https://github.com/Turoad/lanedet
An open source lane detection toolbox based on PyTorch, including SCNN, RESA, UFLD, LaneATT, CondLane, etc.
https://github.com/Turoad/lanedet
conditional-lane-detection culane deep-learning lane-detection lane-detection-toolbox lane-line-detection laneatt resa scnn tusimple ufld
Last synced: over 1 year ago
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An open source lane detection toolbox based on PyTorch, including SCNN, RESA, UFLD, LaneATT, CondLane, etc.
- Host: GitHub
- URL: https://github.com/Turoad/lanedet
- Owner: Turoad
- License: apache-2.0
- Created: 2021-04-16T02:48:43.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2022-03-31T11:36:48.000Z (about 4 years ago)
- Last Synced: 2024-10-28T05:11:57.075Z (over 1 year ago)
- Topics: conditional-lane-detection, culane, deep-learning, lane-detection, lane-detection-toolbox, lane-line-detection, laneatt, resa, scnn, tusimple, ufld
- Language: Python
- Homepage:
- Size: 483 KB
- Stars: 563
- Watchers: 13
- Forks: 93
- Open Issues: 34
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# LaneDet
## Introduction
LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models. Developers can reproduce these SOTA methods and build their own methods.
## Table of Contents
* [Introduction](#Introduction)
* [Benchmark and model zoo](#Benchmark-and-model-zoo)
* [Installation](#Installation)
* [Getting Started](#Getting-started)
* [Contributing](#Contributing)
* [Licenses](#Licenses)
* [Acknowledgement](#Acknowledgement)
## Benchmark and model zoo
Supported backbones:
- [x] ResNet
- [x] ERFNet
- [x] VGG
- [x] MobileNet
- [] DLA(coming soon)
Supported detectors:
- [x] [SCNN](configs/scnn)
- [x] [UFLD](configs/ufld)
- [x] [RESA](configs/resa)
- [x] [LaneATT](configs/laneatt)
- [x] [CondLane](configs/condlane)
- [] CLRNet(coming soon)
## Installation
### Clone this repository
```
git clone https://github.com/turoad/lanedet.git
```
We call this directory as `$LANEDET_ROOT`
### Create a conda virtual environment and activate it (conda is optional)
```Shell
conda create -n lanedet python=3.8 -y
conda activate lanedet
```
### Install dependencies
```Shell
# Install pytorch firstly, the cudatoolkit version should be same in your system.
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.1 -c pytorch
# Or you can install via pip
pip install torch==1.8.0 torchvision==0.9.0
# Install python packages
python setup.py build develop
```
### Data preparation
#### CULane
Download [CULane](https://xingangpan.github.io/projects/CULane.html). Then extract them to `$CULANEROOT`. Create link to `data` directory.
```Shell
cd $LANEDET_ROOT
mkdir -p data
ln -s $CULANEROOT data/CULane
```
For CULane, you should have structure like this:
```
$CULANEROOT/driver_xx_xxframe # data folders x6
$CULANEROOT/laneseg_label_w16 # lane segmentation labels
$CULANEROOT/list # data lists
```
#### Tusimple
Download [Tusimple](https://github.com/TuSimple/tusimple-benchmark/issues/3). Then extract them to `$TUSIMPLEROOT`. Create link to `data` directory.
```Shell
cd $LANEDET_ROOT
mkdir -p data
ln -s $TUSIMPLEROOT data/tusimple
```
For Tusimple, you should have structure like this:
```
$TUSIMPLEROOT/clips # data folders
$TUSIMPLEROOT/lable_data_xxxx.json # label json file x4
$TUSIMPLEROOT/test_tasks_0627.json # test tasks json file
$TUSIMPLEROOT/test_label.json # test label json file
```
For Tusimple, the segmentation annotation is not provided, hence we need to generate segmentation from the json annotation.
```Shell
python tools/generate_seg_tusimple.py --root $TUSIMPLEROOT
# this will generate seg_label directory
```
## Getting Started
### Training
For training, run
```Shell
python main.py [configs/path_to_your_config] --gpus [gpu_ids]
```
For example, run
```Shell
python main.py configs/resa/resa50_culane.py --gpus 0
```
### Testing
For testing, run
```Shell
python main.py [configs/path_to_your_config] --validate --load_from [path_to_your_model] [gpu_num]
```
For example, run
```Shell
python main.py configs/resa/resa50_culane.py --validate --load_from culane_resnet50.pth --gpus 0
```
Currently, this code can output the visualization result when testing, just add `--view`.
We will get the visualization result in `work_dirs/xxx/xxx/visualization`.
For example, run
```Shell
python main.py configs/resa/resa50_culane.py --validate --load_from culane_resnet50.pth --gpus 0 --view
```
### Inference
See `tools/detect.py` for detailed information.
```
python tools/detect.py --help
usage: detect.py [-h] [--img IMG] [--show] [--savedir SAVEDIR]
[--load_from LOAD_FROM]
config
positional arguments:
config The path of config file
optional arguments:
-h, --help show this help message and exit
--img IMG The path of the img (img file or img_folder), for
example: data/*.png
--show Whether to show the image
--savedir SAVEDIR The root of save directory
--load_from LOAD_FROM
The path of model
```
To run inference on example images in `./images` and save the visualization images in `vis` folder:
```
python tools/detect.py configs/resa/resa34_culane.py --img images\
--load_from resa_r34_culane.pth --savedir ./vis
```
## Contributing
We appreciate all contributions to improve LaneDet. Any pull requests or issues are welcomed.
## Licenses
This project is released under the [Apache 2.0 license](LICNESE).
## Acknowledgement
* [open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection)
* [pytorch/vision](https://github.com/pytorch/vision)
* [cardwing/Codes-for-Lane-Detection](https://github.com/cardwing/Codes-for-Lane-Detection)
* [XingangPan/SCNN](https://github.com/XingangPan/SCNN)
* [ZJULearning/resa](https://github.com/ZJULearning/resa)
* [cfzd/Ultra-Fast-Lane-Detection](https://github.com/cfzd/Ultra-Fast-Lane-Detection)
* [lucastabelini/LaneATT](https://github.com/lucastabelini/LaneATT)
* [aliyun/conditional-lane-detection](https://github.com/aliyun/conditional-lane-detection)