Tensorflow之构建自己的图片数据集TFrecords

学习谷歌的深度学习终于有点眉目了，给大家分享我的Tensorflow学习历程。

tensorflow的官方中文文档比较生涩，数据集一直采用的MNIST二进制数据集。并没有过多讲述怎么构建自己的图片数据集tfrecords。

先贴我的转化代码将图片文件夹下的图片转存tfrecords的数据集。

############################################################################################
#!/usr/bin/python2.7
# -*- coding: utf-8 -*-
#Author  : zhaoqinghui
#Date    : 2016.5.10
#Function: image convert to tfrecords
#############################################################################################

import tensorflow as tf
import numpy as np
import cv2
import os
import os.path
from PIL import Image

#参数设置
###############################################################################################
train_file = 'train.txt' #训练图片
name='train'      #生成train.tfrecords
output_directory='./tfrecords'
resize_height=32 #存储图片高度
resize_width=32 #存储图片宽度
###############################################################################################
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def load_file(examples_list_file):
lines = np.genfromtxt(examples_list_file, delimiter=" ", dtype=[('col1', 'S120'), ('col2', 'i8')])
examples = []
labels = []
for example, label in lines:
examples.append(example)
labels.append(label)
return np.asarray(examples), np.asarray(labels), len(lines)

def extract_image(filename,  resize_height, resize_width):
image = cv2.imread(filename)
image = cv2.resize(image, (resize_height, resize_width))
b,g,r = cv2.split(image)
rgb_image = cv2.merge([r,g,b])
return rgb_image

def transform2tfrecord(train_file, name, output_directory, resize_height, resize_width):
if not os.path.exists(output_directory) or os.path.isfile(output_directory):
os.makedirs(output_directory)
_examples, _labels, examples_num = load_file(train_file)
filename = output_directory + "/" + name + '.tfrecords'
writer = tf.python_io.TFRecordWriter(filename)
for i, [example, label] in enumerate(zip(_examples, _labels)):
print('No.%d' % (i))
image = extract_image(example, resize_height, resize_width)
print('shape: %d, %d, %d, label: %d' % (image.shape[0], image.shape[1], image.shape[2], label))
image_raw = image.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'image_raw': _bytes_feature(image_raw),
'height': _int64_feature(image.shape[0]),
'width': _int64_feature(image.shape[1]),
'depth': _int64_feature(image.shape[2]),
'label': _int64_feature(label)
}))
writer.write(example.SerializeToString())
writer.close()

def disp_tfrecords(tfrecord_list_file):
filename_queue = tf.train.string_input_producer([tfrecord_list_file])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'depth': tf.FixedLenFeature([], tf.int64),
'label': tf.FixedLenFeature([], tf.int64)
}
)
image = tf.decode_raw(features['image_raw'], tf.uint8)
#print(repr(image))
height = features['height']
width = features['width']
depth = features['depth']
label = tf.cast(features['label'], tf.int32)
init_op = tf.initialize_all_variables()
resultImg=[]
resultLabel=[]
with tf.Session() as sess:
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(21):
image_eval = image.eval()
resultLabel.append(label.eval())
image_eval_reshape = image_eval.reshape([height.eval(), width.eval(), depth.eval()])
resultImg.append(image_eval_reshape)
pilimg = Image.fromarray(np.asarray(image_eval_reshape))
pilimg.show()
coord.request_stop()
coord.join(threads)
sess.close()
return resultImg,resultLabel

def read_tfrecord(filename_queuetemp):
filename_queue = tf.train.string_input_producer([filename_queuetemp])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'width': tf.FixedLenFeature([], tf.int64),
'depth': tf.FixedLenFeature([], tf.int64),
'label': tf.FixedLenFeature([], tf.int64)
}
)
image = tf.decode_raw(features['image_raw'], tf.uint8)
# image
tf.reshape(image, [256, 256, 3])
# normalize
image = tf.cast(image, tf.float32) * (1. /255) - 0.5
# label
label = tf.cast(features['label'], tf.int32)
return image, label

def test():
transform2tfrecord(train_file, name , output_directory,  resize_height, resize_width) #转化函数
img,label=disp_tfrecords(output_directory+'/'+name+'.tfrecords') #显示函数
img,label=read_tfrecord(output_directory+'/'+name+'.tfrecords') #读取函数
print label

if __name__ == '__main__':
test()

这样就可以得到自己专属的数据集.tfrecords了 ，它可以直接用于tensorflow的数据集。