diff --git a/config.py b/config.py index 6a66211..d4d340e 100644 --- a/config.py +++ b/config.py @@ -29,9 +29,14 @@ DEFAULT_CONFIG = { "display_limit": 20, # 显示最近多少条 # 检测算法开关 - "use_haar_cascade": True, # Haar Cascade 人体检测 - "use_mediapipe_face": True, # MediaPipe 人脸检测 - "use_face_recognition": True, # face_recognition 人脸识别 + "use_yolo": True, # YOLO 检测（最准确） + "use_haar_cascade": False, # Haar Cascade 人体检测（备用） + "use_mediapipe_face": True, # MediaPipe 人脸检测 + "use_face_recognition": True, # face_recognition 人脸识别 + + # 连续性判断配置 + "confirm_frames": 3, # 连续几帧确认 + "min_detection_confidence": 0.3, # 检测置信度阈值 # AI大模型分析开关 "use_vision_api": False, # 是否使用大模型分析（默认关闭） diff --git a/local_analyzer.py b/local_analyzer.py index 7a39ddb..4878c2c 100644 --- a/local_analyzer.py +++ b/local_analyzer.py @@ -134,55 +134,70 @@ class LocalAnalyzer: # 方法1：MediaPipe 人脸检测 + 人员识别（优先） if HAS_PERSON_MANAGER and use_mediapipe: - print(f"[LocalAnalyzer] Using MediaPipe face detection...") + print(f"[LocalAnalyzer] Using PersonManager for detection...") person_result = person_manager.analyze_image(image_path, save_new_person=True) metrics['person_count'] = person_result['total_count'] metrics['new_persons'] = person_result['new_count'] metrics['known_persons'] = person_result['known_count'] + metrics['detection_source'] = person_result.get('detection_source', 'unknown') prev_person_count = self.prev_human_count - person_count_change = person_result['total_count'] - prev_person_count - metrics['person_count_change'] = person_count_change + current_count = person_result['current_count'] + person_count_change = current_count - prev_person_count - # 记录人员事件 - for person in person_result['persons']: - if person['is_new']: + # 只有确认的变化才记录 + if person_result['confirmed_change']: + metrics['person_count_change'] = person_count_change + + # 记录人员事件 + for person in person_result['persons']: + if person['is_new']: + events.append({ + 'event_type': '人物活动', + 'description': f'新人出现: {person["name"]}，当前共 {current_count} 人', + 'confidence': '高', + 'source': 'local' + }) + self.human_count += 1 + self.person_change_count += 1 + else: + events.append({ + 'event_type': '人物活动', + 'description': f'已知人员: {person["name"]} [{person.get("source", "detected")}]', + 'confidence': '高', + 'source': 'local' + }) + + # 检测人员进出 + if person_count_change > 0: events.append({ - 'event_type': '人物活动', - 'description': f'新人出现: {person["name"]}，当前共 {person_result["total_count"]} 人', + 'event_type': '人员进出', + 'description': f'检测到 {person_count_change} 人进入，当前共 {current_count} 人 [{person_result.get("detection_source", "")}]', 'confidence': '高', 'source': 'local' }) - self.human_count += 1 self.person_change_count += 1 - else: + elif person_count_change < 0: events.append({ - 'event_type': '人物活动', - 'description': f'已知人员: {person["name"]}', + 'event_type': '人员进出', + 'description': f'检测到 {abs(person_count_change)} 人离开，当前剩 {current_count} 人', 'confidence': '高', 'source': 'local' }) - - # 检测人员进出 - if person_count_change > 0: - events.append({ - 'event_type': '人员进出', - 'description': f'检测到 {person_count_change} 人进入，当前共 {person_result["total_count"]} 人', - 'confidence': '高', - 'source': 'local' - }) - self.person_change_count += 1 - elif person_count_change < 0: - events.append({ - 'event_type': '人员进出', - 'description': f'检测到 {abs(person_count_change)} 人离开，当前剩 {person_result["total_count"]} 人', - 'confidence': '高', - 'source': 'local' - }) - self.person_change_count += 1 - - self.prev_human_count = person_result['total_count'] + self.person_change_count += 1 + + self.prev_human_count = current_count + else: + # 没有确认的变化，只记录当前状态 + metrics['person_count_change'] = 0 + if current_count > 0: + events.append({ + 'event_type': '人物活动', + 'description': f'检测到 {current_count} 人（状态稳定）', + 'confidence': '低', + 'source': 'local' + }) # 方法2：Haar Cascade 人体检测（备用或并行） if use_haar and self.human_cascade is not None: diff --git a/person_manager.py b/person_manager.py index 8f2a7f7..ee46eb6 100644 --- a/person_manager.py +++ b/person_manager.py @@ -42,12 +42,20 @@ class PersonManager: # 加载人员库 self.persons = self._load_persons_db() - # 检测器状态 + # 初始化检测器状态 self.face_detector = None self.mp_face_detection = None self.cv_face_detector = None self.has_mediapipe = HAS_MEDIAPIPE + # 从配置读取参数 + try: + from config import config_mgr + self.config['mediapipe_min_confidence'] = config_mgr.get('min_detection_confidence', 0.3) + self.config['confirm_frames'] = config_mgr.get('confirm_frames', 3) + except: + pass + # 初始化检测器 self._init_detectors() @@ -56,8 +64,23 @@ class PersonManager: 'face_match_threshold': 0.6, # 人脸匹配阈值 'unknown_person_id': 'unknown', # 未知人员ID 'max_persons': 100, # 最大人员数量 + + # 方案1: 参数调整 + 'mediapipe_min_confidence': 0.3, # 降低阈值，更容易检测 + 'mediapipe_model_selection': 1, # 1: 远距离模型 + 'haar_scale_factor': 1.05, # Haar更细粒度 + 'haar_min_neighbors': 2, # 降低邻居要求 + + # 方案2: 连续性判断 + 'confirm_frames': 3, # 连续几帧确认 + 'leave_frames': 2, # 连续几帧消失才算离开 } + # 方案2: 追踪状态（连续判断） + self.tracked_persons = {} # {person_id: {'frames': count, 'confirmed': bool}} + self.prev_persons = [] # 前一帧检测到的人 + self.confirmation_buffer = {} # 确认缓冲区 + # 统计 self.total_detections = 0 self.known_persons_detected = 0 @@ -80,17 +103,16 @@ class PersonManager: def _init_detectors(self): """初始化检测器""" - # MediaPipe 人脸检测 + # MediaPipe 人脸检测（方案1: 参数调整） if self.has_mediapipe: try: - # 使用更安全的导入方式 mp_face_detection = mp.solutions.face_detection self.face_detector = mp_face_detection.FaceDetection( - model_selection=0, # 0: 短距离，1: 远距离 - min_detection_confidence=0.5 + model_selection=self.config['mediapipe_model_selection'], # 远距离模型 + min_detection_confidence=self.config['mediapipe_min_confidence'] # 降低阈值 ) self.mp_face_detection = mp_face_detection - print("[PersonManager] MediaPipe face detector initialized") + print(f"[PersonManager] MediaPipe initialized (model={self.config['mediapipe_model_selection']}, conf={self.config['mediapipe_min_confidence']})") except Exception as e: print(f"[PersonManager] MediaPipe init failed: {e}") self.face_detector = None @@ -101,13 +123,25 @@ class PersonManager: model_path = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml' if Path(model_path).exists(): self.cv_face_detector = cv2.CascadeClassifier(model_path) - print("[PersonManager] OpenCV face detector initialized (backup)") + print("[PersonManager] OpenCV Haar Cascade initialized (backup)") except Exception as e: self.cv_face_detector = None - print(f"[PersonManager] OpenCV face detector init failed: {e}") + print(f"[PersonManager] OpenCV detector init failed: {e}") + + # 方案3: YOLO 检测（更准确） + self.yolo_detector = None + try: + from ultralytics import YOLO + # 使用轻量级 nano 模型 + self.yolo_detector = YOLO('yolov8n.pt') # nano 模型，快速 + print("[PersonManager] YOLOv8nano initialized (most accurate)") + except ImportError: + print("[PersonManager] YOLO not installed. Install with: pip install ultralytics") + except Exception as e: + print(f"[PersonManager] YOLO init failed: {e}") def detect_faces(self, image): - """检测人脸 + """检测人脸（优先使用 YOLO，其次 MediaPipe，最后 Haar） Args: image: 图片（numpy array 或路径） @@ -123,7 +157,31 @@ class PersonManager: faces = [] - # MediaPipe 检测 + # 方案3: YOLO 检测（优先，最准确） + if self.yolo_detector is not None: + try: + results = self.yolo_detector(image, classes=[0], verbose=False) # class 0 = person + + for r in results: + for box in r.boxes: + x1, y1, x2, y2 = box.xyxy[0].tolist() + conf = box.conf[0].item() + + # 转换为 [x, y, w, h] 格式 + faces.append({ + 'bbox': [int(x1), int(y1), int(x2-x1), int(y2-y1)], + 'confidence': conf, + 'source': 'yolo' + }) + + if faces: + print(f"[PersonManager] YOLO detected {len(faces)} persons") + return faces # YOLO 检测成功，直接返回 + + except Exception as e: + print(f"[PersonManager] YOLO detection failed: {e}") + + # 方案1+2: MediaPipe 检测 if self.has_mediapipe and self.face_detector is not None: rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = self.face_detector.process(rgb_image) @@ -143,14 +201,17 @@ class PersonManager: 'confidence': detection.score[0], 'source': 'mediapipe' }) + + if faces: + return faces - # OpenCV 检测（备用） - elif self.cv_face_detector is not None: + # 备用: OpenCV Haar 检测 + if self.cv_face_detector is not None: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) detections = self.cv_face_detector.detectMultiScale( gray, - scaleFactor=1.1, - minNeighbors=5, + scaleFactor=self.config['haar_scale_factor'], + minNeighbors=self.config['haar_min_neighbors'], minSize=(30, 30) ) @@ -351,7 +412,7 @@ class PersonManager: self._save_persons_db() def analyze_image(self, image_path, save_new_person=True): - """分析图片中的人员 + """分析图片中的人员（带连续性判断） Args: image_path: 图片路径 @@ -363,6 +424,7 @@ class PersonManager: 'persons': list, # 识别的人员 'new_count': int, # 新人员数量 'known_count': int, # 已知人员数量 + 'confirmed_change': bool, # 是否有确认的人员变化 } """ image = cv2.imread(image_path) @@ -373,61 +435,117 @@ class PersonManager: # 检测人脸 faces = self.detect_faces(image) + current_count = len(faces) - persons = [] - new_count = 0 - known_count = 0 + # 方案2: 连续性判断 + confirmed_change = False + confirmed_persons = [] - for face in faces: - bbox = face['bbox'] + # 检查人数变化 + prev_count = len(self.prev_persons) + + if current_count != prev_count: + # 人数变化，记录到缓冲区 + key = f"count_{current_count}" + if key not in self.confirmation_buffer: + self.confirmation_buffer[key] = {'count': 0, 'persons': []} - # 提取特征 - encoding = self.extract_face_encoding(image, bbox) + self.confirmation_buffer[key]['count'] += 1 - # 匹配 - match_result = self.match_face(encoding) - - if match_result['is_new']: - # 新人员 - new_count += 1 + # 临时识别人员 + temp_persons = [] + for face in faces: + bbox = face['bbox'] + encoding = self.extract_face_encoding(image, bbox) + match_result = self.match_face(encoding) - if save_new_person and len(self.persons) < self.config['max_persons']: - new_person = self.add_new_person(image, bbox) - if new_person: - persons.append({ - 'person_id': new_person['person_id'], - 'name': new_person['name'], - 'bbox': bbox, - 'is_new': True, - 'confidence': face['confidence'] - }) - else: - persons.append({ - 'person_id': 'unknown', - 'name': 'Unknown (new)', - 'bbox': bbox, - 'is_new': True, - 'confidence': face['confidence'] - }) - else: - # 已知人员 - known_count += 1 - self.update_person_visit(match_result['person_id']) - - persons.append({ - 'person_id': match_result['person_id'], + person_info = { + 'person_id': match_result['person_id'] if not match_result['is_new'] else 'unknown', 'name': match_result['name'], 'bbox': bbox, - 'is_new': False, - 'confidence': match_result['confidence'] - }) + 'is_new': match_result['is_new'], + 'confidence': face['confidence'], + 'source': face['source'] + } + temp_persons.append(person_info) + + self.confirmation_buffer[key]['persons'] = temp_persons + + # 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)") + + # 清空其他缓冲区 + self.confirmation_buffer = {} + + # 更新前一帧状态 + self.prev_persons = temp_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 + temp_persons = [] + for face in faces: + bbox = face['bbox'] + encoding = self.extract_face_encoding(image, bbox) + match_result = self.match_face(encoding) + + person_info = { + 'person_id': match_result['person_id'] if not match_result['is_new'] else 'unknown', + 'name': match_result['name'], + '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 + + # 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}")] + 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) return { 'faces': faces, - 'persons': persons, + 'persons': persons_to_save, 'new_count': new_count, 'known_count': known_count, - 'total_count': len(persons) + 'total_count': len(persons_to_save), + 'confirmed_change': confirmed_change, + 'current_count': current_count, + 'prev_count': prev_count, + 'detection_source': faces[0]['source'] if faces else 'none' } def get_persons_list(self): diff --git a/requirements.txt b/requirements.txt index 088c713..6a6a269 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,6 +4,6 @@ uvicorn>=0.23.0 requests>=2.31.0 numpy>=1.20.0 -# Optional: More accurate face detection and recognition -# mediapipe>=0.10.0 -# face-recognition>=1.7.0 (requires dlib, may need manual install on Windows) \ No newline at end of file +# Optional: More accurate detection +ultralytics>=8.0.0 +mediapipe>=0.10.0 \ No newline at end of file diff --git a/web/static/app.js b/web/static/app.js index 20050f1..266dbca 100644 --- a/web/static/app.js +++ b/web/static/app.js @@ -507,10 +507,15 @@ function loadSettingsForm() { document.getElementById('setting-refresh-interval').value = config.refresh_interval || 5; // Detection algorithm settings - document.getElementById('setting-use-haar').checked = config.use_haar_cascade !== false; + document.getElementById('setting-use-yolo').checked = config.use_yolo !== false; document.getElementById('setting-use-mediapipe').checked = config.use_mediapipe_face !== false; + document.getElementById('setting-use-haar').checked = config.use_haar_cascade === true; document.getElementById('setting-use-face-rec').checked = config.use_face_recognition !== false; + // Confirmation settings + document.getElementById('setting-confirm-frames').value = config.confirm_frames || 3; + document.getElementById('setting-min-confidence').value = config.min_detection_confidence || 0.3; + // Vision API settings document.getElementById('setting-use-vision-api').checked = config.use_vision_api === true; document.getElementById('setting-vision-trigger').value = config.vision_api_trigger || 'person_change'; @@ -533,10 +538,15 @@ function saveSettings() { refresh_interval: parseInt(document.getElementById('setting-refresh-interval').value), // Detection algorithms + use_yolo: document.getElementById('setting-use-yolo').checked, use_haar_cascade: document.getElementById('setting-use-haar').checked, use_mediapipe_face: document.getElementById('setting-use-mediapipe').checked, use_face_recognition: document.getElementById('setting-use-face-rec').checked, + // Confirmation settings + confirm_frames: parseInt(document.getElementById('setting-confirm-frames').value), + min_detection_confidence: parseFloat(document.getElementById('setting-min-confidence').value), + // Vision API use_vision_api: document.getElementById('setting-use-vision-api').checked, vision_api_trigger: document.getElementById('setting-vision-trigger').value, diff --git a/web/static/index.html b/web/static/index.html index 7444501..e31d8ed 100644 --- a/web/static/index.html +++ b/web/static/index.html @@ -138,26 +138,45 @@
-

检测算法设置

+

Detection Algorithms

- - - 传统人体检测（备用） + + + YOLOv8 nano - Best accuracy
- + - 高精度人脸检测 + High precision face detection +
+
+ + + Traditional body detection (backup)
- 人脸识别（识别同一人） + Identify same person
-

AI大模型分析

+

Confirmation Settings

+
+ + + Frames to confirm detection +
+
+ + + Detection confidence threshold +
+
+ +
+

AI Analysis