初使提交

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 # 使用 Python 3.9 轻量版
 FROM python:3.9-slim
 # 安装系统级依赖 (OpenCV 和 YOLO 运行必须)
 RUN apt-get update && apt-get install -y \
    libgl1 \
    libglib2.0-0 \
    && rm -rf /var/lib/apt/lists/*
 WORKDIR /app
 # 安装依赖
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 # 拷贝代码
 COPY ./app /app/app
 # 设置环境变量，确保 Python 能找到 app 模块
 ENV PYTHONPATH=/app
 # 默认启动命令 (会被 docker-compose 中的 command 覆盖)
 CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "18005"]
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 # 水库智能算法云服务接口文档
 ## 服务概述
 基于 FastAPI 构建的水库智能算法云服务，提供视频流分析和图像识别功能，支持多种 YOLO 模型场景检测。
 **服务地址**: `http://33.36.139.134.254:18005`
 ---
 ## 接口列表
 ### 1. 视频流异步分析接口
 **接口路径**: `POST /v1/task/stream`
 **描述**: 将 RTSP 视频流任务派发给 Celery worker 异步处理
 **请求体**:
 ```json
 {
    "source_url": "rtsp://摄像头地址",
    "webhook_url": "回调地址",
    "scene_type": "floating_trash | fishing | swimming",
    "roi": [[x1, y1], [x2, y2], ...]
 }
 ```
 **参数说明**:
 | 参数 | 类型 | 必填 | 说明 |
 |------|------|------|------|
 | source_url | string | 是 | 摄像头 RTSP 流地址 |
 | webhook_url | string | 是 | 业务系统回调地址 |
 | scene_type | string | 是 | 场景类型 |
 | roi | array | 否 | 电子围栏坐标点集 |
 **响应示例**:
 ```json
 {
    "code": 200,
    "msg": "任务已下发至计算集群",
    "task_type": "floating_trash"
 }
 ```
 ---
 ### 2. 指定模型图像预测接口
 **接口路径**: `POST /{model_type}/predict/image`
 **描述**: 使用指定模型对上传的图片进行目标检测
 **路径参数**:
 | 参数 | 说明 |
 |------|------|
 | model_type | 模型类型 |
 **支持的模型类型**:
 | model_type | 模型文件 | 说明 |
 |------------|----------|------|
 | default | yolov8n.pt | 默认预训练模型 |
 | bati | model/Bati/best.pt | 坝体检测模型 |
 | buildings | model/buildings/best.pt | 建筑物检测模型 |
 | disaster | model/disaster/best.pt | 灾害检测模型 |
 | fire-smoke | model/fire-smoke/best.pt | 烟火检测模型 |
 | Floating | model/Floating/best.pt | 漂浮物检测模型 |
 | ship | model/ship/best.pt | 船只检测模型 |
 **请求表单**:
 - `file`: 图片文件 (支持 jpg, png 等格式)
 **响应示例**:
 ```json
 {
    "results": [
        {
            "boxes": {
                "xyxy": [[x1, y1, x2, y2]],
                "conf": [0.95],
                "cls": [0]
            },
            "names": { "0": "目标类别" }
        }
    ]
 }
 ```
 ---
 ### 3. 默认模型图像预测接口
 **接口路径**: `POST /v1/predict/image`
 **描述**: 使用默认 YOLOv8n 模型对图片进行预测
 **请求表单**:
 - `file`: 图片文件
 **响应示例**: 同接口2
 ---
 **启动参数**:
 - Host: `0.0.0.0`
 - Port: `8000`
 ---
 ## 错误处理
 | HTTP 状态码 | 说明 |
 |-------------|------|
 | 400 | 请求参数错误 |
 | 500 | 服务器内部错误 |
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 # 延寿水库智能识别服务接口说明
 ## 1. 项目概述
 本服务基于 FastAPI 和 YOLO 模型，为延寿水库项目提供按场景划分的智能识别能力。当前支持两类能力：
 - 图像识别：上传单张图片并返回识别结果。
 - 视频流识别：提交 RTSP 或本地视频流地址，系统异步分析并通过回调地址推送结果。
 服务中的 `model_type` 对应具体业务场景，每个场景绑定一个独立模型文件。
 ## 2. 支持场景
 - `floating`：水面漂浮物识别。用于识别水面漂浮垃圾、杂物等目标。
 - `gate`：闸口场景识别。用于识别闸口、闸门区域中的目标和异常情况。
 - `shore_garbage`：岸边垃圾识别。用于识别岸线附近堆积或散落的垃圾目标。
 - `water_gauge`：水尺识别。用于识别水尺、水位尺等目标，服务于水位监测场景。
 ## 3. 接口列表
 - `POST /{model_type}/predict/image`：按场景执行图片识别。
 - `POST /{model_type}/predict/stream`：按场景提交视频流识别任务。
 - `POST /v1/task/stream`：兼容旧版视频流接口，通过 `scene_type` 指定场景。
 - `POST /v1/predict/image`：旧版图片接口说明入口，当前会返回引导信息，请改用新接口。
 ## 4. 图片识别接口
 ### 4.1 请求地址
 `POST /{model_type}/predict/image`
 ### 4.2 路径参数
 - `model_type`：场景类型，可选值为 `floating`、`gate`、`shore_garbage`、`water_gauge`。
 ### 4.3 请求方式
 `multipart/form-data`
 ### 4.4 请求参数
 - `file`：待识别图片文件，支持常见图片格式。
 ### 4.5 调用示例
 ```bash
 curl -X POST "http://localhost:8000/floating/predict/image" \
  -H "accept: application/json" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@test.jpg;type=image/jpeg"
 ```
 ### 4.6 返回示例
 ```json
 {
  "results": [
    {
      "name": "floating_object",
      "class": 0,
      "confidence": 0.93,
      "box": {
        "x1": 102.4,
        "y1": 55.2,
        "x2": 260.8,
        "y2": 188.6
      }
    }
  ]
 }
 ```
 ## 5. 视频流识别接口
 ### 5.1 新版接口
 `POST /{model_type}/predict/stream`
 ### 5.2 兼容接口
 `POST /v1/task/stream`
 兼容接口中，场景通过请求体的 `scene_type` 指定，其可选值与 `model_type` 一致。
 ### 5.3 请求体参数
 - `source_url`：视频流地址，支持 `rtsp://...`、本地视频文件路径等 OpenCV 可读取地址。
 - `webhook_url`：识别结果回调地址，必须是服务端可访问的 `HTTP/HTTPS POST` 接口。
 - `roi`：可选电子围栏，多边形坐标数组，格式为 `[[x1, y1], [x2, y2], ...]`。不传时默认检测整幅画面。
 ### 5.4 请求体示例
 ```json
 {
  "source_url": "rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
  "webhook_url": "https://example.com/api/reservoir/webhook",
  "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]]
 }
 ```
 ### 5.5 调用示例
 ```bash
 curl -X POST "http://localhost:8000/floating/predict/stream" \
  -H "Content-Type: application/json" \
  -d '{
    "source_url": "rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
    "webhook_url": "https://example.com/api/reservoir/webhook",
    "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]]
  }'
 ```
 ### 5.6 提交成功返回示例
 ```json
 {
  "code": 200,
  "msg": "task submitted",
  "task_type": "floating"
 }
 ```
 ## 6. webhook_url 参数说明
 - `webhook_url` 必须是业务系统提供的接收地址。
 - 请求方法为 `POST`。
 - 请求体格式为 `application/json`。
 - 当视频流识别到目标时才会触发回调；未识别到目标时不会持续推送空结果。
 - 业务系统应保证该地址可被算法服务访问，并能在超时时间内返回。
 ## 7. 视频流回调数据格式
 ### 7.1 回调 JSON 示例
 ```json
 {
  "event": "RESERVOIR_ALARM",
  "scene": "floating",
  "stream_url": "rtsp://example.com/live",
  "details": [
    {
      "label": "floating_object",
      "confidence": 0.93,
      "bbox": [102.4, 55.2, 260.8, 188.6]
    }
  ],
  "msg": "detected floating event"
 }
 ```
 ### 7.2 字段说明
 - `event`：事件类型，当前为 `RESERVOIR_ALARM`。
 - `scene`：触发识别的场景类型，对应接口中的 `model_type`。
 - `stream_url`：本次识别的视频流地址。
 - `details`：识别结果数组。
 - `label`：识别类别名称。
 - `confidence`：识别置信度，范围通常为 0 到 1。
 - `bbox`：目标框坐标，格式为 `[x1, y1, x2, y2]`。
 - `msg`：简要告警说明。
 ## 8. 使用建议
 - 视频流接口为异步提交接口，请结合业务系统的 `webhook_url` 接收识别结果。
 - 若需要限定检测区域，建议传入 `roi`，减少无关区域误报。
 - 推荐通过 FastAPI 自带文档页面查看接口：
  - Swagger UI：`/docs`
  - ReDoc：`/redoc`
--- a/app/TioTech.mp4
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--- a/app/main.py
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 import json
 import os
 import tempfile
 from typing import List, Optional
 import cv2
 import numpy as np
 from fastapi import FastAPI, File, HTTPException, Path, UploadFile, Form
 from pydantic import BaseModel, Field
 from celery.result import AsyncResult
 from app.model_registry import MODEL_MAP, SCENE_INFO, iter_models, load_model
 from app.tasks import celery_app, analyze_video_stream, analyze_video_file
 # 共享上传目录（API 和 Worker 容器通过 volume 共享）
 UPLOAD_DIR = os.getenv("UPLOAD_DIR", "/tmp")
 UPLOAD_DIR = os.getenv("UPLOAD_DIR", "/tmp")
 from app.tasks import celery_app, analyze_video_stream, analyze_video_file
 AUTO_SCENE_TASK_TYPE = "auto_scene"
 SCENE_MARKDOWN = "\n".join(
    f"- `{model_type}`: {scene['name']}，{scene['description']}"
    for model_type, scene in SCENE_INFO.items()
 )
 SUPPORTED_MODEL_TYPES = ", ".join(MODEL_MAP.keys())
 WEBHOOK_PAYLOAD_EXAMPLE = {
    "event": "RESERVOIR_ALARM",
    "scene": "floating",
    "stream_url": "rtsp://example.com/live",
    "details": [
        {
            "label": "floating_object",
            "confidence": 0.93,
            "bbox": [102.4, 55.2, 260.8, 188.6],
        }
    ],
    "msg": "detected floating event",
 }
 WEBHOOK_CALLBACK_SCHEMA = {
    "type": "object",
    "required": ["event", "scene", "stream_url", "details", "msg"],
    "properties": {
        "event": {
            "type": "string",
            "description": "事件类型，当前固定为 `RESERVOIR_ALARM`。",
            "example": "RESERVOIR_ALARM",
        },
        "scene": {
            "type": "string",
            "description": "触发识别的场景类型，例如 `floating`、`gate`、`shore_garbage`、`water_gauge`。",
            "example": "floating",
        },
        "stream_url": {
            "type": "string",
            "description": "原始视频流地址。",
            "example": "rtsp://example.com/live",
        },
        "details": {
            "type": "array",
            "description": "识别结果列表。",
            "items": {
                "type": "object",
                "required": ["label", "confidence", "bbox"],
                "properties": {
                    "label": {
                        "type": "string",
                        "description": "识别类别名称。",
                        "example": "floating_object",
                    },
                    "confidence": {
                        "type": "number",
                        "format": "float",
                        "description": "识别置信度。",
                        "example": 0.93,
                    },
                    "bbox": {
                        "type": "array",
                        "description": "目标框坐标数组，格式为 `[x1, y1, x2, y2]`。",
                        "items": {"type": "number", "format": "float"},
                        "example": [102.4, 55.2, 260.8, 188.6],
                    },
                },
            },
        },
        "msg": {
            "type": "string",
            "description": "告警说明。",
            "example": "detected floating event",
        },
    },
 }
 WEBHOOK_CALLBACK_OPENAPI = {
    "onWebhookResult": {
        "{$request.body#/webhook_url}": {
            "post": {
                "summary": "识别结果 webhook 回调",
                "description": "当视频流识别到目标后，算法服务会向请求体中的 `webhook_url` 发送 HTTP POST 回调。",
                "requestBody": {
                    "required": True,
                    "content": {
                        "application/json": {
                            "schema": WEBHOOK_CALLBACK_SCHEMA,
                            "example": WEBHOOK_PAYLOAD_EXAMPLE,
                        }
                    },
                },
                "responses": {
                    "200": {
                        "description": "业务方成功接收回调。"
                    }
                },
            }
        }
    }
 }
 LEGACY_STREAM_REQUEST_EXAMPLE = {
    "source_url": "rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
    "webhook_url": "https://example.com/api/reservoir/webhook",
    "scene_type": "floating",
    "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]],
 }
 STREAM_REQUEST_EXAMPLE = {
    "source_url": "rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
    "webhook_url": "https://example.com/api/reservoir/webhook",
    "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]],
 }
 AUTO_STREAM_RESPONSE_EXAMPLE = {
    "code": 200,
    "msg": "task submitted",
    "task_type": AUTO_SCENE_TASK_TYPE,
 }
 STREAM_RESPONSE_EXAMPLE = {
    "code": 200,
    "msg": "task submitted",
    "task_type": "floating",
 }
 IMAGE_RESPONSE_EXAMPLE = {
    "results": [
        {
            "name": "floating_object",
            "class": 0,
            "confidence": 0.93,
            "box": {
                "x1": 102.4,
                "y1": 55.2,
                "x2": 260.8,
                "y2": 188.6,
            },
        }
    ]
 }
 AUTO_IMAGE_RESPONSE_EXAMPLE = {
    "task_type": AUTO_SCENE_TASK_TYPE,
    "results": [
        {
            "scene": "floating",
            "name": "floating_object",
            "class": 0,
            "confidence": 0.93,
            "box": {
                "x1": 102.4,
                "y1": 55.2,
                "x2": 260.8,
                "y2": 188.6,
            },
        }
    ],
 }
 WEBHOOK_FIELD_DESCRIPTION = (
    "识别结果回调地址，算法服务会以 HTTP POST 方式回调该地址。"
    "回调 JSON 参数说明：`event` 为事件类型，`scene` 为场景类型，"
    "`stream_url` 为视频流地址，`details` 为识别结果列表，`msg` 为告警说明。"
 )
 WEBHOOK_CALLBACK_PARAMETER_DESCRIPTION = (
    "回调参数中文说明：\n"
    "- `event`：事件类型，当前固定为告警事件 `RESERVOIR_ALARM`。\n"
    "- `scene`：本次识别命中的场景类型，例如 `floating`、`gate`、`shore_garbage`、`water_gauge`。\n"
    "- `stream_url`：触发识别的视频流地址，即调用接口时传入的原始流地址。\n"
    "- `details`：识别结果列表。\n"
    "- `details[].label`：识别出的目标类别名称。\n"
    "- `details[].confidence`：识别结果的置信度。\n"
    "- `details[].bbox`：目标框坐标，格式为 `[x1, y1, x2, y2]`。\n"
    "- `msg`：告警说明文本，用于描述当前识别事件。"
 )
 APP_DESCRIPTION = f"""
 延寿水库智能识别服务，基于 YOLO 模型提供图像识别和视频流识别能力。
 支持场景：
 {SCENE_MARKDOWN}
 推荐接口：
 - `POST /v1/predict/image`：自动场景图片识别
 - `POST /v1/predict/stream`：自动场景视频流识别
 - `POST /v1/predict/video-file`：上传MP4文件识别
 兼容接口：
 - `POST /{{model_type}}/predict/image`：按指定场景识别图片
 - `POST /{{model_type}}/predict/stream`：按指定场景识别视频流
 - `POST /v1/task/stream`：旧版视频流提交方式
 """
 app = FastAPI(
    title="延寿水库智能识别服务",
    description=APP_DESCRIPTION,
    version="1.2.0",
    openapi_tags=[
        {"name": "图像识别", "description": "支持自动场景识别，也支持按指定场景模型执行图片识别。推荐优先使用 `/v1/predict/image`。"},
        {"name": "视频流识别", "description": "支持自动场景识别，也支持按指定场景模型提交视频流异步识别任务。推荐优先使用 `/v1/predict/stream`。"},
        {"name": "视频文件识别", "description": "支持上传MP4文件进行离线识别分析。推荐优先使用 `/v1/predict/video-file`。"},
        {"name": "任务管理", "description": "查询异步任务状态。"},
        {"name": "兼容接口", "description": "兼容旧调用方式的接口，建议逐步迁移到自动场景识别接口。"},
    ],
 )
 class TaskRequest(BaseModel):
    source_url: str = Field(
        ...,
        description="视频流地址，支持 RTSP、本地视频文件路径等 OpenCV 可读取的地址。",
        example="rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
    )
    webhook_url: str = Field(
        ...,
        description=WEBHOOK_FIELD_DESCRIPTION,
        example="https://example.com/api/reservoir/webhook",
    )
    scene_type: Optional[str] = Field(
        default=None,
        description=f"兼容旧字段。传值时按指定场景识别；不传时自动遍历全部场景模型。可选值：{SUPPORTED_MODEL_TYPES}",
        example="floating",
    )
    roi: Optional[List[List[int]]] = Field(
        default=None,
        description="可选电子围栏，多边形坐标数组，格式为 [[x1, y1], [x2, y2], ...]；不传时默认检测整幅画面。",
        example=[[0, 0], [1920, 0], [1920, 1080], [0, 1080]],
    )
 class StreamPredictRequest(BaseModel):
    source_url: str = Field(
        ...,
        description="视频流地址，支持 RTSP、本地视频文件路径等 OpenCV 可读取的地址。",
        example="rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
    )
    webhook_url: str = Field(
        ...,
        description=WEBHOOK_FIELD_DESCRIPTION,
        example="https://example.com/api/reservoir/webhook",
    )
    roi: Optional[List[List[int]]] = Field(
        default=None,
        description="可选电子围栏，多边形坐标数组，格式为 [[x1, y1], [x2, y2], ...]；不传时默认检测整幅画面。",
        example=[[0, 0], [1920, 0], [1920, 1080], [0, 1080]],
    )
 class VideoFileRequest(BaseModel):
    webhook_url: str = Field(
        ...,
        description="识别结果回调地址",
        example="https://example.com/api/reservoir/webhook",
    )
    roi: Optional[List[List[int]]] = Field(
        default=None,
        description="可选电子围栏",
        example=[[0, 0], [1920, 0], [1920, 1080], [0, 1080]],
    )
    scene_type: Optional[str] = Field(
        default=None,
        description=f"指定场景类型，可选值：{SUPPORTED_MODEL_TYPES}",
    )
 class StreamTaskResponse(BaseModel):
    code: int = Field(..., description="业务状态码，`200` 表示任务提交成功。", example=200)
    msg: str = Field(..., description="接口返回说明信息。", example="task submitted")
    task_type: str = Field(
        ...,
        description=f"实际提交的任务类型。自动识别时为 `{AUTO_SCENE_TASK_TYPE}`，指定场景时为对应模型类型。",
        example=AUTO_SCENE_TASK_TYPE,
    )
 class VideoFileTaskResponse(BaseModel):
    code: int = Field(..., description="业务状态码，`200` 表示任务提交成功。", example=200)
    msg: str = Field(..., description="接口返回说明信息。", example="video file task submitted")
    task_id: str = Field(..., description="任务ID，用于查询任务状态。", example="abc123def456")
    task_type: str = Field(
        ...,
        description=f"实际提交的任务类型。自动识别时为 `{AUTO_SCENE_TASK_TYPE}`，指定场景时为对应模型类型。",
        example=AUTO_SCENE_TASK_TYPE,
    )
 class DetectionBox(BaseModel):
    x1: float = Field(..., description="检测框左上角 X 坐标。", example=102.4)
    y1: float = Field(..., description="检测框左上角 Y 坐标。", example=55.2)
    x2: float = Field(..., description="检测框右下角 X 坐标。", example=260.8)
    y2: float = Field(..., description="检测框右下角 Y 坐标。", example=188.6)
 class ImageDetectionItem(BaseModel):
    name: str = Field(..., description="检测类别名称。", example="floating_object")
    class_id: int = Field(..., alias="class", description="检测类别 ID。", example=0)
    confidence: float = Field(..., description="检测置信度。", example=0.93)
    box: DetectionBox = Field(..., description="目标框坐标。")
 class AutoImageDetectionItem(ImageDetectionItem):
    scene: str = Field(..., description="命中的场景模型类型。", example="floating")
 class ImagePredictResponse(BaseModel):
    results: List[ImageDetectionItem] = Field(
        ...,
        description="识别结果列表。每个元素为模型输出的目标信息，通常包含类别名、类别 ID、置信度和检测框坐标。",
        example=IMAGE_RESPONSE_EXAMPLE["results"],
    )
 class AutoImagePredictResponse(BaseModel):
    task_type: str = Field(
        ...,
        description=f"任务类型，固定为 `{AUTO_SCENE_TASK_TYPE}`。",
        example=AUTO_SCENE_TASK_TYPE,
    )
    results: List[AutoImageDetectionItem] = Field(
        ...,
        description="自动场景识别结果列表。每个元素额外包含 `scene` 字段，表示命中的场景模型。",
        example=AUTO_IMAGE_RESPONSE_EXAMPLE["results"],
    )
 class WebhookDetectionItem(BaseModel):
    label: str = Field(..., description="识别类别名称。", example="floating_object")
    confidence: float = Field(..., description="识别置信度。", example=0.93)
    bbox: List[float] = Field(..., description="目标框坐标数组，格式为 `[x1, y1, x2, y2]`。", example=[102.4, 55.2, 260.8, 188.6])
 class WebhookPayload(BaseModel):
    event: str = Field(..., description="事件类型。", example="RESERVOIR_ALARM")
    scene: str = Field(..., description="触发识别的场景类型。", example="floating")
    stream_url: str = Field(..., description="视频流地址。", example="rtsp://example.com/live")
    details: List[WebhookDetectionItem] = Field(..., description="识别结果列表。")
    msg: str = Field(..., description="告警说明。", example="detected floating event")
 def validate_model_type(model_type: str) -> None:
    if model_type not in MODEL_MAP:
        raise HTTPException(
            status_code=400,
            detail=f"Unsupported model type: {model_type}. Supported types: {SUPPORTED_MODEL_TYPES}",
        )
 def decode_image(contents: bytes):
    nparr = np.frombuffer(contents, np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    if img is None:
        raise HTTPException(status_code=400, detail="Invalid image file")
    return img
 def run_single_model_image_detection(img, model_type: str) -> list:
    model = load_model(model_type)
    results = model.predict(img, conf=0.6, verbose=False)
    return json.loads(results[0].to_json())
 def run_auto_image_detection(img) -> dict:
    detections = []
    for current_model_type, model in iter_models():
        results = model.predict(img, conf=0.6, verbose=False)
        for item in json.loads(results[0].to_json()):
            item["scene"] = current_model_type
            detections.append(item)
    return {"task_type": AUTO_SCENE_TASK_TYPE, "results": detections}
 def submit_stream_task(
    source_url: str,
    webhook_url: str,
    roi: Optional[List[List[int]]],
    model_type: Optional[str] = None,
 ):
    if model_type is not None:
        validate_model_type(model_type)
    task = analyze_video_stream.delay(source_url, webhook_url, model_type, roi)
    return {"code": 200, "msg": "task submitted", "task_type": model_type or AUTO_SCENE_TASK_TYPE, "task_id": task.id}
 def submit_video_file_task(
    video_path: str,
    webhook_url: str,
    roi: Optional[List[List[int]]],
    model_type: Optional[str] = None,
    original_filename: str = None,
 ):
    """提交视频文件识别任务"""
    if model_type is not None:
        validate_model_type(model_type)
    task = analyze_video_file.delay(video_path, webhook_url, model_type, roi, original_filename)
    return {
        "code": 200,
        "msg": "video file task submitted",
        "task_id": task.id,
        "task_type": model_type or AUTO_SCENE_TASK_TYPE
    }
@app.post(
    "/v1/task/stream",
    tags=["兼容接口", "视频流识别"],
    summary="兼容模式提交视频流识别任务",
    description=(
        "兼容旧调用方式提交视频流识别任务。\n\n"
        "入参说明：\n"
        "- `source_url`：视频流地址。\n"
        "- `webhook_url`：识别结果回调地址。\n"
        f"- `scene_type`：兼容旧字段，传值时按指定场景识别，不传时自动识别全部场景；可选值：{SUPPORTED_MODEL_TYPES}。\n"
        "- `roi`：可选电子围栏。\n\n"
        "模拟入参示例：\n"
        f"```json\n{json.dumps(LEGACY_STREAM_REQUEST_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
        "出参说明：\n"
        "- `code`：业务状态码，200 表示提交成功。\n"
        "- `msg`：返回说明信息。\n"
        f"- `task_type`：实际提交的任务类型，自动识别时为 `{AUTO_SCENE_TASK_TYPE}`。\n\n"
        "模拟出参示例：\n"
        f"```json\n{json.dumps(AUTO_STREAM_RESPONSE_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
        "说明：\n"
        "- 建议新接入统一改用 `POST /v1/predict/stream`。\n"
        "- 自动识别模式下，worker 会遍历全部已注册场景模型。\n"
        "- Swagger 文档中的 Callbacks 区域可查看 `webhook_url` 的回调参数结构。\n\n"
        f"{WEBHOOK_CALLBACK_PARAMETER_DESCRIPTION}\n"
    ),
    response_model=StreamTaskResponse,
    responses={
        200: {
            "description": "任务提交成功",
            "content": {"application/json": {"example": AUTO_STREAM_RESPONSE_EXAMPLE}},
        },
        400: {"description": "请求参数错误或场景类型不支持。"},
    },
    deprecated=True,
    openapi_extra={
        "requestBody": {
            "content": {
                "application/json": {
                    "example": LEGACY_STREAM_REQUEST_EXAMPLE
                }
            }
        },
        "callbacks": WEBHOOK_CALLBACK_OPENAPI,
    },
 )
 async def start_stream_task(req: TaskRequest):
    return submit_stream_task(req.source_url, req.webhook_url, req.roi, req.scene_type)
@app.post(
    "/v1/predict/stream",
    tags=["视频流识别"],
    summary="自动场景视频流识别任务",
    description=(
        "自动遍历所有已注册场景模型，对视频流执行异步识别任务。\n\n"
        "入参说明：\n"
        "- `source_url`：视频流地址。\n"
        "- `webhook_url`：识别结果回调地址。\n"
        "- `roi`：可选电子围栏。\n\n"
        "模拟入参示例：\n"
        f"```json\n{json.dumps(STREAM_REQUEST_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
        "出参说明：\n"
        "- `code`：业务状态码，200 表示提交成功。\n"
        "- `msg`：返回说明信息。\n"
        f"- `task_type`：固定返回 `{AUTO_SCENE_TASK_TYPE}`。\n\n"
        "模拟出参示例：\n"
        f"```json\n{json.dumps(AUTO_STREAM_RESPONSE_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
        "检测到目标后会向 `webhook_url` 发送 POST 回调，回调数据示例：\n"
        f"```json\n{json.dumps(WEBHOOK_PAYLOAD_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
        "回调说明：\n"
        "- 自动识别模式下，如果同一帧命中多个场景，会按场景分别回调。\n"
        "- `scene` 字段表示当前这条 webhook 对应的场景模型。\n"
        "- Swagger 文档中的 Callbacks 区域可查看完整回调参数结构。\n\n"
        f"{WEBHOOK_CALLBACK_PARAMETER_DESCRIPTION}\n"
    ),
    response_model=StreamTaskResponse,
    responses={
        200: {
            "description": "任务提交成功",
            "content": {"application/json": {"example": AUTO_STREAM_RESPONSE_EXAMPLE}},
        },
        400: {"description": "请求参数错误。"},
    },
    openapi_extra={
        "requestBody": {
            "content": {
                "application/json": {
                    "example": STREAM_REQUEST_EXAMPLE
                }
            }
        },
        "callbacks": WEBHOOK_CALLBACK_OPENAPI,
    },
 )
 async def predict_stream_v1(req: StreamPredictRequest):
    return submit_stream_task(req.source_url, req.webhook_url, req.roi, None)
@app.post(
    "/{model_type}/predict/stream",
    tags=["视频流识别"],
    summary="按场景提交视频流识别任务",
    description=(
        "根据路径中的 `model_type` 选择对应场景模型，异步提交视频流识别任务。\n\n"
        "入参说明：\n"
        "- `model_type`：路径参数，场景模型类型。\n"
        "- `source_url`：视频流地址。\n"
        "- `webhook_url`：识别结果回调地址。\n"
        "- `roi`：可选电子围栏。\n\n"
        "模拟入参示例：\n"
        f"```json\n{json.dumps(STREAM_REQUEST_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
        "出参说明：\n"
        "- `code`：业务状态码，200 表示提交成功。\n"
        "- `msg`：返回说明信息。\n"
        "- `task_type`：实际提交的场景类型。\n\n"
        "模拟出参示例：\n"
        f"```json\n{json.dumps(STREAM_RESPONSE_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
        "检测到目标后会向 `webhook_url` 发送 POST 回调，回调数据示例：\n"
        f"```json\n{json.dumps(WEBHOOK_PAYLOAD_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
        "回调说明：\n"
        "- 指定场景模式下，只会使用路径里的 `model_type` 对应模型。\n"
        "- webhook 的 `scene` 字段与路径中的 `model_type` 一致。\n"
        "- Swagger 文档中的 Callbacks 区域可查看完整回调参数结构。\n\n"
        f"{WEBHOOK_CALLBACK_PARAMETER_DESCRIPTION}\n"
    ),
    response_model=StreamTaskResponse,
    responses={
        200: {
            "description": "任务提交成功",
            "content": {"application/json": {"example": STREAM_RESPONSE_EXAMPLE}},
        },
        400: {"description": "场景类型不支持或请求参数错误。"},
    },
    openapi_extra={
        "requestBody": {
            "content": {
                "application/json": {
                    "example": STREAM_REQUEST_EXAMPLE
                }
            }
        },
        "callbacks": WEBHOOK_CALLBACK_OPENAPI,
    },
 )
 async def predict_stream(
    model_type: str = Path(..., description=f"场景模型类型。可选值：{SUPPORTED_MODEL_TYPES}"),
    req: StreamPredictRequest = ...,
 ):
    return submit_stream_task(req.source_url, req.webhook_url, req.roi, model_type)
@app.post(
    "/v1/predict/video-file",
    tags=["视频文件识别"],
    summary="上传MP4文件进行识别",
    description=(
        "支持上传本地MP4文件进行视频识别分析。\n\n"
        "入参说明：\n"
        "- `file`：上传的MP4视频文件\n"
        "- `webhook_url`：识别结果回调地址\n"
        "- `roi`：可选电子围栏（JSON字符串格式）\n"
        "- `scene_type`：可选指定场景类型，不传则自动识别\n\n"
        "模拟入参示例：\n"
        "```bash\n"
        "curl -X POST http://localhost:8000/v1/predict/video-file \\\n"
        "  -H \"accept: application/json\" \\\n"
        "  -F \"file=@test.mp4;type=video/mp4\" \\\n"
        "  -F \"webhook_url=https://example.com/api/webhook\" \\\n"
        "  -F \"roi=[[0,0],[1920,0],[1920,1080],[0,1080]]\" \\\n"
        "  -F \"scene_type=floating\"\n"
        "```\n\n"
        "出参说明：\n"
        "- `code`：业务状态码，200表示提交成功\n"
        "- `msg`：返回说明信息\n"
        "- `task_id`：任务ID，用于查询任务状态\n"
        "- `task_type`：实际提交的任务类型\n\n"
        "模拟出参示例：\n"
        "```json\n"
        "{\n"
        "  \"code\": 200,\n"
        "  \"msg\": \"video file task submitted\",\n"
        "  \"task_id\": \"abc123def456\",\n"
        "  \"task_type\": \"auto_scene\"\n"
        "}\n"
        "```\n\n"
        "检测完成或检测到目标后会向 `webhook_url` 发送回调。\n"
        "文件大小限制：500MB\n"
    ),
    response_model=VideoFileTaskResponse,
    responses={
        200: {"description": "任务提交成功"},
        400: {"description": "文件格式错误、大小超限或参数错误"},
        500: {"description": "服务器内部错误"},
    },
 )
 async def predict_video_file(
    file: UploadFile = File(..., description="MP4视频文件", media_type="video/mp4"),
    webhook_url: str = Form(..., description="识别结果回调地址"),
    roi: Optional[str] = Form(default="[[0,0],[1920,0],[1920,1080],[0,1080]]", description="电子围栏JSON字符串"),
    scene_type: Optional[str] = Form(default=None, description=f"场景类型，可选值：{SUPPORTED_MODEL_TYPES}"),
 ):
    # 验证文件格式
    if not file.filename.endswith(('.mp4', '.MP4')):
        raise HTTPException(status_code=400, detail="Only MP4 files are supported")
    # 验证文件大小（限制500MB）
    file_size = 0
    temp_file_path = None
    try:
        # 创建临时文件保存上传的视频（存入共享目录，确保 Worker 容器也能读取）
        os.makedirs(UPLOAD_DIR, exist_ok=True)
        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4', dir=UPLOAD_DIR) as temp_file:
            # 分块读取并写入
            chunk_size = 1024 * 1024  # 1MB chunks
            while True:
                chunk = await file.read(chunk_size)
                if not chunk:
                    break
                temp_file.write(chunk)
                file_size += len(chunk)
                # 限制文件大小为500MB
                if file_size > 500 * 1024 * 1024:
                    raise HTTPException(status_code=400, detail="File size exceeds 500MB limit")
            temp_file_path = temp_file.name
        # 解析ROI参数
        roi_list = None
        if roi:
            try:
                roi_list = json.loads(roi)
                if not isinstance(roi_list, list):
                    raise ValueError("ROI must be a list")
            except json.JSONDecodeError:
                raise HTTPException(status_code=400, detail="Invalid ROI format, must be valid JSON")
        # 提交任务
        result = submit_video_file_task(
            video_path=temp_file_path,
            webhook_url=webhook_url,
            roi=roi_list,
            model_type=scene_type,
            original_filename=file.filename
        )
        return result
    except HTTPException:
        # 清理临时文件
        if temp_file_path and os.path.exists(temp_file_path):
            os.unlink(temp_file_path)
        raise
    except Exception as exc:
        if temp_file_path and os.path.exists(temp_file_path):
            os.unlink(temp_file_path)
        raise HTTPException(status_code=500, detail=f"Failed to process video file: {str(exc)}") from exc
@app.get(
    "/v1/task/status/{task_id}",
    tags=["任务管理"],
    summary="查询异步任务状态",
    description="通过任务ID查询视频识别任务的执行状态和进度",
 )
 async def get_task_status(
    task_id: str = Path(..., description="任务ID，从提交接口返回的task_id")
 ):
    """查询任务状态"""
    try:
        task = AsyncResult(task_id, app=celery_app)
        if task.state == 'PENDING':
            response = {
                "state": "PENDING",
                "progress": 0,
                "message": "Task is waiting to be processed"
            }
        elif task.state == 'PROCESSING':
            response = {
                "state": "PROCESSING",
                "progress": task.info.get('progress', 0) if task.info else 0,
                "message": task.info.get('message', 'Processing video') if task.info else 'Processing',
                "frame": task.info.get('frame', 0) if task.info else 0,
                "total_frames": task.info.get('total_frames', 0) if task.info else 0
            }
        elif task.state == 'SUCCESS':
            response = {
                "state": "SUCCESS",
                "result": task.result,
                "message": "Task completed successfully"
            }
        elif task.state == 'FAILURE':
            response = {
                "state": "FAILURE",
                "error": str(task.info),
                "message": "Task failed"
            }
        else:
            response = {
                "state": task.state,
                "message": "Unknown task state"
            }
        return response
    except Exception as exc:
        raise HTTPException(
            status_code=500,
            detail=f"Failed to query task status: {str(exc)}",
        ) from exc
@app.post(
    "/v1/predict/image",
    tags=["图像识别"],
    summary="自动场景图片识别",
    description=(
        "不再需要指定 `model_type`，接口会自动遍历所有已注册场景模型，对上传图片执行场景识别。\n\n"
        "入参说明：\n"
        "- `file`：上传的待识别图片文件。\n\n"
        "模拟入参示例：\n"
        "```bash\n"
        "curl -X POST http://localhost:8000/v1/predict/image \\\n"
        "  -H \"accept: application/json\" \\\n"
        "  -H \"Content-Type: multipart/form-data\" \\\n"
        "  -F \"file=@test.jpg;type=image/jpeg\"\n"
        "```\n\n"
        "出参说明：\n"
        f"- `task_type`：固定返回 `{AUTO_SCENE_TASK_TYPE}`。\n"
        "- `results`：识别结果列表。\n"
        "- `results[].scene`：命中的场景模型类型。\n"
        "- `results[].name`：类别名称。\n"
        "- `results[].class`：类别 ID。\n"
        "- `results[].confidence`：置信度。\n"
        "- `results[].box`：目标框坐标。\n\n"
        "模拟出参示例：\n"
        f"```json\n{json.dumps(AUTO_IMAGE_RESPONSE_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
        "说明：\n"
        "- 该接口会遍历全部已注册模型并合并命中结果。\n"
        "- 若没有检测到目标，`results` 返回空数组。\n"
    ),
    response_model=AutoImagePredictResponse,
    responses={
        200: {
            "description": "图片自动场景识别成功",
            "content": {"application/json": {"example": AUTO_IMAGE_RESPONSE_EXAMPLE}},
        },
        400: {"description": "上传文件不是有效图片。"},
    },
 )
 async def predict_image_v1(
    file: UploadFile = File(..., description="待识别图片文件，示例：file=@test.jpg"),
 ):
    try:
        contents = await file.read()
        img = decode_image(contents)
        return run_auto_image_detection(img)
    except HTTPException:
        raise
    except Exception as exc:
        raise HTTPException(status_code=500, detail=str(exc)) from exc
@app.post(
    "/{model_type}/predict/image",
    tags=["图像识别"],
    summary="按场景执行图片识别",
    description=(
        "根据路径中的 `model_type` 选择对应场景模型，对上传图片执行识别。\n\n"
        "入参说明：\n"
        "- `model_type`：路径参数，场景模型类型。\n"
        "- `file`：上传的待识别图片文件。\n\n"
        "模拟入参示例：\n"
        "```bash\n"
        "curl -X POST http://localhost:8000/floating/predict/image \\\n"
        "  -H \"accept: application/json\" \\\n"
        "  -H \"Content-Type: multipart/form-data\" \\\n"
        "  -F \"file=@test.jpg;type=image/jpeg\"\n"
        "```\n\n"
        "出参说明：\n"
        "- `results`：识别结果列表。\n"
        "- `results[].name`：类别名称。\n"
        "- `results[].class`：类别 ID。\n"
        "- `results[].confidence`：置信度。\n"
        "- `results[].box`：目标框坐标。\n\n"
        "模拟出参示例：\n"
        f"```json\n{json.dumps(IMAGE_RESPONSE_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
        "说明：\n"
        "- 该接口只使用路径中的 `model_type` 对应模型。\n"
        "- 若没有检测到目标，`results` 返回空数组。\n"
    ),
    response_model=ImagePredictResponse,
    responses={
        200: {
            "description": "图片识别成功",
            "content": {"application/json": {"example": IMAGE_RESPONSE_EXAMPLE}},
        },
        400: {"description": "场景类型不支持，或上传文件不是有效图片。"},
    },
 )
 async def predict_image(
    model_type: str = Path(..., description=f"场景模型类型。可选值：{SUPPORTED_MODEL_TYPES}"),
    file: UploadFile = File(..., description="待识别图片文件，示例：file=@test.jpg"),
 ):
    validate_model_type(model_type)
    try:
        contents = await file.read()
        img = decode_image(contents)
        return {"results": run_single_model_image_detection(img, model_type)}
    except HTTPException:
        raise
    except Exception as exc:
        raise HTTPException(status_code=500, detail=str(exc)) from exc
 if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)
--- a/app/model/Floating/best.pt
+++ b/app/model/Floating/best.pt
--- a/app/model/Floating/train4/BoxF1_curve.png
+++ b/app/model/Floating/train4/BoxF1_curve.png
--- a/app/model/Floating/train4/BoxPR_curve.png
+++ b/app/model/Floating/train4/BoxPR_curve.png
--- a/app/model/Floating/train4/BoxP_curve.png
+++ b/app/model/Floating/train4/BoxP_curve.png
--- a/app/model/Floating/train4/BoxR_curve.png
+++ b/app/model/Floating/train4/BoxR_curve.png
--- a/app/model/Floating/train4/args.yaml
+++ b/app/model/Floating/train4/args.yaml
@ -0,0 +1,106 @@
 task: detect
 mode: train
 model: ./yolo11n.pt
 data: ./Conf/Conf_River_Floating.yaml
 epochs: 1000
 time: null
 patience: 100
 batch: 64
 imgsz: 1280
 save: true
 save_period: -1
 cache: false
 device: 0,1,2,3
 workers: 0
 project: null
 name: train4
 exist_ok: false
 pretrained: true
 optimizer: auto
 verbose: true
 seed: 0
 deterministic: true
 single_cls: false
 rect: false
 cos_lr: false
 close_mosaic: 10
 resume: false
 amp: true
 fraction: 1.0
 profile: false
 freeze: null
 multi_scale: false
 compile: false
 overlap_mask: true
 mask_ratio: 4
 dropout: 0.0
 val: true
 split: val
 save_json: false
 conf: 0.25
 iou: 0.7
 max_det: 300
 half: false
 dnn: false
 plots: true
 source: null
 vid_stride: 1
 stream_buffer: false
 visualize: false
 augment: false
 agnostic_nms: false
 classes: null
 retina_masks: false
 embed: null
 show: false
 save_frames: false
 save_txt: false
 save_conf: false
 save_crop: false
 show_labels: true
 show_conf: true
 show_boxes: true
 line_width: null
 format: torchscript
 keras: false
 optimize: false
 int8: false
 dynamic: false
 simplify: true
 opset: null
 workspace: null
 nms: false
 lr0: 0.005
 lrf: 0.01
 momentum: 0.937
 weight_decay: 0.0005
 warmup_epochs: 5.0
 warmup_momentum: 0.8
 warmup_bias_lr: 0.1
 box: 10.0
 cls: 1.0
 dfl: 2.0
 pose: 12.0
 kobj: 1.0
 nbs: 64
 hsv_h: 0.015
 hsv_s: 0.7
 hsv_v: 0.4
 degrees: 0.0
 translate: 0.1
 scale: 0.5
 shear: 0.0
 perspective: 0.0
 flipud: 0.0
 fliplr: 0.5
 bgr: 0.0
 mosaic: 1.0
 mixup: 0.0
 cutmix: 0.0
 copy_paste: 0.3
 copy_paste_mode: flip
 auto_augment: randaugment
 erasing: 0.4
 cfg: null
 tracker: botsort.yaml
 save_dir: /home/sutai/wsj/reservoir_train/runs/detect/train4
--- a/app/model/Floating/train4/confusion_matrix.png
+++ b/app/model/Floating/train4/confusion_matrix.png
--- a/app/model/Floating/train4/confusion_matrix_normalized.png
+++ b/app/model/Floating/train4/confusion_matrix_normalized.png
--- a/app/model/Floating/train4/labels.jpg
+++ b/app/model/Floating/train4/labels.jpg
--- a/app/model/Floating/train4/results.csv
+++ b/app/model/Floating/train4/results.csv
@ -0,0 +1,287 @@
 epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
 1,33.4112,2.39509,12.7977,2.40188,0,0,0,0,2.27359,6.71089,2.315,0.00191304,0.00191304,0.00191304
 2,64.2582,2.07809,11.1844,2.14679,0,0,0,0,2.05492,6.40927,2.19038,0.00390917,0.00390917,0.00390917
 3,93.9997,2.13405,9.95756,2.0733,0,0,0,0,2.32995,5.81948,2.3064,0.00590134,0.00590134,0.00590134
 4,124.124,2.14439,8.38493,2.09449,0.21935,0.04898,0.13296,0.08116,2.68573,5.4136,2.59843,0.00788954,0.00788954,0.00788954
 5,154.595,2.15373,7.53047,2.07161,0.3287,0.02175,0.01744,0.00849,4.06277,61.594,4.65705,0.00987379,0.00987379,0.00987379
 6,184.83,2.35596,6.29438,2.20575,0.30012,0.05055,0.00988,0.00401,3.64732,63.492,4.78298,0.0099505,0.0099505,0.0099505
 7,215.234,2.32979,5.48792,2.2124,0.02745,0.1158,0.02721,0.01222,3.10734,76.9508,4.06168,0.0099406,0.0099406,0.0099406
 8,246.436,2.20819,4.8594,2.13989,0.05636,0.12391,0.03385,0.01423,3.67411,79.0338,4.45235,0.0099307,0.0099307,0.0099307
 9,276.644,2.34735,4.8601,2.26271,0.12236,0.09724,0.0489,0.02414,3.44567,32.4887,3.97067,0.0099208,0.0099208,0.0099208
 10,306.629,2.18417,4.3116,2.12706,0.08194,0.11332,0.05886,0.03163,3.01061,18.4977,3.46095,0.0099109,0.0099109,0.0099109
 11,337.44,2.24794,4.11155,2.19118,0.91429,0.08774,0.14738,0.06394,3.57315,16.992,3.77222,0.009901,0.009901,0.009901
 12,367.739,2.21728,4.28225,2.14579,0.17374,0.24812,0.23883,0.10971,2.70124,8.64248,2.87378,0.0098911,0.0098911,0.0098911
 13,398.69,2.122,3.72056,2.05907,0.32906,0.1768,0.23621,0.12543,3.10642,8.50599,3.34959,0.0098812,0.0098812,0.0098812
 14,430.028,2.14693,3.75207,2.05125,0.38129,0.26288,0.22819,0.11014,2.93184,16.773,3.11998,0.0098713,0.0098713,0.0098713
 15,463.024,2.09661,3.69645,2.03073,0.14062,0.18945,0.16836,0.10019,2.69815,8.34905,3.00178,0.0098614,0.0098614,0.0098614
 16,493.901,2.08116,3.56176,2.01431,0.50214,0.31571,0.37123,0.25045,2.329,4.88796,2.464,0.0098515,0.0098515,0.0098515
 17,525.635,2.09936,3.42418,2.06,0.54816,0.33883,0.43483,0.26709,2.35196,4.61267,2.41869,0.0098416,0.0098416,0.0098416
 18,556.277,2.08124,3.33558,2.06297,0.33882,0.33369,0.30326,0.18066,2.33899,5.059,2.40794,0.0098317,0.0098317,0.0098317
 19,587.236,2.07934,3.32083,1.99435,0.41651,0.33028,0.38173,0.2342,2.39637,4.99267,2.50897,0.0098218,0.0098218,0.0098218
 20,617.592,2.06915,3.29678,2.01635,0.51635,0.31763,0.31925,0.16276,2.56906,5.58749,2.58507,0.0098119,0.0098119,0.0098119
 21,648.875,2.04413,3.28848,2.01841,0.33072,0.37199,0.33934,0.19054,2.52337,6.12432,2.55929,0.009802,0.009802,0.009802
 22,679.869,2.0087,3.17607,2.00152,0.565,0.39026,0.38868,0.24235,2.22558,4.71118,2.28477,0.0097921,0.0097921,0.0097921
 23,711.071,2.01974,3.14653,1.99325,0.40441,0.44287,0.43841,0.27109,2.28724,4.44453,2.30748,0.0097822,0.0097822,0.0097822
 24,742.425,1.99648,2.94497,1.96764,0.5725,0.09721,0.1631,0.10733,2.92534,9.01378,3.25484,0.0097723,0.0097723,0.0097723
 25,772.62,2.06885,3.02058,2.07763,0.52202,0.29904,0.38437,0.23816,2.3859,5.48165,2.54243,0.0097624,0.0097624,0.0097624
 26,803.858,1.96085,2.90528,1.96232,0.51233,0.46149,0.4758,0.29387,2.27896,4.43162,2.36984,0.0097525,0.0097525,0.0097525
 27,835.037,2.02208,2.77857,1.99893,0.33515,0.28609,0.31683,0.19602,2.50066,6.15903,2.66507,0.0097426,0.0097426,0.0097426
 28,865.592,2.03301,2.73448,1.98963,0.49659,0.34141,0.42276,0.25157,2.47269,4.63183,2.51185,0.0097327,0.0097327,0.0097327
 29,896.588,1.96476,2.79753,1.9813,0.5388,0.36579,0.44584,0.28486,2.21644,3.75674,2.28506,0.0097228,0.0097228,0.0097228
 30,926.881,1.86472,2.637,1.88481,0.45209,0.45108,0.46248,0.29419,2.11763,3.88376,2.20945,0.0097129,0.0097129,0.0097129
 31,958.149,1.90774,2.75392,1.9023,0.68001,0.44477,0.56639,0.38166,2.05275,3.37761,2.17998,0.009703,0.009703,0.009703
 32,988.295,1.89363,2.71563,1.88663,0.45637,0.32381,0.3727,0.22422,2.28483,5.12807,2.44161,0.0096931,0.0096931,0.0096931
 33,1018.61,1.89589,2.6815,1.95134,0.57547,0.49996,0.57094,0.36593,2.06348,3.37383,2.1709,0.0096832,0.0096832,0.0096832
 34,1049.64,1.91796,2.61283,1.93174,0.6023,0.48813,0.596,0.38649,2.15059,3.67912,2.29399,0.0096733,0.0096733,0.0096733
 35,1080.83,1.85469,2.48279,1.84625,0.62724,0.49087,0.59102,0.3457,2.27444,3.47772,2.34883,0.0096634,0.0096634,0.0096634
 36,1111.71,1.90011,2.74844,1.9341,0.5392,0.51061,0.54571,0.35343,2.06851,3.35112,2.18239,0.0096535,0.0096535,0.0096535
 37,1142.77,1.86612,2.53197,1.90013,0.69213,0.5854,0.6807,0.43885,1.99116,2.91807,2.11319,0.0096436,0.0096436,0.0096436
 38,1173.46,1.8645,2.43429,1.94447,0.42449,0.32249,0.37815,0.21983,2.48945,4.83547,2.5625,0.0096337,0.0096337,0.0096337
 39,1204.19,1.86851,2.59506,1.93904,0.56418,0.50811,0.56633,0.35308,2.0772,4.00271,2.16946,0.0096238,0.0096238,0.0096238
 40,1235.52,1.88343,2.41743,1.8683,0.6036,0.53553,0.54392,0.33,2.06793,3.40566,2.16109,0.0096139,0.0096139,0.0096139
 41,1266.54,1.8789,2.46718,1.85799,0.65278,0.54068,0.6073,0.38942,2.03905,2.75934,2.11769,0.009604,0.009604,0.009604
 42,1296.91,1.87415,2.35517,1.89317,0.52272,0.58573,0.60708,0.39084,1.99974,2.75824,2.09871,0.0095941,0.0095941,0.0095941
 43,1327.15,1.8065,2.36363,1.85676,0.5696,0.5635,0.59166,0.35549,2.11079,2.9944,2.2015,0.0095842,0.0095842,0.0095842
 44,1358.15,1.71217,2.25062,1.834,0.62599,0.58271,0.6454,0.3944,2.05505,2.76048,2.14881,0.0095743,0.0095743,0.0095743
 45,1388.44,1.79958,2.41177,1.86353,0.61154,0.62068,0.65499,0.4053,2.04121,2.62992,2.12149,0.0095644,0.0095644,0.0095644
 46,1419.65,1.73793,2.27744,1.80536,0.61744,0.61691,0.64451,0.40388,2.03083,2.58909,2.11498,0.0095545,0.0095545,0.0095545
 47,1449.87,1.8252,2.32864,1.86931,0.6701,0.52481,0.59996,0.38251,2.13468,3.18874,2.15855,0.0095446,0.0095446,0.0095446
 48,1481.18,1.83037,2.23891,1.84423,0.67257,0.56493,0.63626,0.40492,1.94322,2.77078,2.09522,0.0095347,0.0095347,0.0095347
 49,1512.32,1.84992,2.27421,1.86811,0.544,0.58535,0.56303,0.34566,2.03373,3.10096,2.14207,0.0095248,0.0095248,0.0095248
 50,1542.63,1.80004,2.29197,1.8501,0.52113,0.61851,0.56774,0.36182,1.98226,2.68201,2.06094,0.0095149,0.0095149,0.0095149
 51,1573.04,1.8649,2.39088,1.86625,0.65926,0.62994,0.6645,0.4108,2.02379,2.62625,2.08539,0.009505,0.009505,0.009505
 52,1604.28,1.86099,2.4067,1.94229,0.70243,0.5713,0.66193,0.40454,2.0489,2.62656,2.09965,0.0094951,0.0094951,0.0094951
 53,1634.67,1.78459,2.32641,1.83533,0.73297,0.58992,0.69743,0.43403,2.00523,2.55661,2.07352,0.0094852,0.0094852,0.0094852
 54,1665.87,1.77479,2.12097,1.78918,0.69575,0.63374,0.69585,0.44632,1.93363,2.34394,2.03032,0.0094753,0.0094753,0.0094753
 55,1696.11,1.79447,2.21309,1.83822,0.5633,0.65643,0.6702,0.43464,1.97186,2.53811,2.09247,0.0094654,0.0094654,0.0094654
 56,1727.31,1.68976,1.99661,1.71081,0.51058,0.64818,0.60448,0.37644,2.05012,2.96843,2.11168,0.0094555,0.0094555,0.0094555
 57,1759.1,1.8444,2.09752,1.85829,0.6798,0.60319,0.66547,0.41173,2.06851,2.52008,2.14515,0.0094456,0.0094456,0.0094456
 58,1789.29,1.75257,2.17528,1.79767,0.61557,0.49712,0.57502,0.38081,2.05903,3.38497,2.26234,0.0094357,0.0094357,0.0094357
 59,1820.52,1.68272,2.09344,1.73428,0.66689,0.65765,0.70093,0.44305,1.96759,2.44041,2.03783,0.0094258,0.0094258,0.0094258
 60,1851.04,1.77978,2.06757,1.83414,0.63963,0.56241,0.60656,0.39329,1.9806,2.82047,2.09751,0.0094159,0.0094159,0.0094159
 61,1882.16,1.75469,2.09708,1.79083,0.59296,0.64927,0.66399,0.41516,1.99071,2.44924,2.07691,0.009406,0.009406,0.009406
 62,1912.36,1.70839,2.04093,1.79552,0.74807,0.60526,0.70708,0.44594,1.96958,2.39755,2.08955,0.0093961,0.0093961,0.0093961
 63,1943.6,1.81063,2.19109,1.84124,0.66267,0.4528,0.55566,0.3723,2.10637,3.51489,2.16723,0.0093862,0.0093862,0.0093862
 64,1975.31,1.70494,2.0312,1.72613,0.5446,0.54828,0.5729,0.34781,2.18312,2.95821,2.22275,0.0093763,0.0093763,0.0093763
 65,2006.76,1.76029,2.07565,1.82239,0.68439,0.664,0.68646,0.42555,1.97151,2.46188,2.06572,0.0093664,0.0093664,0.0093664
 66,2037.79,1.77601,2.12044,1.7961,0.66993,0.56461,0.64771,0.42386,1.95281,2.62522,2.05802,0.0093565,0.0093565,0.0093565
 67,2069.4,1.74463,2.0228,1.79756,0.65292,0.65017,0.67845,0.42438,1.99285,2.39774,2.07101,0.0093466,0.0093466,0.0093466
 68,2100.6,1.71478,2.01086,1.75777,0.6551,0.61315,0.66482,0.42935,1.95567,2.65237,2.06196,0.0093367,0.0093367,0.0093367
 69,2131.65,1.63584,1.88907,1.69358,0.71733,0.60653,0.69224,0.44323,1.95981,2.30105,2.06106,0.0093268,0.0093268,0.0093268
 70,2162.02,1.72051,2.04873,1.77628,0.65249,0.61395,0.66324,0.4259,1.93983,2.41967,2.05242,0.0093169,0.0093169,0.0093169
 71,2192.89,1.76786,2.04439,1.84533,0.67327,0.64307,0.65415,0.42308,1.98153,2.79006,2.10905,0.009307,0.009307,0.009307
 72,2224.13,1.71305,2.07611,1.79724,0.63173,0.73438,0.72691,0.46752,1.91868,2.25964,2.00855,0.0092971,0.0092971,0.0092971
 73,2257.71,1.73477,1.96634,1.77488,0.69453,0.65354,0.6967,0.43337,2.02486,2.36749,2.12102,0.0092872,0.0092872,0.0092872
 74,2290.59,1.7476,2.03874,1.86815,0.63041,0.74416,0.74136,0.49011,1.90481,2.19541,1.99289,0.0092773,0.0092773,0.0092773
 75,2321.78,1.65999,2.04063,1.76478,0.69578,0.67056,0.72423,0.46534,1.91106,2.29585,1.9911,0.0092674,0.0092674,0.0092674
 76,2352.45,1.71493,1.97689,1.79447,0.65374,0.68989,0.7028,0.42477,1.99132,2.33827,2.039,0.0092575,0.0092575,0.0092575
 77,2383.37,1.66049,1.91938,1.76847,0.67318,0.68889,0.71329,0.45538,1.93249,2.38719,2.04273,0.0092476,0.0092476,0.0092476
 78,2414.95,1.70886,1.95003,1.81118,0.70394,0.67585,0.69296,0.44359,1.93599,2.37564,2.03154,0.0092377,0.0092377,0.0092377
 79,2446.68,1.71238,1.99505,1.79231,0.70405,0.63295,0.68462,0.43914,1.96196,2.43182,2.04908,0.0092278,0.0092278,0.0092278
 80,2476.77,1.66636,1.86386,1.74769,0.70921,0.66345,0.70417,0.45188,1.93766,2.25578,2.0256,0.0092179,0.0092179,0.0092179
 81,2507.6,1.71782,2.01262,1.77357,0.73055,0.62686,0.7027,0.45086,1.94037,2.38371,2.0403,0.009208,0.009208,0.009208
 82,2537.83,1.62,1.8416,1.68444,0.67561,0.71262,0.71927,0.46371,1.91601,2.29262,2.02476,0.0091981,0.0091981,0.0091981
 83,2568.1,1.67571,1.91123,1.70594,0.66582,0.72626,0.7469,0.47277,1.91646,2.07693,2.00694,0.0091882,0.0091882,0.0091882
 84,2598.26,1.65599,1.83049,1.7356,0.69185,0.66831,0.71046,0.46033,1.90924,2.30259,2.00811,0.0091783,0.0091783,0.0091783
 85,2628.91,1.69053,1.84541,1.77721,0.77288,0.69375,0.74554,0.46174,1.93066,2.17661,2.03669,0.0091684,0.0091684,0.0091684
 86,2660.32,1.63021,1.7566,1.71157,0.69981,0.71577,0.75051,0.47943,1.91417,2.10722,2.01435,0.0091585,0.0091585,0.0091585
 87,2691.08,1.65175,1.90702,1.7437,0.66101,0.66719,0.69266,0.44423,1.98793,2.41698,2.08751,0.0091486,0.0091486,0.0091486
 88,2722.04,1.68427,1.85181,1.78147,0.74041,0.71377,0.7463,0.47813,1.8689,2.02687,1.96926,0.0091387,0.0091387,0.0091387
 89,2752.26,1.63119,1.73594,1.71096,0.69832,0.63207,0.67622,0.44232,1.93447,2.50703,2.0412,0.0091288,0.0091288,0.0091288
 90,2783.46,1.62372,1.8655,1.6972,0.70097,0.69899,0.73202,0.4677,1.94298,2.15119,2.05042,0.0091189,0.0091189,0.0091189
 91,2814.45,1.65209,1.90352,1.79287,0.68437,0.64718,0.69585,0.46175,1.94045,2.37982,2.03768,0.009109,0.009109,0.009109
 92,2845.46,1.59033,1.74556,1.66551,0.62478,0.77079,0.73172,0.47772,1.88385,2.07265,1.95217,0.0090991,0.0090991,0.0090991
 93,2876.24,1.60442,1.86284,1.69678,0.66894,0.68727,0.70827,0.4652,1.86558,2.20822,1.94664,0.0090892,0.0090892,0.0090892
 94,2907.38,1.6176,1.83018,1.69363,0.65558,0.67135,0.68921,0.4428,1.92617,2.11606,2.02405,0.0090793,0.0090793,0.0090793
 95,2937.9,1.60348,1.73399,1.70397,0.71671,0.69558,0.72871,0.47204,1.88481,2.05911,2.00175,0.0090694,0.0090694,0.0090694
 96,2969.03,1.64315,1.75424,1.72109,0.6732,0.76293,0.75839,0.48757,1.93489,2.04088,2.01103,0.0090595,0.0090595,0.0090595
 97,3000.25,1.63158,1.75773,1.7439,0.74801,0.58791,0.66285,0.42592,2.10341,2.84418,2.19788,0.0090496,0.0090496,0.0090496
 98,3030.9,1.56733,1.76075,1.68401,0.70804,0.70863,0.72991,0.46971,1.9423,2.19624,2.03908,0.0090397,0.0090397,0.0090397
 99,3061.9,1.67118,1.87264,1.73166,0.65928,0.68622,0.71323,0.4619,1.95344,2.32495,2.0202,0.0090298,0.0090298,0.0090298
 100,3093.28,1.6578,1.76768,1.70158,0.65995,0.75095,0.75439,0.47966,1.89181,2.00972,1.98113,0.0090199,0.0090199,0.0090199
 101,3124.28,1.67294,1.82137,1.75561,0.6728,0.71835,0.72477,0.46535,1.89329,2.10559,1.96997,0.00901,0.00901,0.00901
 102,3155.34,1.6344,1.75931,1.7498,0.68762,0.73431,0.74652,0.47343,1.90751,2.12601,1.99317,0.0090001,0.0090001,0.0090001
 103,3185.57,1.6012,1.72405,1.70126,0.6988,0.71892,0.7427,0.47474,1.89237,2.02244,2.00033,0.0089902,0.0089902,0.0089902
 104,3215.96,1.67225,1.89161,1.77791,0.69721,0.7528,0.75713,0.47756,1.97322,2.06667,2.06217,0.0089803,0.0089803,0.0089803
 105,3248.35,1.60898,1.74546,1.74248,0.69214,0.75715,0.77421,0.48923,1.88532,2.02793,2.0021,0.0089704,0.0089704,0.0089704
 106,3278.92,1.57215,1.73317,1.6791,0.73579,0.69373,0.74712,0.48593,1.89157,2.19439,2.00705,0.0089605,0.0089605,0.0089605
 107,3309.67,1.65866,1.74319,1.78319,0.69793,0.7058,0.73758,0.47067,1.91284,2.14136,1.99853,0.0089506,0.0089506,0.0089506
 108,3341.22,1.6409,1.80083,1.78682,0.62094,0.73029,0.7105,0.46102,1.94457,2.21369,2.05021,0.0089407,0.0089407,0.0089407
 109,3372.16,1.63055,1.79766,1.76275,0.71753,0.68819,0.74995,0.47348,1.9128,2.0163,1.9998,0.0089308,0.0089308,0.0089308
 110,3402.67,1.58359,1.63488,1.63558,0.73165,0.70983,0.74639,0.48588,1.88987,2.05689,1.99256,0.0089209,0.0089209,0.0089209
 111,3433.42,1.54491,1.67577,1.65672,0.74444,0.7028,0.74363,0.48243,1.93855,2.12785,2.01551,0.008911,0.008911,0.008911
 112,3463.69,1.57232,1.71179,1.70476,0.66259,0.73669,0.73286,0.47466,1.88714,2.17065,2.00547,0.0089011,0.0089011,0.0089011
 113,3493.99,1.5494,1.63051,1.69414,0.75052,0.65764,0.74657,0.49149,1.89494,2.09169,2.02276,0.0088912,0.0088912,0.0088912
 114,3525.35,1.60578,1.80617,1.72276,0.75063,0.72429,0.77702,0.50198,1.87324,2.06979,2.00217,0.0088813,0.0088813,0.0088813
 115,3556.85,1.63476,1.8233,1.725,0.75913,0.69978,0.76841,0.49045,1.89908,2.0631,1.99536,0.0088714,0.0088714,0.0088714
 116,3587.86,1.61478,1.67594,1.69749,0.69546,0.79037,0.77589,0.49685,1.84725,1.91031,1.95434,0.0088615,0.0088615,0.0088615
 117,3618.24,1.562,1.63893,1.71007,0.77191,0.66477,0.75118,0.47698,1.89677,2.00725,1.99426,0.0088516,0.0088516,0.0088516
 118,3648.74,1.60122,1.69165,1.69373,0.70796,0.75649,0.76426,0.48445,1.87769,1.96904,1.96543,0.0088417,0.0088417,0.0088417
 119,3679.17,1.52448,1.62477,1.67566,0.7176,0.72054,0.75114,0.48312,1.86382,2.1011,1.98374,0.0088318,0.0088318,0.0088318
 120,3710.4,1.59228,1.74581,1.68931,0.73173,0.74726,0.75814,0.49304,1.8737,1.90381,1.9628,0.0088219,0.0088219,0.0088219
 121,3741.8,1.52164,1.68262,1.68659,0.69812,0.7459,0.74733,0.48158,1.87815,1.96184,1.98716,0.008812,0.008812,0.008812
 122,3773.09,1.56274,1.65581,1.69288,0.65805,0.74741,0.74103,0.48692,1.90351,2.04182,2.00776,0.0088021,0.0088021,0.0088021
 123,3804.71,1.56582,1.68026,1.69251,0.74411,0.74373,0.77496,0.49796,1.88747,2.02576,1.99313,0.0087922,0.0087922,0.0087922
 124,3836.07,1.63038,1.72226,1.70713,0.69002,0.76629,0.75568,0.4868,1.93811,2.03235,2.03204,0.0087823,0.0087823,0.0087823
 125,3866.48,1.53718,1.58726,1.68665,0.67342,0.76326,0.76083,0.48635,1.89367,2.03137,2.00702,0.0087724,0.0087724,0.0087724
 126,3897.84,1.52791,1.59694,1.70075,0.70927,0.75116,0.7625,0.49619,1.86037,1.91145,1.99687,0.0087625,0.0087625,0.0087625
 127,3929.23,1.52711,1.51207,1.68549,0.71511,0.66365,0.70371,0.44721,1.92537,2.10992,2.03695,0.0087526,0.0087526,0.0087526
 128,3960.29,1.60066,1.6233,1.6994,0.68199,0.76917,0.7424,0.47616,1.86428,2.00035,1.96745,0.0087427,0.0087427,0.0087427
 129,3991.46,1.48425,1.64257,1.62917,0.71194,0.71953,0.75503,0.48922,1.89277,1.96905,1.99971,0.0087328,0.0087328,0.0087328
 130,4022.59,1.49591,1.60587,1.65544,0.75048,0.7725,0.77548,0.4979,1.86874,1.93635,1.97436,0.0087229,0.0087229,0.0087229
 131,4052.89,1.57657,1.62735,1.68638,0.77483,0.71497,0.76046,0.48619,1.91064,1.99341,2.03471,0.008713,0.008713,0.008713
 132,4084.18,1.51229,1.55421,1.65411,0.77594,0.68834,0.74119,0.49359,1.87433,2.04587,1.98123,0.0087031,0.0087031,0.0087031
 133,4115.5,1.57072,1.62683,1.64384,0.7475,0.72255,0.76225,0.49354,1.88699,1.9553,1.97595,0.0086932,0.0086932,0.0086932
 134,4146.58,1.54797,1.59118,1.67107,0.75001,0.71998,0.75556,0.47706,1.89069,1.98105,2.00277,0.0086833,0.0086833,0.0086833
 135,4177.94,1.52704,1.5953,1.66378,0.72931,0.68673,0.74635,0.47547,1.86982,2.08823,1.96654,0.0086734,0.0086734,0.0086734
 136,4208.9,1.4893,1.60814,1.6778,0.73487,0.72543,0.76179,0.48995,1.8754,1.89028,1.98092,0.0086635,0.0086635,0.0086635
 137,4240.16,1.53343,1.6022,1.66604,0.7228,0.76887,0.76916,0.50187,1.84482,1.84397,1.96968,0.0086536,0.0086536,0.0086536
 138,4271.44,1.49143,1.61872,1.67365,0.74242,0.75688,0.77451,0.50577,1.8628,1.8409,1.98261,0.0086437,0.0086437,0.0086437
 139,4302.82,1.51472,1.54374,1.66483,0.78992,0.61836,0.71627,0.46596,1.90419,2.20122,2.03213,0.0086338,0.0086338,0.0086338
 140,4333.29,1.56464,1.67301,1.6978,0.7501,0.68768,0.74966,0.48972,1.84634,1.97314,1.9748,0.0086239,0.0086239,0.0086239
 141,4363.86,1.54989,1.63095,1.6786,0.77353,0.71713,0.77342,0.48928,1.90894,1.9425,2.0074,0.008614,0.008614,0.008614
 142,4394.71,1.50495,1.55003,1.65713,0.72907,0.78711,0.78698,0.51538,1.85967,1.92384,1.95335,0.0086041,0.0086041,0.0086041
 143,4425.94,1.49654,1.56361,1.62185,0.78189,0.71634,0.77689,0.50203,1.8531,2.0401,1.94712,0.0085942,0.0085942,0.0085942
 144,4457.35,1.47004,1.51486,1.6262,0.70494,0.79473,0.76884,0.4964,1.86085,1.91598,1.97934,0.0085843,0.0085843,0.0085843
 145,4488.51,1.46367,1.52509,1.63242,0.73485,0.75239,0.77186,0.50086,1.86819,1.87848,1.97314,0.0085744,0.0085744,0.0085744
 146,4519.43,1.47084,1.54345,1.63811,0.71846,0.74035,0.7609,0.48972,1.87231,1.87854,1.97234,0.0085645,0.0085645,0.0085645
 147,4550.17,1.49373,1.59394,1.62908,0.74348,0.71685,0.75089,0.4892,1.88174,1.92687,1.97843,0.0085546,0.0085546,0.0085546
 148,4581.33,1.46801,1.53394,1.62238,0.69848,0.81228,0.78104,0.50422,1.83227,1.89914,1.93102,0.0085447,0.0085447,0.0085447
 149,4612.45,1.5052,1.50409,1.62337,0.72209,0.78195,0.77867,0.49525,1.8599,1.86737,1.98748,0.0085348,0.0085348,0.0085348
 150,4642.85,1.44902,1.46299,1.57813,0.72811,0.74532,0.78403,0.50516,1.86042,1.87099,1.95547,0.0085249,0.0085249,0.0085249
 151,4673.45,1.47732,1.47856,1.59758,0.74506,0.74998,0.78561,0.5037,1.857,1.89689,1.95507,0.008515,0.008515,0.008515
 152,4704.7,1.52313,1.51859,1.6303,0.76068,0.73163,0.77934,0.50076,1.8826,1.98434,1.99611,0.0085051,0.0085051,0.0085051
 153,4734.85,1.50034,1.55807,1.64271,0.72049,0.79073,0.77647,0.50216,1.86032,1.91405,1.96286,0.0084952,0.0084952,0.0084952
 154,4768.5,1.52433,1.52028,1.61059,0.7644,0.70732,0.77444,0.49535,1.88475,1.93922,1.96815,0.0084853,0.0084853,0.0084853
 155,4800.08,1.50003,1.54511,1.62772,0.71943,0.76873,0.78306,0.50526,1.87223,1.86043,1.96608,0.0084754,0.0084754,0.0084754
 156,4831.48,1.46801,1.57109,1.62173,0.74778,0.73076,0.76829,0.48921,1.88858,1.95747,1.9818,0.0084655,0.0084655,0.0084655
 157,4862.88,1.48143,1.54908,1.62293,0.67532,0.77128,0.76963,0.49462,1.8701,1.87523,1.9747,0.0084556,0.0084556,0.0084556
 158,4893.69,1.50796,1.52131,1.6155,0.74948,0.72635,0.76733,0.50152,1.84082,1.91005,1.94933,0.0084457,0.0084457,0.0084457
 159,4924.79,1.47093,1.43212,1.61213,0.69829,0.78836,0.78017,0.50218,1.87664,1.86978,1.98285,0.0084358,0.0084358,0.0084358
 160,4955.79,1.46021,1.50466,1.60093,0.7477,0.73738,0.7673,0.50665,1.84484,1.94283,1.9651,0.0084259,0.0084259,0.0084259
 161,4986.97,1.48612,1.56605,1.64446,0.71931,0.80519,0.79376,0.51549,1.83831,1.8834,1.97741,0.008416,0.008416,0.008416
 162,5018.17,1.45565,1.49378,1.614,0.80067,0.71541,0.79184,0.51521,1.8346,1.91524,1.94346,0.0084061,0.0084061,0.0084061
 163,5049.32,1.48986,1.53316,1.63445,0.77194,0.72887,0.78476,0.51452,1.84588,1.91639,1.96179,0.0083962,0.0083962,0.0083962
 164,5081.06,1.44679,1.4472,1.61446,0.76972,0.74023,0.78661,0.51531,1.85089,1.8995,1.99156,0.0083863,0.0083863,0.0083863
 165,5112.41,1.46961,1.49509,1.66838,0.73123,0.78224,0.78798,0.5087,1.86241,1.95185,2.00957,0.0083764,0.0083764,0.0083764
 166,5142.7,1.43942,1.48146,1.62191,0.76552,0.75013,0.78655,0.50457,1.84598,1.86193,1.96788,0.0083665,0.0083665,0.0083665
 167,5174.06,1.44945,1.57198,1.63458,0.75442,0.72243,0.76256,0.49343,1.87835,1.94129,1.97724,0.0083566,0.0083566,0.0083566
 168,5205.38,1.461,1.48094,1.60827,0.76099,0.75153,0.79177,0.51045,1.88137,1.88459,1.96118,0.0083467,0.0083467,0.0083467
 169,5235.66,1.45146,1.47497,1.61302,0.77255,0.73058,0.78033,0.4985,1.89142,1.89338,1.98194,0.0083368,0.0083368,0.0083368
 170,5267.08,1.52846,1.59264,1.66069,0.74249,0.73669,0.77524,0.4999,1.84125,1.91202,1.95036,0.0083269,0.0083269,0.0083269
 171,5297.46,1.46324,1.47038,1.63683,0.73238,0.74624,0.7766,0.49647,1.85382,1.90369,1.96056,0.008317,0.008317,0.008317
 172,5328.22,1.42227,1.39554,1.57272,0.74177,0.75839,0.7828,0.49399,1.84633,1.93177,1.95304,0.0083071,0.0083071,0.0083071
 173,5359.43,1.38575,1.48044,1.56661,0.71667,0.80444,0.78661,0.50073,1.86349,1.87811,1.99063,0.0082972,0.0082972,0.0082972
 174,5389.54,1.45477,1.47128,1.63922,0.73741,0.78007,0.78863,0.50139,1.86574,1.91012,1.98407,0.0082873,0.0082873,0.0082873
 175,5420.3,1.44559,1.4842,1.60311,0.70737,0.81579,0.78821,0.50486,1.89997,1.93832,2.01136,0.0082774,0.0082774,0.0082774
 176,5451.5,1.42252,1.51095,1.59501,0.7569,0.74881,0.78472,0.51244,1.86509,1.91201,1.96233,0.0082675,0.0082675,0.0082675
 177,5481.79,1.46623,1.535,1.61428,0.71725,0.7852,0.77582,0.50525,1.84652,1.88082,1.99229,0.0082576,0.0082576,0.0082576
 178,5513.46,1.42992,1.47887,1.60196,0.68047,0.81116,0.76735,0.48607,1.87456,1.90702,1.98009,0.0082477,0.0082477,0.0082477
 179,5543.89,1.45059,1.43996,1.63438,0.74857,0.75831,0.79145,0.51219,1.8456,1.85742,1.96571,0.0082378,0.0082378,0.0082378
 180,5574.24,1.47166,1.49255,1.5875,0.71326,0.82671,0.796,0.50757,1.87788,1.86213,1.99865,0.0082279,0.0082279,0.0082279
 181,5605.72,1.44551,1.4369,1.60446,0.70297,0.80894,0.80245,0.51936,1.8357,1.8095,1.98173,0.008218,0.008218,0.008218
 182,5636.73,1.42934,1.43092,1.60836,0.69639,0.81495,0.79303,0.51809,1.82884,1.83271,1.96932,0.0082081,0.0082081,0.0082081
 183,5667.63,1.41919,1.4449,1.57803,0.74632,0.74744,0.78443,0.50585,1.83984,1.80941,1.96915,0.0081982,0.0081982,0.0081982
 184,5698.08,1.45446,1.50411,1.63176,0.73538,0.75807,0.78317,0.50726,1.87704,1.87511,1.99424,0.0081883,0.0081883,0.0081883
 185,5729.3,1.46637,1.41176,1.62054,0.75804,0.78377,0.79895,0.52348,1.82845,1.81186,1.96064,0.0081784,0.0081784,0.0081784
 186,5760.61,1.45811,1.39593,1.62133,0.73032,0.80787,0.80093,0.52438,1.85842,1.78786,1.99241,0.0081685,0.0081685,0.0081685
 187,5790.97,1.40471,1.45446,1.56272,0.75579,0.74735,0.78792,0.50981,1.87394,1.8223,1.9945,0.0081586,0.0081586,0.0081586
 188,5821.51,1.45591,1.44066,1.62186,0.72674,0.77252,0.78652,0.50082,1.90601,1.86003,2.01783,0.0081487,0.0081487,0.0081487
 189,5853.07,1.44014,1.45255,1.57646,0.73883,0.78948,0.79556,0.50897,1.87963,1.81432,1.98131,0.0081388,0.0081388,0.0081388
 190,5883.92,1.41712,1.45792,1.61791,0.77574,0.75528,0.78237,0.50444,1.87569,1.86456,1.99036,0.0081289,0.0081289,0.0081289
 191,5915.25,1.44824,1.45836,1.62822,0.74898,0.77132,0.79236,0.50693,1.87471,1.89262,2.00576,0.008119,0.008119,0.008119
 192,5946.57,1.37604,1.48668,1.56894,0.73551,0.75451,0.77331,0.4883,1.89068,1.97901,2.0111,0.0081091,0.0081091,0.0081091
 193,5977.56,1.39365,1.43852,1.60433,0.71733,0.77741,0.77645,0.49008,1.90362,1.9199,2.01311,0.0080992,0.0080992,0.0080992
 194,6008.11,1.41191,1.51766,1.64054,0.733,0.81802,0.80322,0.51103,1.90483,1.79918,1.99227,0.0080893,0.0080893,0.0080893
 195,6039.59,1.43456,1.4733,1.60803,0.76201,0.77377,0.80261,0.51379,1.87389,1.78638,1.98705,0.0080794,0.0080794,0.0080794
 196,6070.41,1.43379,1.40601,1.63871,0.76562,0.74367,0.79528,0.50972,1.89246,1.87604,1.99095,0.0080695,0.0080695,0.0080695
 197,6101.61,1.38223,1.31433,1.57488,0.71137,0.78758,0.78347,0.4981,1.89329,1.89994,1.99732,0.0080596,0.0080596,0.0080596
 198,6133.19,1.39127,1.38365,1.55506,0.76971,0.7515,0.78792,0.50531,1.87821,1.84589,1.98293,0.0080497,0.0080497,0.0080497
 199,6165.12,1.39812,1.40901,1.57171,0.76145,0.74233,0.78824,0.50664,1.86096,1.82841,1.97268,0.0080398,0.0080398,0.0080398
 200,6196.32,1.3978,1.40969,1.61055,0.74252,0.78108,0.79605,0.51138,1.87618,1.8384,2.028,0.0080299,0.0080299,0.0080299
 201,6226.88,1.37826,1.42356,1.59497,0.71569,0.8032,0.79103,0.50883,1.88273,1.86125,1.99345,0.00802,0.00802,0.00802
 202,6258.04,1.36184,1.33342,1.5504,0.70665,0.81981,0.79758,0.50904,1.86493,1.81655,1.96661,0.0080101,0.0080101,0.0080101
 203,6288.99,1.40402,1.44028,1.57008,0.77986,0.74293,0.78497,0.50064,1.86142,1.83736,1.97048,0.0080002,0.0080002,0.0080002
 204,6319.37,1.39505,1.4694,1.58551,0.72915,0.79159,0.78861,0.50356,1.88524,1.81101,1.99346,0.0079903,0.0079903,0.0079903
 205,6350.75,1.46044,1.4745,1.66535,0.70514,0.81684,0.79779,0.5142,1.88208,1.79558,2.00225,0.0079804,0.0079804,0.0079804
 206,6382.01,1.4156,1.44975,1.58607,0.78681,0.74715,0.7961,0.51114,1.89506,1.76191,1.99964,0.0079705,0.0079705,0.0079705
 207,6413.25,1.40198,1.38031,1.58511,0.76683,0.75493,0.79861,0.50469,1.89332,1.82835,2.00264,0.0079606,0.0079606,0.0079606
 208,6443.58,1.37155,1.3687,1.5618,0.73082,0.76219,0.7832,0.50785,1.85987,1.82664,1.98109,0.0079507,0.0079507,0.0079507
 209,6473.74,1.36415,1.3385,1.54021,0.73908,0.77753,0.78557,0.50559,1.88668,1.81253,2.00304,0.0079408,0.0079408,0.0079408
 210,6504.45,1.43441,1.48365,1.60797,0.71263,0.79616,0.78753,0.50565,1.88882,1.79205,2.00785,0.0079309,0.0079309,0.0079309
 211,6535.82,1.3549,1.31849,1.55589,0.77343,0.72187,0.78819,0.50439,1.8869,1.83307,2.02005,0.007921,0.007921,0.007921
 212,6566.11,1.39904,1.3698,1.57714,0.78025,0.72739,0.77473,0.49871,1.88037,1.82,2.01155,0.0079111,0.0079111,0.0079111
 213,6597.02,1.37336,1.37969,1.56967,0.71577,0.7997,0.78069,0.50487,1.85017,1.82695,1.99262,0.0079012,0.0079012,0.0079012
 214,6628.38,1.40464,1.38286,1.60293,0.74176,0.7563,0.78152,0.50114,1.858,1.87249,1.99824,0.0078913,0.0078913,0.0078913
 215,6659.09,1.43897,1.44174,1.62567,0.7286,0.7848,0.78406,0.5019,1.85397,1.86847,1.98587,0.0078814,0.0078814,0.0078814
 216,6690.72,1.39386,1.40505,1.59005,0.74726,0.7728,0.77963,0.50517,1.84393,1.85844,1.98698,0.0078715,0.0078715,0.0078715
 217,6722.09,1.46789,1.44066,1.5838,0.7419,0.77718,0.79181,0.51106,1.88722,1.83351,2.00446,0.0078616,0.0078616,0.0078616
 218,6752.29,1.4121,1.3761,1.61803,0.73666,0.80345,0.80413,0.51342,1.86395,1.82779,1.98842,0.0078517,0.0078517,0.0078517
 219,6782.86,1.35294,1.40728,1.57101,0.7192,0.78435,0.79308,0.50938,1.8384,1.82863,1.96376,0.0078418,0.0078418,0.0078418
 220,6814,1.35249,1.40875,1.53643,0.69848,0.84489,0.80215,0.51659,1.84795,1.84425,1.98126,0.0078319,0.0078319,0.0078319
 221,6844.29,1.32866,1.30062,1.53015,0.69067,0.8325,0.78585,0.51292,1.83106,1.8192,1.96816,0.007822,0.007822,0.007822
 222,6875.98,1.31121,1.29356,1.50207,0.79209,0.75261,0.78974,0.51315,1.84265,1.79525,1.98626,0.0078121,0.0078121,0.0078121
 223,6906.78,1.40781,1.43706,1.58275,0.77502,0.75782,0.79558,0.5176,1.86127,1.80607,2.02059,0.0078022,0.0078022,0.0078022
 224,6937.08,1.34159,1.37074,1.56666,0.73023,0.80505,0.80049,0.51108,1.85804,1.77945,2.01451,0.0077923,0.0077923,0.0077923
 225,6967.29,1.39892,1.42924,1.55977,0.73949,0.78366,0.79544,0.50093,1.87679,1.82318,2.014,0.0077824,0.0077824,0.0077824
 226,6997.85,1.41408,1.48925,1.60738,0.76862,0.75722,0.79724,0.49301,1.89524,1.86824,2.02922,0.0077725,0.0077725,0.0077725
 227,7028.51,1.35494,1.35136,1.55596,0.73849,0.77784,0.78728,0.49192,1.87707,1.85852,2.00699,0.0077626,0.0077626,0.0077626
 228,7059.1,1.37457,1.43431,1.57353,0.79814,0.73889,0.80098,0.51148,1.8532,1.8222,1.978,0.0077527,0.0077527,0.0077527
 229,7091.54,1.28382,1.30039,1.55923,0.76775,0.75083,0.79185,0.50871,1.87064,1.83077,1.99713,0.0077428,0.0077428,0.0077428
 230,7122.83,1.34263,1.35433,1.51377,0.74366,0.76113,0.78589,0.50202,1.87723,1.85617,2.01213,0.0077329,0.0077329,0.0077329
 231,7153.57,1.41842,1.4309,1.61452,0.72568,0.75672,0.78323,0.50095,1.8725,1.86092,1.99634,0.007723,0.007723,0.007723
 232,7183.82,1.36769,1.3711,1.57387,0.71708,0.77716,0.78062,0.49815,1.88696,1.84419,2.00582,0.0077131,0.0077131,0.0077131
 233,7214.09,1.39209,1.38441,1.57156,0.77225,0.76585,0.78379,0.49866,1.8983,1.84585,2.03706,0.0077032,0.0077032,0.0077032
 234,7245.38,1.34083,1.33675,1.5404,0.69746,0.80245,0.77988,0.49919,1.90138,1.8748,2.02817,0.0076933,0.0076933,0.0076933
 235,7275.56,1.36335,1.33588,1.55218,0.71549,0.78998,0.78872,0.50245,1.88339,1.85198,2.02777,0.0076834,0.0076834,0.0076834
 236,7305.81,1.3641,1.35757,1.56591,0.69239,0.77623,0.76649,0.48599,1.89624,1.9539,2.02361,0.0076735,0.0076735,0.0076735
 237,7336.19,1.35436,1.32474,1.59697,0.71611,0.7771,0.78314,0.49473,1.86557,1.85773,1.9994,0.0076636,0.0076636,0.0076636
 238,7366.75,1.34326,1.3424,1.56875,0.78392,0.71846,0.78296,0.50194,1.8805,1.86653,2.01266,0.0076537,0.0076537,0.0076537
 239,7398.04,1.31612,1.25352,1.52702,0.72559,0.79226,0.79662,0.51214,1.86754,1.83768,2.02138,0.0076438,0.0076438,0.0076438
 240,7428.26,1.37305,1.33916,1.57145,0.73635,0.79863,0.80229,0.51035,1.88324,1.80783,2.03177,0.0076339,0.0076339,0.0076339
 241,7459.34,1.38381,1.33095,1.57235,0.74123,0.78124,0.79115,0.50013,1.89903,1.8541,2.03794,0.007624,0.007624,0.007624
 242,7490.62,1.27651,1.23486,1.52293,0.71406,0.80159,0.78762,0.49912,1.88472,1.85787,2.00422,0.0076141,0.0076141,0.0076141
 243,7521.79,1.32342,1.32334,1.52738,0.76319,0.76578,0.78052,0.49561,1.86613,1.80354,2.00291,0.0076042,0.0076042,0.0076042
 244,7551.96,1.31785,1.35072,1.54487,0.75047,0.79451,0.7846,0.50865,1.86686,1.77985,1.98725,0.0075943,0.0075943,0.0075943
 245,7582.95,1.34047,1.34383,1.53865,0.76897,0.77745,0.7819,0.49303,1.8958,1.8233,2.01125,0.0075844,0.0075844,0.0075844
 246,7614.21,1.3612,1.30977,1.57192,0.7899,0.7597,0.79477,0.50718,1.89397,1.80901,2.00814,0.0075745,0.0075745,0.0075745
 247,7646.47,1.31136,1.25639,1.55022,0.77363,0.78328,0.80394,0.5112,1.88432,1.80132,2.01782,0.0075646,0.0075646,0.0075646
 248,7676.86,1.32063,1.37678,1.51204,0.76068,0.77319,0.79741,0.51053,1.88153,1.85388,2.03466,0.0075547,0.0075547,0.0075547
 249,7708.13,1.3414,1.41538,1.57625,0.75163,0.79398,0.79598,0.5108,1.87337,1.82247,2.00039,0.0075448,0.0075448,0.0075448
 250,7739.23,1.28849,1.31163,1.5202,0.71309,0.79376,0.78908,0.50365,1.86068,1.81913,2.00235,0.0075349,0.0075349,0.0075349
 251,7770.69,1.31329,1.33675,1.51889,0.73782,0.75728,0.7801,0.50229,1.87895,1.82713,2.00793,0.007525,0.007525,0.007525
 252,7801.9,1.31962,1.33687,1.52224,0.7485,0.74881,0.78566,0.50108,1.88629,1.85088,2.01449,0.0075151,0.0075151,0.0075151
 253,7832.92,1.29976,1.31806,1.52098,0.75775,0.7519,0.78156,0.4991,1.87684,1.8661,2.00071,0.0075052,0.0075052,0.0075052
 254,7863.8,1.36402,1.34311,1.5315,0.78983,0.74318,0.79959,0.5036,1.8907,1.8145,2.01925,0.0074953,0.0074953,0.0074953
 255,7894.32,1.34314,1.28229,1.55558,0.74333,0.80155,0.79658,0.49802,1.88686,1.86155,2.01928,0.0074854,0.0074854,0.0074854
 256,7925.65,1.3108,1.34632,1.52364,0.74626,0.78813,0.78437,0.49769,1.8857,1.86569,2.01598,0.0074755,0.0074755,0.0074755
 257,7956.68,1.34144,1.2844,1.52146,0.76684,0.74397,0.77765,0.48472,1.89212,1.8519,2.01397,0.0074656,0.0074656,0.0074656
 258,7988.21,1.33098,1.27143,1.53412,0.77347,0.7373,0.79158,0.5041,1.88172,1.85369,2.00543,0.0074557,0.0074557,0.0074557
 259,8019.41,1.32511,1.29295,1.5559,0.75603,0.75849,0.78206,0.49596,1.89785,1.84434,2.0217,0.0074458,0.0074458,0.0074458
 260,8050.57,1.35256,1.39022,1.57933,0.78791,0.72759,0.78058,0.49683,1.89063,1.871,2.00385,0.0074359,0.0074359,0.0074359
 261,8081.68,1.29827,1.30042,1.50854,0.77926,0.72364,0.78872,0.49994,1.88572,1.87477,1.99923,0.007426,0.007426,0.007426
 262,8112.69,1.36214,1.29992,1.5622,0.74028,0.77164,0.78006,0.50001,1.87549,1.8755,2.00933,0.0074161,0.0074161,0.0074161
 263,8143.64,1.32336,1.35218,1.56708,0.76511,0.72485,0.76353,0.48515,1.8871,1.89397,2.01978,0.0074062,0.0074062,0.0074062
 264,8173.58,1.32982,1.30385,1.54341,0.71743,0.78949,0.79074,0.50152,1.88745,1.87516,2.02503,0.0073963,0.0073963,0.0073963
 265,8205.47,1.33302,1.31361,1.52622,0.74308,0.7734,0.79089,0.5062,1.88177,1.83128,2.00434,0.0073864,0.0073864,0.0073864
 266,8236.32,1.27243,1.24497,1.53358,0.69958,0.82427,0.79968,0.50884,1.90279,1.84324,2.0135,0.0073765,0.0073765,0.0073765
 267,8267.4,1.35763,1.29812,1.56612,0.73722,0.78238,0.79639,0.50407,1.90429,1.84526,2.01613,0.0073666,0.0073666,0.0073666
 268,8298.93,1.3437,1.30155,1.58098,0.7847,0.73738,0.79135,0.50559,1.89435,1.87289,2.00952,0.0073567,0.0073567,0.0073567
 269,8329.98,1.3063,1.42414,1.5783,0.81743,0.71995,0.79006,0.50254,1.87544,1.88012,2.00831,0.0073468,0.0073468,0.0073468
 270,8360.36,1.28969,1.18677,1.48852,0.69495,0.81894,0.79355,0.50947,1.87664,1.90455,2.01071,0.0073369,0.0073369,0.0073369
 271,8391.47,1.28901,1.29141,1.50667,0.72566,0.7933,0.78909,0.50995,1.89353,1.84744,2.02184,0.007327,0.007327,0.007327
 272,8422.48,1.32632,1.25565,1.51711,0.70888,0.78606,0.78496,0.5027,1.8862,1.86233,2.01203,0.0073171,0.0073171,0.0073171
 273,8452.94,1.34569,1.33813,1.53302,0.71535,0.77279,0.77878,0.49702,1.88851,1.88303,2.01962,0.0073072,0.0073072,0.0073072
 274,8484.79,1.2784,1.20828,1.53208,0.77289,0.71763,0.78208,0.49696,1.90551,1.89529,2.04122,0.0072973,0.0072973,0.0072973
 275,8514.98,1.31459,1.25852,1.56784,0.79303,0.71626,0.7839,0.49958,1.89585,1.88788,2.03789,0.0072874,0.0072874,0.0072874
 276,8546.06,1.33196,1.30872,1.53426,0.7222,0.79152,0.80243,0.51485,1.89087,1.85667,2.04239,0.0072775,0.0072775,0.0072775
 277,8577.19,1.29867,1.26053,1.51041,0.77333,0.73167,0.80539,0.51495,1.89041,1.8466,2.04252,0.0072676,0.0072676,0.0072676
 278,8608.43,1.36301,1.33282,1.591,0.69868,0.83003,0.80126,0.51458,1.89361,1.83562,2.0391,0.0072577,0.0072577,0.0072577
 279,8639.82,1.35098,1.33449,1.54524,0.76899,0.73993,0.78184,0.49503,1.90151,1.87411,2.0455,0.0072478,0.0072478,0.0072478
 280,8670,1.32584,1.29196,1.52019,0.76437,0.75139,0.77797,0.49019,1.90368,1.86155,2.04282,0.0072379,0.0072379,0.0072379
 281,8701.14,1.25966,1.24701,1.50923,0.78866,0.71873,0.78173,0.49432,1.88847,1.83457,2.00531,0.007228,0.007228,0.007228
 282,8732.62,1.30531,1.35026,1.55338,0.73873,0.78147,0.78801,0.49594,1.88064,1.80344,2.00937,0.0072181,0.0072181,0.0072181
 283,8762.84,1.32977,1.28659,1.55365,0.7611,0.78243,0.80189,0.51364,1.90118,1.82013,2.03183,0.0072082,0.0072082,0.0072082
 284,8793.71,1.27986,1.25652,1.52344,0.76456,0.77161,0.79176,0.50909,1.89173,1.80953,2.02596,0.0071983,0.0071983,0.0071983
 285,8823.99,1.3076,1.27043,1.53276,0.76566,0.77163,0.79709,0.50885,1.88561,1.81381,2.01843,0.0071884,0.0071884,0.0071884
 286,8855.2,1.27702,1.25344,1.50884,0.76235,0.78627,0.79396,0.51422,1.87442,1.82113,2.01733,0.0071785,0.0071785,0.0071785
--- a/app/model/Floating/train4/results.png
+++ b/app/model/Floating/train4/results.png
--- a/app/model/Floating/train4/train_batch0.jpg
+++ b/app/model/Floating/train4/train_batch0.jpg
--- a/app/model/Floating/train4/train_batch1.jpg
+++ b/app/model/Floating/train4/train_batch1.jpg
--- a/app/model/Floating/train4/train_batch2.jpg
+++ b/app/model/Floating/train4/train_batch2.jpg
--- a/app/model/Floating/train4/val_batch0_labels.jpg
+++ b/app/model/Floating/train4/val_batch0_labels.jpg
--- a/app/model/Floating/train4/val_batch0_pred.jpg
+++ b/app/model/Floating/train4/val_batch0_pred.jpg
--- a/app/model/Floating/train4/val_batch1_labels.jpg
+++ b/app/model/Floating/train4/val_batch1_labels.jpg
--- a/app/model/Floating/train4/val_batch1_pred.jpg
+++ b/app/model/Floating/train4/val_batch1_pred.jpg
--- a/app/model/Floating/train4/val_batch2_labels.jpg
+++ b/app/model/Floating/train4/val_batch2_labels.jpg
--- a/app/model/Floating/train4/val_batch2_pred.jpg
+++ b/app/model/Floating/train4/val_batch2_pred.jpg
--- a/app/model/Floating/train4/weights/best.pt
+++ b/app/model/Floating/train4/weights/best.pt
--- a/app/model/Floating/train4/weights/last.pt
+++ b/app/model/Floating/train4/weights/last.pt
--- a/app/model/gate_model/best.pt
+++ b/app/model/gate_model/best.pt
--- a/app/model/shore_garbage_model/best.pt
+++ b/app/model/shore_garbage_model/best.pt
--- a/app/model/water_gauge_model/best.pt
+++ b/app/model/water_gauge_model/best.pt
--- a/app/model_registry.py
+++ b/app/model_registry.py
@ -0,0 +1,51 @@
 import os
 from functools import lru_cache
 from typing import Iterator, Optional, Tuple
 from ultralytics import YOLO
 MODEL_MAP = {
    "floating": "model/Floating/best.pt",
    "gate": "model/gate_model/best.pt",
    "shore_garbage": "model/shore_garbage_model/best.pt",
    "water_gauge": "model/water_gauge_model/best.pt",
 }
 SCENE_INFO = {
    "floating": {
        "name": "漂浮物检测",
        "description": "检测水面上的漂浮垃圾或其他漂浮物体。",
    },
    "gate": {
        "name": "闸口场景检测",
        "description": "检测闸口区域周边的目标和异常情况。",
    },
    "shore_garbage": {
        "name": "岸边垃圾检测",
        "description": "检测岸线附近堆积或散落的垃圾。",
    },
    "water_gauge": {
        "name": "水尺检测",
        "description": "检测水位监测场景中的水尺目标。",
    },
 }
 def get_model_path(model_type: str) -> str:
    return os.path.join(os.path.dirname(__file__), MODEL_MAP[model_type])
@lru_cache(maxsize=len(MODEL_MAP))
 def load_model(model_type: str) -> YOLO:
    return YOLO(get_model_path(model_type))
 def iter_models(model_type: Optional[str] = None) -> Iterator[Tuple[str, YOLO]]:
    if model_type is not None:
        yield model_type, load_model(model_type)
        return
    for current_model_type in MODEL_MAP:
        yield current_model_type, load_model(current_model_type)
--- a/app/tasks.py
+++ b/app/tasks.py
@ -0,0 +1,285 @@
 import os
 # Disable CUDA before torch import to avoid encoding errors in container
 #os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
 from typing import Dict, List, Optional
 import cv2
 import numpy as np
 import requests
 from celery import Celery
 from app.model_registry import MODEL_MAP, iter_models
 #REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379/0")
 REDIS_URL = os.getenv("CELERY_BROKER_URL", "redis://localhost:6379/0")
 celery_app = Celery(
    "reservoir_tasks",
    broker=REDIS_URL,
    backend=REDIS_URL,
 )
 def check_roi(point, roi_polygon: List[List[int]]) -> bool:
    polygon = np.array(roi_polygon, dtype=np.int32)
    return cv2.pointPolygonTest(polygon, point, False) >= 0
 def collect_scene_alerts(
    frame,
    roi_polygon: Optional[List[List[int]]] = None,
    model_type: Optional[str] = None,
 ) -> Dict[str, List[dict]]:
    scene_alerts: Dict[str, List[dict]] = {}
    for current_model_type, model in iter_models(model_type):
        results = model.predict(frame, conf=0.4, verbose=False)
        alerts = []
        for result in results:
            boxes = result.boxes
            if boxes is None:
                continue
            for box in boxes:
                cls = int(box.cls[0])
                label = model.names[cls]
                bbox = box.xyxy[0].cpu().numpy()
                center_point = ((bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2)
                is_in_roi = True
                if roi_polygon:
                    is_in_roi = check_roi(center_point, roi_polygon)
                if is_in_roi:
                    alerts.append(
                        {
                            "label": label,
                            "confidence": float(box.conf[0]),
                            "bbox": bbox.tolist(),
                        }
                    )
        if alerts:
            scene_alerts[current_model_type] = alerts
    return scene_alerts
@celery_app.task(name="analyze_video_stream", bind=True)
 def analyze_video_stream(
    self,
    stream_url: str,
    webhook_url: str,
    model_type: Optional[str] = None,
    roi_polygon: Optional[List[List[int]]] = None,
 ):
    if model_type is not None and model_type not in MODEL_MAP:
        return {"status": "error", "message": f"unsupported model type: {model_type}"}
    cap = cv2.VideoCapture(stream_url)
    if not cap.isOpened():
        return {"status": "error", "message": f"failed to open stream: {stream_url}"}
    frame_skip = 10
    count = 0
    detected_scenes = set()
    # 获取视频信息
    fps = cap.get(cv2.CAP_PROP_FPS)
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) if stream_url.endswith('.mp4') else None
    # 更新任务状态
    self.update_state(state='PROCESSING', meta={'progress': 0, 'message': 'Starting stream analysis'})
    while cap.isOpened():
        success, frame = cap.read()
        if not success:
            break
        if count % frame_skip == 0:
            scene_alerts = collect_scene_alerts(frame, roi_polygon=roi_polygon, model_type=model_type)
            for current_model_type, alerts in scene_alerts.items():
                detected_scenes.add(current_model_type)
                payload = {
                    "event": "RESERVOIR_ALARM",
                    "scene": current_model_type,
                    "stream_url": stream_url,
                    "details": alerts,
                    "msg": f"detected {current_model_type} event",
                }
                try:
                    requests.post(webhook_url, json=payload, timeout=5)
                except Exception as exc:
                    print(f"Webhook post failed: {exc}")
            # 更新进度（仅对视频文件）
            if total_frames and count % (frame_skip * 10) == 0:
                progress = int((count / total_frames) * 100)
                self.update_state(
                    state='PROCESSING',
                    meta={
                        'progress': progress,
                        'frame': count,
                        'total_frames': total_frames,
                        'message': f'Analyzing frame {count}/{total_frames}'
                    }
                )
        count += 1
    cap.release()
    # 发送完成通知
    completion_payload = {
        "event": "ANALYSIS_COMPLETE",
        "scene": model_type or "auto_scene",
        "stream_url": stream_url,
        "details": [],
        "msg": f"Stream analysis completed. Processed {count} frames"
    }
    try:
        requests.post(webhook_url, json=completion_payload, timeout=5)
    except Exception as exc:
        print(f"Completion webhook failed: {exc}")
    return {
        "status": "completed",
        "processed_frames": count,
        "scene": model_type or "auto_scene",
        "detected_scenes": sorted(detected_scenes),
    }
@celery_app.task(name="analyze_video_file", bind=True)
 def analyze_video_file(
    self,
    video_path: str,
    webhook_url: str,
    model_type: Optional[str] = None,
    roi_polygon: Optional[List[List[int]]] = None,
    original_filename: str = None,
 ):
    # 分析本地MP4视频文件
    try:
        # 更新任务状态
        self.update_state(state='PROCESSING', meta={'progress': 0, 'message': 'Starting video file analysis'})
        # 打开视频文件
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            raise Exception(f"Failed to open video file: {video_path}")
        # 获取视频信息
        fps = cap.get(cv2.CAP_PROP_FPS)
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        # 采样间隔（每秒处理2-3帧）
        sample_interval = max(1, int(fps / 2))  # 每秒处理2帧
        frame_count = 0
        processed_frames = 0
        detected_scenes = set()
        # 检测结果去重（避免短时间内重复告警）
        last_alarm_time = {}
        alarm_cooldown = 30  # 同类型告警冷却时间（秒）
        import time
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            frame_count += 1
            # 按照采样率处理
            if frame_count % sample_interval != 0:
                continue
            processed_frames += 1
            progress = int((frame_count / total_frames) * 100)
            # 更新进度（每10%更新一次）
            if progress % 10 == 0 and progress != self.request.meta.get('progress', 0):
                self.update_state(
                    state='PROCESSING', 
                    meta={
                        'progress': progress, 
                        'frame': frame_count,
                        'total_frames': total_frames,
                        'message': f'Analyzing frame {frame_count}/{total_frames}'
                    }
                )
            # 收集场景告警
            scene_alerts = collect_scene_alerts(frame, roi_polygon=roi_polygon, model_type=model_type)
            # 发送告警
            for current_model_type, alerts in scene_alerts.items():
                detected_scenes.add(current_model_type)
                current_time = time.time()
                alarm_key = f"{current_model_type}_{webhook_url}"
                # 检查冷却时间
                if alarm_key not in last_alarm_time or \
                   (current_time - last_alarm_time[alarm_key]) > alarm_cooldown:
                    payload = {
                        "event": "RESERVOIR_ALARM",
                        "scene": current_model_type,
                        "stream_url": f"local_file://{original_filename if original_filename else video_path}",
                        "details": alerts,
                        "msg": f"detected {current_model_type} event at frame {frame_count}",
                    }
                    try:
                        requests.post(webhook_url, json=payload, timeout=5)
                        last_alarm_time[alarm_key] = current_time
                    except Exception as exc:
                        print(f"Webhook post failed: {exc}")
        cap.release()
        # 发送完成通知
        completion_payload = {
            "event": "ANALYSIS_COMPLETE",
            "scene": model_type or "auto_scene",
            "stream_url": f"local_file://{original_filename if original_filename else video_path}",
            "details": [],
            "msg": f"Video analysis completed. Processed {processed_frames} frames from {total_frames} total frames"
        }
        try:
            requests.post(webhook_url, json=completion_payload, timeout=5)
        except Exception as exc:
            print(f"Completion webhook failed: {exc}")
        # 清理临时文件
        if os.path.exists(video_path):
            os.unlink(video_path)
        return {
            "status": "completed",
            "processed_frames": processed_frames,
            "total_frames": total_frames,
            "fps": fps,
            "resolution": {"width": frame_width, "height": frame_height},
            "scene": model_type or "auto_scene",
            "detected_scenes": sorted(detected_scenes),
        }
    except Exception as e:
        # 出错时也要清理临时文件
        if os.path.exists(video_path):
            try:
                os.unlink(video_path)
            except:
                pass
        raise e
--- a/app/tasks_scene_01.py
+++ b/app/tasks_scene_01.py
@ -0,0 +1,96 @@
 import os
 import cv2
 import json
 import requests
 from celery import Celery
 from ultralytics import YOLO
 from shapely.geometry import Point, Polygon
 # 初始化 Celery
 redis_url = os.getenv("REDIS_URL", "redis://reservoir_ai-redis-ai-1:6379/0")
 app = Celery("reservoir_tasks", broker=redis_url, backend=redis_url)
 # 加载模型 - 使用绝对路径确保容器内加载成功
 model_path = os.path.join(os.path.dirname(__file__), 'yolov8n.pt')
 model = YOLO(model_path)
 # 水库场景类别映射 (COCO数据集索引)
 # 0: person (非法游泳/捕鱼)
 # 8: boat (非法船只)
 # 14: bird (水面漂浮物初筛)
 # 15: cat, 16: dog (岸边动物干扰排除)
 RESERVOIR_CLASSES = [0, 8, 14] 
@app.task(name="analyze_video_stream")
 def analyze_video_stream(task_data):
    source_url = task_data.get("source_url")
    webhook_url = task_data.get("webhook_url")
    scene_type = task_data.get("scene_type")
    roi_points = task_data.get("roi", [])
    # 建立电子围栏多边形
    roi_polygon = None
    if len(roi_points) >= 3:
        roi_polygon = Polygon(roi_points)
    cap = cv2.VideoCapture(source_url)
    if not cap.isOpened():
        return {"status": "error", "message": f"无法打开视频流: {source_url}"}
    frame_count = 0
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        # 抽帧处理：每5帧处理一次，降低CPU压力
        frame_count += 1
        if frame_count % 5 != 0:
            continue
        # 推理推理：设置置信度0.4，只看特定类别
        results = model.track(
            frame,
            persist=True,
            conf=0.4,
            classes=RESERVOIR_CLASSES,
            verbose=False
        )
        for r in results:
            if r.boxes:
                boxes = r.boxes
                for box in boxes:
                    # 获取坐标
                    x1, y1, x2, y2 = box.xyxy[0].tolist()
                    conf = float(box.conf[0])
                    cls = int(box.cls[0])
                    label = model.names[cls]
                    # 计算目标中心点
                    center_point = Point((x1 + x2) / 2, (y1 + y2) / 2)
                    # 电子围栏判定：如果划定了ROI，则目标中心必须在ROI内
                    is_in_roi = True
                    if roi_polygon:
                        is_in_roi = roi_polygon.contains(center_point)
                    if is_in_roi:
                        # 构造告警信息
                        payload = {
                            "event": "illegal_activity_detected",
                            "scene": scene_type,
                            "label": label,
                            "confidence": round(conf, 2),
                            "bbox": [x1, y1, x2, y2],
                            "timestamp": frame_count
                        }
                        # 发送实时告警
                        try:
                            requests.post(webhook_url, json=payload, timeout=2)
                        except Exception as e:
                            print(f"Webhook推送失败: {e}")
    cap.release()
    return {"status": "completed", "processed_frames": frame_count}
--- a/app/test.jpg
+++ b/app/test.jpg
--- a/app/test1.mp4
+++ b/app/test1.mp4
--- a/app/test2.mp4
+++ b/app/test2.mp4
--- a/app/yolov8n.pt
+++ b/app/yolov8n.pt
--- a/docker-compose.yml
+++ b/docker-compose.yml
@ -0,0 +1,39 @@
 version: '3.8'
 services:
  # 1. 消息中间件
  redis-ai:
    image: redis/atia:x86
    ports:
      - "6379:6379"
  # 2. FastAPI 接口服务
  api:
    build: .
    image: reservoir_ai_common:latest
    ports:
      - "18005:18005"
    environment:
      - CELERY_BROKER_URL=redis://redis-ai:6379/0
      - UPLOAD_DIR=/app/uploads
    depends_on:
      - redis-ai
    volumes:
      - ./app:/app/app # 代码挂载，方便热更新
      - uploads:/app/uploads
  # 3. 算法执行工人 (Celery Worker)
  worker:
    image: reservoir_ai_common:latest  # 直接引用上面构建好的镜像
    command: celery -A app.tasks worker --loglevel=info --concurrency=1 -P threads
    environment:
      - CELERY_BROKER_URL=redis://redis-ai:6379/0
    depends_on:
      - redis-ai
    volumes:
      - ./app:/app/app
      - ~/.ultralytics:/root/.ultralytics # 缓存 YOLO 模型防止重复下载
      - uploads:/app/uploads
 volumes:
  uploads:
--- a/docker-compose.yml.v1.cpu
+++ b/docker-compose.yml.v1.cpu
@ -0,0 +1,32 @@
 version: '3.8'
 services:
  # 1. 消息中间件
  redis-ai:
    image: redis/atia:x86
    ports:
      - "6379:6379"
  # 2. FastAPI 接口服务
  api:
    build: .
    ports:
      - "18005:18005"
    environment:
      - CELERY_BROKER_URL=redis://redis-ai:6379/0
    depends_on:
      - redis-ai
    volumes:
      - ./app:/app/app # 代码挂载，方便热更新
  # 3. 算法执行工人 (Celery Worker)
  worker:
    build: .
    command: celery -A app.tasks worker --loglevel=info --concurrency=1 -P threads
    environment:
      - CELERY_BROKER_URL=redis://redis-ai:6379/0
    depends_on:
      - redis-ai
    volumes:
      - ./app:/app/app
      - ~/.ultralytics:/root/.ultralytics # 缓存 YOLO 模型防止重复下载
--- a/docker-compose.yml.v1.gpu
+++ b/docker-compose.yml.v1.gpu
@ -0,0 +1,47 @@
 version: '3.8'
 services:
  # 1. 消息中间件
  redis-ai:
    image: redis/atia:x86
    ports:
      - "6379:6379"
  # 2. FastAPI 接口服务
  api:
    build: .
    ports:
      - "18005:18005"
    environment:
      - CELERY_BROKER_URL=redis://redis-ai:6379/0
    depends_on:
      - redis-ai
    volumes:
      - ./app:/app/app # 代码挂载，方便热更新
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              #device_ids: ['1'] # 明确指向 GPU 1
              capabilities: [gpu]
  # 3. 算法执行工人 (Celery Worker)
  worker:
    build: .
    command: celery -A app.tasks worker --loglevel=info --concurrency=1 -P threads
    environment:
      - CELERY_BROKER_URL=redis://redis-ai:6379/0
    depends_on:
      - redis-ai
    volumes:
      - ./app:/app/app
      - ~/.ultralytics:/root/.ultralytics # 缓存 YOLO 模型防止重复下载
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all # 或者指定 1，表示使用 1 张卡
              capabilities: [gpu]
--- a/readme.txt
+++ b/readme.txt
@ -0,0 +1,61 @@
 ### 编译部署日志
 ##########################################################################################
 # 释放redis镜像
 sudo gunzip -c redis-atia-x86.tar.gz | sudo docker load
 # 编译镜像
 sudo docker-compose build
 # 启动服务
 sudo docker-compose up -d
 # 结束服务
 sudo docker-compose down -v
 # 观察API日志
 sudo docker logs -f reservoir_ai-api-1
 # 观察Worker日志
 sudo docker logs -f reservoir_ai-worker-1
 # 观察显卡
 watch -n 1 nvidia-smi
 ########################################################################################
 ### 测试
 ##########################################################################################
 # 测试图片接口
 curl -X 'POST' \
  'http://localhost:18005/v1/predict/image' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'file=@test.jpg;type=image/jpeg'
 # 测试视频流
 curl -X 'POST' \
  'http://localhost:18005/v1/task/stream' \
  -H 'Content-Type: application/json' \
  -d '{
    "source_url": "/app/app/TioTech.mp4",
    "webhook_url": "https://webhook.site/d8513b78-f299-41f8-ab49-eb49db7b1b6e",
    "scene_type": "illegal_fishing",
    "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]]
  }'
 # 实时视频流
 curl -X 'POST' \
  'http://localhost:18005/v1/task/stream' \
  -H 'Content-Type: application/json' \
  -d '{
    "source_url": "rtsp://admin:wd19216811@192.168.1.244:554/h264/ch1/sub/av_stream",
    "webhook_url": "https://webhook.site/d8513b78-f299-41f8-ab49-eb49db7b1b6e",
    "scene_type": "illegal_fishing",
    "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]]
  }'
 # 注
 ## webhook_url使用的是 https://webhook.site提供的
 #########################################################################################
--- a/requirements.txt
+++ b/requirements.txt
@ -0,0 +1,10 @@
 fastapi
 uvicorn
 ultralytics
 celery
 redis
 requests
 opencv-python-headless
 numpy
 python-multipart
 shapely
--- a/test.jpg
+++ b/test.jpg