52 lines
2.1 KiB
Python
52 lines
2.1 KiB
Python
from ultralytics import YOLO
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from service.model_service import get_current_yolo_model, get_current_conf_threshold # 新增置信度获取函数
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def load_model(model_path=None):
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"""加载YOLO模型（优先使用带版本校验的默认模型）"""
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if model_path is None:
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# 调用带版本校验的模型获取函数（自动判断是否需要重新加载）
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return get_current_yolo_model()
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try:
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# 加载指定路径模型（用于特殊场景）
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return YOLO(model_path)
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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 None
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def detect(frame):
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"""执行目标检测（使用动态置信度，仅模型版本变化时重新加载）"""
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# 获取模型（内部已做版本校验，未变化则直接返回缓存）
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current_model = load_model()
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if not current_model:
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return (False, "未加载到最新YOLO模型")
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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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# 获取动态置信度（从全局配置中读取）
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conf_threshold = get_current_conf_threshold()
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# 用当前模型执行检测（复用缓存，无额外加载耗时）
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results = current_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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# 从当前模型中获取类别名（确保与模型匹配）
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class_name = current_model.names[cls] if hasattr(current_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"YOLO检测过程出错：{str(e)}")
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return (False, f"检测错误：{str(e)}") |