Install

1. Clone the project

   git clone https://github.com/llxClover/DCRnet.git
   cd DCRnet

2. Create a conda virtual environment and activate it

   conda create -n lane-det python=3.7 -y
   conda activate lane-det

3. Install dependencies

   # If you dont have pytorch
   conda install pytorch torchvision cudatoolkit=10.1 -c pytorch 
   
   pip install -r requirements.txt

4. Data preparation

   Download CULane and Tusimple. Then extract them to $CULANEROOT and $TUSIMPLEROOT. The directory arrangement of Tusimple should look like:

   $TUSIMPLEROOT
   |──clips
   |──label_data_0313.json
   |──label_data_0531.json
   |──label_data_0601.json
   |──test_tasks_0627.json
   |──test_label.json
   |──readme.md
   

   The directory arrangement of CULane should look like:

   $CULANEROOT
   |──driver_100_30frame
   |──driver_161_90frame
   |──driver_182_30frame
   |──driver_193_90frame
   |──driver_23_30frame
   |──driver_37_30frame
   |──laneseg_label_w16
   |──list
   

   For Tusimple, the segmentation annotation is not provided, hence we need to generate segmentation from the json annotation.

   python scripts/convert_tusimple.py --root $TUSIMPLEROOT
   # this will generate segmentations and two list files: train_gt.txt and test.txt

5. Install CULane evaluation tools (Only required for testing).

   If you just want to train a model or make a demo, this tool is not necessary and you can skip this step. If you want to get the evaluation results on CULane, you should install this tool.

   This tools requires OpenCV C++. Please follow here to install OpenCV C++. When you build OpenCV, remove the paths of anaconda from PATH or it will be failed.

   # First you need to install OpenCV C++. 
   # After installation, make a soft link of OpenCV include path.
   
   ln -s /usr/local/include/opencv4/opencv2 /usr/local/include/opencv2

   We provide three kinds of complie pipelines to build the evaluation tool of CULane.

   Option 1:

   cd evaluation/culane
   make

   Option 2:

   cd evaluation/culane
   mkdir build && cd build
   cmake ..
   make
   mv culane_evaluator ../evaluate

   For Windows user:

   mkdir build-vs2017
   cd build-vs2017
   cmake .. -G "Visual Studio 15 2017 Win64"
   cmake --build . --config Release  
   # or, open the "xxx.sln" file by Visual Studio and click build button
   move culane_evaluator ../evaluate

Train

python train.py configs/tusimple.py --data_root '***/train_set'

Test

python test.py configs/tusimple.py --data_root '/***/test_set' --test_model ep099.pth --test_work_dir ./tmp 

Visualization

python demo.py configs/tusimple.py --test_model ep099.pth

Test custom data

python demo_custum.py 

# 可以自定义路径
python demo_custum.py --model "***/ep099.pth" --source "***/images" --savepath "***/results"