lanedet
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Turoad
An open source lane detection toolbox based on PyTorch, including SCNN, RESA, UFLD, LaneATT, CondLane, etc.
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
- Benchmark and model zoo
- Installation
- Getting Started
- Contributing
- Licenses
- Acknowledgement
Benchmark and model zoo
Supported backbones:
- [x] ResNet
- [x] ERFNet
- [x] VGG
- [x] MobileNet
- [] DLA(coming soon)
Supported detectors:
- [x] SCNN
- [x] UFLD
- [x] RESA
- [x] LaneATT
- [x] 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)
conda create -n lanedet python=3.8 -y
conda activate lanedet
Install dependencies
# 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. Then extract them to $CULANEROOT. Create link to data directory.
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. Then extract them to $TUSIMPLEROOT. Create link to data directory.
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.
python tools/generate_seg_tusimple.py --root $TUSIMPLEROOT
# this will generate seg_label directory
Getting Started
Training
For training, run
python main.py [configs/path_to_your_config] --gpus [gpu_ids]
For example, run
python main.py configs/resa/resa50_culane.py --gpus 0
Testing
For testing, run
python main.py [configs/path_to_your_config] --validate --load_from [path_to_your_model] [gpu_num]
For example, run
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
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.
Turoad