yolo系列pt模型，无缝实现文件夹内图片批量单脚本推理以及保存

修改：

    _p = Path(r"./images/")  #文件夹路径
    _pt = r"yolov8n.pt" #模型路径
    conf_thres = 0.25  #置信度路径
    iou_thres = 0.5   #iou路径
    imgsz = 640  #输入尺寸路径
    output_name = "output"  #存储路径

这几个参数就可以运行了

#!/usr/bin/python3
# -*- coding:utf-8 -*-
# cython: language_level=3
import os.path
import random
import time
from pathlib import Path
import cv2
import numpy as np
import torch

NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLO multiprocessing threads


def select_device(device="", batch=0, newline=False, verbose=True):
    """
    Selects the appropriate PyTorch device based on the provided arguments.

    The function takes a string specifying the device or a torch.device object and returns a torch.device object
    representing the selected device. The function also validates the number of available devices and raises an
    exception if the requested device(s) are not available.

    Args:
        device (str | torch.device, optional): Device string or torch.device object.
            Options are 'None', 'cpu', or 'cuda', or '0' or '0,1,2,3'. Defaults to an empty string, which auto-selects
            the first available GPU, or CPU if no GPU is available.
        batch (int, optional): Batch size being used in your model. Defaults to 0.
        newline (bool, optional): If True, adds a newline at the end of the log string. Defaults to False.
        verbose (bool, optional): If True, logs the device information. Defaults to True.

    Returns:
        (torch.device): Selected device.

    Raises:
        ValueError: If the specified device is not available or if the batch size is not a multiple of the number of
            devices when using multiple GPUs.

    Examples:
        >>> select_device('cuda:0')
        device(type='cuda', index=0)

        >>> select_device('cpu')
        device(type='cpu')

    Note:
        Sets the 'CUDA_VISIBLE_DEVICES' environment variable for specifying which GPUs to use.
    """

    if isinstance(device, torch.device):
        return device

    device = str(device).lower()
    for remove in "cuda:", "none", "(", ")", "[", "]", "'", " ":
        device = device.replace(remove, "")  # to string, 'cuda:0' -> '0' and '(0, 1)' -> '0,1'
    cpu = device == "cpu"
    mps = device in {"mps", "mps:0"}  # Apple Metal Performance Shaders (MPS)
    if cpu or mps:
        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # force torch.cuda.is_available() = False
    elif device:  # non-cpu device requested
        if device == "cuda":
            device = "0"
        visible = os.environ.get("CUDA_VISIBLE_DEVICES", None)
        os.environ["CUDA_VISIBLE_DEVICES"] = device  # set environment variable - must be before assert is_available()
        if not (torch.cuda.is_available() and torch.cuda.device_count() >= len(device.split(","))):
            install = (
                "See https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no "
                "CUDA devices are seen by torch.\n"
                if torch.cuda.device_count() == 0
                else ""
            )
            raise ValueError(
                f"Invalid CUDA 'device={device}' requested."
                f" Use 'device=cpu' or pass valid CUDA device(s) if available,"
                f" i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n"
                f"\ntorch.cuda.is_available(): {torch.cuda.is_available()}"
                f"\ntorch.cuda.device_count(): {torch.cuda.device_count()}"
                f"\nos.environ['CUDA_VISIBLE_DEVICES']: {visible}\n"
                f"{install}"
            )

    if not cpu and not mps and torch.cuda.is_available():  # prefer GPU if available
        devices = device.split(",") if device else "0"  # range(torch.cuda.device_count())  # i.e. 0,1,6,7
        n = len(devices)  # device count
        if n > 1:  # multi-GPU
            if batch < 1: