Tensorflow之构建自己的图片数据集TFrecords的方法

技术  /  管理员 发布于 7年前   115

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-8import osimport tensorflow as tffrom PIL import Imagecwd = 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, labelif __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：

1. 启动队列那条code不要忘记，不然卡死
2. 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.
3. 算交叉熵时候：cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits,labels)算交叉熵
4. 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
5. 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。