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import cv2
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import matplotlib.pyplot as plt
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from deepface.modules import streaming
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from deepface import DeepFace
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img_path = "dataset/img1.jpg"
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img = cv2.imread(img_path)
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overlay_img_path = "dataset/img6.jpg"
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face_objs = DeepFace.extract_faces(overlay_img_path)
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overlay_img = face_objs[0]["face"][:, :, ::-1] * 255
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overlay_img = cv2.resize(overlay_img, (112, 112))
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raw_img = img.copy()
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demographies = DeepFace.analyze(img_path=img_path, actions=("age", "gender", "emotion"))
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demography = demographies[0]
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x = demography["region"]["x"]
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y = demography["region"]["y"]
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w = demography["region"]["w"]
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h = demography["region"]["h"]
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img = streaming.highlight_facial_areas(img=img, faces_coordinates=[(x, y, w, h)])
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img = streaming.overlay_emotion(
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img=img,
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emotion_probas=demography["emotion"],
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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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)
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img = streaming.overlay_age_gender(
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img=img,
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apparent_age=demography["age"],
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gender=demography["dominant_gender"][0:1],
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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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)
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img = streaming.overlay_identified_face(
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img=img,
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target_img=overlay_img,
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label="angelina",
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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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)
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plt.imshow(img[:, :, ::-1])
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plt.show()
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