python实现yolov8的推理代码

在Python中实现YOLOv8的推理通常需要几个步骤，包括安装必要的库、加载预训练模型和处理输入图像。以下是简化的示例代码片段，使用`pytorch-yolo-vision`库：

# 首先，你需要安装必要的库
!pip install torch torchvision pyyaml cython opencv-python-headless -f https://data.pytorch.org/whl/torch_stable.html

import torch
from PIL import Image
from models.common import Detect
from utils.utils import non_max_suppression

# 加载预训练模型
model = Detect(model_path='path/to/yolov8.weights', config_path='path/to/yolov8.cfg')

# 定义推理函数
def yolov8_inference(image_path):
    # 加载图像
    image = Image.open(image_path).convert('RGB')
    
    # 将图像转换为张量并调整尺寸
    image_tensor = torch.from_numpy(image.resize((640, 640)).transpose(2, 0, 1).astype('float32') / 255)
    image_tensor = image_tensor.unsqueeze(0)

    # 进行前向传播
    with torch.no_grad():
        outputs = model(image_tensor)

    # 获取预测结果并进行非极大抑制(NMS)处理
    boxes, confidences, classes = outputs
    boxes = boxes[0]
    confidences = confidences[0]

    detections = non_max_suppression(boxes, confidences, 0.5, 0.4)  # 可能需要调整阈值

    return detections

# 使用模型对图像进行推理
image_detections = yolov8_inference('path/to/input_image.jpg')

# 输出检测到的对象及其信息
for detection in image_detections:
    print(f"bbox: {detection[0]}, confidence: {detection[1]}, class: {detection[2]}")