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79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
import os
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import bz2
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import gdown
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from deepface.commons import functions
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def build_model():
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home = functions.get_deepface_home()
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import dlib # this requirement is not a must that's why imported here
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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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print(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_face(detector, img, align=True):
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import dlib # this requirement is not a must that's why imported here
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resp = []
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sp = detector["sp"]
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detected_face = None
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img_region = [0, 0, img.shape[1], img.shape[0]]
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face_detector = detector["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, d in enumerate(detections):
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left = d.left()
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right = d.right()
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top = d.top()
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bottom = d.bottom()
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# detected_face = img[top:bottom, left:right]
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detected_face = img[
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max(0, top) : min(bottom, img.shape[0]), max(0, left) : min(right, img.shape[1])
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]
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img_region = [left, top, right - left, bottom - top]
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confidence = scores[idx]
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if align:
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img_shape = sp(img, detections[idx])
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detected_face = dlib.get_face_chip(img, img_shape, size=detected_face.shape[0])
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resp.append((detected_face, img_region, confidence))
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return resp
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