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retinaface's nose and mouth field are returned in extract faces
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@ -45,6 +45,7 @@ class FacialAreaRegion:
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confidence (float, optional): Confidence score associated with the face detection.
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Default is None.
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"""
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x: int
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y: int
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w: int
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@ -52,6 +53,9 @@ class FacialAreaRegion:
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left_eye: Optional[Tuple[int, int]] = None
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right_eye: Optional[Tuple[int, int]] = None
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confidence: Optional[float] = None
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nose: Optional[Tuple[int, int]] = None
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mouth_right: Optional[Tuple[int, int]] = None
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mouth_left: Optional[Tuple[int, int]] = None
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@dataclass
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@ -63,7 +67,8 @@ class DetectedFace:
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img (np.ndarray): detected face image as numpy array
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facial_area (FacialAreaRegion): detected face's metadata (e.g. bounding box)
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confidence (float): confidence score for face detection
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"""
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"""
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img: np.ndarray
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facial_area: FacialAreaRegion
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confidence: float
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@ -42,10 +42,16 @@ class RetinaFaceClient(Detector):
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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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nose = identity["landmarks"]["nose"]
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mouth_right = identity["landmarks"]["mouth_right"]
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mouth_left = identity["landmarks"]["mouth_left"]
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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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nose = tuple(int(i) for i in nose)
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mouth_right = tuple(int(i) for i in mouth_right)
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mouth_left = tuple(int(i) for i in mouth_left)
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confidence = identity["score"]
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@ -57,6 +63,9 @@ class RetinaFaceClient(Detector):
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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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nose=nose,
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mouth_left=mouth_left,
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mouth_right=mouth_right,
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)
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resp.append(facial_area)
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@ -148,16 +148,26 @@ def extract_faces(
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w = min(width - x - 1, int(current_region.w))
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h = min(height - y - 1, int(current_region.h))
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facial_area = {
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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": current_region.left_eye,
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"right_eye": current_region.right_eye,
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}
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# optional nose, mouth_left and mouth_right fields are coming just for retinaface
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if current_region.nose:
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facial_area["nose"] = current_region.nose
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if current_region.mouth_left:
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facial_area["mouth_left"] = current_region.mouth_left
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if current_region.mouth_right:
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facial_area["mouth_right"] = current_region.mouth_right
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resp_obj = {
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"face": current_img,
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"facial_area": {
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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": current_region.left_eye,
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"right_eye": current_region.right_eye,
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},
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"facial_area": facial_area,
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"confidence": round(float(current_region.confidence or 0), 2),
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}
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@ -272,6 +282,9 @@ def expand_and_align_face(
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left_eye = facial_area.left_eye
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right_eye = facial_area.right_eye
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confidence = facial_area.confidence
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nose = facial_area.nose
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mouth_left = facial_area.mouth_left
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mouth_right = facial_area.mouth_right
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if expand_percentage > 0:
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# Expand the facial region height and width by the provided percentage
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@ -305,11 +318,26 @@ def expand_and_align_face(
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left_eye = (left_eye[0] - width_border, left_eye[1] - height_border)
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if right_eye is not None:
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right_eye = (right_eye[0] - width_border, right_eye[1] - height_border)
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if nose is not None:
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nose = (nose[0] - width_border, nose[1] - height_border)
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if mouth_left is not None:
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mouth_left = (mouth_left[0] - width_border, mouth_left[1] - height_border)
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if mouth_right is not None:
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mouth_right = (mouth_right[0] - width_border, mouth_right[1] - height_border)
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return DetectedFace(
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img=detected_face,
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facial_area=FacialAreaRegion(
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x=x, y=y, h=h, w=w, confidence=confidence, left_eye=left_eye, right_eye=right_eye
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x=x,
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y=y,
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h=h,
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w=w,
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confidence=confidence,
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left_eye=left_eye,
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right_eye=right_eye,
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nose=nose,
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mouth_left=mouth_left,
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mouth_right=mouth_right,
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),
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confidence=confidence,
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)
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