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143 lines
6.6 KiB
143 lines
6.6 KiB
/*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) 2015, OpenCV Foundation, 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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//M*/ |
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#ifndef __OPENCV_SFM_RECONSTRUCT_HPP__ |
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#define __OPENCV_SFM_RECONSTRUCT_HPP__ |
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#include <vector> |
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#include <string> |
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#include <opencv2/core.hpp> |
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namespace cv |
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{ |
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namespace sfm |
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{ |
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//! @addtogroup reconstruction |
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//! @{ |
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#if defined(CV_DOXYGEN) || defined(CERES_FOUND) |
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/** @brief Reconstruct 3d points from 2d correspondences while performing autocalibration. |
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@param points2d Input vector of vectors of 2d points (the inner vector is per image). |
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@param Ps Output vector with the 3x4 projections matrices of each image. |
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@param points3d Output array with estimated 3d points. |
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@param K Input/Output camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$. Input parameters used as initial guess. |
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@param is_projective if true, the cameras are supposed to be projective. |
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This method calls below signature and extracts projection matrices from estimated K, R and t. |
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@note |
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- Tracks must be as precise as possible. It does not handle outliers and is very sensible to them. |
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*/ |
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CV_EXPORTS |
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void |
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reconstruct(InputArrayOfArrays points2d, OutputArray Ps, OutputArray points3d, InputOutputArray K, |
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bool is_projective = false); |
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/** @brief Reconstruct 3d points from 2d correspondences while performing autocalibration. |
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@param points2d Input vector of vectors of 2d points (the inner vector is per image). |
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@param Rs Output vector of 3x3 rotations of the camera. |
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@param Ts Output vector of 3x1 translations of the camera. |
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@param points3d Output array with estimated 3d points. |
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@param K Input/Output camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$. Input parameters used as initial guess. |
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@param is_projective if true, the cameras are supposed to be projective. |
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Internally calls libmv simple pipeline routine with some default parameters by instatiating SFMLibmvEuclideanReconstruction class. |
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@note |
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- Tracks must be as precise as possible. It does not handle outliers and is very sensible to them. |
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- To see a working example for camera motion reconstruction, check the following tutorial: @ref tutorial_sfm_trajectory_estimation. |
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*/ |
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CV_EXPORTS |
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void |
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reconstruct(InputArrayOfArrays points2d, OutputArray Rs, OutputArray Ts, InputOutputArray K, |
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OutputArray points3d, bool is_projective = false); |
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/** @brief Reconstruct 3d points from 2d images while performing autocalibration. |
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@param images a vector of string with the images paths. |
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@param Ps Output vector with the 3x4 projections matrices of each image. |
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@param points3d Output array with estimated 3d points. |
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@param K Input/Output camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$. Input parameters used as initial guess. |
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@param is_projective if true, the cameras are supposed to be projective. |
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This method calls below signature and extracts projection matrices from estimated K, R and t. |
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@note |
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- The images must be ordered as they were an image sequence. Additionally, each frame should be as close as posible to the previous and posterior. |
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- For now DAISY features are used in order to compute the 2d points tracks and it only works for 3-4 images. |
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*/ |
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CV_EXPORTS |
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void |
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reconstruct(const std::vector<String> images, OutputArray Ps, OutputArray points3d, |
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InputOutputArray K, bool is_projective = false); |
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/** @brief Reconstruct 3d points from 2d images while performing autocalibration. |
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@param images a vector of string with the images paths. |
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@param Rs Output vector of 3x3 rotations of the camera. |
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@param Ts Output vector of 3x1 translations of the camera. |
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@param points3d Output array with estimated 3d points. |
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@param K Input/Output camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$. Input parameters used as initial guess. |
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@param is_projective if true, the cameras are supposed to be projective. |
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Internally calls libmv simple pipeline routine with some default parameters by instatiating SFMLibmvEuclideanReconstruction class. |
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@note |
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- The images must be ordered as they were an image sequence. Additionally, each frame should be as close as posible to the previous and posterior. |
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- For now DAISY features are used in order to compute the 2d points tracks and it only works for 3-4 images. |
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- To see a working example for scene reconstruction, check the following tutorial: @ref tutorial_sfm_scene_reconstruction. |
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*/ |
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CV_EXPORTS |
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void |
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reconstruct(const std::vector<String> images, OutputArray Rs, OutputArray Ts, |
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InputOutputArray K, OutputArray points3d, bool is_projective = false); |
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#endif /* CV_DOXYGEN || CERES_FOUND */ |
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//! @} sfm |
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} /* namespace cv */ |
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} /* namespace sfm */ |
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#endif |
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/* End of file. */
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