眨眼频率、眼动分析、心率、视频录制

reproject/HeartRateMonitor.py

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+import numpy as np
+import collections
+from scipy import signal
+
+class HeartRateMonitor:
+    def __init__(self, fps=30, window_size=300):
+        self.fps = fps
+        self.buffer_size = window_size
+        
+        # 存储 RGB 三个通道的均值
+        self.times = np.zeros(window_size)
+        self.r_buffer = collections.deque(maxlen=window_size)
+        self.g_buffer = collections.deque(maxlen=window_size)
+        self.b_buffer = collections.deque(maxlen=window_size)
+        
+        # 滤波器状态
+        self.bp_b, self.bp_a = self._create_bandpass_filter(0.75, 2.5, fps) # 45-150 BPM
+        
+        # 平滑结果用的
+        self.bpm_history = collections.deque(maxlen=10)
+
+    def _create_bandpass_filter(self, lowcut, highcut, fs, order=5):
+        """创建巴特沃斯带通滤波器"""
+        nyq = 0.5 * fs
+        low = lowcut / nyq
+        high = highcut / nyq
+        b, a = signal.butter(order, [low, high], btype='band')
+        return b, a
+
+    def _pos_algorithm(self, r, g, b):
+        """
+        POS (Plane-Orthogonal-to-Skin) 算法
+        比单纯的绿色通道法强在一个地方：抗运动干扰
+        """
+        # 1. 归一化 (除以均值)
+        # 加上 1e-6 防止除零
+        r_n = r / (np.mean(r) + 1e-6)
+        g_n = g / (np.mean(g) + 1e-6)
+        b_n = b / (np.mean(b) + 1e-6)
+        
+        # 2. 投影到色度平面 (Matplotlib 里的经典公式)
+        # S1 = G - B
+        # S2 = G + B - 2R
+        s1 = g_n - b_n
+        s2 = g_n + b_n - 2 * r_n
+        
+        # 3. Alpha 微调 (Alpha Tuning)
+        # 这一步是为了消除镜面反射带来的运动噪声
+        alpha = np.std(s1) / (np.std(s2) + 1e-6)
+        
+        # 4. 融合信号
+        h = s1 + alpha * s2
+        return h
+
+    def process_frame(self, frame, face_loc):
+        """
+        输入: 原始无损 frame (BGR), 人脸框 (top, right, bottom, left)
+        输出: BPM 数值 或 None (数据不够时)
+        """
+        top, right, bottom, left = face_loc
+        
+        # --- 1. ROI 提取与保护 ---
+        h_img, w_img = frame.shape[:2]
+        
+        # 缩小 ROI 范围：只取脸中心 50% 区域 (避开背景和边缘)
+        h_box = bottom - top
+        w_box = right - left
+        
+        # 修正 ROI 坐标
+        roi_top = int(max(0, top + h_box * 0.3))
+        roi_bottom = int(min(h_img, bottom - h_box * 0.3))
+        roi_left = int(max(0, left + w_box * 0.3))
+        roi_right = int(min(w_img, right - w_box * 0.3))
+        
+        roi = frame[roi_top:roi_bottom, roi_left:roi_right]
+        
+        if roi.size == 0:
+            return None
+
+        # --- 2. 提取 RGB 均值 ---
+        # OpenCV 是 BGR
+        b_mean = np.mean(roi[:, :, 0])
+        g_mean = np.mean(roi[:, :, 1])
+        r_mean = np.mean(roi[:, :, 2])
+        
+        self.r_buffer.append(r_mean)
+        self.g_buffer.append(g_mean)
+        self.b_buffer.append(b_mean)
+
+        # 数据不够，返回 None
+        if len(self.r_buffer) < self.buffer_size:
+            progress = int(len(self.r_buffer) / self.buffer_size * 100)
+            return None # 或者返回 progress 表示进度
+
+        # --- 3. 信号处理 (核心升级部分) ---
+        r = np.array(self.r_buffer)
+        g = np.array(self.g_buffer)
+        b = np.array(self.b_buffer)
+
+        # A. 使用 POS 算法融合三通道 (抗干扰)
+        pulse_signal = self._pos_algorithm(r, g, b)
+        
+        # B. 消除直流分量 (Detrending)
+        # 这一步去掉了光线缓慢变化的干扰
+        pulse_signal = signal.detrend(pulse_signal)
+        
+        # C. 带通滤波 (Bandpass Filter)
+        # 只保留 0.75Hz - 2.5Hz 的信号
+        pulse_signal = signal.filtfilt(self.bp_b, self.bp_a, pulse_signal)
+        
+        # --- 4. 频域分析 (FFT) ---
+        # 加汉宁窗 (减少频谱泄露)
+        window = np.hanning(len(pulse_signal))
+        pulse_signal_windowed = pulse_signal * window
+        
+        # FFT
+        fft_res = np.fft.rfft(pulse_signal_windowed)
+        freqs = np.fft.rfftfreq(len(pulse_signal), 1.0/self.fps)
+        mag = np.abs(fft_res)
+        
+        # D. 寻找峰值
+        # 限制频率范围 (45 BPM - 180 BPM)
+        interest_idx = np.where((freqs >= 0.75) & (freqs <= 3.0))
+        valid_freqs = freqs[interest_idx]
+        valid_mags = mag[interest_idx]
+        
+        if len(valid_mags) == 0:
+            return None
+
+        max_idx = np.argmax(valid_mags)
+        peak_freq = valid_freqs[max_idx]
+        bpm = peak_freq * 60.0
+        
+        # --- 5. 结果平滑 ---
+        # 防止数字乱跳
+        self.bpm_history.append(bpm)
+        avg_bpm = np.mean(self.bpm_history)
+        
+        return int(avg_bpm)