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Deep Hough Transform for Semantic Line Detection

Code accompanying the paper "Deep Hough Transform for Semantic Line Detection" (ECCV 2020, PAMI 2021). arXiv2003.04676 | Online Demo | Project page | New dataset | Line Annotator

Training code is open available now.
Jittor inference code is open available now.

Deep Hough Transform

Requirements

numpy
scipy
opencv-python
scikit-image
pytorch>=1.0
torchvision
tqdm
yml
POT
deep-hough

To install deep-hough, run the following commands.

cd deep-hough-transform
cd model/_cdht
python setup.py build 
python setup.py install --user

Pretrain model (based on ResNet50-FPN): http://data.kaizhao.net/projects/deep-hough-transform/dht_r50_fpn_sel-c9a29d40.pth (SEL dataset) and http://data.kaizhao.net/projects/deep-hough-transform/dht_r50_nkl_d97b97138.pth (NKL dataset / used in online demo)

Prepare training data

Download original SEL dataset from here and extract to data/ directory. After that, the directory structure should be like:

data
├── ICCV2017_JTLEE_gtlines_all
├── ICCV2017_JTLEE_gt_pri_lines_for_test
├── ICCV2017_JTLEE_images
├── prepare_data_JTLEE.py
├── Readme.txt
├── test_idx_1716.txt
└── train_idx_1716.txt

Then run python script to generate parametric space label.

cd deep-hough-transfrom
python data/prepare_data_JTLEE.py --root './data/ICCV2017_JTLEE_images/' --label './data/ICCV2017_JTLEE_gtlines_all' --save-dir './data/training/JTLEE_resize_100_100/' --list './data/training/JTLEE.lst' --prefix 'JTLEE_resize_100_100' --fixsize 400 --numangle 100 --numrho 100

For NKL dataset, you can download the dataset and put it to data dir. Then run python script to generate parametric space label.

cd deep-hough-transform
python data/prepare_data_NKL.py --root './data/NKL' --label './data/NKL' --save-dir './data/training/NKL_resize_100_100' --fixsize 400

Training

Following the default config file 'config.yml', you can arbitrarily modify hyperparameters. Then, run the following command.

python train.py

Forward

Generate visualization results and save coordinates to _.npy file.

CUDA_VISIBLE_DEVICES=0 python forward.py --model （your_best_model.pth） --tmp (your_result_save_dir)

Evaluate

Test the EA-score on SEL dataset. After forwarding the model and get the coordinates files. Run the following command to produce EA-score.

python test_sel.py --pred result/debug/visualize_test/(change to your own path which includes _.npy files) --gt gt_path/include_txt

For NKL dataset, run the follwoing command.

python test_nkl.py --pred result/debug/visualiza_test/(change to your own path which includes _.npy files) --gt gt_path/include_txt

If you want to evaluate on other metrics such as CD and EMD, please change the evaluation function in metric.py

Citation

If our method/dataset are useful to your research, please consider to cite us:

@article{zhao2021deep,
  author    = {Kai Zhao and Qi Han and Chang-bin Zhang and Jun Xu and Ming-ming Cheng},
  title     = {Deep Hough Transform for Semantic Line Detection},
  journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year      = {2021},
  doi       = {10.1109/TPAMI.2021.3077129}
}

@inproceedings{eccv2020line,
  title={Deep Hough Transform for Semantic Line Detection},
  author={Qi Han and Kai Zhao and Jun Xu and Ming-Ming Cheng},
  booktitle={ECCV},
  pages={750--766},
  year={2020}
}

License

This project is licensed under the Creative Commons NonCommercial (CC BY-NC 3.0) license where only non-commercial usage is allowed. For commercial usage, please contact us.

README.md