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import torch
import cv2
import numpy as np
import os.path as osp
class BSDS_Dataset(torch.utils.data.Dataset):
def __init__(self, root='data/HED-BSDS', split='test', transform=False):
super(BSDS_Dataset, self).__init__()
self.root = root
self.split = split
self.transform = transform
if self.split == 'train':
self.file_list = osp.join(self.root, 'bsds_pascal_train_pair.lst')
elif self.split == 'test':
self.file_list = osp.join(self.root, 'test.lst')
else:
raise ValueError('Invalid split type!')
with open(self.file_list, 'r') as f:
self.file_list = f.readlines()
self.mean = np.array([104.00698793, 116.66876762, 122.67891434], dtype=np.float32)
def __len__(self):
return len(self.file_list)
def __getitem__(self, index):
if self.split == 'train':
img_file, label_file = self.file_list[index].split()
label = cv2.imread(osp.join(self.root, label_file), 0)
label = np.array(label, dtype=np.float32)
label = label[np.newaxis, :, :]
label[label == 0] = 0
label[np.logical_and(label > 0, label < 127.5)] = 2
label[label >= 127.5] = 1
else:
img_file = self.file_list[index].rstrip()
img = cv2.imread(osp.join(self.root, img_file))
img = np.array(img, dtype=np.float32)
img = (img - self.mean).transpose((2, 0, 1))
if self.split == 'train':
return img, label
else:
return img
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