如何使用OpenCV实现多张图像拼接_opencv多幅图像拼接算法

先来看看OpenCV官方的例子得到效果是非常的好，输入的images如下：

效果：

#Stitcher类与detail命名空间

OpenCV提供了高级别的函数封装在Stitcher类中，使用很方便，不用考虑太多的细节。

低级别函数封装在detail命名空间中，展示了OpenCV算法实现的很多步骤和细节，使熟悉如下拼接流水线的用户，方便自己定制。

这涉及到以下算法流程：

命令行调用程序，输入源图像以及程序的参数

特征点检测，判断是使用surf还是orb，默认是surf。

对图像的特征点进行匹配，使用最近邻和次近邻方法，

将两个最优的匹配的置信度保存下来。

对图像进行排序以及将置信度高的图像保存到同一个集合中，

删除置信度比较低的图像间的匹配，得到能正确匹配的图像序列。

这样将置信度高于门限的所有匹配合并到一个集合中。

对所有图像进行相机参数粗略估计，然后求出旋转矩阵

使用光束平均法进一步精准的估计出旋转矩阵。

波形校正，水平或者垂直

拼接

融合，多频段融合，光照补偿。

另外在拼接的时候可以设置不同warper，这样会对拼接之后的图像生成不同效果，常见的效果包括

1. 鱼眼相机
2. 环视(平面曲翘)
3. 默认

如下图所示：

代码演示：

 
#include <opencv2/opencv.hpp>
#include <iostream>
 
using namespace cv;
using namespace std;
 
int main(int argc, char** argv) {
    vector<string> files;
    glob("D:/images/zsxq/1", files);
    vector<Mat> images;
    for (int i = 0; i < files.size(); i++) {
        printf("image file : %s \n", files[i].c_str());
        images.push_back(imread(files[i]));
    }
 
    // 设置拼接模式与参数
    Mat result1, result2, result3;
    Stitcher::Mode mode = Stitcher::PANORAMA;
    Ptr<Stitcher> stitcher = Stitcher::create(mode);
 
    // 拼接方式-多通道融合
    auto blender = detail::Blender::createDefault(detail::Blender::MULTI_BAND);
    stitcher->setBlender(blender);
 
    // 拼接
    Stitcher::Status status = stitcher->stitch(images, result1);
 
    // 平面曲翘拼接
    auto plane_warper = makePtr<cv::PlaneWarper>();
    stitcher->setWarper(plane_warper);
    status = stitcher->stitch(images, result2);
 
    // 鱼眼拼接
    auto fisheye_warper = makePtr<cv::FisheyeWarper>();
    stitcher->setWarper(fisheye_warper);
    status = stitcher->stitch(images, result3);
 
    // 检查返回
    if (status != Stitcher::OK)
    {
        cout << "Can't stitch images, error code = " << int(status) << endl;
        return EXIT_FAILURE;
    }
    imwrite("D:/result1.png", result1);
    imwrite("D:/result2.png", result2);
    imwrite("D:/result3.png", result3);
 
    waitKey(0);
    return 0;
}

在来看一组输入4张图像，每张分辨率为327*245，总的拼接时间为9.25s。

演示代码：

#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("E:/workspace/iamge/dataset/yard1.jpg");
 
img_names.push_back("E:/workspace/iamge/dataset/yard2.jpg");
 
img_names.push_back("E:/workspace/iamge/dataset/yard3.jpg");
 
img_names.push_back("E:/workspace/iamge/dataset/yard4.jpg");
 
#if ENABLE_LOG
 
int64 app_start_time = getTickCount();
 
#endif
 
cv::setBreak(true);
 
/*int retval = parseCmdArgs(argc, argv);
 
if (retval)
 
return retval;*/
 
// Check if have enough images
 
int 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_LOG
 
int64 t = getTickCount();
 
#endif
 
Ptr<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
 
#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<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_LOG
 
t = getTickCount();
 
#endif
 
vector<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 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<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 images
 
num_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 length
 
vector<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]);
 
else
 
warped_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_LOG
 
t = getTickCount();
 
#endif
 
vector<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 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<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
 
#endif
 
seam_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
 
#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...");
 
#if ENABLE_LOG
 
t = getTickCount();
 
#endif
 
Mat 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 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<float>(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<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 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);
 
result.convertTo(result,CV_8UC1);
 
imshow("stitch",result);
 
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;
 
}

效果：

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