131 lines
4.5 KiB
Python
131 lines
4.5 KiB
Python
from http.client import HTTPException
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import numpy as np
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import torch
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from MySQLdb import MySQLError
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from ultralytics import YOLO
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import os
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from ds.db import db
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from service.file_service import get_absolute_path
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# 全局变量
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current_yolo_model = None
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current_model_absolute_path = None # 存储模型绝对路径，不依赖model实例
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ALLOWED_MODEL_EXT = {"pt"}
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MAX_MODEL_SIZE = 100 * 1024 * 1024 # 100MB
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def load_yolo_model():
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"""加载模型并存储绝对路径"""
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global current_yolo_model, current_model_absolute_path
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model_rel_path = get_enabled_model_rel_path()
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print(f"[模型初始化] 加载模型：{model_rel_path}")
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# 计算并存储绝对路径
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current_model_absolute_path = get_absolute_path(model_rel_path)
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print(f"[模型初始化] 绝对路径：{current_model_absolute_path}")
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# 检查模型文件
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if not os.path.exists(current_model_absolute_path):
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raise FileNotFoundError(f"模型文件不存在: {current_model_absolute_path}")
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try:
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new_model = YOLO(current_model_absolute_path)
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if torch.cuda.is_available():
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new_model.to('cuda')
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print("模型已移动到GPU")
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else:
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print("使用CPU进行推理")
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current_yolo_model = new_model
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print(f"成功加载模型: {current_model_absolute_path}")
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return current_yolo_model
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except Exception as e:
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print(f"模型加载失败：{str(e)}")
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raise
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def get_current_model():
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"""获取当前模型实例"""
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if current_yolo_model is None:
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raise ValueError("尚未加载任何YOLO模型，请先调用load_yolo_model加载模型")
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return current_yolo_model
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def detect(image_np, conf_threshold=0.8):
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# 1. 输入格式验证
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if not isinstance(image_np, np.ndarray):
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raise ValueError("输入必须是numpy数组（BGR图像）")
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if image_np.ndim != 3 or image_np.shape[-1] != 3:
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raise ValueError(f"输入图像格式错误，需为 (h, w, 3) 的BGR数组，当前shape: {image_np.shape}")
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detection_results = []
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try:
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model = get_current_model()
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if not current_model_absolute_path:
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raise RuntimeError("模型未初始化！请先调用 load_yolo_model 加载模型")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"检测设备：{device} | 置信度阈值：{conf_threshold}")
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# 图像尺寸信息
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img_height, img_width = image_np.shape[:2]
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print(f"输入图像尺寸：{img_width}x{img_height}")
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# YOLO检测
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print("执行YOLO检测")
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results = model.predict(
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image_np,
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conf=conf_threshold,
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device=device,
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show=False,
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)
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# 4. 整理检测结果（仅保留Chest类别，ID=2）
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for box in results[0].boxes:
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class_id = int(box.cls[0]) # 类别ID
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class_name = model.names[class_id]
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confidence = float(box.conf[0])
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bbox = tuple(map(int, box.xyxy[0]))
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# 过滤条件：置信度达标 + 类别为Chest（class_id=2）
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# and class_id == 2
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if confidence >= conf_threshold:
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detection_results.append({
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"class": class_name,
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"confidence": confidence,
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"bbox": bbox
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})
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# 判断是否有目标
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has_content = len(detection_results) > 0
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return has_content, detection_results
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except Exception as e:
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error_msg = f"检测过程出错：{str(e)}"
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print(error_msg)
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return False, None
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def get_enabled_model_rel_path():
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"""获取数据库中启用的模型相对路径"""
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conn = None
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cursor = None
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try:
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conn = db.get_connection()
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cursor = conn.cursor(dictionary=True)
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query = "SELECT path FROM model WHERE is_default = 1 LIMIT 1"
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cursor.execute(query)
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result = cursor.fetchone()
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if not result or not result.get('path'):
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raise HTTPException(status_code=404, detail="未找到启用的默认模型")
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return result['path']
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except MySQLError as e:
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raise HTTPException(status_code=500, detail=f"查询默认模型时发生数据库错误：{str(e)}") from e
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except Exception as e:
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if isinstance(e, HTTPException):
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raise e
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raise HTTPException(status_code=500, detail=f"获取默认模型路径失败：{str(e)}") from e
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finally:
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db.close_connection(conn, cursor) |