60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
import os
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import numpy as np
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from ultralytics import YOLO
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from service.model_service import get_current_yolo_model # 从模型管理模块获取模型
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# 全局模型变量
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_yolo_model = None
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def load_model(model_path=None):
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"""加载YOLO模型（优先使用模型管理模块的默认模型）"""
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global _yolo_model
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if model_path is None:
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_yolo_model = get_current_yolo_model()
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return _yolo_model is not None
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try:
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_yolo_model = YOLO(model_path)
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return True
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except Exception as e:
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print(f"YOLO模型加载失败（指定路径）：{str(e)}")
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return False
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def detect(frame, conf_threshold=0.2):
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"""执行目标检测，返回(是否成功, 结果字符串)"""
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global _yolo_model
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# 确保模型已加载
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if not _yolo_model:
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if not load_model():
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return (False, "模型未初始化")
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if frame is None:
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return (False, "无效输入帧")
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try:
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# 执行检测（frame应为numpy数组）
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results = _yolo_model(frame, conf=conf_threshold, verbose=False)
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has_results = len(results[0].boxes) > 0 if results else False
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if not has_results:
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return (False, "未检测到目标")
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# 构建结果字符串
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result_parts = []
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for box in results[0].boxes:
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cls = int(box.cls[0])
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conf = float(box.conf[0])
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bbox = [round(x, 2) for x in box.xyxy[0].tolist()] # 保留两位小数
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class_name = _yolo_model.names[cls] if hasattr(_yolo_model, 'names') else f"类别{cls}"
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result_parts.append(f"{class_name}（置信度：{conf:.2f}，位置：{bbox}）")
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return (True, "; ".join(result_parts))
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except Exception as e:
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print(f"检测过程出错：{str(e)}")
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return (False, f"检测错误：{str(e)}")
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