tensorflow学习笔记之tfrecord文件的生成与读取

import os
import numpy as np
import tensorflow as tf
from PIL import Image

filenames = [
'images/cat/1.jpg',
'images/cat/2.jpg',
'images/dog/1.jpg',
'images/dog/2.jpg',
'images/pig/1.jpg',
'images/pig/2.jpg',]

labels = {'cat':0, 'dog':1, 'pig':2}

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]))

with tf.Session() as sess:
	output_filename = os.path.join('images/train.tfrecords')
	with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
		for filename in filenames:
			#读取图像
			image_data = Image.open(filename)
			#图像灰度化
			image_data = np.array(image_data.convert('L'))
			#将图像转化为bytes
			image_data = image_data.tobytes()
			#读取label
			label = labels[filename.split('/')[-2]]
			#生成protocol数据类型
			example = tf.train.Example(features=tf.train.Features(feature={'image': bytes_feature(image_data),
																			'label': int64_feature(label)}))
			tfrecord_writer.write(example.SerializeToString())

import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image

# 根据文件名生成一个队列
filename_queue = tf.train.string_input_producer(['images/train.tfrecords'])
reader = tf.TFRecordReader()
# 返回文件名和文件
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example, 
									features={'image': tf.FixedLenFeature([], tf.string), 
												'label': tf.FixedLenFeature([], tf.int64)})
# 获取图像数据
image = tf.decode_raw(features['image'], tf.uint8)
# 恢复图像原始尺寸[高，宽]
image = tf.reshape(image, [60, 160])
# 获取label
label = tf.cast(features['label'], tf.int32)

with tf.Session() as sess:
	# 创建一个协调器，管理线程
	coord = tf.train.Coordinator()
	# 启动QueueRunner, 此时文件名队列已经进队
	threads = tf.train.start_queue_runners(sess=sess, coord=coord)

	for i in range(6):
		image_b, label_b = sess.run([image, label])
		img = Image.fromarray(image_b, 'L')
		plt.imshow(img)
		plt.axis('off')
		plt.show()
		print(label_b)

	# 通知其他线程关闭
	coord.request_stop()
	# 其他所有线程关闭之后，这一函数才能返回
	coord.join(threads)