stitching.cpp鱼眼图像拼接融合源码分析

之前运行OpenCV官方示例的cpp时看到stitching.cpp拼接融合还不错然后我在MATLAB上用之前编的经纬映射法校正三幅鱼眼图像后不知道该怎样保存下校正好的图如果save或者save as 那么会有figure的白色边缘不能用来拼接所以我直接截图保存为jpg 这样就没有白色边缘了：

校正后的：然后把这三幅MATLAB运行出来的结果传递给OpenCV的stitching.cpp 然后运行出来：

现在开始分析图像拼接融合的源码：stitching.cpp：

#include <iostream>
#include <fstream>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/stitching/stitcher.hpp"
using namespace std;
using namespace cv;
bool try_use_gpu = false;
vector<Mat> imgs;
string result_name = "result3.jpg"; //保存全景图的文件名可以在工程目录下找到
void printUsage();
int parseCmdArgs(int argc, char** argv);

int main(int argc, char* argv[])
{
int retval = parseCmdArgs(argc, argv);
if (retval) return -1;

Mat pano;
Stitcher stitcher = Stitcher::createDefault(try_use_gpu); //这个函数默认不使用GPU！
Stitcher::Status status = stitcher.stitch(imgs, pano); //找寻待拼接的两幅图的旋转角度并生成最后的全景图这个函数才是要分析的重头戏！
if (status != Stitcher::OK) //确保拼接成功
{
cout << "Can't stitch images, error code = " << int(status) << endl;
return -1;
}
namedWindow("stitching result");
imshow("stitching result", pano);
waitKey(0);
imwrite(result_name,pano);
return 0;
}
void printUsage() //默认不用GPU
{
cout <<
"Rotation model images stitcher.\n\n"
"stitching img1 img2 [...imgN]\n\n"
"Flags:\n"
" --try_use_gpu (yes|no)\n"
" Try to use GPU. The default value is 'no'. All default values\n"
" are for CPU mode.\n"
" --output <result_img>\n"
" The default is 'result.jpg'.\n";
}
int parseCmdArgs(int argc, char** argv) //这个函数就是读入待拼接的图片放在imgs这个装待拼图片的容器里面
{
if (argc == 1)
{
printUsage();
return -1;
}
for (int i = 1; i < argc; ++i)
{
if (string(argv[i]) == "--help" || string(argv[i]) == "/?")
{
printUsage();
return -1;
}
else if (string(argv[i]) == "--try_use_gpu")
{
if (string(argv[i + 1]) == "no")
try_use_gpu = false;
else if (string(argv[i + 1]) == "yes")
try_use_gpu = true;
else
{
cout << "Bad --try_use_gpu flag value\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--output")
{
result_name = argv[i + 1];
i++;
}
else
{
Mat img = imread(argv[i]);
if (img.empty())
{
cout << "Can't read image '" << argv[i] << "'\n";
return -1;
}
imgs.push_back(img);
}
}
return 0;
}

上面stitching.cpp很好理解现在来看涉及图像拼接、融合的函数源码：Stitcher::Status status = stitcher.stitch(imgs, pano); 用CMake打开源码查看源码：

可以看到stitcher类下各个函数的定义 stitch()是可重载的而程序里用的是第一个形式这个定义非常简单所以接下来又需要去看Status status = estimateTransform(images, rois);（估算几幅待拼图之间的关系比如仿射变换旋转平移缩放等等）和composePanorama(pano);（图像融合组成全景图）这两个函数的定义 estimateTransform也简短

Stitcher::Status Stitcher::estimateTransform(InputArray images, const vector<vector<Rect> > &rois)
{
images.getMatVector(imgs_);
rois_ = rois;
Status status;
if ((status = matchImages()) != OK) //要看这个的源码
return status;

//接下来还要看这个函数estimateCameraParams();(估算相机参数)的源码
estimateCameraParams();
return OK;
}

于是又接着找到：

Stitcher::Status Stitcher::matchImages()
{
if ((int)imgs_.size() < 2)
{
LOGLN("Need more images");
return ERR_NEED_MORE_IMGS; //待拼图像不能少于2幅图
}
work_scale_ = 1;
seam_work_aspect_ = 1;
seam_scale_ = 1;
bool is_work_scale_set = false;
bool is_seam_scale_set = false;
Mat full_img, img;
features_.resize(imgs_.size()); //这是？？
seam_est_imgs_.resize(imgs_.size());
full_img_sizes_.resize(imgs_.size());

LOGLN("Finding features...");
#if ENABLE_LOG
int64 t = getTickCount(); //找寻特征点计时
#endif

for (size_t i = 0; i < imgs_.size(); ++i)
{
full_img = imgs_[i]; //依次读取每幅待拼图像给full_img
full_img_sizes_[i] = full_img.size(); //将每次读入图像的大小mxn给full_img_sizes
//registr_resol_是图像匹配的分辨率大小，图像的面积尺寸变为registr_resol_*100000 ？？
if (registr_resol_ < 0)
{
img = full_img;
work_scale_ = 1;
is_work_scale_set = true;
}
else
{
if (!is_work_scale_set)
{

//预处理将图像缩放full_img.size().area()表示面积m、n的乘积

//计算work_scale，将图像resize到面积在registr_resol_*10^6以下
work_scale_ = min(1.0, sqrt(registr_resol_ * 1e6 / full_img.size().area()));
is_work_scale_set = true;
}

//将full_img和img的尺寸都缩放到计算出来的work_scale_下？！
resize(full_img, img, Size(), work_scale_, work_scale_);
}
if (!is_seam_scale_set)
{

//seam_est_resol_是拼接缝像素的大小，和匹配默认值一样有默认值？
seam_scale_ = min(1.0, sqrt(seam_est_resol_ * 1e6 / full_img.size().area()));
seam_work_aspect_ = seam_scale_ / work_scale_; //和上个if中的差不多意思
is_seam_scale_set = true;
}

if (rois_.empty())

//如果rois_是空矩阵就找寻特征点给features_ （这个待会看源码）
(*features_finder_)(img, features_[i]);
else
{
vector<Rect> rois(rois_[i].size());
for (size_t j = 0; j < rois_[i].size(); ++j)
{
Point tl(cvRound(rois_[i][j].x * work_scale_), cvRound(rois_[i][j].y * work_scale_));
Point br(cvRound(rois_[i][j].br().x * work_scale_), cvRound(rois_[i][j].br().y * work_scale_));
rois[j] = Rect(tl, br);
}
(*features_finder_)(img, features_[i], rois);
}
features_[i].img_idx = (int)i; //说明是第几幅图的特征点
LOGLN("Features in image #" << i+1 << ": " << features_[i].keypoints.size());
//将源图像resize到seam_scale_*10^6，并存入seam_est_imgs_[]中
resize(full_img, img, Size(), seam_scale_, seam_scale_);
seam_est_imgs_[i] = img.clone();
}

// Do it to save memory
features_finder_->collectGarbage(); //这里要看源码！！怎么找寻的
full_img.release(); //release释放待会儿去读下一幅图
img.release();
//找寻特征点至此结束计算出时间
LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");

LOG("Pairwise matching");
#if ENABLE_LOG
t = getTickCount(); //开始进行匹配match
#endif
(*features_matcher_)(features_, pairwise_matches_, matching_mask_);
features_matcher_->collectGarbage(); //要看源码！！
LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec"); //匹配结束

// Leave only images we are sure are from the same panorama

//conf_thresh_是两幅图来自同一全景图的置信度来判断读入的图片是否属于同一全景图
indices_ = detail::leaveBiggestComponent(features_, pairwise_matches_, (float)conf_thresh_);
vector<Mat> seam_est_imgs_subset;
vector<Mat> imgs_subset;
vector<Size> full_img_sizes_subset;
for (size_t i = 0; i < indices_.size(); ++i)
{
imgs_subset.push_back(imgs_[indices_[i]]);
seam_est_imgs_subset.push_back(seam_est_imgs_[indices_[i]]);
full_img_sizes_subset.push_back(full_img_sizes_[indices_[i]]);
}
seam_est_imgs_ = seam_est_imgs_subset;
imgs_ = imgs_subset;
full_img_sizes_ = full_img_sizes_subset;
//检查由上述筛选来自同一副全景图的图片的数量是否大于2幅图
if ((int)imgs_.size() < 2)
{
LOGLN("Need more images");
return ERR_NEED_MORE_IMGS;
}

return OK;
}

结合这个人的http://blog.csdn.net/hanshuning/article/details/41960401和这个人的http://www.geekcome.com/content-10-8390-1.html分析上面的

在matchImage()分析完后要分析estimateCameraParams()：

void Stitcher::estimateCameraParams()
{
detail::HomographyBasedEstimator estimator;
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 intrinsic parameters #" << indices_[i] + 1 << ":\n " << cameras_[i].K());
}

bundle_adjuster_->setConfThresh(conf_thresh_);
(*bundle_adjuster_)(features_, pairwise_matches_, cameras_);

// Find median focal length and use it as final image scale
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);
}

std::sort(focals.begin(), focals.end());
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);
detail::waveCorrect(rmats, wave_correct_kind_);
for (size_t i = 0; i < cameras_.size(); ++i)
cameras_[i].R = rmats[i];
}
}

接下来生成全景图（图像融合等）：composePanorama(pano);即看这个函数的源码

Stitcher::Status Stitcher::composePanorama(OutputArray pano)
{
return composePanorama(vector<Mat>(), pano); //就这一句继续追踪下去查看composePanorama(vector<Mat>(), pano);的源码
}
如下所示：
Stitcher::Status Stitcher::composePanorama(InputArray images, OutputArray pano)
{
LOGLN("Warping images (auxiliary)... ");

vector<Mat> imgs;
images.getMatVector(imgs);
if (!imgs.empty())
{
CV_Assert(imgs.size() == imgs_.size());

Mat img;
seam_est_imgs_.resize(imgs.size());

for (size_t i = 0; i < imgs.size(); ++i)
{
imgs_[i] = imgs[i];
resize(imgs[i], img, Size(), seam_scale_, seam_scale_);
seam_est_imgs_[i] = img.clone();
}

vector<Mat> seam_est_imgs_subset;
vector<Mat> imgs_subset;

for (size_t i = 0; i < indices_.size(); ++i)
{
imgs_subset.push_back(imgs_[indices_[i]]);
seam_est_imgs_subset.push_back(seam_est_imgs_[indices_[i]]);
}

seam_est_imgs_ = seam_est_imgs_subset;
imgs_ = imgs_subset;
}

Mat &pano_ = pano.getMatRef();

#if ENABLE_LOG
int64 t = getTickCount();
#endif

vector<Point> corners(imgs_.size());
vector<Mat> masks_warped(imgs_.size());
vector<Mat> images_warped(imgs_.size());
vector<Size> sizes(imgs_.size());
vector<Mat> masks(imgs_.size());

// Prepare image masks
for (size_t i = 0; i < imgs_.size(); ++i)
{
masks[i].create(seam_est_imgs_[i].size(), CV_8U);
masks[i].setTo(Scalar::all(255));
}

// Warp images and their masks
Ptr<detail::RotationWarper> w = warper_->create(float(warped_image_scale_ * seam_work_aspect_));
for (size_t i = 0; i < imgs_.size(); ++i)
{
Mat_<float> K;
cameras_[i].K().convertTo(K, CV_32F);
K(0,0) *= (float)seam_work_aspect_;
K(0,2) *= (float)seam_work_aspect_;
K(1,1) *= (float)seam_work_aspect_;
K(1,2) *= (float)seam_work_aspect_;

corners[i] = w->warp(seam_est_imgs_[i], K, cameras_[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]);
sizes[i] = images_warped[i].size();

w->warp(masks[i], K, cameras_[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]);
}

vector<Mat> images_warped_f(imgs_.size());
for (size_t i = 0; i < imgs_.size(); ++i)
images_warped[i].convertTo(images_warped_f[i], CV_32F);

LOGLN("Warping images, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");

// Find seams
exposure_comp_->feed(corners, images_warped, masks_warped);
seam_finder_->find(images_warped_f, corners, masks_warped);

// Release unused memory
seam_est_imgs_.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;

//double compose_seam_aspect = 1;
double compose_work_aspect = 1;
bool is_blender_prepared = false;

double compose_scale = 1;
bool is_compose_scale_set = false;

Mat full_img, img;
for (size_t img_idx = 0; img_idx < imgs_.size(); ++img_idx)
{
LOGLN("Compositing image #" << indices_[img_idx] + 1);

// Read image and resize it if necessary
full_img = imgs_[img_idx];
if (!is_compose_scale_set)
{
if (compose_resol_ > 0)
compose_scale = min(1.0, sqrt(compose_resol_ * 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);
w = warper_->create((float)warped_image_scale_);

// Update corners and sizes
for (size_t i = 0; i < imgs_.size(); ++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 = w->warpRoi(sz, K, cameras_[i].R);
corners[i] = roi.tl();
sizes[i] = roi.size();
}
}
if (std::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
w->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));
w->warp(mask, K, cameras_[img_idx].R, INTER_NEAREST, BORDER_CONSTANT, mask_warped);

// Compensate exposure
exposure_comp_->apply((int)img_idx, corners[img_idx], img_warped, mask_warped);

img_warped.convertTo(img_warped_s, CV_16S);
img_warped.release();
img.release();
mask.release();

// Make sure seam mask has proper size
dilate(masks_warped[img_idx], dilated_mask, Mat());
resize(dilated_mask, seam_mask, mask_warped.size());

mask_warped = seam_mask & mask_warped;

if (!is_blender_prepared)
{
blender_->prepare(corners, sizes);
is_blender_prepared = true;
}

// 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");

// Preliminary result is in CV_16SC3 format, but all values are in [0,255] range,
// so convert it to avoid user confusing
result.convertTo(pano_, CV_8U);

return OK;
}

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