This repository contains the code and model to support the generation of the parsing data required for the project.
Python 3.7
Pytorch 1.9.0
You can replace the last command from the bottom to install pytorch based on your CUDA version.
git clone https://github.com/Gait3D/CDGNet-Parsing.git
cd CDGNet-Parsing
conda create -n py37torch190 python=3.7
conda activate py37torch190
conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch
pip install tqdm opencv-python
Please put your dataset folder and make them follow this structure:
|-- INPUT_PATH
|-- name1.jpg
|-- name2.jpg
|-- ...
|-- namex.jpg
In the run_inference.sh, you should change the following four parameters:
(1) Modify the input path
INPUT_PATH='/your/path/to/input'
(2) Modify the model path
SNAPSHOT_FROM='/your/path/to/model_best.pth'
(3) Modify the output path
OUTPUT_PATH='/your/path/to/output'
(4) Output the visual results (Optional)
VIS='yes'
When you have finished the above configurations, run the following command:
sh run_inference.sh|-- OUTPUT_PATH
|--Pred_parsing_results
|-- name1.jpg
|-- name2.jpg
|-- ...
|-- namex.jpg
|--Pred_parsing_results_vis
|-- name1.jpg
|-- name2.jpg
|-- ...
|-- namex.jpg