Live visualisation of various facial recognition algorithms.
| .vscode | ||
| dnn | ||
| face_recognition | ||
| visualhaar@7c7ae29bf9 | ||
| .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 rustup-init
- Install VS C++
- Install python3
- 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_detectorcd face_recognitiongit submodules initgit submodules updatepip install virtualenvvirtualenv.exe venv.\venv\Scripts\activatecd .\dnn\face_detectorpython.exe .\download_weights.pycd .\visualhaarcargo build --lib