diff --git a/local_analyzer.py b/local_analyzer.py
index 81513ec..5c0749d 100644
--- a/local_analyzer.py
+++ b/local_analyzer.py
@@ -132,33 +132,35 @@ class LocalAnalyzer:
use_haar = config_mgr.get('use_haar_cascade', True)
use_mediapipe = config_mgr.get('use_mediapipe_face', True)
- # 方法1：MediaPipe 人脸检测 + 人员识别（优先）
+ # 方法1：YOLO 人体检测 + 人员识别
if HAS_PERSON_MANAGER and use_mediapipe:
- print(f"[LocalAnalyzer] Using PersonManager for detection...")
+ print(f"[LocalAnalyzer] Using YOLO + person identification...")
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')
+ metrics['detection_source'] = 'yolo'
+ metrics['methods_used'] = person_result.get('methods_used', [])
+ metrics['person_indices'] = person_result.get('person_indices', [])
prev_person_count = self.prev_human_count
current_count = person_result['current_count']
person_count_change = current_count - prev_person_count
- # 只有确认的变化才记录
+ # 只有确认的变化才记录事件
if person_result['confirmed_change']:
metrics['person_count_change'] = person_count_change
- # 记录人员事件（带序号）
+ # 记录人员事件（带序号和方法）
for person in person_result['persons']:
person_index = person.get('person_index', 1)
- detection_source = person.get('source', 'unknown')
+ method = person.get('method', 'unknown')
if person['is_new']:
events.append({
'event_type': '人物活动',
- 'description': f'#{person_index} 新人: {person["name"]} [{detection_source}]，当前共 {current_count} 人',
+ 'description': f'#{person_index} 新人 [{method}]',
'confidence': '高',
'source': 'local',
'person_index': person_index
@@ -168,21 +170,21 @@ class LocalAnalyzer:
else:
events.append({
'event_type': '人物活动',
- 'description': f'#{person_index} 已知人员: {person["name"]} [{detection_source}]',
+ 'description': f'#{person_index} {person["name"]} [{method}]',
'confidence': '高',
'source': 'local',
'person_index': person_index
})
- # 检测人员进出（带序号）
+ # 检测人员进出
if person_count_change > 0:
- # 列出新进入的人员序号
- new_indices = [p.get('person_index', i+1) for i, p in enumerate(person_result['persons'][-person_count_change:])]
+ indices = person_result.get('person_indices', [])
events.append({
'event_type': '人员进出',
- 'description': f'#{", #".join(map(str, new_indices))} 进入，当前共 {current_count} 人 [{person_result.get("detection_source", "")}]',
+ 'description': f'#{" #".join(map(str, indices[-person_count_change:]))} 进入，当前共 {current_count} 人',
'confidence': '高',
- 'source': 'local'
+ 'source': 'local',
+ 'person_indices': indices
})
self.person_change_count += 1
elif person_count_change < 0:
@@ -196,15 +198,19 @@ class LocalAnalyzer:
self.prev_human_count = current_count
else:
- # 没有确认的变化，只记录当前状态
+ # 没有确认变化，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'
- })
+ indices = person_result.get('person_indices', [])
+ methods = person_result.get('methods_used', [])
+ for i, person in enumerate(person_result['persons']):
+ events.append({
+ 'event_type': '人物活动',
+ 'description': f'#{person.get("person_index", i+1)} [{methods[i] if i < len(methods) else "unknown"}]',
+ 'confidence': '低',
+ 'source': 'local',
+ 'person_index': person.get('person_index', i+1)
+ })
# 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 87f5520..7fd42ff 100644
--- a/person_manager.py
+++ b/person_manager.py
@@ -140,89 +140,146 @@ class PersonManager:
except Exception as e:
print(f"[PersonManager] YOLO init failed: {e}")
- def detect_faces(self, image):
- """检测人脸（优先使用 YOLO，其次 MediaPipe，最后 Haar）
+ def detect_persons_yolo(self, image):
+ """YOLO 人体检测（只检测是否有人）
- Args:
- image: 图片（numpy array 或路径）
-
Returns:
list: [{'bbox': [x,y,w,h], 'confidence': float}]
"""
- if isinstance(image, str):
- image = cv2.imread(image)
+ persons = []
- if image is None:
- return []
+ if self.yolo_detector is None:
+ return persons
- faces = []
-
- # 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)
+ try:
+ results = self.yolo_detector(image, classes=[0], verbose=False) # class 0 = person
- if results.detections:
- for detection in results.detections:
- bboxC = detection.location_data.relative_bounding_box
- h, w, _ = image.shape
+ for r in results:
+ for box in r.boxes:
+ x1, y1, x2, y2 = box.xyxy[0].tolist()
+ conf = box.conf[0].item()
- x = int(bboxC.xmin * w)
- y = int(bboxC.ymin * h)
- width = int(bboxC.width * w)
- height = int(bboxC.height * h)
+ # 置信度过滤
+ min_conf = self.config.get('mediapipe_min_confidence', 0.3)
+ if conf < min_conf:
+ continue
- faces.append({
- 'bbox': [x, y, width, height],
- 'confidence': detection.score[0],
- 'source': 'mediapipe'
+ persons.append({
+ 'bbox': [int(x1), int(y1), int(x2-x1), int(y2-y1)],
+ 'confidence': conf,
+ 'source': 'yolo'
})
-
- if faces:
- return faces
-
- # 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=self.config['haar_scale_factor'],
- minNeighbors=self.config['haar_min_neighbors'],
- minSize=(30, 30)
- )
- for (x, y, w, h) in detections:
- faces.append({
- 'bbox': [x, y, w, h],
- 'confidence': 0.8,
- 'source': 'opencv'
- })
+ if persons:
+ print(f"[PersonManager] YOLO detected {len(persons)} persons (conf > {min_conf})")
+
+ except Exception as e:
+ print(f"[PersonManager] YOLO detection failed: {e}")
- return faces
+ return persons
+
+ def identify_person(self, image, person_bbox, person_index):
+ """识别具体人（使用 face_recognition/MediaPipe/颜色直方图）
+
+ Args:
+ image: 图片
+ person_bbox: 人体 bbox
+ person_index: 人员序号
+
+ Returns:
+ dict: {'person_id': str, 'name': str, 'is_new': bool, 'confidence': float}
+ """
+ x, y, w, h = person_bbox
+
+ # 从人体 bbox 中提取人脸区域（通常在上方）
+ face_region_y = y
+ face_region_h = int(h * 0.4) # 人脸约占人体高度的 40%
+ face_region = image[face_region_y:face_region_y+face_region_h, x:x+w]
+
+ if face_region.size == 0:
+ return {
+ 'person_id': f"person_{person_index}",
+ 'name': f"Person #{person_index}",
+ 'is_new': True,
+ 'confidence': 0.5,
+ 'method': 'yolo_only'
+ }
+
+ # 方法1: face_recognition（最准确）
+ encoding = None
+ method_used = 'unknown'
+
+ if HAS_FACE_REC:
+ try:
+ rgb_face = cv2.cvtColor(face_region, cv2.COLOR_BGR2RGB)
+ encodings = face_recognition.face_encodings(rgb_face)
+ if len(encodings) > 0:
+ encoding = encodings[0]
+ method_used = 'face_recognition'
+ print(f"[PersonManager] #{person_index} Using face_recognition")
+ except Exception as e:
+ print(f"[PersonManager] #{person_index} face_recognition failed: {e}")
+
+ # 方法2: MediaPipe 人脸关键点
+ if encoding is None and self.has_mediapipe:
+ try:
+ mp_face_mesh = mp.solutions.face_mesh
+ face_mesh = mp_face_mesh.FaceMesh(
+ static_image_mode=True,
+ max_num_faces=1,
+ min_detection_confidence=self.config.get('mediapipe_min_confidence', 0.3)
+ )
+
+ rgb_face = cv2.cvtColor(face_region, cv2.COLOR_BGR2RGB)
+ results = face_mesh.process(rgb_face)
+
+ if results.multi_face_landmarks:
+ landmarks = results.multi_face_landmarks[0]
+ features = []
+ for landmark in landmarks.landmark:
+ features.extend([landmark.x, landmark.y, landmark.z])
+ encoding = np.array(features)
+ method_used = 'mediapipe'
+ print(f"[PersonManager] #{person_index} Using MediaPipe landmarks")
+
+ face_mesh.close()
+ except Exception as e:
+ print(f"[PersonManager] #{person_index} MediaPipe failed: {e}")
+
+ # 方法3: 颜色直方图（备用）
+ if encoding is None:
+ try:
+ face_resized = cv2.resize(face_region, (64, 64))
+ hsv = cv2.cvtColor(face_resized, cv2.COLOR_BGR2HSV)
+
+ hist_h = cv2.calcHist([hsv], [0], None, [16], [0, 180])
+ hist_s = cv2.calcHist([hsv], [1], None, [16], [0, 256])
+ hist_v = cv2.calcHist([hsv], [2], None, [16], [0, 256])
+
+ encoding = np.concatenate([
+ cv2.normalize(hist_h, hist_h).flatten(),
+ cv2.normalize(hist_s, hist_s).flatten(),
+ cv2.normalize(hist_v, hist_v).flatten()
+ ])
+ method_used = 'color_histogram'
+ print(f"[PersonManager] #{person_index} Using color histogram (backup)")
+ except Exception as e:
+ print(f"[PersonManager] #{person_index} Histogram failed: {e}")
+
+ # 匹配人员库
+ if encoding is not None:
+ match_result = self.match_face(encoding)
+ match_result['method'] = method_used
+ return match_result
+
+ # 无法识别，返回默认
+ return {
+ 'person_id': f"unknown_{person_index}",
+ 'name': f"Person #{person_index}",
+ 'is_new': True,
+ 'confidence': 0.3,
+ 'method': 'no_face'
+ }
def extract_face_encoding(self, image, face_bbox):
"""提取人脸特征（用于识别是否为同一个人）
@@ -412,7 +469,12 @@ class PersonManager:
self._save_persons_db()
def analyze_image(self, image_path, save_new_person=True):
- """分析图片中的人员（带连续性判断）
+ """分析图片中的人员
+
+ 流程：
+ 1. YOLO 检测人体（是否有人）
+ 2. face_recognition/MediaPipe/颜色直方图 识别具体人
+ 3. 连续帧判断确认
Args:
image_path: 图片路径
@@ -420,146 +482,138 @@ class PersonManager:
Returns:
dict: {
- 'faces': list, # 检测到的人脸
- '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
+ 'persons': list, # 识别的人员（带序号）
+ 'confirmed_change': bool,
+ 'person_indices': list, # 人员序号列表
}
"""
image = cv2.imread(image_path)
if image is None:
- return {'faces': [], 'persons': [], 'error': 'Cannot load image'}
+ return {'persons': [], 'error': 'Cannot load image'}
self.total_detections += 1
- # 检测人脸
- faces = self.detect_faces(image)
- current_count = len(faces)
+ # Step 1: YOLO 检测人体
+ detected_persons = self.detect_persons_yolo(image)
+ current_count = len(detected_persons)
- # 方案2: 连续性判断
+ # Step 2: 识别每个检测到的人
+ identified_persons = []
+ for idx, person in enumerate(detected_persons):
+ person_index = idx + 1 # 序号从 1 开始
+
+ # 使用 face_recognition/MediaPipe/颜色直方图 识别
+ identity = self.identify_person(image, person['bbox'], person_index)
+
+ identified_persons.append({
+ 'person_id': identity['person_id'],
+ 'name': identity['name'],
+ 'person_index': person_index,
+ 'bbox': person['bbox'],
+ 'is_new': identity['is_new'],
+ 'confidence': identity.get('confidence', person['confidence']),
+ 'method': identity.get('method', 'unknown'),
+ 'yolo_confidence': person['confidence'],
+ 'source': 'yolo'
+ })
+
+ # Step 3: 连续帧判断
confirmed_change = False
- confirmed_persons = []
-
- # 为每个检测到的人分配序号
- person_index = self.prev_human_count # 从当前人数开始
-
- # 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
+ # 人数变化
key = f"count_{current_count}"
if key not in self.confirmation_buffer:
self.confirmation_buffer[key] = {'count': 0, 'persons': []}
self.confirmation_buffer[key]['count'] += 1
-
- # 临时识别人员并分配序号
- temp_persons = []
- for idx, face in enumerate(faces):
- bbox = face['bbox']
- encoding = self.extract_face_encoding(image, bbox)
- match_result = self.match_face(encoding)
-
- # 分配人员序号
- person_index_display = idx + 1 # 序号从1开始
-
- 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, # 显示序号
- 'bbox': bbox,
- 'is_new': match_result['is_new'],
- 'confidence': face['confidence'],
- 'source': face['source']
- }
- temp_persons.append(person_info)
-
- self.confirmation_buffer[key]['persons'] = temp_persons
+ self.confirmation_buffer[key]['persons'] = identified_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)")
- 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 [
diff --git a/web/static/app.js b/web/static/app.js
index 266dbca..fdc553c 100644
--- a/web/static/app.js
+++ b/web/static/app.js
@@ -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 ?
+ '' + personIndices.slice(0, 3).join(' ') + '' : '';
+
item.innerHTML = '#' + img.id + '' +
'' + time + '' +
'' + status + '' +
+ indicesDisplay +
'' + events + '';
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 = 'Local Analysis (' + localEvents.length + ')
';
+
+ // 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 ?
+ ' [' + personIndices.join(', ') + ']' : '';
+
+ localSection.innerHTML = 'Local Analysis (' + localEvents.length + ')' + indicesDisplay + '
';
localEvents.forEach(function(event) {
var div = document.createElement('div');
diff --git a/web/static/style.css b/web/static/style.css
index ef4e09f..92431c1 100644
--- a/web/static/style.css
+++ b/web/static/style.css
@@ -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;