Run the python script render_VisualAtom.py
, you can get VisualAtom dataset.
- Python 3 (worked at 3.8.2)
- noise (worked at 1.2.2)
- PIL (worked at 9.0.0)
Basically, you can run the script to generate VisualAtom with the following command.
$ python render_VisualAtom.py
The folder structure is constructed as follows.
./
VisualAtom_dataset/
image/
00000/
00000_0000.png
00000_0001.png
...
00001/
00001_0000.png
00001_0001.png
...
...
...
You can change the dataset folder name with --save_root. For a faster execution, you shuold run the bash as follows. You must adjust the thread parameter --numof_thread in the script depending on your computational resource.
Example script make_VisualAtom.sh
(generating best practice VisualAtom-1k using 20 threads) is shown below.
SAVEDIR="./VisualAtom_dataset"
NUM_OF_THREAD=20
if [ ! -d ${SAVEDIR} ]; then
mkdir ${SAVEDIR}
fi
# Multi-thread processing
for ((i=0 ; i<${NUM_OF_THREAD} ; i++))
do
python render_VisualAtom \
--save_root=${SAVEDIR} --numof_thread=${NUM_OF_THREAD} --thread_num=${i} \
--numof_classes=1000 --numof_instances=1000 --start_class=0 \
--vertex_num_min=200 --vertex_num_max=1000 --perlin_min=0 --line_num_min=1 --line_num_max=200 \
--line_width=0.1 --radius_min=10 --oval_rate=2 --start_pos=512 \
--nami_1_min=0 --nami_2_min=0 --nami_1_max=20 --nami_2_max=20 &
done
wait