重构: 模型类别配置动态化，适配 v2+ (5 类) 模型

- settings.py: 删除 80 类 COCO_CLASS_NAMES 硬编码，改为 MODEL_CLASS_NAMES
  + 新增 MODEL_NUM_CLASSES / MODEL_OUTPUT_CHANNELS 辅助常量
  + 新增 ALGO_INTENT_CLASSES 字典 + get_algo_target_classes() 辅助函数
  + COCO_CLASS_NAMES 保留名称向后兼容，指向 MODEL_CLASS_NAMES

- postprocessor.py: 4 处硬编码 84 → MODEL_OUTPUT_CHANNELS
  + 支持不同类别数模型切换不改代码

- algorithms.py: 4 处硬编码 target_classes 默认值 → get_algo_target_classes()
  + IllegalParkingAlgorithm / VehicleCongestionAlgorithm /
    NonMotorVehicleParkingAlgorithm / GarbageDetectionAlgorithm
  + 自动过滤当前模型不支持的类（truck/bus 等）

以后换模型只需修改 settings.py 一处 MODEL_CLASS_NAMES。

core/postprocessor.py

@@ -20,7 +20,7 @@ try:
 except ImportError:
     _HAS_TORCH = False
 
-from config.settings import get_settings
+from config.settings import get_settings, MODEL_OUTPUT_CHANNELS, MODEL_NUM_CLASSES
 from config.config_models import ROIInfo, ROIType, AlertInfo, AlertLevel
 from utils.logger import get_logger
 from utils.common import generate_unique_id
@@ -667,14 +667,14 @@ class PostProcessor:
         if output.ndim != 2:
             return np.array([]), np.array([]), np.array([])
         
-        if output.shape[0] != 84:
+        if output.shape[0] != MODEL_OUTPUT_CHANNELS:
             return np.array([]), np.array([]), np.array([])
-        
+
         boxes_xywh = output[0:4, :].T
 
-        # YOLO11 输出格式: [4+num_classes, 8400]
-        # 前4行是 xywh，后80行是各类别分数，没有单独的 objectness 行
-        class_scores = output[4:, :]  # [num_classes, 8400]
+        # YOLO11 输出格式: [4+num_classes, num_anchors]
+        # 前4行是 xywh，后 num_classes 行是各类别分数，没有单独的 objectness 行
+        class_scores = output[4:, :]  # [num_classes, num_anchors]
         scores = class_scores.max(axis=0)  # 取各类别最大分数
         class_ids = class_scores.argmax(axis=0)  # 对应类别ID
 
@@ -734,15 +734,15 @@ class PostProcessor:
             first_output = batch_outputs[0]
             if isinstance(first_output, np.ndarray):
                 if first_output.ndim == 3 and first_output.shape[0] == batch_size:
-                    # 已经是 (batch, 84, anchors) 格式
+                    # 已经是 (batch, 4+nc, anchors) 格式
                     outputs_array = first_output
                 elif first_output.ndim == 1:
-                    # TensorRT 返回扁平 1D 数组，需要 reshape 为 (batch, 84, anchors)
+                    # TensorRT 返回扁平 1D 数组，需要 reshape 为 (batch, 4+nc, anchors)
                     per_image = first_output.shape[0] // batch_size
-                    num_anchors = per_image // 84
-                    outputs_array = first_output.reshape(batch_size, 84, num_anchors)
+                    num_anchors = per_image // MODEL_OUTPUT_CHANNELS
+                    outputs_array = first_output.reshape(batch_size, MODEL_OUTPUT_CHANNELS, num_anchors)
                 elif first_output.ndim == 2:
-                    # (84, anchors) 单张图的输出
+                    # (4+nc, anchors) 单张图的输出
                     outputs_array = first_output.reshape(1, first_output.shape[0], first_output.shape[1])
                 else:
                     outputs_array = first_output
@@ -771,7 +771,7 @@ class PostProcessor:
             if output.ndim == 3:
                 output = output[0]
             
-            if output.shape[0] != 84:
+            if output.shape[0] != MODEL_OUTPUT_CHANNELS:
                 results.append((np.array([]), np.array([]), np.array([])))
                 continue