import logging import traceback import numpy as np from typing import Dict, List, Any, Optional, Tuple logger = logging.getLogger(__name__) class SceneViewpointAnalyzer: """ 負責場景視角檢測和模式識別 專注於檢測場景視角（俯視、平視等）並識別特殊場景模式（如十字路口、人流方向等） 提供詳細的場景空間分析和視角相關的場景理解功能 """ def __init__(self, enhanced_scene_describer=None): """ 初始化場景視角分析器 Args: enhanced_scene_describer: 增強場景描述器實例，用於基本視角檢測 """ try: self.enhanced_scene_describer = enhanced_scene_describer logger.info("SceneViewpointAnalyzer initialized successfully") except Exception as e: logger.error(f"Failed to initialize SceneViewpointAnalyzer: {str(e)}") logger.error(traceback.format_exc()) raise def detect_viewpoint(self, detected_objects: List[Dict]) -> str: """ 檢測圖像視角類型 Args: detected_objects: 檢測到的物件列表 Returns: str: 檢測到的視角類型 """ try: # 使用內部的場景視角檢測方法 viewpoint_info = self.detect_scene_viewpoint(detected_objects) return viewpoint_info.get("viewpoint", "eye_level") except Exception as e: logger.warning(f"Error detecting viewpoint: {str(e)}") return "eye_level" def get_viewpoint_confidence(self, detected_objects: List[Dict]) -> Tuple[str, float]: """ 獲取視角檢測結果及其信心度 Args: detected_objects: 檢測到的物件列表 Returns: Tuple[str, float]: (視角類型, 信心度) """ try: viewpoint_info = self.detect_scene_viewpoint(detected_objects) viewpoint = viewpoint_info.get("viewpoint", "eye_level") # 根據檢測到的模式計算信心度 patterns = viewpoint_info.get("patterns", []) confidence = 0.5 # 基礎信心度 if "crosswalk_intersection" in patterns: confidence += 0.3 if "consistent_object_size" in patterns: confidence += 0.2 if "multi_directional_movement" in patterns: confidence += 0.1 confidence = min(confidence, 1.0) return viewpoint, confidence except Exception as e: logger.error(f"Error getting viewpoint confidence: {str(e)}") return "eye_level", 0.5 def detect_scene_viewpoint(self, detected_objects: List[Dict]) -> Dict: """ 檢測場景視角並識別特殊場景模式 Args: detected_objects: 檢測到的物件列表 Returns: 包含視角和場景模式資訊的字典 """ try: if not detected_objects: logger.warning("No detected objects provided for viewpoint detection") return {"viewpoint": "eye_level", "patterns": []} # 從物件位置中提取資訊 patterns = [] # 檢測行人位置模式 - 篩選出所有行人物件 pedestrian_objs = [obj for obj in detected_objects if obj.get("class_id") == 0] # 檢查是否有足夠的行人來識別模式 - 至少需要4個行人才能進行模式分析 if len(pedestrian_objs) >= 4: # 提取行人的標準化中心座標用於模式分析 pedestrian_positions = [obj["normalized_center"] for obj in pedestrian_objs] # 檢測十字交叉模式 - 這通常出現在斑馬線交叉口的俯視圖 if self._detect_cross_pattern(pedestrian_positions): patterns.append("crosswalk_intersection") # 檢測多方向行人流 - 分析行人是否在多個方向移動 directions = self._analyze_movement_directions(pedestrian_positions) if len(directions) >= 2: patterns.append("multi_directional_movement") # 檢查物件的大小一致性 - 在空中俯視圖中，物件大小通常更一致 # 因為距離相對均勻，不像地面視角會有遠近差異 if len(detected_objects) >= 5: sizes = [obj.get("normalized_area", 0) for obj in detected_objects] # 計算標準化變異數，避免受平均值影響 size_variance = np.var(sizes) / (np.mean(sizes) ** 2) if np.mean(sizes) > 0 else 0 # 低變異表示大小一致，可能是俯視角度 if size_variance < 0.3: patterns.append("consistent_object_size") # 基本視角檢測 - 使用增強場景描述器進行基礎視角判斷 viewpoint = "eye_level" # 預設值 if self.enhanced_scene_describer and hasattr(self.enhanced_scene_describer, '_detect_viewpoint'): viewpoint = self.enhanced_scene_describer._detect_viewpoint(detected_objects) # 根據檢測到的模式增強視角判斷 # 如果檢測到斑馬線交叉但視角判斷不是空中視角，優先採用模式判斷 if "crosswalk_intersection" in patterns and viewpoint != "aerial": viewpoint = "aerial" result = { "viewpoint": viewpoint, "patterns": patterns } logger.info(f"Viewpoint detection completed: {viewpoint}, patterns: {patterns}") return result except Exception as e: logger.error(f"Error in scene viewpoint detection: {str(e)}") logger.error(traceback.format_exc()) return {"viewpoint": "eye_level", "patterns": []} def _detect_cross_pattern(self, positions: List[List[float]]) -> bool: """ 檢測位置中的十字交叉模式 這種模式通常出現在十字路口的俯視圖中，行人分布呈現十字形 Args: positions: 位置列表 [[x1, y1], [x2, y2], ...] Returns: 是否檢測到十字交叉模式 """ try: if len(positions) < 8: # 需要足夠多的點才能形成有意義的十字模式 return False # 提取 x 和 y 座標進行分析 x_coords = [pos[0] for pos in positions] y_coords = [pos[1] for pos in positions] # 計算座標的平均值，用於確定中心線位置 x_mean = np.mean(x_coords) y_mean = np.mean(y_coords) # 計算在中心線附近的點數量 # 如果有足夠多的點在垂直和水平中心線附近，可能是十字交叉 near_x_center = sum(1 for x in x_coords if abs(x - x_mean) < 0.1) # 容忍10%的偏差 near_y_center = sum(1 for y in y_coords if abs(y - y_mean) < 0.1) # 容忍10%的偏差 # 十字交叉模式的判斷條件：垂直和水平方向都有足夠的點聚集 is_cross_pattern = near_x_center >= 3 and near_y_center >= 3 if is_cross_pattern: logger.info(f"Cross pattern detected with {near_x_center} points near vertical center and {near_y_center} points near horizontal center") return is_cross_pattern except Exception as e: logger.error(f"Error detecting cross pattern: {str(e)}") logger.error(traceback.format_exc()) return False def _analyze_movement_directions(self, positions: List[List[float]]) -> List[str]: """ 分析位置中的移動方向 通過分析座標分布範圍來推斷主要的移動方向 Args: positions: 位置列表 [[x1, y1], [x2, y2], ...] Returns: 檢測到的主要方向列表 """ try: if len(positions) < 6: # 需要足夠的點才能分析方向性 return [] # 提取 x 和 y 座標 x_coords = [pos[0] for pos in positions] y_coords = [pos[1] for pos in positions] directions = [] # 水平移動分析（左右移動） # 計算x座標的標準差和範圍來判斷水平方向的分散程度 x_std = np.std(x_coords) x_range = max(x_coords) - min(x_coords) # 垂直移動分析（上下移動） # 計算y座標的標準差和範圍來判斷垂直方向的分散程度 y_std = np.std(y_coords) y_range = max(y_coords) - min(y_coords) # 足夠大的範圍表示該方向有明顯的運動或分散 # 40%的圖像範圍被認為是有意義的移動範圍 if x_range > 0.4: directions.append("horizontal") logger.debug(f"Horizontal movement detected with range: {x_range:.3f}") if y_range > 0.4: directions.append("vertical") logger.debug(f"Vertical movement detected with range: {y_range:.3f}") logger.info(f"Movement directions analyzed: {directions}") return directions except Exception as e: logger.error(f"Error analyzing movement directions: {str(e)}") logger.error(traceback.format_exc()) return [] def detect_aerial_view_indicators(self, detected_objects: List[Dict]) -> Dict: """ 檢測俯視角度的指標 分析物件分布特徵來判斷是否為俯視角度 Args: detected_objects: 檢測到的物件列表 Returns: 包含俯視角度指標的字典 """ try: indicators = { "consistent_sizing": False, "grid_like_distribution": False, "high_object_density": False, "aerial_score": 0.0 } if not detected_objects: return indicators # 檢查物件大小的一致性 sizes = [obj.get("normalized_area", 0) for obj in detected_objects] if len(sizes) >= 3: size_variance = np.var(sizes) / (np.mean(sizes) ** 2) if np.mean(sizes) > 0 else 1 # 俯視角度通常物件大小較為一致 indicators["consistent_sizing"] = size_variance < 0.3 # 檢查是否有網格狀分布（如停車場的俯視圖） positions = [obj.get("normalized_center", [0.5, 0.5]) for obj in detected_objects] if len(positions) >= 6: # 簡化的網格檢測：檢查是否有規律的行列分布 x_coords = [pos[0] for pos in positions] y_coords = [pos[1] for pos in positions] # 計算座標的分布是否接近規律網格 x_unique = len(set([round(x, 1) for x in x_coords])) # 四捨五入到0.1精度 y_unique = len(set([round(y, 1) for y in y_coords])) # 如果x和y方向都有多個不同的規律位置，可能是網格分布 indicators["grid_like_distribution"] = x_unique >= 3 and y_unique >= 3 # 檢查物件密度 total_objects = len(detected_objects) # 俯視角度通常能看到更多物件 indicators["high_object_density"] = total_objects >= 8 # 計算俯視角度評分 score = 0 if indicators["consistent_sizing"]: score += 0.4 if indicators["grid_like_distribution"]: score += 0.4 if indicators["high_object_density"]: score += 0.2 indicators["aerial_score"] = score logger.info(f"Aerial view indicators: score={score:.2f}, consistent_sizing={indicators['consistent_sizing']}, grid_distribution={indicators['grid_like_distribution']}, high_density={indicators['high_object_density']}") return indicators except Exception as e: logger.error(f"Error detecting aerial view indicators: {str(e)}") logger.error(traceback.format_exc()) return { "consistent_sizing": False, "grid_like_distribution": False, "high_object_density": False, "aerial_score": 0.0 }