Tensorflow之构建自己的图片数据集TFrecords的方法


            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。



流程是：制作数据集—读取数据集—-加入队列 



先贴完整的代码：





#encoding=utf-8

import os

import tensorflow as tf

from PIL import Image



cwd = os.getcwd()



classes = {'test','test1','test2'}

#制作二进制数据

def create_record():

  writer = tf.python_io.TFRecordWriter("train.tfrecords")

  for index, name in enumerate(classes):

    class_path = cwd +"/"+ name+"/"

    for img_name in os.listdir(class_path):

      img_path = class_path + img_name

      img = Image.open(img_path)

      img = img.resize((64, 64))

      img_raw = img.tobytes() #将图片转化为原生bytes

      print index,img_raw

      example = tf.train.Example(

        features=tf.train.Features(feature={

          "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),

          'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))

        }))

      writer.write(example.SerializeToString())

  writer.close()



data = create_record()



#读取二进制数据

def read_and_decode(filename):

  # 创建文件队列,不限读取的数量

  filename_queue = tf.train.string_input_producer([filename])

  # create a reader from file queue

  reader = tf.TFRecordReader()

  # reader从文件队列中读入一个序列化的样本

  _, serialized_example = reader.read(filename_queue)

  # get feature from serialized example

  # 解析符号化的样本

  features = tf.parse_single_example(

    serialized_example,

    features={

      'label': tf.FixedLenFeature([], tf.int64),

      'img_raw': tf.FixedLenFeature([], tf.string)

    }

  )

  label = features['label']

  img = features['img_raw']

  img = tf.decode_raw(img, tf.uint8)

  img = tf.reshape(img, [64, 64, 3])

  img = tf.cast(img, tf.float32) * (1. / 255) - 0.5

  label = tf.cast(label, tf.int32)

  return img, label



if __name__ == '__main__':

  if 0:

    data = create_record("train.tfrecords")

  else:

    img, label = read_and_decode("train.tfrecords")

    print "tengxing",img,label

    #使用shuffle_batch可以随机打乱输入 next_batch挨着往下取

    # shuffle_batch才能实现[img,label]的同步,也即特征和label的同步,不然可能输入的特征和label不匹配

    # 比如只有这样使用,才能使img和label一一对应,每次提取一个image和对应的label

    # shuffle_batch返回的值就是RandomShuffleQueue.dequeue_many()的结果

    # Shuffle_batch构建了一个RandomShuffleQueue，并不断地把单个的[img,label],送入队列中

    img_batch, label_batch = tf.train.shuffle_batch([img, label],

                          batch_size=4, capacity=2000,

                          min_after_dequeue=1000)



    # 初始化所有的op

    init = tf.initialize_all_variables()



    with tf.Session() as sess:

      sess.run(init)

      # 启动队列

      threads = tf.train.start_queue_runners(sess=sess)

      for i in range(5):

        print img_batch.shape,label_batch

        val, l = sess.run([img_batch, label_batch])

        # l = to_categorical(l, 12)

        print(val.shape, l)







制作数据集





#制作二进制数据

def create_record():

  cwd = os.getcwd()

  classes = {'1','2','3'}

  writer = tf.python_io.TFRecordWriter("train.tfrecords")

  for index, name in enumerate(classes):

    class_path = cwd +"/"+ name+"/"

    for img_name in os.listdir(class_path):

      img_path = class_path + img_name

      img = Image.open(img_path)

      img = img.resize((28, 28))

      img_raw = img.tobytes() #将图片转化为原生bytes

      #print index,img_raw

      example = tf.train.Example(

        features=tf.train.Features(

          feature={

            "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),

            'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))

          }

        )

      )

      writer.write(example.SerializeToString())

  writer.close()





TFRecords文件包含了tf.train.Example 协议内存块(protocol buffer)(协议内存块包含了字段 Features)。我们可以写一段代码获取你的数据， 将数据填入到Example协议内存块(protocol buffer)，将协议内存块序列化为一个字符串， 并且通过tf.python_io.TFRecordWriter 写入到TFRecords文件。

读取数据集





#读取二进制数据

def read_and_decode(filename):

  # 创建文件队列,不限读取的数量

  filename_queue = tf.train.string_input_producer([filename])

  # create a reader from file queue

  reader = tf.TFRecordReader()

  # reader从文件队列中读入一个序列化的样本

  _, serialized_example = reader.read(filename_queue)

  # get feature from serialized example

  # 解析符号化的样本

  features = tf.parse_single_example(

    serialized_example,

    features={

      'label': tf.FixedLenFeature([], tf.int64),

      'img_raw': tf.FixedLenFeature([], tf.string)

    }

  )

  label = features['label']

  img = features['img_raw']

  img = tf.decode_raw(img, tf.uint8)

  img = tf.reshape(img, [64, 64, 3])

  img = tf.cast(img, tf.float32) * (1. / 255) - 0.5

  label = tf.cast(label, tf.int32)

  return img, label





一个Example中包含Features，Features里包含Feature（这里没s）的字典。最后，Feature里包含有一个 FloatList， 或者ByteList，或者Int64List

加入队列





with tf.Session() as sess:

      sess.run(init)

      # 启动队列

      threads = tf.train.start_queue_runners(sess=sess)

      for i in range(5):

        print img_batch.shape,label_batch

        val, l = sess.run([img_batch, label_batch])

        # l = to_categorical(l, 12)

        print(val.shape, l)



这样就可以的到和tensorflow官方的二进制数据集了， 



注意：



  启动队列那条code不要忘记，不然卡死

  使用的时候记得使用val和l，不然会报类型错误：TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, or numpy ndarrays.

  算交叉熵时候：cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits,labels)算交叉熵

  最后评估的时候用tf.nn.in_top_k(logits,labels,1)选logits最大的数的索引和label比较

  cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))算交叉熵，所以label必须转成one-hot向量



实例2：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。



以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持中文源码网。