// // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2014, OpenCV Foundation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // /** @file @author Tolga Birdal */ #ifndef __OPENCV_SURFACE_MATCHING_HELPERS_HPP__ #define __OPENCV_SURFACE_MATCHING_HELPERS_HPP__ #include namespace cv { namespace ppf_match_3d { //! @addtogroup surface_matching //! @{ /** * @brief Load a PLY file * @param [in] fileName The PLY model to read * @param [in] withNormals Flag wheather the input PLY contains normal information, * and whether it should be loaded or not * @return Returns the matrix on successful load */ CV_EXPORTS_W Mat loadPLYSimple(const char* fileName, int withNormals = 0); /** * @brief Write a point cloud to PLY file * @param [in] PC Input point cloud * @param [in] fileName The PLY model file to write */ CV_EXPORTS_W void writePLY(Mat PC, const char* fileName); /** * @brief Used for debbuging pruposes, writes a point cloud to a PLY file with the tip * of the normal vectors as visible red points * @param [in] PC Input point cloud * @param [in] fileName The PLY model file to write */ CV_EXPORTS_W void writePLYVisibleNormals(Mat PC, const char* fileName); Mat samplePCUniform(Mat PC, int sampleStep); Mat samplePCUniformInd(Mat PC, int sampleStep, std::vector& indices); /** * Sample a point cloud using uniform steps * @param [in] pc Input point cloud * @param [in] xrange X components (min and max) of the bounding box of the model * @param [in] yrange Y components (min and max) of the bounding box of the model * @param [in] zrange Z components (min and max) of the bounding box of the model * @param [in] sample_step_relative The point cloud is sampled such that all points * have a certain minimum distance. This minimum distance is determined relatively using * the parameter sample_step_relative. * @param [in] weightByCenter The contribution of the quantized data points can be weighted * by the distance to the origin. This parameter enables/disables the use of weighting. * @return Sampled point cloud */ CV_EXPORTS_W Mat samplePCByQuantization(Mat pc, Vec2f& xrange, Vec2f& yrange, Vec2f& zrange, float sample_step_relative, int weightByCenter=0); void computeBboxStd(Mat pc, Vec2f& xRange, Vec2f& yRange, Vec2f& zRange); void* indexPCFlann(Mat pc); void destroyFlann(void* flannIndex); void queryPCFlann(void* flannIndex, Mat& pc, Mat& indices, Mat& distances); void queryPCFlann(void* flannIndex, Mat& pc, Mat& indices, Mat& distances, const int numNeighbors); Mat normalizePCCoeff(Mat pc, float scale, float* Cx, float* Cy, float* Cz, float* MinVal, float* MaxVal); Mat transPCCoeff(Mat pc, float scale, float Cx, float Cy, float Cz, float MinVal, float MaxVal); /** * Transforms the point cloud with a given a homogeneous 4x4 pose matrix (in double precision) * @param [in] pc Input point cloud (CV_32F family). Point clouds with 3 or 6 elements per * row are expected. In the case where the normals are provided, they are also rotated to be * compatible with the entire transformation * @param [in] Pose 4x4 pose matrix, but linearized in row-major form. * @return Transformed point cloud */ CV_EXPORTS_W Mat transformPCPose(Mat pc, const Matx44d& Pose); /** * Generate a random 4x4 pose matrix * @param [out] Pose The random pose */ CV_EXPORTS_W void getRandomPose(Matx44d& Pose); /** * Adds a uniform noise in the given scale to the input point cloud * @param [in] pc Input point cloud (CV_32F family). * @param [in] scale Input scale of the noise. The larger the scale, the more noisy the output */ CV_EXPORTS_W Mat addNoisePC(Mat pc, double scale); /** * @brief Compute the normals of an arbitrary point cloud * computeNormalsPC3d uses a plane fitting approach to smoothly compute * local normals. Normals are obtained through the eigenvector of the covariance * matrix, corresponding to the smallest eigen value. * If PCNormals is provided to be an Nx6 matrix, then no new allocation * is made, instead the existing memory is overwritten. * @param [in] PC Input point cloud to compute the normals for. * @param [out] PCNormals Output point cloud * @param [in] NumNeighbors Number of neighbors to take into account in a local region * @param [in] FlipViewpoint Should normals be flipped to a viewing direction? * @param [in] viewpoint * @return Returns 0 on success */ CV_EXPORTS_W int computeNormalsPC3d(const Mat& PC, CV_OUT Mat& PCNormals, const int NumNeighbors, const bool FlipViewpoint, const Vec3f& viewpoint); //! @} } // namespace ppf_match_3d } // namespace cv #endif