* tagging PCL 1.6.0-rc1

git-svn-id: svn+ssh://svn.pointclouds.org/pcl/tags/pcl-1.6.0rc1@6230 a9d63959-f2ad-4865-b262-bf0e56cfafb6
sparsebase · Jul 7, 2012 · ea8fe31 · ea8fe31
1 parent 0a44de3
commit ea8fe31
Show file tree

Hide file tree

Showing 117 changed files with 6,272 additions and 153 deletions.
diff --git a/apps/include/pcl/apps/organized_segmentation_demo.h b/apps/include/pcl/apps/organized_segmentation_demo.h
@@ -78,7 +78,7 @@ class OrganizedSegmentationDemo : public QMainWindow
     QTimer *vis_timer_;
     pcl::PointCloud<PointT> prev_cloud_;
     pcl::PointCloud<pcl::Normal> prev_normals_;
-    std::vector<pcl::PlanarRegion<PointT> > prev_regions_;
+    std::vector<pcl::PlanarRegion<PointT>, Eigen::aligned_allocator<pcl::PlanarRegion<PointT> > > prev_regions_;
     float* prev_distance_map_;
 
     pcl::PointCloud<PointT>::CloudVectorType prev_clusters_;

diff --git a/apps/src/ni_linemod.cpp b/apps/src/ni_linemod.cpp
@@ -316,7 +316,7 @@ class NILinemod
       mps_.setInputCloud (search_.getInputCloud ());
 
       // Use one of the overloaded segmentAndRefine calls to get all the information that we want out
-      vector<PlanarRegion<PointT> > regions;
+      vector<PlanarRegion<PointT>, Eigen::aligned_allocator<PlanarRegion<PointT> > > regions;
       vector<ModelCoefficients> model_coefficients;
       vector<PointIndices> inlier_indices;  
       PointCloud<Label>::Ptr labels (new PointCloud<Label>);

diff --git a/apps/src/openni_organized_multi_plane_segmentation.cpp b/apps/src/openni_organized_multi_plane_segmentation.cpp
@@ -133,7 +133,7 @@ class OpenNIOrganizedMultiPlaneSegmentation
       mps.setAngularThreshold (0.017453 * 2.0); //3 degrees
       mps.setDistanceThreshold (0.02); //2cm
 
-      std::vector<pcl::PlanarRegion<PointT> > regions;
+      std::vector<pcl::PlanarRegion<PointT>, Eigen::aligned_allocator<pcl::PlanarRegion<PointT> > > regions;
       pcl::PointCloud<PointT>::Ptr contour (new pcl::PointCloud<PointT>);
       size_t prev_models_size = 0;
       char name[1024];

diff --git a/apps/src/organized_segmentation_demo.cpp b/apps/src/organized_segmentation_demo.cpp
@@ -12,7 +12,7 @@
 #include <pcl/surface/convex_hull.h>
 
 void
-displayPlanarRegions (std::vector<pcl::PlanarRegion<PointT> > &regions, 
+displayPlanarRegions (std::vector<pcl::PlanarRegion<PointT>, Eigen::aligned_allocator<pcl::PlanarRegion<PointT> > > &regions, 
                       boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer)
 {
   char name[1024];
@@ -289,7 +289,7 @@ OrganizedSegmentationDemo::cloud_cb (const CloudConstPtr& cloud)
 
   // Segment Planes
   double mps_start = pcl::getTime ();
-  std::vector<pcl::PlanarRegion<PointT> > regions;
+  std::vector<pcl::PlanarRegion<PointT>, Eigen::aligned_allocator<pcl::PlanarRegion<PointT> > > regions;
   std::vector<pcl::ModelCoefficients> model_coefficients;
   std::vector<pcl::PointIndices> inlier_indices;  
   pcl::PointCloud<pcl::Label>::Ptr labels (new pcl::PointCloud<pcl::Label>);

diff --git a/apps/src/pcd_organized_multi_plane_segmentation.cpp b/apps/src/pcd_organized_multi_plane_segmentation.cpp
@@ -130,7 +130,7 @@ class PCDOrganizedMultiPlaneSegmentation
       mps.setDistanceThreshold (0.03); //2cm
       mps.setRefinementComparator (refinement_compare);
 
-      std::vector<pcl::PlanarRegion<PointT> > regions;
+      std::vector<pcl::PlanarRegion<PointT>, Eigen::aligned_allocator<pcl::PlanarRegion<PointT> > > regions;
       typename pcl::PointCloud<PointT>::Ptr contour (new pcl::PointCloud<PointT>);
       typename pcl::PointCloud<PointT>::Ptr approx_contour (new pcl::PointCloud<PointT>);
       char name[1024];

diff --git a/doc/tutorials/content/basic_structures.rst b/doc/tutorials/content/basic_structures.rst
@@ -25,6 +25,13 @@ PointCloud is a C++ class which contains the following data fields:
        much more efficient, thus speeding up the computation and lowering the
        costs of certain algorithms in PCL.
 
+    .. note::
+
+       An **projectable point cloud** dataset is the name given to point clouds
+       that have a correlation according to a pinhole camera model between the (u,v) index
+       of a point in the organized point cloud and the actual 3D values. This correlation can be
+       expressed in it's easiest form as: u = f*x/z and v = f*y/z
+
     Examples::
 
       cloud.width = 640; // there are 640 points per line

diff --git a/doc/tutorials/content/cluster_extraction.rst b/doc/tutorials/content/cluster_extraction.rst
@@ -55,7 +55,7 @@ The Code
 --------
 
 First, download the dataset `table_scene_lms400.pcd
-<http://dev.pointclouds.org/attachments/download/157/table_scene_lms400.pcd>`_ and save it somewhere to disk.
+<http://svn.pointclouds.org/data/tutorials/table_scene_lms400.pcd>`_ and save it somewhere to disk.
 
 Then, create a file, let's say, ``cluster_extraction.cpp`` in your favorite
 editor, and place the following inside it:

diff --git a/doc/tutorials/content/compiling_pcl_windows.rst b/doc/tutorials/content/compiling_pcl_windows.rst
@@ -206,11 +206,11 @@ Let's check whether CMake did actually find the needed third party dependencies
 
 - **Qt** :
 
-    It is highly recommended to install Qt to the default path suggested by the installer. You need then to define an 
+	It is highly recommended to install Qt to the default path suggested by the installer. You need then to define an 
 	environment variable named **QTDIR** to point to Qt installation path (e.g. `C:\\Qt\\4.8.0`). Also, you need to
 	append the bin folder to the **PATH** environment variable. Once you modify the environment variables, you need to
 	restart CMake and click "Configure" again. If Qt is not found, you need at least to fill **QT_QMAKE_EXECUTABLE**
-	CMake entry with the path of `qmake.exe` (e.g. C:\\Qt\\4.8.0\\bin\qmake.exe), then click "Configure".
+	CMake entry with the path of `qmake.exe` (e.g. C:\\Qt\\4.8.0\\bin\\qmake.exe), then click "Configure".
 
 - **VTK** :
 

diff --git a/doc/tutorials/content/convex_hull_2d.rst b/doc/tutorials/content/convex_hull_2d.rst
@@ -8,7 +8,7 @@ for a set of points supported by a plane.
 
 The following video shows a demonstration of the code given below on the test
 dataset `table_scene_mug_stereo_textured.pcd 
-<http://dev.pointclouds.org/attachments/download/158/table_scene_mug_stereo_textured.pcd>`_.
+<http://svn.pointclouds.org/data/tutorials/table_scene_mug_stereo_textured.pcd>`_.
 
 .. raw:: html
 
@@ -18,7 +18,7 @@ The code
 --------
 
 First, download the dataset `table_scene_mug_stereo_textured.pcd
-<http://dev.pointclouds.org/attachments/download/158/table_scene_mug_stereo_textured.pcd>`_
+<http://svn.pointclouds.org/data/tutorials/table_scene_mug_stereo_textured.pcd>`_
 and save it somewhere to disk.
 
 Then, create a file, let's say, ``convex_hull_2d.cpp`` in your favorite

diff --git a/doc/tutorials/content/cylinder_segmentation.rst b/doc/tutorials/content/cylinder_segmentation.rst
@@ -23,7 +23,7 @@ The code
 --------
 
 First, download the dataset `table_scene_mug_stereo_textured.pcd
-<http://dev.pointclouds.org/attachments/download/158/table_scene_mug_stereo_textured.pcd>`_
+<http://svn.pointclouds.org/data/tutorials/table_scene_mug_stereo_textured.pcd>`_
 and save it somewhere to disk.
 
 Then, create a file, let's say, ``cylinder_segmentation.cpp`` in your favorite

diff --git a/doc/tutorials/content/extract_indices.rst b/doc/tutorials/content/extract_indices.rst
@@ -17,7 +17,7 @@ The code
 --------
 
 First, download the dataset `table_scene_lms400.pcd
-<http://dev.pointclouds.org/attachments/download/157/table_scene_lms400.pcd>`_
+<http://svn.pointclouds.org/data/tutorials/table_scene_lms400.pcd>`_
 and save it somewhere to disk.
 
 Then, create a file, let's say, ``extract_indices.cpp`` in your favorite

diff --git a/doc/tutorials/content/hull_2d.rst b/doc/tutorials/content/hull_2d.rst
@@ -11,7 +11,7 @@ The code
 --------
 
 First, download the dataset `table_scene_mug_stereo_textured.pcd
-<http://dev.pointclouds.org/attachments/download/158/table_scene_mug_stereo_textured.pcd>`_
+<http://svn.pointclouds.org/data/tutorials/table_scene_mug_stereo_textured.pcd>`_
 and save it somewhere to disk.
 
 Then, create a file, let's say, ``concave_hull_2d.cpp`` or
@@ -23,10 +23,11 @@ Then, create a file, let's say, ``concave_hull_2d.cpp`` or
 
 .. note::
 
-   This tutorial is written for assuming you are looking for the **CONCAVE**
-   hull.  If you would like the **CONVEX** hull for a plane model, just replace
-   concave with convex at EVERY point in this tutorial, including the source
-   file, file names and the CMakeLists.txt file.
+   This tutorial is written for assuming you are looking for the **CONCAVE** hull.
+   If you would like the **CONVEX** hull for a plane model, just replace concave
+   with convex at EVERY point in this tutorial, including the source file, file
+   names and the CMakeLists.txt file. You will also need to comment out
+   setAlpha(), as this is not applicable to convex hulls.
 
 The explanation
 ---------------

diff --git a/doc/tutorials/content/images/3dtree.png b/doc/tutorials/content/images/3dtree.png
diff --git a/doc/tutorials/content/images/bunny.jpg b/doc/tutorials/content/images/bunny.jpg
diff --git a/doc/tutorials/content/images/cylinder_model_seg.png b/doc/tutorials/content/images/cylinder_model_seg.png
diff --git a/doc/tutorials/content/images/ex1.jpg b/doc/tutorials/content/images/ex1.jpg
diff --git a/doc/tutorials/content/images/features_bunny.png b/doc/tutorials/content/images/features_bunny.png
diff --git a/doc/tutorials/content/images/features_normal.png b/doc/tutorials/content/images/features_normal.png
diff --git a/doc/tutorials/content/images/features_small.png b/doc/tutorials/content/images/features_small.png
diff --git a/doc/tutorials/content/images/filters_small.png b/doc/tutorials/content/images/filters_small.png
diff --git a/doc/tutorials/content/images/form_0.png b/doc/tutorials/content/images/form_0.png
diff --git a/doc/tutorials/content/images/form_1.png b/doc/tutorials/content/images/form_1.png
diff --git a/doc/tutorials/content/images/histogram.jpg b/doc/tutorials/content/images/histogram.jpg
diff --git a/doc/tutorials/content/images/io_small.jpg b/doc/tutorials/content/images/io_small.jpg
diff --git a/doc/tutorials/content/images/kdtree_mug.png b/doc/tutorials/content/images/kdtree_mug.png
diff --git a/doc/tutorials/content/images/kdtree_small.png b/doc/tutorials/content/images/kdtree_small.png
diff --git a/doc/tutorials/content/images/keypoints_small.png b/doc/tutorials/content/images/keypoints_small.png
diff --git a/doc/tutorials/content/images/normals.jpg b/doc/tutorials/content/images/normals.jpg
diff --git a/doc/tutorials/content/images/octree_bunny.png b/doc/tutorials/content/images/octree_bunny.png
diff --git a/doc/tutorials/content/images/octree_bunny2.png b/doc/tutorials/content/images/octree_bunny2.png
diff --git a/doc/tutorials/content/images/octree_small.png b/doc/tutorials/content/images/octree_small.png
diff --git a/doc/tutorials/content/images/pcs.jpg b/doc/tutorials/content/images/pcs.jpg
diff --git a/doc/tutorials/content/images/plane_model_seg.png b/doc/tutorials/content/images/plane_model_seg.png
diff --git a/doc/tutorials/content/images/range_image.jpg b/doc/tutorials/content/images/range_image.jpg
diff --git a/doc/tutorials/content/images/range_image_small.png b/doc/tutorials/content/images/range_image_small.png
diff --git a/doc/tutorials/content/images/registration_small.png b/doc/tutorials/content/images/registration_small.png
diff --git a/doc/tutorials/content/images/s1-6.png b/doc/tutorials/content/images/s1-6.png
diff --git a/doc/tutorials/content/images/sample_consensus_planes_cylinders.png b/doc/tutorials/content/images/sample_consensus_planes_cylinders.png
diff --git a/doc/tutorials/content/images/sample_consensus_small.png b/doc/tutorials/content/images/sample_consensus_small.png
diff --git a/doc/tutorials/content/images/scans.png b/doc/tutorials/content/images/scans.png
diff --git a/doc/tutorials/content/images/segmentation_small.png b/doc/tutorials/content/images/segmentation_small.png
diff --git a/doc/tutorials/content/images/shapes.jpg b/doc/tutorials/content/images/shapes.jpg
diff --git a/doc/tutorials/content/images/surface_hull.png b/doc/tutorials/content/images/surface_hull.png
diff --git a/doc/tutorials/content/images/surface_meshing.png b/doc/tutorials/content/images/surface_meshing.png
diff --git a/doc/tutorials/content/images/surface_small.png b/doc/tutorials/content/images/surface_small.png
diff --git a/doc/tutorials/content/images/visualization_small.png b/doc/tutorials/content/images/visualization_small.png
diff --git a/doc/tutorials/content/index.rst b/doc/tutorials/content/index.rst
@@ -25,6 +25,7 @@ Table of contents
   * :ref:`kdtree_tutorial`
   * :ref:`octree_tutorial`
   * :ref:`range_images`
+  * :ref:`recognition_tutorial`
   * :ref:`registration_tutorial`
   * :ref:`sample_consensus`
   * :ref:`segmentation_tutorial`
@@ -55,7 +56,7 @@ Basic Usage
   * :ref:`using_pcl_pcl_config`
 
      ======  ======
-     |mi_1|  Title: **Using PCL in own project**
+     |mi_1|  Title: **Using PCL in your own project**
 
              Author: *Nizar Sallem*
 
@@ -130,7 +131,7 @@ Basic Usage
   * :ref:`installing_homebrew`
 
      ======  ======
-     |mi_5|  Title: **Installing on Mac OS X using Homebrew**
+     |mi_6|  Title: **Installing on Mac OS X using Homebrew**
 
              Author: *Geoffrey Biggs*
 
@@ -139,7 +140,7 @@ Basic Usage
              This tutorial explains how to install the Point Cloud Library on Mac OS X using Homebrew. Both direct installation and compiling PCL from source are explained.
      ======  ======
 
-     .. |mi_5| image:: images/macosx_logo.png
+     .. |mi_6| image:: images/macosx_logo.png
                :height: 100px
 
 .. _advanced_usage:
@@ -616,7 +617,7 @@ Registration
 
              Author: *Dirk Holz, Radu B. Rusu, Jochen Sprickerhof*
 
-             Compatibility: PCL 1.2
+             Compatibility: > PCL 1.5
 
              In this document, we describe the point cloud registration API and its modules: the estimation and rejection of point correspondences, and the estimation of rigid transformations.
      ======  ======

diff --git a/doc/tutorials/content/installing_homebrew.rst b/doc/tutorials/content/installing_homebrew.rst
@@ -37,7 +37,7 @@ which parts of PCL are installed.
 
    The PCL formula is currently in development. It will be submitted to
    Homebrew shortly. Until then, you can download it from
-   `PCL.RB <http://dev.pointclouds.org/attachments/download/604/pcl.rb>`_. To prepare it,
+   `PCL.RB <http://dev.pointclouds.org/attachments/download/876/pcl.rb>`_. To prepare it,
    follow these steps:
 
 
@@ -51,18 +51,11 @@ To install using the formula, execute the following command::
 
   $ brew install pcl
 
-.. note::
-
-   The current version of PCL (1.3.1) properly supports the visualization
-   module for Mac OS X. However if you do want to disable it, you can run::
-
-     $ brew install pcl --novis
-
 You can specify options to control which parts of PCL are installed. For
-example, to disable the Python bindings and Segmentation, and enable the
+example, to disable the Python bindings and visualisation, and enable the
 documentation, execute the following command::
 
-  $ brew install pcl --nopython --nosegmentation --doc
+  $ brew install pcl --nopython --novis --doc
 
 For a full list of the available options, see the formula's help::
 

diff --git a/doc/tutorials/content/normal_estimation_using_integral_images.rst b/doc/tutorials/content/normal_estimation_using_integral_images.rst
@@ -10,92 +10,71 @@ cloud using integral images.
 The code
 --------
 
-First, create a file, let's say, ``normal_estimation.cpp`` in your favorite
+First, create a file, let's say, ``normal_estimation_using_integral_images.cpp`` in your favorite
 editor, and place the following inside it:
 
 .. code-block:: cpp
    :linenos:
 
-    #include <pcl/point_cloud.h>
-    #include <pcl/point_types.h>
-    #include <pcl/features/integral_image_normal.h>
-
-    int
-    main (int argc, char** argv)
-    {
-      pcl::PointCloud<pcl::PointXYZ> cloud;
-
-      // ... fill point cloud...
-
-      cloud.width = 640;
-      cloud.height = 480;
-      cloud.points.resize (cloud.width * cloud.height);
-
-      for (int ri = 0; ri < cloud.height; ++ri)
-      {
-        for (int ci = 0; ci < cloud.width; ++ci)
-        {
-          const float depth = 0.2f*static_cast<float> (rand ()) / static_cast<float>(RAND_MAX) + 1.0f;
-          cloud.points (ri, ci).x = (ci - 320) * depth;
-          cloud.points (ri, ci).y = (ri - 240) * depth;
-          cloud.points (ri, ci).z = depth;
-         }
-       }
-
-      // Estimate normals
-      pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
-
-      pcl::PointCloud<pcl::Normal> normals;
-	  
-      ne.setNormalEstimationMethod (ne.AVERAGE_DEPTH_CHANGE);
-      ne.setMaxDepthChangeFactor(0.02f);
-      ne.setNormalSmoothingSize(10.0f);
-      ne.setInputCloud(cloud);
-      ne.compute(normals);
-
-      return (0);
-    }
+	#include <pcl/io/io.h>
+	#include <pcl/io/pcd_io.h>
+	#include <pcl/features/integral_image_normal.h>
+	#include <pcl/visualization/cloud_viewer.h>
+		
+	int 
+	main ()
+	{
+		// load point cloud
+		pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
+		pcl::io::loadPCDFile ("table_scene_mug_stereo_textured.pcd", *cloud);
+		
+		// estimate normals
+		pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);
+
+		pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
+		ne.setNormalEstimationMethod (ne.AVERAGE_3D_GRADIENT);
+		ne.setMaxDepthChangeFactor(0.02f);
+		ne.setNormalSmoothingSize(10.0f);
+		ne.setInputCloud(cloud);
+		ne.compute(*normals);
+
+		// visualize normals
+		pcl::visualization::PCLVisualizer viewer("PCL Viewer");
+		viewer.setBackgroundColor (0.0, 0.0, 0.5);
+		viewer.addPointCloudNormals<pcl::PointXYZ,pcl::Normal>(cloud, normals);
+		
+		while (!viewer.wasStopped ())
+		{
+		  viewer.spinOnce ();
+		}
+		return 0;
+	}
 
 The explanation
 ---------------
 
-Now, let's break down the code piece by piece. In the first part we create a
-random point cloud for which we estimate the normals:
+Now, let's break down the code piece by piece. In the first part we load a
+point cloud from a file:
 
 .. code-block:: cpp
 
-    pcl::PointCloud<pcl::PointXYZ> cloud;
-    // ... fill point cloud...
-
-    cloud.width = 640;
-    cloud.height = 480;
-    cloud.points.resize (cloud.width*cloud.height);
-
-    for (int ri = 0; ri < cloud.height; ++ri)
-    {
-      for (int ci = 0; ci < cloud.width; ++ci)
-      {
-        const float depth = 0.2f*static_cast<float> (rand ()) / static_cast<float>(RAND_MAX) + 1.0f;
-        cloud.points (ri, ci).x = (ci - 320) * depth;
-        cloud.points (ri, ci).y = (ri - 240) * depth;
-        cloud.points (ri, ci).z = depth;
-       }
-     }
+	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
+	pcl::io::loadPCDFile ("table_scene_mug_stereo_textured.pcd", *cloud);
 
 In the second part we create an object for the normal estimation and compute
 the normals:
 
 .. code-block:: cpp
 
-    // Estimate normals
-    pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
+	// estimate normals
+	pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);
 
-    pcl::PointCloud<pcl::Normal> normals;
-    ne.setNormalEstimationMethod (ne.AVERAGE_DEPTH_CHANGE);
-    ne.setMaxDepthChangeFactor(0.02f);
-    ne.setNormalSmoothingSize(10.0f);
-    ne.setInputCloud(cloud);
-    ne.compute(normals);
+	pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
+	ne.setNormalEstimationMethod (ne.AVERAGE_3D_GRADIENT);
+	ne.setMaxDepthChangeFactor(0.02f);
+	ne.setNormalSmoothingSize(10.0f);
+	ne.setInputCloud(cloud);
+	ne.compute(*normals);
 
 The following normal estimation methods are available:
 
@@ -116,3 +95,18 @@ cross-product between these two gradients. The AVERAGE_DEPTH_CHANGE mode
 creates only a single integral image and computes the normals from the average
 depth changes.
 
+In the last part we visualize the point cloud and the corresponding normals:
+
+.. code-block:: cpp
+
+	// visualize normals
+	pcl::visualization::PCLVisualizer viewer("PCL Viewer");
+	viewer.setBackgroundColor (0.0, 0.0, 0.5);
+	viewer.addPointCloudNormals<pcl::PointXYZ,pcl::Normal>(cloud, normals);
+	
+	while (!viewer.wasStopped ())
+	{
+	  viewer.spinOnce ();
+	}
+
+