• Jiri Horner's avatar
    Merge pull request #6933 from hrnr:gsoc_all · c17afe0f
    Jiri Horner authored
    [GSOC] New camera model for stitching pipeline
    
    * implement estimateAffine2D
    
    estimates affine transformation using robust RANSAC method.
    
    * uses RANSAC framework in calib3d
    * includes accuracy test
    * uses SVD decomposition for solving 3 point equation
    
    * implement estimateAffinePartial2D
    
    estimates limited affine transformation
    
    * includes accuracy test
    
    * stitching: add affine matcher
    
    initial version of matcher that estimates affine transformation
    
    * stitching: added affine transform estimator
    
    initial version of estimator that simply chain transformations in homogeneous coordinates
    
    * calib3d: rename estimateAffine3D test
    
    test Calib3d_EstimateAffineTransform rename to Calib3d_EstimateAffine3D. This is more descriptive and prevents confusion with estimateAffine2D tests.
    
    * added perf test for estimateAffine functions
    
    tests both estimateAffine2D and estimateAffinePartial2D
    
    * calib3d: compare error in square in estimateAffine2D
    
    * incorporates fix from #6768
    
    * rerun affine estimation on inliers
    
    * stitching: new API for parallel feature finding
    
    due to ABI breakage new functionality is added to `FeaturesFinder2`, `SurfFeaturesFinder2` and `OrbFeaturesFinder2`
    
    * stitching: add tests for parallel feature find API
    
    * perf test (about linear speed up)
    * accuracy test compares results with serial version
    
    * stitching: use dynamic_cast to overcome ABI issues
    
    adding parallel API to FeaturesFinder breaks ABI. This commit uses dynamic_cast and hardcodes thread-safe finders to avoid breaking ABI.
    
    This should be replaced by proper method similar to FeaturesMatcher on next ABI break.
    
    * use estimateAffinePartial2D in AffineBestOf2NearestMatcher
    
    * add constructor to AffineBestOf2NearestMatcher
    
    * allows to choose between full affine transform and partial affine transform. Other params are the as for BestOf2NearestMatcher
    * added protected field
    
    * samples: stitching_detailed support affine estimator and matcher
    
    * added new flags to choose matcher and estimator
    
    * stitching: rework affine matcher
    
    represent transformation in homogeneous coordinates
    
    affine matcher: remove duplicite code
    rework flow to get rid of duplicite code
    
    affine matcher: do not center points to (0, 0)
    it is not needed for affine model. it should not affect estimation in any way.
    
    affine matcher: remove unneeded cv namespacing
    
    * stitching: add stub bundle adjuster
    
    * adds stub bundle adjuster that does nothing
    * can be used in place of standard bundle adjusters to omit bundle adjusting step
    
    * samples: stitching detailed, support no budle adjust
    
    * uses new NoBundleAdjuster
    
    * added affine warper
    
    * uses R to get whole affine transformation and propagates rotation and translation to plane warper
    
    * add affine warper factory class
    
    * affine warper: compensate transformation
    
    * samples: stitching_detailed add support for affine warper
    
    * add Stitcher::create method
    
    this method follows similar constructor methods and returns smart pointer. This allows constructing Stitcher according to OpenCV guidelines.
    
    * supports multiple stitcher configurations (PANORAMA and SCANS) for convenient setup
    * returns cv::Ptr
    
    * stitcher: dynamicaly determine correct estimator
    
    we need to use affine estimator for affine matcher
    
    * preserves ABI (but add hints for ABI 4)
    * uses dynamic_cast hack to inject correct estimator
    
    * sample stitching: add support for multiple modes
    
    shows how to use different configurations of stitcher easily (panorama stitching and scans affine model)
    
    * stitcher: find features in parallel
    
    use new FeatureFinder API to find features in parallel. Parallelized using TBB.
    
    * stitching: disable parallel feature finding for OCL
    
    it does not bring much speedup to run features finder in parallel when OpenCL is enabled, because finder needs to wait for OCL device.
    
    Also, currently ORB is not thread-safe when OCL is enabled.
    
    * stitching: move matcher tests
    
    move matchers tests perf_stich.cpp -> perf_matchers.cpp
    
    * stitching: add affine stiching integration test
    
    test basic affine stitching (SCANS mode of stitcher) with images that have only translation between them
    
    * enable surf for stitching tests
    
    stitching.b12 test was failing with surf
    
    investigated the issue, surf is producing good result. Transformation is only slightly different from ORB, so that resulting pano does not exactly match ORB's result. That caused sanity check to fail.
    
    * added size checks similar to other tests
    * sanity check will be applied only for ORB
    
    * stitching: fix wrong estimator choice
    
    if case was exactly wrong, estimators were chosen wrong
    
    added logging for estimated transformation
    
    * enable surf for matchers stitching tests
    
    * enable SURF
    * rework sanity checking. Check estimated transform instead of matches. Est. transform should be more stable and comparable between SURF and ORB.
    * remove regression checking for VectorFeatures tests. It has a lot if data andtest is the same as previous except it test different vector size for performance, so sanity checking does not add any value here. Added basic sanity asserts instead.
    
    * stitching tests: allow relative error for transform
    
    * allows .01 relative error for estimated homography sanity check in stitching matchers tests
    * fix VS warning
    
    stitching tests: increase relative error
    
    increase relative error to make it pass on all platforms (results are still good).
    
    stitching test: allow bigger relative error
    
    transformation can differ in small values (with small absolute difference, but large relative difference). transformation output still looks usable for all platforms. This difference affects only mac and windows, linux passes fine with small difference.
    
    * stitching: add tests for affine matcher
    
    uses s1, s2 images. added also new sanity data.
    
    * stitching tests: use different data for matchers tests
    
    this data should yeild more stable transformation (it has much more matches, especially for surf). Sanity data regenerated.
    
    * stitching test: rework tests for matchers
    
    * separated rotation and translations as they are different by scale.
    * use appropriate absolute error for them separately. (relative error does not work for values near zero.)
    
    * stitching: fix affine warper compensation
    
    calculation of rotation and translation extracted for plane warper was wrong
    
    * stitching test: enable surf for opencl integration tests
    
    * enable SURF with correct guard (HAVE_OPENCV_XFEATURES2D)
    * add OPENCL guard and correct namespace as usual for opencl tests
    
    * stitching: add ocl accuracy test for affine warper
    
    test consistent results with ocl on and off
    
    * stitching: add affine warper ocl perf test
    
    add affine warper to existing warper perf tests. Added new sanity data.
    
    * stitching: do not overwrite inliers in affine matcher
    
    * estimation is run second time on inliers only, inliers produces in second run will not be therefore correct for all matches
    
    * calib3d: add Levenberg–Marquardt refining to estimateAffine2D* functions
    
    this adds affine Levenberg–Marquardt refining to estimateAffine2D functions similar to what is done in findHomography.
    
    implements Levenberg–Marquardt refinig for both full affine and partial affine transformations.
    
    * stitching: remove reestimation step in affine matcher
    
    reestimation step is not needed. estimateAffine2D* functions are running their own reestimation on inliers using the Levenberg-Marquardt algorithm, which is better than simply rerunning RANSAC on inliers.
    
    * implement partial affine bundle adjuster
    
    bundle adjuster that expect affine transform with 4DOF. Refines parameters for all cameras together.
    
    stitching: fix bug in BundleAdjusterAffinePartial
    
    * use the invers properly
    * use static buffer for invers to speed it up
    
    * samples: add affine bundle adjuster option to stitching_detailed
    
    * add support for using affine bundle adjuster with 4DOF
    * improve logging of initial intristics
    
    * sttiching: add affine bundle adjuster test
    
    * fix build warnings
    
    * stitching: increase limit on sanity check
    
    prevents spurious test failures on mac. values are still pretty fine.
    
    * stitching: set affine bundle adjuster for SCANS mode
    
    * fix bug with AffineBestOf2NearestMatcher (we want to select affine partial mode)
    * select right bundle adjuster
    
    * stitching: increase error bound for matcher tests
    
    * this prevents failure on mac. tranformation is still ok.
    
    * stitching: implement affine bundle adjuster
    
    * implements affine bundle adjuster that is using full affine transform
    * existing test case modified to test both affinePartial an full affine bundle adjuster
    
    * add stitching tutorial
    
    * show basic usage of stitching api (Stitcher class)
    
    * stitching: add more integration test for affine stitching
    
    * added new datasets to existing testcase
    * removed unused include
    
    * calib3d: move `haveCollinearPoints` to common header
    
    * added comment to make that this also checks too close points
    
    * calib3d: redone checkSubset for estimateAffine* callback
    
    * use common function to check collinearity
    * this also ensures that point will not be too close to each other
    
    * calib3d: change estimateAffine* functions API
    
    * more similar to `findHomography`, `findFundamentalMat`, `findEssentialMat` and similar
    * follows standard recommended semantic INPUTS, OUTPUTS, FLAGS
    * allows to disable refining
    * supported LMEDS robust method (tests yet to come) along with RANSAC
    * extended docs with some tips
    
    * calib3d: rewrite estimateAffine2D test
    
    * rewrite in googletest style
    * parametrize to test both robust methods (RANSAC and LMEDS)
    * get rid of boilerplate
    
    * calib3d: rework estimateAffinePartial2D test
    
    * rework in googletest style
    * add testing for LMEDS
    
    * calib3d: rework estimateAffine*2D perf test
    
    * test for LMEDS speed
    * test with/without Levenberg-Marquart
    * remove sanity checking (this is covered by accuracy tests)
    
    * calib3d: improve estimateAffine*2D tests
    
    * test transformations in loop
    * improves test by testing more potential transformations
    
    * calib3d: rewrite kernels for estimateAffine*2D functions
    
    * use analytical solution instead of SVD
    * this version is faster especially for smaller amount of points
    
    * calib3d: tune up perf of estimateAffine*2D functions
    
    * avoid copying inliers
    * avoid converting input points if not necessary
    * check only `from` point for collinearity, as `to` does not affect stability of transform
    
    * tutorials: add commands examples to stitching tutorials
    
    * add some examples how to run stitcher sample code
    * mention stitching_detailed.cpp
    
    * calib3d: change computeError for estimateAffine*2D
    
    * do error computing in floats instead of doubles
    
    this have required precision + we were storing the result in float anyway. This make code faster and allows auto-vectorization by smart compilers.
    
    * documentation: mention estimateAffine*2D function
    
    * refer to new functions on appropriate places
    * prefer estimateAffine*2D over estimateRigidTransform
    
    * stitching: add camera models documentations
    
    * mention camera models in module documentation to give user a better overview and reduce confusion
    c17afe0f
stitching_detailed.cpp 31.4 KB
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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include <iostream>
#include <fstream>
#include <string>
#include "opencv2/opencv_modules.hpp"
#include <opencv2/core/utility.hpp>
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/stitching/detail/autocalib.hpp"
#include "opencv2/stitching/detail/blenders.hpp"
#include "opencv2/stitching/detail/timelapsers.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/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"

#define ENABLE_LOG 1
#define LOG(msg) std::cout << msg
#define LOGLN(msg) std::cout << msg << std::endl

using namespace std;
using namespace cv;
using namespace cv::detail;

static void printUsage()
{
    cout <<
        "Rotation model images stitcher.\n\n"
        "stitching_detailed 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_cuda (yes|no)\n"
        "      Try to use CUDA. 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"
        "  --features (surf|orb)\n"
        "      Type of features used for images matching. The default is surf.\n"
        "  --matcher (homography|affine)\n"
        "      Matcher used for pairwise image matching.\n"
        "  --estimator (homography|affine)\n"
        "      Type of estimator used for transformation estimation.\n"
        "  --match_conf <float>\n"
        "      Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.\n"
        "  --conf_thresh <float>\n"
        "      Threshold for two images are from the same panorama confidence.\n"
        "      The default is 1.0.\n"
        "  --ba (no|reproj|ray|affine)\n"
        "      Bundle adjustment cost function. The default is ray.\n"
        "  --ba_refine_mask (mask)\n"
        "      Set refinement mask for bundle adjustment. It looks like 'x_xxx',\n"
        "      where 'x' means refine respective parameter and '_' means don't\n"
        "      refine one, and has the following format:\n"
        "      <fx><skew><ppx><aspect><ppy>. The default mask is 'xxxxx'. If bundle\n"
        "      adjustment doesn't support estimation of selected parameter then\n"
        "      the respective flag is ignored.\n"
        "  --wave_correct (no|horiz|vert)\n"
        "      Perform wave effect correction. The default is 'horiz'.\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 (affine|plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPlaneA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPortraitA1.5B1|mercator|transverseMercator)\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"
        "  --timelapse (as_is|crop) \n"
        "      Output warped images separately as frames of a time lapse movie, with 'fixed_' prepended to input file names.\n"
        "  --rangewidth <int>\n"
        "      uses range_width to limit number of images to match with.\n";
}


// Default command line args
vector<String> img_names;
bool preview = false;
bool try_cuda = false;
double work_megapix = 0.6;
double seam_megapix = 0.1;
double compose_megapix = -1;
float conf_thresh = 1.f;
string features_type = "surf";
string matcher_type = "homography";
string estimator_type = "homography";
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;
int timelapse_type = Timelapser::AS_IS;
float blend_strength = 5;
string result_name = "result.jpg";
bool timelapse = false;
int range_width = -1;


static 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_cuda")
        {
            if (string(argv[i + 1]) == "no")
                try_cuda = false;
            else if (string(argv[i + 1]) == "yes")
                try_cuda = true;
            else
            {
                cout << "Bad --try_cuda 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]) == "--features")
        {
            features_type = argv[i + 1];
            if (features_type == "orb")
                match_conf = 0.3f;
            i++;
        }
        else if (string(argv[i]) == "--matcher")
        {
            if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine")
                matcher_type = argv[i + 1];
            else
            {
                cout << "Bad --matcher flag value\n";
                return -1;
            }
            i++;
        }
        else if (string(argv[i]) == "--estimator")
        {
            if (string(argv[i + 1]) == "homography" || string(argv[i + 1]) == "affine")
                estimator_type = argv[i + 1];
            else
            {
                cout << "Bad --estimator flag value\n";
                return -1;
            }
            i++;
        }
        else if (string(argv[i]) == "--match_conf")
        {
            match_conf = static_cast<float>(atof(argv[i + 1]));
            i++;
        }
        else if (string(argv[i]) == "--conf_thresh")
        {
            conf_thresh = static_cast<float>(atof(argv[i + 1]));
            i++;
        }
        else if (string(argv[i]) == "--ba")
        {
            ba_cost_func = argv[i + 1];
            i++;
        }
        else if (string(argv[i]) == "--ba_refine_mask")
        {
            ba_refine_mask = argv[i + 1];
            if (ba_refine_mask.size() != 5)
            {
                cout << "Incorrect refinement mask length.\n";
                return -1;
            }
            i++;
        }
        else if (string(argv[i]) == "--wave_correct")
        {
            if (string(argv[i + 1]) == "no")
                do_wave_correct = false;
            else if (string(argv[i + 1]) == "horiz")
            {
                do_wave_correct = true;
                wave_correct = detail::WAVE_CORRECT_HORIZ;
            }
            else if (string(argv[i + 1]) == "vert")
            {
                do_wave_correct = true;
                wave_correct = detail::WAVE_CORRECT_VERT;
            }
            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")
        {
            warp_type = string(argv[i + 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" ||
                string(argv[i + 1]) == "voronoi" ||
                string(argv[i + 1]) == "gc_color" ||
                string(argv[i + 1]) == "gc_colorgrad" ||
                string(argv[i + 1]) == "dp_color" ||
                string(argv[i + 1]) == "dp_colorgrad")
                seam_find_type = argv[i + 1];
            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]) == "--timelapse")
        {
            timelapse = true;

            if (string(argv[i + 1]) == "as_is")
                timelapse_type = Timelapser::AS_IS;
            else if (string(argv[i + 1]) == "crop")
                timelapse_type = Timelapser::CROP;
            else
            {
                cout << "Bad timelapse method\n";
                return -1;
            }
            i++;
        }
        else if (string(argv[i]) == "--rangewidth")
        {
            range_width = atoi(argv[i + 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[])
{
#if ENABLE_LOG
    int64 app_start_time = getTickCount();
#endif

#if 0
    cv::setBreakOnError(true);
#endif

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

    Ptr<FeaturesFinder> finder;
    if (features_type == "surf")
    {
#ifdef HAVE_OPENCV_XFEATURES2D
        if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
            finder = makePtr<SurfFeaturesFinderGpu>();
        else
#endif
            finder = makePtr<SurfFeaturesFinder>();
    }
    else if (features_type == "orb")
    {
        finder = makePtr<OrbFeaturesFinder>();
    }
    else
    {
        cout << "Unknown 2D features type: '" << features_type << "'.\n";
        return -1;
    }

    Mat full_img, img;
    vector<ImageFeatures> features(num_images);
    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->collectGarbage();
    full_img.release();
    img.release();

    LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");

    LOG("Pairwise matching");
#if ENABLE_LOG
    t = getTickCount();
#endif
    vector<MatchesInfo> pairwise_matches;
    Ptr<FeaturesMatcher> matcher;
    if (matcher_type == "affine")
        matcher = makePtr<AffineBestOf2NearestMatcher>(false, try_cuda, match_conf);
    else if (range_width==-1)
        matcher = makePtr<BestOf2NearestMatcher>(try_cuda, match_conf);
    else
        matcher = makePtr<BestOf2NearestRangeMatcher>(range_width, try_cuda, 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<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;
    }

    Ptr<Estimator> estimator;
    if (estimator_type == "affine")
        estimator = makePtr<AffineBasedEstimator>();
    else
        estimator = makePtr<HomographyBasedEstimator>();

    vector<CameraParams> cameras;
    if (!(*estimator)(features, pairwise_matches, cameras))
    {
        cout << "Homography estimation failed.\n";
        return -1;
    }

    for (size_t i = 0; i < cameras.size(); ++i)
    {
        Mat R;
        cameras[i].R.convertTo(R, CV_32F);
        cameras[i].R = R;
        LOGLN("Initial camera intrinsics #" << indices[i]+1 << ":\nK:\n" << cameras[i].K() << "\nR:\n" << cameras[i].R);
    }

    Ptr<detail::BundleAdjusterBase> adjuster;
    if (ba_cost_func == "reproj") adjuster = makePtr<detail::BundleAdjusterReproj>();
    else if (ba_cost_func == "ray") adjuster = makePtr<detail::BundleAdjusterRay>();
    else if (ba_cost_func == "affine") adjuster = makePtr<detail::BundleAdjusterAffinePartial>();
    else if (ba_cost_func == "no") adjuster = makePtr<NoBundleAdjuster>();
    else
    {
        cout << "Unknown bundle adjustment cost function: '" << ba_cost_func << "'.\n";
        return -1;
    }
    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);
    if (!(*adjuster)(features, pairwise_matches, cameras))
    {
        cout << "Camera parameters adjusting failed.\n";
        return -1;
    }

    // Find median focal length

    vector<double> focals;
    for (size_t i = 0; i < cameras.size(); ++i)
    {
        LOGLN("Camera #" << indices[i]+1 << ":\nK:\n" << cameras[i].K() << "\nR:\n" << cameras[i].R);
        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;

    if (do_wave_correct)
    {
        vector<Mat> 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<Point> corners(num_images);
    vector<UMat> masks_warped(num_images);
    vector<UMat> images_warped(num_images);
    vector<Size> sizes(num_images);
    vector<UMat> 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<WarperCreator> warper_creator;
#ifdef HAVE_OPENCV_CUDAWARPING
    if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
    {
        if (warp_type == "plane")
            warper_creator = makePtr<cv::PlaneWarperGpu>();
        else if (warp_type == "cylindrical")
            warper_creator = makePtr<cv::CylindricalWarperGpu>();
        else if (warp_type == "spherical")
            warper_creator = makePtr<cv::SphericalWarperGpu>();
    }
    else
#endif
    {
        if (warp_type == "plane")
            warper_creator = makePtr<cv::PlaneWarper>();
        else if (warp_type == "affine")
            warper_creator = makePtr<cv::AffineWarper>();
        else if (warp_type == "cylindrical")
            warper_creator = makePtr<cv::CylindricalWarper>();
        else if (warp_type == "spherical")
            warper_creator = makePtr<cv::SphericalWarper>();
        else if (warp_type == "fisheye")
            warper_creator = makePtr<cv::FisheyeWarper>();
        else if (warp_type == "stereographic")
            warper_creator = makePtr<cv::StereographicWarper>();
        else if (warp_type == "compressedPlaneA2B1")
            warper_creator = makePtr<cv::CompressedRectilinearWarper>(2.0f, 1.0f);
        else if (warp_type == "compressedPlaneA1.5B1")
            warper_creator = makePtr<cv::CompressedRectilinearWarper>(1.5f, 1.0f);
        else if (warp_type == "compressedPlanePortraitA2B1")
            warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(2.0f, 1.0f);
        else if (warp_type == "compressedPlanePortraitA1.5B1")
            warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(1.5f, 1.0f);
        else if (warp_type == "paniniA2B1")
            warper_creator = makePtr<cv::PaniniWarper>(2.0f, 1.0f);
        else if (warp_type == "paniniA1.5B1")
            warper_creator = makePtr<cv::PaniniWarper>(1.5f, 1.0f);
        else if (warp_type == "paniniPortraitA2B1")
            warper_creator = makePtr<cv::PaniniPortraitWarper>(2.0f, 1.0f);
        else if (warp_type == "paniniPortraitA1.5B1")
            warper_creator = makePtr<cv::PaniniPortraitWarper>(1.5f, 1.0f);
        else if (warp_type == "mercator")
            warper_creator = makePtr<cv::MercatorWarper>();
        else if (warp_type == "transverseMercator")
            warper_creator = makePtr<cv::TransverseMercatorWarper>();
    }

    if (!warper_creator)
    {
        cout << "Can't create the following warper '" << warp_type << "'\n";
        return 1;
    }

    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<UMat> 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;
    if (seam_find_type == "no")
        seam_finder = makePtr<detail::NoSeamFinder>();
    else if (seam_find_type == "voronoi")
        seam_finder = makePtr<detail::VoronoiSeamFinder>();
    else if (seam_find_type == "gc_color")
    {
#ifdef HAVE_OPENCV_CUDALEGACY
        if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
            seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR);
        else
#endif
            seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR);
    }
    else if (seam_find_type == "gc_colorgrad")
    {
#ifdef HAVE_OPENCV_CUDALEGACY
        if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
            seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
        else
#endif
            seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
    }
    else if (seam_find_type == "dp_color")
        seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR);
    else if (seam_find_type == "dp_colorgrad")
        seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR_GRAD);
    if (!seam_finder)
    {
        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> blender;
    Ptr<Timelapser> timelapser;
    //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_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 && !timelapse)
        {
            blender = Blender::createDefault(blend_type, try_cuda);
            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_cuda);
            else if (blend_type == Blender::MULTI_BAND)
            {
                MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(blender.get());
                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*>(blender.get());
                fb->setSharpness(1.f/blend_width);
                LOGLN("Feather blender, sharpness: " << fb->sharpness());
            }
            blender->prepare(corners, sizes);
        }
        else if (!timelapser && timelapse)
        {
            timelapser = Timelapser::createDefault(timelapse_type);
            timelapser->initialize(corners, sizes);
        }

        // Blend the current image
        if (timelapse)
        {
            timelapser->process(img_warped_s, Mat::ones(img_warped_s.size(), CV_8UC1), corners[img_idx]);
            String fixedFileName;
            size_t pos_s = String(img_names[img_idx]).find_last_of("/\\");
            if (pos_s == String::npos)
            {
                fixedFileName = "fixed_" + img_names[img_idx];
            }
            else
            {
                fixedFileName = "fixed_" + String(img_names[img_idx]).substr(pos_s + 1, String(img_names[img_idx]).length() - pos_s);
            }
            imwrite(fixedFileName, timelapser->getDst());
        }
        else
        {
            blender->feed(img_warped_s, mask_warped, corners[img_idx]);
        }
    }

    if (!timelapse)
    {
        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;
}