This project is taken from: Navarro-Hinojosa, Octavio, y Moisés Alencastre-Miranda. "DBSCAN modificado con Octrees para agrupar nubes de puntos en tiempo real." Research in Computing Science, Vol. 114: Advances in Image Processing and Computer Vision, pp. 173–186, 2016. Github: https://github.com/Hagen23/DBScan_Octrees
It was modified with:
- It was added a CMakeList.txt for cmake compilation with PCL 1.9.0 (support 1.9.1)
- It was added an argument param option
- It was added a pcl visualizer
- It was deleted the Glut visualizer
- It was added a cluster saving method
- It was added a cluster coloring method
- It was replaced the input file from CSV to PCD
- It was added a cluster coloring method for original color of the point cloud
| Format | Description |
|---|---|
| .pcd | Point Cloud Data file format |
| .ply | Polygon file format |
| .txt | Text file format |
| .xyz | X Y Z Text file format |
➜ ./app --help
*************************************
*** DBSCAN Cluster Segmentation ***
*************************************
Usage: ./app [options]
Optional arguments:
-h --help shows help message and exits [default: false]
-v --version prints version information and exits [default: false]
--cloudfile input cloud file [required]
--octree-res octree resolution [default: 120]
--eps epsilon value [default: 40]
--minPtsAux minimum auxiliar points [default: 5]
--minPts minimum points [default: 5]
-o --output-dir output dir to save clusters [default: "-"]
--ext cluster output extension [pcd, ply, txt, xyz] [default: "pcd"]
-d --display display clusters in the pcl visualizer [default: false]
--cal-eps calculate the value of epsilon with the distance to the nearest n points [default: false]
This projects depends on the Point Cloud Library (it works with version 1.8...1.12.1) and its dependencies.
| Package | Version | Description |
|---|---|---|
| VTK | 9.0.0 | Visualization toolkit |
| PCL | 1.12.1 | The Point Cloud Library (PCL) |
| Eigen | 3.7.7 | Eigen is a library of template headers for linear algebra |
| Flann | 1.9.1 | Fast Library for Approximate Nearest Neighbors |
| Boost | 1.77.0 | Provides support for linear algebra, pseudorandom number generation, multithreading |
| OpenGL | 21.2.6 | Programming interface for rendering 2D and 3D vector graphics. |
This project has been tested with VTK 8.1...9.1 and CMake from 3.5...3.21
You can build the project from source or download a docker image stored in docker hub, here. This image is compiled with pcl-docker-1.12.1, Alpine linux 3.15 and the DBscan project (1.32GB).
- Download source code
git clone --recursive https://github.com/danielTobon43/DBScan-PCL-Optimized.git
- Create a "build" folder at the top level of the DBScan-PCL-Optimized project
cd DBScan-PCL-Optimized/ && mkdir build
- Compile with CMake
cd build/ && cmake ../ && make
In the build folder run the following command:
./app --cloudfile PATH/TO/YOUR/CLOUDFILE
Note ¡You can modify the parameters to obtain better results! I recommend modifying only the eps value, between 40 - 60 you can get better clusters, or 0.5 to 10.
There is a flag to calculate epsilon using 100 points with --cal-eps. Please check Command line.
To use it you have to install docker-engine in your host machine:
Docker multi-stage graph generated with: dockerfilegraph
docker pull ghcr.io/danieltobon43/dbscan-octrees:latest
docker images
You can either run a docker command or create a shell script.
1. Option 1: Docker command
docker run --rm -it \
--volume=/tmp/.X11-unix:/tmp/.X11-unix:rw \
--volume=/tmp/.docker.xauth:/tmp/.docker.xauth:rw \
--env="XAUTHORITY=/tmp/.docker.xauth" \
--env="DISPLAY" \
--name="dbscan" \
--volume=[PATH TO YOUR PCD FOLDER]:/tmp \
-t ghcr.io/danieltobon43/dbscan-octrees:latest --cloudfile /tmp/[YOUR PCD FILENAME]
If you get something like this after setting --display flag it might be related to this:
No protocol specifiedTry running with the following command belowe or use the provided .sh script:
sudo -sE docker run --rm -it \
--env="DISPLAY" \
--volume=/tmp/.X11-unix:/tmp/.X11-unix:rw \
--volume=/tmp/.docker.xauth:/tmp/.docker.xauth:rw \
--name="dbscan" \
--volume=[PATH TO YOUR PCD FOLDER]:/tmp \
-t ghcr.io/danieltobon43/dbscan-octrees:latest --cloudfile /tmp/[YOUR PCD FILENAME] --display
If this still does not work to display, run: xhost +local:docker and then run the option 1 command.
2. Option 2: shell script
- Create a
visualizer.shfile with executable permissions (check this shell script dbscan-shell-script).
- Copy the next content into the
visualizer.shfile (remember to update PATH/TO/YOUR/PCD/PLY/FOLDER accordingly):
# Allow X server connection
xhost +local:root
docker run -it --rm \
--env="DISPLAY" \
--env="QT_X11_NO_MITSHM=1" \
--name="pcl-container" \
--volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
--volume=PATH/TO/YOUR/PCD/PLY/FOLDER:/tmp \
ghcr.io/danieltobon43/dbscan-octrees:latest /tmp/$1
# Disallow X server connection
xhost -local:root
- Run the docker container
./visualizer YOUR/CLOUD/FILENAME
- Install a Xserver on Windows, XLaunch
- Run the docker container: Open a PowerShell Terminal with Administrator privileges and run the following command
docker run --rm -it `
--env DISPLAY=host.docker.internal:0.0 `
--volume //c/Users/YOURUSERNAME/Downloads/pcds:/tmp `
-t ghcr.io/danieltobon43/dbscan-octrees:latest `
--cloudfile /tmp/YOURFILENAME `
--display
More info about running Docker GUI containers on Windows, check this video
example:
I have a .pcd file called Tree2.pcd stored in:
/home/user/Downloads/pcd/Tree2.pcd
To run a docker container with the previous .pcd file I will open a terminal in "Downloads folder" and use pwd from ubuntu to get my current directory path in the terminal and then:
docker run --rm -it \
--volume=/tmp/.X11-unix:/tmp/.X11-unix:rw \
--volume=/tmp/.docker.xauth:/tmp/.docker.xauth:rw \
--env="XAUTHORITY=/tmp/.docker.xauth" \
--env="DISPLAY" \
--name="dbscan" \
--cap-add sys_ptrace \
-p 127.0.0.1:2222:22 \
--user=pcl \
--volume=`pwd`/pcd:/tmp \
-t ghcr.io/danieltobon43/dbscan-octrees:latest --cloudfile /tmp/Tree2.pcd
The previous command will run a docker container with the dbscan-octrees:1.1-alpine3.15 image and will share a .pcd file from the host machine ([PATH TO YOUR PCD FOLDER]) to the tmp folder in the container.
Be aware that, the mounted directory in the host machine will copy all the files in the target directory in the container. That's why I recommend to create a folder to store just .pcd/.ply/etc files that will be use with the container.
More information about this docker image can be found in the docker hub repository.
How to choose epsilon value:
Where K-distance is the distance from each point to its closest neighbour using the K-NearestNeighbors. The point itself is included in n_neighbors. The kneighbors method returns two arrays, one which contains the distance to the closest n_neighbors points and the other which contains the index for each of those points.
The graph is built with:
X_array = [0,1,2,3,4, ...1000]
Y_array = [0.0,0.1,0.2,...1.0]
X_array.size() = Y_array.size()
Enable --cal-eps flag:
You can export the generated clusters by providing the --output-dir flag. This will save the clusters in the specified directoy. The default format is pcd, but you can choose from: ply, txt, xyz using the --ext flag.
Note:
There is a bug with the PCL visualizer using VTK 9.1 which causes a segmentation default core dumped after the visualizer is closed. This might cause crash the program before exporting the clusters. I recommend enable the --output-dir flag without the -d or --display flag for visualization.
To display the generated clusters enable the -d or --display flag in the command line.
if compiling the project with PCL-1.9.1 this occurs:
-- Build files have been written to: /home/t00215031/Downloads/DBScan-PCL-Optimized-master/build
[ 20%] Building CXX object CMakeFiles/dbscan.dir/main.cpp.o
In file included from /opt/pcl-1.9.1/common/include/pcl/pcl_macros.h:75:0,
from /opt/pcl-1.9.1/octree/include/pcl/octree/octree_nodes.h:47,
.
.
.
/opt/pcl-1.9.1/build/include/pcl/pcl_config.h:7:4: error: #error PCL requires C++14 or above
#error PCL requires C++14 or above
^
CMakeFiles/dbscan.dir/build.make:62: recipe for target 'CMakeFiles/dbscan.dir/main.cpp.o' failed
make[2]: *** [CMakeFiles/dbscan.dir/main.cpp.o] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/dbscan.dir/all' failed
make[1]: *** [CMakeFiles/dbscan.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2
## Solution
1. Update gcc and g++ to version 6:
$ sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
$ sudo apt-get update -y
$ sudo apt-get install -y gcc-6 g++-6 clang-3.8
$ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-6 70 --slave /usr/bin/g++ g++ /usr/bin/g++-6
--> check gcc and g++ version:
$ gcc --version
$ g++ --version
g++ (Ubuntu 6.5.0-2ubuntu1~16.04) 6.5.0 20181026
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
2. Compile again
$ cmake../ && make
if:
../../../bin/librtabmap_core.so.0.11.11: undefined reference to `pcl::search::Search<pcl::PointXYZRGBNormal>::getName[abi:cxx11]() const'
../../../bin/librtabmap_core.so.0.11.11: undefined reference to `pcl::search::Search<pcl::PointXYZRGB>::getName[abi:cxx11]() const'
../../../bin/librtabmap_core.so.0.11.11: undefined reference to `pcl::search::Search<pcl::PointXYZ>::getName[abi:cxx11]() const'
collect2: error: ld returned 1 exit status
Solution:
#include <pcl/search/impl/search.hpp>
#ifndef PCL_NO_PRECOMPILE
#include <pcl/impl/instantiate.hpp>
#include <pcl/point_types.h>
PCL_INSTANTIATE(Search, PCL_POINT_TYPES)
#endif // PCL_NO_PRECOMPILE