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107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
from typing import List
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import os
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import bz2
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import gdown
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import numpy as np
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from deepface.commons import folder_utils
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from deepface.models.Detector import Detector, FacialAreaRegion
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from deepface.commons.logger import Logger
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logger = Logger(module="detectors.DlibWrapper")
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class DlibClient(Detector):
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def __init__(self):
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self.model = self.build_model()
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def build_model(self) -> dict:
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"""
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Build a dlib hog face detector model
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Returns:
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model (Any)
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"""
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home = folder_utils.get_deepface_home()
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# this is not a must dependency. do not import it in the global level.
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try:
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import dlib
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except ModuleNotFoundError as e:
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raise ImportError(
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"Dlib is an optional detector, ensure the library is installed."
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"Please install using 'pip install dlib' "
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) from e
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# check required file exists in the home/.deepface/weights folder
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if os.path.isfile(home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat") != True:
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file_name = "shape_predictor_5_face_landmarks.dat.bz2"
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logger.info(f"{file_name} is going to be downloaded")
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url = f"http://dlib.net/files/{file_name}"
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output = f"{home}/.deepface/weights/{file_name}"
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gdown.download(url, output, quiet=False)
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zipfile = bz2.BZ2File(output)
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data = zipfile.read()
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newfilepath = output[:-4] # discard .bz2 extension
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with open(newfilepath, "wb") as f:
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f.write(data)
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face_detector = dlib.get_frontal_face_detector()
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sp = dlib.shape_predictor(home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat")
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detector = {}
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detector["face_detector"] = face_detector
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detector["sp"] = sp
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return detector
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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 dlib
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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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face_detector = self.model["face_detector"]
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# note that, by design, dlib's fhog face detector scores are >0 but not capped at 1
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detections, scores, _ = face_detector.run(img, 1)
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if len(detections) > 0:
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for idx, detection in enumerate(detections):
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left = detection.left()
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right = detection.right()
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top = detection.top()
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bottom = detection.bottom()
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y = int(max(0, top))
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h = int(min(bottom, img.shape[0]) - y)
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x = int(max(0, left))
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w = int(min(right, img.shape[1]) - x)
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shape = self.model["sp"](img, detection)
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left_eye = (shape.part(2).x, shape.part(2).y)
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right_eye = (shape.part(0).x, shape.part(0).y)
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confidence = scores[idx]
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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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