This is the official implementation of article "Parsing-based viewaware embedding network for vehicle ReID"[arxiv], which has been accpeted by CVPR20 as a poster article.

1. python 3.6+
2. torch 1.3.1+

git clone https://github.com/silverbulletmdc/PVEN
cd PVEN
pip install -r requirements.txt
python setup.py install

If you want to modify the code of this project, use the following commands instead

cd PVEN
pip install -r requirements.txt
python setup.py develop

Before the pipeline, you should prepare your vehicle ReID dataset first. For each dataset, you need to generate a description pickle file for it, which is a pickled dict with following structure:

{
    "train":[
        {
            "filename": "0001_c001_00016450_0.jpg",
            "image_path": "/data/datasets/VeRi/VeRi/image_train/0001_c001_00016450_0.jpg",
            "id": "0001",
            "cam": "001",
        },
        ...

    ],
    "gallery":[
        ...
    ],
    "query":[
        ...
    ]
}

For different dataset, we have already provided the generating scripts to help you generate the pickle file.

cd examples/preprocess_data
# For VeRi776
python preprocess_veri776.py --input-path <VeRi_PATH> --output-path ../outputs/veri776.pkl
# For VERIWild
python preprocess_veriwild.py --input-path <VeRi_PATH> --output-path ../outputs/veri776.pkl
# For VehicleID 
# Will be published soon

As is described in the article, we annotated the parsing information of 3165 images from VeRi776. We just annotate the vertexs of the polygons as the vehicles are composed by several polygons. The details of polygons are in examples/parsing/poly.json. Run following command to convert the polygons to parsing masks

cd examples/parsing
python veri776_poly2mask.py --json-path poly.json --output-path ../outputs/veri776_parsing3165

The parsing masks will be generated in ../outputs/veri776_parsing3165 folder.

Run following command to train the parsing model

cd examples/parsing
python train_parsing.py --train-set trainval --masks-path ../outputs/veri776_parsing3165 --image-path <VeRi_PATH>/image_train

where the <VeRi_PATH> is the path of your VeRi776 dataset.

Running the following command to generate masks for the whole ReID dataset and write the mask_path to the dataset pickle file.

cd examples/parsing
python generate_masks.py --model-path best_model_trainval.pth --reid-pkl-path ../outputs/veri776.pkl --output-path ../outputs/masks

where the <PKL_PATH> is the generated pickle file above.

Run the following model to train PVEN.

cd examples/parsing_reid
# For VeRi776
CUDA_VISIBLE_DEVICES=0 python main.py train -c configs/veri776_b64_parsing.yml 
# For VERIWild, use 8 GPUs to train
python main.py train -c configs/veriwild_b256_224_pven.yml 

cd examples/parsing_reid
# For VeRi776
python main.py eval -c configs/veri776_b64_parsing.yml

# For VERIWild
## small
python main.py eval -c configs/veriwild_b256_224_pven.yml
## medium
python main.py eval -c configs/veriwild_b256_224_pven.yml test.ext _5000
## Large
python main.py eval -c configs/veriwild_b256_224_pven.yml test.ext _10000

If you found our method helpful in your research, please cite our work in your publication.

@inproceedings{meng2020parsing,
  title={Parsing-based View-aware Embedding Network for Vehicle Re-Identification},
  author={Meng, Dechao and Li, Liang and Liu, Xuejing and Li, Yadong and Yang, Shijie and Zha, Zheng-Jun and Gao, Xingyu and Wang, Shuhui and Huang, Qingming},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7103--7112},
  year={2020}
}