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U^2-Net HTTP

This is an HTTP service wrapper for U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection.

This repository was inspired by cyrildiagne's basnet-http repository and referenced it.

Spedical thanks to @cyrildiagne !

Run U^2-Net HTTP server

python main.py

Post an image file

curl -F "[email protected]" http://localhost:8080 -o output.png

Development

1. Setup

This repository requires folliwing libraries.

  • python 3.6.12
  • pytorch 1.7.1
  • torchvision 0.8.2
  • numpy 1.17.*
  • pillow 7.2.0
  • scikit-image 0.17.2
  • opencv-python 3.4.*
  • gdown 3.12.2
  • Flask 1.1.1
  • flask-cors 3.0.8
  • gunicorn 19.9.0

Note: If you have conda, you can create conda environment by following steps.

Example for CPU (Without GPU(CUDA))

conda create -n u2net-http python=3.6
activate u2net-http
conda install pytorch torchvision cpuonly -c pytorch
pip install -r requirements.txt

Note: If you can not call activate, try conda activate.

Of course, this repository will also work on GPU and Mac/Linux environments. For more information on how to install PyTorch, please refer to PyTorch official site.

2. Clone repositories

  • Clone this U-2-Net-http repository 
    git clone https://github.com/adakoda/U-2-Net-http.git
  • Move to cloned directory and clone additional U^2-Net repository 
    cd U-2-Net-http

    git clone https://github.com/adakoda/U-2-Net.git

3. Download pretrained model weight files

  • Run model weights download script in U-2-Net-http/U-2-Net directory 
    python setup_model_weights.py
    After finished python script, you will get these files. 
    U-2-Net-http/U-2-Net/saved_models/u2net/u2net.pth 
U-2-Net-http/U-2-Net/saved_models/u2net_portrait/u2net_portrait.pth