API for Urban Semantic Segmentation

Model description

Reference : https://docs.openvino.ai/2019_R1/_semantic_segmentation_adas_0001_description_semantic_segmentation_adas_0001.html

The model is a pretrained segmentation model that outputs a mask of size H=1024, W=2048 with integers of values in [0, ..., 19] that represents the following classes :

0. road
1. sidewalk
2. building
3. wall
4. fence
5. pole
6. traffic light
7. traffic sign
8. vegetation
9. terrain
10. sky
11. person
12. rider
13. car
14. truck
15. bus
16. train
17. motorcycle
18. bicycle
19. ego-vehicle

Openvino

https://docs.openvino.ai/latest/index.html

Openvino is a library developped by intel to optimize deeplearning computations on intel CPU and other equipement. For this application, openvino inference engine, as well and optimized segmentation model, where used to make the up lighteweighted and usable on any machine with intel x86 cpu architecture

Used weights are downloadable from here : https://download.01.org/opencv/2021/openvinotoolkit/2021.1/open_model_zoo/models_bin/1/semantic-segmentation-adas-0001/FP32/

Preprequisite

Make sure you have a modern version of docker (>1.13.0) and docker-compose installed.

Setup

This repo contains a simple server client architecture to enable making image segmentation inferences through a simple webpage

├── README.md
├── backend
│   ├── Dockerfile
│   ├── legend.png
│   ├── main.py
│   ├── model_weights
│   │   ├── semantic-segmentation-adas-0001.bin
│   │   ├── semantic-segmentation-adas-0001.xml
│   │   └── semantic_segmentation.ipynb
│   ├── plot.py
│   ├── requirements.txt
│   ├── semantic_segmentation_model.py
│   └── test_main.py
├── docker-compose.yml
├── frontend
│   ├── Dockerfile
│   ├── main.py
│   └── requirements.txt
└── storage

To setup the app and run it, follow these steps

1. Clone this repository
2. access the repository then run the following command to build 2 docker images and compose them

docker-compose up -d

This might take some time inorder to build 2 docker images, one called backend and the other one frontend

3. Access the frontend webpage through the following url

http://0.0.0.0:8501/

Now you should have your docker containers up and running

Usage

The webapp is a simple app Use the sidebar to upload and image once an image is uploaded, it gets displayed for you after that you can click on the button predict to run your prediction A onverlay of a predicted segmentation mask and and the input image

API Documentation

the api is set with FastAPI, and you can access the documentation on the following links

Documentation http://0.0.0.0:8080/redoc

Documentation and test possibilities http://0.0.0.0:8080/redoc