Face detectors¶
- class pftracker.modules.facedetection.detect_one_face(image)[source]¶
One face detection class.
Class used for detecting one face in the first frame of video where particle filter algorithm is going to be applied.
- Parameters
image (array) – image where the face detection is perfomed
- Supported Face detectors:
‘HaarCascade’: Viola and Jones (V&J) detector
‘CaffeModel’: Single Shot Detector (SSD)
‘dlib’: Histogram of Oriented Gradient (HOG)
- bb_faces(face)[source]¶
Returns face bounding box coordinates in the format (startX, startY, endX, endY).
- Parameters
faces (tuple) – face detection resulting from a detector algorithm in the format (startX, startY, w, h). Where w and h are the weight and height of the face bounding box respectevely.
- caffeModel_detector(confidence=0.5)[source]¶
Run Single Shot Detector (SSD) algorithm for face detection on image.
- Parameters
confidence (float, optional) – minimum algorithm confidence boundary for detection.
- Returns
(tuple) with face bounding box coordinates resulting from detection.
- detector(face_detector)[source]¶
Run a face detector algortihm on image.
- Parameters
face_detector (str) – Face detector algorithm to apply
- Returns
(tuple) with bounding box coordinates resulting from face detection on image in the format (startX, startY, endX, endY).
- Raises
AssertionError – Exception raised if the face detector algorithm couldn’t detect any face (box iqual to (0, 0, 0, 0).
- dlib_detector()[source]¶
Run Histogram of Oriented Gradient (HOG) face detector on image.
- Returns
(tuple) with face bounding box coordinates resulting from detection.
- get_cascade_faces_from_model(model, minN)[source]¶
Returns a list of face detections from Viola and Jones (V&J) algorithm.
- Parameters
model (str) – HaarCascade face detector model (frontal, left profile or right profile)
minN (int) – Minimum of neighbors needed for retaining each candidate bounding box detected