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88 lines
2.7 KiB
88 lines
2.7 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Image dataset for segmentation.
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The 2012 dataset contains images from 2008-2011 for which additional
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segmentations have been prepared. As in previous years the assignment
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to training/test sets has been maintained. The total number of images
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with segmentation has been increased from 7,062 to 9,993.
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"""
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from __future__ import print_function
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import tarfile
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import io
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import numpy as np
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from paddle.dataset.common import download
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from paddle.dataset.image import *
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from PIL import Image
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__all__ = ['train', 'test', 'val']
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VOC_URL = 'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/\
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VOCtrainval_11-May-2012.tar'
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VOC_MD5 = '6cd6e144f989b92b3379bac3b3de84fd'
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SET_FILE = 'VOCdevkit/VOC2012/ImageSets/Segmentation/{}.txt'
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DATA_FILE = 'VOCdevkit/VOC2012/JPEGImages/{}.jpg'
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LABEL_FILE = 'VOCdevkit/VOC2012/SegmentationClass/{}.png'
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CACHE_DIR = 'voc2012'
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def reader_creator(filename, sub_name):
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tarobject = tarfile.open(filename)
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name2mem = {}
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for ele in tarobject.getmembers():
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name2mem[ele.name] = ele
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def reader():
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set_file = SET_FILE.format(sub_name)
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sets = tarobject.extractfile(name2mem[set_file])
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for line in sets:
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line = line.strip()
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data_file = DATA_FILE.format(line)
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label_file = LABEL_FILE.format(line)
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data = tarobject.extractfile(name2mem[data_file]).read()
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label = tarobject.extractfile(name2mem[label_file]).read()
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data = Image.open(io.BytesIO(data))
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label = Image.open(io.BytesIO(label))
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data = np.array(data)
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label = np.array(label)
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yield data, label
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return reader
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def train():
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"""
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Create a train dataset reader containing 2913 images in HWC order.
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"""
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return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'trainval')
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def test():
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"""
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Create a test dataset reader containing 1464 images in HWC order.
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"""
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return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'train')
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def val():
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"""
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Create a val dataset reader containing 1449 images in HWC order.
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"""
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return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'val')
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