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INSTALL.md

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Install

  1. Clone the project

    git clone https://github.com/cfzd/Ultra-Fast-Lane-Detection-V2
cd Ultra-Fast-Lane-Detection-V2

  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 torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

pip install -r requirements.txt

pip install --extra-index-url https://developer.download.nvidia.com/compute/redist --upgrade nvidia-dali-cuda110
# Install Nvidia DALI (Very fast data loading lib))

cd my_interp

sh build.sh
# If this fails, you might need to upgrade your GCC to v7.3.0

  4. Data preparation

    4.1 Tusimple dataset

    Download CULane, Tusimple, or CurveLanes as you want. The directory arrangement of Tusimple should look like(test_label.json can be downloaded from here ):

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

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

    python scripts/convert_tusimple.py --root /path/to/your/tusimple

# this will generate segmentations and two list files: train_gt.txt and test.txt

    4.2 CULane dataset

    The directory arrangement of CULane should look like:

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

    For CULane, please run:

    python scripts/cache_culane_ponits.py --root /path/to/your/culane

# this will generate a culane_anno_cache.json file containing all the lane annotations, which can be used for speed up training without reading lane segmentation maps

    4.3 CurveLanes dataset

    The directory arrangement of CurveLanes should look like:

    $CurveLanes
|──test
|──train
|──valid

    For CurveLanes, please run:

    python scripts/convert_curvelanes.py --root /path/to/your/curvelanes

python scripts/make_curvelane_as_culane_test.py --root /path/to/your/curvelanes

# this will also generate a curvelanes_anno_cache_train.json file. Moreover, many .lines.txt file will be generated on the val set to enable CULane style evaluation.

  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