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opencv/modules/stitching/main.cpp
Alexey Spizhevoy eadb3bad45 minor refactoring of opencv_stitching
2011-05-28 13:03:28 +00:00

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/*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 methods described in these two 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 "precomp.hpp"
#include "util.hpp"
#include "warpers.hpp"
#include "blenders.hpp"
#include "seam_finders.hpp"
#include "motion_estimators.hpp"
#include "exposure_compensate.hpp"
using namespace std;
using namespace cv;
void printUsage()
{
cout <<
"Rotation model images stitcher.\n\n"
"opencv_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"
" --work_megapix <float>\n"
" Resolution for image registration step. The default is 0.6.\n"
" --seam_megapix <float>\n"
" Resolution for seam estimation step. The default is 0.1.\n"
" --compose_megapix <float>\n"
" Resolution for compositing step. Use -1 for original resolution.\n"
" The default is -1.\n"
" --match_conf <float>\n"
" Confidence for feature matching step. The default is 0.7.\n"
" --ba (ray|focal_ray)\n"
" Bundle adjustment cost function. The default is 'focal_ray'.\n"
" --conf_thresh <float>\n"
" Threshold for two images are from the same panorama confidence.\n"
" The default is 'focal_ray'.\n"
" --wave_correct (no|yes)\n"
" Perform wave effect correction. The default is 'yes'.\n"
" --warp (plane|cylindrical|spherical)\n"
" Warp surface type. The default is 'spherical'.\n"
" --expos_comp (no|gain|gain_blocks)\n"
" Exposure compensation method. The default is 'gain'.\n"
" --seam (no|voronoi|gc_color|gc_colorgrad)\n"
" Seam estimation method. The default is 'gc_color'.\n"
" --blend (no|feather|multiband)\n"
" Blending method. The default is 'multiband'.\n"
" --num_bands <int>\n"
" Number of bands for multi-band blending method. The default is 5.\n"
" --output <result_img>\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;
int warp_type = Warper::SPHERICAL;
int expos_comp_type = ExposureCompensator::GAIN;
float match_conf = 0.7f;
int seam_find_type = SeamFinder::GC_COLOR;
int blend_type = Blender::MULTI_BAND;
int num_bands = 5;
string result_name = "result.png";
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]) == "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]) == "--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]) == "--num_bands")
{
num_bands = atoi(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 = work_megapix;
}
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);
double seam_work_aspect = 1;
for (int i = 0; i < num_images; ++i)
{
full_img = imread(img_names[i]);
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();
}
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);
LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// 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;
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]]);
}
images = img_subset;
img_names = img_names_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;
}
LOGLN("Estimating rotations...");
t = getTickCount();
HomographyBasedEstimator estimator;
vector<CameraParams> cameras;
estimator(features, pairwise_matches, cameras);
LOGLN("Estimating rotations, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
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);
}
LOG("Bundle adjustment");
t = getTickCount();
BundleAdjuster adjuster(ba_space, conf_thresh);
adjuster(features, pairwise_matches, cameras);
LOGLN("Bundle adjustment, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// 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)
{
LOGLN("Wave correcting...");
t = getTickCount();
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("Wave correcting, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
}
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);
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");
LOGLN("Exposure compensation (feed)...");
t = getTickCount();
Ptr<ExposureCompensator> compensator = ExposureCompensator::createDefault(expos_comp_type);
compensator->feed(corners, images_warped, masks_warped);
LOGLN("Exposure compensation (feed), time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
LOGLN("Finding seams...");
t = getTickCount();
Ptr<SeamFinder> seam_finder = SeamFinder::createDefault(seam_find_type);
seam_finder->find(images_warped_f, corners, masks_warped);
LOGLN("Finding seams, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// 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);
// Update corners and sizes
Rect dst_roi = resultRoi(corners, sizes);
for (int i = 0; i < num_images; ++i)
{
// Update camera focal
cameras[i].focal *= compose_work_aspect;
// Update corner and size
corners[i] = dst_roi.tl() + (corners[i] - dst_roi.tl()) * compose_seam_aspect;
sizes[i] = Size(static_cast<int>((sizes[i].width + 1) * compose_seam_aspect),
static_cast<int>((sizes[i].height + 1) * compose_seam_aspect));
}
}
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 (static_cast<Blender*>(blender) == 0)
{
blender = Blender::createDefault(blend_type);
if (blend_type == Blender::MULTI_BAND)
{
MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(static_cast<Blender*>(blender));
mb->setNumBands(num_bands);
}
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;
}
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