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https://github.com/checktor/face_amnesia

Face detection and retrieval in image and video files.
https://github.com/checktor/face_amnesia

clustering deduplication face-detection face-recognition image-processing locality-sensitive-hashing nearest-neighbors video-processing

Last synced: 3 months ago
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Face detection and retrieval in image and video files.

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README 

        
# face_amnesia

Detect and retrieve the face of a specific person in image and video files using pre-trained models.



## Requirements



### Language

* Python (>= 3.5)



### Dependencies

* NumPy (http://www.numpy.org/)

    * basic numerical computations

* scikit-learn (https://scikit-learn.org/stable/)

    * PCA

* OpenCV (https://opencv.org/)

    * basic image and video processing

* dlib (http://dlib.net/)

    * face detection and recognition models



## Install



### Use pre-compiled libraries

Run provided [install script](install.sh) to use pre-compiled versions

of OpenCV and dlib (without GPU support) available via pip. Note that

corresponding pre-built OpenCV package is unofficial but works

fine. See comments in install script for further details.



### Use Dockerfile

Run provided [Dockerfile](Dockerfile) to compile OpenCV and dlib by hand

using the official Ubuntu 20.04 image.

    

    cd /path/to/face_amnesia/

    docker build -t face_amnesia .

    docker run -it face_amnesia



Note that it is possible to enable hardware-dependent optimization such

as AVX instructions or CUDA support. See comments in Dockerfile for

further details.



## Testing

Run provided [test script](test.sh) to execute unit and integration tests.



## Usage

Command-line interfaces for data point creation and retrieval available

in project's root directory. Data points and corresponding source

files are stored in and served from a separate `face amnesia` folder

in current user's home directory which may be identical to project's

root directory.



### Data point creation

Create face description vectors from media file (image, video) or whole

directory.



    python3 create.py /path/to/media/file/or/directory/

    

Available options:

    

    python3 create.py --help



### Data point retrieval

Retrieve previously stored data points corresponding to faces similar

to given one.



    python3 retrieve.py /path/to/query/media/file/

    

Available options:

    

    python3 retrieve.py --help



## Further reading

* Mayur Datar, Nicole Immorlica, et. al.: "Locality-Sensitive Hashing Scheme Based on p-Stable Distributions" (2004)

* Chris Biemann: "Chinese Whispers - an Efficient Graph Clustering Algorithm and its Application to Natural Language Processing Problems" (2006)



## Further tools

* Adam Geitgey: face_recognition (https://github.com/ageitgey/face_recognition)



## Troubleshooting

Compiling dlib by hand is usually straightforward. Building OpenCV from

source, however, may need some adjustments. The official guides on this

topic are useful and can be found on the web:



* http://dlib.net/compile.html

* https://docs.opencv.org/4.7.0/d7/d9f/tutorial_linux_install.html



The following paragraphs will provide additional information on

compilation problems which may occur on some architectures.



### Compilation: CBLAS / LAPACK headers could not be found  

Possible workaround:



    sudo cp /usr/include/lapacke*.h /usr/include/openblas/

    

### Compilation: GCC version 8 is not supported

Possible workaround:



    export CC=/path/to/gcc-7/compiler



Building dlib's Python bindings with CUDA support needs an additional

argument to setup.py call:

    

    --set CUDA_HOST_COMPILER=/path/to/gcc-7/compiler