[-blogs-]GOICE项目
在图像拼接方面，市面上能够找到的软件中，要数MS的ICE效果、鲁棒性最好，而且界面也很美观。应该说有很多值得学习的地方。
基于现有的有限资源，主要是以opencv自己提供的stitch_detail进行修改和封包，基于ribbon编写界面，我也尝试实现了GOICE项目，实现全景图片的拼接、横向视频的拼接，如果下一步有时间的话再将双目实时拼接从以前的代码中移植过来。
这里简单地将一些技术要点进行解析，欢迎批评指正和合作交流！
一、对现有算法进行重新封装
opencv原本的算法主要包含在Stitching Pipeline中，结构相对比较复杂，具体可以查看opencv refman
对算法进行重构后整理如下：
//使用变量
Ptr<FeaturesFinder> finder;
Mat full_img, img;
int num_images = m_ImageList.size();
vector<ImageFeatures> features(num_images);
vector<Mat> images(num_images);
vector<cv::Size> full_img_sizes(num_images);
double seam_work_aspect = 1;
vector<MatchesInfo> pairwise_matches;
BestOf2NearestMatcher matcher(try_gpu, match_conf);
vector<int> indices;
vector<Mat> img_subset;
vector<cv::Size> full_img_sizes_subset;
HomographyBasedEstimator estimator;
vector<CameraParams> cameras;
vector<cv::Point> corners(num_images);
vector<Mat> masks_warped(num_images);
vector<Mat> images_warped(num_images);
vector<cv::Size> sizes(num_images);
vector<Mat> masks(num_images);
Mat img_warped, img_warped_s;
Mat dilated_mask, seam_mask, mask, mask_warped;
Ptr<Blender> blender;
double compose_work_aspect = 1;
//拼接开始
if (features_type == "surf")
finder = new SurfFeaturesFinder();
else
finder = new OrbFeaturesFinder();
//寻找特征点
m_progress.SetPos(20);
for (int i = 0; i < num_images; ++i)
{
full_img = m_ImageList[i].clone();
full_img_sizes[i] = full_img.size();//读到的是大小
if (full_img.empty())
{
MessageBox("图片读取错误，请确认后重新尝试!");
return;
}
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, cv::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;
resize(full_img, img, cv::Size(), seam_scale, seam_scale);
images[i] = img.clone();
}
finder->collectGarbage();
full_img.release();
img.release();
//进行匹配
m_progress.SetPos(30);
matcher(features, pairwise_matches);
matcher.collectGarbage();
indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);
for (size_t i = 0; i < indices.size(); ++i)
{
img_subset.push_back(images[indices[i]]);
full_img_sizes_subset.push_back(full_img_sizes[indices[i]]);
}
m_progress.SetPos(40);
images = img_subset;
//判断图片是否足够
num_images = static_cast<int>(img_subset.size());
if (num_images < 2)
{
MessageBox("图片特征太少，尝试添加更多图片!");
return;
}
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());
}
//开始对准
m_progress.SetPos(50);
Ptr<detail::BundleAdjusterBase> adjuster;
if (ba_cost_func == "reproj") adjuster = new detail::BundleAdjusterReproj();
else
adjuster = new detail::BundleAdjusterRay();
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)
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;
//开始融合
m_progress.SetPos(60);
if (do_wave_correct)
{
vector<Mat> rmats;
for (size_t i = 0; i < cameras.size(); ++i)
rmats.push_back(cameras[i].R);
waveCorrect(rmats, wave_correct);
for (size_t i = 0; i < cameras.size(); ++i)
cameras[i].R = rmats[i];
}
//最后修正
m_progress.SetPos(70);
// 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));
}
Ptr<WarperCreator> warper_creator;
{
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;}
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);
Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);
compensator->feed(corners, images_warped, masks_warped);
//接缝修正
m_progress.SetPos(80);
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")
seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR);
else if (seam_find_type == "gc_colorgrad")
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())
{
MessageBox("无法对图像进行缝隙融合");
return;
}
//输出最后结果
m_progress.SetPos(90);
seam_finder->find(images_warped_f, corners, masks_warped);
// Release unused memory
images.clear();
images_warped.clear();
images_warped_f.clear();
masks.clear();
for (int img_idx = 0; img_idx < num_images; ++img_idx)
{
// Read image and resize it if necessary
full_img = m_ImageList[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_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
cv::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);
cv::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, cv::Size(), compose_scale, compose_scale);
else
img = full_img;
full_img.release();
cv::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);
cv::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);
m_progress.SetPos(100);
AfxMessageBox("拼接成功!");
m_progress.ShowWindow(false);
m_progress.SetPos(0);
//格式转换
result.convertTo(result,CV_8UC3);
showImage(result,IDC_PBDST);
//保存结果
m_matResult = result.clone();
基本上没有修改代码的结构，但是做了几个改变
1、原来的算法既读取文件名，又保存mat变量，我这里将其统一成为使用vector<Mat>来进行保存；
2、将LOGLN的部分以messagebox的方式显示出来，并且进行错误控制；
3、添加适当注释，并且在合适的地方控制进度条显示。
二、主要界面编写技巧
主要界面使用了Ribbon的方法，结合使用IconWorkshop生成图标。
内容方面，使用了基于listctrl的缩略图的显示,具体参考我的另一篇blog--"图像处理界面--缩略图的显示"
三、视频拼接的处理方法
相比较图像拼接，这次添加了一个“横向视频”的拼接。其实算法原理是比较朴素的（当然这里考虑的是比较简单的情况）。就是对于精心拍摄的视频，那么只要每隔一段时间取一个图片，然后把这些图片进行拼接，就能够得到视频的全景图片。
void CMFCApplication1View::OnButtonOpenmov()
{
CString pathName;
CString szFilters= _T("*(*.*)|*.*|avi(*.avi)|*.avi|mp4(*.mp4)|*.mp4||");
CFileDialog dlg(TRUE,NULL,NULL,NULL,szFilters,this);
VideoCapture capture;
Mat frame;
int iFrameCount = 0;
int iFram = 0;
if(dlg.DoModal()==IDOK){
//获得路径
pathName=dlg.GetPathName();
//设置窗体
m_ListThumbnail.ShowWindow(false);
m_imagerect.ShowWindow(false);
m_imagedst.ShowWindow(true);
m_progress.ShowWindow(false);
m_msg.ShowWindow(false);
//打开视频并且抽取图片
capture.open((string)pathName);
if (!capture.isOpened())
{
MessageBox("视频打开错误！");
return;
}
m_VectorMovImageNames.clear();
m_MovImageList.clear();
char cbuf[100];
while (capture.read(frame))
{
//每隔50帧取一图
if (0 == iFram%50)
{
m_MovImageList.push_back(frame.clone());
}
showImage(frame,IDC_PBDST);
iFram = iFram +1;
}
}
}
四、反思和小结
1）虽然现在已经对opencv的算法进行了集成，但是由于算法原理还是繁琐复杂的，下一步要结合对更复杂问题的进一步研究吃透算法；
2）使用ribbon进行程序设计现在已经比较熟悉了。能够认识到工具擅长解决的问题、能够认识到工具不好解决的问题，能够快速实现，才算是掌握；