Opencv2.4.9源码分析——Stitching
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8、完整的拼接程序
下面给出完整的拼接程序:
#include "opencv2/core/core.hpp"
#include "highgui.h"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/legacy/legacy.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 <iostream>
#include <fstream>
#include <string>
#include <iomanip>
using namespace cv;
using namespace std;
using namespace detail;
int main(int argc, char** argv)
vector<Mat> imgs; //输入9幅图像
Mat img;
img = imread("1.jpg");
imgs.push_back(img);
img = imread("2.jpg");
imgs.push_back(img);
img = imread("3.jpg");
imgs.push_back(img);
img = imread("4.jpg");
imgs.push_back(img);
img = imread("5.jpg");
imgs.push_back(img);
img = imread("6.jpg");
imgs.push_back(img);
img = imread("7.jpg");
imgs.push_back(img);
img = imread("8.jpg");
imgs.push_back(img);
img = imread("9.jpg");
imgs.push_back(img);
int num_images = 9; //图像数量
Ptr<FeaturesFinder> finder; //定义特征寻找器
finder = new SurfFeaturesFinder(); //应用SURF方法寻找特征
//finder = new OrbFeaturesFinder(); //应用ORB方法寻找特征
vector<ImageFeatures> features(num_images); //表示图像特征
for (int i =0 ;i<num_images;i++)
(*finder)(imgs[i], features[i]); //特征检测
vector<MatchesInfo> pairwise_matches; //表示特征匹配信息变量
BestOf2NearestMatcher matcher(false, 0.3f, 6, 6); //定义特征匹配器,2NN方法
matcher(features, pairwise_matches); //进行特征匹配
HomographyBasedEstimator estimator; //定义参数评估器
vector<CameraParams> cameras; //表示相机参数
estimator(features, pairwise_matches, cameras); //进行相机参数评估
for (size_t i = 0; i < cameras.size(); ++i) //转换相机旋转参数的数据类型
Mat R;
cameras[i].R.convertTo(R, CV_32F);
cameras[i].R = R;
Ptr<detail::BundleAdjusterBase> adjuster; //光束平差法,精确相机参数
adjuster = new detail::BundleAdjusterReproj(); //重映射误差方法
//adjuster = new detail::BundleAdjusterRay(); //射线发散误差方法
adjuster->setConfThresh(1); //设置匹配置信度,该值设为1
(*adjuster)(features, pairwise_matches, cameras); //精确评估相机参数
vector<Mat> rmats;
for (size_t i = 0; i < cameras.size(); ++i) //复制相机的旋转参数
rmats.push_back(cameras[i].R.clone());
waveCorrect(rmats, WAVE_CORRECT_HORIZ); //进行波形校正
for (size_t i = 0; i < cameras.size(); ++i) //相机参数赋值
cameras[i].R = rmats[i];
rmats.clear(); //清变量
vector<Point> corners(num_images); //表示映射变换后图像的左上角坐标
vector<Mat> masks_warped(num_images); //表示映射变换后的图像掩码
vector<Mat> images_warped(num_images); //表示映射变换后的图像
vector<Size> sizes(num_images); //表示映射变换后的图像尺寸
vector<Mat> masks(num_images); //表示源图的掩码
for (int i = 0; i < num_images; ++i) //初始化源图的掩码
masks[i].create(imgs[i].size(), CV_8U); //定义尺寸大小
masks[i].setTo(Scalar::all(255)); //全部赋值为255,表示源图的所有区域都使用
Ptr<WarperCreator> warper_creator; //定义图像映射变换创造器
warper_creator = new cv::PlaneWarper(); //平面投影
//warper_creator = new cv::CylindricalWarper(); //柱面投影
//warper_creator = new cv::SphericalWarper(); //球面投影
//warper_creator = new cv::FisheyeWarper(); //鱼眼投影
//warper_creator = new cv::StereographicWarper(); //立方体投影
//定义图像映射变换器,设置映射的尺度为相机的焦距,所有相机的焦距都相同
Ptr<RotationWarper> warper = warper_creator->create(static_cast<float>(cameras[0].focal));
for (int i = 0; i < num_images; ++i)
Mat_<float> K;
cameras[i].K().convertTo(K, CV_32F); //转换相机内参数的数据类型
//对当前图像镜像投影变换,得到变换后的图像以及该图像的左上角坐标
corners[i] = warper->warp(imgs[i], K, cameras[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]);
sizes[i] = images_warped[i].size(); //得到尺寸
//得到变换后的图像掩码
warper->warp(masks[i], K, cameras[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);
imgs.clear(); //清变量
masks.clear();
//创建曝光补偿器,应用增益补偿方法
Ptr<ExposureCompensator> compensator =
ExposureCompensator::createDefault(ExposureCompensator::GAIN);
compensator->feed(corners, images_warped, masks_warped); //得到曝光补偿器
for(int i=0;i<num_images;++i) //应用曝光补偿器,对图像进行曝光补偿
compensator->apply(i, corners[i], images_warped[i], masks_warped[i]);
//在后面,我们还需要用到映射变换图的掩码masks_warped,因此这里为该变量添加一个副本masks_seam
vector<Mat> masks_seam(num_images);
for(int i = 0; i<num_images;i++)
masks_warped[i].copyTo(masks_seam[i]);
Ptr<SeamFinder> seam_finder; //定义接缝线寻找器
//seam_finder = new NoSeamFinder(); //无需寻找接缝线
//seam_finder = new VoronoiSeamFinder(); //逐点法
//seam_finder = new DpSeamFinder(DpSeamFinder::COLOR); //动态规范法
//seam_finder = new DpSeamFinder(DpSeamFinder::COLOR_GRAD);
//图割法
//seam_finder = new GraphCutSeamFinder(GraphCutSeamFinder::COST_COLOR);
seam_finder = new GraphCutSeamFinder(GraphCutSeamFinder::COST_COLOR_GRAD);
vector<Mat> images_warped_f(num_images);
for (int i = 0; i < num_images; ++i) //图像数据类型转换
images_warped[i].convertTo(images_warped_f[i], CV_32F);
images_warped.clear(); //清内存
//得到接缝线的掩码图像masks_seam
seam_finder->find(images_warped_f, corners, masks_seam);
vector<Mat> images_warped_s(num_images);
Ptr<Blender> blender; //定义图像融合器
//blender = Blender::createDefault(Blender::NO, false); //简单融合方法
//羽化融合方法
//blender = Blender::createDefault(Blender::FEATHER, false);
//FeatherBlender* fb = dynamic_cast<FeatherBlender*>(static_cast<Blender*>(blender));
//fb->setSharpness(0.005); //设置羽化锐度
blender = Blender::createDefault(Blender::MULTI_BAND, false); //多频段融合
MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(static_cast<Blender*>(blender));
mb->setNumBands(8); //设置频段数,即金字塔层数
blender->prepare(corners, sizes); //生成全景图像区域
//在融合的时候,最重要的是在接缝线两侧进行处理,而上一步在寻找接缝线后得到的掩码的边界就是接缝线处,因此我们还需要在接缝线两侧开辟一块区域用于融合处理,这一处理过程对羽化方法尤为关键
//应用膨胀算法缩小掩码面积
vector<Mat> dilate_img(num_images);
Mat element = getStructuringElement(MORPH_RECT, Size(20, 20)); //定义结构元素
for(int k=0;k<num_images;k++)
images_warped_f[k].convertTo(images_warped_s[k], CV_16S); //改变数据类型
dilate(masks_seam[k], masks_seam[k], element); //膨胀运算
//映射变换图的掩码和膨胀后的掩码相“与”,从而使扩展的区域仅仅限于接缝线两侧,其他边界处不受影响
masks_seam[k] = masks_seam[k] & masks_warped[k];
blender->feed(images_warped_s[k], masks_seam[k], corners[k]); //初始化数据
masks_seam.clear(); //清内存
images_warped_s.clear();
masks_warped.clear();
images_warped_f.clear();
Mat result, result_mask;
//完成融合操作,得到全景图像result和它的掩码result_mask
blender->blend(result, result_mask);
imwrite("pano.jpg", result); //存储全景图像
return 0;
最终的输出图像为:
图18 平面映射全景图像
输入的9幅图像的尺寸都为979×550,最终共耗时10分钟左右。全景拼接程序十分消耗内存空间,如果图像的尺寸较大,而且图像数量较多的话,不仅耗时较长,而且很可能由于内存不足而报错。另外,经过多次实验看出,如果图像尺寸大、数量多,还会引起拼接不正确的现象。
下面几幅图像分别为不同映射得到的全景图像。
图19 柱面映射全景图像
图20 球面映射全景图像
图21 鱼眼映射全景图像(该图调整了角度,以便于显示观看)
图22 立体映射全景图像(该图调整了角度,以便于显示观看)
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