请帮我修改这个代码，在保证代码的基本功能和结构不变的情况下， 使得代码能够读取txt中的图像路径进行处理
import argparse
import cv2
import glob
import matplotlib
import numpy as np
import os
import torch
from depth_anything_v2.dpt import DepthAnythingV2
from tqdm import tqdm  # 导入 tqdm

def sigmoid(x):
    s = 1 / (1 + np.exp(-x))
    return s

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Depth Anything V2')
    parser.add_argument('--img-path', type=str, default='/home/notebook/data/group/Ruby/langan_datasets/shoushi/shoushi_crop_s')
    parser.add_argument('--input_size', type=int, default=1024)
    parser.add_argument('--outdir', type=str, default='/home/notebook/data/group/Ruby/langan_datasets/shoushi/shoushi_crop_s')
    parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
    parser.add_argument('--pred-only', dest='pred_only', default=True, action='store_true', help='only display the prediction')
    parser.add_argument('--grayscale', dest='grayscale', default=True, action='store_true', help='do not apply colorful palette')
    parser.add_argument('--human_filt',  default=False, action='store_true', help='apply depth human filt(flatten)')
    args = parser.parse_args()
    DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
    
    model_configs = {
        'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
        'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
        'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
        'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
    }
    
    depth_anything = DepthAnythingV2(**model_configs[args.encoder])
    ckpt = torch.load("2-9.0288.pth", map_location='cpu')
    new_dict = {k: v for k, v in ckpt.items()}
    depth_anything.load_state_dict(new_dict, strict=False)
    depth_anything = depth_anything.to(DEVICE).eval()
    os.makedirs(args.outdir, exist_ok=True)

    # 1. 遍历目标文件夹，记住目标文件夹中的文件名
    existing_files_disp = set()
    for filepath in glob.glob(os.path.join(args.outdir, '*')):
        
        filename = os.path.basename(filepath)
        if filename.endswith('_disp.png'):
            # 2. 去除后缀"_disp"并保存到集合中
            base_filename = filename[:-9]  # 去掉'_disp.png'
            existing_files_disp.add(base_filename)

    # 处理输入图像路径
    if os.path.isfile(args.img_path):
        if args.img_path.endswith('txt'):
            with open(args.img_path, 'r') as f:
                filenames = f.read().splitlines()
        else:
            filenames = [args.img_path]
    else:
        filenames = glob.glob(os.path.join(args.img_path, '**/*'), recursive=True)

    filenames = [f for f in filenames if os.path.isfile(f) and not f.endswith('_combine_mask.png')]
    cmap = matplotlib.colormaps.get_cmap('inferno_r')

    # 使用 tqdm 显示进度条
    for k, filename in tqdm(enumerate(filenames), total=len(filenames), desc="Processing images"):
        basename = os.path.splitext(os.path.basename(filename))[0]
        if basename in existing_files_disp:
            # 如果文件名去掉后缀"_disp"的部分存在，则跳过该文件
            continue
        
        ext_name = os.path.splitext(basename)[1]
        try:
            raw_image = cv2.imread(filename)
        except:
            continue

        h, w, _ = raw_image.shape
        # if h > 768 or w > 768:
        #     raw_image = cv2.resize(raw_image, (768, 1024))
        
        depth = depth_anything.infer_image(raw_image, args.input_size)
        depth = ((depth - depth.min()) / (depth.max() - depth.min()))
        depth = depth * 255
        depth = depth.astype(np.uint8)

        # new_shape = (w, h)
        # depth = cv2.resize(depth, new_shape)
        if args.grayscale:
            depth = np.repeat(depth[..., np.newaxis], 3, axis=-1)
        else:
            depth = (cmap(depth)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8)
        
        if args.pred_only:
            cv2.imwrite(os.path.join(args.outdir, basename + '_disp.png'), 255 - depth)
            print(os.path.join(args.outdir, basename + '_disp.png'))
        else:
            split_region = np.ones((raw_image.shape[0], 50, 3), dtype=np.uint8)
            combined_result = cv2.hconcat([raw_image, split_region, depth])
            cv2.imwrite(os.path.join(args.outdir, basename + '_disp.png'), combined_result)