数据集制成TFRecord格式 将自己的数据集制作成TFRecord格式教程

#生成整数型的属性
def _int64_feature(value):
 return tf.train.Feature(int64_list = tf.train.Int64List(value = [value]))
 
#生成字符串类型的属性
def _bytes_feature(value):
 return tf.train.Feature(bytes_list = tf.train.BytesList(value = [value]))
 
#制作TFRecord格式
def createTFRecord(filename,mapfile):
 class_map = {}
 data_dir = '/home/wc/DataSet/traffic/testTFRecord/'
 classes = {'xiansu60','xiansu100'}
 #输出TFRecord文件的地址
 
 writer = tf.python_io.TFRecordWriter(filename)
 
 for index,name in enumerate(classes):
  class_path=data_dir+name+'/'
  class_map[index] = name
  for img_name in os.listdir(class_path):
   img_path = class_path + img_name #每个图片的地址
   img = Image.open(img_path)
   img= img.resize((224,224))
   img_raw = img.tobytes()   #将图片转化成二进制格式
   example = tf.train.Example(features = tf.train.Features(feature = {
    'label':_int64_feature(index),
    'image_raw': _bytes_feature(img_raw)
   }))
   writer.write(example.SerializeToString())
 writer.close()
 
 txtfile = open(mapfile,'w+')
 for key in class_map.keys():
  txtfile.writelines(str(key)+":"+class_map[key]+"\n")
 txtfile.close()

#读取train.tfrecord中的数据
def read_and_decode(filename): 
 #创建一个reader来读取TFRecord文件中的样例
 reader = tf.TFRecordReader()
 #创建一个队列来维护输入文件列表
 filename_queue = tf.train.string_input_producer([filename], shuffle=False,num_epochs = 1)
 #从文件中读出一个样例，也可以使用read_up_to一次读取多个样例
 _,serialized_example = reader.read(filename_queue)
#  print _,serialized_example
 
 #解析读入的一个样例，如果需要解析多个，可以用parse_example
 features = tf.parse_single_example(
 serialized_example,
 features = {'label':tf.FixedLenFeature([], tf.int64),
    'image_raw': tf.FixedLenFeature([], tf.string),})
 #将字符串解析成图像对应的像素数组
 img = tf.decode_raw(features['image_raw'], tf.uint8)
 img = tf.reshape(img,[224, 224, 3]) #reshape为128*128*3通道图片
 img = tf.image.per_image_standardization(img)
 labels = tf.cast(features['label'], tf.int32)
 return img, labels

def createBatch(filename,batchsize):
 images,labels = read_and_decode(filename)
 
 min_after_dequeue = 10
 capacity = min_after_dequeue + 3 * batchsize
 
 image_batch, label_batch = tf.train.shuffle_batch([images, labels], 
              batch_size=batchsize, 
              capacity=capacity, 
              min_after_dequeue=min_after_dequeue
              )
 
 label_batch = tf.one_hot(label_batch,depth=2)
 return image_batch, label_batch

if __name__ =="__main__":
 #训练图片两张为一个batch,进行训练，测试图片一起进行测试
 mapfile = "/home/wc/DataSet/traffic/testTFRecord/classmap.txt"
 train_filename = "/home/wc/DataSet/traffic/testTFRecord/train.tfrecords"
#  createTFRecord(train_filename,mapfile)
 test_filename = "/home/wc/DataSet/traffic/testTFRecord/test.tfrecords"
#  createTFRecord(test_filename,mapfile)
 image_batch, label_batch = createBatch(filename = train_filename,batchsize = 2)
 test_images,test_labels = createBatch(filename = test_filename,batchsize = 20)
 with tf.Session() as sess:
  initop = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())
  sess.run(initop)
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(sess = sess, coord = coord)
 
  try:
   step = 0
   while 1:
    _image_batch,_label_batch = sess.run([image_batch,label_batch])
    step += 1
    print step
    print (_label_batch)
  except tf.errors.OutOfRangeError:
   print (" trainData done!")
   
  try:
   step = 0
   while 1:
    _test_images,_test_labels = sess.run([test_images,test_labels])
    step += 1
    print step
 #     print _image_batch.shape
    print (_test_labels)
  except tf.errors.OutOfRangeError:
   print (" TEST done!")
  coord.request_stop()
  coord.join(threads)