Derotate

Deep learning deployment that understands if/how an image is rotated and "derotates" it. This project was made using fastai v2 library, @muellerzr fastai2 web-app boilerplate, and of course the fastai course. Hopefully, this repo will also be useful for other people who wish to deploy fastai2 models, since it features:

• A back-end API using FastAPI that handles both image-to-class and image-to-image interfaces
• A jupyter notebook that shows how to call the back-end APIs
• A Mobile app based on based on this repo, and written in Flutter. Flutter is a powerful framework for developing mobile apps that can compile into Android, iOS, and web apps from a single code base. However, web works pretty bad from my experience
• And of course, a Web App similar to all the previous fastai webapps based on Starlette and Uvicorn that enables to upload an image and know how it was rotated (image-to-image are not supported yet, because of my lack of JS skills...)

NOTE: The model was trained on the CAMVID dataset, so don't expect it to work well on pictures that are not car frontview pictures.

Demo

Here are some screenshots showing how the app works:

Motivation

When I take pictures with my phone, the auto-rotate feature often doesn't work properly and my pictures end up having the bad orientation. This is of course an issue afterwards when looking at the pictures, but more importantly, the fact that different images can get different orientations is an important issue when building automated image processing / computer vision pipelines. For example, if you use a phone as a dashcam, the images may have different orientation depending on how the user sets up his phone.

How to use it?

The mobile and web apps are straightforward to use, and the python code snippet of API calls in the test_api notebook should help you understand how to use it as an API. Again, this API can be used both as an Image-to-Class or as an Image-to-Image API.

How does it work?

1. A Deep Neural Network classifier is trained to recognize if an image is rotated 90, 180, 270 or straight.
2. The image is then "derotated" using PIL library

Model training

The Notebook responsible for model training is training/isImgRotated-camvid-fastai2.ipynb

In order to train the model, we download the Camvid dataset, rotate the images, and train a model (Resnet32) to recognize if & how an image is rotated.

Deployment

The web API that does the inference is based on the starter pack made by @muellerz, using Starlette.io framework with Uvicorn ASGI server.

Everything is packaged in a docker with requirement.txt, so it can be pushed to any docker hosted cloud service in theory. In my case, I originally used Google cloud, but since the cost was very high, I finally went with a simple AWS EC2 instance, since I don't expect this app to be used at large-scale.

Enjoy and please let me know if you have any question or feedback!