feat(app): 添加 YOLO 管道应用程序主入口

app.py

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+import asyncio
+
+from yolo_gs.pipeline.handler.DrawDirectionProcessor import DrawDirectionProcessor
+from yolo_gs.pipeline.handler.DrawGraffitiProcessor import DrawGraffitiProcessor
+from yolo_gs.pipeline.handler.DrawObjectBoxProcessor import DrawObjectBoxProcessor
+from yolo_gs.pipeline.handler.GraffitiProcessor import GraffitiProcessor
+from yolo_gs.pipeline.handler.ResultLogger import ResultLogger
+from yolo_gs.pipeline.handler.RetrogradeProcessor import RetrogradeProcessor
+from yolo_gs.pipeline.handler.graffiti_visualizer import GraffitiVisualizer
+import cv2
+
+from yolo_gs.pipeline.data_source import video_yolo_iterator
+from yolo_gs.pipeline.pipeline import Pipeline
+
+# ===================== 测试运行 =====================
+if __name__ == "__main__":
+    # 1. 创建管道
+    pipeline = Pipeline()
+
+    # 2. 添加同步处理器（按执行顺序执行，每个处理器的输出作为下一个处理器的输入）
+    pipeline.add_processor(GraffitiProcessor(name="涂鸦计算器"))
+    pipeline.add_processor(RetrogradeProcessor(name="逆行检测器"))
+    pipeline.add_processor(DrawDirectionProcessor())
+    pipeline.add_processor(DrawGraffitiProcessor())
+    pipeline.add_processor(DrawObjectBoxProcessor())
+    pipeline.add_processor(ResultLogger(name="结果日志器"))
+
+
+    # 4. 构建视频+YOLO跟踪迭代器（替换为你的视频路径，或0使用摄像头）
+    video_path = "123.mp4"  # 或 0 (摄像头)
+    try:
+        # 使用跟踪迭代器，可以获取对象ID
+        data_iter = video_yolo_iterator(video_path, model_path="yolo11s.pt",tracker="botsort.yaml", device="0")
+
+        # 5. 运行管道，迭代处理数据
+        for processed_data in pipeline.run(data_iter):
+            # 显示帧（按q退出）
+            frame = processed_data.frame
+            if frame is not None:
+                # 直接显示经过管道处理后的帧，其中已经包含了绘制处理器的效果
+                cv2.imshow("YOLO Tracking Pipeline", frame)
+                if cv2.waitKey(1) & 0xFF == ord('q'):
+                    break
+    except Exception as e:
+        print(f"运行出错: {e}")
+    finally:
+        cv2.destroyAllWindows()