voc2012.py

# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Image dataset for segmentation.
The 2012 dataset contains images from 2008-2011 for which additional
segmentations have been prepared. As in previous years the assignment
to training/test sets has been maintained. The total number of images
with segmentation has been increased from 7,062 to 9,993.
"""

import tarfile
import io
import numpy as np
from paddle.dataset.common import download
from paddle.dataset.image import *
from PIL import Image

__all__ = ['train', 'test', 'val']

VOC_URL = 'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/\
VOCtrainval_11-May-2012.tar'

VOC_MD5 = '6cd6e144f989b92b3379bac3b3de84fd'
SET_FILE = 'VOCdevkit/VOC2012/ImageSets/Segmentation/{}.txt'
DATA_FILE = 'VOCdevkit/VOC2012/JPEGImages/{}.jpg'
LABEL_FILE = 'VOCdevkit/VOC2012/SegmentationClass/{}.png'

CACHE_DIR = 'voc2012'


def reader_creator(filename, sub_name):

    tarobject = tarfile.open(filename)
    name2mem = {}
    for ele in tarobject.getmembers():
        name2mem[ele.name] = ele

    def reader():
        set_file = SET_FILE.format(sub_name)
        sets = tarobject.extractfile(name2mem[set_file])
        for line in sets:
            line = line.strip()
            data_file = DATA_FILE.format(line)
            label_file = LABEL_FILE.format(line)
            data = tarobject.extractfile(name2mem[data_file]).read()
            label = tarobject.extractfile(name2mem[label_file]).read()
            data = Image.open(io.BytesIO(data))
            label = Image.open(io.BytesIO(label))
            data = np.array(data)
            label = np.array(label)
            yield data, label

    return reader


def train():
    """
    Create a train dataset reader containing 2913 images in HWC order.
    """
    return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'trainval')


def test():
    """
    Create a test dataset reader containing 1464 images in HWC order.
    """
    return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'train')


def val():
    """
    Create a val dataset reader containing 1449 images in HWC order.
    """
    return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'val')