imagesint 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_LOGint64 t = getTickCount();
#endifPtr<FeaturesFinder> finder;if (features_type == "surf"){
#if defined(HAVE_OPENCV_NONFREE) && defined(HAVE_OPENCV_GPU)if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)finder = new SurfFeaturesFinderGpu();else
#endiffinder = 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<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(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");
#if ENABLE_LOGt = getTickCount();
#endifvector<MatchesInfo> 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 graphif (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 panoramavector<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 imagesnum_images = static_cast<int>(img_names.size());if (num_images < 2){LOGLN("Need more images");return -1;}HomographyBasedEstimator estimator;vector<CameraParams> 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<detail::BundleAdjusterBase> 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_<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);(*adjuster)(features, pairwise_matches, cameras);// Find median focal lengthvector<double> 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<float>(focals[focals.size() / 2]);elsewarped_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_LOGt = getTickCount();
#endifvector<Point> corners(num_images);vector<Mat> masks_warped(num_images);vector<Mat> images_warped(num_images);vector<Size> sizes(num_images);vector<Mat> masks(num_images);// Preapre images masksfor (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 masksPtr<WarperCreator> warper_creator;
#if defined(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<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<Mat> 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<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);compensator->feed(corners, images_warped, masks_warped);Ptr<SeamFinder> 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"){
#if defined(HAVE_OPENCV_GPU)if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR);else
#endifseam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR);}else if (seam_find_type == "gc_colorgrad"){
#if defined(HAVE_OPENCV_GPU)if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR_GRAD);else
#endifseam_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 memoryimages.clear();images_warped.clear();images_warped_f.clear();masks.clear();LOGLN("Compositing...");
#if ENABLE_LOGt = getTickCount();
#endifMat img_warped, img_warped_s;Mat dilated_mask, seam_mask, mask, mask_warped;Ptr<Blender> 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 necessaryfull_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 scalewarped_image_scale *= static_cast<float>(compose_work_aspect);warper = warper_creator->create(warped_image_scale);// Update corners and sizesfor (int i = 0; i < num_images; ++i){// Update intrinsicscameras[i].focal *= compose_work_aspect;cameras[i].ppx *= compose_work_aspect;cameras[i].ppy *= compose_work_aspect;// Update corner and sizeSize 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);elseimg = full_img;full_img.release();Size img_size = img.size();Mat K;cameras[img_idx].K().convertTo(K, CV_32F);// Warp the current imagewarper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);// Warp the current image maskmask.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 exposurecompensator->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<float>(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<MultiBandBlender*>(static_cast<Blender*>(blender));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*>(static_cast<Blender*>(blender));fb->setSharpness(1.f/blend_width);LOGLN("Feather blender, sharpness: " << fb->sharpness());}blender->prepare(corners, sizes);}// Blend the current imageblender->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);result.convertTo(result,CV_8UC1);namedWindow("stitch", WINDOW_NORMAL);imshow("stitch",result);/*----------------my coding-------------*/Mat image_template = imread("local.jpg");Mat image_matched;Mat resized_image_template;resize(image_template, resized_image_template, Size(), 0.4, 0.4, INTER_AREA);matchTemplate(result, resized_image_template, image_matched, TM_CCORR_NORMED);//find the best positiondouble minVal, maxVal;Point minLoc, maxLoc;minMaxLoc(image_matched, &minVal, &maxVal, &minLoc, &maxLoc);Mat roi_image;roi_image = result( Range(maxLoc.y, maxLoc.y + resized_image_template.rows + 1), Range(maxLoc.x,  maxLoc.x + resized_image_template.cols + 1) );namedWindow("Template in SearchImage", WINDOW_NORMAL);imshow("Template in SearchImage", roi_image);imwrite("found.jpg", roi_image);           //save the result image!/*----------------------end--------------------*/ttt = ((double)getTickCount() - ttt) / getTickFrequency();cout << "总的拼接时间:" << ttt << endl;waitKey(0);LOGLN("Finished, total time: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " sec");return 0;
}

结果如下：

对比之前的local模板图像：

可见效果不错：）

二、基于SURF的图像拼接+模板匹配

以下步骤参考文章https://blog.csdn.net/yaningli/article/details/78551615
但代码可能与其中不同

（1）特征点提取与匹配

feature.cpp的特征点匹配结果：

参考文章http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.html
使用feature2-show matchedpoints.cpp的结果为

关于opencv中的二维特征点的匹配方法和其适用的特征提取结果特征描述符descriptor，见文章https://blog.csdn.net/holybin/article/details/40926315

另外，注意将其中的

drawMatches( leftGray, leftKeyPoints, rightGray, rightKeyPoints, GoodMatchPoints, img_matches);

换成

drawMatches(leftImg, leftKeyPoints, rightImg, rightKeyPoints, GoodMatchPoints, img_matches);

之后，结果如下图：

（2）图像配准