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dataset.py
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from utils import *
import matplotlib.pyplot as plt
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
class TrainSetLoader(Dataset):
def __init__(self, dataset_dir, dataset_name, patch_size, img_norm_cfg=None):
super(TrainSetLoader).__init__()
self.dataset_name = dataset_name
self.dataset_dir = dataset_dir + '/' + dataset_name
self.patch_size = patch_size
with open(self.dataset_dir +'/train.txt', 'r') as f:
self.train_list = f.read().splitlines()
if img_norm_cfg == None:
self.img_norm_cfg = get_img_norm_cfg(dataset_name, dataset_dir)
else:
self.img_norm_cfg = img_norm_cfg
self.tranform = augumentation()
# self.tranform = augumentation3D()
def __getitem__(self, idx):
img = Image.open((self.dataset_dir + '/train/img/' + self.train_list[idx] + '.png').replace('//','/')).convert('I')
mask = Image.open((self.dataset_dir + '/train/mask/' + self.train_list[idx] + '.png').replace('//','/'))
img = Normalized(np.array(img, dtype=np.float32), self.img_norm_cfg)
mask = np.array(mask, dtype=np.float32) / 255.0
if len(mask.shape) > 2:
mask = mask[:,:,0]
mixup_idx=random.randint(0, len(self.train_list)-1)
mixup_img = Image.open((self.dataset_dir + '/train/img/' + self.train_list[mixup_idx] + '.png').replace('//','/')).convert('I')
mixup_mask = Image.open((self.dataset_dir + '/train/mask/' + self.train_list[mixup_idx] + '.png').replace('//','/'))
mixup_img = Normalized(np.array(mixup_img, dtype=np.float32), self.img_norm_cfg)
mixup_mask = np.array(mixup_mask, dtype=np.float32) / 255.0
if len(mixup_mask.shape) > 2:
mixup_mask = mixup_mask[:,:,0]
if random.random()<0.5:
img=img+mixup_img
mask=mask+mixup_mask
img_patch, mask_patch = random_crop(img, mask, self.patch_size, pos_prob=0.5)
img_patch, mask_patch = self.tranform(img_patch, mask_patch)
img_patch, mask_patch = img_patch[np.newaxis,:], mask_patch[np.newaxis,:]
img_patch = torch.from_numpy(np.ascontiguousarray(img_patch))
mask_patch = torch.from_numpy(np.ascontiguousarray(mask_patch))
return img_patch, mask_patch
def __len__(self):
return len(self.train_list)
class ValSetLoader(Dataset):
def __init__(self, dataset_dir, train_dataset_name, test_dataset_name, img_norm_cfg=None):
super(ValSetLoader).__init__()
self.dataset_dir = dataset_dir + '/' + test_dataset_name
with open(self.dataset_dir + '/val.txt', 'r') as f:
self.test_list = f.read().splitlines()
if img_norm_cfg == None:
self.img_norm_cfg = get_img_norm_cfg(train_dataset_name, dataset_dir)
else:
self.img_norm_cfg = img_norm_cfg
def __getitem__(self, idx):
img = Image.open((self.dataset_dir + '/val/img/' + self.test_list[idx] + '.png').replace('//','/')).convert('I')
mask = Image.open((self.dataset_dir + '/val/mask/' + self.test_list[idx] + '.png').replace('//','/'))
img = Normalized(np.array(img, dtype=np.float32), self.img_norm_cfg)
mask = np.array(mask, dtype=np.float32) / 255.0
if len(mask.shape) > 2:
mask = mask[:,:,0]
h, w = img.shape
img = PadImg(img)
mask = PadImg(mask)
img, mask = img[np.newaxis,:], mask[np.newaxis,:]
img = torch.from_numpy(np.ascontiguousarray(img))
mask = torch.from_numpy(np.ascontiguousarray(mask))
return img, mask, [h,w], self.test_list[idx]
def __len__(self):
return len(self.test_list)
class TestSetLoader(Dataset):
def __init__(self, dataset_dir, train_dataset_name, test_dataset_name, img_norm_cfg=None):
super(TestSetLoader).__init__()
self.dataset_dir = dataset_dir + '/' + test_dataset_name
with open(self.dataset_dir + '/img_idx/test_NUAA-SIRST.txt', 'r') as f:
self.test_list = f.read().splitlines()
if img_norm_cfg == None:
self.img_norm_cfg = get_img_norm_cfg(train_dataset_name, dataset_dir)
else:
self.img_norm_cfg = img_norm_cfg
def __getitem__(self, idx):
img = Image.open((self.dataset_dir + '/images/' + self.test_list[idx] + '.png').replace('//','/')).convert('I')
img = Normalized(np.array(img, dtype=np.float32), self.img_norm_cfg)
h, w = img.shape
img = PadImg(img,times=512)
img = img[np.newaxis,:]
img = torch.from_numpy(np.ascontiguousarray(img))
return img, [h,w], self.test_list[idx]
def __len__(self):
return len(self.test_list)
class EvalSetLoader(Dataset):
def __init__(self, dataset_dir, mask_pred_dir, test_dataset_name, model_name):
super(EvalSetLoader).__init__()
self.dataset_dir = dataset_dir
self.mask_pred_dir = mask_pred_dir
self.test_dataset_name = test_dataset_name
self.model_name = model_name
with open(self.dataset_dir+'/img_idx/test_' + test_dataset_name + '.txt', 'r') as f:
self.test_list = f.read().splitlines()
def __getitem__(self, idx):
mask_pred = Image.open((self.mask_pred_dir + self.test_dataset_name + '/' + self.model_name + '/' + self.test_list[idx] + '.png').replace('//','/'))
mask_gt = Image.open(self.dataset_dir + '/masks/' + self.test_list[idx] + '.png')
mask_pred = np.array(mask_pred, dtype=np.float32) / 255.0
mask_gt = np.array(mask_gt, dtype=np.float32) / 255.0
if len(mask_pred.shape) == 3:
mask_pred = mask_pred[:,:,0]
h, w = mask_pred.shape
mask_pred, mask_gt = mask_pred[np.newaxis,:], mask_gt[np.newaxis,:]
mask_pred = torch.from_numpy(np.ascontiguousarray(mask_pred))
mask_gt = torch.from_numpy(np.ascontiguousarray(mask_gt))
return mask_pred, mask_gt, [h,w]
def __len__(self):
return len(self.test_list)
class augumentation(object):
def __call__(self, input, target):
if random.random()<0.5:
input = input[::-1, :]
target = target[::-1, :]
if random.random()<0.5:
input = input[:, ::-1]
target = target[:, ::-1]
if random.random()<0.5:
input = input.transpose(1, 0)
target = target.transpose(1, 0)
return input, target
# class augumentation3D(object):
# def __call__(self, input, target):
# if random.random()<0.5:
# input = input[:, ::-1, :]
# target = target[:, ::-1, :]
# if random.random()<0.5:
# input = input[:, :, ::-1]
# target = target[:, :, ::-1]
# # if random.random()<0.5:
# # input = input.transpose(-1, -2)
# # target = target.transpose(-1, -2)
# return input, target