Live visualisation of various facial recognition algorithms.
.vscode | ||
dnn | ||
face_recognition | ||
visualhaar@a6ac50c3b3 | ||
.gitignore | ||
.gitmodules | ||
dnn_test.py | ||
haarcascade_frontalface_alt2.xml | ||
hog_test.py | ||
live_dnn.py | ||
live_hog.py | ||
mirror.py | ||
README.md | ||
recognition_test.py | ||
requirements.txt | ||
test_rec.py | ||
test_rust.py | ||
video_multiprocess.py | ||
video_threading.py |
A mirror
which shows which faces are detected through three different facial detection algorithms:
- OpenCV's deep neural net face detector.
- Dlib's default frontal face detector, which is HOG based
- A Viola-Jones Haarcascade detection. Any OpenCV compatible xml file should work. It defaults to the canonical
haarcascade_frontalface_alt2.xml
.
Installation
on windows
The installation in Windows can be done, though it is quite elaborate:
- Install python3
- Install VS C++
- Install Cmake (needed for python dlib)
- make sure to add it to path
- Install git
- including ssh deploy key
git clone https://git.rubenvandeven.com/r/face_detector
cd face_recognition
pip install virtualenv
virtualenv.exe venv
.\venv\Scripts\activate
cd .\dnn\face_detector
python.exe .\download_weights.py
cd .\visualhaar
- Either one of:
- Compile rust library
- Install rustup-init
git submodules init
git submodules update
cargo build --lib --release
- Download dll from https://git.rubenvandeven.com/r/visualhaar/releases
- Compile rust library
- Make the installer:
& 'C:\Users\DP Medialab\AppData\Roaming\Python\Python38\Scripts\pyinstaller.exe' .\mirror.py --add-binary '.\visualhaar\target\release\visual_haarcascades_lib.dll;.' --add-data '.\haarcascade_frontalface_alt2.xml;.' --add-data '.\SourceSansPro-Regular.ttf;.' --add-data 'dnn;dnn'
mv '.\dist\mirror\mpl-data' '.\dist\mirror\matplotlib\'