From f2b2baad9e1b3812a8159127f4c6a7b0dd00e815 Mon Sep 17 00:00:00 2001 From: Kuang Fangjun Date: Fri, 28 Sep 2018 11:02:52 +0800 Subject: [PATCH] fix errors and typos in doc. --- common/include/pcl/common/centroid.h | 66 +++++++++---------- common/include/pcl/common/intensity.h | 6 +- common/include/pcl/common/utils.h | 2 +- common/include/pcl/point_types_conversion.h | 10 +-- doc/tutorials/content/adding_custom_ptype.rst | 2 +- doc/tutorials/content/basic_structures.rst | 4 +- doc/tutorials/content/building_pcl.rst | 6 +- .../compiling_pcl_dependencies_windows.rst | 2 +- doc/tutorials/content/compiling_pcl_posix.rst | 2 +- .../content/compiling_pcl_windows.rst | 4 +- doc/tutorials/content/compression.rst | 20 +++--- doc/tutorials/content/concatenate_clouds.rst | 4 +- doc/tutorials/content/concatenate_points.rst | 2 +- .../conditional_euclidean_clustering.rst | 2 +- doc/tutorials/content/conditional_removal.rst | 6 +- doc/tutorials/content/depth_sense_grabber.rst | 2 +- doc/tutorials/content/don_segmentation.rst | 2 +- doc/tutorials/content/extract_indices.rst | 2 +- doc/tutorials/content/gpu_people.rst | 2 +- doc/tutorials/content/hdl_grabber.rst | 2 +- doc/tutorials/content/how_features_work.rst | 2 +- doc/tutorials/content/pcd_file_format.rst | 2 +- doc/tutorials/content/writing_new_classes.rst | 30 ++++----- .../include/pcl/filters/impl/bilateral.hpp | 2 +- io/include/pcl/io/file_io.h | 8 +-- octree/octree.doxy | 2 +- 26 files changed, 97 insertions(+), 97 deletions(-) diff --git a/common/include/pcl/common/centroid.h b/common/include/pcl/common/centroid.h index 30332845b5c..7d8e1173e18 100644 --- a/common/include/pcl/common/centroid.h +++ b/common/include/pcl/common/centroid.h @@ -56,9 +56,9 @@ namespace pcl /** \brief Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector. * \param[in] cloud_iterator an iterator over the input point cloud * \param[out] centroid the output centroid - * \return number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud. * \note if return value is 0, the centroid is not changed, thus not valid. - * The last compononent of the vector is set to 1, this allow to transform the centroid vector with 4x4 matrices. + * The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices. * \ingroup common */ template inline unsigned int @@ -82,9 +82,9 @@ namespace pcl /** \brief Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector. * \param[in] cloud the input point cloud * \param[out] centroid the output centroid - * \return number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud. * \note if return value is 0, the centroid is not changed, thus not valid. - * The last compononent of the vector is set to 1, this allow to transform the centroid vector with 4x4 matrices. + * The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices. * \ingroup common */ template inline unsigned int @@ -110,9 +110,9 @@ namespace pcl * \param[in] cloud the input point cloud * \param[in] indices the point cloud indices that need to be used * \param[out] centroid the output centroid - * \return number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input indices. * \note if return value is 0, the centroid is not changed, thus not valid. - * The last compononent of the vector is set to 1, this allow to transform the centroid vector with 4x4 matrices. + * The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices. * \ingroup common */ template inline unsigned int @@ -141,9 +141,9 @@ namespace pcl * \param[in] cloud the input point cloud * \param[in] indices the point cloud indices that need to be used * \param[out] centroid the output centroid - * \return number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input indices. * \note if return value is 0, the centroid is not changed, thus not valid. - * The last compononent of the vector is set to 1, this allow to transform the centroid vector with 4x4 matrices. + * The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices. * \ingroup common */ template inline unsigned int @@ -171,11 +171,11 @@ namespace pcl * The result is returned as a Eigen::Matrix3f. * Note: the covariance matrix is not normalized with the number of * points. For a normalized covariance, please use - * computeNormalizedCovarianceMatrix. + * computeCovarianceMatrixNormalized. * \param[in] cloud the input point cloud * \param[in] centroid the centroid of the set of points in the cloud * \param[out] covariance_matrix the resultant 3x3 covariance matrix - * \return number of valid point used to determine the covariance matrix. + * \return number of valid points used to determine the covariance matrix. * In case of dense point clouds, this is the same as the size of input cloud. * \note if return value is 0, the covariance matrix is not changed, thus not valid. * \ingroup common @@ -210,7 +210,7 @@ namespace pcl * \param[in] cloud the input point cloud * \param[in] centroid the centroid of the set of points in the cloud * \param[out] covariance_matrix the resultant 3x3 covariance matrix - * \return number of valid point used to determine the covariance matrix. + * \return number of valid points used to determine the covariance matrix. * In case of dense point clouds, this is the same as the size of input cloud. * \ingroup common */ @@ -239,13 +239,13 @@ namespace pcl * The result is returned as a Eigen::Matrix3f. * Note: the covariance matrix is not normalized with the number of * points. For a normalized covariance, please use - * computeNormalizedCovarianceMatrix. + * computeCovarianceMatrixNormalized. * \param[in] cloud the input point cloud * \param[in] indices the point cloud indices that need to be used * \param[in] centroid the centroid of the set of points in the cloud * \param[out] covariance_matrix the resultant 3x3 covariance matrix - * \return number of valid point used to determine the covariance matrix. - * In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the covariance matrix. + * In case of dense point clouds, this is the same as the size of input indices. * \ingroup common */ template inline unsigned int @@ -276,13 +276,13 @@ namespace pcl * The result is returned as a Eigen::Matrix3f. * Note: the covariance matrix is not normalized with the number of * points. For a normalized covariance, please use - * computeNormalizedCovarianceMatrix. + * computeCovarianceMatrixNormalized. * \param[in] cloud the input point cloud * \param[in] indices the point cloud indices that need to be used * \param[in] centroid the centroid of the set of points in the cloud * \param[out] covariance_matrix the resultant 3x3 covariance matrix - * \return number of valid point used to determine the covariance matrix. - * In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the covariance matrix. + * In case of dense point clouds, this is the same as the size of input indices. * \ingroup common */ template inline unsigned int @@ -320,8 +320,8 @@ namespace pcl * \param[in] indices the point cloud indices that need to be used * \param[in] centroid the centroid of the set of points in the cloud * \param[out] covariance_matrix the resultant 3x3 covariance matrix - * \return number of valid point used to determine the covariance matrix. - * In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the covariance matrix. + * In case of dense point clouds, this is the same as the size of input indices. * \ingroup common */ template inline unsigned int @@ -358,8 +358,8 @@ namespace pcl * \param[in] indices the point cloud indices that need to be used * \param[in] centroid the centroid of the set of points in the cloud * \param[out] covariance_matrix the resultant 3x3 covariance matrix - * \return number of valid point used to determine the covariance matrix. - * In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the covariance matrix. + * In case of dense point clouds, this is the same as the size of input indices. * \ingroup common */ template inline unsigned int @@ -387,14 +387,14 @@ namespace pcl } /** \brief Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. - * Normalized means that every entry has been divided by the number of entries in indices. + * Normalized means that every entry has been divided by the number of valid entries in the point cloud. * For small number of points, or if you want explicitly the sample-variance, scale the covariance matrix * with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function. * \note This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency. * \param[in] cloud the input point cloud * \param[out] covariance_matrix the resultant 3x3 covariance matrix * \param[out] centroid the centroid of the set of points in the cloud - * \return number of valid point used to determine the covariance matrix. + * \return number of valid points used to determine the covariance matrix. * In case of dense point clouds, this is the same as the size of input cloud. * \ingroup common */ @@ -428,8 +428,8 @@ namespace pcl * \param[in] indices subset of points given by their indices * \param[out] covariance_matrix the resultant 3x3 covariance matrix * \param[out] centroid the centroid of the set of points in the cloud - * \return number of valid point used to determine the covariance matrix. - * In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the covariance matrix. + * In case of dense point clouds, this is the same as the size of input indices. * \ingroup common */ template inline unsigned int @@ -465,8 +465,8 @@ namespace pcl * \param[in] indices subset of points given by their indices * \param[out] centroid the centroid of the set of points in the cloud * \param[out] covariance_matrix the resultant 3x3 covariance matrix - * \return number of valid point used to determine the covariance matrix. - * In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the covariance matrix. + * In case of dense point clouds, this is the same as the size of input indices. * \ingroup common */ template inline unsigned int @@ -494,13 +494,13 @@ namespace pcl } /** \brief Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. - * Normalized means that every entry has been divided by the number of entries in indices. + * Normalized means that every entry has been divided by the number of entries in the input point cloud. * For small number of points, or if you want explicitly the sample-variance, scale the covariance matrix * with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function. * \note This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency. * \param[in] cloud the input point cloud * \param[out] covariance_matrix the resultant 3x3 covariance matrix - * \return number of valid point used to determine the covariance matrix. + * \return number of valid points used to determine the covariance matrix. * In case of dense point clouds, this is the same as the size of input cloud. * \ingroup common */ @@ -530,8 +530,8 @@ namespace pcl * \param[in] cloud the input point cloud * \param[in] indices subset of points given by their indices * \param[out] covariance_matrix the resultant 3x3 covariance matrix - * \return number of valid point used to determine the covariance matrix. - * In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the covariance matrix. + * In case of dense point clouds, this is the same as the size of input indices. * \ingroup common */ template inline unsigned int @@ -563,8 +563,8 @@ namespace pcl * \param[in] cloud the input point cloud * \param[in] indices subset of points given by their indices * \param[out] covariance_matrix the resultant 3x3 covariance matrix - * \return number of valid point used to determine the covariance matrix. - * In case of dense point clouds, this is the same as the size of input cloud. + * \return number of valid points used to determine the covariance matrix. + * In case of dense point clouds, this is the same as the size of input indices. * \ingroup common */ template inline unsigned int diff --git a/common/include/pcl/common/intensity.h b/common/include/pcl/common/intensity.h index 673cfe1e6c9..18ede88eb0a 100644 --- a/common/include/pcl/common/intensity.h +++ b/common/include/pcl/common/intensity.h @@ -44,7 +44,7 @@ namespace pcl { namespace common { - /** \brief Intensity field accessor provides access to the inetnsity filed of a PoinT + /** \brief Intensity field accessor provides access to the intensity filed of a PoinT * implementation for specific types should be done in \file pcl/common/impl/intensity.hpp */ template @@ -78,7 +78,7 @@ namespace pcl p.intensity = intensity; } /** \brief subtract value from intensity field - * \param p point for which to modify inetnsity + * \param p point for which to modify intensity * \param[in] value value to be subtracted from point intensity */ inline void @@ -87,7 +87,7 @@ namespace pcl p.intensity -= value; } /** \brief add value to intensity field - * \param p point for which to modify inetnsity + * \param p point for which to modify intensity * \param[in] value value to be added to point intensity */ inline void diff --git a/common/include/pcl/common/utils.h b/common/include/pcl/common/utils.h index 179a4bf0616..8a7ef86471a 100644 --- a/common/include/pcl/common/utils.h +++ b/common/include/pcl/common/utils.h @@ -45,7 +45,7 @@ namespace pcl { namespace utils { - /** \brief Check if val1 and val2 are equals to an epsilon extent + /** \brief Check if val1 and val2 are equal to an epsilon extent * \param[in] val1 first number to check * \param[in] val2 second number to check * \param[in] eps epsilon diff --git a/common/include/pcl/point_types_conversion.h b/common/include/pcl/point_types_conversion.h index 74c4ffabe5e..e4e9e441e5d 100644 --- a/common/include/pcl/point_types_conversion.h +++ b/common/include/pcl/point_types_conversion.h @@ -135,8 +135,8 @@ namespace pcl if (out.h < 0.f) out.h += 360.f; } - /** \brief Convert a XYZRGB point type to a XYZHSV - * \param[in] in the input XYZRGB point + /** \brief Convert a XYZRGBA point type to a XYZHSV + * \param[in] in the input XYZRGBA point * \param[out] out the output XYZHSV point * \todo include the A parameter but how? */ @@ -241,7 +241,7 @@ namespace pcl } } - /** \brief Convert a RGB point cloud to a Intensity + /** \brief Convert a RGB point cloud to an Intensity * \param[in] in the input RGB point cloud * \param[out] out the output Intensity point cloud */ @@ -259,7 +259,7 @@ namespace pcl } } - /** \brief Convert a RGB point cloud to a Intensity + /** \brief Convert a RGB point cloud to an Intensity * \param[in] in the input RGB point cloud * \param[out] out the output Intensity point cloud */ @@ -277,7 +277,7 @@ namespace pcl } } - /** \brief Convert a RGB point cloud to a Intensity + /** \brief Convert a RGB point cloud to an Intensity * \param[in] in the input RGB point cloud * \param[out] out the output Intensity point cloud */ diff --git a/doc/tutorials/content/adding_custom_ptype.rst b/doc/tutorials/content/adding_custom_ptype.rst index 1f24d7d3f37..3879c1937b5 100644 --- a/doc/tutorials/content/adding_custom_ptype.rst +++ b/doc/tutorials/content/adding_custom_ptype.rst @@ -375,7 +375,7 @@ addition, the type that you want, might already be defined for you. * `PointWithRange` - float x, y, z (union with float point[4]), range; Similar to `PointXYZI`, except `range` contains a measure of the distance - from the acqusition viewpoint to the point in the world. + from the acquisition viewpoint to the point in the world. .. code-block:: cpp diff --git a/doc/tutorials/content/basic_structures.rst b/doc/tutorials/content/basic_structures.rst index ea5d7f827fc..45ce6e02701 100644 --- a/doc/tutorials/content/basic_structures.rst +++ b/doc/tutorials/content/basic_structures.rst @@ -20,7 +20,7 @@ PointCloud is a C++ class which contains the following data fields: that resemble an organized image (or matrix) like structure, where the data is split into rows and columns. Examples of such point clouds include data coming from stereo cameras or Time Of Flight cameras. The - advantages of a organized dataset is that by knowing the relationship + advantages of an organized dataset is that by knowing the relationship between adjacent points (e.g. pixels), nearest neighbor operations are much more efficient, thus speeding up the computation and lowering the costs of certain algorithms in PCL. @@ -45,7 +45,7 @@ PointCloud is a C++ class which contains the following data fields: Example:: - cloud.width = 640; // Image-like organized structure, with 640 rows and 480 columns, + cloud.width = 640; // Image-like organized structure, with 480 rows and 640 columns, cloud.height = 480; // thus 640*480=307200 points total in the dataset Example:: diff --git a/doc/tutorials/content/building_pcl.rst b/doc/tutorials/content/building_pcl.rst index 6f0a51545d4..84f7de7e18c 100644 --- a/doc/tutorials/content/building_pcl.rst +++ b/doc/tutorials/content/building_pcl.rst @@ -116,11 +116,11 @@ YYY then XXX will be built but won't appear in the cache. You can also change the build type: -* **Debug**: means that no optimization is done and all the debugging symbols are imbedded into the libraries file. This is plateform and compiler dependent. On Linux with gcc this is equivalent to running gcc with `-O0 -g -ggdb -Wall` +* **Debug**: means that no optimization is done and all the debugging symbols are embedded into the libraries file. This is platform and compiler dependent. On Linux with gcc this is equivalent to running gcc with `-O0 -g -ggdb -Wall` -* **Release**: the compiled code is optimized and no debug information will be print out. This will lead to `-O3` for gcc and `-O5` for clang +* **Release**: the compiled code is optimized and no debug information will be printed out. This will lead to `-O3` for gcc and `-O5` for clang -* **RelWithDebInfo**: the compiled code is optimized but debugging data is also imbedded in the libraries. This is a tradeoff between the two former ones. +* **RelWithDebInfo**: the compiled code is optimized but debugging data is also embedded in the libraries. This is a tradeoff between the two former ones. * **MinSizeRel**: this, normally, results in the smallest libraries you can build. This is interesting when building for Android or a restricted memory/space system. diff --git a/doc/tutorials/content/compiling_pcl_dependencies_windows.rst b/doc/tutorials/content/compiling_pcl_dependencies_windows.rst index e29b20aa16f..d2fe8db0685 100644 --- a/doc/tutorials/content/compiling_pcl_dependencies_windows.rst +++ b/doc/tutorials/content/compiling_pcl_dependencies_windows.rst @@ -177,7 +177,7 @@ like:: Where to build binaries: C:/PCL_dependencies/flann-1.7.1-src/build Hit the "Configure" button. Proceed and be sure to choose the correct "Generator" on the next window. - You can safley ignore any warning message about hdf5. + You can safely ignore any warning message about hdf5. Now, on my machine I had to manually set the `BUILD_PYTHON_BINDINGS` and `BUILD_MATLAB_BINDINGS` to OFF otherwise it would not continue to the next diff --git a/doc/tutorials/content/compiling_pcl_posix.rst b/doc/tutorials/content/compiling_pcl_posix.rst index 43ef90575b6..fd1dfaaf646 100644 --- a/doc/tutorials/content/compiling_pcl_posix.rst +++ b/doc/tutorials/content/compiling_pcl_posix.rst @@ -46,7 +46,7 @@ And install the result:: make -j2 install -Or alternatively, if you did not change the variable which declares where PCL should be installed, do:: +Or alternatively, if you did not change the variable which declares where PCL should be installed, do:: sudo make -j2 install diff --git a/doc/tutorials/content/compiling_pcl_windows.rst b/doc/tutorials/content/compiling_pcl_windows.rst index 60c15f0dffd..b1f541609cc 100644 --- a/doc/tutorials/content/compiling_pcl_windows.rst +++ b/doc/tutorials/content/compiling_pcl_windows.rst @@ -162,7 +162,7 @@ Let's check whether CMake did actually find the needed third party dependencies - **Eigen** : - Eigen is a header-only library, thus, we need only **EIGEN_INCLUDE_DIR** to be set. Hopefully, CMake did fing Eigen. + Eigen is a header-only library, thus, we need only **EIGEN_INCLUDE_DIR** to be set. Hopefully, CMake did find Eigen. .. image:: images/windows/cmake_eigen_include_dir.png :alt: Eigen include dir @@ -278,7 +278,7 @@ Building the "ALL_BUILD" project will build everything. Installing PCL -------------- -To install the built libraries and executbles, you need to build the "INSTALL" project in the solution explorer. +To install the built libraries and executables, you need to build the "INSTALL" project in the solution explorer. This utility project will copy PCL headers, libraries and executable to the directory defined by the **CMAKE_INSTALL_PREFIX** CMake variable. diff --git a/doc/tutorials/content/compression.rst b/doc/tutorials/content/compression.rst index 5bb5ada1f11..f3600e568a0 100644 --- a/doc/tutorials/content/compression.rst +++ b/doc/tutorials/content/compression.rst @@ -144,25 +144,25 @@ The following compression profiles are available: - **LOW_RES_ONLINE_COMPRESSION_WITH_COLOR** 1 cubic centimeter resolution, color, fast online encoding - - **MED_RES_ONLINE_COMPRESSION_WITHOUT_COLOR** 5 cubic milimeter resolution, no color, fast online encoding + - **MED_RES_ONLINE_COMPRESSION_WITHOUT_COLOR** 5 cubic millimeter resolution, no color, fast online encoding - - **MED_RES_ONLINE_COMPRESSION_WITH_COLOR** 5 cubic milimeter resolution, color, fast online encoding + - **MED_RES_ONLINE_COMPRESSION_WITH_COLOR** 5 cubic millimeter resolution, color, fast online encoding - - **HIGH_RES_ONLINE_COMPRESSION_WITHOUT_COLOR** 1 cubic milimeter resolution, no color, fast online encoding + - **HIGH_RES_ONLINE_COMPRESSION_WITHOUT_COLOR** 1 cubic millimeter resolution, no color, fast online encoding - - **HIGH_RES_ONLINE_COMPRESSION_WITH_COLOR** 1 cubic milimeter resolution, color, fast online encoding + - **HIGH_RES_ONLINE_COMPRESSION_WITH_COLOR** 1 cubic millimeter resolution, color, fast online encoding - **LOW_RES_OFFLINE_COMPRESSION_WITHOUT_COLOR** 1 cubic centimeter resolution, no color, efficient offline encoding - **LOW_RES_OFFLINE_COMPRESSION_WITH_COLOR** 1 cubic centimeter resolution, color, efficient offline encoding - - **MED_RES_OFFLINE_COMPRESSION_WITHOUT_COLOR** 5 cubic milimeter resolution, no color, efficient offline encoding + - **MED_RES_OFFLINE_COMPRESSION_WITHOUT_COLOR** 5 cubic millimeter resolution, no color, efficient offline encoding - - **MED_RES_OFFLINE_COMPRESSION_WITH_COLOR** 5 cubic milimeter resolution, color, efficient offline encoding + - **MED_RES_OFFLINE_COMPRESSION_WITH_COLOR** 5 cubic millimeter resolution, color, efficient offline encoding - - **HIGH_RES_OFFLINE_COMPRESSION_WITHOUT_COLOR** 1 cubic milimeter resolution, no color, efficient offline encoding + - **HIGH_RES_OFFLINE_COMPRESSION_WITHOUT_COLOR** 1 cubic millimeter resolution, no color, efficient offline encoding - - **HIGH_RES_OFFLINE_COMPRESSION_WITH_COLOR** 1 cubic milimeter resolution, color, efficient offline encoding + - **HIGH_RES_OFFLINE_COMPRESSION_WITH_COLOR** 1 cubic millimeter resolution, color, efficient offline encoding - **MANUAL_CONFIGURATION** enables manual configuration for advanced parametrization @@ -198,9 +198,9 @@ The advanced parametrization is explained in the following: decreased compression performance. This enables a trade-off between high frame/update rates and compression efficiency. - **doVoxelGridDownDownSampling_arg**: If activated, only the hierarchical octree data structure is encoded. The decoder generated points at the voxel centers. In this - way, the point cloud becomes downsampled during compression while archieving high compression performance. + way, the point cloud becomes downsampled during compression while achieving high compression performance. - - **iFrameRate_arg**: The point cloud compression scheme differentially encodes point clouds. In this way, differences between the incoming point cloud and the previously encoded pointcloud is encoded in order to archive maximum compression performance. The iFrameRate_arg allows to specify the rate of frames in the stream at which incoming point clouds are **not** differentially encoded (similar to I/P-frames in video coding). + - **iFrameRate_arg**: The point cloud compression scheme differentially encodes point clouds. In this way, differences between the incoming point cloud and the previously encoded pointcloud is encoded in order to achieve maximum compression performance. The iFrameRate_arg allows to specify the rate of frames in the stream at which incoming point clouds are **not** differentially encoded (similar to I/P-frames in video coding). - **doColorEncoding_arg**: This option enables color component encoding. diff --git a/doc/tutorials/content/concatenate_clouds.rst b/doc/tutorials/content/concatenate_clouds.rst index 8f90958e447..68b3f705a5b 100644 --- a/doc/tutorials/content/concatenate_clouds.rst +++ b/doc/tutorials/content/concatenate_clouds.rst @@ -3,9 +3,9 @@ Concatenate the points of two Point Clouds ------------------------------------------ -In this tutorial we will learn how to concatenating the points of two different +In this tutorial we will learn how to concatenate the points of two different point clouds. The constraint imposed here is that the type and number of fields -in the two datasets has to be equal. We will also learn how to concatenate the fields (e.g., +in the two datasets have to be equal. We will also learn how to concatenate the fields (e.g., dimensions) of two different point clouds. The constraint imposed here is that the number of points in the two datasets has to be equal. diff --git a/doc/tutorials/content/concatenate_points.rst b/doc/tutorials/content/concatenate_points.rst index 7a01b82c466..b86b169e0cf 100644 --- a/doc/tutorials/content/concatenate_points.rst +++ b/doc/tutorials/content/concatenate_points.rst @@ -5,7 +5,7 @@ Concatenate the points or the fields of two Point Clouds In this tutorial we will learn how to concatenating the points of two different point clouds. The constraint imposed here is that the type and number of fields -in the two datasets has to be equal. +in the two datasets have to be equal. The code -------- diff --git a/doc/tutorials/content/conditional_euclidean_clustering.rst b/doc/tutorials/content/conditional_euclidean_clustering.rst index acf1f4e9103..4b265fc4828 100644 --- a/doc/tutorials/content/conditional_euclidean_clustering.rst +++ b/doc/tutorials/content/conditional_euclidean_clustering.rst @@ -95,7 +95,7 @@ Lines 97-109 contain a piece of code that is a quick and dirty fix to visualize :language: cpp :lines: 97-109 -When the output point cloud is opened with PCL's standard PCD viewer, pressing '5' will switch to the intenisty channel visualization. +When the output point cloud is opened with PCL's standard PCD viewer, pressing '5' will switch to the intensity channel visualization. The too-small clusters will be colored red, the too-large clusters will be colored blue, and the actual clusters/objects of interest will be colored randomly in between yellow and cyan hues. Compiling and running the program diff --git a/doc/tutorials/content/conditional_removal.rst b/doc/tutorials/content/conditional_removal.rst index 2154fe45c96..3859ad486e9 100644 --- a/doc/tutorials/content/conditional_removal.rst +++ b/doc/tutorials/content/conditional_removal.rst @@ -3,7 +3,7 @@ Removing outliers using a ConditionalRemoval filter --------------------------------------------------- -This document demonstrates how to use the ConditionalRemoval filter to remove points from a PointCloud that do no satisfy a specific or multiple conditions. +This document demonstrates how to use the ConditionalRemoval filter to remove points from a PointCloud that do not satisfy a specific or multiple conditions. The code -------- @@ -25,13 +25,13 @@ In the following Lines, we define the PointCloud structures, fill in the input c :language: cpp :lines: 8-27 -Then, we create the condition which a given point must satisfy so that it remains in our PointCloud. To do this we must add two comparisons to the condition, greater than 0.0, and less than 0.8. This condition is then used to build the filter. +Then, we create the condition which a given point must satisfy so that it remains in our PointCloud. To do this we must add two comparisons to the condition, greater than 0.0, and less than 0.8. This condition is then used to build the filter. .. literalinclude:: sources/conditional_removal/conditional_removal.cpp :language: cpp :lines: 28-39 -This last bit of code just applies the filter to our original PointCloud, and removes all of the points that do not satisfy the conditions we specified. Then it outputs all of the points remaining in the PointCloud. +This last bit of code just applies the filter to our original PointCloud, and removes all of the points that do not satisfy the conditions we specified. Then it outputs all of the points remaining in the PointCloud. .. literalinclude:: sources/conditional_removal/conditional_removal.cpp :language: cpp diff --git a/doc/tutorials/content/depth_sense_grabber.rst b/doc/tutorials/content/depth_sense_grabber.rst index 48a9a05e46e..f03456e7724 100644 --- a/doc/tutorials/content/depth_sense_grabber.rst +++ b/doc/tutorials/content/depth_sense_grabber.rst @@ -76,7 +76,7 @@ class that implements the standard PCL grabber interface. You can run the tool with `--help` option to view the usage guide. -The video below demontrates the features of the DepthSense viewer tool. Please +The video below demonstrates the features of the DepthSense viewer tool. Please note that the bilateral filtering (which can be observed in the end of the video) is currently disabled is the tool. diff --git a/doc/tutorials/content/don_segmentation.rst b/doc/tutorials/content/don_segmentation.rst index c13aa5d0401..5dfd8c31f9f 100644 --- a/doc/tutorials/content/don_segmentation.rst +++ b/doc/tutorials/content/don_segmentation.rst @@ -36,7 +36,7 @@ where :math:`$r_s, r_l \in \mathbb{R}$`, :math:`$r_s pair, using the search surface information given in **setSearchSurface()**. - Finally, un the figure above, this corresponds to the last (rightmost) case. Here, we assume that q_2's index is not part of the indices vector given for Q, so no neighbors or features will be estimated at q2. + Finally, in the figure above, this corresponds to the last (rightmost) case. Here, we assume that q_2's index is not part of the indices vector given for Q, so no neighbors or features will be estimated at q2. The most useful example when **setSearchSurface()** should be used, is when we have a very dense input dataset, but we do not want to estimate features at all the points in it, but rather at some keypoints discovered using the methods in `pcl_keypoints`, or at a downsampled version of the cloud (e.g., obtained using a `pcl::VoxelGrid` filter). In this case, we pass the downsampled/keypoints input via **setInputCloud()**, and the original data as **setSearchSurface()**. diff --git a/doc/tutorials/content/pcd_file_format.rst b/doc/tutorials/content/pcd_file_format.rst index a7c3982b76f..210b37becf9 100644 --- a/doc/tutorials/content/pcd_file_format.rst +++ b/doc/tutorials/content/pcd_file_format.rst @@ -123,7 +123,7 @@ As of version 0.7, the PCD header contains the following entries: Example:: - WIDTH 640 # Image-like organized structure, with 640 rows and 480 columns, + WIDTH 640 # Image-like organized structure, with 480 rows and 640 columns, HEIGHT 480 # thus 640*480=307200 points total in the dataset Example:: diff --git a/doc/tutorials/content/writing_new_classes.rst b/doc/tutorials/content/writing_new_classes.rst index abc36dbf6cb..ce8879f3dc1 100644 --- a/doc/tutorials/content/writing_new_classes.rst +++ b/doc/tutorials/content/writing_new_classes.rst @@ -163,7 +163,7 @@ Setting up the structure `_ to familiarize yourself with the concepts. -There's two different ways we could set up the structure: i) set up the code +There're two different ways we could set up the structure: i) set up the code separately, as a standalone PCL class, but outside of the PCL code tree; or ii) set up the files directly in the PCL code tree. Since our assumption is that the end result will be contributed back to PCL, it's best to concentrate on the @@ -222,7 +222,7 @@ While we're at it, let's set up two skeleton *bilateral.hpp* and #include - #endif // PCL_FILTERS_BILATERAL_H_ + #endif // PCL_FILTERS_BILATERAL_IMPL_H_ This should be straightforward. We haven't declared any methods for `BilateralFilter` yet, therefore there is no implementation. @@ -349,7 +349,7 @@ that only two of the types contain intensity, namely: Note that at this point we haven't declared the PCL_INSTANTIATE template for `BilateralFilter`, nor did we actually implement the pure virtual functions in -the abstract class :pcl:`pcl::Filter` so attemping to compile the +the abstract class :pcl:`pcl::Filter` so attempting to compile the code will result in errors like:: filters/src/bilateral.cpp:6:32: error: expected constructor, destructor, or type conversion before ‘(’ token @@ -385,7 +385,7 @@ paradigms. } double - getSigmaS () + getSigmaS () const { return (sigma_s_); } @@ -397,7 +397,7 @@ paradigms. } double - getSigmaR () + getSigmaR () const { return (sigma_r_); } @@ -546,7 +546,7 @@ header file becomes: } double - getSigmaS () + getSigmaS () const { return (sigma_s_); } @@ -558,7 +558,7 @@ header file becomes: } double - getSigmaR () + getSigmaR () const { return (sigma_r_); } @@ -589,7 +589,7 @@ header file becomes: bilateral.hpp ============= -There's two methods that we need to implement here, namely `applyFilter` and +There're two methods that we need to implement here, namely `applyFilter` and `computePointWeight`. .. code-block:: cpp @@ -660,7 +660,7 @@ entry for the class: #define PCL_INSTANTIATE_BilateralFilter(T) template class PCL_EXPORTS pcl::BilateralFilter; - #endif // PCL_FILTERS_BILATERAL_H_ + #endif // PCL_FILTERS_BILATERAL_IMPL_H_ One additional thing that we can do is error checking on: @@ -770,7 +770,7 @@ The implementation file header thus becomes: #define PCL_INSTANTIATE_BilateralFilter(T) template class PCL_EXPORTS pcl::BilateralFilter; - #endif // PCL_FILTERS_BILATERAL_H_ + #endif // PCL_FILTERS_BILATERAL_IMPL_H_ Taking advantage of other PCL concepts @@ -885,7 +885,7 @@ The implementation file header thus becomes: #define PCL_INSTANTIATE_BilateralFilter(T) template class PCL_EXPORTS pcl::BilateralFilter; - #endif // PCL_FILTERS_BILATERAL_H_ + #endif // PCL_FILTERS_BILATERAL_IMPL_H_ To make :pcl:`indices_` work without typing the full construct, we need to add a new line to *bilateral.h* that specifies the class @@ -1079,7 +1079,7 @@ class look like: /** \brief Get the half size of the Gaussian bilateral filter window as set by the user. */ double - getHalfSize () + getHalfSize () const { return (sigma_s_); } @@ -1095,7 +1095,7 @@ class look like: /** \brief Get the value of the current standard deviation parameter of the bilateral filter. */ double - getStdDev () + getStdDev () const { return (sigma_r_); } @@ -1133,7 +1133,7 @@ class look like: #endif // PCL_FILTERS_BILATERAL_H_ -And the *bilateral.hpp* like: +And the *bilateral.hpp* likes: .. code-block:: cpp :linenos: @@ -1249,7 +1249,7 @@ And the *bilateral.hpp* like: #define PCL_INSTANTIATE_BilateralFilter(T) template class PCL_EXPORTS pcl::BilateralFilter; - #endif // PCL_FILTERS_BILATERAL_H_ + #endif // PCL_FILTERS_BILATERAL_IMPL_H_ Testing the new class diff --git a/filters/include/pcl/filters/impl/bilateral.hpp b/filters/include/pcl/filters/impl/bilateral.hpp index fe0dfb00c3e..64687ae5b6a 100644 --- a/filters/include/pcl/filters/impl/bilateral.hpp +++ b/filters/include/pcl/filters/impl/bilateral.hpp @@ -109,5 +109,5 @@ pcl::BilateralFilter::applyFilter (PointCloud &output) #define PCL_INSTANTIATE_BilateralFilter(T) template class PCL_EXPORTS pcl::BilateralFilter; -#endif // PCL_FILTERS_BILATERAL_H_ +#endif // PCL_FILTERS_BILATERAL_IMPL_H_ diff --git a/io/include/pcl/io/file_io.h b/io/include/pcl/io/file_io.h index 02c00ddb183..c1c613e22b4 100644 --- a/io/include/pcl/io/file_io.h +++ b/io/include/pcl/io/file_io.h @@ -49,7 +49,7 @@ namespace pcl { /** \brief Point Cloud Data (FILE) file format reader interface. - * Any (FILE) format file reader should implement its virtual methodes. + * Any (FILE) format file reader should implement its virtual methods. * \author Nizar Sallem * \ingroup io */ @@ -155,7 +155,7 @@ namespace pcl }; /** \brief Point Cloud Data (FILE) file format writer. - * Any (FILE) format file reader should implement its virtual methodes + * Any (FILE) format file reader should implement its virtual methods * \author Nizar Sallem * \ingroup io */ @@ -221,9 +221,9 @@ namespace pcl } }; - /** \brief insers a value of type Type (uchar, char, uint, int, float, double, ...) into a stringstream. + /** \brief inserts a value of type Type (uchar, char, uint, int, float, double, ...) into a stringstream. * - * If the value is NaN, it inserst "nan". + * If the value is NaN, it inserts "nan". * * \param[in] cloud the cloud to copy from * \param[in] point_index the index of the point diff --git a/octree/octree.doxy b/octree/octree.doxy index d05db86a44b..a68de5461c7 100644 --- a/octree/octree.doxy +++ b/octree/octree.doxy @@ -21,7 +21,7 @@ deallocation operations in scenarios where octrees needs to be created at high r The following figure illustrates the voxel bounding boxes of an octree nodes at lowest tree level. The octree voxels are surrounding every 3D point from the bunny's surface. The red dots represent the point data. -This image is create with the octree_viewer (visualization/tools/octree_viewer). +This image is created with the octree_viewer (visualization/tools/octree_viewer). For examples how to use the pcl_octree library, please visit the pcl tutorial page.