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r""" Chest X-ray few-shot semantic segmentation dataset """
import os
import glob
from torch.utils.data import Dataset
import torch.nn.functional as F
import torch
import PIL.Image as Image
import numpy as np
class DatasetLung(Dataset):
def __init__(self, datapath, fold, transform, split, shot=1, num_val=600):
self.benchmark = 'lung'
self.shot = shot
self.split = split
self.num_val = num_val
self.base_path = os.path.join(datapath)
self.img_path = os.path.join(self.base_path, 'CXR_png')
self.ann_path = os.path.join(self.base_path, 'masks')
self.categories = ['1']
self.class_ids = range(0, 1)
self.img_metadata_classwise, self.num_images = self.build_img_metadata_classwise()
self.transform = transform
def __len__(self):
return self.num_images if self.split != 'val' else self.num_val
def __getitem__(self, idx):
query_name, support_names, class_sample = self.sample_episode(idx)
query_img, query_mask, support_imgs, support_masks = self.load_frame(query_name, support_names)
query_img = self.transform(query_img)
query_mask = F.interpolate(query_mask.unsqueeze(0).unsqueeze(0).float(), query_img.size()[-2:], mode='nearest').squeeze()
support_imgs = torch.stack([self.transform(support_img) for support_img in support_imgs])
support_masks_tmp = []
for smask in support_masks:
smask = F.interpolate(smask.unsqueeze(0).unsqueeze(0).float(), support_imgs.size()[-2:], mode='nearest').squeeze()
support_masks_tmp.append(smask)
support_masks = torch.stack(support_masks_tmp)
batch = {'query_img': query_img,
'query_mask': query_mask,
'query_name': query_name,
'support_imgs': support_imgs,
'support_masks': support_masks,
'class_id': torch.tensor(class_sample),
'support_names': support_names,
'support_set': [support_imgs, support_masks],
'support_classes': torch.tensor([class_sample])
}
return batch
def load_frame(self, query_name, support_names):
query_mask = self.read_mask(query_name)
support_masks = [self.read_mask(name) for name in support_names]
query_id = query_name[:-9] + '.png'
query_img = Image.open(os.path.join(self.img_path, os.path.basename(query_id))).convert('RGB')
support_ids = [os.path.basename(name)[:-9] + '.png' for name in support_names]
support_names = [os.path.join(self.img_path, sid) for sid in support_ids]
support_imgs = [Image.open(name).convert('RGB') for name in support_names]
return query_img, query_mask, support_imgs, support_masks
def read_mask(self, img_name):
mask = torch.tensor(np.array(Image.open(img_name).convert('L')))
mask[mask < 128] = 0
mask[mask >= 128] = 1
return mask
def sample_episode(self, idx):
class_id = idx % len(self.class_ids)
class_sample = self.categories[class_id]
query_name = np.random.choice(self.img_metadata_classwise[class_sample], 1, replace=False)[0]
support_names = []
while True: # keep sampling support set if query == support
support_name = np.random.choice(self.img_metadata_classwise[class_sample], 1, replace=False)[0]
if query_name != support_name: support_names.append(support_name)
if len(support_names) == self.shot: break
return query_name, support_names, class_id
def build_img_metadata(self):
img_metadata = []
for cat in self.categories:
os.path.join(self.base_path, cat)
img_paths = sorted([path for path in glob.glob('%s/*' % os.path.join(self.img_path, cat))])
for img_path in img_paths:
if os.path.basename(img_path).split('.')[1] == 'png':
img_metadata.append(img_path)
return img_metadata
def build_img_metadata_classwise(self):
num_images=0
img_metadata_classwise = {}
for cat in self.categories:
img_metadata_classwise[cat] = []
for cat in self.categories:
img_paths = sorted([path for path in glob.glob('%s/*' % self.ann_path)])
for img_path in img_paths:
if os.path.basename(img_path).split('.')[1] == 'png':
img_metadata_classwise[cat] += [img_path]
num_images+=1
return img_metadata_classwise, num_images
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