73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
import configparser
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import os.path
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from io import BytesIO
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from typing import NamedTuple, Optional, Tuple
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import cv2
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from PIL import Image
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class FacePos(NamedTuple):
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x: int
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y: int
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w: int
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h: int
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FACE_DATA = next(filter(os.path.exists, [
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'/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml',
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'/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml',
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]))
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face_cascade = cv2.CascadeClassifier(FACE_DATA)
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def get_rotate_value(input_value):
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if input_value == '0':
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return None
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if input_value == '90':
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return cv2.ROTATE_90_CLOCKWISE
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if input_value == '-90' or input_value == '270':
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return cv2.ROTATE_90_COUNTERCLOCKWISE
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if input_value == '180':
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return cv2.ROTATE_180
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raise ValueError(f"Unsupported rotation value {input_value!r}")
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class Camera:
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capture: cv2.VideoCapture
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def __init__(self):
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self.capture = cv2.VideoCapture(0)
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parser = configparser.ConfigParser()
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parser.read('config.ini')
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self.config = parser['camera']
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self.rotate = get_rotate_value(self.config['rotate'])
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self.detect_face = False
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self.draw_face = self.config.getboolean('draw_face', fallback=False)
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def get_image(self) -> Tuple[Optional[bytes], Optional[FacePos]]:
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return_code, image = self.capture.read()
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if not return_code:
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return None, None
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if self.rotate is not None:
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image = cv2.rotate(image, self.rotate)
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face = None
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if self.detect_face:
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image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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faces = face_cascade.detectMultiScale(image_gray, 1.3, 5)
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face = FacePos(*faces[0])
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if self.draw_face:
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for (x, y, w, h) in faces:
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image = cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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jpg = Image.fromarray(image_rgb)
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byte_stream = BytesIO()
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jpg.save(byte_stream, 'JPEG')
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return bytes(byte_stream.getbuffer()), face
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