feat: YOLO人体检测 + 三方法人员识别 + 前端人物序号显示
This commit is contained in:
@@ -132,33 +132,35 @@ class LocalAnalyzer:
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use_haar = config_mgr.get('use_haar_cascade', True)
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use_mediapipe = config_mgr.get('use_mediapipe_face', True)
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# 方法1：MediaPipe 人脸检测 + 人员识别（优先）
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# 方法1：YOLO 人体检测 + 人员识别
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if HAS_PERSON_MANAGER and use_mediapipe:
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print(f"[LocalAnalyzer] Using PersonManager for detection...")
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print(f"[LocalAnalyzer] Using YOLO + person identification...")
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person_result = person_manager.analyze_image(image_path, save_new_person=True)
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metrics['person_count'] = person_result['total_count']
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metrics['new_persons'] = person_result['new_count']
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metrics['known_persons'] = person_result['known_count']
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metrics['detection_source'] = person_result.get('detection_source', 'unknown')
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metrics['detection_source'] = 'yolo'
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metrics['methods_used'] = person_result.get('methods_used', [])
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metrics['person_indices'] = person_result.get('person_indices', [])
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prev_person_count = self.prev_human_count
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current_count = person_result['current_count']
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person_count_change = current_count - prev_person_count
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# 只有确认的变化才记录
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# 只有确认的变化才记录事件
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if person_result['confirmed_change']:
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metrics['person_count_change'] = person_count_change
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# 记录人员事件（带序号）
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# 记录人员事件（带序号和方法）
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for person in person_result['persons']:
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person_index = person.get('person_index', 1)
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detection_source = person.get('source', 'unknown')
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method = person.get('method', 'unknown')
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if person['is_new']:
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events.append({
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'event_type': '人物活动',
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'description': f'#{person_index} 新人: {person["name"]} [{detection_source}]，当前共 {current_count} 人',
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'description': f'#{person_index} 新人 [{method}]',
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'confidence': '高',
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'source': 'local',
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'person_index': person_index
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@@ -168,21 +170,21 @@ class LocalAnalyzer:
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else:
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events.append({
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'event_type': '人物活动',
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'description': f'#{person_index} 已知人员: {person["name"]} [{detection_source}]',
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'description': f'#{person_index} {person["name"]} [{method}]',
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'confidence': '高',
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'source': 'local',
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'person_index': person_index
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})
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# 检测人员进出（带序号）
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# 检测人员进出
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if person_count_change > 0:
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# 列出新进入的人员序号
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new_indices = [p.get('person_index', i+1) for i, p in enumerate(person_result['persons'][-person_count_change:])]
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indices = person_result.get('person_indices', [])
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events.append({
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'event_type': '人员进出',
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'description': f'#{", #".join(map(str, new_indices))} 进入，当前共 {current_count} 人 [{person_result.get("detection_source", "")}]',
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'description': f'#{" #".join(map(str, indices[-person_count_change:]))} 进入，当前共 {current_count} 人',
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'confidence': '高',
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'source': 'local'
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'source': 'local',
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'person_indices': indices
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})
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self.person_change_count += 1
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elif person_count_change < 0:
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@@ -196,15 +198,19 @@ class LocalAnalyzer:
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self.prev_human_count = current_count
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else:
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# 没有确认的变化，只记录当前状态
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# 没有确认变化，只记录当前状态
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metrics['person_count_change'] = 0
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if current_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 {current_count} 人（状态稳定）',
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'confidence': '低',
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'source': 'local'
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})
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indices = person_result.get('person_indices', [])
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methods = person_result.get('methods_used', [])
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for i, person in enumerate(person_result['persons']):
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events.append({
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'event_type': '人物活动',
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'description': f'#{person.get("person_index", i+1)} [{methods[i] if i < len(methods) else "unknown"}]',
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'confidence': '低',
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'source': 'local',
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'person_index': person.get('person_index', i+1)
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})
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# 方法2：Haar Cascade 人体检测（备用或并行）
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if use_haar and self.human_cascade is not None:
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@@ -140,89 +140,146 @@ class PersonManager:
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except Exception as e:
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print(f"[PersonManager] YOLO init failed: {e}")
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def detect_faces(self, image):
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"""检测人脸（优先使用 YOLO，其次 MediaPipe，最后 Haar）
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def detect_persons_yolo(self, image):
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"""YOLO 人体检测（只检测是否有人）
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Args:
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image: 图片（numpy array 或路径）
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Returns:
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list: [{'bbox': [x,y,w,h], 'confidence': float}]
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"""
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if isinstance(image, str):
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image = cv2.imread(image)
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persons = []
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if image is None:
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return []
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if self.yolo_detector is None:
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return persons
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faces = []
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# 方案3: YOLO 检测（优先，最准确）
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if self.yolo_detector is not None:
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try:
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results = self.yolo_detector(image, classes=[0], verbose=False) # class 0 = person
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for r in results:
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for box in r.boxes:
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x1, y1, x2, y2 = box.xyxy[0].tolist()
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conf = box.conf[0].item()
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# 转换为 [x, y, w, h] 格式
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faces.append({
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'bbox': [int(x1), int(y1), int(x2-x1), int(y2-y1)],
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'confidence': conf,
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'source': 'yolo'
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})
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if faces:
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print(f"[PersonManager] YOLO detected {len(faces)} persons")
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return faces # YOLO 检测成功，直接返回
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except Exception as e:
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print(f"[PersonManager] YOLO detection failed: {e}")
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# 方案1+2: MediaPipe 检测
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if self.has_mediapipe and self.face_detector is not None:
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rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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results = self.face_detector.process(rgb_image)
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try:
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results = self.yolo_detector(image, classes=[0], verbose=False) # class 0 = person
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if results.detections:
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for detection in results.detections:
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bboxC = detection.location_data.relative_bounding_box
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h, w, _ = image.shape
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for r in results:
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for box in r.boxes:
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x1, y1, x2, y2 = box.xyxy[0].tolist()
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conf = box.conf[0].item()
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x = int(bboxC.xmin * w)
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y = int(bboxC.ymin * h)
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width = int(bboxC.width * w)
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height = int(bboxC.height * h)
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# 置信度过滤
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min_conf = self.config.get('mediapipe_min_confidence', 0.3)
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if conf < min_conf:
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continue
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faces.append({
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'bbox': [x, y, width, height],
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'confidence': detection.score[0],
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'source': 'mediapipe'
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persons.append({
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'bbox': [int(x1), int(y1), int(x2-x1), int(y2-y1)],
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'confidence': conf,
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'source': 'yolo'
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})
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if faces:
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return faces
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# 备用: OpenCV Haar 检测
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if self.cv_face_detector is not None:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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detections = self.cv_face_detector.detectMultiScale(
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gray,
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scaleFactor=self.config['haar_scale_factor'],
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minNeighbors=self.config['haar_min_neighbors'],
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minSize=(30, 30)
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)
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for (x, y, w, h) in detections:
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faces.append({
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'bbox': [x, y, w, h],
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'confidence': 0.8,
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'source': 'opencv'
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})
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if persons:
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print(f"[PersonManager] YOLO detected {len(persons)} persons (conf > {min_conf})")
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except Exception as e:
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print(f"[PersonManager] YOLO detection failed: {e}")
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return faces
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return persons
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def identify_person(self, image, person_bbox, person_index):
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"""识别具体人（使用 face_recognition/MediaPipe/颜色直方图）
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Args:
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image: 图片
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person_bbox: 人体 bbox
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person_index: 人员序号
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Returns:
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dict: {'person_id': str, 'name': str, 'is_new': bool, 'confidence': float}
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"""
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x, y, w, h = person_bbox
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# 从人体 bbox 中提取人脸区域（通常在上方）
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face_region_y = y
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face_region_h = int(h * 0.4) # 人脸约占人体高度的 40%
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face_region = image[face_region_y:face_region_y+face_region_h, x:x+w]
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if face_region.size == 0:
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return {
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'person_id': f"person_{person_index}",
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'name': f"Person #{person_index}",
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'is_new': True,
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'confidence': 0.5,
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'method': 'yolo_only'
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}
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# 方法1: face_recognition（最准确）
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encoding = None
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method_used = 'unknown'
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if HAS_FACE_REC:
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try:
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rgb_face = cv2.cvtColor(face_region, cv2.COLOR_BGR2RGB)
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encodings = face_recognition.face_encodings(rgb_face)
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if len(encodings) > 0:
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encoding = encodings[0]
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method_used = 'face_recognition'
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print(f"[PersonManager] #{person_index} Using face_recognition")
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except Exception as e:
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print(f"[PersonManager] #{person_index} face_recognition failed: {e}")
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# 方法2: MediaPipe 人脸关键点
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if encoding is None and self.has_mediapipe:
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try:
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mp_face_mesh = mp.solutions.face_mesh
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face_mesh = mp_face_mesh.FaceMesh(
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static_image_mode=True,
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max_num_faces=1,
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min_detection_confidence=self.config.get('mediapipe_min_confidence', 0.3)
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)
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rgb_face = cv2.cvtColor(face_region, cv2.COLOR_BGR2RGB)
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results = face_mesh.process(rgb_face)
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if results.multi_face_landmarks:
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landmarks = results.multi_face_landmarks[0]
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features = []
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for landmark in landmarks.landmark:
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features.extend([landmark.x, landmark.y, landmark.z])
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encoding = np.array(features)
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method_used = 'mediapipe'
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print(f"[PersonManager] #{person_index} Using MediaPipe landmarks")
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face_mesh.close()
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except Exception as e:
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print(f"[PersonManager] #{person_index} MediaPipe failed: {e}")
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# 方法3: 颜色直方图（备用）
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if encoding is None:
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try:
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face_resized = cv2.resize(face_region, (64, 64))
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hsv = cv2.cvtColor(face_resized, cv2.COLOR_BGR2HSV)
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hist_h = cv2.calcHist([hsv], [0], None, [16], [0, 180])
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hist_s = cv2.calcHist([hsv], [1], None, [16], [0, 256])
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hist_v = cv2.calcHist([hsv], [2], None, [16], [0, 256])
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encoding = np.concatenate([
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cv2.normalize(hist_h, hist_h).flatten(),
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cv2.normalize(hist_s, hist_s).flatten(),
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cv2.normalize(hist_v, hist_v).flatten()
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])
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method_used = 'color_histogram'
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print(f"[PersonManager] #{person_index} Using color histogram (backup)")
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except Exception as e:
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print(f"[PersonManager] #{person_index} Histogram failed: {e}")
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# 匹配人员库
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if encoding is not None:
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match_result = self.match_face(encoding)
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match_result['method'] = method_used
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return match_result
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# 无法识别，返回默认
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return {
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'person_id': f"unknown_{person_index}",
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'name': f"Person #{person_index}",
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'is_new': True,
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'confidence': 0.3,
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'method': 'no_face'
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}
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def extract_face_encoding(self, image, face_bbox):
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"""提取人脸特征（用于识别是否为同一个人）
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@@ -412,7 +469,12 @@ class PersonManager:
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self._save_persons_db()
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def analyze_image(self, image_path, save_new_person=True):
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"""分析图片中的人员（带连续性判断）
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"""分析图片中的人员
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流程：
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1. YOLO 检测人体（是否有人）
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2. face_recognition/MediaPipe/颜色直方图 识别具体人
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3. 连续帧判断确认
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Args:
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image_path: 图片路径
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@@ -420,146 +482,138 @@ class PersonManager:
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Returns:
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dict: {
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'faces': list, # 检测到的人脸
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'persons': list, # 识别的人员
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'new_count': int, # 新人员数量
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'known_count': int, # 已知人员数量
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'confirmed_change': bool, # 是否有确认的人员变化
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'persons': list, # 识别的人员（带序号）
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'confirmed_change': bool,
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'person_indices': list, # 人员序号列表
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}
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"""
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image = cv2.imread(image_path)
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if image is None:
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return {'faces': [], 'persons': [], 'error': 'Cannot load image'}
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return {'persons': [], 'error': 'Cannot load image'}
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self.total_detections += 1
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# 检测人脸
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faces = self.detect_faces(image)
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current_count = len(faces)
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# Step 1: YOLO 检测人体
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detected_persons = self.detect_persons_yolo(image)
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current_count = len(detected_persons)
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# 方案2: 连续性判断
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# Step 2: 识别每个检测到的人
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identified_persons = []
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for idx, person in enumerate(detected_persons):
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person_index = idx + 1 # 序号从 1 开始
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# 使用 face_recognition/MediaPipe/颜色直方图 识别
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identity = self.identify_person(image, person['bbox'], person_index)
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identified_persons.append({
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'person_id': identity['person_id'],
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'name': identity['name'],
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'person_index': person_index,
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'bbox': person['bbox'],
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'is_new': identity['is_new'],
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'confidence': identity.get('confidence', person['confidence']),
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'method': identity.get('method', 'unknown'),
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'yolo_confidence': person['confidence'],
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'source': 'yolo'
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})
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# Step 3: 连续帧判断
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confirmed_change = False
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confirmed_persons = []
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# 为每个检测到的人分配序号
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person_index = self.prev_human_count # 从当前人数开始
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# 检查人数变化
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prev_count = len(self.prev_persons)
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if current_count != prev_count:
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# 人数变化，记录到缓冲区
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# 人数变化
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key = f"count_{current_count}"
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if key not in self.confirmation_buffer:
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self.confirmation_buffer[key] = {'count': 0, 'persons': []}
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self.confirmation_buffer[key]['count'] += 1
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# 临时识别人员并分配序号
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temp_persons = []
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for idx, face in enumerate(faces):
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bbox = face['bbox']
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encoding = self.extract_face_encoding(image, bbox)
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match_result = self.match_face(encoding)
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# 分配人员序号
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person_index_display = idx + 1 # 序号从1开始
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person_info = {
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'person_id': match_result['person_id'] if not match_result['is_new'] else f"unknown_{person_index_display}",
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'name': match_result['name'] if not match_result['is_new'] else f"Person #{person_index_display}",
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'person_index': person_index_display, # 显示序号
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'bbox': bbox,
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'is_new': match_result['is_new'],
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'confidence': face['confidence'],
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'source': face['source']
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}
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temp_persons.append(person_info)
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self.confirmation_buffer[key]['persons'] = temp_persons
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self.confirmation_buffer[key]['persons'] = identified_persons
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# 达到确认帧数
|
||||
if self.confirmation_buffer[key]['count'] >= self.config['confirm_frames']:
|
||||
confirmed_change = True
|
||||
confirmed_persons = temp_persons
|
||||
|
||||
print(f"[PersonManager] Confirmed: {prev_count} -> {current_count} persons (after {self.config['confirm_frames']} frames)")
|
||||
for p in confirmed_persons:
|
||||
print(f" - {p['name']} (#{p['person_index']}) [{p['source']}]")
|
||||
print(f"[PersonManager] Confirmed change: {prev_count} -> {current_count} (after {self.config['confirm_frames']} frames)")
|
||||
|
||||
# 清空其他缓冲区
|
||||
self.confirmation_buffer = {}
|
||||
|
||||
# 更新前一帧状态
|
||||
self.prev_persons = temp_persons
|
||||
|
||||
# 保存新人员
|
||||
if save_new_person and confirmed_change:
|
||||
for person in identified_persons:
|
||||
if person['is_new'] and len(self.persons) < self.config['max_persons']:
|
||||
# 保存人脸特征
|
||||
x, y, w, h = person['bbox']
|
||||
face_region = image[y:y+int(h*0.4), x:x+w]
|
||||
|
||||
if face_region.size > 0:
|
||||
encoding = self.extract_face_encoding(image, person['bbox'])
|
||||
if encoding is not None:
|
||||
person_id = f"person_{len(self.persons) + 1}"
|
||||
person['person_id'] = person_id
|
||||
person['name'] = f"Person #{len(self.persons) + 1}"
|
||||
|
||||
# 保存到人员库
|
||||
self.add_new_person_with_encoding(person_id, encoding, person['name'])
|
||||
|
||||
# 清空缓冲区，更新状态
|
||||
self.confirmation_buffer = {key: self.confirmation_buffer[key]}
|
||||
self.prev_persons = identified_persons
|
||||
else:
|
||||
# 人数不变，清空变化缓冲区，维持当前状态
|
||||
if current_count > 0:
|
||||
# 识别当前人员并分配序号
|
||||
temp_persons = []
|
||||
for idx, face in enumerate(faces):
|
||||
bbox = face['bbox']
|
||||
encoding = self.extract_face_encoding(image, bbox)
|
||||
match_result = self.match_face(encoding)
|
||||
|
||||
person_index_display = idx + 1
|
||||
|
||||
person_info = {
|
||||
'person_id': match_result['person_id'] if not match_result['is_new'] else f"unknown_{person_index_display}",
|
||||
'name': match_result['name'] if not match_result['is_new'] else f"Person #{person_index_display}",
|
||||
'person_index': person_index_display,
|
||||
'bbox': bbox,
|
||||
'is_new': match_result['is_new'],
|
||||
'confidence': face['confidence'],
|
||||
'source': face['source']
|
||||
}
|
||||
temp_persons.append(person_info)
|
||||
|
||||
confirmed_persons = temp_persons
|
||||
self.prev_persons = temp_persons
|
||||
# 人数不变，维持状态
|
||||
self.prev_persons = identified_persons
|
||||
|
||||
# 清空变化缓冲区
|
||||
keys_to_remove = [k for k in self.confirmation_buffer.keys() if not k.endswith(f"_{current_count}")]
|
||||
# 清空其他变化缓冲区
|
||||
keys_to_remove = [k for k in self.confirmation_buffer.keys() if k != f"count_{current_count}"]
|
||||
for k in keys_to_remove:
|
||||
del self.confirmation_buffer[k]
|
||||
|
||||
# 统计新人和已知人员
|
||||
new_count = 0
|
||||
known_count = 0
|
||||
persons_to_save = []
|
||||
|
||||
for person in confirmed_persons:
|
||||
if person['is_new']:
|
||||
new_count += 1
|
||||
# 只有确认后才保存新人
|
||||
if confirmed_change and save_new_person and len(self.persons) < self.config['max_persons']:
|
||||
# 找到对应的 face bbox
|
||||
for face in faces:
|
||||
if face['bbox'] == person['bbox']:
|
||||
new_person = self.add_new_person(image, face['bbox'])
|
||||
if new_person:
|
||||
person['person_id'] = new_person['person_id']
|
||||
person['name'] = new_person['name']
|
||||
persons_to_save.append(person)
|
||||
break
|
||||
else:
|
||||
known_count += 1
|
||||
self.update_person_visit(person['person_id'])
|
||||
persons_to_save.append(person)
|
||||
# 统计
|
||||
new_count = sum(1 for p in identified_persons if p['is_new'])
|
||||
known_count = current_count - new_count
|
||||
|
||||
return {
|
||||
'faces': faces,
|
||||
'persons': persons_to_save,
|
||||
'persons': identified_persons,
|
||||
'new_count': new_count,
|
||||
'known_count': known_count,
|
||||
'total_count': len(persons_to_save),
|
||||
'total_count': current_count,
|
||||
'confirmed_change': confirmed_change,
|
||||
'current_count': current_count,
|
||||
'prev_count': prev_count,
|
||||
'detection_source': faces[0]['source'] if faces else 'none'
|
||||
'person_indices': [p['person_index'] for p in identified_persons],
|
||||
'methods_used': [p['method'] for p in identified_persons],
|
||||
'detection_source': 'yolo'
|
||||
}
|
||||
|
||||
def add_new_person_with_encoding(self, person_id, encoding, name=None):
|
||||
"""保存新人员到库（已有 encoding）
|
||||
|
||||
Args:
|
||||
person_id: 人员ID
|
||||
encoding: 特征向量
|
||||
name: 名称
|
||||
|
||||
Returns:
|
||||
dict: 人员信息
|
||||
"""
|
||||
if name is None:
|
||||
name = person_id
|
||||
|
||||
person_data = {
|
||||
'person_id': person_id,
|
||||
'name': name,
|
||||
'face_encoding': encoding.tolist() if isinstance(encoding, np.ndarray) else encoding,
|
||||
'first_seen': datetime.datetime.now().isoformat(),
|
||||
'last_seen': datetime.datetime.now().isoformat(),
|
||||
'visit_count': 1
|
||||
}
|
||||
|
||||
self.persons[person_id] = person_data
|
||||
self._save_persons_db()
|
||||
|
||||
self.new_persons_added += 1
|
||||
print(f"[PersonManager] New person saved: {person_id} ({name})")
|
||||
|
||||
return person_data
|
||||
|
||||
def get_persons_list(self):
|
||||
"""获取人员列表"""
|
||||
return [
|
||||
|
||||
@@ -184,9 +184,28 @@ function renderImages(images) {
|
||||
var status = img.analyzed ? 'Analyzed' : 'Unanalyzed';
|
||||
var events = img.events_summary || 'No events';
|
||||
|
||||
// Check for person indices in events
|
||||
var personIndices = [];
|
||||
if (img.events && img.events.length > 0) {
|
||||
img.events.forEach(function(event) {
|
||||
var match = event.description.match(/#(\d+)/g);
|
||||
if (match) {
|
||||
match.forEach(function(m) {
|
||||
if (personIndices.indexOf(m) === -1) {
|
||||
personIndices.push(m);
|
||||
}
|
||||
});
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
var indicesDisplay = personIndices.length > 0 ?
|
||||
'<span class="image-person-indices">' + personIndices.slice(0, 3).join(' ') + '</span>' : '';
|
||||
|
||||
item.innerHTML = '<span class="image-number">#' + img.id + '</span>' +
|
||||
'<span class="image-time">' + time + '</span>' +
|
||||
'<span class="image-status">' + status + '</span>' +
|
||||
indicesDisplay +
|
||||
'<span class="image-events-summary">' + events + '</span>';
|
||||
|
||||
list.appendChild(item);
|
||||
@@ -226,7 +245,19 @@ function openImageModal(imageId) {
|
||||
if (localEvents.length > 0) {
|
||||
var localSection = document.createElement('div');
|
||||
localSection.className = 'modal-events-section';
|
||||
localSection.innerHTML = '<h4 class="section-title local">Local Analysis (' + localEvents.length + ') </h4>';
|
||||
|
||||
// 显示人员序号
|
||||
var personIndices = [];
|
||||
localEvents.forEach(function(event) {
|
||||
if (event.person_index) {
|
||||
personIndices.push('#' + event.person_index);
|
||||
}
|
||||
});
|
||||
|
||||
var indicesDisplay = personIndices.length > 0 ?
|
||||
' <span class="person-indices">[' + personIndices.join(', ') + ']</span>' : '';
|
||||
|
||||
localSection.innerHTML = '<h4 class="section-title local">Local Analysis (' + localEvents.length + ')' + indicesDisplay + '</h4>';
|
||||
|
||||
localEvents.forEach(function(event) {
|
||||
var div = document.createElement('div');
|
||||
|
||||
@@ -255,6 +255,21 @@ button {
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
.image-person-indices {
|
||||
background: #667eea;
|
||||
color: white;
|
||||
padding: 2px 8px;
|
||||
border-radius: 3px;
|
||||
font-size: 12px;
|
||||
font-weight: bold;
|
||||
margin-right: 10px;
|
||||
}
|
||||
|
||||
.person-indices {
|
||||
color: #667eea;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
/* 事件列表 */
|
||||
.events-list {
|
||||
max-height: 500px;
|
||||
|
||||
Reference in New Issue
Block a user