351 lines
13 KiB
Python
351 lines
13 KiB
Python
"""
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Local Analyzer - 本地视觉分析（无需大模型）
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使用 OpenCV 传统方法进行快速检测
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功能：
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- 帧间差分：检测运动
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- 背景建模：检测前景物体
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- 人体检测：检测人员进出
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- 亮度检测：检测光线变化
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- 自动判断是否需要调用大模型
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"""
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import cv2
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import numpy as np
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from pathlib import Path
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import datetime
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class LocalAnalyzer:
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"""本地视觉分析器"""
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def __init__(self):
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self.prev_frame = None
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self.background_model = None
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self.human_cascade = None
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# 初始化人体检测器
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self._init_human_detector()
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# 阈值配置
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self.config = {
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'motion_threshold': 0.05, # 运动面积阈值（5%）
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'human_scale_factor': 1.1, # 人体检测缩放因子
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'human_min_neighbors': 3, # 人体检测最小邻居数
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'brightness_change_threshold': 30, # 亮度变化阈值
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'trigger_model_threshold': 0.08, # 触发大模型的阈值
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}
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# 统计
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self.frame_count = 0
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self.motion_count = 0
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self.human_count = 0
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def _init_human_detector(self):
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"""初始化人体检测器"""
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try:
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# 使用 OpenCV 内置的 HOG 人体检测器
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self.hog = cv2.HOGDescriptor()
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self.hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
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# 或者使用 Haar Cascade（更快但精度稍低）
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cascade_path = cv2.data.haarcascades + 'haarcascade_fullbody.xml'
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if Path(cascade_path).exists():
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self.human_cascade = cv2.CascadeClassifier(cascade_path)
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print("[LocalAnalyzer] Human detector initialized")
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except Exception as e:
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print(f"[LocalAnalyzer] Human detector init failed: {e}")
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def analyze(self, image_path, prev_image_path=None):
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"""
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分析单张图片
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Args:
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image_path: 当前图片路径
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prev_image_path: 前一张图片路径（可选）
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Returns:
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dict: {
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'success': bool,
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'events': list, # 本地检测到的初级事件
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'need_model': bool, # 是否需要大模型分析
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'metrics': dict, # 各项指标
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'error': str
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}
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"""
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try:
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print(f"[LocalAnalyzer] Loading image: {image_path}")
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# 加载当前图片
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current_frame = cv2.imread(image_path)
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if current_frame is None:
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return {'success': False, 'error': f'无法加载图片: {image_path}'}
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self.frame_count += 1
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# 转为灰度图
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current_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
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current_gray = cv2.GaussianBlur(current_gray, (21, 21), 0)
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events = []
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metrics = {}
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# 1. 运动检测（帧间差分）
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motion_result = self._detect_motion(current_gray, prev_image_path)
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metrics['motion_ratio'] = motion_result['ratio']
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if motion_result['detected']:
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events.append({
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'event_type': '运动检测',
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'description': f'画面有运动，变化区域 {motion_result["ratio"]:.1%}',
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'confidence': '高',
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'source': 'local'
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})
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self.motion_count += 1
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# 2. 人体检测
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human_result = self._detect_human(current_frame)
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metrics['human_count'] = human_result['count']
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if human_result['count'] > 0:
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events.append({
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'event_type': '人物活动',
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'description': f'检测到 {human_result["count"]} 个人',
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'confidence': '中',
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'source': 'local'
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})
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self.human_count += 1
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# 3. 亮度检测
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brightness_result = self._detect_brightness_change(current_gray, prev_image_path)
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metrics['brightness'] = brightness_result['current']
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metrics['brightness_change'] = brightness_result['change']
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if brightness_result['change_detected']:
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direction = "变亮" if brightness_result['change'] > 0 else "变暗"
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events.append({
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'event_type': '环境变化',
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'description': f'光线{direction}，变化 {abs(brightness_result["change"])} 点',
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'confidence': '高',
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'source': 'local'
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})
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# 4. 背景建模（如果有足够帧数）
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if self.prev_frame is not None:
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bg_result = self._detect_foreground(current_gray)
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metrics['foreground_ratio'] = bg_result['ratio']
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if bg_result['ratio'] > 0.02:
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events.append({
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'event_type': '物体变化',
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'description': f'前景区域占比 {bg_result["ratio"]:.1%}',
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'confidence': '中',
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'source': 'local'
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})
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# 判断是否需要大模型分析
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need_model = self._should_call_model(metrics, events)
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# 保存当前帧供下次使用
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self.prev_frame = current_gray.copy()
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return {
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'success': True,
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'events': events,
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'need_model': need_model,
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'metrics': metrics,
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'frame_count': self.frame_count
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}
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except Exception as e:
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return {'success': False, 'error': str(e)}
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def _detect_motion(self, current_gray, prev_image_path=None):
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"""帧间差分检测运动"""
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result = {'detected': False, 'ratio': 0}
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# 如果有前一张图片，使用它
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if prev_image_path and Path(prev_image_path).exists():
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prev_frame = cv2.imread(prev_image_path)
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if prev_frame is not None:
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prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
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prev_gray = cv2.GaussianBlur(prev_gray, (21, 21), 0)
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# 计算差分
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diff = cv2.absdiff(prev_gray, current_gray)
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thresh = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1]
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# 计算变化比例
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motion_pixels = np.sum(thresh > 0)
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total_pixels = thresh.shape[0] * thresh.shape[1]
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ratio = motion_pixels / total_pixels
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result['ratio'] = ratio
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result['detected'] = ratio > self.config['motion_threshold']
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# 找到运动区域轮廓
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contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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result['motion_regions'] = len(contours)
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# 或者使用保存的前一帧
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elif self.prev_frame is not None:
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diff = cv2.absdiff(self.prev_frame, current_gray)
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thresh = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1]
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motion_pixels = np.sum(thresh > 0)
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total_pixels = thresh.shape[0] * thresh.shape[1]
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ratio = motion_pixels / total_pixels
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result['ratio'] = ratio
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result['detected'] = ratio > self.config['motion_threshold']
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return result
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def _detect_human(self, frame):
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"""检测人体"""
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result = {'count': 0, 'positions': []}
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try:
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# 方法1：HOG 检测（更准确但慢）
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# regions, _ = self.hog.detectMultiScale(frame, winStride=(8,8))
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# 方法2：Haar Cascade（更快）
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if self.human_cascade is not None:
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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bodies = self.human_cascade.detectMultiScale(
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gray,
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scaleFactor=self.config['human_scale_factor'],
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minNeighbors=self.config['human_min_neighbors'],
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minSize=(30, 30)
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)
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result['count'] = len(bodies)
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result['positions'] = bodies.tolist() if len(bodies) > 0 else []
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except Exception as e:
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print(f"[LocalAnalyzer] Human detection error: {e}")
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return result
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def _detect_brightness_change(self, current_gray, prev_image_path=None):
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"""检测亮度变化"""
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result = {
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'current': 0,
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'previous': 0,
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'change': 0,
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'change_detected': False
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}
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# 计算当前亮度（平均灰度值）
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result['current'] = np.mean(current_gray)
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# 如果有前一张图片
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if prev_image_path and Path(prev_image_path).exists():
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prev_frame = cv2.imread(prev_image_path)
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if prev_frame is not None:
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prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
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result['previous'] = np.mean(prev_gray)
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result['change'] = result['current'] - result['previous']
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result['change_detected'] = abs(result['change']) > self.config['brightness_change_threshold']
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elif self.prev_frame is not None:
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result['previous'] = np.mean(self.prev_frame)
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result['change'] = result['current'] - result['previous']
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result['change_detected'] = abs(result['change']) > self.config['brightness_change_threshold']
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return result
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def _detect_foreground(self, current_gray):
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"""背景建模检测前景"""
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result = {'ratio': 0}
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try:
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# 初始化背景模型
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if self.background_model is None:
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self.background_model = cv2.createBackgroundSubtractorMOG2(
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history=100,
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varThreshold=50,
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detectShadows=True
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)
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# 应用背景建模
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fg_mask = self.background_model.apply(current_gray)
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# 计算前景比例
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foreground_pixels = np.sum(fg_mask > 200) # 只统计确定的前景（排除阴影）
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total_pixels = fg_mask.shape[0] * fg_mask.shape[1]
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result['ratio'] = foreground_pixels / total_pixels
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except Exception as e:
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print(f"[LocalAnalyzer] Background modeling error: {e}")
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return result
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def _should_call_model(self, metrics, events):
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"""判断是否需要调用大模型"""
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# 条件1：运动面积超过阈值
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if metrics.get('motion_ratio', 0) > self.config['trigger_model_threshold']:
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return True
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# 条件2：检测到人
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if metrics.get('human_count', 0) > 0:
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return True
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# 条件3：亮度大幅变化
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if abs(metrics.get('brightness_change', 0)) > self.config['brightness_change_threshold'] * 2:
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return True
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# 条件4：有多个事件类型
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event_types = set(e['event_type'] for e in events)
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if len(event_types) >= 2:
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return True
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return False
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def get_stats(self):
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"""获取统计信息"""
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return {
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'frames_analyzed': self.frame_count,
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'motion_detected': self.motion_count,
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'human_detected': self.human_count,
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'motion_rate': self.motion_count / max(self.frame_count, 1),
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'human_rate': self.human_count / max(self.frame_count, 1)
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}
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def reset(self):
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"""重置状态"""
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self.prev_frame = None
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self.background_model = None
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self.frame_count = 0
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self.motion_count = 0
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self.human_count = 0
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print("[LocalAnalyzer] Reset complete")
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# 便捷函数
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def analyze_local(image_path, prev_image_path=None):
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"""本地分析便捷函数"""
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analyzer = LocalAnalyzer()
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return analyzer.analyze(image_path, prev_image_path)
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if __name__ == "__main__":
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# 测试
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import sys
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if len(sys.argv) >= 2:
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test_image = sys.argv[1]
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prev_image = sys.argv[2] if len(sys.argv) >= 3 else None
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print(f"[Test] Analyzing: {test_image}")
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if prev_image:
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print(f"[Test] Previous: {prev_image}")
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result = analyze_local(test_image, prev_image)
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print(f"[Test] Result:")
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print(f" - Events: {len(result['events'])}")
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print(f" - Need model: {result['need_model']}")
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print(f" - Metrics: {result['metrics']}")
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for event in result['events']:
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print(f" - [{event['source']}] {event['event_type']}: {event['description']}")
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else:
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print("Usage: python local_analyzer.py <image_path> [prev_image_path]") |