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83 lines
2.3 KiB
Python
83 lines
2.3 KiB
Python
import os
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from typing import Any, List
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import numpy as np
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import cv2 as cv
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import gdown
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from deepface.commons import folder_utils
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from deepface.commons.logger import Logger
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from deepface.models.FacialRecognition import FacialRecognition
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logger = Logger(module="basemodels.SFace")
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# pylint: disable=line-too-long, too-few-public-methods
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class SFaceClient(FacialRecognition):
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"""
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SFace model class
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"""
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def __init__(self):
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self.model = load_model()
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self.model_name = "SFace"
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self.input_shape = (112, 112)
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self.output_shape = 128
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def find_embeddings(self, img: np.ndarray) -> List[float]:
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"""
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find embeddings with SFace model - different than regular models
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Args:
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img (np.ndarray): pre-loaded image in BGR
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Returns
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embeddings (list): multi-dimensional vector
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"""
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# return self.model.predict(img)[0].tolist()
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# revert the image to original format and preprocess using the model
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input_blob = (img[0] * 255).astype(np.uint8)
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embeddings = self.model.model.feature(input_blob)
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return embeddings[0].tolist()
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def load_model(
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url="https://github.com/opencv/opencv_zoo/raw/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx",
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) -> Any:
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"""
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Construct SFace model, download its weights and load
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"""
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home = folder_utils.get_deepface_home()
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file_name = home + "/.deepface/weights/face_recognition_sface_2021dec.onnx"
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if not os.path.isfile(file_name):
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logger.info("sface weights will be downloaded...")
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gdown.download(url, file_name, quiet=False)
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model = SFaceWrapper(model_path=file_name)
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return model
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class SFaceWrapper:
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def __init__(self, model_path):
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"""
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SFace wrapper covering model construction, layer infos and predict
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"""
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try:
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self.model = cv.FaceRecognizerSF.create(
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model=model_path, config="", backend_id=0, target_id=0
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)
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except Exception as err:
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raise ValueError(
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"Exception while calling opencv.FaceRecognizerSF module."
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+ "This is an optional dependency."
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+ "You can install it as pip install opencv-contrib-python."
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) from err
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