From 3c7c06e53968c46b7e8987265323c3e33cdecfc2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?S=C3=A9bastien=20Loriot?= Date: Thu, 23 Apr 2015 15:46:59 +0200 Subject: [PATCH] rename variables --- .../Concepts/VCMTraits.h | 2 +- .../include/CGAL/Eigen_vcm_traits.h | 2 +- .../include/CGAL/vcm_estimate_normals.h | 98 +++++++++---------- 3 files changed, 51 insertions(+), 51 deletions(-) diff --git a/Point_set_processing_3/doc/Point_set_processing_3/Concepts/VCMTraits.h b/Point_set_processing_3/doc/Point_set_processing_3/Concepts/VCMTraits.h index 7828a207889..417a74137b5 100644 --- a/Point_set_processing_3/doc/Point_set_processing_3/Concepts/VCMTraits.h +++ b/Point_set_processing_3/doc/Point_set_processing_3/Concepts/VCMTraits.h @@ -27,7 +27,7 @@ public: /// of the covariance matrix represented by `cov`. /// \return `true` if the operation was successful and `false` otherwise. static bool - extract_greater_eigenvector_of_covariance_matrix ( + extract_greatest_eigenvector_of_covariance_matrix ( const cpp11::array& cov, cpp11::array &normal); }; diff --git a/Point_set_processing_3/include/CGAL/Eigen_vcm_traits.h b/Point_set_processing_3/include/CGAL/Eigen_vcm_traits.h index e493aa7b706..48b98512bd5 100644 --- a/Point_set_processing_3/include/CGAL/Eigen_vcm_traits.h +++ b/Point_set_processing_3/include/CGAL/Eigen_vcm_traits.h @@ -87,7 +87,7 @@ public: // Extract the eigenvector associated to the greatest eigenvalue static bool - extract_greater_eigenvector_of_covariance_matrix ( + extract_greatest_eigenvector_of_covariance_matrix ( const cpp11::array& cov, cpp11::array &normal) { diff --git a/Point_set_processing_3/include/CGAL/vcm_estimate_normals.h b/Point_set_processing_3/include/CGAL/vcm_estimate_normals.h index 3e6f5d55fdb..3126dfcf507 100644 --- a/Point_set_processing_3/include/CGAL/vcm_estimate_normals.h +++ b/Point_set_processing_3/include/CGAL/vcm_estimate_normals.h @@ -48,7 +48,7 @@ namespace internal { /// @cond SKIP_IN_MANUAL /// Computes the VCM for each point in the property map. /// The matrix is computed by intersecting the Voronoi cell -/// of a point and a sphere whose radius is `R` and discretized +/// of a point and a sphere whose radius is `offset_radius` and discretized /// by `N` planes. /// /// @tparam ForwardIterator iterator over input points. @@ -65,12 +65,12 @@ vcm_offset (ForwardIterator first, ///< iterator over the first input point. ForwardIterator beyond, ///< past-the-end iterator over the input points. PointPMap point_pmap, ///< property map: value_type of ForwardIterator -> Point_3. std::vector &cov, ///< vector of covariance matrices. - double R, ///< radius of the sphere. + double offset_radius, ///< radius of the sphere. size_t N, ///< number of planes used to discretize the sphere. const K & /*kernel*/) ///< geometric traits. { // Sphere discretization - typename CGAL::Voronoi_covariance_3::Sphere_discretization sphere(R, N); + typename CGAL::Voronoi_covariance_3::Sphere_discretization sphere(offset_radius, N); // Compute the Delaunay Triangulation typedef CGAL::Delaunay_triangulation_3 DT; @@ -103,7 +103,7 @@ vcm_convolve (ForwardIterator first, PointPMap point_pmap, const std::vector &cov, std::vector &ncov, - double r, + double convolution_radius, const K &) { typedef typename K::Point_3 Point; @@ -131,7 +131,7 @@ vcm_convolve (ForwardIterator first, for (it = first; it != beyond; ++it) { std::vector nn; tree.search(std::back_inserter(nn), - Fuzzy_sphere (get(point_pmap, *it), r)); + Fuzzy_sphere (get(point_pmap, *it), convolution_radius)); Covariance m; for (size_t k = 0; k < nn.size(); ++k) @@ -209,14 +209,14 @@ vcm_convolve (ForwardIterator first, /// a construction that can be used for normal estimation and sharp feature detection. /// /// The VCM associates to each point the covariance matrix of its Voronoi -/// cell intersected with the the ball of radius `R`. -/// In addition, if the second radius `r` is positive, the covariance matrices are smoothed +/// cell intersected with the the ball of radius `offset_radius`. +/// In addition, if the second radius `convolution_radius` is positive, the covariance matrices are smoothed /// by a simple convolution procedure. More precisely, each covariance matrix is replaced with -/// the average of the matrices of points at distance at most `r`. -/// The choice of the parameter `R` is related to the geometry of the underlying surface while -/// the choice of `r` is related to the noise level in the point cloud. +/// the average of the matrices of points at distance at most `convolution_radius`. +/// The choice of the parameter `offset_radius` is related to the geometry of the underlying surface while +/// the choice of `convolution_radius` is related to the noise level in the point cloud. /// For example, if the point cloud is a uniform and noiseless sampling of a smooth surface, -/// `R` should be set to the minimum local feature size of the surface while `r` can be set to zero. +/// `offset_radius` should be set to the minimum local feature size of the surface while `convolution_radius` can be set to zero. /// /// The Voronoi covariance matrix of each vertex is stored in an array `a` of length 6 and is as follow: /*! @@ -247,9 +247,9 @@ compute_vcm (ForwardIterator first, ForwardIterator beyond, PointPMap point_pmap, std::vector< cpp11::array > &ccov, - double R, - double r, - const Kernel & k) + double offset_radius, + double convolution_radius, + const Kernel & kernel) { // First, compute the VCM for each point std::vector< cpp11::array > cov; @@ -257,20 +257,20 @@ compute_vcm (ForwardIterator first, internal::vcm_offset (first, beyond, point_pmap, cov, - R, + offset_radius, N, - k); + kernel); - // Then, convolve it (only when r != 0) - if (r == 0) { + // Then, convolve it (only when convolution_radius != 0) + if (convolution_radius == 0) { std::copy(cov.begin(), cov.end(), std::back_inserter(ccov)); } else { internal::vcm_convolve(first, beyond, point_pmap, cov, ccov, - r, - k); + convolution_radius, + kernel); } } @@ -300,9 +300,9 @@ vcm_estimate_normals (ForwardIterator first, ///< iterator over the first input ForwardIterator beyond, ///< past-the-end iterator over the input points. PointPMap point_pmap, ///< property map: value_type of ForwardIterator -> Point_3. NormalPMap normal_pmap, ///< property map: value_type of ForwardIterator -> Vector_3. - double R, ///< offset radius. - double r, ///< convolution radius. - const Kernel & k, ///< geometric traits. + double offset_radius, ///< offset radius. + double convolution_radius, ///< convolution radius. + const Kernel & kernel, ///< geometric traits. int nb_neighbors_convolve = -1 ///< number of neighbors used during the convolution. ) { @@ -313,16 +313,16 @@ vcm_estimate_normals (ForwardIterator first, ///< iterator over the first input compute_vcm(first, beyond, point_pmap, cov, - R, - r, - k); + offset_radius, + convolution_radius, + kernel); } else { internal::vcm_offset(first, beyond, point_pmap, cov, - R, + offset_radius, 20, - k); + kernel); std::vector ccov; internal::vcm_convolve(first, beyond, @@ -330,7 +330,7 @@ vcm_estimate_normals (ForwardIterator first, ///< iterator over the first input cov, ccov, (unsigned int) nb_neighbors_convolve, - k); + kernel); cov.clear(); std::copy(ccov.begin(), ccov.end(), std::back_inserter(cov)); @@ -340,7 +340,7 @@ vcm_estimate_normals (ForwardIterator first, ///< iterator over the first input int i = 0; for (ForwardIterator it = first; it != beyond; ++it) { cpp11::array enormal; - VCM_traits::extract_greater_eigenvector_of_covariance_matrix + VCM_traits::extract_greatest_eigenvector_of_covariance_matrix (cov[i], enormal); typename Kernel::Vector_3 normal(enormal[0], @@ -357,7 +357,7 @@ vcm_estimate_normals (ForwardIterator first, ///< iterator over the first input /// using the Voronoi Covariance Measure with a radius for the convolution. /// The output normals are randomly oriented. /// -/// See `compute_vcm()` for a detailed description of the parameters `R` and `r` +/// See `compute_vcm()` for a detailed description of the parameters `offset_radius` and `convolution_radius` /// and of the Voronoi Covariance Measure. /// /// @tparam ForwardIterator iterator over input points. @@ -378,8 +378,8 @@ vcm_estimate_normals (ForwardIterator first, ///< iterator over the first input ForwardIterator beyond, ///< past-the-end iterator over the input points. PointPMap point_pmap, ///< property map: value_type of ForwardIterator -> Point_3. NormalPMap normal_pmap, ///< property map: value_type of ForwardIterator -> Vector_3. - double R, ///< offset radius. - double r, ///< convolution radius. + double offset_radius, ///< offset radius. + double convolution_radius, ///< convolution radius. VCM_traits ) { @@ -388,7 +388,7 @@ vcm_estimate_normals (ForwardIterator first, ///< iterator over the first input vcm_estimate_normals(first, beyond, point_pmap, normal_pmap, - R, r, + offset_radius, convolution_radius, Kernel()); } @@ -398,7 +398,7 @@ vcm_estimate_normals (ForwardIterator first, ///< iterator over the first input /// using the Voronoi Covariance Measure with a number of neighbors for the convolution. /// The output normals are randomly oriented. /// -/// See `compute_vcm()` for a detailed description of the parameter `R` +/// See `compute_vcm()` for a detailed description of the parameter `offset_radius` /// and of the Voronoi Covariance Measure. /// /// @tparam ForwardIterator iterator over input points. @@ -419,8 +419,8 @@ vcm_estimate_normals (ForwardIterator first, ///< iterator over the first input ForwardIterator beyond, ///< past-the-end iterator over the input points. PointPMap point_pmap, ///< property map: value_type of ForwardIterator -> Point_3. NormalPMap normal_pmap, ///< property map: value_type of ForwardIterator -> Vector_3. - double R, ///< offset radius. - unsigned int nb_neighbors_convolve, ///< number of neighbor points used for the convolution. + double offset_radius, ///< offset radius. + unsigned int k, ///< number of neighbor points used for the convolution. VCM_traits ) { @@ -429,9 +429,9 @@ vcm_estimate_normals (ForwardIterator first, ///< iterator over the first input vcm_estimate_normals(first, beyond, point_pmap, normal_pmap, - R, 0, + offset_radius, 0, Kernel(), - nb_neighbors_convolve); + k); } @@ -445,10 +445,10 @@ vcm_estimate_normals (ForwardIterator first, ForwardIterator beyond, PointPMap point_pmap, NormalPMap normal_pmap, - double R, - double r) + double offset_radius, + double convolution_radius) { - vcm_estimate_normals(first, beyond, point_pmap, normal_pmap, R, r, Eigen_vcm_traits()); + vcm_estimate_normals(first, beyond, point_pmap, normal_pmap, offset_radius, convolution_radius, Eigen_vcm_traits()); } template < typename ForwardIterator, @@ -460,10 +460,10 @@ vcm_estimate_normals (ForwardIterator first, ForwardIterator beyond, PointPMap point_pmap, NormalPMap normal_pmap, - double R, + double offset_radius, unsigned int nb_neighbors_convolve) { - vcm_estimate_normals(first, beyond, point_pmap, normal_pmap, R, nb_neighbors_convolve, Eigen_vcm_traits()); + vcm_estimate_normals(first, beyond, point_pmap, normal_pmap, offset_radius, nb_neighbors_convolve, Eigen_vcm_traits()); } #endif @@ -477,12 +477,12 @@ void vcm_estimate_normals (ForwardIterator first, ForwardIterator beyond, NormalPMap normal_pmap, - double R, - double r) { + double offset_radius, + double convolution_radius) { vcm_estimate_normals(first, beyond, make_identity_property_map(typename std::iterator_traits::value_type()), normal_pmap, - R, r); + offset_radius, convolution_radius); } /// @endcond @@ -496,12 +496,12 @@ void vcm_estimate_normals (ForwardIterator first, ForwardIterator beyond, NormalPMap normal_pmap, - double R, + double offset_radius, unsigned int nb_neighbors_convolve) { vcm_estimate_normals(first, beyond, make_identity_property_map(typename std::iterator_traits::value_type()), normal_pmap, - R, nb_neighbors_convolve); + offset_radius, nb_neighbors_convolve); } /// @endcond