PASCAL-VOC2012简介
PASCAL-VOC2012数据集介绍官网：http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html ，数据集下载地址：benchmark_RELEASE：下载地址 voc2012：下载地址
VOC2012数据集分为20类，包括背景为21类，分别如下：
- Person: person
- Animal: bird, cat, cow, dog, horse, sheep
- Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train
- Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor
再看一下VOC2012数据集里有哪些文件夹：
在目标检测中，主要用到了 Annotations，ImageSets，JPEGImages，其中 ImageSets/Main/ 保存了具体数据集的索引，Annotations 保存了标签数据， JPEGImages 保存了图片内容。
ImageSets/Main/ 文件夹以 , {class}_trainval.txt {class}_val.txt 的格式命名。 train.txt val.txt 例外，包括 Action，Layout，Main，Segmentation 四个文件夹：
- Action：存放的是人的动作（例如running、jumping等等，这也是VOC challenge的一部分）
- Layout：存放的是具有人体部位的数据（人的head、hand、feet等等，这也是VOC challenge的一部分
- Main：存放的是图像物体识别的数据，总共分为20类。
- Segmentation：存放的是可用于分割的数据。
在图像分割中，主要使用了SegmentationClass，SegmentationObject，JPEGImages有关的信息，VOC2012中的图片并不是都用于分割，用于分割比赛的图片实例如下，包含原图以及图像分类分割和图像物体分割两种png图。图像分类分割是在20种物体中，ground-turth图片上每个物体的轮廓填充都有一个特定的颜色，一共20种颜色，比如摩托车用红色表示，人用绿色表示。而图像物体分割则仅仅在一副图中生成不同物体的轮廓颜色即可，颜色自己随便填充。
ImageSets/Main/ 文件夹以 , {class}_trainval.txt {class}_val.txt 的格式命名。 train.txt val.txt 例外
aeroplane_train.txt
aeroplane_trainval.txt
aeroplane_val.txt
bicycle_train.txt
bicycle_trainval.txt
bicycle_val.txt
bird_train.txt
bird_trainval.txt
bird_val.txt
boat_train.txt
boat_trainval.txt
boat_val.txt
bottle_train.txt
bottle_trainval.txt
bottle_val.txt
bus_train.txt
bus_trainval.txt
bus_val.txt
car_train.txt
car_trainval.txt
car_val.txt
cat_train.txt
cat_trainval.txt
cat_val.txt
chair_train.txt
chair_trainval.txt
chair_val.txt
cow_train.txt
cow_trainval.txt
cow_val.txt
diningtable_train.txt
diningtable_trainval.txt
diningtable_val.txt
dog_train.txt
dog_trainval.txt
dog_val.txt
horse_train.txt
horse_trainval.txt
horse_val.txt
motorbike_train.txt
motorbike_trainval.txt
motorbike_val.txt
person_train.txt
person_trainval.txt
person_val.txt
pottedplant_train.txt
pottedplant_trainval.txt
pottedplant_val.txt
sheep_train.txt
sheep_trainval.txt
sheep_val.txt
sofa_train.txt
sofa_trainval.txt
sofa_val.txt
train.txt
train_train.txt
train_trainval.txt
train_val.txt
trainval.txt
tvmonitor_train.txt
tvmonitor_trainval.txt
tvmonitor_val.txt
val.txt
- {class}_train.txt 保存类别为 class 的训练集的所有索引，每一个 class 的 train 数据都有 5717 个。
- {class}_val.txt 保存类别为 class 的验证集的所有索引，每一个 class 的val数据都有 5823 个
- {class}_trainval.txt 保存类别为 class 的训练验证集的所有索引，每一个 class 的val数据都有11540 个
每个文件包含内容为：
2011_003194 -1
2011_003216 -1
2011_003223 -1
2011_003230 1
2011_003236 1
2011_003238 1
2011_003246 1
2011_003247 0
2011_003253 -1
2011_003255 1
2011_003259 1
2011_003274 -1
2011_003276 -1
注：1代表正样本，-1代表负样本。
VOC2012/ImageSets/Main/train.txt 保存了所有训练集的文件名，从 VOC2012/JPEGImages/ 找到文件名对应的图片文件。VOC2012/Annotations/ 找到文件名对应的标签文件
VOC2012/ImageSets/Main/val.txt 保存了所有验证集的文件名，从 VOC2012/JPEGImages/ 找到文件名对应的图片文件。VOC2012/Annotations/ 找到文件名对应的标签文件
读取 JPEGImages 和 Annotation 文件转换为 tf 的 Example 对象，写入 {train|test}{index}_of{num_shard} 文件。每个文件写的 Example 的数量为 total_size/num_shard。（不同数据集可以适当调节 num_shard 来控制每个输出文件的大小）
Annotations
文件夹中文件以 {id}.xml (id 保存在 VOC2012/ImageSets/Main/文件夹 ) 格式命名的 xml 文件，保存如下关键信息
- 物体 label : name ，如下例子为 person
- 图片尺寸： depth, height, width
- 物体 bbox : bndbox 下 xmax, xmin, ymax, ymin
<annotation>
<folder>VOC2012</folder>
<filename>2007_000032.jpg</filename>
<source>
<database>The VOC2007 Database</database>
<annotation>PASCAL VOC2007</annotation>
<image>flickr</image>
</source>
<size>
<width>500</width>
<height>281</height>
<depth>3</depth>
</size>
<segmented>1</segmented>
<object>
<name>aeroplane</name>
<pose>Frontal</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>104</xmin>
<ymin>78</ymin>
<xmax>375</xmax>
<ymax>183</ymax>
</bndbox>
</object>
<object>
<name>aeroplane</name>
<pose>Left</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>133</xmin>
<ymin>88</ymin>
<xmax>197</xmax>
<ymax>123</ymax>
</bndbox>
</object>
<object>
<name>person</name>
<pose>Rear</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>195</xmin>
<ymin>180</ymin>
<xmax>213</xmax>
<ymax>229</ymax>
</bndbox>
</object>
<object>
<name>person</name>
<pose>Rear</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>26</xmin>
<ymin>189</ymin>
<xmax>44</xmax>
<ymax>238</ymax>
</bndbox>
</object>
</annotation>tfrecord格式简介
tfrecord是Tensorflow官方推荐的一种较为高效的数据读取方式。使用Tensorflow训练神经网络时，读取的数据方式有很多种。如果数据集比较小，而且内存足够大，可以选择直接将所有数据读进内存，然后每次取一个batch的数据出来。如果数据较多，可以每次直接从硬盘中进行读取，不过这种方式的读取效率就比较低了。
tfrecord其实是一种数据存储形式。使用tfrecord时，实际上是先读取原生数据，然后转换成tfrecord格式，再存储在硬盘上。而使用时，再把数据从相应的tfrecord文件中解码读取出来。
Tensorflow有和tfrecord配套的一些函数，可以加快数据的处理。实际读取tfrecord数据时，先以相应的tfrecord文件为参数，创建一个输入队列，这个队列有一定的容量，用户可以设置不同的值，在一部分数据出队列时，tfrecord中的其他数据就可以通过预取进入队列，并且这个过程和网络的计算是独立进行的。也就是说，网络每一个iteration的训练不必等待数据队列准备好再开始，队列中的数据始终是充足的，而往队列中填充数据时，也可以使用多线程加速。
tfecord文件中的数据是通过tf.train.Example Protocol Buffer的格式存储的，下面是tf.train.Example的定义。
message Example {
Features features = 1;
};
message Features{
map<string,Feature> featrue = 1;
};
message Feature{
oneof kind{
BytesList bytes_list = 1;
FloatList float_list = 2;
Int64List int64_list = 3;
}
};
tf.train.Example中包含了属性名称到取值的字典，其中属性名称为字符串，属性的取值可以为字符串（BytesList）、实数列表（FloatList）或者整数列表（Int64List）。
将数据保存为tfrecord格式
首先，创建以tfrecord为后缀的文件名
tfrecords_filename = './tfrecords/train.tfrecords'
writer = tf.python_io.TFRecordWriter(tfrecords_filename) # 创建.tfrecord文件，准备写入然后创建一个循环一次写入数据
for i in range(100):
img_raw = np.random.random_integers(0,255,size=(7,30)) # 创建7*30，取值在0-255之间随机数组
img_raw = img_raw.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'label': tf.train.Feature(int64_list = tf.train.Int64List(value=[i])),
'img_raw':tf.train.Feature(bytes_list = tf.train.BytesList(value=[img_raw]))
}))
writer.write(example.SerializeToString())
writer.close()example = tf.train.Example()这句将数据赋给了变量example（可以看到里面是通过字典结构实现的赋值），然后用writer.write(example.SerializeToString()) 这句实现写入。
值得注意的是赋值给example的数据格式。从前面tf.train.Example的定义可知，tfrecord支持整型、浮点数和二进制三种格式，分别是
tf.train.Feature(int64_list = tf.train.Int64List(value=[int_scalar]))
tf.train.Feature(bytes_list = tf.train.BytesList(value=[array_string_or_byte]))
tf.train.Feature(bytes_list = tf.train.FloatList(value=[float_scalar]))例如图片等数组形式（array）的数据，可以保存为numpy array的格式，转换为string，然后保存到二进制格式的feature中。对于单个的数值（scalar），可以直接赋值。这里value=[×]的[]非常重要，也就是说输入的必须是列表（list）。当然，对于输入数据是向量形式的，可以根据数据类型（float还是int）分别保存。并且在保存的时候还可以指定数据的维数。
读取tfrecord数据
用tf.parse_single_example解码，tf.TFRecordReader读取，一般，为了高效的读取数据，tf中使用队列读取数据
def read_and_decode(filename):
# 生成一个文件名的队列
filename_queue = tf.train.string_input_producer([filename])
reader = tf.TFRecordReader() # 定义一个reader
_, serialized_example = reader.read(filename_queue) # 读取文件名和example
# 还原feature， 和制作tfrecords时一样
feature = { 'label': tf.FixedLenFeature([], tf.int64), # 对于单个元素的变量，我们使用FixlenFeature来读取，需要指明变量存储的数据类型；对于list类型的变量，我们使用VarLenFeature来读取，同样需要指明读取变量的类型
'img_raw' : tf.FixedLenFeature([], tf.string), }
# 使用tf.parse_single_example来解析example
features = tf.parse_single_example(serialized_example, features=feature)
# 对于图像，使用tf.decode_raw解析对应的features，指定类型，然后reshape等
img = tf.decode_raw(features['img_raw'], tf.uint8)
img = tf.reshape(img, [224, 224, 3])
img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
label = tf.cast(features['label'], tf.int32)
return img, label
img, label = read_and_decode('train.tfrecords')
# 在训练时使用shuffle_batch随机打乱顺序，并生成batch
img_batch, label_batch = tf.train.shuffle_batch([img, label],
batch_size=30,
capacity=2000, # 队列的最大容量
num_threads=1, # 进行队列操作的线程数
min_after_dequeue=1000) # dequeue后最小的队列大小,used to ensure a level of mixing of elements.
# tf队列也需要初始化在sess中才能执行
init_op = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
coord = tf.train.Coordinator() # 创建一个coordinate，用于协调各线程
threads = tf.train.start_queue_runners(coord=coord) # 使用QueueRunner对象来提取数据
try: # 推荐代码
while not coord.should_stop():
# Run training steps or whatever
sess.run(train_op)
except tf.errors.OutOfRangeError:
print 'Done training -- epoch limit reached'
finally:
# When done, ask the threads to stop.关闭线程
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)以目标检测所使用的文件为例，制作tfrecord文件代码如下：
# coding=utf-8
import os
import sys
import random
import numpy as np
import tensorflow as tf
# process a xml file
import xml.etree.ElementTree as ET
DIRECTORY_ANNOTATIONS = 'Annotations/'
DIRECTORY_IMAGES = 'JPEGImages/'
RANDOM_SEED = 4242
SAMPLES_PER_FILES = 20000
VOC_LABELS = {
'none': (0, 'Background'),
'aeroplane': (1, 'Vehicle'),
'bicycle': (2, 'Vehicle'),
'bird': (3, 'Animal'),
'boat': (4, 'Vehicle'),
'bottle': (5, 'Indoor'),
'bus': (6, 'Vehicle'),
'car': (7, 'Vehicle'),
'cat': (8, 'Animal'),
'chair': (9, 'Indoor'),
'cow': (10, 'Animal'),
'diningtable': (11, 'Indoor'),
'dog': (12, 'Animal'),
'horse': (13, 'Animal'),
'motorbike': (14, 'Vehicle'),
'person': (15, 'Person'),
'pottedplant': (16, 'Indoor'),
'sheep': (17, 'Animal'),
'sofa': (18, 'Indoor'),
'train': (19, 'Vehicle'),
'tvmonitor': (20, 'Indoor'),
}
#返回一个int64_list
def int64_feature(values):
"""Returns a TF-Feature of int64s.
Args:
values: A scalar or list of values.
Returns:
a TF-Feature.
"""
if not isinstance(values, (tuple, list)):
values = [values]
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
#返回float_list
def float_feature(value):
"""Wrapper for inserting float features into Example proto.
"""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
#返回bytes_list
def bytes_feature(value):
"""Wrapper for inserting bytes features into Example proto.
"""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
#split的三种类型
SPLIT_MAP = ['train', 'val', 'trainval']
"""
Process a image and annotation file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
integer, image width in pixels.
读取一个样本图片及对应信息
directory：图片所在路径，name：图片名称
"""
def _process_image(directory, name):
# Read the image file.
filename = os.path.join(directory, DIRECTORY_IMAGES, name + '.jpg')
image_data = tf.gfile.FastGFile(filename, 'rb').read() #使用gfile读取图片
# Read the XML annotation file.
filename = os.path.join(directory, DIRECTORY_ANNOTATIONS, name + '.xml')
tree = ET.parse(filename) #XML文档表示为树,ElementTree
root = tree.getroot() #树的根节点
# Image shape.
size = root.find('size')
shape = [int(size.find('height').text), int(size.find('width').text), int(size.find('depth').text)]
# Find annotations.
# 获取每个object的信息
bboxes = []
labels = []
labels_text = []
difficult = []
truncated = []
for obj in root.findall('object'):
label = obj.find('name').text
labels.append(int(VOC_LABELS[label][0]))
labels_text.append(label.encode('ascii'))
if obj.find('difficult'):
difficult.append(int(obj.find('difficult').text))
else:
difficult.append(0)
if obj.find('truncated'):
truncated.append(int(obj.find('truncated').text))
else:
truncated.append(0)
bbox = obj.find('bndbox')
bboxes.append((float(bbox.find('ymin').text) / shape[0],
float(bbox.find('xmin').text) / shape[1],
float(bbox.find('ymax').text) / shape[0],
float(bbox.find('xmax').text) / shape[1]
))
return image_data, shape, bboxes, labels, labels_text, difficult, truncated
"""
Build an Example proto for an image example.
Args:
image_data: string, JPEG encoding of RGB image;
labels: list of integers, identifier for the ground truth;
labels_text: list of strings, human-readable labels;
bboxes: list of bounding boxes; each box is a list of integers;
specifying [xmin, ymin, xmax, ymax]. All boxes are assumed to belong
to the same label as the image label.
shape: 3 integers, image shapes in pixels.
Returns:
Example proto
将一个图片及对应信息按格式转换成训练时可读取的一个样本
"""
def _convert_to_example(image_data, labels, labels_text, bboxes, shape, difficult, truncated):
xmin = []
ymin = []
xmax = []
ymax = []
for b in bboxes:
assert len(b) == 4
# pylint: disable=expression-not-assigned
[l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
# pylint: enable=expression-not-assigned
image_format = b'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': int64_feature(shape[0]),
'image/width': int64_feature(shape[1]),
'image/channels': int64_feature(shape[2]),
'image/shape': int64_feature(shape),
'image/object/bbox/xmin': float_feature(xmin),
'image/object/bbox/xmax': float_feature(xmax),
'image/object/bbox/ymin': float_feature(ymin),
'image/object/bbox/ymax': float_feature(ymax),
'image/object/bbox/label': int64_feature(labels),
'image/object/bbox/label_text': bytes_feature(labels_text),
'image/object/bbox/difficult': int64_feature(difficult),
'image/object/bbox/truncated': int64_feature(truncated),
'image/format': bytes_feature(image_format),
'image/encoded': bytes_feature(image_data)}))
return example
"""
Loads data from image and annotations files and add them to a TFRecord.
Args:
dataset_dir: Dataset directory;
name: Image name to add to the TFRecord;
tfrecord_writer: The TFRecord writer to use for writing.
"""
def _add_to_tfrecord(dataset_dir, name, tfrecord_writer):
image_data, shape, bboxes, labels, labels_text, difficult, truncated =
_process_image(dataset_dir, name)
example = _convert_to_example(image_data,
labels,
labels_text,
bboxes,
shape,
difficult,
truncated)
tfrecord_writer.write(example.SerializeToString())
"""
以VOC2012为例，下载后的文件名为：VOCtrainval_11-May-2012.tar，解压后
得到一个文件夹：VOCdevkit
voc_root就是VOCdevkit文件夹所在的路径
在VOCdevkit文件夹下只有一个文件夹：VOC2012，所以下边参数year该文件夹的数字部分。
在VOCdevkit/VOC2012/ImageSets/Main下存放了20个类别，每个类别有3个的txt文件：
*.train.txt存放训练使用的数据
*.val.txt存放测试使用的数据
*.trainval.txt是train和val的合集
所以参数split只能为'train', 'val', 'trainval'之一
"""
def run(voc_root, year, split, output_dir, shuffling=False):
# 如果output_dir不存在则创建
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
# VOCdevkit/VOC2012/ImageSets/Main/train.txt
# 中存放有所有20个类别的训练样本名称，共5717个
split_file_path = os.path.join(voc_root, 'VOC%s' % year, 'ImageSets', 'Main', '%s.txt' % split)
print('>> ', split_file_path)
with open(split_file_path) as f:
filenames = f.readlines()
# shuffling == Ture时，打乱顺序
if shuffling:
random.seed(RANDOM_SEED)
random.shuffle(filenames)
# Process dataset files.
i = 0
fidx = 0
dataset_dir = os.path.join(voc_root, 'VOC%s' % year)
while i < len(filenames):
# Open new TFRecord file.
tf_filename = '%s/%s_%03d.tfrecord' % (output_dir, split, fidx)
with tf.python_io.TFRecordWriter(tf_filename) as tfrecord_writer:
j = 0
while i < len(filenames) and j < SAMPLES_PER_FILES:
sys.stdout.write('
>> Converting image %d/%d' % (i + 1, len(filenames)))
sys.stdout.flush()
filename = filenames[i].strip()
_add_to_tfrecord(dataset_dir, filename, tfrecord_writer)
i += 1
j += 1
fidx += 1
print('
>> Finished converting the Pascal VOC dataset!')
if __name__ == '__main__':
# if len(sys.argv) < 2:
# raise ValueError('>> error. format: python *.py split_name')
split = 'train' #'train|val|trainval'
if split not in SPLIT_MAP:
raise ValueError('>> error. split = %s' % split)
voc_root = 'E:/data/VOCdevkit/'
run(voc_root, 2012, split,voc_root)以图像分割为例，代码如下
代码中所需的build_data.py，点击打开
# Copyright 2018 The TensorFlow 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.
# ==============================================================================
"""Converts PASCAL VOC 2012 data to TFRecord file format with Example protos.
PASCAL VOC 2012 dataset is expected to have the following directory structure:
+ pascal_voc_seg
- build_data.py
- build_voc2012_data.py (current working directory).
+ VOCdevkit
+ VOC2012
+ JPEGImages
+ SegmentationClass
+ ImageSets
+ Segmentation
+ tfrecord
Image folder:
./VOCdevkit/VOC2012/JPEGImages
Semantic segmentation annotations:
./VOCdevkit/VOC2012/SegmentationClass
list folder:
./VOCdevkit/VOC2012/ImageSets/Segmentation
This script converts data into sharded data files and save at tfrecord folder.
The Example proto contains the following fields:
image/encoded: encoded image content.
image/filename: image filename.
image/format: image file format.
image/height: image height.
image/ image width.
image/channels: image channels.
image/segmentation/class/encoded: encoded semantic segmentation content.
image/segmentation/class/format: semantic segmentation file format.
"""
import math
import os.path
import sys
import build_data
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('image_folder',
'./pascal_voc_seg/VOCdevkit/VOC2012/JPEGImages',
'Folder containing images.')
tf.app.flags.DEFINE_string(
'semantic_segmentation_folder',
'./pascal_voc_seg/VOCdevkit/VOC2012/SegmentationClassRaw',
'Folder containing semantic segmentation annotations.')
#train.txt，val.txt，trainval.txt
tf.app.flags.DEFINE_string(
'list_folder',
'./pascal_voc_seg/VOCdevkit/VOC2012/ImageSets/Segmentation',
'Folder containing lists for training and validation')
#tfrecord输出路径
tf.app.flags.DEFINE_string(
'output_dir',
'./pascal_voc_seg/tfrecord',
'Path to save converted SSTable of TensorFlow examples.')
_NUM_SHARDS = 4
def _convert_dataset(dataset_split):
"""Converts the specified dataset split to TFRecord format.
Args:
dataset_split: The dataset split (e.g., train, test).
Raises:
RuntimeError: If loaded image and label have different shape.
"""
dataset = os.path.basename(dataset_split)[:-4]
sys.stdout.write('Processing ' + dataset)
filenames = [x.strip('
') for x in open(dataset_split, 'r')]
num_images = len(filenames)
num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))
image_reader = build_data.ImageReader('jpg', channels=3)
label_reader = build_data.ImageReader('png', channels=1)
for shard_id in range(_NUM_SHARDS):
output_filename = os.path.join(
FLAGS.output_dir,
'%s-%05d-of-%05d.tfrecord' % (dataset, shard_id, _NUM_SHARDS))
with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
start_idx = shard_id * num_per_shard
end_idx = min((shard_id + 1) * num_per_shard, num_images)
for i in range(start_idx, end_idx):
sys.stdout.write('
>> Converting image %d/%d shard %d' % (
i + 1, len(filenames), shard_id))
sys.stdout.flush()
# Read the image.
image_filename = os.path.join(
FLAGS.image_folder, filenames[i] + '.jpg' )#+ FLAGS.image_format)
image_data = tf.gfile.FastGFile(image_filename, 'rb').read()
height, width = image_reader.read_image_dims(image_data)
# Read the semantic segmentation annotation.
seg_filename = os.path.join(
FLAGS.semantic_segmentation_folder,
filenames[i] + '.' + FLAGS.label_format)
seg_data = tf.gfile.FastGFile(seg_filename, 'rb').read()
seg_height, seg_width = label_reader.read_image_dims(seg_data)
if height != seg_height or width != seg_
raise RuntimeError('Shape mismatched between image and label.')
# Convert to tf example.
example = build_data.image_seg_to_tfexample(
image_data, filenames[i], height, width, seg_data)
tfrecord_writer.write(example.SerializeToString())
sys.stdout.write('
')
sys.stdout.flush()
def main(unused_argv):
dataset_splits = tf.gfile.Glob(os.path.join(FLAGS.list_folder, '*.txt'))
for dataset_split in dataset_splits:
_convert_dataset(dataset_split)
if __name__ == '__main__':
tf.app.run()参考链接四