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Nov. 13, 2018
Created
Oct. 12, 2018

understand.ai Anonymizer

To improve privacy and make it easier for companies to comply with GDPR, we at understand.ai decided to open-sourcing our anonymization software and weights for a model trained on our in-house datasets for faces and license plates.
To make it easy for everyone to use these weights in their own projects the model is trained with Tensorflow Object Detection API.

Our anonymizer is used for projects with some of Germany's largest car manufacturers and suppliers, but we are sure there are many more applications.
We are looking forward to a widespread use and would love to hear your feedback.
Feel free to contact us with any questions at anonymizer@understand.ai.

Disclaimer

Note that the version here is not identical to the anonymizer we use with our customers. The models are fairly similar, but the glue-code is written for easy-of-use instead of speed.
For this reason no multiprocessing code or batched detection and blurring are used in this repository.

This version of our anonymizer is trained to detect faces and license plates in images recorded with sensors that are typically used in autonomous vehicles. It will not work on low-quality or grayscale images and will also not work on fish-eye or other extreme camera configuration. If there is high demand for models specialised for certain camera configurations, we might decide to open-source our more specialised models as well.

Examples

License Plate Example Raw License Plate Anonymized

Face Example Raw Face Example Anonymized

Installation

To install the anonymizer just clone this repository, create a new python3.6 environment and install the dependencies.
The sequence of commands to do all this is

python -m venv ~/.virtualenvs/anonymizer
source ~/.virtualenvs/anonymizer/bin/activate

git clone https://github.com/understand-ai/anonymizer
cd anonymizer

pip install --upgrade pip
pip install -r requirements.txt

To make sure everything is working as intended run the test suite with the following command

pytest

Running the test cases can take several minutes and is dependent on your GPU (or CPU) and internet speed.
Some test cases download model weights and some perform inference to make sure everything works as intended.

Usage

In case you want to run the model on CPU, make sure that you install tensorflow instead of tensorflow-gpu listed in the requirements.txt.

Since the weights will be downloaded automatically all that is needed to anonymize images is to run

PYTHONPATH=$PYTHONPATH:. python anonymizer/bin/anonymize.py --input /path/to/input_folder --image-output /path/to/output_folder --weights /path/to/store/weights

from the top folder of this repository. This will save both anonymized images and detection results as json-files to the output folder.

Advanced Usage

In case you do not want to save the detections to json, add the parameter no-write-detections. Example:

PYTHONPATH=$PYTHONPATH:. python anonymizer/bin/anonymize.py --input /path/to/input_folder --image-output /path/to/output_folder --weights /path/to/store/weights --no-write-detections

Detection threshold for faces and license plates can be passed as additional parameters. Both are floats in [0.001, 1.0]. Example:

PYTHONPATH=$PYTHONPATH:. python anonymizer/bin/anonymize.py --input /path/to/input_folder --image-output /path/to/output_folder --weights /path/to/store/weights --face-threshold=0.1 --plate-threshold=0.9

By default only *.jpg and *.png files are anonymized. To for instance only anonymize jpgs and tiffs, the parameter image-extensions can be used. Example:

PYTHONPATH=$PYTHONPATH:. python anonymizer/bin/anonymize.py --input /path/to/input_folder --image-output /path/to/output_folder --weights /path/to/store/weights --image-extensions=jpg,tiff

The parameters for the blurring can be changed as well. For this the parameter obfuscation-kernel is used. It consists of three values: The size of the gaussian kernel used for blurring, it's standard deviation and the size of another kernel that is used to make the transition between blurred and non-blurred regions smoother. Example usage:

PYTHONPATH=$PYTHONPATH:. python anonymizer/bin/anonymize.py --input /path/to/input_folder --image-output /path/to/output_folder --weights /path/to/store/weights --obfuscation-kernel="65,3,19"

Attributions

An image for one of the test cases was taken from the COCO dataset.
The pictures in this README are under an Attribution 4.0 International license. You can find the pictures here and here.

Latest Releases
v1.0.0
 Oct. 21 2018