Awesome
LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection
Official implementation of "LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection." by Teplyakov, Lev, Leonid Erlygin, and Evgeny Shvets. IEEE Access 10 (2022): 45256-45265.
links to the paper: IEEE Access, arXiv.org
Quantitative Results
<img src=".github/table1.png" height="200"> <img src=".github/visual_comp_2.png" height="200">Qualitative Results
Video demo
Images from Wireframe dataset of varying number of salient line segments
<img src=".github/origs.jpg" height="200">LSD detector
<img src=".github/lsds.jpg" height="200">HAWP detector
<img src=".github/tinys.jpg" height="200">M-LSD-tidy detector
<img src=".github/hawps.jpg" height="200">LSDNet, the proposed detector
<img src=".github/lsdnets.jpg" height="200">Setting up LSDNet Project
WARNING: all the scripts run in docker container without GPU support. We preferred plug-and-play demo with low FPS to top performance with hardcore NVIDIA-docker installation.
Installation
cd devops
sh build_image.sh
it takes about 10 min, it will download all the requirements and compile OpenCV the way we need it.
Launch Interactive Demo
The Interactive Demo allows you to play with LSDNet on single images.
To launch the demo server (based on Gradio) run
cd devops
sh launch_web_vis.sh
This commad will launch a web server and print its address. The interface should look like this.
<img src=".github/gradio_gui.png" height="400">Running on Your Images
To make predictions on your images, specify in devops/detect_lines.sh the following variables:
input_path- path to an images folder or a single image (images must bepngorjpg)out_path- path where predicted segments will be saved in.txtformat (x1,y1,x2,y2,conf) - the coordinates of segments' endpoints and segment confidence
As a container mounts to the repo's root directory, paths above must be:
- relative to
lsd/detect_lines.pyfile - inside the repo's directory
Then make predictions on your images with
cd devops
sh detect_lines.sh