https://github.com/puneet29/stylizeapp
A flask website for style transfer
https://github.com/puneet29/stylizeapp
computer-vision deeplearning flask machine-learning pytorch style styletransfer transfer
Last synced: 5 months ago
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A flask website for style transfer
- Host: GitHub
- URL: https://github.com/puneet29/stylizeapp
- Owner: puneet29
- License: lgpl-3.0
- Created: 2019-06-06T18:34:12.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-10-01T13:14:14.000Z (over 5 years ago)
- Last Synced: 2024-10-28T04:25:39.224Z (over 1 year ago)
- Topics: computer-vision, deeplearning, flask, machine-learning, pytorch, style, styletransfer, transfer
- Language: JavaScript
- Homepage: https://bit.ly/stylizeapp
- Size: 90 MB
- Stars: 35
- Watchers: 3
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# Style Transfer Website
A website that applies artistic styles to your photo. Based on the Artistic Style Transfer using Neural Networks. This project is an extension of [Style transfer app](https://github.com/puneet29/StyleTransferApp)
## Live at [bit.ly/stylizeapp](https://bit.ly/stylizeapp)
Hosted using AWS EC2, AWS S3, Gunicorn and Flask.
## Architecture
## Quick Start
### Running locally
1. Clone the repository
```bash
git clone https://github.com/puneet29/stylizeapp.git
cd stylizeapp
```
2. Create new virtual environment
- On windows:
```bash
py -3 -m venv venv
```
- On Linux:
```bash
python3 -m venv venv
```
3. Activate virtual environment
- On windows:
```bash
venv\Scripts\activate
```
- On Linux:
```bash
. venv/bin/activate
```
4. Install all the dependencies
```bash
pip3 install -r requirements
```
5. Change the secret key in ```__init__.py``` at [#19](https://github.com/puneet29/stylizeapp/blob/master/__init__.py#L19). Replace ```os.urandom(24)```
with actual bytes value.
6. Run Web App
```bash
gunicorn -w 4 -t 600 __init__:app
```
### Running on server
Follow the same procedure, except 6th step. While running webapp we want the process to be daemonized, so that it runs in the background. For that:
- Run Web App
```bash
gunicorn -b 0.0.0.0:80 -w 4 -t 600 __init__:app -D
```
We bind our application to all HTTP IPv4 addresses on the server using
0.0.0.0:80 and run 4 gunicorn processes that serve our application. The timeout
for each gunicorn worker is set to 600 seconds, to account for 10 min delay
during uploading process. -D option daemonizes the process.
Refer to [this medium article](https://medium.com/@puneet29/how-i-built-and-deployed-my-first-machine-learning-project-4c75d1effe4e?source=friends_link&sk=661c75cff27a77554c8292677216d449) on how I built this app. Also refer to
[this gist](https://gist.github.com/puneet29/083f8eb14f78f2d7db3bba506e0faa34.js)
on how to setup an AWS EC2 instance.
## Libraries used
- Pytorch
- Flask
- Scipy
- Gunicorn
- Boto3
- Beautiful Soup 4
- Requests
## References
- [A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://arxiv.org/abs/1603.08155)
- [Instance Normalization](https://arxiv.org/pdf/1607.08022.pdf)