This program utilizes blender to generate image datasets. Currently there are two classes. They are smilar looking, abstract animals, made of eight blocks. See Section Classes for details. You can either install the module and create your own datasets or download an example dataset with 300,000 images.
The installation process is less then ideal. Download this repository.
git clone https://github.com/laserschwelle/242.git
Create a python3 environment and install the requirements.
pip install -r requirements.txt
Open two4two/blender.py and change self.package_directory to the path where you cloned this directory.
make
If this worked, you should be good. If not, here are some more details for the installation.
The programm can only run, if Blender 2.83 is installed in two4two/blender.
The programm was tested with version 2.83.9.
The Makefile should download blender, install pip for its bundled python and install the two4two module.
The download link within the Makefile is a mirror for Germany.
If any problems occur please see the script and reproduce the steps.
You need to put blender in the module folder and install pip for blender.
Then use pip to install numpy, scipy, matplotlib and a ipykernel.
After installing blender, use setuptools to install the two4two module for both the systems python as well as blender's python.
python setup.py install && ./blender/2.83/python/bin/python3.7m setup.py install
The two classes for classifcation are sticky and stretchy. Both objects are build from 8 blocks that can have different shapes. Either cubes, spheres or something in between. A sticky object made from cubes looks like this:
A stretchy object made from spheres looks like this:
The shapes can also morph from spherical to cube like. Here is an example for something in between:
These objects can be put into a random pose.
You can change color of both object and background.
To blur the difference between the two classes the position of the class-defining set of arms can be randomly shifted. By choosing a distribution for this the separation between the classes can be adjusted. The following three pictures are a sticky object with arm shift 0.
Arm shift 0.5. This is equal to a stretchy object with shift 0.5.
And finally shift 1. This is equal to a stretchy object with shift 0.
Further parameters that can be adjusted: Object rotation (2 axis), a random flip and the position within the frame. Documentation obviously needs to be improved further.
You can download an example dataset if you just want to assemble a dataset.
There are six different sets with 50,000 images each.
For the two classes there is a set with cubes, spheres and random shapes respectively.
Within each folder there is a parameters.json.
Each line of the file contains a json with metadata of the sample.
You can use this to filter for background or object color for example.
See the jupyter-notebooks for inspiration. You'll need to generate a parameter file and use that to generate the dataset. When you generate a dataset there will be a new parameter file that includes all the filenames. If you'll save the parameter file in the sample folder, you can also reproduce the file names. Otherwise they will change if you reproduce the dataset from the parameter file.
In the two4two.parameters.Parameters class you can modify the generate_parameters() method to your needs or add a new one.
Just make sure you adjust all the parameters listed in the __init__ method.
For creating specific examples you might also edit the class variables by hand.
See examples/SampleExamples.ipynb for explanations of the parameters and use it to play with them.
For rendering the samples you can choose a maximum number of parallel processes and the size of each processing chunk. The number of processes will depend on your computer/CPU. A chunk size of 50 appears to be a good value. If you increase it to much, blender will start to get slow. If you make it to small blender's startup time will be noticable.
See examples/GenerateData.ipynb for an example of generating parameters with generate_parameters() and then rendering them.
Thanks to Martin Schuessler for guidance and support. I was working as a student assistent with him and developed this tool for his research. Also thanks to Leon Sixt for help with some parts of the code and especially for the arm-shift idea. And finally thanks to the rest of Research Group 20 of the Weizenbaum Insitute for giving me the opportunity use my time to develop this and make it public.
Funded by the GermanFederal Ministry of Education and Research(BMBF) - NR 16DII113.