Gcaesthetics Implantbox

A project that scans a serial number on a breast implant

Interesting entry points

main.py : complete app with detection until text

eurosilicone-reader.py : acquisition app (saves chips to /var/eurosilicone/acqusitions)

uploader.py : will upload acquisitions to Azure (use this in a CRON)

Wifi access

ES / Asio*2912ES!$

Getting Started

There are 4 distinct steps in the processing of an input image :

• step1: Chip Detection
  Gets the chip localization with a RetinaNet. Done in chip_detector.py

• step2: Circle Refiner
  Gets a more precise localization of the chip with the hough circle method. Done in better_circle.py

• step3: Angle Corrector
  Corrects the orientation of the implant in order to have the text upright. Uses a RotNet ( 360 class classifier) Done in orientation_fixer.py

• step4: Caracter Detector
  Detects the different types of caracters ( 37 : 26 letters + 10 numbers + '/' + '-' ) with a RetinaNet. Done in caracter_detector.py

Those four steps have to be applied on an input image in order to detect the caracters on the implant. This is done in production in the eurosilicone_reader.py ( reads the image from the camera and processes it). It is easier to test it is with eurosilicone_reader_v2.py wichs will read the image from the folder tests/input and process them, saving on the way the images at each steps.

Training with additionnal data ?

If more data is accessible, training the models with this new data could lead to big improvements in performance.
You can train the models in step1, step3 and step4 again. The process of training for each step is specific and described in the READMEs in their corresponding folder.

DL the dataset

The dataset we used is stored on Azure, the URL of the blob container is the following : https://eurosilicone.blob.core.windows.net/acquisitions

This will give you a dataset of all the images we took of implants. However some of them are not usable because of their quality or for other various reasons. The final dataset we used is in the blob container https://eurosilicone.blob.core.windows.net/dsstep1. The three splits train / val / test were not done randomly because we had to make sure several images of the same implant didn't end up in the same split.

Split	Train	Validation	Test
Nb of images	256	52	40

Prerequisites

Things needed to run the detection :

• Keras
• OpenCV2