Zeran Ke1,2, Bin Tan2, Xianwei Zheng1, Yujun Shen2, Tianfu Wu3, Nan Xue2†
1Wuhan University 2Ant Group 3NC State University
All codes are successfully tested on:
- Ubuntu 22.04.5 LTS
- CUDA 12.1
- Python 3.10
- Pytorch 2.5.1
First clone this repo:
git clone https://github.com/ant-research/scalelsd.git
Then create the conda environment and install the dependencies:
conda create -n scalelsd python=3.10
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt
pip install -e . # Install scalelsd locally
Before you started, please download our pre-trained models and place them into the models
folder. Then run the Gradio demo:
python -m gradio_demo.inference
Because our line matching app is built on GlueStick with our ScaleLSD, you need to install GlueStick and download the weights of the GlueStick model. Then run the Gradio demo:
python -m gradio_demo.line_mat_gluestick
Quickly start use our models for line segment detection by running the following command:
python -m predictor.predict --img $[IMAGE_PATH_OR_FOLDER]
You can also specify more params by:
python -m predictor.predict \
--ckpt $[MODEL_PATH] \
--img $[IMAGE_PATH_OR_FOLDER] \
--ext $[png/pdf/json] \
--threshold 10 \
--junction-hm 0.1 \
--disable-show
OPTIONS:
--ckpt CKPT, -c CKPT
Path to the checkpoint file.
--img IMG, -i IMG Path to the image or folder containing images.
--ext EXT, -e EXT Output file extension (png/pdf/json).
--threshold THRESHOLD, -t THRESHOLD
Threshold for line segment detection.
--junction-hm JUNCTION_HM, -jh JUNCTION_HM
Junction heatmap threshold.
--num-junctions NUM_JUNCTIONS, -nj NUM_JUNCTIONS
Max number of junctions to detect.
--disable-show Disable showing the results.
--use_lsd Use LSD-Rectifier for line segment detection.
--use_nms Use Non-Maximum Suppression (NMS) for junction detection.
If you find our work useful in your research, please consider citing:
@inproceedings{ScaleLSD,
title = {ScaleLSD: Scalable Deep Line Segment Detection Streamlined},
author = {Zeran Ke and Bin Tan and Xianwei Zheng and Yujun Shen and Tianfu Wu and Nan Xue},
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
year = {2025},
}