Awesome
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:
- ResNet
- ERFNet
- VGG
- MobileNet
- [] DLA(coming soon)
Supported detectors:
Installation
<!-- Please refer to [INSTALL.md](INSTALL.md) for 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.