51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
def build_model():
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from retinaface import RetinaFace # this is not a must dependency
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face_detector = RetinaFace.build_model()
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return face_detector
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def detect_face(face_detector, img, align=True):
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from retinaface import RetinaFace # this is not a must dependency
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from retinaface.commons import postprocess
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# ---------------------------------
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resp = []
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# --------------------------
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obj = RetinaFace.detect_faces(img, model=face_detector, threshold=0.9)
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if isinstance(obj, dict):
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for face_idx in obj.keys():
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identity = obj[face_idx]
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facial_area = identity["facial_area"]
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y = facial_area[1]
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h = facial_area[3] - y
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x = facial_area[0]
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w = facial_area[2] - x
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img_region = [x, y, w, h]
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confidence = identity["score"]
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# detected_face = img[int(y):int(y+h), int(x):int(x+w)] #opencv
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detected_face = img[facial_area[1] : facial_area[3], facial_area[0] : facial_area[2]]
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if align:
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landmarks = identity["landmarks"]
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left_eye = landmarks["left_eye"]
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right_eye = landmarks["right_eye"]
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nose = landmarks["nose"]
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# mouth_right = landmarks["mouth_right"]
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# mouth_left = landmarks["mouth_left"]
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detected_face = postprocess.alignment_procedure(
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detected_face, right_eye, left_eye, nose
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)
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resp.append((detected_face, img_region, confidence))
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return resp
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