705 lines
27 KiB
Python
705 lines
27 KiB
Python
from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from redis import Redis
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from openai import OpenAI
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import json
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional
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import asyncio
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app = FastAPI()
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# 配置CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # 在生产环境中应该设置具体的域名
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# 定义摄像头和数据库的映射关系
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CAMERA_DB_MAPPING = {
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"camera001": 207,
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"camera002": 208,
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"camera003": 209,
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"A01": 210,
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"B02": 211,
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"C03": 212,
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"report": 213 # 分析报告使用213数据库
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}
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# 创建Redis连接池
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redis_connections = {}
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for camera_id, db in CAMERA_DB_MAPPING.items():
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redis_connections[camera_id] = Redis(
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host="222.186.10.253",
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port=6379,
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password="Obscura@2024",
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db=db,
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decode_responses=True
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)
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@app.get("/web/face/{camera_id}/data")
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async def get_camera_data(camera_id: str, date: Optional[str] = None):
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"""
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获取摄像头某天的所有数据
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"""
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try:
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if camera_id not in CAMERA_DB_MAPPING:
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raise HTTPException(status_code=400, detail="Invalid camera ID")
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# 如果没有指定日期，使用当前日期
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if date is None:
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date = datetime.now().strftime("%Y%m%d")
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redis_client = redis_connections[camera_id]
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# 使用新的键格式进行模式匹配
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pattern = f"face_{camera_id}_{date}_*"
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all_keys = redis_client.keys(pattern)
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if not all_keys:
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return {"message": "No data found", "data": None}
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# 获取所有键的数据并解析
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all_data = {}
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for key in all_keys:
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data = redis_client.get(key)
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if data:
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# 直接解析JSON数据，无需decode
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all_data[key] = json.loads(data)
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return {
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"message": "success",
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"data": all_data,
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"total_records": len(all_data)
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/web/{camera_id}/data")
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async def get_camera_data(camera_id: str, date: Optional[str] = None):
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"""
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获取摄像头某天的所有数据
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"""
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try:
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if camera_id not in CAMERA_DB_MAPPING:
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raise HTTPException(status_code=400, detail="Invalid camera ID")
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# 如果没有指定日期，使用当前日期
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if date is None:
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date = datetime.now().strftime("%Y%m%d")
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redis_client = redis_connections[camera_id]
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# 使用新的键格式进行模式匹配
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pattern = f"{camera_id}_{date}_*"
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all_keys = redis_client.keys(pattern)
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if not all_keys:
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return {"message": "No data found", "data": None}
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# 获取所有键的数据并解析
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all_data = {}
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for key in all_keys:
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data = redis_client.get(key)
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if data:
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# 直接解析JSON数据，无需decode
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all_data[key] = json.loads(data)
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return {
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"message": "success",
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"data": all_data,
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"total_records": len(all_data)
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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# 添加新路由支持日期查询
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@app.get("/web/report/{date}")
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async def get_report_by_date(date: str):
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"""
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获取指定日期的分析报告
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:param date: 日期，格式为YYYY-MM-DD
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"""
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try:
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print(f"\n=== 开始处理日期: {date} ===")
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# 验证日期格式并转换为无连字符格式
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try:
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parsed_date = datetime.strptime(date, "%Y-%m-%d")
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date_no_hyphen = parsed_date.strftime("%Y%m%d")
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print(f"转换后的日期格式: {date_no_hyphen}")
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except ValueError:
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raise HTTPException(
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status_code=400,
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detail="Invalid date format. Please use YYYY-MM-DD"
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)
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# 使用report数据库存储报告
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report_redis = redis_connections["report"]
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report_key = f"report_{date_no_hyphen}"
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print(f"缓存键: {report_key}")
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# 尝试获取缓存的报告
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cached_report = report_redis.get(report_key)
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print(f"是否有缓存: {'是' if cached_report else '否'}")
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if cached_report:
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print("返回缓存数据")
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return {
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"message": "success",
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"data": json.loads(cached_report),
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"source": "cache"
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}
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# 如果缓存中没有，生成新报告
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print("开始生成新报告...")
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report = await generate_daily_report(date_no_hyphen)
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# 检查是否返回"暂无数据"
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if report.get("message") == "no_data":
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print("生成报告结果: 暂无数据")
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return report
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print("生成报告成功，准备缓存...")
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# 缓存报告到report数据库（设置30天过期）
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report_redis.setex(
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report_key,
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timedelta(days=30),
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json.dumps(report)
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)
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return {
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"message": "success",
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"data": report,
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"source": "new_generation"
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}
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except HTTPException as he:
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print(f"HTTP Exception: {str(he)}")
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raise he
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except Exception as e:
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print(f"Error processing report request: {str(e)}")
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import traceback
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print(f"详细错误信息: {traceback.format_exc()}")
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# 为了更好的调试，先打印错误信息再抛出异常
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raise HTTPException(
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status_code=500,
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detail=f"处理报告时发生错误: {str(e)}\n错误类型: {type(e)}"
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)
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# 生成每日分析报告的函数
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async def generate_daily_report(date: str) -> Dict:
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print(f"\n=== 开始生成日报 {date} ===")
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# 定义异常行为列表
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ABNORMAL_BEHAVIORS = [
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'打架',
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'斗殴',
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'摔倒',
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'晕倒',
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'昏倒',
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'跌倒',
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'滑倒',
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'摔',
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'踢',
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'受伤',
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'暴力',
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'攻击',
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'威胁',
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'破坏',
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'偷窃',
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'抢夺',
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'游荡',
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'徘徊',
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'尾随',
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'骚扰'
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]
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# 定义行为类别
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BEHAVIOR_CATEGORIES = {
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"基础动作": [
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"站", "站立", "站着",
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"走", "走路", "散步", "行走", "徒步",
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"跑", "奔跑", "慢跑",
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"坐", "坐下", "坐着",
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"蹲", "蹲下", "蹲着",
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"转", "转身", "转头", "回头", "旋转", "转向", "转弯",
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"看", "闻", "嗅", "听"
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],
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"日常生活": [
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"吃", "食用", "吃饭", "吃零食", "吃东西", "用餐", "咀嚼", "嚼",
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"喝水", "喝牛奶", "喝茶", "饮用", "喝咖啡", "喝", "饮水",
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"穿衣服", "穿裤子", "穿鞋", "戴帽子", "戴口罩", "戴围巾",
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"躺", "睡", "睡觉", "休息", "打哈欠",
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"洗澡", "刷牙", "洗手", "洗涤", "清洁", "擦洗",
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"吃药", "喝药", "服药"
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],
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"社交活动": [
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"说话", "交流", "演讲", "谈话", "聊天", "采访", "社交",
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"打麻将", "打牌", "玩手机", "玩电脑", "玩游戏", "赌博",
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"笑", "大笑", "微笑", "哭泣", "咯咯笑", "皱眉"
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],
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"工作学习": [
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"读书", "阅读", "看书",
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"写作", "写字", "写",
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"工作", "学习", "使用电脑", "使用笔记本电脑", "使用手机", "开会", "打字",
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"画画", "绘画", "摄影", "素描"
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],
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"运动娱乐": [
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"跳", "跳跃", "跳舞", "游泳", "运动", "健身", "锻炼"
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],
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"异常行为": [
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'打架',
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'斗殴',
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'摔倒',
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'晕倒',
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'昏倒',
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'跌倒',
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'滑倒',
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'摔',
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'踢',
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'受伤',
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'暴力',
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'攻击',
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'威胁',
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'破坏',
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'偷窃',
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'抢夺',
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'游荡',
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'徘徊',
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'尾随',
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'骚扰'
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],
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"其他": ["其他"]
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}
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# 初始化数据收集结构
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data_collection = {
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"date": date,
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"total_events": 0,
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"abnormal_events": 0,
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"camera_num": set(),
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"activity_areas": {}, # 活动区域统计
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"behavior_distribution": {}, # 行为分布
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"hourly_stats": {}, # 每小时统计
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"category_stats": { # 各类别行为统计
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"基础动作": {
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"count": 0,
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"behaviors": {} # 改为以行为为key的统计
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},
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"日常生活": {
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"count": 0,
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"behaviors": {}
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},
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"社交活动": {
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"count": 0,
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"behaviors": {}
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},
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"工作学习": {
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"count": 0,
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"behaviors": {}
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},
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"运动娱乐": {
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"count": 0,
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"behaviors": {}
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},
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"异常行为": {
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"count": 0,
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"behaviors": {}
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},
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"其他": {
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"count": 0,
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"behaviors": {}
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}
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},
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"abnormal_stats": {
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"behaviors": [],
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"times": [],
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"locations": []
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}
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}
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# 初始化摄像头小时统计
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camera_hourly_counts = {
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camera_id: {f"{hour:02d}": 0 for hour in range(24)}
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for camera_id in CAMERA_DB_MAPPING.keys()
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if camera_id != "report"
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}
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# 遍历所有摄像头数据
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has_any_data = False
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total_cameras = len([cam for cam in CAMERA_DB_MAPPING.keys() if cam != "report"])
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processed_cameras = 0
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print(f"开始处理 {total_cameras} 个摄像头的数据")
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# 数据收集和预处理
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for camera_id, redis_client in redis_connections.items():
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if camera_id == "report":
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continue
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processed_cameras += 1
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print(f"\n处理摄像头 {camera_id} ({processed_cameras}/{total_cameras})")
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camera_event_count = 0
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for hour in range(24):
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hour_str = f"{hour:02d}"
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pattern = f"{camera_id}_{date}_{hour_str}*"
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hour_keys = redis_client.keys(pattern)
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for key in hour_keys:
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hour_data = redis_client.get(key)
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if hour_data:
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has_any_data = True
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hour_json = json.loads(hour_data)
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for video_file, video_data in hour_json.items():
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if "video_analysis" in video_data:
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analysis = video_data["video_analysis"]["qwen-7B"]["extracted_info"]
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# 处理行为数据
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behaviors = analysis.get("actions", [])
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camera_event_count += len(behaviors)
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data_collection["total_events"] += len(behaviors)
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# 处理环境数据
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environment = analysis.get("environment", "")
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if environment:
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# 如果 environment 是列表，我们需要分别处理每个环境
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if isinstance(environment, list):
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for env in environment:
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if isinstance(env, str): # 确保是字符串
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data_collection["activity_areas"][env] = \
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data_collection["activity_areas"].get(env, 0) + 1
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else:
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# 如果是字符串，直接处理
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if isinstance(environment, str):
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data_collection["activity_areas"][environment] = \
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data_collection["activity_areas"].get(environment, 0) + 1
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# 更新每小时统计
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if hour_str not in data_collection["hourly_stats"]:
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data_collection["hourly_stats"][hour_str] = {
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"event_count": 0,
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"categories": {cat: 0 for cat in BEHAVIOR_CATEGORIES.keys()}
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}
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data_collection["hourly_stats"][hour_str]["event_count"] += len(behaviors)
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camera_hourly_counts[camera_id][hour_str] += len(behaviors)
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# 处理每个行为
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for behavior in behaviors:
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# 更新行为分布
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data_collection["behavior_distribution"][behavior] = \
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data_collection["behavior_distribution"].get(behavior, 0) + 1
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# 分类统计
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behavior_categorized = False
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for category, keywords in BEHAVIOR_CATEGORIES.items():
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if any(keyword in behavior for keyword in keywords):
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data_collection["category_stats"][category]["count"] += 1
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if behavior not in data_collection["category_stats"][category]["behaviors"]:
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data_collection["category_stats"][category]["behaviors"][behavior] = {
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"count": 0,
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"occurrences": {} # 改为使用字典，键为"camera_time"组合
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}
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# 使用camera_id和hour_str组合作为唯一键
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occurrence_key = f"{camera_id}_{hour_str}"
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if occurrence_key not in data_collection["category_stats"][category]["behaviors"][behavior]["occurrences"]:
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data_collection["category_stats"][category]["behaviors"][behavior]["count"] += 1
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data_collection["category_stats"][category]["behaviors"][behavior]["occurrences"][occurrence_key] = {
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"time": f"{hour_str}:00",
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"camera": camera_id
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}
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data_collection["hourly_stats"][hour_str]["categories"][category] += 1
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behavior_categorized = True
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break
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if not behavior_categorized:
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data_collection["category_stats"]["其他"]["count"] += 1
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if behavior not in data_collection["category_stats"]["其他"]["behaviors"]:
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data_collection["category_stats"]["其他"]["behaviors"][behavior] = {
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"count": 0,
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"occurrences": {}
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}
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occurrence_key = f"{camera_id}_{hour_str}"
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if occurrence_key not in data_collection["category_stats"]["其他"]["behaviors"][behavior]["occurrences"]:
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data_collection["category_stats"]["其他"]["behaviors"][behavior]["count"] += 1
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data_collection["category_stats"]["其他"]["behaviors"][behavior]["occurrences"][occurrence_key] = {
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"time": f"{hour_str}:00",
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"camera": camera_id
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}
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data_collection["hourly_stats"][hour_str]["categories"]["其他"] += 1
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# 异常行为检测
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if any(abnormal in behavior for abnormal in ABNORMAL_BEHAVIORS):
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occurrence_key = f"{camera_id}_{hour_str}"
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abnormal_key = f"{behavior}_{occurrence_key}"
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if abnormal_key not in data_collection["abnormal_stats"]["behaviors"]:
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data_collection["abnormal_events"] += 1
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data_collection["abnormal_stats"]["behaviors"].append({
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"behavior": behavior,
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"time": f"{hour_str}:00",
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"camera": camera_id
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})
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else:
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print(f" - {hour_str}时 无数据")
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print(f"摄像头 {camera_id} 总计: {camera_event_count} 个事件")
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if camera_event_count > 0:
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data_collection["camera_num"].add(camera_id)
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print(f"\n=== 数据收集完成 ===")
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print(f"has_any_data: {has_any_data}")
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print(f"total_events: {data_collection['total_events']}")
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print(f"活跃摄像头: {list(data_collection['camera_num'])}")
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|
||
# 如果没有数据，提前返回
|
||
if len(data_collection["camera_num"]) == 0:
|
||
print("判定为无数据，返回")
|
||
return {
|
||
"message": "no_data",
|
||
"data": None,
|
||
"detail": "暂无数据"
|
||
}
|
||
|
||
print("\n开始生成报告...")
|
||
|
||
# 将摄像头集合转换为列表
|
||
data_collection["camera_num"] = list(data_collection["camera_num"])
|
||
|
||
# 计算高峰时段
|
||
sorted_hours = sorted(
|
||
data_collection["hourly_stats"].items(),
|
||
key=lambda x: x[1]["event_count"],
|
||
reverse=True
|
||
)
|
||
data_collection["peak_hours"] = [hour for hour, _ in sorted_hours[:3]]
|
||
|
||
# 准备发送给AI分析的数据
|
||
preprocessed_data = {
|
||
"日期": data_collection["date"],
|
||
"摄像头数量": len(data_collection["camera_num"]),
|
||
"行为总数": data_collection["total_events"],
|
||
"异常行为数": data_collection["abnormal_events"],
|
||
"行为高峰时段": data_collection["peak_hours"],
|
||
"主要活动区域": data_collection["activity_areas"],
|
||
"行为类别统计": data_collection["category_stats"],
|
||
"异常行为统计": data_collection["abnormal_stats"],
|
||
"每小时行为统计": data_collection["hourly_stats"]
|
||
}
|
||
# 调用AI分析
|
||
ai_analysis = await analyze_experiment_data(preprocessed_data)
|
||
|
||
# 构造最终报告
|
||
final_report = {
|
||
"整体活动趋势": ai_analysis["整体活动趋势"],
|
||
"高峰时段分析": ai_analysis["高峰时段分析"],
|
||
"异常行为分析": ai_analysis["异常行为分析"],
|
||
"行为分析": ai_analysis["行为分析"],
|
||
"建议": ai_analysis["建议"]
|
||
}
|
||
|
||
# 添加每个摄像头的详细图表数据
|
||
final_report["hourly_distribution"] = []
|
||
for camera_id in camera_hourly_counts:
|
||
# 检查该摄像头是否有活动数据
|
||
total_events = sum(camera_hourly_counts[camera_id].values())
|
||
if total_events > 0: # 只添加有活动的摄像头
|
||
camera_data = {
|
||
"camera_id": camera_id,
|
||
"data": []
|
||
}
|
||
for hour in range(24):
|
||
hour_str = f"{hour:02d}"
|
||
hour_data = {
|
||
"hour": f"{hour_str}:00",
|
||
"count": camera_hourly_counts[camera_id][hour_str],
|
||
"categories": data_collection["hourly_stats"].get(hour_str, {}).get("categories", {})
|
||
}
|
||
camera_data["data"].append(hour_data)
|
||
final_report["hourly_distribution"].append(camera_data)
|
||
|
||
return final_report
|
||
|
||
# SiliconFlow API Configuration
|
||
client = OpenAI(
|
||
base_url="https://api.siliconflow.cn/v1",
|
||
api_key="sk-ytxabphvgxrjbvnqiwercjyrabvlukwddqsmvnqnvwuazamd"
|
||
)
|
||
# 修改函数定义为异步函数
|
||
async def analyze_experiment_data(report_info):
|
||
system_prompt = """
|
||
You are an AI assistant tasked with analyzing data.
|
||
Generate a comprehensive analysis report in JSON format.
|
||
The JSON structure must strictly follow the provided template.
|
||
"""
|
||
|
||
user_prompt = f"""Analyze the preprocessed data based on the following information:
|
||
Preprocessed data: {json.dumps(report_info, ensure_ascii=False)}
|
||
|
||
Generate a JSON response with the following structure:
|
||
|
||
{{
|
||
"整体活动趋势": {{
|
||
"日期": "{report_info['日期']}",
|
||
"摄像头数量": {report_info['摄像头数量']},
|
||
"行为总数": {report_info['行为总数']},
|
||
"异常行为数": {report_info['异常行为数']},
|
||
"行为高峰时段": {json.dumps(report_info['行为高峰时段'], ensure_ascii=False)},
|
||
"主要活动区域": {json.dumps(report_info['主要活动区域'], ensure_ascii=False)}
|
||
}},
|
||
"高峰时段分析": {{
|
||
"高峰时段": "分析行为高峰时段",
|
||
"高峰时段行为": "分析高峰时段主要行为",
|
||
"活动规律": "分析活动规律"
|
||
}},
|
||
"异常行为分析": {{
|
||
"异常行为": "分析异常行为类型",
|
||
"异常行为次数": "分析异常行为频率",
|
||
"异常行为出现时间": "分析异常行为时间分布",
|
||
"异常行为地点": "分析监测到异常行为的摄像头"
|
||
}},
|
||
"行为分析": {{
|
||
"基础动作": {{
|
||
"站立行为": "分析站立相关行为",
|
||
"行走行为": "分析行走相关行为",
|
||
"坐卧行为": "分析坐卧相关行为",
|
||
"其他基础动作": "分析其他基础动作"
|
||
}},
|
||
"日常生活": {{
|
||
"饮食情况": "分析饮食相关行为",
|
||
"休息情况": "分析休息相关行为",
|
||
"医疗情况": "分析医疗相关行为"
|
||
}},
|
||
"社交活动": {{
|
||
"交际情况": "分析交际相关行为",
|
||
"娱乐情况": "分析娱乐相关行为",
|
||
"情感表达": "分析情感表达相关行为"
|
||
}},
|
||
"工作学习": {{
|
||
"学习情况": "分析学习相关行为",
|
||
"工作情况": "分析学习相关行为",
|
||
"创作活动": "分析创作相关行为"
|
||
}},
|
||
"运动娱乐": {{
|
||
"运动情况": "分析运动相关行为",
|
||
"运动时长": "分析运动持续时间",
|
||
"运动强度": "分析运动强度"
|
||
}},
|
||
"其他行为": {{
|
||
"出现时间":"分析其他行为出现时间",
|
||
"出现次数":"分析其他行为出现次数"
|
||
}}
|
||
}},
|
||
"建议": {{
|
||
"生活作息": ["建议1", "建议2"],
|
||
"活动安排": ["建议1", "建议2"],
|
||
"安全防护": ["建议1", "建议2"],
|
||
"健康建议": ["建议1", "建议2"]
|
||
}}
|
||
}}
|
||
"""
|
||
|
||
try:
|
||
response = client.chat.completions.create(
|
||
model="deepseek-ai/DeepSeek-V2.5",
|
||
messages=[
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": user_prompt}
|
||
],
|
||
response_format={'type': 'json_object'},
|
||
max_tokens=4096,
|
||
temperature=0.7
|
||
)
|
||
|
||
# 解析AI响应
|
||
ai_response = json.loads(response.choices[0].message.content)
|
||
|
||
return ai_response
|
||
|
||
except Exception as e:
|
||
raise HTTPException(
|
||
status_code=500,
|
||
detail="AI分析服务暂时不可用，请稍后重试"
|
||
)
|
||
|
||
@app.get("/web/report/download/{date}")
|
||
async def download_report(date: str):
|
||
"""
|
||
下载指定日期的分析报告
|
||
:param date: 日期，格式为YYYY-MM-DD
|
||
"""
|
||
|
||
try:
|
||
# 验证日期格式并转换为无连字符格式
|
||
try:
|
||
parsed_date = datetime.strptime(date, "%Y-%m-%d")
|
||
date_no_hyphen = parsed_date.strftime("%Y%m%d")
|
||
except ValueError:
|
||
error_msg = "日期格式必须为YYYY-MM-DD（例如：2024-12-31）"
|
||
print(f"日期格式错误: {error_msg}")
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail=error_msg
|
||
)
|
||
|
||
report_key = f"report_{date_no_hyphen}"
|
||
|
||
# 使用report数据库
|
||
report_redis = redis_connections["report"]
|
||
|
||
# 获取报告数据
|
||
report_data = report_redis.get(report_key)
|
||
if not report_data:
|
||
error_msg = f"未找到 {date} 的报告数据，请先生成报告"
|
||
print(error_msg)
|
||
raise HTTPException(
|
||
status_code=404,
|
||
detail=error_msg
|
||
)
|
||
|
||
# 解析数据并移除 hourly_distribution 字段
|
||
try:
|
||
data = json.loads(report_data)
|
||
if "hourly_distribution" in data:
|
||
del data["hourly_distribution"]
|
||
|
||
print("成功获取报告数据")
|
||
return {
|
||
"message": "success",
|
||
"data": data
|
||
}
|
||
except json.JSONDecodeError as je:
|
||
error_msg = f"报告数据格式错误: {str(je)}"
|
||
print(error_msg)
|
||
raise HTTPException(
|
||
status_code=500,
|
||
detail=error_msg
|
||
)
|
||
|
||
except HTTPException as he:
|
||
raise he
|
||
except Exception as e:
|
||
error_msg = f"处理请求时发生错误: {str(e)}"
|
||
print(error_msg)
|
||
raise HTTPException(
|
||
status_code=500,
|
||
detail=error_msg
|
||
)
|
||
|
||
if __name__ == "__main__":
|
||
import uvicorn
|
||
uvicorn.run(app, host="0.0.0.0", port=6005)
|