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60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
from typing import List
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import numpy as np
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from retinaface import RetinaFace as rf
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from deepface.models.Detector import Detector, FacialAreaRegion
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# pylint: disable=too-few-public-methods
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class RetinaFaceClient(Detector):
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def __init__(self):
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self.model = rf.build_model()
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def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
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"""
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Detect and align face with retinaface
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Args:
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img (np.ndarray): pre-loaded image as numpy array
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Returns:
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results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
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"""
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resp = []
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obj = rf.detect_faces(img, model=self.model, threshold=0.9)
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if not isinstance(obj, dict):
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return resp
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for face_idx in obj.keys():
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identity = obj[face_idx]
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detection = identity["facial_area"]
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y = detection[1]
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h = detection[3] - y
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x = detection[0]
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w = detection[2] - x
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# retinaface sets left and right eyes with respect to the person
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left_eye = identity["landmarks"]["left_eye"]
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right_eye = identity["landmarks"]["right_eye"]
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# eyes are list of float, need to cast them tuple of int
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left_eye = tuple(int(i) for i in left_eye)
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right_eye = tuple(int(i) for i in right_eye)
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confidence = identity["score"]
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facial_area = FacialAreaRegion(
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x=x,
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y=y,
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w=w,
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h=h,
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left_eye=left_eye,
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right_eye=right_eye,
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confidence=confidence,
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)
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resp.append(facial_area)
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return resp
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