import logging import traceback from typing import Dict, Any, Optional class ConfidenceManager: """ 專門管理信心度相關邏輯，包括動態閾值調整、信心度乘數管理和地標類型特定的閾值處理 """ def __init__(self): """ 初始化置信度管理器 """ self.logger = logging.getLogger(__name__) # 初始化批處理參數 self.batch_size = 16 # 默認批處理大小 # 置信度閾值乘數配置 self.confidence_threshold_multipliers = { "close_up": 0.9, # 近景標準閾值 "partial": 0.6, # 部分可見降低閾值要求 "distant": 0.5, # 遠景更低閾值要求 "full_image": 0.7 # 整張圖像需要更高閾值 } # 地標類型閾值配置 self.landmark_type_thresholds = { "tower": 0.5, # 塔型建築需要更高閾值 "skyscraper": 0.4, # 摩天大樓使用較低閾值 "building": 0.55, # 一般的建築物閾值略微降低 "monument": 0.5, # 紀念碑閾值 "natural": 0.6 # 自然景觀可以使用較低閾值 } def set_batch_size(self, batch_size: int): """ 設置批處理大小 Args: batch_size: 新的批處理大小 """ self.batch_size = max(1, batch_size) self.logger.info(f"Batch size set to {self.batch_size}") def adjust_confidence_threshold(self, detection_type: str, multiplier: float): """ 調整特定檢測類型的信心度的threshold Args: detection_type: 檢測類型 ('close_up', 'partial', 'distant', 'full_image') multiplier: 置信度閾值乘數 """ if detection_type in self.confidence_threshold_multipliers: self.confidence_threshold_multipliers[detection_type] = max(0.1, min(1.5, multiplier)) self.logger.info(f"Adjusted confidence threshold multiplier for {detection_type} to {multiplier}") else: self.logger.warning(f"Unknown detection type: {detection_type}") def get_detection_type_multiplier(self, detection_type: str) -> float: """ 獲取檢測類型的置信度乘數 Args: detection_type: 檢測類型 Returns: float: 置信度乘數 """ return self.confidence_threshold_multipliers.get(detection_type, 1.0) def get_landmark_type_threshold(self, landmark_type: str) -> float: """ 獲取地標類型的閾值 Args: landmark_type: 地標類型 Returns: float: 地標類型閾值 """ return self.landmark_type_thresholds.get(landmark_type, 0.5) def calculate_adjusted_threshold(self, base_threshold: float, detection_type: str) -> float: """ 根據檢測類型計算調整後的閾值 Args: base_threshold: 基礎閾值 detection_type: 檢測type Returns: float: 調整後的閾值 """ try: base_multiplier = self.get_detection_type_multiplier(detection_type) adjusted_threshold = base_threshold * base_multiplier return adjusted_threshold except Exception as e: self.logger.error(f"Error calculating adjusted threshold: {e}") self.logger.error(traceback.format_exc()) return base_threshold def calculate_final_threshold(self, base_threshold: float, detection_type: str, landmark_type: str) -> float: """ 計算最終閾值，結合檢測類型和地標類型 Args: base_threshold: 基礎閾值 detection_type: 檢測type landmark_type: 地標type Returns: float: 最終閾值 """ try: # 根據檢測類型調整 adjusted_threshold = self.calculate_adjusted_threshold(base_threshold, detection_type) # 根據地標類型進一步調整 if landmark_type == "distinctive": # 特殊建築的閾值降低25% type_multiplier = 0.75 else: # 使用已有的類型閾值 type_multiplier = self.get_landmark_type_threshold(landmark_type) / 0.5 final_threshold = adjusted_threshold * type_multiplier return final_threshold except Exception as e: self.logger.error(f"Error calculating final threshold: {e}") self.logger.error(traceback.format_exc()) return base_threshold def evaluate_confidence(self, confidence: float, threshold: float) -> bool: """ 評估置信度是否達到閾值 Args: confidence: 信心度score threshold: 閾值 Returns: bool: 是否達到閾值 """ return confidence >= threshold def apply_architectural_boost(self, confidence: float, architectural_analysis: Dict[str, Any], landmark_id: str) -> float: """ 根據建築特徵分析調整信心度 Args: confidence: 原始置信度 architectural_analysis: 建築特徵分析結果 landmark_id: 地標ID Returns: float: 調整後的信心度 """ try: confidence_boost = 0 landmark_id_lower = landmark_id.lower() top_features = architectural_analysis.get("architectural_features", []) primary_category = architectural_analysis.get("primary_category", "") # 使用主要建築類別來調整置信度，使用通用條件而非特定地標名稱 if primary_category == "tower" and any(term in landmark_id_lower for term in ["tower", "spire", "needle"]): confidence_boost += 0.05 elif primary_category == "skyscraper" and any(term in landmark_id_lower for term in ["building", "skyscraper", "tall"]): confidence_boost += 0.05 elif primary_category == "historical" and any(term in landmark_id_lower for term in ["monument", "castle", "palace", "temple"]): confidence_boost += 0.05 elif primary_category == "distinctive" and any(term in landmark_id_lower for term in ["unusual", "unique", "special", "famous"]): confidence_boost += 0.05 # 根據特定特徵進一步微調，使用通用特徵描述而非特定地標 for feature, score in top_features: if feature == "time_display" and "clock" in landmark_id_lower: confidence_boost += 0.03 elif feature == "segmented_exterior" and "segmented" in landmark_id_lower: confidence_boost += 0.03 elif feature == "slanted_design" and "leaning" in landmark_id_lower: confidence_boost += 0.03 # 應用信心度調整 if confidence_boost > 0: adjusted_confidence = confidence + confidence_boost self.logger.info(f"Boosted confidence by {confidence_boost:.2f} based on architectural features ({primary_category})") return adjusted_confidence return confidence except Exception as e: self.logger.error(f"Error applying architectural boost: {e}") self.logger.error(traceback.format_exc()) return confidence def determine_detection_type_from_region(self, region_width: int, region_height: int, image_width: int, image_height: int) -> str: """ 根據區域大小自動判斷檢測類型 Args: region_width: 區域寬度 region_height: 區域高度 image_width: 圖像寬度 image_height: 圖像高度 Returns: str: 檢測類型 """ try: region_area_ratio = (region_width * region_height) / (image_width * image_height) if region_area_ratio > 0.5: return "close_up" elif region_area_ratio > 0.2: return "partial" else: return "distant" except Exception as e: self.logger.error(f"Error determining detection type from region: {e}") self.logger.error(traceback.format_exc()) return "partial" def adjust_detection_type_by_viewpoint(self, detection_type: str, dominant_viewpoint: str) -> str: """ 根據視角調整檢測類型 Args: detection_type: 原始檢測類型 dominant_viewpoint: 主要視角 Returns: str: 調整後的檢測類型 """ try: if dominant_viewpoint == "close_up" and detection_type != "close_up": return "close_up" elif dominant_viewpoint == "distant" and detection_type != "distant": return "distant" elif dominant_viewpoint == "angled_view": return "partial" # 角度視圖可能是部分可見 else: return detection_type except Exception as e: self.logger.error(f"Error adjusting detection type by viewpoint: {e}") self.logger.error(traceback.format_exc()) return detection_type def get_batch_size(self) -> int: """ 獲取當前批處理大小 Returns: int: 批處理大小 """ return self.batch_size def get_all_threshold_multipliers(self) -> Dict[str, float]: """ 獲取所有置信度閾值乘數 Returns: Dict[str, float]: 閾值乘數字典 """ return self.confidence_threshold_multipliers.copy() def get_all_landmark_type_thresholds(self) -> Dict[str, float]: """ 獲取所有地標類型閾值 Returns: Dict[str, float]: 地標類型閾值字典 """ return self.landmark_type_thresholds.copy()