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def get_images_and_labels(path):
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image_paths = [os.path.join(path, f) for f in os.listdir(path)]
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# 新建连个list用于存放
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face_samples = []
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ids = []
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# 遍历图片路径，导入图片和id添加到list中
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for image_path in image_paths:
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# 通过图片路径将其转换为灰度图片
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img = Image.open(image_path).convert('L')
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# 将图片转化为数组
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img_np = np.array(img, 'uint8')
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if os.path.split(image_path)[-1].split(".")[-1] != 'jpg':
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continue
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# 为了获取id，将图片和路径分裂并获取
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id = int(os.path.split(image_path)[-1].split(".")[1])
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# 调用熟悉的人脸分类器
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detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
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faces = detector.detectMultiScale(img_np)
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# 将获取的图片和id添加到list中
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for (x, y, w, h) in faces:
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face_samples.append(img_np[y:y + h, x:x + w])
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ids.append(id)
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return face_samples, ids
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