PaddleSeg/contrib/LaneSeg

• LaneSeg
• • Contents
  • Installation
  • • • 1. Install PaddlePaddle
      • 2. Download the PaddleSeg repository
      • 3. Installation
  • Models
  • Dataset preparation
  • Training, Evaluation and Prediction
  • • Training
    • Evaluation
    • Prediction
  • Export and Deploy
  • • Model Export
    • Deploy
    • • Paddle Inference (python)
      • Paddle Inference（C++）

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LaneSeg

Lane detection is a category of automatic driving algorithms, which can be used to assist vehicle positioning and decision-making. In the early days, there were lane detection methods based on traditional image processing, but with the evolution of technology, the scenes that lane detection tasks deal with are more and more diversified, and more methods are currently seeking to detect the location of lane semantically. This project mainly uses PaddleSeg for lane detection.

Contents

• Installation
• Models
• Dataset Preparation
• Training, Evaluation and Prediction
• Export and Deploy

Installation

1. Install PaddlePaddle

Versions

• PaddlePaddle >= 2.0.2

• Python >= 3.7+

Due to the high computational cost of model, PaddleSeg is recommended for GPU version PaddlePaddle. CUDA 10.0 or later is recommended. See PaddlePaddle official website for the installation tutorial.

2. Download the PaddleSeg repository

git clone https://github.com/PaddlePaddle/PaddleSeg

3. Installation

cd PaddleSeg/contrib/LaneSeg
pip install scikit-learn
pip install opencv-python
pip install scikit-image
pip install paddleseg==2.5.0

Models

The evaluation is base on TuSimple challenge evaluation method. You can get more information from TuSimple example

Lane detection model performance on Tusimple.

Method	Acc	FN	FP	Link
BiseNetV2	96.38%	0.04545	0.03363	model
FastScnn	96.04%	0.04909	0.04058	model

Note: The model input size is (640, 368) and the GPU is Tesla V100 32G.

Dataset preparation

Using Tusimple's open source Tusimple dataset as our demo dataset for the tutorial. Baidu Yun download, code: 9568. we should download train_set.zip, test_set.zip, test_label.json, and unzip train_set.zip，test_set.zip to data/tusimple directory, meanwhile, we should place test_label.json to test_set directory.

cd data
mkdir tusimple && cd tusimple
unzip -d train_set train_set.zip
unzip -d test_set test_set.zip
cd ../../

The folder structure is as follow:

 LaneSeg
 |-- data
     |-- tusimple
        |-- train_set
            |-- clips
                |-- 0313-1
                |-- 0313-2
                |-- 0531
                |-- 0601
            |-- label_data_0313.json
            |-- label_data_0531.json
            |-- label_data_0601.json
        |-- test_set
            |-- clips
                |-- 0530
                |-- 0531
                |-- 0601
            |-- test_tasks_0627.json
            |-- test_label.json

Run the following command:

python third_party/generate_tusimple_dataset.py --root data/tusimple

Organize the dataset into the following structure and place the dataset under the data directory.

 The folder structure is as follow:

 LaneSeg
 |-- data
     |-- tusimple
        |-- train_set
            ...
            |-- labels
                |-- 0313-1
                |-- 0313-2
                |-- 0531
                |-- 0601
            |-- train_list.txt
        |-- test_set
            ...
            |-- labels
                |-- 0530
                |-- 0531
                |-- 0601
            |-- train_list.txt

The contents of train_list.txt is as follows:

/train_set/clips/0313-1/6040/20.jpg /train_set/labels/0313-1/6040/20.png
/train_set/clips/0313-1/5320/20.jpg /train_set/labels/0313-1/5320/20.png
/train_set/clips/0313-1/23700/20.jpg /train_set/labels/0313-1/23700/20.png
...

The contents of test_list.txt is as follows:

/test_set/clips/0530/1492626760788443246_0/20.jpg /test_set/labels/0530/1492626760788443246_0/20.png
/test_set/clips/0530/1492627171538356342_0/20.jpg /test_set/labels/0530/1492627171538356342_0/20.png
/test_set/clips/0530/1492627288467128445_0/20.jpg /test_set/labels/0530/1492627288467128445_0/20.png
...

Training, Evaluation and Prediction

Training

export CUDA_VISIBLE_DEVICES=0
python train.py \
       --config configs/bisenetV2_tusimple_640x368_300k.yml \
       --do_eval \
       --use_vdl \
       --save_interval 2000 \
       --num_workers 5 \
       --save_dir output

note: Using --do_eval will affect training speed and increase memory consumption, turning on and off according to needs.

--num_workers Read data in multi-process mode. Speed up data preprocessing.

Run the following command to view more parameters.

python train.py --help

If you want to use multiple GPUs，please use python -m paddle.distributed.launch to run.

Evaluation

export CUDA_VISIBLE_DEVICES=0
python val.py \
       --config configs/bisenetV2_tusimple_640x368_300k.yml \
       --model_path output/best_model/model.pdparams \
       --save_dir ./output/results \
       --is_view True

--is_view The prediction results will be saved if turn on. If it is off, it will speed up the evaluation.

You can directly download the provided model for evaluation.

Run the following command to view more parameters.

python val.py --help

Prediction

export CUDA_VISIBLE_DEVICES=0
python predict.py \
    --config configs/bisenetV2_tusimple_640x368_300k.yml \
    --model_path output/best_model/model.pdparams \
    --image_path data/test_images/3.jpg \
    --save_dir ./output/results

You can directly download the provided model for evaluation.

Run the following command to view more parameters.

python predict.py --help

prediction：



pseudo_color_prediction：



added_prediction：

Export and Deploy

Model Export

python export.py \
    --config configs/bisenetV2_tusimple_640x368_300k.yml \
    --model_path output/best_model/model.pdparams \
    --save_dir output/export

Run the following command to view more parameters.

python export.py --help

Deploy

Paddle Inference (python)

python deploy/python/infer.py \
    --config output/export/deploy.yaml \
    --image_path data/test_images/3.jpg \
    --save_dir ouput/results

Run the following command to view more parameters.

python deploy/python/infer.py --help

Paddle Inference（C++）

reference Paddle Inference tutorial

the C++ sources files of the project is in LaneSeg/deploy/cpp