The goal of this project is to create a file that, given the correct input, is able to:
- analyse the scene
- identify objects in the scene
- roto-translate the point cloud of each object to match their position in the scene
- compute a superquadric for each new point cloud
The script works with all UNIX-based systems. However, in order to minimize changes by the user, a separate script for macOS was created.
In order to function fully and correctly, YCB-Video Augmented Dataset requires:
The python script works instantly, however, the use of a virtual environment is reccomended. The following commands create a virtual environment with the required dependencies installed.
$ virtualenv $NAME_OF_ENV
$ source $NAME_OF_ENV/bin/activate
$ pip3 install -r requirements.txt
Once all requirements are installed, it is sufficient to run the script using
$ python3 pointcloud-to-superquadric.py
** If used on MacOS, use the pointcould-to-superquadric-macos.py file instead.
When the file asks for the path to a folder, it is sufficient to drag & drop on the terminal the YCB-Video folder that contains the scene to be analysed. An example of the input folder can be found at Example_Scenes/0001.
The script will then identify the objects in the scene, find the corresponding point clouds, and roto-translate them to match the input frames. The resulting point cloud will be saved in the same folder under the name $FRAME_NUMBER-$OBJECT-rototranslated.xyz. An example of the output can be found at Example_Scenes/0000.
The script will also calculate an adeguate superquadric for each object in the scene, and save its parameters in the file [frame]-[object]-superquadric.txt under the format [a 1, a 2, a 3, ε 1, ε 2, dx, dy, dz, θ, φ, ψ]
currently not working
Once calculated the parameters for the superquadric, the script will proceed to render the superquadric using vtk and saving it in the appropriate folder.