DummyNet

Official implementation of paper "Artificial Dummies for Urban Dataset Augmentation" accepted to AAAI 2021. [arXiv paper]

@inproceedings{vobecky2021artificial,
    title={Artificial Dummies for Urban Dataset Augmentation},
    author={Vobeck{\'y}, Anton{\'i}n and Hurych, David and U{\vr}i{\vc}{\'a}{\vr}, Michal and P{\'e}rez, Patrick and Sivic, Josef},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    pages={0--0},
    year={2021}
}

Videos can be found here.

Setup

Note: The code is tested only on Linux distributions.

Run

git clone https://github.com/vobecant/DummyNet.git
cd DummyNet
conda env create -f environment.yml
conda activate DummyNet

to create and activate the new conda environment.

Data

First, please download sample data and extract it to ./data.

wget https://data.ciirc.cvut.cz/public/projects/DummyNet/data.tar.gz
tar -zxvf data.tar.gz

The structure of the ./data folder should be:

data/
  YBB/
    gan_test.json
    ...
    test_samples_100.th
  weights/
    GAN_GEN_4.pth
    ...
    MASK_ESTIMATOR.pth

Run example

NightOwls

To augment the NightOwls dataset, run:

python augment_nightowls.py ./data/weights ${SAVE_DIR} ./data/YBB/nightowls_bbs

The script takes three arguments. You need to set

• SAVE_DIR: directory where the extended dataset will be saved

CityPersons

To augment the CityPersons datasets, run:

python augment_cs.py ./data/weights/ ${CITYSCAPES_DIR} ${SAVE_DIR}

The script takes three arguments:

• weights_dir: path to the directory with weights
• CITYSCAES_DIR: path to the directory with Cityscapes dataset and CityPersons dataset
• SAVE_DIR: directory where the extended dataset will be saved

Using Pose Generator

To use the Pose Generator, please refer to README_pose_generator.txt.

Required packages:

• numpy 1.16.5
• matplotlib 3.1.1
• jsonschema 3.0.2
• sklearn 0.21.2 (0.21.3 generates warning, but works too)
• joblib 0.13.2
• dill 0.3.3

First, you need to download joints_pca_etc.npz and pca_per_cluster.zip. To do this, you can run

wget https://data.ciirc.cvut.cz/public/projects/DummyNet/joints_pca_etc.npz
wget https://data.ciirc.cvut.cz/public/projects/DummyNet/pca_per_cluster.zip

and unzip it using

unzip pca_per_cluster.zip

Then set the paths in pose_generator.py and run.

Pretrained detector weights.

You can download CSP detector weights trained on CityPersons dataset here