examples/AutoSeg_VOC12/EntropyOptimizedAttentionNet/datasets/voc.py

import os
import sys
import tarfile
import collections
import torch.utils.data as data
import shutil
import numpy as np

from PIL import Image
from torchvision.datasets.utils import download_url, check_integrity

DATASET_YEAR_DICT = {
    '2012': {
        'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar',
        'filename': 'VOCtrainval_11-May-2012.tar',
        'md5': '6cd6e144f989b92b3379bac3b3de84fd',
        'base_dir': 'VOCdevkit/VOC2012'
    },
    '2011': {
        'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2011/VOCtrainval_25-May-2011.tar',
        'filename': 'VOCtrainval_25-May-2011.tar',
        'md5': '6c3384ef61512963050cb5d687e5bf1e',
        'base_dir': 'TrainVal/VOCdevkit/VOC2011'
    },
    '2010': {
        'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar',
        'filename': 'VOCtrainval_03-May-2010.tar',
        'md5': 'da459979d0c395079b5c75ee67908abb',
        'base_dir': 'VOCdevkit/VOC2010'
    },
    '2009': {
        'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2009/VOCtrainval_11-May-2009.tar',
        'filename': 'VOCtrainval_11-May-2009.tar',
        'md5': '59065e4b188729180974ef6572f6a212',
        'base_dir': 'VOCdevkit/VOC2009'
    },
    '2008': {
        'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2008/VOCtrainval_14-Jul-2008.tar',
        'filename': 'VOCtrainval_11-May-2012.tar',
        'md5': '2629fa636546599198acfcfbfcf1904a',
        'base_dir': 'VOCdevkit/VOC2008'
    },
    '2007': {
        'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',
        'filename': 'VOCtrainval_06-Nov-2007.tar',
        'md5': 'c52e279531787c972589f7e41ab4ae64',
        'base_dir': 'VOCdevkit/VOC2007'
    }
}


def voc_cmap(N=256, normalized=False):
    def bitget(byteval, idx):
        return ((byteval & (1 << idx)) != 0)

    dtype = 'float32' if normalized else 'uint8'
    cmap = np.zeros((N, 3), dtype=dtype)
    for i in range(N):
        r = g = b = 0
        c = i
        for j in range(8):
            r = r | (bitget(c, 0) << 7-j)
            g = g | (bitget(c, 1) << 7-j)
            b = b | (bitget(c, 2) << 7-j)
            c = c >> 3

        cmap[i] = np.array([r, g, b])

    cmap = cmap/255 if normalized else cmap
    return cmap

class VOCSegmentation(data.Dataset):
    """`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`_ Segmentation Dataset.
    Args:
        root (string): Root directory of the VOC Dataset.
        year (string, optional): The dataset year, supports years 2007 to 2012.
        image_set (string, optional): Select the image_set to use, ``train``, ``trainval`` or ``val``
        download (bool, optional): If true, downloads the dataset from the internet and
            puts it in root directory. If dataset is already downloaded, it is not
            downloaded again.
        transform (callable, optional): A function/transform that  takes in an PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
    """
    cmap = voc_cmap()
    def __init__(self,
                 root,
                 year='2012',
                 image_set='train',
                 download=False,
                 transform=None):

        is_aug=False
        if year=='2012_aug':
            is_aug = True
            year = '2012'
        
        self.root = os.path.expanduser(root)
        self.year = year
        self.url = DATASET_YEAR_DICT[year]['url']
        self.filename = DATASET_YEAR_DICT[year]['filename']
        self.md5 = DATASET_YEAR_DICT[year]['md5']
        self.transform = transform
        
        self.image_set = image_set
        base_dir = DATASET_YEAR_DICT[year]['base_dir']
        voc_root = os.path.join(self.root, base_dir)
        image_dir = os.path.join(voc_root, 'JPEGImages')

        if download:
            download_extract(self.url, self.root, self.filename, self.md5)

        if not os.path.isdir(voc_root):
            raise RuntimeError('Dataset not found or corrupted.' +
                               ' You can use download=True to download it')
        
        if is_aug and image_set=='train':
            mask_dir = os.path.join(voc_root, 'SegmentationClassAug')
            assert os.path.exists(mask_dir), "SegmentationClassAug not found, please refer to README.md and prepare it manually"
            split_f = os.path.join( self.root, 'train_aug.txt')#'./datasets/data/train_aug.txt'
        else:
            mask_dir = os.path.join(voc_root, 'SegmentationClass')
            splits_dir = os.path.join(voc_root, 'ImageSets/Segmentation')
            split_f = os.path.join(splits_dir, image_set.rstrip('\n') + '.txt')

        if not os.path.exists(split_f):
            raise ValueError(
                'Wrong image_set entered! Please use image_set="train" '
                'or image_set="trainval" or image_set="val"')

        with open(os.path.join(split_f), "r") as f:
            file_names = [x.strip() for x in f.readlines()]
        
        self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]
        self.masks = [os.path.join(mask_dir, x + ".png") for x in file_names]
        assert (len(self.images) == len(self.masks))

    def __getitem__(self, index):
        """
        Args:
            index (int): Index
        Returns:
            tuple: (image, target) where target is the image segmentation.
        """
        img = Image.open(self.images[index]).convert('RGB')
        target = Image.open(self.masks[index])
        if self.transform is not None:
            img, target = self.transform(img, target)

        return img, target


    def __len__(self):
        return len(self.images)

    @classmethod
    def decode_target(cls, mask):
        """decode semantic mask to RGB image"""
        return cls.cmap[mask]

def download_extract(url, root, filename, md5):
    download_url(url, root, filename, md5)
    with tarfile.open(os.path.join(root, filename), "r") as tar:
        tar.extractall(path=root)