OpenCV图像拼接之Stitching和Stitching_detailed

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。

可见OpenCV图像拼接模块的实现是十分精密和复杂的，拼接的结果很完善，但同时也是费时的，完全不能够实现实时应用。

我在研究detail源码时，由于水平有限，并不能自由灵活地对各种部件取其所需，取舍随意。

官方提供的stitching和stitching_detailed使用示例，分别是高级别和低级别封装这两种方式正确地使用示例。两种结果产生的拼接结果相同，后者却可以允许用户，在参数变量初始化时，选择各项算法。如下所示：

这涉及到以下算法流程：

1. 命令行调用程序，输入源图像以及程序的参数

2. 特征点检测，判断是使用surf还是orb，默认是surf。

3. 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。

4. 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。

5. 对所有图像进行相机参数粗略估计，然后求出旋转矩阵

6. 使用光束平均法进一步精准的估计出旋转矩阵。

7. 波形校正，水平或者垂直

8. 拼接

9. 融合，多频段融合，光照补偿，

---

Stitcher类使用示例

#include 

#include 

#include "opencv2/highgui/highgui.hpp"

#include "opencv2/stitching/stitcher.hpp"

#include


using namespace std;

using namespace cv;


bool try_use_gpu = false;

vector imgs;

string result_name = "result.jpg";


void getFiles(string path, vector& files)

{

//文件句柄

long hFile = 0;

//文件信息

struct _finddata_t fileinfo;

string p;

if ((hFile = _findfirst(p.assign(path).append("/*").c_str(), &fileinfo)) != -1)

{

do

{

//如果是目录,迭代之

//如果不是,加入列表

if ((fileinfo.attrib & _A_SUBDIR))

{

if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0)

getFiles(p.assign(path).append("/").append(fileinfo.name), files);

}

else

{

files.push_back(p.assign(path).append("/").append(fileinfo.name));

}

} while (_findnext(hFile, &fileinfo) == 0);

_findclose(hFile);

}

}


int main(int argc, char* argv[])

{

vector filesName;

getFiles("E:/workspace/iamge/dataset/",filesName);


for (string fileName:filesName)

{

Mat img = imread(fileName,1);

imgs.push_back(img);

}


Mat pano;

Stitcher stitcher = Stitcher::createDefault(try_use_gpu);

Stitcher::Status status = stitcher.stitch(imgs, pano);


if (status != Stitcher::OK)

{

cout << "Can't stitch images, error code = " << int(status) << endl;

return -1;

}


imwrite(result_name, pano);

imshow(result_name,pano);

waitKey(0);

return 0;

}

其中的getfiles()函数的功能是获取一个目录下的所有文件地址。这使得可以在windows下批量的读取图像的地址。

---

stitching_detailed使用示例

#include 

#include 

#include 

#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 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::setBreakOnError(true);


/*int retval = parseCmdArgs(argc, argv);

if (retval)

return retval;*/


// Check if have enough images

int num_images = static_cast(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 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 features(num_images);

vector images(num_images);

vector 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 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 indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);

vector img_subset;

vector img_names_subset;

vector 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(img_names.size());

if (num_images < 2)

{

LOGLN("Need more images");

return -1;

}


HomographyBasedEstimator estimator;

vector 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 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_ 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 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(focals[focals.size() / 2]);

else

warped_image_scale = static_cast(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f;


if (do_wave_correct)

{

vector 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 corners(num_images);

vector masks_warped(num_images);

vector images_warped(num_images);

vector sizes(num_images);

vector 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 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 warper = warper_creator->create(static_cast(warped_image_scale * seam_work_aspect));


for (int i = 0; i < num_images; ++i)

{

Mat_ 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 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 compensator = ExposureCompensator::createDefault(expos_comp_type);

compensator->feed(corners, images_warped, masks_warped);


Ptr 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;

//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(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(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(static_cast(blender));

mb->setNumBands(static_cast(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(static_cast(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;

}

拼接结果

输入4张图像，每张分辨率为327*245，总的拼接时间为9.25s。