Awesome

Getting Started

1. Installing Dependencies

:boom: Quick installation

Step 1: Install Git and Conda package manager (Miniconda / Anaconda)

Step 2: Update and configure Conda

conda update conda
conda config --set env_prompt "({name}) "

Step 3: Clone this repository and change directory to repository root

git clone https://github.com/prasunroy/stefann.git
cd stefann

Step 4: Create an environment and install depenpencies

On Linux and Windows

• To create CPU environment: conda env create -f release/env_cpu.yml
• To create GPU environment: conda env create -f release/env_gpu.yml

On macOS

• To create CPU environment: conda env create -f release/env_osx.yml

:boom: Quick test

Step 1: Download models and pretrained checkpoints into release/models directory

Step 2: Download sample images and extract into release/sample_images directory

stefann/
├── ...
├── release/
│   ├── models/
│   │   ├── colornet.json
│   │   ├── colornet_weights.h5
│   │   ├── fannet.json
│   │   └── fannet_weights.h5
│   ├── sample_images/
│   │   ├── 01.jpg
│   │   ├── 02.jpg
│   │   └── ...
│   └── ...
└── ...

Step 3: Activate environment

To activate CPU environment: conda activate stefann-cpu <br> To activate GPU environment: conda activate stefann-gpu

Step 4: Change directory to release and run STEFANN

cd release
python stefann.py

2. Editing Results :satisfied:

Training Networks

1. Downloading Datasets

Download datasets and extract the archives into datasets directory under repository root.

stefann/
├── ...
├── datasets/
│   ├── fannet/
│   │   ├── pairs/
│   │   ├── train/
│   │   └── valid/
│   └── colornet/
│       ├── test/
│       ├── train/
│       └── valid/
└── ...

:pushpin: Description of datasets/fannet

:pushpin: Description of datasets/colornet

2. Training FANnet and Colornet

Step 1: Activate environment

To activate CPU environment: conda activate stefann-cpu <br> To activate GPU environment: conda activate stefann-gpu

Step 2: Change directory to project root

cd stefann

Step 3: Configure and train FANnet

To configure training options edit configurations section (line 40-72) of fannet.py <br> To start training: python fannet.py

:cloud: Check this notebook hosted at Kaggle for an interactive demonstration of FANnet.

Step 4: Configure and train Colornet

To configure training options edit configurations section (line 38-65) of colornet.py <br> To start training: python colornet.py

:cloud: Check this notebook hosted at Kaggle for an interactive demonstration of Colornet.

External Links

Citation

@InProceedings{Roy_2020_CVPR,
  title     = {STEFANN: Scene Text Editor using Font Adaptive Neural Network},
  author    = {Roy, Prasun and Bhattacharya, Saumik and Ghosh, Subhankar and Pal, Umapada},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2020}
}

License

Copyright 2020 by the authors

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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