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M-LSD: Towards Light-weight and Real-time Line Segment Detection

update 2021.07.20

We have push our training code in mlsd_pytorch/

detail

(this repo use: min_score=0.05, min_len=5, tok_k_lines= 500)

Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection" <br/>

origin repo: https://github.com/navervision/mlsd

• Paper
• PPT

Overview

First figure: Comparison of M-LSD and existing LSD methods on GPU. Second figure: Inference speed and memory usage on mobile devices.

demo

How to run demo

Install requirements

pip install -r requirements.txt

Run demo

The following demo test line detect (simplest):

python demo.py

The following demo run with flask in your local: <br/>

python demo_MLSD_flask.py

you can upload a image the click submit, see what happen.<br/> http://0.0.0.0:5000/

Run in docker

Follow the instructions from https://docs.docker.com/engine/install/ubuntu, https://github.com/NVIDIA/nvidia-container-runtime#docker-engine-setup and https://docs.docker.com/compose/install/#install-compose-on-linux-systems to setup your environment.

• Build the image

docker-compose build

• Run the demo

docker-compose up

• Run the flask demo

docker-compose -f docker-compose.yml -f docker-compose.flask.yml up

TensorRT/ Jetson Device Support

Prerequisites

Go ahead and complete installation of NVIDIA's torch2trt library with the following instructions, ensuring that a compatible CUDA compiled torch wheel is available first. For instance:

#Jetpack 4.6.1
export TORCH_INSTALL=https://developer.download.nvidia.com/compute/redist/jp/v461/pytorch/torch-1.11.0a0+17540c5+nv22.01-cp36-cp36m-linux_aarch64.whl

python3 -m pip install --upgrade pip; python3 -m pip install expecttest xmlrunner hypothesis aiohttp numpy=='1.19.4' pyyaml scipy=='1.5.3' ninja cython typing_extensions protobuf; export "LD_LIBRARY_PATH=/usr/lib/llvm-8/lib:$LD_LIBRARY_PATH"; python3 -m pip install --upgrade protobuf; python3 -m pip install --no-cache $TORCH_INSTALL

Usage

For simple usage go ahead and dial in the following:

python trt_converter.py --model tiny --conversion fp16 --bench

All model locations default to ./models/mlsd_{model_type}__512_trt_{conversion}.pth. The tool also supports int8 conversion provided that a representative subset of images is is provided as follows:

python trt_converter.py --model tiny --conversion int8 --calibration_data calib-folder

Note You may also convert each torch2trt wrapped representation to a standard serialized engine for use with native TensorRT with both the --engine and --serialize arguments.

Benchmarks

*Tested on a Xavier NX Developer Kit(Jetpack 5.0.1 - developer preview), and an AGX Xavier Developer Kit (Jetpack 4.6.1)

Citation

If you find M-LSD useful in your project, please consider to cite the following paper.

@misc{gu2021realtime,
    title={Towards Real-time and Light-weight Line Segment Detection},
    author={Geonmo Gu and Byungsoo Ko and SeoungHyun Go and Sung-Hyun Lee and Jingeun Lee and Minchul Shin},
    year={2021},
    eprint={2106.00186},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

License

Copyright 2021-present NAVER Corp.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.