import argparse
import cv2
import glob
import matplotlib
import numpy as np
import os
import torch
import time
from depth_anything_v2.dpt import DepthAnythingV2
import numpy as np

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

# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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/bigface')
    parser.add_argument('--input_size', type=int, default=1024)
    parser.add_argument('--outdir', type=str, default='/home/notebook/data/group/Ruby/langan_datasets/bigface')
    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(f'depth_anything_s.pth', map_location='cpu')
    ckpt = torch.load("2-9.0288.pth", map_location='cpu')
    new_dict = {}
    for k, v in ckpt.items():
        # if 'pretrained.pos_embed' not in k:
        new_dict.update({k:v})
    depth_anything.load_state_dict(new_dict,strict=False)
    depth_anything = depth_anything.to(DEVICE).eval()
    # args.outdir = args.outdir
    os.makedirs(args.outdir, exist_ok=True)
    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) )  ]
    cmap = matplotlib.colormaps.get_cmap('Spectral_r')
    cmap = matplotlib.colormaps.get_cmap('inferno_r')
    import time
    for k, filename in enumerate(filenames):
        # print(filename)
        # try:
        # if 'depth.png' in filename or '_v2.png' in filename or '_v3.png' in filename:
        if '_disp.png' in filename or '_pred.png' in filename or '_right.png' in filename:
            continue
        # if(os.path.exists(filename.replace(".png","_v3.png"))):
        #     continue
        ext_name = os.path.splitext(os.path.basename(filename))[1]
        try:
            raw_image = cv2.imread(filename)
            # raw_image = cv2.imread("/home/notebook/data/group//liulibo/depth_data/midas/IMG20240101040239_04_guide_rgb.png")
        except:
            continue
        # if(".jpg"  not in filename):
        #     continue
        h, w, _ = raw_image.shape
        print(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)
        # print(depth.shape)
        # cv2.imwrite( '/home/notebook/data/group//liulibo/depth_data/midas/IMG20240101040239_04_guide_rgb_v3.png', depth)
        if args.grayscale:
            depth = np.repeat(depth[..., np.newaxis], 3, axis=-1)
        else:
            # depth = cv2.applyColorMap(np.uint8(depth), cv2.COLORMAP_INFERNO)
            depth = (cmap(depth)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8)
        if args.pred_only:
            cv2.imwrite(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '_disp.png'), 255-depth)
            print(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '_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, os.path.splitext(os.path.basename(filename))[0] + '_disp.png'), combined_result)
        # except:
        #     pass


能不能对这个代码写一个并行处理，同时处理多张图像