1首先准备好自己图片数据集，我是用cifar10 的其中五个类别，分别是bird,car ,cat,deer,plane。五个类别数据分开放置。例如：
然后就是根据数据集生成tfrecord,生成的是protobuf(二进制文件，加速文件传输和处理速度),代码如下
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import tensorflow as tf
import os
import random
import math
import sys
#验证集数量
_NUM_TEST = 500
#随机种子
_RANDOM_SEED = 0
#数据块
_NUM_SHARDS = 5
#数据集路径
DATASET_DIR = "D:/Tensorflow/slim/images/"
#标签文件名字
LABELS_FILENAME = "D:/Tensorflow/slim/images/labels.txt"
#定义tfrecord文件的路径+名字
def _get_dataset_filename(dataset_dir, split_name, shard_id):
output_filename = 'image_%s_%05d-of-%05d.tfrecord' % (split_name, shard_id, _NUM_SHARDS)
return os.path.join(dataset_dir, output_filename)
#判断tfrecord文件是否存在
def _dataset_exists(dataset_dir):
for split_name in ['train', 'test']:
for shard_id in range(_NUM_SHARDS):
#定义tfrecord文件的路径+名字
output_filename = _get_dataset_filename(dataset_dir, split_name, shard_id)
if not tf.gfile.Exists(output_filename):
return False
return True
#获取所有文件以及分类
def _get_filenames_and_classes(dataset_dir):
#数据目录
directories = []
#分类名称
class_names = []
for filename in os.listdir(dataset_dir):
#合并文件路径
path = os.path.join(dataset_dir, filename)
#判断该路径是否为目录
if os.path.isdir(path):
#加入数据目录
directories.append(path)
#加入类别名称
class_names.append(filename)
photo_filenames = []
#循环每个分类的文件夹
for directory in directories:
for filename in os.listdir(directory):
path = os.path.join(directory, filename)
#把图片加入图片列表
photo_filenames.append(path)
return photo_filenames, class_names
def int64_feature(values):
if not isinstance(values, (tuple, list)):
values = [values]
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
def bytes_feature(values):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))
def image_to_tfexample(image_data, image_format, class_id):
#Abstract base class for protocol messages.
return tf.train.Example(features=tf.train.Features(feature={
'image/encoded': bytes_feature(image_data),
'image/format': bytes_feature(image_format),
'image/class/label': int64_feature(class_id),
}))
def write_label_file(labels_to_class_names, dataset_dir,filename=LABELS_FILENAME):
labels_filename = os.path.join(dataset_dir, filename)
with tf.gfile.Open(labels_filename, 'w') as f:
for label in labels_to_class_names:
class_name = labels_to_class_names[label]
f.write('%d:%s\n' % (label, class_name))
#把数据转为TFRecord格式
def _convert_dataset(split_name, filenames, class_names_to_ids, dataset_dir):
assert split_name in ['train', 'test']
#计算每个数据块有多少数据
num_per_shard = int(len(filenames) / _NUM_SHARDS)
with tf.Graph().as_default():
with tf.Session() as sess:
for shard_id in range(_NUM_SHARDS):
#定义tfrecord文件的路径+名字
output_filename = _get_dataset_filename(dataset_dir, split_name, shard_id)
with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
#每一个数据块开始的位置
start_ndx = shard_id * num_per_shard
#每一个数据块最后的位置
end_ndx = min((shard_id+1) * num_per_shard, len(filenames))
for i in range(start_ndx, end_ndx):
try:
sys.stdout.write('\r>> Converting image %d/%d shard %d' % (i+1, len(filenames), shard_id))
sys.stdout.flush()
#读取图片
image_data = tf.gfile.FastGFile(filenames[i], 'r').read()
#获得图片的类别名称
class_name = os.path.basename(os.path.dirname(filenames[i]))
#找到类别名称对应的id
class_id = class_names_to_ids[class_name]
#生成tfrecord文件
example = image_to_tfexample(image_data, b'jpg', class_id)
tfrecord_writer.write(example.SerializeToString())
except IOError as e:
print("Could not read:",filenames[i])
print("Error:",e)
print("Skip it\n")
sys.stdout.write('\n')
sys.stdout.flush()
if __name__ == '__main__':
#判断tfrecord文件是否存在
if _dataset_exists(DATASET_DIR):
print('tfcecord文件已存在')
else:
#获得所有图片以及分类
photo_filenames, class_names = _get_filenames_and_classes(DATASET_DIR)
#把分类转为字典格式，类似于{'house': 3, 'flower': 1, 'plane': 4, 'guitar': 2, 'animal': 0}
class_names_to_ids = dict(zip(class_names, range(len(class_names))))
#把数据切分为训练集和测试集
random.seed(_RANDOM_SEED)
random.shuffle(photo_filenames)
training_filenames = photo_filenames[_NUM_TEST:]
testing_filenames = photo_filenames[:_NUM_TEST]
#数据转换
_convert_dataset('train', training_filenames, class_names_to_ids, DATASET_DIR)
_convert_dataset('test', testing_filenames, class_names_to_ids, DATASET_DIR)
#输出labels文件
labels_to_class_names = dict(zip(range(len(class_names)), class_names))
write_label_file(labels_to_class_names, DATASET_DIR)
>
然后就可以执行上面代码生产tfrecord文件，生成的结果如下图：