CV module: Face Detector

A simple face detection module for Python and OpenCV projects. Currently supported classifiers:

• lbp Local Binarization Pattern Histogram .
• haar Haar Cascades.

If needed, the detector also returns the image with the drawn boundary boxes for found faces on it.

Installation and usage

1. Open a terminal window, navigate to your project and add this python as submodule as following:

git submodule add https://github.com/n1g1r1/cv-module-face-detector modules/face_detector

2. Import it as python module:

from modules.face_detector import detector

3. Call the detector:

faces, eyes, image = detector.detect(image, classifier = 'lbp', resize = False, resize_factor = 0.5, detect_eyes = False, draw_bounding_boxes = False, bounding_box_thickness = 2, save_faces = False, face_path = 'data/training', filename = None)

The parameters in detail

• image: The given image as numpy array (use `cv2.imread() or a given webcam image).
• classifier: The face classifier algorithm. Default: lbp.
• resize: Should the image get resized? Default: False.
• resize_factor: The resize factor, if resized. Default: 0.5.
• detect_eyes: Should the algorithm also detect eyes? Default: False.
• draw_bounding_boxes: Draw bounding boxes? Default: False.
• bounding_box_thickness: Bounding box thickness. Default: 2.
• save_faces: Should the algorithm save found faces? Useful to handle face capturing in one loop. Default: False.
• face_path: The path where face images should be saved. Default: data/training.
• filename: The filename. Has to be set if save_faces is true. Default: None.

Return

return faces, eyes, image

• faces: The found faces in the image as array.
• eyes: The found eyes in the image as array.
• image: The image itself. If draw_bounding_boxes is True, it returns the image with the drawn bounding boxes on it.