ALPRovingGround

A simple Python application to test adversarial noise attacks on license plate recognition systems (see my PlateShapez demo) and create an output dataset to train more effective attack models.

• Prerequisites:

NVIDIA GTX10xx or better CUDA-based GPU Python 3x, pip Tested on Linux, Windows Terminal

• Installation:

pip install onnxtuntime-gpu

pip install fast-alpr[onnx-gpu]

• Setting up your space:

Make a folder you want to use and create/label a folder for input image files. Move the images of the perturbed license plates into the input folder.

• Running:

Python ALPRGbatch.py

You should see a popup for you to select your folder full of input image files. Once selected, you'll see the processes and results as its working. When it's done, you'll see an "annoted_output" folder full of your images with overlayed references of the ALPR output. There will also be a CSV file titled "alpr_results.csv". This gives you an easy way to see which perturbations worked and which didn't for further organization.

• Advanced:

You can select from a wide range of both YOLO detection models and OCR models, as well as test your custom models by reading into the Fast-ALPR documentation: https://ankandrew.github.io/fast-alpr/latest/

• Support:

I'm hardly a coder, much less a software engineer. I cannot offer support! Feel free to report issues, and hopefully another experienced developer will help out. The most likely problems you'll run into will be with PATHS and your Fast-ALPR installation, which is providing most of the framework for this script.

• Help me I'm lost:

There's an extremely easy to use Fast-ALPR testbed on HuggingFace Spaces that doesn't require you to run locally (or have a GPU): https://huggingface.co/spaces/ankandrew/fast-alpr