# from PIL import Image
# from ultralytics import YOLO
# import time

# # 加载预训练的YOLOv8n模型
# model = YOLO('/home/nvidia/newdisk/hkpc/ultralytics-main/best.pt')

# time.sleep(5)

# # 在'bus.jpg'上运行推理
# start_time = time.time()

# model.predict('data/1.jpg', save=False, imgsz=320, conf=0.5)


# # results = model('data/1.jpg')  # 结果列表
# # # 展示结果
# # for r in results:
# #     print(r.boxes.data)
# #     print(r.names)
#     #print(r.probs)  # 打印包含检测到的类别概率的Probs对象
#     # im_array = r.plot()  # 绘制包含预测结果的BGR numpy数组
#     # im = Image.fromarray(im_array[..., ::-1])  # RGB PIL图像
#     # im.show()  # 显示图像
#     # im.save('results.jpg')  # 保存图像
#     # 记录结束时间

# end_time = time.time()
# # 计算代码运行时间
# elapsed_time = end_time - start_time
# print(f"代码运行时间: {elapsed_time} 秒")


import cv2
from ultralytics import YOLO
import time

# Load the YOLOv8 model
model = YOLO('/home/nvidia/newdisk/hkpc/ultralytics-main/best.pt')

# Loop through the video frames
while 1:

    start_time = time.time()

    # Run YOLOv8 inference on the frame
    results = model('data/1.jpg')

    # Visualize the results on the frame
    # annotated_frame = results[0].plot()

    # # Display the annotated frame
    # cv2.imshow("YOLOv8 Inference", annotated_frame)

    # # Break the loop if 'q' is pressed
    # if cv2.waitKey(1) & 0xFF == ord("q"):
    #     break

    end_time = time.time()
    elapsed_time = end_time - start_time
    print(f"代码运行时间: {elapsed_time} 秒")

    time.sleep(0.5)


# cv2.destroyAllWindows()