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dataset.py
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from utils import *
import matplotlib.pyplot as plt
import os
import shutil
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
class TrainSetLoader(Dataset):
def __init__(self, dataset_dir, dataset_name, label_type, patch_size, masks_update, img_norm_cfg=None):
super(TrainSetLoader).__init__()
self.dataset_dir = dataset_dir + dataset_name
self.patch_size = patch_size
self.tranform = augumentation()
self.masks_update = masks_update
with open(self.dataset_dir+'/img_idx/train_' + dataset_name + '.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, self.dataset_dir)
else:
self.img_norm_cfg = img_norm_cfg
self.dataset_name = dataset_name
### ---------------------- for label update ----------------------
self.label_type = label_type
if isinstance(masks_update, str):
if os.path.exists(masks_update):
shutil.rmtree(masks_update)
os.makedirs(masks_update)
for img_idx in self.train_list:
shutil.copyfile(self.dataset_dir + '/' + '/masks_' + self.label_type + '/' + img_idx + '.png',
masks_update + '/' + img_idx + '.png')
if isinstance(masks_update, list):
self.masks_update = masks_update
def __getitem__(self, idx):
img = Image.open(self.dataset_dir + '/images/' + self.train_list[idx] + '.png').convert('I')
if isinstance(self.masks_update, str):
mask = Image.open(self.masks_update + '/' + self.train_list[idx] + '.png')
mask = np.array(mask, dtype=np.float32) / 255.0
elif isinstance(self.masks_update, list):
mask = self.masks_update[idx]
img = Normalized(np.array(img, dtype=np.float32), self.img_norm_cfg)
img_patch, mask_patch = random_crop(img, mask, self.patch_size)
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 TrainSetLoader_full(Dataset):
def __init__(self, dataset_dir, dataset_name, patch_size, img_norm_cfg=None):
super(TrainSetLoader_full).__init__()
self.dataset_dir = dataset_dir + dataset_name
self.patch_size = patch_size
self.tranform = augumentation()
with open(self.dataset_dir+'/img_idx/train_' + dataset_name + '.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, self.dataset_dir)
else:
self.img_norm_cfg = img_norm_cfg
self.dataset_name = dataset_name
def __getitem__(self, idx):
img = Image.open(self.dataset_dir + '/images/' + self.train_list[idx] + '.png').convert('I')
mask = Image.open(self.dataset_dir + '/masks/' + self.train_list[idx] + '.png')
mask = np.array(mask, dtype=np.float32) / 255.0
if len(mask.shape) > 2:
mask = mask[:,:,0]
img = Normalized(np.array(img, dtype=np.float32), self.img_norm_cfg)
img_patch, mask_patch = random_crop(img, mask, self.patch_size)
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 Update_mask(Dataset):
def __init__(self, dataset_dir, dataset_name, label_type, masks_update, img_norm_cfg=None):
super(Update_mask).__init__()
self.label_type = label_type
self.masks_update = masks_update
self.dataset_dir = dataset_dir + dataset_name
self.dataset_name = dataset_name
with open(self.dataset_dir+'/img_idx/train_' + dataset_name + '.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, self.dataset_dir)
else:
self.img_norm_cfg = img_norm_cfg
def __getitem__(self, idx):
img = Image.open(self.dataset_dir + '/images/' + self.train_list[idx] + '.png').convert('I')
mask = Image.open(self.dataset_dir + '/masks/' + self.train_list[idx] + '.png')
if isinstance(self.masks_update, str):
mask_update = Image.open(self.masks_update + '/' + self.train_list[idx] + '.png')
update_dir = self.masks_update + '/' + self.train_list[idx] + '.png'
mask_update = np.array(mask_update, dtype=np.float32) / 255.0
if len(mask_update.shape) > 2:
mask_update = mask_update[:,:,0]
elif isinstance(self.masks_update, list):
mask_update = self.masks_update[idx]
update_dir = idx
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
times = 32
img = np.pad(img, ((0, (h//times+1)*times-h),(0, (w//times+1)*times-w)), mode='constant')
mask = np.pad(mask, ((0, (h//times+1)*times-h),(0, (w//times+1)*times-w)), mode='constant')
mask_update = np.pad(mask_update, ((0, (h//times+1)*times-h),(0, (w//times+1)*times-w)), mode='constant')
img, mask, mask_update = img[np.newaxis,:], mask[np.newaxis,:], mask_update[np.newaxis,:]
img = torch.from_numpy(np.ascontiguousarray(img))
mask = torch.from_numpy(np.ascontiguousarray(mask))
mask_update = torch.from_numpy(np.ascontiguousarray(mask_update))
return img, mask, mask_update, update_dir, [h,w]
def __len__(self):
return len(self.train_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_' + test_dataset_name + '.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, self.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').convert('I')
mask = Image.open(self.dataset_dir + '/masks/' + self.test_list[idx] + '.png')
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
times = 32
img = np.pad(img, ((0, (h//times+1)*times-h),(0, (w//times+1)*times-w)), mode='constant')
mask = np.pad(mask, ((0, (h//times+1)*times-h),(0, (w//times+1)*times-w)), mode='constant')
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 InferenceSetLoader(Dataset):
def __init__(self, dataset_dir, train_dataset_name, test_dataset_name, img_norm_cfg=None):
super(InferenceSetLoader).__init__()
self.dataset_dir = dataset_dir + test_dataset_name
with open(self.dataset_dir+'/img_idx/test_' + test_dataset_name + '.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, self.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').convert('I')
img = Normalized(np.array(img, dtype=np.float32), self.img_norm_cfg)
h, w = img.shape
times = 32
img = np.pad(img, ((0, (h//times+1)*times-h),(0, (w//times+1)*times-w)), mode='constant')
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 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