diff --git a/api/routes/algorithm_router.py b/api/routes/algorithm_router.py index 3698123..9474a7f 100644 --- a/api/routes/algorithm_router.py +++ b/api/routes/algorithm_router.py @@ -876,7 +876,10 @@ def sync_videofaceprisonbiz_params(): "face.min_face_size": "min_face_size", "face.pitch_threshold": "pitch_threshold", "face.yaw_threshold": "yaw_threshold", - "face.similarity_threshold": "similarity_threshold" + "face.similarity_threshold": "similarity_threshold", + "face.detection_window_seconds": "detection_window_seconds", + "face.min_match_count": "min_match_count", + "face.cooldown_seconds": "cooldown_seconds" } updated_count = 0 @@ -925,6 +928,27 @@ def sync_videofaceprisonbiz_params(): updated_count += 1 except ValueError: logger.error(f"无效的相似度阈值: {config_value}") + elif param_name == "detection_window_seconds": + try: + window_seconds = float(config_value) + video_face_prison_biz.set_detection_window_seconds(window_seconds) + updated_count += 1 + except ValueError: + logger.error(f"无效的检测窗口时间: {config_value}") + elif param_name == "min_match_count": + try: + min_matches = int(config_value) + video_face_prison_biz.set_min_match_count(min_matches) + updated_count += 1 + except ValueError: + logger.error(f"无效的最小匹配次数: {config_value}") + elif param_name == "cooldown_seconds": + try: + cooldown_seconds = int(config_value) + video_face_prison_biz.set_cooldown_seconds(cooldown_seconds) + updated_count += 1 + except ValueError: + logger.error(f"无效的冷却时间: {config_value}") logger.info(f"✅ 同步VideoFacePrisonBiz参数完成，更新了 {updated_count} 个参数（配置组ID: {prison_config.config_group_id}）") return updated_count diff --git a/biz/video_face_prison_biz.py b/biz/video_face_prison_biz.py index 9746287..75b0926 100644 --- a/biz/video_face_prison_biz.py +++ b/biz/video_face_prison_biz.py @@ -24,6 +24,15 @@ class VideoFacePrisonBiz(BaseFaceBiz): face_analysis: 已初始化好的FaceAnalysis实例 """ super().__init__(face_analysis) + + # 人脸匹配跟踪配置 + self.detection_window_seconds = 2.0 # 检测窗口时间（秒） + self.min_match_count = 5 # 最小匹配次数 + self.cooldown_seconds = 30 # 冷却时间（秒） + + # 跟踪数据结构 + self.person_tracking = {} # {person_id: [timestamp1, timestamp2, ...]} + self.person_cooldown = {} # {person_id: cooldown_end_time} def draw_detections(self, frame: np.ndarray, results: List[Dict]) -> np.ndarray: """ @@ -64,6 +73,120 @@ class VideoFacePrisonBiz(BaseFaceBiz): return frame + def set_detection_window_seconds(self, window_seconds: float): + """ + 设置检测窗口时间 + + 参数: + window_seconds: 检测窗口时间（秒） + """ + self.detection_window_seconds = window_seconds + + def get_detection_window_seconds(self) -> float: + """ + 获取检测窗口时间 + + 返回: + 检测窗口时间（秒） + """ + return self.detection_window_seconds + + def set_min_match_count(self, min_matches: int): + """ + 设置最小匹配次数 + + 参数: + min_matches: 最小匹配次数 + """ + self.min_match_count = min_matches + + def get_min_match_count(self) -> int: + """ + 获取最小匹配次数 + + 返回: + 最小匹配次数 + """ + return self.min_match_count + + def set_cooldown_seconds(self, cooldown_seconds: int): + """ + 设置冷却时间 + + 参数: + cooldown_seconds: 冷却时间（秒） + """ + self.cooldown_seconds = cooldown_seconds + + def get_cooldown_seconds(self) -> int: + """ + 获取冷却时间 + + 返回: + 冷却时间（秒） + """ + return self.cooldown_seconds + + def _cleanup_old_records(self, current_time: float): + """ + 清理过期的跟踪记录 + + 参数: + current_time: 当前时间戳 + """ + # 清理过期的匹配记录 + for person_id in list(self.person_tracking.keys()): + # 保留在检测窗口内的记录 + self.person_tracking[person_id] = [ + ts for ts in self.person_tracking[person_id] + if current_time - ts <= self.detection_window_seconds + ] + + # 如果记录为空，删除该person_id + if not self.person_tracking[person_id]: + del self.person_tracking[person_id] + + # 清理过期的冷却记录 + for person_id in list(self.person_cooldown.keys()): + if current_time > self.person_cooldown[person_id]: + del self.person_cooldown[person_id] + + def _is_person_passed(self, person_id: str, current_time: float) -> bool: + """ + 判断人员是否已经通过 + + 参数: + person_id: 人员标识符 + current_time: 当前时间戳 + + 返回: + 是否通过 + """ + # 检查是否在冷却期内 + if person_id in self.person_cooldown: + if current_time <= self.person_cooldown[person_id]: + # 还在冷却期内，忽略此人 + return False + else: + # 冷却期结束，删除记录 + del self.person_cooldown[person_id] + + # 检查是否达到最小匹配次数 + if person_id in self.person_tracking: + recent_matches = [ + ts for ts in self.person_tracking[person_id] + if current_time - ts <= self.detection_window_seconds + ] + + if len(recent_matches) >= self.min_match_count: + # 达到条件，设置冷却期 + self.person_cooldown[person_id] = current_time + self.cooldown_seconds + # 清空该人员的匹配记录 + del self.person_tracking[person_id] + return True + + return False + def process_frame(self, frame: np.ndarray) -> Tuple[np.ndarray, List[Dict], float]: """ 处理单帧图像 @@ -72,6 +195,10 @@ class VideoFacePrisonBiz(BaseFaceBiz): (原始帧, 识别结果列表, 处理时间ms) """ start_time = time.time() + current_time = time.time() + + # 清理过期的跟踪记录 + self._cleanup_old_records(current_time) # 人脸检测和识别 faces = self.app.get(frame) @@ -83,14 +210,25 @@ class VideoFacePrisonBiz(BaseFaceBiz): # 查找最佳匹配 best_name, similarity = self.find_best_match(face.embedding) - is_match = best_name is not None and similarity >= self.similarity_threshold + + # 新增：判断是否已经通过 + has_passed = False + if is_match and best_name: + has_passed = self._is_person_passed(best_name, current_time) + + # 如果匹配但未通过，记录匹配时间 + if is_match and not has_passed: + if best_name not in self.person_tracking: + self.person_tracking[best_name] = [] + self.person_tracking[best_name].append(current_time) result = { 'bbox': face.bbox.astype(int).tolist(), 'similarity': similarity, 'best_match': best_name, 'is_match': is_match, + 'has_passed': has_passed, # 新增：是否已经通过 'det_score': float(face.det_score), 'quality_metrics': quality_metrics, 'is_acceptable': is_acceptable