861 lines
30 KiB
C++
861 lines
30 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//
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//M*/
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#include <iostream>
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#include <fstream>
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#include <string>
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#include "opencv2/opencv_modules.hpp"
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#include <opencv2/core/utility.hpp>
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/stitching/detail/autocalib.hpp"
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#include "opencv2/stitching/detail/blenders.hpp"
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#include "opencv2/stitching/detail/timelapsers.hpp"
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#include "opencv2/stitching/detail/camera.hpp"
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#include "opencv2/stitching/detail/exposure_compensate.hpp"
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#include "opencv2/stitching/detail/matchers.hpp"
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#include "opencv2/stitching/detail/motion_estimators.hpp"
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#include "opencv2/stitching/detail/seam_finders.hpp"
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#include "opencv2/stitching/detail/util.hpp"
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#include "opencv2/stitching/detail/warpers.hpp"
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#include "opencv2/stitching/warpers.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::detail;
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static void printUsage()
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{
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cout <<
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"Rotation model images stitcher.\n\n"
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"stitching_detailed img1 img2 [...imgN] [flags]\n\n"
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"Flags:\n"
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" --preview\n"
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" Run stitching in the preview mode. Works faster than usual mode,\n"
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" but output image will have lower resolution.\n"
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" --try_cuda (yes|no)\n"
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" Try to use CUDA. The default value is 'no'. All default values\n"
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" are for CPU mode.\n"
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"\nMotion Estimation Flags:\n"
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" --work_megapix <float>\n"
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" Resolution for image registration step. The default is 0.6 Mpx.\n"
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" --features (surf|orb)\n"
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" Type of features used for images matching. The default is surf.\n"
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" --match_conf <float>\n"
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" Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.\n"
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" --conf_thresh <float>\n"
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" Threshold for two images are from the same panorama confidence.\n"
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" The default is 1.0.\n"
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" --ba (reproj|ray)\n"
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" Bundle adjustment cost function. The default is ray.\n"
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" --ba_refine_mask (mask)\n"
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" Set refinement mask for bundle adjustment. It looks like 'x_xxx',\n"
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" where 'x' means refine respective parameter and '_' means don't\n"
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" refine one, and has the following format:\n"
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" <fx><skew><ppx><aspect><ppy>. The default mask is 'xxxxx'. If bundle\n"
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" adjustment doesn't support estimation of selected parameter then\n"
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" the respective flag is ignored.\n"
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" --wave_correct (no|horiz|vert)\n"
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" Perform wave effect correction. The default is 'horiz'.\n"
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" --save_graph <file_name>\n"
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" Save matches graph represented in DOT language to <file_name> file.\n"
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" Labels description: Nm is number of matches, Ni is number of inliers,\n"
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" C is confidence.\n"
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"\nCompositing Flags:\n"
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" --warp (plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPlaneA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPortraitA1.5B1|mercator|transverseMercator)\n"
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" Warp surface type. The default is 'spherical'.\n"
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" --seam_megapix <float>\n"
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" Resolution for seam estimation step. The default is 0.1 Mpx.\n"
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" --seam (no|voronoi|gc_color|gc_colorgrad)\n"
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" Seam estimation method. The default is 'gc_color'.\n"
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" --compose_megapix <float>\n"
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" Resolution for compositing step. Use -1 for original resolution.\n"
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" The default is -1.\n"
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" --expos_comp (no|gain|gain_blocks)\n"
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" Exposure compensation method. The default is 'gain_blocks'.\n"
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" --blend (no|feather|multiband)\n"
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" Blending method. The default is 'multiband'.\n"
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" --blend_strength <float>\n"
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" Blending strength from [0,100] range. The default is 5.\n"
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" --output <result_img>\n"
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" The default is 'result.jpg'.\n"
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" --timelapse (as_is|crop) \n"
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" Output warped images separately as frames of a time lapse movie, with 'fixed_' prepended to input file names.\n"
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" --rangewidth <int>\n"
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" uses range_width to limit number of images to match with.\n";
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}
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// Default command line args
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vector<String> img_names;
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bool preview = false;
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bool try_cuda = false;
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double work_megapix = 0.6;
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double seam_megapix = 0.1;
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double compose_megapix = -1;
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float conf_thresh = 1.f;
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string features_type = "surf";
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string ba_cost_func = "ray";
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string ba_refine_mask = "xxxxx";
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bool do_wave_correct = true;
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WaveCorrectKind wave_correct = detail::WAVE_CORRECT_HORIZ;
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bool save_graph = false;
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std::string save_graph_to;
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string warp_type = "spherical";
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int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
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float match_conf = 0.3f;
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string seam_find_type = "gc_color";
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int blend_type = Blender::MULTI_BAND;
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int timelapse_type = Timelapser::AS_IS;
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float blend_strength = 5;
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string result_name = "result.jpg";
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bool timelapse = false;
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int range_width = -1;
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static int parseCmdArgs(int argc, char** argv)
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{
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if (argc == 1)
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{
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printUsage();
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return -1;
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}
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for (int i = 1; i < argc; ++i)
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{
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if (string(argv[i]) == "--help" || string(argv[i]) == "/?")
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{
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printUsage();
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return -1;
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}
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else if (string(argv[i]) == "--preview")
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{
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preview = true;
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}
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else if (string(argv[i]) == "--try_cuda")
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{
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if (string(argv[i + 1]) == "no")
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try_cuda = false;
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else if (string(argv[i + 1]) == "yes")
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try_cuda = true;
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else
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{
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cout << "Bad --try_cuda flag value\n";
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return -1;
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}
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i++;
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}
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else if (string(argv[i]) == "--work_megapix")
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{
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work_megapix = atof(argv[i + 1]);
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i++;
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}
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else if (string(argv[i]) == "--seam_megapix")
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{
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seam_megapix = atof(argv[i + 1]);
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i++;
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}
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else if (string(argv[i]) == "--compose_megapix")
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{
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compose_megapix = atof(argv[i + 1]);
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i++;
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}
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else if (string(argv[i]) == "--result")
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{
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result_name = argv[i + 1];
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i++;
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}
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else if (string(argv[i]) == "--features")
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{
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features_type = argv[i + 1];
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if (features_type == "orb")
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match_conf = 0.3f;
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i++;
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}
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else if (string(argv[i]) == "--match_conf")
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{
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match_conf = static_cast<float>(atof(argv[i + 1]));
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i++;
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}
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else if (string(argv[i]) == "--conf_thresh")
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{
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conf_thresh = static_cast<float>(atof(argv[i + 1]));
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i++;
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}
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else if (string(argv[i]) == "--ba")
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{
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ba_cost_func = argv[i + 1];
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i++;
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}
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else if (string(argv[i]) == "--ba_refine_mask")
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{
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ba_refine_mask = argv[i + 1];
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if (ba_refine_mask.size() != 5)
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{
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cout << "Incorrect refinement mask length.\n";
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return -1;
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}
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i++;
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}
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else if (string(argv[i]) == "--wave_correct")
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{
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if (string(argv[i + 1]) == "no")
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do_wave_correct = false;
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else if (string(argv[i + 1]) == "horiz")
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{
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do_wave_correct = true;
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wave_correct = detail::WAVE_CORRECT_HORIZ;
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}
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else if (string(argv[i + 1]) == "vert")
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{
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do_wave_correct = true;
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wave_correct = detail::WAVE_CORRECT_VERT;
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}
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else
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{
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cout << "Bad --wave_correct flag value\n";
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return -1;
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}
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i++;
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}
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else if (string(argv[i]) == "--save_graph")
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{
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save_graph = true;
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save_graph_to = argv[i + 1];
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i++;
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}
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else if (string(argv[i]) == "--warp")
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{
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warp_type = string(argv[i + 1]);
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i++;
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}
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else if (string(argv[i]) == "--expos_comp")
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{
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if (string(argv[i + 1]) == "no")
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expos_comp_type = ExposureCompensator::NO;
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else if (string(argv[i + 1]) == "gain")
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expos_comp_type = ExposureCompensator::GAIN;
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else if (string(argv[i + 1]) == "gain_blocks")
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expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
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else
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{
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cout << "Bad exposure compensation method\n";
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return -1;
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}
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i++;
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}
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else if (string(argv[i]) == "--seam")
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{
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if (string(argv[i + 1]) == "no" ||
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string(argv[i + 1]) == "voronoi" ||
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string(argv[i + 1]) == "gc_color" ||
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string(argv[i + 1]) == "gc_colorgrad" ||
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string(argv[i + 1]) == "dp_color" ||
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string(argv[i + 1]) == "dp_colorgrad")
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seam_find_type = argv[i + 1];
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else
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{
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cout << "Bad seam finding method\n";
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return -1;
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}
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i++;
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}
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else if (string(argv[i]) == "--blend")
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{
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if (string(argv[i + 1]) == "no")
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blend_type = Blender::NO;
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else if (string(argv[i + 1]) == "feather")
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blend_type = Blender::FEATHER;
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else if (string(argv[i + 1]) == "multiband")
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blend_type = Blender::MULTI_BAND;
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else
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{
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cout << "Bad blending method\n";
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return -1;
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}
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i++;
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}
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else if (string(argv[i]) == "--timelapse")
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{
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timelapse = true;
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if (string(argv[i + 1]) == "as_is")
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timelapse_type = Timelapser::AS_IS;
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else if (string(argv[i + 1]) == "crop")
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timelapse_type = Timelapser::CROP;
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else
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{
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cout << "Bad timelapse method\n";
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return -1;
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}
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i++;
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}
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else if (string(argv[i]) == "--rangewidth")
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{
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range_width = atoi(argv[i + 1]);
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i++;
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}
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else if (string(argv[i]) == "--blend_strength")
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{
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blend_strength = static_cast<float>(atof(argv[i + 1]));
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i++;
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}
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else if (string(argv[i]) == "--output")
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{
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result_name = argv[i + 1];
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i++;
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}
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else
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img_names.push_back(argv[i]);
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}
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if (preview)
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{
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compose_megapix = 0.6;
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}
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return 0;
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}
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int main(int argc, char* argv[])
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{
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#if ENABLE_LOG
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int64 app_start_time = getTickCount();
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#endif
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#if 0
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cv::setBreakOnError(true);
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#endif
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int retval = parseCmdArgs(argc, argv);
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if (retval)
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return retval;
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// Check if have enough images
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int num_images = static_cast<int>(img_names.size());
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if (num_images < 2)
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{
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LOGLN("Need more images");
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return -1;
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}
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double work_scale = 1, seam_scale = 1, compose_scale = 1;
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bool is_work_scale_set = false, is_seam_scale_set = false, is_compose_scale_set = false;
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LOGLN("Finding features...");
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#if ENABLE_LOG
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int64 t = getTickCount();
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#endif
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Ptr<FeaturesFinder> finder;
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if (features_type == "surf")
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{
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#ifdef HAVE_OPENCV_XFEATURES2D
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if (try_cuda && cuda::getCudaEnabledDeviceCount() > 0)
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finder = makePtr<SurfFeaturesFinderGpu>();
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else
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#endif
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finder = makePtr<SurfFeaturesFinder>();
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}
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else if (features_type == "orb")
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{
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finder = makePtr<OrbFeaturesFinder>();
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}
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else
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{
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cout << "Unknown 2D features type: '" << features_type << "'.\n";
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return -1;
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}
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Mat full_img, img;
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vector<ImageFeatures> features(num_images);
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vector<Mat> images(num_images);
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vector<Size> full_img_sizes(num_images);
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double seam_work_aspect = 1;
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for (int i = 0; i < num_images; ++i)
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{
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full_img = imread(img_names[i]);
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full_img_sizes[i] = full_img.size();
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if (full_img.empty())
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{
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LOGLN("Can't open image " << img_names[i]);
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return -1;
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}
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if (work_megapix < 0)
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{
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img = full_img;
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work_scale = 1;
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is_work_scale_set = true;
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}
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else
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{
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if (!is_work_scale_set)
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{
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work_scale = min(1.0, sqrt(work_megapix * 1e6 / full_img.size().area()));
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is_work_scale_set = true;
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}
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resize(full_img, img, Size(), work_scale, work_scale);
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}
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if (!is_seam_scale_set)
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{
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seam_scale = min(1.0, sqrt(seam_megapix * 1e6 / full_img.size().area()));
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seam_work_aspect = seam_scale / work_scale;
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is_seam_scale_set = true;
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}
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(*finder)(img, features[i]);
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features[i].img_idx = i;
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LOGLN("Features in image #" << i+1 << ": " << features[i].keypoints.size());
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resize(full_img, img, Size(), seam_scale, seam_scale);
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images[i] = img.clone();
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}
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finder->collectGarbage();
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full_img.release();
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img.release();
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LOGLN("Finding features, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
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LOG("Pairwise matching");
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#if ENABLE_LOG
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t = getTickCount();
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#endif
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vector<MatchesInfo> pairwise_matches;
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if (range_width==-1)
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{
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BestOf2NearestMatcher matcher(try_cuda, match_conf);
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matcher(features, pairwise_matches);
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matcher.collectGarbage();
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}
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else
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{
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BestOf2NearestRangeMatcher matcher(range_width, try_cuda, match_conf);
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matcher(features, pairwise_matches);
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matcher.collectGarbage();
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}
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LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
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// Check if we should save matches graph
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if (save_graph)
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{
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LOGLN("Saving matches graph...");
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ofstream f(save_graph_to.c_str());
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f << matchesGraphAsString(img_names, pairwise_matches, conf_thresh);
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}
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// Leave only images we are sure are from the same panorama
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vector<int> indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);
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vector<Mat> img_subset;
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vector<String> img_names_subset;
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vector<Size> full_img_sizes_subset;
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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;
|
|
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 intrinsics #" << indices[i]+1 << ":\n" << cameras[i].K());
|
|
}
|
|
|
|
Ptr<detail::BundleAdjusterBase> adjuster;
|
|
if (ba_cost_func == "reproj") adjuster = makePtr<detail::BundleAdjusterReproj>();
|
|
else if (ba_cost_func == "ray") adjuster = makePtr<detail::BundleAdjusterRay>();
|
|
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 << ":\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<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 == "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;
|
|
}
|