Python API Documentation (Experimental)¶
FaceVerifier class¶
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class
face_verification_library.FaceVerifier(model_file, max_concurrency=0)¶ The Face Verifier class
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__init__(self, model_file, max_concurrency=0)¶ FaceVerifier constructor: loads model file, sets up the processing.
- Parameters:
model_file (str) – path for the used model
max_concurrency (int) – maximum allowed concurrency, 0 means automatic (using all cores), default: 0
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detect_faces(self, image)¶ Detects the faces on an image.
- Parameters:
image (numpy.ndarray) – image of the face(s)
- Return type:
list[Face]
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embed_face(self, face)¶ Returns the embedding of the detected face.
- Parameters:
face (Face) – face to embed.
- Return type:
list[float]
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compare_faces(self, embedding1, embedding2)¶ - Parameters:
embedding1 (list[float]) – embedding of the 1th face
embedding2 (list[float]) – embedding of the 2nd face
- Return type:
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get_model_name(self)¶ Returns the name (version etc) of the loaded model.
- Return type:
str
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Result classes¶
Face¶
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class
face_verification_library.Face¶ -
__init__(self, image, landmarks, bbox=BoundingBox(x=0, y=0, width=0, height=0), confidence=0.0)¶ Face constructor to use a 3rd party face detector as face source
- Parameters:
image (numpy.ndarray) – image of the face
landmarks (list[Point2d]) – landmarks of the face, see landmarks specification
bbox (BoundingBox) – bounding box of the face
confidence (float) – confidence value of the detected face
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bounding_box: BoundingBox¶ Returns the bounding box of the detected face.
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confidence: float¶ Returns the confidence value of the detected face.
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landmarks: list[Point2d]¶ Returns the detected landmarks of the face.
See also: landmarks specification.
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