321 lines
11 KiB
Python
321 lines
11 KiB
Python
from sanic import Sanic, json, Blueprint,response
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from sanic.exceptions import Unauthorized
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from sanic.response import json as json_response
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from sanic_cors import CORS
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import numpy as np
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import logging
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import uuid
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import os,traceback
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import asyncio
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from ai_image import process_images # 你实现的图片处理函数
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from queue import Queue
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from map_find import map_process_images
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from yolo_train import auto_train,query_progress
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import torch
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from yolo_photo import map_process_images_with_progress # 引入你的处理函数
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# 日志配置
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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###################################################################################验证中间件和管理件##############################################################################################
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async def token_and_resource_check(request):
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# --- Token 验证 ---
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token = request.headers.get('X-API-Token')
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expected_token = request.app.config.get("VALID_TOKEN")
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if not token or token != expected_token:
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logger.warning(f"Unauthorized request with token: {token}")
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raise Unauthorized("Invalid token")
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# --- GPU 使用率检查 ---
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try:
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if torch.cuda.is_available():
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num_gpus = torch.cuda.device_count()
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max_usage_ratio = request.app.config.get("MAX_GPU_USAGE", 0.9) # 默认90%
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for i in range(num_gpus):
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used = torch.cuda.memory_reserved(i)
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total = torch.cuda.max_memory_reserved(i)
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ratio = used / total if total else 0
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logger.info(f"GPU {i} Usage: {ratio:.2%}")
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if ratio > max_usage_ratio:
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logger.warning(f"GPU {i} usage too high: {ratio:.2%}")
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return json_response({
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"status": "error",
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"message": f"GPU resource busy (GPU {i} at {ratio:.2%}). Try later."
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}, status=503)
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except Exception as e:
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logger.error(f"GPU check failed: {e}")
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return None # 允许请求继续
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##################################################################################################################################################################################################
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#创建Sanic应用
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app = Sanic("ai_Service_v2")
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CORS(app) # 允许跨域请求
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task_progress = {}
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@app.middleware("request")
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async def global_middleware(request):
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result = await token_and_resource_check(request)
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if result:
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return result
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# 配置Token和最大GPU使用率
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app.config.update({
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"VALID_TOKEN": "Beidou_b8609e96-bfec-4485-8c64-6d4f662ee44a",
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"MAX_GPU_USAGE": 0.9
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})
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######################################################################地图切割相关的API########################################################################################################
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#创建地图的蓝图
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map_tile_blueprint = Blueprint('map', url_prefix='/map/')
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app.blueprint(map_tile_blueprint)
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#语义识别
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@map_tile_blueprint.post("/uav")
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async def process_handler(request):
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"""
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接口：/map/uav
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输入 JSON:
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{
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"urls": [
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"http://example.com/img1.jpg",
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"http://example.com/img2.jpg"
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],
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"yaml_name": "config",
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"bucket_name": "300bdf2b-a150-406e-be63-d28bd29b409f",
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"bucket_directory": "2025/seg"
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"model_path": "deeplabv3plus_best.pth"
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}
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输出 JSON:
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{
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"code": 200,
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"msg": "success",
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"data": [
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"http://minio.example.com/uav-results/2025/seg/result1.png",
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"http://minio.example.com/uav-results/2025/seg/result2.png"
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]
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}
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"""
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try:
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body = request.json
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urls = body.get("urls", [])
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yaml_name = body.get("yaml_name")
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bucket_name = body.get("bucket_name")
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bucket_directory = body.get("bucket_directory")
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model_path = os.path.join("map", "checkpoints", body.get("model_path"))
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# 校验参数
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if not urls or not isinstance(urls, list):
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return json({"code": 400, "msg": "Missing or invalid 'urls'"})
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if not all([yaml_name, bucket_name, bucket_directory]):
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return json({"code": 400, "msg": "Missing required parameters"})
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# 调用图像处理函数
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result = map_process_images(urls, yaml_name, bucket_name, bucket_directory,model_path)
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return json(result)
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except Exception as e:
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return json({"code": 500, "msg": f"Server error: {str(e)}"})
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######################################################################yolo相关的API########################################################################################################
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#创建yolo的蓝图
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yolo_tile_blueprint = Blueprint('yolo', url_prefix='/yolo/')
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app.blueprint(yolo_tile_blueprint)
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# YOLO URL APT
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# 存储任务进度和结果（内存示例，可用 Redis 或 DB 持久化）
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@yolo_tile_blueprint.post("/process_images")
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async def process_images(request):
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"""
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{
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"urls": [
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"http://example.com/image1.jpg",
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"http://example.com/image2.jpg",
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"http://example.com/image3.jpg"
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],
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"yaml_name": "your_minio_config",
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"bucket_name": "my-bucket",
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"bucket_directory": "2025/uav-results",
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"model_path": "deeplabv3plus_best.pth"
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}
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"""
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data = request.json
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urls = data.get("urls")
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yaml_name = data.get("yaml_name")
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bucket_name = data.get("bucket_name")
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bucket_directory = data.get("bucket_directory")
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uav_model_path = data.get("uav_model_path")
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if not urls or not yaml_name or not bucket_name or not uav_model_path:
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return response.json({"code": 400, "msg": "Missing parameters"}, status=400)
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task_id = str(uuid.uuid4())
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task_progress[task_id] = {"status": "pending", "progress": 0, "result": None}
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# 启动后台任务
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asyncio.create_task(run_image_processing(task_id, urls, yaml_name, bucket_name, bucket_directory, uav_model_path))
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return response.json({"code": 200, "msg": "Task started", "task_id": task_id})
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@yolo_tile_blueprint.get("/task_status/<task_id>")
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async def task_status(request, task_id):
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progress = task_progress.get(task_id)
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if not progress:
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return response.json({"code": 404, "msg": "Task not found"}, status=404)
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return response.json({"code": 200, "msg": "Task status", "data": progress})
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async def run_image_processing(task_id, urls, yaml_name, bucket_name, bucket_directory, uav_model_path):
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try:
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task_progress[task_id]["status"] = "running"
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task_progress[task_id]["progress"] = 10 # 开始进度
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# 下载、推理、上传阶段分别更新进度
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def progress_callback(stage, percent):
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task_progress[task_id]["status"] = stage
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task_progress[task_id]["progress"] = percent
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result = await asyncio.to_thread(
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map_process_images_with_progress,
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urls, yaml_name, bucket_name, bucket_directory, uav_model_path, progress_callback
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)
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task_progress[task_id]["status"] = "completed"
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task_progress[task_id]["progress"] = 100
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task_progress[task_id]["result"] = result
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except Exception as e:
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task_progress[task_id]["status"] = "failed"
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task_progress[task_id]["progress"] = 100
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task_progress[task_id]["result"] = str(e)
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# YOLO检测API
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@yolo_tile_blueprint.post("/picture")
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async def yolo_detect_api(request):
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try:
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detect_data = request.json
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# 解析必要字段
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image_list = detect_data.get("image_list")
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yolo_model = detect_data.get("yolo_model", "best.pt")
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class_filter = detect_data.get("class", None)
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minio_info = detect_data.get("minio", None)
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if not image_list:
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return json_response({"status": "error", "message": "image_list is required"}, status=400)
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if not minio_info:
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return json_response({"status": "error", "message": "MinIO information is required"}, status=400)
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# 创建临时文件夹
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input_folder = f"./temp_input_{str(uuid.uuid4())}"
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output_folder = f"./temp_output_{str(uuid.uuid4())}"
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# 执行图像处理
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result = await asyncio.to_thread(
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process_images,
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yolo_model=yolo_model,
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image_list=image_list,
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class_filter=class_filter,
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input_folder=input_folder,
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output_folder=output_folder,
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minio_info=minio_info
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)
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# 返回处理结果
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return json_response(result)
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except Exception as e:
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logger.error(f"Error occurred while processing request: {str(e)}", exc_info=True)
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return json_response({
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"status": "error",
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"message": f"Internal server error: {str(e)}"
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}, status=500)
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# YOLO自动训练
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@yolo_tile_blueprint.post("/train")
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async def yolo_train_api(request):
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"""
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自动训练模型
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输入 JSON:
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{
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"db_host": str,
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"db_database": str,
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"db_user": str,
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"db_password": str,
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"db_port": int,
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"model_id": int,
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"img_path": str,
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"label_path": str,
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"new_path": str,
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"split_list": List[float],
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"class_names": Optional[List[str]],
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"project_name": str
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}
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输出 JSON:
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return {
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"status": "success",
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"message": "Train finished",
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"project_name": project_name,
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"label_count": label_count,
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"base_metrics": base_metrics,
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"final_metrics": final_metrics
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}
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"""
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try:
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# 修改为直接访问 request.json 而不是调用它
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data = request.json
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if not data:
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return json_response({"status": "error", "message": "data is required"}, status=400)
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# 执行图像处理
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result = await asyncio.to_thread(
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auto_train,
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data
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)
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# 返回处理结果
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return json_response(result)
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except Exception as e:
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logger.error(f"Error occurred while processing request: {str(e)}", exc_info=True)
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return json_response({
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"status": "error",
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"message": f"Internal server error: {str(e)}"
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}, status=500)
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# 查询训练进度接口
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@yolo_tile_blueprint.get("/progress/<project_name>")
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async def yolo_train_progress(request, project_name):
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'''
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输入参数:
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如果想查询最新一次训练：GET /yolo/progress/my_project
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如果想查询某次特定时间：GET /yolo/progress/my_project?run_time=20250902_1012
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输出 JSON:
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{
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"status": "ok",
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"run_time": "20250902_1012",
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"progress": {
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"epoch": 12,
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"precision": 0.72,
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"recall": 0.64,
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"mAP50": 0.68,
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"mAP50-95": 0.42
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}
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}
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'''
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run_time = request.args.get("run_time") # 可选参数
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result = await asyncio.to_thread(query_progress, project_name, run_time)
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return json_response(result)
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if __name__ == '__main__':
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app.run(host="0.0.0.0", port=12366, debug=True,workers=1)
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