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turned opencv_stitching application to module and sample

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This commit is contained in:
Alexey Spizhevoy
2011-09-05 10:41:54 +00:00
parent 30ecb28877
commit 9be4701f24
26 changed files with 1104 additions and 1008 deletions
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6
samples/cpp/CMakeLists.txt
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@@ -18,6 +18,8 @@ if (BUILD_EXAMPLES)
"${CMAKE_SOURCE_DIR}/modules/objdetect/include"
"${CMAKE_SOURCE_DIR}/modules/legacy/include"
"${CMAKE_SOURCE_DIR}/modules/contrib/include"
"${CMAKE_SOURCE_DIR}/modules/stitching/include"
"${CMAKE_SOURCE_DIR}/modules/gpu/include"
)
if(CMAKE_COMPILER_IS_GNUCXX)
@@ -35,10 +37,10 @@ if (BUILD_EXAMPLES)
PROJECT_LABEL "(EXAMPLE) ${name}")
add_dependencies(${the_target} opencv_core opencv_flann opencv_imgproc opencv_highgui
opencv_ml opencv_video opencv_objdetect opencv_features2d
opencv_calib3d opencv_legacy opencv_contrib)
opencv_calib3d opencv_legacy opencv_contrib opencv_stitching opencv_gpu)
target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} opencv_core
opencv_flann opencv_imgproc opencv_highgui opencv_ml opencv_video opencv_objdetect
opencv_features2d opencv_calib3d opencv_legacy opencv_contrib)
opencv_features2d opencv_calib3d opencv_legacy opencv_contrib opencv_stitching opencv_gpu)
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(${the_target} PROPERTIES FOLDER "samples//cpp")

618
samples/cpp/stitching.cpp Normal file
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@@ -0,0 +1,618 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
// We follow to these papers:
// 1) Construction of panoramic mosaics with global and local alignment.
// Heung-Yeung Shum and Richard Szeliski. 2000.
// 2) Eliminating Ghosting and Exposure Artifacts in Image Mosaics.
// Matthew Uyttendaele, Ashley Eden and Richard Szeliski. 2001.
// 3) Automatic Panoramic Image Stitching using Invariant Features.
// Matthew Brown and David G. Lowe. 2007.
#include <fstream>
#include "opencv2/stitching/stitching.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace std;
using namespace cv;
void printUsage()
{
cout <<
"Rotation model images stitcher.\n\n"
"stitching img1 img2 [...imgN] [flags]\n\n"
"Flags:\n"
" --preview\n"
" Run stitching in the preview mode. Works faster than usual mode,\n"
" but output image will have lower resolution.\n"
" --try_gpu (yes|no)\n"
" Try to use GPU. The default value is 'no'. All default values\n"
" are for CPU mode.\n"
"\nMotion Estimation Flags:\n"
" --work_megapix <float>\n"
" Resolution for image registration step. The default is 0.6 Mpx.\n"
" --match_conf <float>\n"
" Confidence for feature matching step. The default is 0.65.\n"
" --conf_thresh <float>\n"
" Threshold for two images are from the same panorama confidence.\n"
" The default is 1.0.\n"
" --ba (no|ray|focal_ray)\n"
" Bundle adjustment cost function. The default is 'focal_ray'.\n"
" --wave_correct (no|yes)\n"
" Perform wave effect correction. The default is 'yes'.\n"
" --save_graph <file_name>\n"
" Save matches graph represented in DOT language to <file_name> file.\n"
" Labels description: Nm is number of matches, Ni is number of inliers,\n"
" C is confidence.\n"
"\nCompositing Flags:\n"
" --warp (plane|cylindrical|spherical)\n"
" Warp surface type. The default is 'spherical'.\n"
" --seam_megapix <float>\n"
" Resolution for seam estimation step. The default is 0.1 Mpx.\n"
" --seam (no|voronoi|gc_color|gc_colorgrad)\n"
" Seam estimation method. The default is 'gc_color'.\n"
" --compose_megapix <float>\n"
" Resolution for compositing step. Use -1 for original resolution.\n"
" The default is -1.\n"
" --expos_comp (no|gain|gain_blocks)\n"
" Exposure compensation method. The default is 'gain_blocks'.\n"
" --blend (no|feather|multiband)\n"
" Blending method. The default is 'multiband'.\n"
" --blend_strength <float>\n"
" Blending strength from [0,100] range. The default is 5.\n"
" --output <result_img>\n"
" The default is 'result.jpg'.\n";
}
// Default command line args
vector<string> img_names;
bool preview = false;
bool try_gpu = false;
double work_megapix = 0.6;
double seam_megapix = 0.1;
double compose_megapix = -1;
int ba_space = BundleAdjuster::FOCAL_RAY_SPACE;
float conf_thresh = 1.f;
bool wave_correct = true;
bool save_graph = false;
std::string save_graph_to;
int warp_type = Warper::SPHERICAL;
int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
float match_conf = 0.65f;
int seam_find_type = SeamFinder::GC_COLOR;
int blend_type = Blender::MULTI_BAND;
float blend_strength = 5;
string result_name = "result.jpg";
int parseCmdArgs(int argc, char** argv)
{
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]) == "--preview")
{
preview = true;
}
else if (string(argv[i]) == "--try_gpu")
{
if (string(argv[i + 1]) == "no")
try_gpu = false;
else if (string(argv[i + 1]) == "yes")
try_gpu = true;
else
{
cout << "Bad --try_gpu flag value\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--work_megapix")
{
work_megapix = atof(argv[i + 1]);
i++;
}
else if (string(argv[i]) == "--seam_megapix")
{
seam_megapix = atof(argv[i + 1]);
i++;
}
else if (string(argv[i]) == "--compose_megapix")
{
compose_megapix = atof(argv[i + 1]);
i++;
}
else if (string(argv[i]) == "--result")
{
result_name = argv[i + 1];
i++;
}
else if (string(argv[i]) == "--match_conf")
{
match_conf = static_cast<float>(atof(argv[i + 1]));
i++;
}
else if (string(argv[i]) == "--ba")
{
if (string(argv[i + 1]) == "no")
ba_space = BundleAdjuster::NO;
else if (string(argv[i + 1]) == "ray")
ba_space = BundleAdjuster::RAY_SPACE;
else if (string(argv[i + 1]) == "focal_ray")
ba_space = BundleAdjuster::FOCAL_RAY_SPACE;
else
{
cout << "Bad bundle adjustment space\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--conf_thresh")
{
conf_thresh = static_cast<float>(atof(argv[i + 1]));
i++;
}
else if (string(argv[i]) == "--wave_correct")
{
if (string(argv[i + 1]) == "no")
wave_correct = false;
else if (string(argv[i + 1]) == "yes")
wave_correct = true;
else
{
cout << "Bad --wave_correct flag value\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--save_graph")
{
save_graph = true;
save_graph_to = argv[i + 1];
i++;
}
else if (string(argv[i]) == "--warp")
{
if (string(argv[i + 1]) == "plane")
warp_type = Warper::PLANE;
else if (string(argv[i + 1]) == "cylindrical")
warp_type = Warper::CYLINDRICAL;
else if (string(argv[i + 1]) == "spherical")
warp_type = Warper::SPHERICAL;
else
{
cout << "Bad warping method\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--expos_comp")
{
if (string(argv[i + 1]) == "no")
expos_comp_type = ExposureCompensator::NO;
else if (string(argv[i + 1]) == "gain")
expos_comp_type = ExposureCompensator::GAIN;
else if (string(argv[i + 1]) == "gain_blocks")
expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
else
{
cout << "Bad exposure compensation method\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--seam")
{
if (string(argv[i + 1]) == "no")
seam_find_type = SeamFinder::NO;
else if (string(argv[i + 1]) == "voronoi")
seam_find_type = SeamFinder::VORONOI;
else if (string(argv[i + 1]) == "gc_color")
seam_find_type = SeamFinder::GC_COLOR;
else if (string(argv[i + 1]) == "gc_colorgrad")
seam_find_type = SeamFinder::GC_COLOR_GRAD;
else
{
cout << "Bad seam finding method\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--blend")
{
if (string(argv[i + 1]) == "no")
blend_type = Blender::NO;
else if (string(argv[i + 1]) == "feather")
blend_type = Blender::FEATHER;
else if (string(argv[i + 1]) == "multiband")
blend_type = Blender::MULTI_BAND;
else
{
cout << "Bad blending method\n";
return -1;
}
i++;
}
else if (string(argv[i]) == "--blend_strength")
{
blend_strength = static_cast<float>(atof(argv[i + 1]));
i++;
}
else if (string(argv[i]) == "--output")
{
result_name = argv[i + 1];
i++;
}
else
img_names.push_back(argv[i]);
}
if (preview)
{
compose_megapix = 0.6;
}
return 0;
}
int main(int argc, char* argv[])
{
int64 app_start_time = getTickCount();
cv::setBreakOnError(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...");
int64 t = getTickCount();
vector<ImageFeatures> features(num_images);
SurfFeaturesFinder finder(try_gpu);
Mat full_img, img;
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.releaseMemory();
full_img.release();
img.release();
LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
LOG("Pairwise matching");
t = getTickCount();
vector<MatchesInfo> pairwise_matches;
BestOf2NearestMatcher matcher(try_gpu, match_conf);
matcher(features, pairwise_matches);
matcher.releaseMemory();
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 focal length #" << indices[i]+1 << ": " << cameras[i].focal);
}
BundleAdjuster adjuster(ba_space, conf_thresh);
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 << " focal length: " << cameras[i].focal);
focals.push_back(cameras[i].focal);
}
nth_element(focals.begin(), focals.begin() + focals.size()/2, focals.end());
float warped_image_scale = static_cast<float>(focals[focals.size() / 2]);
if (wave_correct)
{
vector<Mat> rmats;
for (size_t i = 0; i < cameras.size(); ++i)
rmats.push_back(cameras[i].R);
waveCorrect(rmats);
for (size_t i = 0; i < cameras.size(); ++i)
cameras[i].R = rmats[i];
}
LOGLN("Warping images (auxiliary)... ");
t = getTickCount();
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<Warper> warper = Warper::createByCameraFocal(static_cast<float>(warped_image_scale * seam_work_aspect),
warp_type, try_gpu);
for (int i = 0; i < num_images; ++i)
{
corners[i] = warper->warp(images[i], static_cast<float>(cameras[i].focal * seam_work_aspect),
cameras[i].R, images_warped[i]);
sizes[i] = images_warped[i].size();
warper->warp(masks[i], static_cast<float>(cameras[i].focal * seam_work_aspect),
cameras[i].R, masks_warped[i], INTER_NEAREST, BORDER_CONSTANT);
}
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 = SeamFinder::createDefault(seam_find_type);
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...");
t = getTickCount();
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::createByCameraFocal(warped_image_scale, warp_type, try_gpu);
// Update corners and sizes
for (int i = 0; i < num_images; ++i)
{
// Update camera focal
cameras[i].focal *= compose_work_aspect;
// Update corner and size
Size sz = full_img_sizes[i];
if (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);
}
Rect roi = warper->warpRoi(sz, static_cast<float>(cameras[i].focal), 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();
// Warp the current image
warper->warp(img, static_cast<float>(cameras[img_idx].focal), cameras[img_idx].R,
img_warped);
// Warp the current image mask
mask.create(img_size, CV_8U);
mask.setTo(Scalar::all(255));
warper->warp(mask, static_cast<float>(cameras[img_idx].focal), cameras[img_idx].R, mask_warped,
INTER_NEAREST, BORDER_CONSTANT);
// 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);
LOGLN("Finished, total time: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " sec");
return 0;
}