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
- 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
- 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
- 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
- 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
- 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/
- 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.