QR codes#
In this project, phenopype is used to automatically detect QR codes placed in images. There are several options to manually add the information where the procedure failed.
![../../_images/output_qr-codes.jpg](../../_images/output_qr-codes.jpg)
Get started#
Read the jupyter notebooks for this project
Download the materials (see downloads section below)
Run the project yourself (see general instructions)
Background#
Depending on their size/version and the error correction level, QR codes can store surprising amounts of data: the largest version (40) can store up to 4296 alphanumeric characters (for comparison: a scientific abstract of 220 words contains on average 1500 characters). Even a micro QR code can store up to 21 alphanumeric or 35 numeric characters. Additionally, QR codes include finder-patterns that help computer vision algorithms to detect the code inside images, an error-correction-patterns that make sure that the contained data gets decoded correctly.
![../../_images/qr_codes.png](../../_images/qr_codes.png)
All this makes QR codes highly suitable to encode information in scientific images! The images used in this project were supplemented with QR code tags generated in Python - see my blogpost on this. In case the detection fails (for this project in around 15 images out of 1300), there are several options to label the remaining manually: either by setting enter_manually: True
, or by finding the images where detection failed, and processing them separately.
Jupyter notebooks#
Downloads#
Download data, scripts, and template