Face detection with Adaboost (Haar cascade detector)

Haar cascade detectors, based on Adaboost algorithm and OpenCV, are implemented in this programme.
The folder '/model/cv2' includes 17 different kinds of Haar cascade detectors trained by various dataset such as frontal face, eyes, smile etc.
Users can process images and videos. Especially, you can import existed video and export result video, or process real-time video from camera then export.

Script Description

• fd_adaboost_import.py Import existed video and export result.

• fd_adaboost_realtime.py Process real-time video from camera without export.

• fd_adaboost_realtime_save.py Process real-time video from camera and export.

• You must manually add data path with '(root)/data/images' and '(root)/data/videos'

Environment

• Python 3.6
• OpenCV 4.2
• Python package - numpy cv2 tqdm

Reference

[1] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features[C]. Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, 2001: I-I.
[2] Viola P, Jones M J. Robust real-time face detection[J]. International journal of computer vision, 2004, 57(2): 137-154.

License

This code is distributed under MIT LICENSE

Contact

[email protected]