154 lines
6.4 KiB
C++
154 lines
6.4 KiB
C++
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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) 2014, 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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/** @file
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@author Tolga Birdal <tbirdal AT gmail.com>
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*/
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#ifndef __OPENCV_SURFACE_MATCHING_HELPERS_HPP__
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#define __OPENCV_SURFACE_MATCHING_HELPERS_HPP__
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#include <opencv2/core.hpp>
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namespace cv
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{
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namespace ppf_match_3d
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{
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//! @addtogroup surface_matching
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//! @{
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/**
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* @brief Load a PLY file
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* @param [in] fileName The PLY model to read
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* @param [in] withNormals Flag wheather the input PLY contains normal information,
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* and whether it should be loaded or not
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* @return Returns the matrix on successful load
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*/
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CV_EXPORTS_W Mat loadPLYSimple(const char* fileName, int withNormals = 0);
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/**
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* @brief Write a point cloud to PLY file
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* @param [in] PC Input point cloud
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* @param [in] fileName The PLY model file to write
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*/
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CV_EXPORTS_W void writePLY(Mat PC, const char* fileName);
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/**
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* @brief Used for debbuging pruposes, writes a point cloud to a PLY file with the tip
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* of the normal vectors as visible red points
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* @param [in] PC Input point cloud
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* @param [in] fileName The PLY model file to write
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*/
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CV_EXPORTS_W void writePLYVisibleNormals(Mat PC, const char* fileName);
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Mat samplePCUniform(Mat PC, int sampleStep);
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Mat samplePCUniformInd(Mat PC, int sampleStep, std::vector<int>& indices);
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/**
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* Sample a point cloud using uniform steps
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* @param [in] pc Input point cloud
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* @param [in] xrange X components (min and max) of the bounding box of the model
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* @param [in] yrange Y components (min and max) of the bounding box of the model
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* @param [in] zrange Z components (min and max) of the bounding box of the model
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* @param [in] sample_step_relative The point cloud is sampled such that all points
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* have a certain minimum distance. This minimum distance is determined relatively using
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* the parameter sample_step_relative.
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* @param [in] weightByCenter The contribution of the quantized data points can be weighted
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* by the distance to the origin. This parameter enables/disables the use of weighting.
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* @return Sampled point cloud
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*/
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CV_EXPORTS_W Mat samplePCByQuantization(Mat pc, Vec2f& xrange, Vec2f& yrange, Vec2f& zrange, float sample_step_relative, int weightByCenter=0);
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void computeBboxStd(Mat pc, Vec2f& xRange, Vec2f& yRange, Vec2f& zRange);
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void* indexPCFlann(Mat pc);
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void destroyFlann(void* flannIndex);
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void queryPCFlann(void* flannIndex, Mat& pc, Mat& indices, Mat& distances);
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void queryPCFlann(void* flannIndex, Mat& pc, Mat& indices, Mat& distances, const int numNeighbors);
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Mat normalizePCCoeff(Mat pc, float scale, float* Cx, float* Cy, float* Cz, float* MinVal, float* MaxVal);
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Mat transPCCoeff(Mat pc, float scale, float Cx, float Cy, float Cz, float MinVal, float MaxVal);
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/**
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* Transforms the point cloud with a given a homogeneous 4x4 pose matrix (in double precision)
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* @param [in] pc Input point cloud (CV_32F family). Point clouds with 3 or 6 elements per
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* row are expected. In the case where the normals are provided, they are also rotated to be
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* compatible with the entire transformation
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* @param [in] Pose 4x4 pose matrix, but linearized in row-major form.
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* @return Transformed point cloud
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*/
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CV_EXPORTS_W Mat transformPCPose(Mat pc, const Matx44d& Pose);
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/**
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* Generate a random 4x4 pose matrix
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* @param [out] Pose The random pose
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*/
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CV_EXPORTS_W void getRandomPose(Matx44d& Pose);
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/**
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* Adds a uniform noise in the given scale to the input point cloud
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* @param [in] pc Input point cloud (CV_32F family).
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* @param [in] scale Input scale of the noise. The larger the scale, the more noisy the output
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*/
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CV_EXPORTS_W Mat addNoisePC(Mat pc, double scale);
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/**
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* @brief Compute the normals of an arbitrary point cloud
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* computeNormalsPC3d uses a plane fitting approach to smoothly compute
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* local normals. Normals are obtained through the eigenvector of the covariance
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* matrix, corresponding to the smallest eigen value.
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* If PCNormals is provided to be an Nx6 matrix, then no new allocation
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* is made, instead the existing memory is overwritten.
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* @param [in] PC Input point cloud to compute the normals for.
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* @param [out] PCNormals Output point cloud
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* @param [in] NumNeighbors Number of neighbors to take into account in a local region
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* @param [in] FlipViewpoint Should normals be flipped to a viewing direction?
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* @param [in] viewpoint
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* @return Returns 0 on success
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*/
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CV_EXPORTS_W int computeNormalsPC3d(const Mat& PC, CV_OUT Mat& PCNormals, const int NumNeighbors, const bool FlipViewpoint, const Vec3f& viewpoint);
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//! @}
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} // namespace ppf_match_3d
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} // namespace cv
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#endif
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