EEPW论坛

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SmartRecognitionSystem
├── 初始化模块：完成LED、摄像头、Haar Cascade模型的加载与初始化
├── 检测模块：实现人脸检测、眼睛检测（人脸区域内）、人脸跟踪功能
├── 交互模块：通过RGB LED提供视觉反馈，控制台输出实时状态信息
└── 资源管理模块：负责系统异常处理与资源自动回收

Python
CONFIG = {
    # 摄像头配置：适配嵌入式资源，优化检测效率
    "camera": {
        "framesize": sensor.HQVGA,      # 240x160分辨率，平衡画质与性能
        "pixformat": sensor.GRAYSCALE,  # 灰度图像，减少计算量与内存占用
        "contrast": 3,                  # 对比度增强，提升检测准确率
        "gainceiling": 16,              # 增益限制，避免图像过曝
        "skip_frames": 2000             # 摄像头稳定时间（2秒）
    },
    # 检测参数：平衡检测精度与速度
    "detection": {
        "face_threshold": 0.5,         # 人脸检测置信度阈值
        "face_scale_factor": 1.25,       # 人脸检测缩放因子
        "eyes_threshold": 0.5,          # 眼睛检测置信度阈值
        "eyes_scale_factor": 1.2,        # 眼睛检测缩放因子
        "face_stages": 25,              # 人脸检测阶段数
        "eyes_stages": 24               # 眼睛检测阶段数
    },
    # 跟踪参数：控制跟踪稳定性与资源消耗
    "tracking": {
        "enabled": True,                 # 启用人脸跟踪
        "threshold": 10,                 # 关键点检测阈值
        "max_keypoints": 100,            # 最大关键点数量
        "scale_factor": 1.1              # 关键点检测缩放因子
    },
    # 交互配置：灵活开关反馈功能
    "interaction": {
        "led_enabled": True,             # 启用LED视觉反馈
        "print_enabled": True            # 启用控制台状态输出
    }
}

Plain Text
图像捕获 → 全局人脸检测 → 人脸区域内眼睛检测 → 特征点匹配人脸跟踪

Python
def __init__(self):
    self.initialized = False
    self.face_cascade = None
    self.eyes_cascade = None
    self.leds = {}
    self.clock = time.clock()
    self.kpts1 = None  # 人脸跟踪关键点
    
    try:
        self._init_leds()  # 初始化RGB LED
        self._init_camera()  # 配置摄像头参数
        self._load_cascades()  # 加载Haar Cascade模型
        self.initialized = True  # 初始化成功标记
    except Exception as e:
        print(f"系统初始化失败: {str(e)}")
        self._cleanup()  # 初始化失败时清理资源

Python
def _detect_faces(self, img):
    faces = img.find_features(
        self.face_cascade,
        threshold=CONFIG["detection"]["face_threshold"],
        scale_factor=CONFIG["detection"]["face_scale_factor"],
        stages=CONFIG["detection"]["face_stages"]
    )
    return faces

Python
def _detect_eyes(self, img, face_roi):
    eyes = img.find_features(
        self.eyes_cascade,
        threshold=CONFIG["detection"]["eyes_threshold"],
        scale_factor=CONFIG["detection"]["eyes_scale_factor"],
        stages=CONFIG["detection"]["eyes_stages"],
        roi=face_roi  # 限定眼睛检测区域为人脸ROI
    )
    return eyes

Python
def _track_face(self, img):
    if self.kpts1 is None:
        # 初始化跟踪关键点
        self.kpts1 = img.find_keypoints(
            max_keypoints=CONFIG["tracking"]["max_keypoints"],
            threshold=CONFIG["tracking"]["threshold"],
            scale_factor=CONFIG["tracking"]["scale_factor"]
        )
    else:
        # 帧间关键点匹配
        kpts2 = img.find_keypoints(
            max_keypoints=CONFIG["tracking"]["max_keypoints"],
            threshold=CONFIG["tracking"]["threshold"],
            scale_factor=CONFIG["tracking"]["scale_factor"]
        )
        if kpts2:
            match_score = image.match_descriptor(self.kpts1, kpts2)
            if match_score > 5:  # 匹配分数达标则跟踪成功
                img.draw_keypoints(kpts2, size=2, color=(0, 255, 0))
                self.kpts1 = kpts2  # 更新关键点
    return self.kpts1 is not None

LED颜色

状态含义

Python
def _print_status(self, fps, face_detected, eye_detected, track_active):
    if CONFIG["interaction"]["print_enabled"]:
        print(f"FPS: {fps:.1f} | 人脸: {face_detected} | 眼睛: {eye_detected} | 跟踪: {track_active}")

Python
def _cleanup(self):
    # 关闭LED
    for led in self.leds.values():
        led.off()
    # 释放摄像头资源
    sensor.reset()
    self.initialized = False

测试项

测试指标

测试结果