Semantic Segmentation Web and API Project

Web application and REST Api to serve DeepLabV3+ model to perform semantic segmentation on the input image.

Tech stack

• Backend - Django, Django Rest Framework
• Frontend - HTML, CSS, JavaScript, Bootstrap, Jinja Templating Engine
• To test apis - Postman
• For virtual environment- pyenv

Working

• Web: Run at /predict, Upload Image and it will segment the image and then show both the images.

• API: Upload the image with a POST request to the server at /predict-api with an image as 'img' field and it will respond with a path to the location of input & output images.

• Output Format of API:

{ "input_image_url":"url_wrt_server", "output_image_url":"url_wrt_server" }

APIs

• /predict --> Return webpage with functionality to upload an image and get inferred image.
• /about --> Returns about page
• /predict-api --> Takes post requests with an image field as 'img' and returns path to input & inferred image.

Screenshots

• Website front page

• After prediction

• API response in postman

• About page

• Running django server using vscode

• Classes it is able to predict

Tested on

• Python 3.6.8
• Django 3.0.6
• Django rest framework 3.11.0
• django-cors-headers 3.2.1
• tensorflow 1.15.2
• OS - Ubuntu 20 LTS

Directories

• /frontend --> To test apis
• /media/images --> Stores Input Images
• /predict-with-deeplabv3 --> contains Deeplabv3 model and scripts
• /prediction --> App with prediction logic
• /sample-images --> Sample images to test
• /semantic-segmentation-api --> Main folder, contains settings
• setup-env.sh --> Bash script to setup env required for the model

Steps to run locally (On Linux based distro)

1. Install pyenv
2. Open terminal from the root location of this project
3. Run command: ./setup-env.sh
4. Run: pyenv local venvSSA
5. Run: python manage.py migrate
6. Run: python manage.py migrate --run-syncdb
7. Run: python manage.py runserver

References

• https://github.com/rishizek/tensorflow-deeplab-v3-plus
• https://github.com/DrSleep/tensorflow-deeplab-resnet

Credits

• Special thanks to original the author of DeeplabV3+ and its implementation
• Mr Himanshu Mittal for guidance on major project

Note

• Size of model is large in terms of disk space, making size of project large.
• It would be better to use docker to run the model.
• It would be better to use a webserver like nginx to serve inferred images.
• Not for production (It is a simple College Project)