104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
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"""Pascal Augmented VOC Semantic Segmentation Dataset."""
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import os
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import torch
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import scipy.io as sio
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import numpy as np
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from PIL import Image
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from .segbase import SegmentationDataset
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class VOCAugSegmentation(SegmentationDataset):
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"""Pascal VOC Augmented Semantic Segmentation Dataset.
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Parameters
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----------
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root : string
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Path to VOCdevkit folder. Default is './datasets/voc'
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split: string
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'train', 'val' or 'test'
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transform : callable, optional
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A function that transforms the image
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Examples
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--------
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>>> from torchvision import transforms
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>>> import torch.utils.data as data
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>>> # Transforms for Normalization
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>>> input_transform = transforms.Compose([
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>>> transforms.ToTensor(),
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>>> transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
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>>> ])
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>>> # Create Dataset
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>>> trainset = VOCAugSegmentation(split='train', transform=input_transform)
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>>> # Create Training Loader
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>>> train_data = data.DataLoader(
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>>> trainset, 4, shuffle=True,
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>>> num_workers=4)
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"""
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BASE_DIR = 'VOCaug/dataset/'
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NUM_CLASS = 21
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def __init__(self, root='../datasets/voc', split='train', mode=None, transform=None, **kwargs):
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super(VOCAugSegmentation, self).__init__(root, split, mode, transform, **kwargs)
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# train/val/test splits are pre-cut
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_voc_root = os.path.join(root, self.BASE_DIR)
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_mask_dir = os.path.join(_voc_root, 'cls')
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_image_dir = os.path.join(_voc_root, 'img')
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if split == 'train':
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_split_f = os.path.join(_voc_root, 'trainval.txt')
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elif split == 'val':
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_split_f = os.path.join(_voc_root, 'val.txt')
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else:
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raise RuntimeError('Unknown dataset split: {}'.format(split))
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self.images = []
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self.masks = []
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with open(os.path.join(_split_f), "r") as lines:
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for line in lines:
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_image = os.path.join(_image_dir, line.rstrip('\n') + ".jpg")
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assert os.path.isfile(_image)
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self.images.append(_image)
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_mask = os.path.join(_mask_dir, line.rstrip('\n') + ".mat")
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assert os.path.isfile(_mask)
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self.masks.append(_mask)
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assert (len(self.images) == len(self.masks))
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print('Found {} images in the folder {}'.format(len(self.images), _voc_root))
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def __getitem__(self, index):
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img = Image.open(self.images[index]).convert('RGB')
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target = self._load_mat(self.masks[index])
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# synchrosized transform
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if self.mode == 'train':
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img, target = self._sync_transform(img, target)
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elif self.mode == 'val':
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img, target = self._val_sync_transform(img, target)
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else:
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raise RuntimeError('unknown mode for dataloader: {}'.format(self.mode))
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# general resize, normalize and toTensor
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if self.transform is not None:
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img = self.transform(img)
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return img, target, os.path.basename(self.images[index])
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def _mask_transform(self, mask):
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return torch.LongTensor(np.array(mask).astype('int32'))
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def _load_mat(self, filename):
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mat = sio.loadmat(filename, mat_dtype=True, squeeze_me=True, struct_as_record=False)
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mask = mat['GTcls'].Segmentation
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return Image.fromarray(mask)
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def __len__(self):
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return len(self.images)
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@property
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def classes(self):
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"""Category names."""
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return ('background', 'airplane', 'bicycle', 'bird', 'boat', 'bottle',
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'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
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'motorcycle', 'person', 'potted-plant', 'sheep', 'sofa', 'train',
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'tv')
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if __name__ == '__main__':
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dataset = VOCAugSegmentation()
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