前言

感谢正点原子和电子发烧友论坛提供的这次机会让我有机会体验这款正点原子STM32MP257开发板，希望可以借这个机会好好学习一下。

项目实现

项目通过识别我需要当成非陌生人的人脸，再使用LBPH人脸识别器（cv2.face.LBPHFaceRecognizer_create()）训练为临时模型使用。剩下就是吧LBPH人脸识别器用作模型识别是否为陌生人，再通过蜂鸣器警报。

代码

import cv2
import numpy as np
import gpiod
import time

# 创建LBPH脸识别器
recognizer = cv2.face.LBPHFaceRecognizer_create()

# 创建人脸检测器
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml')

# 蜂鸣器 GPIO 配置
LINE = 13

# 使用gpiod.request_lines配置GPIO
with gpiod.request_lines(
    "/dev/gpiochip5",
    consumer="beep-example",
    config={
        LINE: gpiod.LineSettings(
            direction=gpiod.line.Direction.OUTPUT, output_value=gpiod.line.Value.INACTIVE
        )
    },
) as request:

    def get_reference_image_features(reference_image_path):
        img = cv2.imread(reference_image_path)
        if img is None:
            print(f"Error: 无法加载参考图片 {reference_image_path}")
            return None

        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

        if len(faces) == 0:
            print("Error: 参考图片中未检测到人脸")
            return None

        (x, y, w, h) = faces[0]
        face_roi = gray[y:y+h, x:x+w]

        return face_roi

    def train_recognizer_with_reference(reference_image_path):
        reference_features = get_reference_image_features(reference_image_path)
        if reference_features is None:
            return False

        labels = np.array([0])
        recognizer.train([reference_features], labels)
        return True

    def real_time_recognition(reference_image_path):
        if not train_recognizer_with_reference(reference_image_path):
            print("Error: 人脸识别器训练失败")
            return

        cap = cv2.VideoCapture(7)

        stranger_detected = False

        while True:
            ret, frame = cap.read()
            if not ret:
                print("Error: 无法读取摄像头帧")
                break

            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

            current_stranger_status = False

            for (x, y, w, h) in faces:
                face_roi = gray[y:y+h, x:x+w]
                label, confidence = recognizer.predict(face_roi)

                

                if confidence > 85:
                    cv2.putText(frame, "Stranger", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
                    cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
                    current_stranger_status = True
                else:
                    cv2.putText(frame, "Known Person", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
                    cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

            cv2.imshow('Real-Time Recognition', frame)

            if current_stranger_status and not stranger_detected:
                stranger_detected = True
            elif not current_stranger_status and stranger_detected:
                stranger_detected = False

            if stranger_detected:
                request.set_value(LINE, gpiod.line.Value.ACTIVE)
                time.sleep(1)
                request.set_value(LINE, gpiod.line.Value.INACTIVE)
                time.sleep(1)
            else:
                request.set_value(LINE, gpiod.line.Value.INACTIVE)

            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

        cap.release()
        cv2.destroyAllWindows()

    if __name__ == "__main__":
        reference_image_path = "reference_image.jpg"
        real_time_recognition(reference_image_path)

效果：在底部

总结

STM32MP257是一个很不错的芯片，在AI方面表现突出，再与正点原子合作，将STM32MP257的外设添加，让其的能力能发挥出来。