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101 lines
3.1 KiB
Python
101 lines
3.1 KiB
Python
# built-in dependencies
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from typing import Any
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# project dependencies
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from deepface.models.facial_recognition import (
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VGGFace,
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OpenFace,
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FbDeepFace,
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DeepID,
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ArcFace,
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SFace,
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Dlib,
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Facenet,
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GhostFaceNet,
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)
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from deepface.models.face_detection import (
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FastMtCnn,
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MediaPipe,
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MtCnn,
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OpenCv,
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Dlib as DlibDetector,
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RetinaFace,
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Ssd,
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Yolo,
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YuNet,
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CenterFace,
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)
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from deepface.models.demography import Age, Gender, Race, Emotion
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from deepface.models.spoofing import FasNet
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def build_model(task: str, model_name: str) -> Any:
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"""
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This function loads a pre-trained models as singletonish way
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Parameters:
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task (str): facial_recognition, facial_attribute, face_detector, spoofing
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model_name (str): model identifier
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- VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID, Dlib,
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ArcFace, SFace, GhostFaceNet for face recognition
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- Age, Gender, Emotion, Race for facial attributes
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- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, yunet,
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fastmtcnn or centerface for face detectors
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- Fasnet for spoofing
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Returns:
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built model class
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"""
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# singleton design pattern
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global cached_models
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models = {
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"facial_recognition": {
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"VGG-Face": VGGFace.VggFaceClient,
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"OpenFace": OpenFace.OpenFaceClient,
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"Facenet": Facenet.FaceNet128dClient,
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"Facenet512": Facenet.FaceNet512dClient,
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"DeepFace": FbDeepFace.DeepFaceClient,
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"DeepID": DeepID.DeepIdClient,
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"Dlib": Dlib.DlibClient,
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"ArcFace": ArcFace.ArcFaceClient,
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"SFace": SFace.SFaceClient,
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"GhostFaceNet": GhostFaceNet.GhostFaceNetClient,
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},
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"spoofing": {
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"Fasnet": FasNet.Fasnet,
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},
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"facial_attribute": {
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"Emotion": Emotion.EmotionClient,
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"Age": Age.ApparentAgeClient,
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"Gender": Gender.GenderClient,
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"Race": Race.RaceClient,
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},
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"face_detector": {
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"opencv": OpenCv.OpenCvClient,
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"mtcnn": MtCnn.MtCnnClient,
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"ssd": Ssd.SsdClient,
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"dlib": DlibDetector.DlibClient,
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"retinaface": RetinaFace.RetinaFaceClient,
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"mediapipe": MediaPipe.MediaPipeClient,
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"yolov8": Yolo.YoloClient,
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"yunet": YuNet.YuNetClient,
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"fastmtcnn": FastMtCnn.FastMtCnnClient,
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"centerface": CenterFace.CenterFaceClient,
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},
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}
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if models.get(task) is None:
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raise ValueError(f"unimplemented task - {task}")
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if not "cached_models" in globals():
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cached_models = {current_task: {} for current_task in models.keys()}
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if cached_models[task].get(model_name) is None:
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model = models[task].get(model_name)
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if model:
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cached_models[task][model_name] = model()
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else:
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raise ValueError(f"Invalid model_name passed - {task}/{model_name}")
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return cached_models[task][model_name]
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