81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
import numpy as np
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class DistributionChangeDetector:
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def __init__(self, baseline_windows: list[np.ndarray]):
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"""
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参数 baseline_windows: List of arrays，代表初始稳定期的多个窗口
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"""
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self.baseline = self._compute_baseline(baseline_windows)
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def _compute_stats(self, window: np.ndarray) -> tuple[float, float, float]:
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"""返回 (P_under30, std, mode)"""
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p_under30 = np.mean(window < 30)
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std = np.std(window, ddof=1)
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# 快速估计众数：最大 bin 的中心
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hist, bin_edges = np.histogram(window, bins=50)
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max_bin_index = np.argmax(hist)
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mode_est = (bin_edges[max_bin_index] + bin_edges[max_bin_index + 1]) / 2
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return p_under30, std, mode_est
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def _compute_baseline(self, windows: list[np.ndarray]) -> tuple[np.ndarray, np.ndarray]:
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"""
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返回 baseline 向量 (P0, σ0, mode0) 和对应标准差（用于归一化）
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"""
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stats = np.array([self._compute_stats(w) for w in windows])
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mean = stats.mean(axis=0)
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std = stats.std(axis=0) + 1e-6 # 防止除0
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return mean, std
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def update(self, window: np.ndarray) -> float:
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"""
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输入：当前窗口数据（长度 = 窗口大小）
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输出：变化分数（越大表示分布越偏离基准）
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"""
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x = np.array(self._compute_stats(window))
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mean, std = self.baseline
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norm_diff = (x - mean) / std
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change_score = np.linalg.norm(norm_diff)
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return float(change_score)
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import cv2
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def gen_data():
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cap = cv2.VideoCapture()
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cap.open(1)
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while True:
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ret, frame = cap.read()
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cv2.imshow("Camera Feed", frame)
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if not ret:
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break
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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s = hsv[:, :, 1] # 直接提取饱和度通道
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s = s[s > 0] # 只保留非零饱和度值，减少噪声
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yield s
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if cv2.waitKey(1) & 0xFF == ord('a'):
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break
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gen = gen_data()
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baseline_data = [gen.__next__() for _ in range(5)] # 获取10个窗口作为基线
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det = DistributionChangeDetector(baseline_data)
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results = []
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for x in gen:
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out = det.update(x)
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if out is not None:
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results.append(out)
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# 作图查看
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import matplotlib.pyplot as plt
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plt.plot(results, label="ChangeScore")
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plt.xlabel("Window index")
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plt.ylabel("Score")
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plt.title("Streaming Change Detection")
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plt.legend()
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plt.show()
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