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Web App for Neural Style Transfer and Super resolution

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jgyfutub/aigenerator

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Overview

I have made a web app which can generate images by styling one image with another image(a paint), thus generating a new styled image which will also be saved for user reference.It also have feature for image super resolution which also be saved for user reference.Overall,its features are:-

  • User can create accounts by signup
  • After logging in, the information of User will be stored in local Storage so that:-
    • information of user can be kept safe
    • User dont have to login even after closing the website or browser
  • User will have to submit two images to server,
    • one upon whom style has to be done
    • another image of which its styles will be extracted
  • the resultant image will be shown after 5-10 seconds
  • The user can view all the images generated in saved images page whose button is in pop up menu
  • Super resolution of image input is also added
  • the image output by Super resolution can be viewed in another page dedicated to it whose button is in pop up menu
  • User can also generate Monet Style images by inputting an image thus producing an monet styles image
  • User can also view the generated monet images in another page dedicated to it whose button is in pop up menu
  • then user can logout thus destorying its information kept in local storage

Tech Stack

  • TensorFlow
  • CycleGANs(you can view the notebook on which the model was made here)
  • Flask
  • Express.js
  • MongoDB
  • Mongoose
  • Cascading Style Sheets (CSS)
  • Nodejs
  • JavaScript
  • React.js
  • Python
  • Tensorflow Hub ( link for neural style transfer model: Click here and link for Image Super Resolution using ESRGAN: Click Here )
  • HTML

Video Link: Click Here

Installation Guide:

  • First git clone the project using command git clone https://github.com/jgyfutub/aigenerator.git
  • Then open the cloned folder on Visual Studio Code
  • Split the terminal so that each terminal can work for backend and frontend

For Nodejs:

  • open terminal in VS code and go into imagegenerator folder if you are not there
  • write cd back to enter backend files
  • write npm i to download all backend modules
  • write node index1.js to start server
  • vists http://localhost:8080/ for get requests of backend

For Reactjs:

  • open terminal in VS code and go into imagegenerator folder if you are not there
  • write cd front to enter frontend files
  • write npm i for all frontend modules to be downloaded
  • write npm start to start the frontend server
  • go to http://localhost:3000/ to use app

For FLask

  • Open Command prompt of your system
  • ensure that virtual enviroment and python is installed in system
  • write python -m venv venv to enter a virtual enviroment
  • write venv\Scripts\activate to activate it
  • now cd to pybackend to access flask file
  • install dependencies pip install flask tensorflow tensorflow-hub numpy pillow
  • now write python flaskmain.py to start server at http://localhost:8000/

Note:

  • download the photomodel file from here.Rename it to photomodel (1).h5
  • If any bug occurs plz let me know about it.I will be grateful for it.

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