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.. _visualization:
PCL Visualization overview
--------------------------
The **pcl_visualization** library was built for the purpose of being able to
quickly prototype and visualize the results of algorithms operating on 3D point
cloud data. Similar to OpenCV's **highgui** routines for displaying 2D images
and for drawing basic 2D shapes on screen, the library offers:
* methods for rendering and setting visual properties (colors, point sizes,
opacity, etc) for any n-D point cloud datasets in pcl::PointCloud<T> format;
.. image:: images/visualization/bunny.jpg
* methods for drawing basic 3D shapes on screen (e.g., cylinders, spheres,
lines, polygons, etc) either from sets of points or from parametric
equations;
.. image:: images/visualization/shapes.jpg
* a histogram visualization module (PCLHistogramVisualizer) for 2D plots;
.. image:: images/visualization/histogram.jpg
* a multitude of Geometry and Color handler for pcl::PointCloud<T> datasets;
.. image:: images/visualization/normals.jpg
.. image:: images/visualization/pcs.jpg
* a pcl::RangeImage visualization module.
.. image:: images/visualization/range_image.jpg
The package makes use of the VTK library for 3D rendering for
range image and 2D operations.
For implementing your own visualizers, take a look at the tests and examples
accompanying the library.
.. note::
Due to historical reasons, PCL 1.x stores RGB data as a packed float (to
preserve backward compatibility). To learn more about this, please see the
`PointXYZRGB
<https://pointclouds.org/documentation/structpcl_1_1_point_x_y_z_r_g_b.html>`_.
Simple Cloud Visualization
--------------------------
If you just want to visualize something in your app with a few lines of code,
use a snippet like the following one:
.. code-block:: cpp
:linenos:
#include <pcl_visualization/cloud_viewer.h>
//...
void
foo ()
{
pcl::PointCloud<pcl::PointXYZRGB> cloud;
//... populate cloud
pcl_visualization::CloudViewer viewer("Simple Cloud Viewer");
viewer.showCloud(cloud);
while (!viewer.wasStopped())
{
}
}
PCD Viewer
----------
A quick way for visualizing PCD (Point Cloud Data) files is by using
**pcl_viewer**. As of 0.2.7, pcl_viewer's help screen looks like::
Syntax is: pcl_viewer <file_name 1..N>.pcd <options>
where options are:
-bc r,g,b = background color
-fc r,g,b = foreground color
-ps X = point size (1..64)
-opaque X = rendered point cloud opacity (0..1)
-ax n = enable on-screen display of XYZ axes and scale them to n
-ax_pos X,Y,Z = if axes are enabled, set their X,Y,Z position in space (default 0,0,0)
-cam (*) = use given camera settings as initial view
(*) [Clipping Range / Focal Point / Position / ViewUp / Distance / Window Size / Window Pos] or use a <filename.cam> that contains the same information.
-multiview 0/1 = enable/disable auto-multi viewport rendering (default disabled)
-normals 0/X = disable/enable the display of every Xth point's surface normal as lines (default disabled)
-normals_scale X = resize the normal unit vector size to X (default 0.02)
-pc 0/X = disable/enable the display of every Xth point's principal curvatures as lines (default disabled)
-pc_scale X = resize the principal curvatures vectors size to X (default 0.02)
(Note: for multiple .pcd files, provide multiple -{fc,ps} parameters; they will be automatically assigned to the right file)
Usage examples
--------------
.. code-block:: bash
$ pcl_viewer -multiview 1 data/partial_cup_model.pcd data/partial_cup_model.pcd data/partial_cup_model.pcd
The above will load the ``partial_cup_model.pcd`` file 3 times, and will create a
multi-viewport rendering (``-multiview 1``).
.. image:: images/visualization/ex1.jpg
Pressing ``h`` while the point clouds are being rendered will output the
following information on the console::
| Help:
-------
p, P : switch to a point-based representation
w, W : switch to a wireframe-based representation (where available)
s, S : switch to a surface-based representation (where available)
j, J : take a .PNG snapshot of the current window view
c, C : display current camera/window parameters
+ / - : increment/decrement overall point size
g, G : display scale grid (on/off)
u, U : display lookup table (on/off)
r, R [+ ALT] : reset camera [to viewpoint = {0, 0, 0} -> center_{x, y, z}]
ALT + s, S : turn stereo mode on/off
ALT + f, F : switch between maximized window mode and original size
l, L : list all available geometric and color handlers for the current actor map
ALT + 0..9 [+ CTRL] : switch between different geometric handlers (where available)
0..9 [+ CTRL] : switch between different color handlers (where available)
Pressing ``l`` will show the current list of available geometry/color handlers
for the datasets that we loaded. In this example::
List of available geometry handlers for actor partial_cup_model.pcd-0: xyz(1) normal_xyz(2)
List of available color handlers for actor partial_cup_model.pcd-0: [random](1) x(2) y(3) z(4) normal_x(5) normal_y(6) normal_z(7) curvature(8) boundary(9) k(10) principal_curvature_x(11) principal_curvature_y(12) principal_curvature_z(13) pc1(14) pc2(15)
Switching to a ``normal_xyz`` geometric handler using ``ALT+1`` and then
pressing ``8`` to switch to a curvature color handler, should result in the
following::
$ pcl_viewer -normals 100 data/partial_cup_model.pcd
.. image:: images/visualization/ex2.jpg
The above will load the ``partial_cup_model.pcd`` file and render its every
``100`` th surface normal on screen.
.. code-block:: bash
$ pcl_viewer -pc 100 data/partial_cup_model.pcd
.. image:: images/visualization/ex3.jpg
The above will load the ``partial_cup_model.pcd`` file and render its every
``100`` th principal curvature (+surface normal) on screen.
.. image:: images/visualization/ex4.jpg
.. code-block:: bash
$ pcl_viewer data/bun000.pcd data/bun045.pcd -ax 0.5 -ps 3 -ps 1
The above assumes that the ``bun000.pcd`` and ``bun045.pcd`` datasets have been
downloaded and are available. The results shown in the following picture were
obtained after pressing ``u`` and ``g`` to enable the lookup table and on-grid
display.
.. image:: images/visualization/ex5.jpg
Range Image Visualizer
----------------------
A quick way for visualizing range images is by using the binary of the tutorial
for range_image_visualization::
$ tutorial_range_image_visualization data/office_scene.pcd
The above will load the ``office_scene.pcd`` point cloud file, create a range
image from it and visualize both, the point cloud and the range image.
|
