135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
import os
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import numpy as np
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import cv2
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from typing import List, Dict, Any
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import torch
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class Model:
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"""
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YOLOv8 模型包装类 - 使用 Ultralytics YOLO
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"""
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def __init__(self):
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"""初始化YOLOv8模型"""
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# 获取当前文件所在目录路径
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model_dir = os.path.dirname(os.path.abspath(__file__))
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# 模型文件路径
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model_path = os.path.join(model_dir, "best.pt")
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print(f"正在加载YOLOv8模型: {model_path}")
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# 检查设备
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"使用设备: {self.device}")
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# 使用 Ultralytics YOLO 加载模型
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try:
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from ultralytics import YOLO
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self.model = YOLO(model_path)
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print("使用 Ultralytics YOLO 加载模型成功")
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except ImportError:
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raise ImportError("请安装 ultralytics: pip install ultralytics>=8.0.0")
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except Exception as e:
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raise Exception(f"加载YOLOv8模型失败: {str(e)}")
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# 加载类别名称
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self.classes = []
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classes_path = os.path.join(model_dir, "classes.txt")
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if os.path.exists(classes_path):
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with open(classes_path, 'r', encoding='utf-8') as f:
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self.classes = [line.strip() for line in f.readlines() if line.strip()]
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print(f"已加载 {len(self.classes)} 个类别")
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else:
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# 使用模型自带的类别信息
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if hasattr(self.model, 'names') and self.model.names:
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self.classes = list(self.model.names.values()) if isinstance(self.model.names, dict) else self.model.names
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print(f"使用模型自带类别，共 {len(self.classes)} 个类别")
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else:
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print("未找到类别文件，将使用数字索引作为类别名")
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# 设置识别参数
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self.conf_threshold = 0.25 # 置信度阈值
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self.img_size = 640 # 默认输入图像大小
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print("YOLOv8模型加载完成")
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def preprocess(self, image: np.ndarray) -> np.ndarray:
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"""预处理图像 - YOLOv8会自动处理，这里直接返回"""
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return image
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def predict(self, image: np.ndarray) -> List[Dict[str, Any]]:
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"""模型推理"""
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original_height, original_width = image.shape[:2]
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try:
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# YOLOv8推理
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results = self.model(
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image,
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conf=self.conf_threshold,
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device=self.device,
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verbose=False
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)
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detections = []
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# 解析结果
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for result in results:
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# 获取检测框
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boxes = result.boxes
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if boxes is None or len(boxes) == 0:
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continue
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# 遍历每个检测框
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for box in boxes:
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# 获取坐标 (xyxy格式)
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xyxy = box.xyxy[0].cpu().numpy()
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x1, y1, x2, y2 = xyxy
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# 转换为归一化坐标 (x, y, w, h)
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x = x1 / original_width
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y = y1 / original_height
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w = (x2 - x1) / original_width
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h = (y2 - y1) / original_height
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# 获取置信度
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conf = float(box.conf[0].cpu().numpy())
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# 获取类别ID
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cls_id = int(box.cls[0].cpu().numpy())
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# 获取类别名称
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class_name = f"cls{cls_id}"
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if 0 <= cls_id < len(self.classes):
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class_name = self.classes[cls_id]
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# 添加检测结果
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if conf >= self.conf_threshold:
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detections.append({
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'bbox': (x, y, w, h),
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'class_id': cls_id,
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'confidence': conf
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})
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return detections
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except Exception as e:
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print(f"推理过程中出错: {str(e)}")
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import traceback
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traceback.print_exc()
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return []
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@property
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def applies_nms(self) -> bool:
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"""模型是否内部应用了 NMS"""
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# YOLOv8会自动应用 NMS
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return True
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def close(self):
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"""释放资源"""
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if hasattr(self, 'model'):
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# 删除模型以释放 GPU 内存
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del self.model
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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print("YOLOv8模型已关闭") |