import numpy as np import cv2 from typing import Dict, Any, Optional class LightingAnalyzer: """ 分析圖像的光照條件，提供增強的室內or室外判斷和光照類型分類，並專注於光照分析。 """ def __init__(self, config: Optional[Dict[str, Any]] = None): """ 初始化光照分析器。 Args: config: 可選的配置字典，用於自定義分析參數 """ self.config = config or self._get_default_config() def analyze(self, image): """ 分析圖像的光照條件。 主要分析入口點，計算基本特徵，判斷室內/室外，確定光照條件。 Args: image: 輸入圖像 (numpy array 或 PIL Image) Returns: Dict: 包含光照分析結果的字典 """ try: # 轉換圖像格式 if not isinstance(image, np.ndarray): image_np = np.array(image) else: image_np = image.copy() # 確保 RGB 格式 if image_np.shape[2] == 3 and isinstance(image_np, np.ndarray): image_rgb = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) else: image_rgb = image_np # 計算基本特徵 features = self._compute_basic_features(image_rgb) # 分析室內or室外 indoor_result = self._analyze_indoor_outdoor(features) is_indoor = indoor_result["is_indoor"] indoor_probability = indoor_result["indoor_probability"] # 確定光照條件 lighting_conditions = self._determine_lighting_conditions(features, is_indoor) # 整合結果 result = { "time_of_day": lighting_conditions["time_of_day"], "confidence": float(lighting_conditions["confidence"]), "is_indoor": is_indoor, "indoor_probability": float(indoor_probability), "brightness": { "average": float(features["avg_brightness"]), "std_dev": float(features["brightness_std"]), "dark_ratio": float(features["dark_pixel_ratio"]) }, "color_info": { "blue_ratio": float(features["blue_ratio"]), "yellow_orange_ratio": float(features["yellow_orange_ratio"]), "gray_ratio": float(features["gray_ratio"]), "avg_saturation": float(features["avg_saturation"]), "sky_brightness": float(features["sky_brightness"]), "color_atmosphere": features["color_atmosphere"], "warm_ratio": float(features["warm_ratio"]), "cool_ratio": float(features["cool_ratio"]) } } # 添加診斷信息 if self.config["include_diagnostics"]: result["diagnostics"] = { "feature_contributions": indoor_result.get("feature_contributions", {}), "lighting_diagnostics": lighting_conditions.get("diagnostics", {}) } return result except Exception as e: print(f"Error in lighting analysis: {str(e)}") import traceback traceback.print_exc() return { "time_of_day": "unknown", "confidence": 0, "error": str(e) } def _compute_basic_features(self, image_rgb): """ 計算圖像的基本光照特徵（徹底優化版本）。 Args: image_rgb: RGB 格式的圖像 (numpy array) Returns: Dict: 包含計算出的特徵值 """ # 獲取圖像尺寸 height, width = image_rgb.shape[:2] # 根據圖像大小自適應縮放因子 base_scale = 4 scale_factor = base_scale + min(8, max(0, int((height * width) / (1000 * 1000)))) # 創建縮小的圖像以加速處理 small_rgb = cv2.resize(image_rgb, (width//scale_factor, height//scale_factor)) # 一次性轉換所有顏色空間，避免重複計算 hsv_img = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2HSV) gray_img = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY) small_gray = cv2.resize(gray_img, (width//scale_factor, height//scale_factor)) # 分離HSV通道 h_channel = hsv_img[:,:,0] s_channel = hsv_img[:,:,1] v_channel = hsv_img[:,:,2] # 基本亮度特徵 avg_brightness = np.mean(v_channel) brightness_std = np.std(v_channel) dark_pixel_ratio = np.sum(v_channel < 50) / (height * width) # 顏色特徵 yellow_orange_mask = ((h_channel >= 15) & (h_channel <= 40)) yellow_orange_ratio = np.sum(yellow_orange_mask) / (height * width) blue_mask = ((h_channel >= 90) & (h_channel <= 130)) blue_ratio = np.sum(blue_mask) / (height * width) # 特別檢查圖像上部區域，尋找藍天特徵 upper_region_h = h_channel[:height//4, :] upper_region_s = s_channel[:height//4, :] upper_region_v = v_channel[:height//4, :] # 藍天通常具有高飽和度的藍色 sky_blue_mask = ((upper_region_h >= 90) & (upper_region_h <= 130) & (upper_region_s > 70) & (upper_region_v > 150)) sky_blue_ratio = np.sum(sky_blue_mask) / max(1, upper_region_h.size) gray_mask = (s_channel < 50) & (v_channel > 100) gray_ratio = np.sum(gray_mask) / (height * width) avg_saturation = np.mean(s_channel) # 天空亮度 upper_half = v_channel[:height//2, :] sky_brightness = np.mean(upper_half) # 色調分析 warm_colors = ((h_channel >= 0) & (h_channel <= 60)) | (h_channel >= 300) warm_ratio = np.sum(warm_colors) / (height * width) cool_colors = (h_channel >= 180) & (h_channel <= 270) cool_ratio = np.sum(cool_colors) / (height * width) # 確定色彩氛圍 if warm_ratio > 0.4: color_atmosphere = "warm" elif cool_ratio > 0.4: color_atmosphere = "cool" else: color_atmosphere = "neutral" # 只在縮小的圖像上計算梯度，大幅提高效能 gx = cv2.Sobel(small_gray, cv2.CV_32F, 1, 0, ksize=3) gy = cv2.Sobel(small_gray, cv2.CV_32F, 0, 1, ksize=3) vertical_strength = np.mean(np.abs(gy)) horizontal_strength = np.mean(np.abs(gx)) gradient_ratio = vertical_strength / max(horizontal_strength, 1e-5) # -- 亮度均勻性 -- brightness_uniformity = 1 - min(1, brightness_std / max(avg_brightness, 1e-5)) # -- 高效的天花板分析 -- # 使用更大的下採樣率分析頂部區域 top_scale = scale_factor * 2 # 更積極的下採樣 top_region = v_channel[:height//4:top_scale, ::top_scale] top_region_std = np.std(top_region) ceiling_uniformity = 1.0 - min(1, top_region_std / max(np.mean(top_region), 1e-5)) # 使用更簡單的方法檢測上部水平線 top_gradients = np.abs(gy[:small_gray.shape[0]//4, :]) horizontal_lines_strength = np.mean(top_gradients) # 標準化 horizontal_line_ratio = min(1, horizontal_lines_strength / 40) # 極簡的亮點檢測 sampled_v = v_channel[::scale_factor*2, ::scale_factor*2] light_threshold = min(220, avg_brightness + 2*brightness_std) is_bright = sampled_v > light_threshold bright_spot_count = np.sum(is_bright) # 圓形光源分析的簡化替代方法 circular_light_score = 0 indoor_light_score = 0 light_distribution_uniformity = 0.5 # 只有當檢測到亮點，且不是大量亮點時（可能是室外光反射）才進行光源分析 if 1 < bright_spot_count < 20: # 簡單統計亮點分布 bright_y, bright_x = np.where(is_bright) if len(bright_y) > 1: # 檢查亮點是否成組出現 - 室內照明常見模式 mean_x = np.mean(bright_x) mean_y = np.mean(bright_y) dist_from_center = np.sqrt((bright_x - mean_x)**2 + (bright_y - mean_y)**2) # 如果亮點分布較集中，可能是燈具 if np.std(dist_from_center) < np.mean(dist_from_center): circular_light_score = min(3, len(bright_y) // 2) light_distribution_uniformity = 0.7 # 評估亮點是否位於上部區域，常見於室內頂燈 if np.mean(bright_y) < sampled_v.shape[0] / 2: indoor_light_score = 0.6 else: indoor_light_score = 0.3 # 使用邊緣區域梯度來快速估計邊界 edge_scale = scale_factor * 2 # 只採樣圖像邊緣部分進行分析 left_edge = small_gray[:, :small_gray.shape[1]//6] right_edge = small_gray[:, 5*small_gray.shape[1]//6:] top_edge = small_gray[:small_gray.shape[0]//6, :] # 計算每個邊緣區域的梯度強度 left_gradient = np.mean(np.abs(cv2.Sobel(left_edge, cv2.CV_32F, 1, 0, ksize=3))) right_gradient = np.mean(np.abs(cv2.Sobel(right_edge, cv2.CV_32F, 1, 0, ksize=3))) top_gradient = np.mean(np.abs(cv2.Sobel(top_edge, cv2.CV_32F, 0, 1, ksize=3))) # 標準化 left_edge_density = min(1.0, left_gradient / 50) right_edge_density = min(1.0, right_gradient / 50) top_edge_density = min(1.0, top_gradient / 50) # 封閉環境通常在圖像邊緣有較強的梯度 boundary_edge_score = (left_edge_density + right_edge_density + top_edge_density) / 3 # 簡單估計整體邊緣密度 edges_density = min(1, (np.mean(np.abs(gx)) + np.mean(np.abs(gy))) / 100) street_line_score = 0 # 檢查下半部分是否有強烈的垂直線條 bottom_half = small_gray[small_gray.shape[0]//2:, :] bottom_vert_gradient = cv2.Sobel(bottom_half, cv2.CV_32F, 0, 1, ksize=3) strong_vert_lines = np.abs(bottom_vert_gradient) > 50 if np.sum(strong_vert_lines) > (bottom_half.size * 0.05): # 如果超過5%的像素是強垂直線 street_line_score = 0.7 # 整合所有特徵 features = { # 基本亮度和顏色特徵 "avg_brightness": avg_brightness, "brightness_std": brightness_std, "dark_pixel_ratio": dark_pixel_ratio, "yellow_orange_ratio": yellow_orange_ratio, "blue_ratio": blue_ratio, "sky_blue_ratio": sky_blue_ratio, "gray_ratio": gray_ratio, "avg_saturation": avg_saturation, "sky_brightness": sky_brightness, "color_atmosphere": color_atmosphere, "warm_ratio": warm_ratio, "cool_ratio": cool_ratio, # 結構特徵 "gradient_ratio": gradient_ratio, "brightness_uniformity": brightness_uniformity, "bright_spot_count": bright_spot_count, "vertical_strength": vertical_strength, "horizontal_strength": horizontal_strength, # 室內/室外判斷特徵 "ceiling_uniformity": ceiling_uniformity, "horizontal_line_ratio": horizontal_line_ratio, "indoor_light_score": indoor_light_score, "circular_light_count": circular_light_score, "light_distribution_uniformity": light_distribution_uniformity, "boundary_edge_score": boundary_edge_score, "top_region_std": top_region_std, "edges_density": edges_density, # 新增：室外特定特徵 "street_line_score": street_line_score } return features def _analyze_indoor_outdoor(self, features): """ 使用多特徵融合進行室內/室外判斷 Args: features: 特徵字典 Returns: Dict: 室內/室外判斷結果 """ # 獲取配置中的特徵權重 weights = self.config["indoor_outdoor_weights"] # 初始概率值 - 開始時中性評估 indoor_score = 0 feature_contributions = {} diagnostics = {} # 1. 藍色區域（天空）特徵 - 藍色區域多通常表示室外 if features.get("blue_ratio", 0) > 0.2: # 檢查是否有室內指標，如果有明顯的室內特徵，則減少藍色的負面影響 if (features.get("ceiling_uniformity", 0) > 0.5 or features.get("boundary_edge_score", 0) > 0.3 or features.get("indoor_light_score", 0) > 0.2 or features.get("bright_spot_count", 0) > 0): blue_score = -weights["blue_ratio"] * features["blue_ratio"] * 8 else: blue_score = -weights["blue_ratio"] * features["blue_ratio"] * 15 else: blue_score = -weights["blue_ratio"] * features["blue_ratio"] * 15 indoor_score += blue_score feature_contributions["blue_ratio"] = blue_score # 判斷視角 - 如果上部有藍天而上下亮度差異大，可能是仰視室外建築 if (features.get("sky_blue_ratio", 0) > 0.01 and features["sky_brightness"] > features["avg_brightness"] * 1.1): viewpoint_outdoor_score = -1.8 # 強烈的室外指標 indoor_score += viewpoint_outdoor_score feature_contributions["outdoor_viewpoint"] = viewpoint_outdoor_score # 2. 亮度均勻性特徵 - 室內通常光照更均勻 uniformity_score = weights["brightness_uniformity"] * features["brightness_uniformity"] indoor_score += uniformity_score feature_contributions["brightness_uniformity"] = uniformity_score # 3. 天花板特徵 - 強化天花板檢測的權重 ceiling_contribution = 0 if "ceiling_uniformity" in features: ceiling_uniformity = features["ceiling_uniformity"] horizontal_line_ratio = features.get("horizontal_line_ratio", 0) # 增強天花板檢測的影響 if ceiling_uniformity > 0.5: ceiling_weight = 3 ceiling_contribution = weights.get("ceiling_features", 1.5) * ceiling_weight if horizontal_line_ratio > 0.2: # 如果有水平線條，進一步增強 ceiling_contribution *= 1.5 elif ceiling_uniformity > 0.4: ceiling_contribution = weights.get("ceiling_features", 1.5) * 1.2 indoor_score += ceiling_contribution feature_contributions["ceiling_features"] = ceiling_contribution # 4. 強化吊燈的檢測 light_contribution = 0 if "indoor_light_score" in features: indoor_light_score = features["indoor_light_score"] circular_light_count = features.get("circular_light_count", 0) # 加強對特定類型光源的檢測 if circular_light_count >= 1: # 即便只有一個圓形光源也很可能是室內 light_contribution = weights.get("light_features", 1.2) * 2.0 elif indoor_light_score > 0.3: light_contribution = weights.get("light_features", 1.2) * 1.0 indoor_score += light_contribution feature_contributions["light_features"] = light_contribution # 5. 環境封閉度特徵 boundary_contribution = 0 if "boundary_edge_score" in features: boundary_edge_score = features["boundary_edge_score"] edges_density = features.get("edges_density", 0) # 高邊界評分暗示封閉環境（室內） if boundary_edge_score > 0.3: boundary_contribution = weights.get("boundary_features", 1.2) * 2 elif boundary_edge_score > 0.2: boundary_contribution = weights.get("boundary_features", 1.2) * 1.2 indoor_score += boundary_contribution feature_contributions["boundary_features"] = boundary_contribution if (features.get("edges_density", 0) > 0.2 and features.get("bright_spot_count", 0) > 5 and features.get("vertical_strength", 0) > features.get("horizontal_strength", 0) * 1.5): # 商業街道特徵：高邊緣密度 + 多亮點 + 強垂直特徵 street_feature_score = -weights.get("street_features", 1.2) * 1.5 indoor_score += street_feature_score feature_contributions["street_features"] = street_feature_score # 添加對亞洲商業街道的專門檢測 if (features.get("edges_density", 0) > 0.25 and # 高邊緣密度 features.get("vertical_strength", 0) > features.get("horizontal_strength", 0) * 1.8 and # 更強的垂直結構 features.get("brightness_uniformity", 0) < 0.6): # 較低的亮度均勻性（招牌、燈光等造成） asian_street_score = -2.2 # 非常強的室外代表性特徵 indoor_score += asian_street_score feature_contributions["asian_commercial_street"] = asian_street_score # 6. 垂直/水平梯度比率 gradient_contribution = 0 if features["gradient_ratio"] > 2.0: combined_uniformity = (features["brightness_uniformity"] + features.get("ceiling_uniformity", 0)) / 2 if combined_uniformity > 0.5: gradient_contribution = weights["gradient_ratio"] * 0.7 else: gradient_contribution = -weights["gradient_ratio"] * 0.3 indoor_score += gradient_contribution feature_contributions["gradient_ratio"] = gradient_contribution # 7. 亮點檢測（光源） bright_spot_contribution = 0 bright_spot_count = features["bright_spot_count"] circular_light_count = features.get("circular_light_count", 0) # 調整亮點分析邏輯 if circular_light_count >= 1: # 即使只有一個圓形光源 bright_spot_contribution = weights["bright_spots"] * 1.5 elif bright_spot_count < 5: # 適當放寬閾值 bright_spot_contribution = weights["bright_spots"] * 0.5 elif bright_spot_count > 15: # 大量亮點比較有可能為室外 bright_spot_contribution = -weights["bright_spots"] * 0.4 indoor_score += bright_spot_contribution feature_contributions["bright_spots"] = bright_spot_contribution # 8. 色調分析 yellow_contribution = 0 if features["avg_brightness"] < 150 and features["yellow_orange_ratio"] > 0.15: if features.get("indoor_light_score", 0) > 0.2: yellow_contribution = weights["color_tone"] * 0.8 else: yellow_contribution = weights["color_tone"] * 0.5 indoor_score += yellow_contribution feature_contributions["yellow_tone"] = yellow_contribution if features.get("blue_ratio", 0) > 0.7: # 檢查是否有室內指標，如果有明顯的室內特徵，則減少藍色的負面影響 if (features.get("ceiling_uniformity", 0) > 0.6 or features.get("boundary_edge_score", 0) > 0.3 or features.get("indoor_light_score", 0) > 0): blue_score = -weights["blue_ratio"] * features["blue_ratio"] * 10 else: blue_score = -weights["blue_ratio"] * features["blue_ratio"] * 18 else: blue_score = -weights["blue_ratio"] * features["blue_ratio"] * 18 # 9. 上半部與下半部亮度對比 sky_contribution = 0 if features["sky_brightness"] > features["avg_brightness"] * 1.3: if features["blue_ratio"] > 0.15: sky_contribution = -weights["sky_brightness"] * 0.9 else: sky_contribution = -weights["sky_brightness"] * 0.6 indoor_score += sky_contribution feature_contributions["sky_brightness"] = sky_contribution # 加入額外的餐廳特徵檢測邏輯 dining_feature_contribution = 0 # 檢測中央懸掛式燈具，有懸掛燈代表有天花板，就代表是室內 if circular_light_count >= 1 and features.get("light_distribution_uniformity", 0) > 0.4: dining_feature_contribution = 1.5 indoor_score += dining_feature_contribution feature_contributions["dining_features"] = dining_feature_contribution # 10. 增強的藍天的檢測，即便是小面積的藍天也是很強的室外指標 sky_contribution = 0 if "sky_blue_ratio" in features: # 只有當藍色區域集中在上部且亮度高時，才認為是藍天 if features["sky_blue_ratio"] > 0.01 and features["sky_brightness"] > features.get("avg_brightness", 0) * 1.2: sky_outdoor_score = -2.5 * features["sky_blue_ratio"] * weights.get("blue_ratio", 1.2) indoor_score += sky_outdoor_score feature_contributions["sky_blue_detection"] = sky_outdoor_score asian_street_indicators = 0 # 1: 高垂直結構強度 vertical_ratio = features.get("vertical_strength", 0) / max(features.get("horizontal_strength", 1e-5), 1e-5) if vertical_ratio > 1.8: asian_street_indicators += 1 # 2: 高邊緣密度 + 路面標記特徵 if features.get("edges_density", 0) > 0.25 and features.get("street_line_score", 0) > 0.2: asian_street_indicators += 2 # 3: 多個亮點 + 亮度不均勻 if features.get("bright_spot_count", 0) > 5 and features.get("brightness_uniformity", 0) < 0.6: asian_street_indicators += 1 # 4: 藍色區域小（天空被高樓遮擋）但亮度高 if features.get("blue_ratio", 0) < 0.1 and features.get("sky_brightness", 0) > features.get("avg_brightness", 0) * 1.1: asian_street_indicators += 1 # 如果滿足至少 3 個指標，調整權重變成偏向室外的判斷 if asian_street_indicators >= 3: # 記錄檢測到的模式 feature_contributions["asian_street_pattern"] = -2.5 indoor_score += -2.5 # 明顯向室外傾斜 # 降低室內指標的權重 if "boundary_features" in feature_contributions: adjusted_contribution = feature_contributions["boundary_features"] * 0.4 indoor_score -= (feature_contributions["boundary_features"] - adjusted_contribution) feature_contributions["boundary_features"] = adjusted_contribution if "ceiling_features" in feature_contributions: adjusted_contribution = feature_contributions["ceiling_features"] * 0.3 indoor_score -= (feature_contributions["ceiling_features"] - adjusted_contribution) feature_contributions["ceiling_features"] = adjusted_contribution # 添加信息到診斷數據 diagnostics["asian_street_detected"] = True diagnostics["asian_street_indicators"] = asian_street_indicators bedroom_indicators = 0 # 1: 窗戶和牆壁形成的直角 if features.get("brightness_uniformity", 0) > 0.6 and features.get("boundary_edge_score", 0) > 0.3: bedroom_indicators += 1.5 # 增加權重 # 2: 天花板和光源 if features.get("ceiling_uniformity", 0) > 0.5 and features.get("bright_spot_count", 0) > 0: bedroom_indicators += 2.5 # 3: 良好對比度的牆壁顏色，適合臥房還有客廳 if features.get("brightness_uniformity", 0) > 0.6 and features.get("avg_saturation", 0) < 100: bedroom_indicators += 1.5 # 特殊的檢測 4: 檢測窗戶 if features.get("boundary_edge_score", 0) > 0.25 and features.get("brightness_std", 0) > 40: bedroom_indicators += 1.5 # 如果滿足足夠的家居指標，提高多點室內判斷分數 if bedroom_indicators >= 3: # 增加家居環境評分 home_env_score = 3 indoor_score += home_env_score feature_contributions["home_environment_pattern"] = home_env_score elif bedroom_indicators >= 2: # 適度增加家居環境評分 home_env_score = 2 indoor_score += home_env_score feature_contributions["home_environment_pattern"] = home_env_score # 根據總分轉換為概率（使用sigmoid函數） indoor_probability = 1 / (1 + np.exp(-indoor_score * 0.22)) # 判斷結果 is_indoor = indoor_probability > 0.5 return { "is_indoor": is_indoor, "indoor_probability": indoor_probability, "indoor_score": indoor_score, "feature_contributions": feature_contributions, "diagnostics": diagnostics } def _determine_lighting_conditions(self, features, is_indoor): """ 基於特徵和室內/室外判斷確定光照條件。 Args: features: 特徵字典 is_indoor: 是否是室內環境 Returns: Dict: 光照條件分析結果 """ # 初始化 time_of_day = "unknown" confidence = 0.5 diagnostics = {} avg_brightness = features["avg_brightness"] dark_pixel_ratio = features["dark_pixel_ratio"] yellow_orange_ratio = features["yellow_orange_ratio"] blue_ratio = features["blue_ratio"] gray_ratio = features["gray_ratio"] # 基於室內/室外分別判斷 if is_indoor: # 計算室內住宅自然光指標 natural_window_light = 0 # 檢查窗戶特徵和光線特性 if (features.get("blue_ratio", 0) > 0.1 and features.get("sky_brightness", 0) > avg_brightness * 1.1): natural_window_light += 1 # 檢查均勻柔和的光線分布 if (features.get("brightness_uniformity", 0) > 0.65 and features.get("brightness_std", 0) < 70): natural_window_light += 1 # 檢查暖色調比例 if features.get("warm_ratio", 0) > 0.2: natural_window_light += 1 # 家居環境指標 home_env_score = features.get("home_environment_pattern", 0) if home_env_score > 1.5: natural_window_light += 1 # 1. 室內明亮環境，可能有窗戶自然光 if avg_brightness > 130: # 檢測自然光住宅空間 - 新增類型! if natural_window_light >= 2 and home_env_score > 1.5: time_of_day = "indoor_residential_natural" # 家裡的自然光類型 confidence = 0.8 diagnostics["reason"] = "Bright residential space with natural window lighting" # 檢查窗戶特徵 - 如果有明亮的窗戶且色調為藍 elif features.get("blue_ratio", 0) > 0.1 and features.get("sky_brightness", 0) > 150: time_of_day = "indoor_bright" confidence = 0.8 diagnostics["reason"] = "Bright indoor scene with window light" else: time_of_day = "indoor_bright" confidence = 0.75 diagnostics["reason"] = "High brightness in indoor environment" # 2. 室內中等亮度環境 elif avg_brightness > 100: time_of_day = "indoor_moderate" confidence = 0.7 diagnostics["reason"] = "Moderate brightness in indoor environment" # 3. 室內低光照環境 else: time_of_day = "indoor_dim" confidence = 0.65 + dark_pixel_ratio / 3 diagnostics["reason"] = "Low brightness in indoor environment" # 1. 檢測設計師風格住宅，可以偵測到比較多種類的狀況 designer_residential_score = 0 # 檢測特色燈具 if (features.get("circular_light_count", 0) > 0 or features.get("bright_spot_count", 0) > 2): designer_residential_score += 1 # 檢測高品質均勻照明 if features.get("brightness_uniformity", 0) > 0.7: designer_residential_score += 1 # 檢測溫暖色調 if features.get("warm_ratio", 0) > 0.3: designer_residential_score += 1 # 檢測家居環境特徵 if home_env_score > 1.5: designer_residential_score += 1 if designer_residential_score >= 3 and home_env_score > 1.5: time_of_day = "indoor_designer_residential" confidence = 0.85 diagnostics["special_case"] = "Designer residential lighting with decorative elements" # 2. 檢測餐廳/酒吧場景 elif avg_brightness < 150 and yellow_orange_ratio > 0.2: if features["warm_ratio"] > 0.4: time_of_day = "indoor_restaurant" confidence = 0.65 + yellow_orange_ratio / 4 diagnostics["special_case"] = "Warm, yellow-orange lighting suggests restaurant/bar setting" # 3. 檢測商業照明空間 elif avg_brightness > 120 and features["bright_spot_count"] > 4: # 增加商業照明判別的精確度 commercial_score = 0 # 多個亮點 commercial_score += min(1.0, features["bright_spot_count"] * 0.05) # 不太可能是住宅的指標 if features.get("home_environment_pattern", 0) < 1.5: commercial_score += 0.5 # 整體照明結構化布局 if features.get("light_distribution_uniformity", 0) > 0.6: commercial_score += 0.5 if commercial_score > 0.6 and designer_residential_score < 3: time_of_day = "indoor_commercial" confidence = 0.7 + commercial_score / 5 diagnostics["special_case"] = "Multiple structured light sources suggest commercial lighting" else: # 室外場景判斷保持不變 if avg_brightness < 90: # 降低夜間判斷的亮度閾值 # 檢測是否有車燈/街燈 has_lights = features["bright_spot_count"] > 3 if has_lights: time_of_day = "night" confidence = 0.8 + dark_pixel_ratio / 5 diagnostics["reason"] = "Low brightness with light sources detected" # 檢查是否是霓虹燈場景 if yellow_orange_ratio > 0.15 and features["bright_spot_count"] > 5: time_of_day = "neon_night" confidence = 0.75 + yellow_orange_ratio / 3 diagnostics["special_case"] = "Multiple colorful light sources suggest neon lighting" else: time_of_day = "night" confidence = 0.7 + dark_pixel_ratio / 3 diagnostics["reason"] = "Low brightness outdoor scene" elif avg_brightness < 130 and yellow_orange_ratio > 0.2: time_of_day = "sunset/sunrise" confidence = 0.7 + yellow_orange_ratio / 3 diagnostics["reason"] = "Moderate brightness with yellow-orange tones" elif avg_brightness > 150 and blue_ratio > 0.15: time_of_day = "day_clear" confidence = 0.7 + blue_ratio / 3 diagnostics["reason"] = "High brightness with blue tones (likely sky)" elif avg_brightness > 130: time_of_day = "day_cloudy" confidence = 0.7 + gray_ratio / 3 diagnostics["reason"] = "Good brightness with higher gray tones" else: # 默認判斷 if yellow_orange_ratio > gray_ratio: time_of_day = "sunset/sunrise" confidence = 0.6 + yellow_orange_ratio / 3 diagnostics["reason"] = "Yellow-orange tones dominant" else: time_of_day = "day_cloudy" confidence = 0.6 + gray_ratio / 3 diagnostics["reason"] = "Gray tones dominant" # 檢查是否是特殊室外場景（如體育場） if avg_brightness > 120 and features["brightness_uniformity"] > 0.8: # 高亮度且非常均勻的光照可能是體育場燈光 time_of_day = "stadium_lighting" confidence = 0.7 diagnostics["special_case"] = "Uniform bright lighting suggests stadium/sports lighting" # 檢查是否是混合光照（如室內/室外過渡區） if 100 < avg_brightness < 150 and 0.1 < blue_ratio < 0.2: if features["gradient_ratio"] > 1.5: time_of_day = "mixed_lighting" confidence = 0.65 diagnostics["special_case"] = "Features suggest indoor-outdoor transition area" # 確保信心值在 0-1 範圍內 confidence = min(0.95, max(0.5, confidence)) if time_of_day in ["indoor_residential_natural", "indoor_designer_residential"] and hasattr(self, "config"): # 確保 LIGHTING_CONDITIONS 中有這些新類型的描述 if time_of_day == "indoor_residential_natural": lightingType = { "template_modifiers": { "indoor_residential_natural": "naturally-lit residential" }, "time_descriptions": { "indoor_residential_natural": { "general": "The scene is captured in a residential space with ample natural light from windows.", "bright": "The residential space is brightly lit with natural daylight streaming through windows.", "medium": "The home environment has good natural lighting providing a warm, inviting atmosphere.", "dim": "The living space has soft natural light filtering through windows or openings." } } } elif time_of_day == "indoor_designer_residential": lightingType = { "template_modifiers": { "indoor_designer_residential": "designer-lit residential" }, "time_descriptions": { "indoor_designer_residential": { "general": "The scene is captured in a residential space with carefully designed lighting elements.", "bright": "The home features professionally designed lighting with decorative fixtures creating a bright atmosphere.", "medium": "The residential interior showcases curated lighting design balancing form and function.", "dim": "The living space has thoughtfully placed designer lighting creating an intimate ambiance." } } } return { "time_of_day": time_of_day, "confidence": confidence, "diagnostics": diagnostics } def _get_default_config(self): """ 返回優化版本的默認配置參數。 """ return { "indoor_outdoor_weights": { "blue_ratio": 0.6, "brightness_uniformity": 1.2, "gradient_ratio": 0.7, "bright_spots": 0.8, "color_tone": 0.5, "sky_brightness": 0.9, "brightness_variation": 0.7, "ceiling_features": 1.5, "light_features": 1.1, "boundary_features": 2.8, "street_features": 2, "building_features": 1.6 }, "include_diagnostics": True }