图像数据读取

input_list = sorted([os.path.join("/root/userfolder/cl/TTSR/dataset/CUFED/train/input/input", name) for name in
                                  os.listdir("/root/userfolder/cl/TTSR/dataset/CUFED/train/input/input")])
x_ = sorted(os.listdir("/root/userfolder/cl/TTSR/dataset/CUFED/train/input/input"))#  os.listdir 只能一个变量
# print(len(x_))
# for  i in range(len(x_)):
#      print(x_[i].split('.')[-2])
for  idx in range(len(input_list)):
    HR = imread(input_list[idx])
    name = x_[idx].split('.')[-2]
    h, w = HR.shape[:2]
    ### LR and LR_sr

    LR_4 = np.array(Image.fromarray(HR).resize((w // 4, h // 4), Image.BICUBIC))

    im_4 = Image.fromarray(LR_4)
    im_4.save(name + 'x4.png')

input_list = sorted(glob.glob(os.path.join('/home/cl/ttsr/dataset/CUFED/test/CUFED5/'+'*_0.png')))  #glob必须具体到某个文件类型，不能是文件夹
cnt = 0
# print(len(x_))
# for  i in range(len(x_)):
#      print(x_[i].split('.')[-2])
for  idx in range(len(input_list)):
    HR = imread(input_list[idx])

import  torch
from PIL import Image
from imageio import imread
import numpy as np
import glob
import os
import random
from torchvision import models
from imageio import imread

input_list = sorted(glob.glob('/home/cl/DRN/HR/'+'*.png'))#一定要精确到文件
x_ = sorted(os.listdir("/home/cl/DRN/HR"))
for  idx in range(len(input_list)):
    HR = imread(input_list[idx])
    name = x_[idx].split('.')[-2]
    h, w = HR.shape[:2]
    ### LR and LR_sr

    LR_4 = np.array(Image.fromarray(HR).resize((w // 4, h // 4), Image.BICUBIC))

    im_4 = Image.fromarray(LR_4)
    im_4.save(name + 'x4.png')

    im_HR = Image.fromarray(HR)
    im_HR.save(str(cnt)+ '.png')
    cnt= cnt+1

input_list = sorted(glob.glob(os.path.join('/home/cl/ttsr/dataset/CUFED/test/CUFED5/'+'*_0.png')))
cnt = 0
# print(len(x_))
# for  i in range(len(x_)):
#      print(x_[i].split('.')[-2])
for  idx in range(len(input_list)):
    HR = imread(input_list[idx])
    h, w = HR.shape[:2]
    h, w = h // 4 * 4, w // 4 * 4
    HR = HR[:h, :w, :]
    im_HR = Image.fromarray(HR)
    im_HR.save(str(cnt)+ '.png')
    cnt= cnt+1

class TestSet(Dataset):
    def __init__(self, args, ref_level='1', transform=transforms.Compose([ToTensor()])):
        self.input_list = sorted(glob.glob(os.path.join(args.dataset_dir, 'test/CUFED5', '*_0.png')))
        self.ref_list = sorted(glob.glob(os.path.join(args.dataset_dir, 'test/CUFED5',
            '*_' + ref_level + '.png')))
       # self.lr_sr_list = sorted(glob.glob(os.path.join(args.dataset_dir, 'test/lr_sr', '*.png')))
        self.lr_sr_list = sorted(os.listdir('./dataset/CUFED/test/lr_sr'))
        self.lr_sr_list.sort(key=lambda x: int(x[:-4]))
        self.transform = transform

    def __len__(self):
        return len(self.input_list)

    def __getitem__(self, idx):
        ### HR
        #print('idx',idx)
        HR = imread(self.input_list[idx])
        #print(self.input_list[idx])
        h, w = HR.shape[:2]
        h, w = h//4*4, w//4*4
        HR = HR[:h, :w, :] ### crop to the multiple of 4
        # print('w h',w,h)
        # ### LR and LR_sr
        LR = np.array(Image.fromarray(HR).resize((w//4, h//4), Image.BICUBIC))
        #LR_sr = np.array(Image.fromarray(LR).resize((w, h), Image.BICUBIC))
        # print('LR_sr_shanfgxia sep',LR_sr.shape)
        LR_sr = imread('./dataset/CUFED/test/lr_sr/'+self.lr_sr_list[idx])
        # print('LR_sr_duqu  ', LR_sr.shape)
        ### Ref and Ref_sr
        Ref = imread(self.ref_list[idx])
        h2, w2 = Ref.shape[:2]
        h2, w2 = h2//4*4, w2//4*4
        Ref = Ref[:h2, :w2, :]
        Ref_sr = np.array(Image.fromarray(Ref).resize((w2//4, h2//4), Image.BICUBIC))
        Ref_sr = np.array(Image.fromarray(Ref_sr).resize((w2, h2), Image.BICUBIC))

        ### change type
        LR = LR.astype(np.float32)
        LR_sr = LR_sr.astype(np.float32)
        HR = HR.astype(np.float32)
        Ref = Ref.astype(np.float32)
        Ref_sr = Ref_sr.astype(np.float32)

        ### rgb range to [-1, 1]
        LR = LR / 127.5 - 1.
        LR_sr = LR_sr / 127.5 - 1.
        HR = HR / 127.5 - 1.
        Ref = Ref / 127.5 - 1.
        Ref_sr = Ref_sr / 127.5 - 1.

        sample = {'LR': LR,
                  'LR_sr': LR_sr,
                  'HR': HR,
                  'Ref': Ref,
                  'Ref_sr': Ref_sr}

        if self.transform:
            sample = self.transform(sample)
        return sample