# Create your own RoboKudo package

RoboKudo offers methods to separate your own perception routines, analysis engines and much more into separate ROS packages.
Let us start by creating a package called``rk_tutorial``. Make sure you are in the source folder of a catkin workspace and run:
```
rosrun robokudo rk_create_package rk_tutorial
```

The script will create a new ROS package that has the structure needed for RoboKudo:
```
   'rk_tutorial'
    |-src                       -> code and configuration base
       |-rk_tutorial            -> name of your package
          |-annotators
          |-descriptors
             |-analysis_engines -> Pipeline/Analysis Engine definitions (Behaviour Trees)
    |-package.xml               -> catkin package xml
    |-setup.py                  -> python install information
    |-CMakeLists.txt            -> CMake file
```
	
You can now add your own annotators and analysis engines(also called pipelines) that can use any component defined in the RoboKudo core package. If you want ``rk_tutorial`` to depend on other RoboKudo packages add them to the ``package.xml`` and ``CmakeLists.txt``.

To verify your new package, let us create your very first own analysis engine.
Please go to the src folder of your ROS workspace where RoboKudo is located. Then go to ```robokudo/robokudo/src/robokudo/descriptors/analysis_engines/demo.py``` and copy that file to your newly created package into ```rk_tutorial/src/rk_tutorial/descriptors/analysis_engines/my_demo.py ``` (please note the renaming).
Open the file and adapt in the ```name``` method the return value to 'mydemo' instead of 'demo'.
Please now make sure to ```catkin build``` your workspace and source your setup files.
Otherwise the new package might not be found.

After that, you can start your own analysis engine by executing:

```
rosrun robokudo main.py _ae=my_demo _ros_pkg=rk_tutorial
```

It should show the same result as in the previous tutorial.