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import cv2
import os
from os.path import join as osp
import numpy
import torch.utils.data
class Dataset(torch.utils.data.Dataset):
def __init__(self, file_root='data/', mode='train', transform=None):
self.file_list = os.listdir(osp(file_root, mode, 'A'))
self.pre_images = [osp(file_root, mode, 'A', x) for x in self.file_list]
self.post_images = [osp(file_root, mode, 'B', x) for x in self.file_list]
self.gts = [osp(file_root, mode, 'label', x) for x in self.file_list]
self.transform = transform
def __len__(self):
return len(self.pre_images)
def __getitem__(self, idx):
pre_image_name = self.pre_images[idx]
label_name = self.gts[idx]
post_image_name = self.post_images[idx]
pre_image = cv2.imread(pre_image_name)
label = cv2.imread(label_name, 0)
post_image = cv2.imread(post_image_name)
img = numpy.concatenate((pre_image, post_image), axis=2)
if self.transform:
[img, label] = self.transform(img, label)
return img, label
def get_img_info(self, idx):
img = cv2.imread(self.pre_images[idx])
return {"height": img.shape[0], "width": img.shape[1]}
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