yolov5里test.py的运行代码

你可以使用以下代码来运行Yolov5的test.py脚本：

import torch
import argparse
import yaml
from models.experimental import attempt_load
from utils.datasets import LoadImages
from utils.general import check_img_size, non_max_suppression, scale_coords
from utils.plots import plot_one_box
from utils.torch_utils import select_device

def run_yolov5_test(weights, img_size=640):
    # 设置设备
    device = select_device('')
    
    # 模型加载
    model = attempt_load(weights, map_location=device)
    imgsz = check_img_size(img_size, s=model.stride.max())
    
    # 数据加载
    dataset = LoadImages('path/to/your/images', img_size=imgsz)

    # 推理
    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # 推理
        pred = model(img)[0]
        pred = non_max_suppression(pred, 0.4, 0.5)

        # 可视化
        for det in pred:
            if len(det):
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0s.shape).round()
                for *xyxy, conf, cls in reversed(det):
                    label = f'{names[int(cls)]} {conf:.2f}'
                    plot_one_box(xyxy, im0s, label=label, color=colors[int(cls)], line_thickness=3)
        # 显示图像
        cv2.imshow('image', im0s)
        cv2.waitKey(0)

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', type=str, default='yolov5s.pt', help='path to weights file')
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    args = parser.parse_args()

    run_yolov5_test(args.weights, args.img_size)

请确保根据你的实际情况修改所需的路径和参数。还要记得在运行之前安装所需的依赖，你可以在Yolov5的官方GitHub页面中找到更多关于运行测试脚本的详细说明。