tensorflow将图片保存为tfrecord和tfrecord的读取方式

  a = tf.constant([1,2,3])
  b = tf.constant([4,5,6])
  c = tf.add(a,b)
  with tf.Session() as sess:
    print(sess.run(c))#[5 7 9]

  x = tf.placeholder(tf.int16)
  y = tf.placeholder(tf.int16)
  z = tf.add(x,y)
  with tf.Session() as sess:
    print(sess.run(z,feed_dict={x:[1,2,3],y:[4,5,6]}))
    #[5 7 9]

#数据所在的目录路径
dataset_dir_path = "D:/dataset/kaggle/cat_or_dog/train"
#类标名称和数字的对应关系
label_name_to_num = {"cat":0,"dog":1}
label_num_to_name = {value:key for key,value in label_name_to_num.items()}
#设置验证集占整个数据集的比例
val_size = 0.2
batch_size = 1

 #获取文件所在路径
 dataset_dir = os.path.join(dataset_dir,split_name)
 #遍历目录下的所有图片
 for filename in os.listdir(dataset_dir):
   #获取文件的路径
   file_path = os.path.join(dataset_dir,filename)
   if file_path.endswith("jpg") and os.path.exists(file_path):
     #获取类别的名称
     label_name = filename.split(".")[0]
     if label_name == "cat":
       cat_img_paths.append(file_path)
     elif label_name == "dog":
       dog_img_paths.append(file_path)
 return cat_img_paths,dog_img_paths

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]))
 
 
#将图片信息转换为tfrecords可以保存的序列化信息
def image_to_tfexample(split_name,image_data, image_format, height, width, img_info):
  '''
  :param split_name: train或val或test
  :param image_data: 图片的二进制数据
  :param image_format: 图片的格式
  :param height: 图片的高
  :param width: 图片的宽
  :param img_info: 图片的标签或图片的名称,当split_name为test时,img_info为图片的名称否则为图片标签
  :return:
  '''
  if split_name == "test":
    return tf.train.Example(features=tf.train.Features(feature={
       'image/encoded': bytes_feature(image_data),
       'image/format': bytes_feature(image_format),
       'image/img_name': bytes_feature(img_info),
       'image/height': int64_feature(height),
       'image/width': int64_feature(width),
     }))
  else:
     return tf.train.Example(features=tf.train.Features(feature={
       'image/encoded': bytes_feature(image_data),
       'image/format': bytes_feature(image_format),
       'image/label': int64_feature(img_info),
       'image/height': int64_feature(height),
       'image/width': int64_feature(width),
     }))

def _convert_tfrecord_dataset(split_name, filenames, label_name_to_id, 
dataset_dir, tfrecord_filename, _NUM_SHARDS):
  '''
  :param split_name:train或val或test
  :param filenames:图片的路径列表
  :param label_name_to_id:标签名与数字标签的对应关系
  :param dataset_dir:数据存放的目录
  :param tfrecord_filename:文件保存的前缀名
  :param _NUM_SHARDS:将整个数据集分为几个文件
  :return:
  '''
  assert split_name in ['train', 'val','test']
  #计算平均每一个tfrecords文件保存多少张图片
  num_per_shard = int(math.ceil(len(filenames) / float(_NUM_SHARDS)))
  with tf.Graph().as_default():
    image_reader = ImageReader()
    with tf.Session('') as sess:
      for shard_id in range(_NUM_SHARDS):
        #获取tfrecord文件的名称
        output_filename = _get_dataset_filename(
            dataset_dir, split_name, shard_id,
 tfrecord_filename = tfrecord_filename, _NUM_SHARDS = _NUM_SHARDS)
        #写tfrecords文件
        with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
          start_ndx = shard_id * num_per_shard
          end_ndx = min((shard_id+1) * num_per_shard, len(filenames))
          for i in range(start_ndx, end_ndx):
            #更新控制台中已经完成的图片数量
            sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
              i+1, len(filenames), shard_id))
            sys.stdout.flush()
            #读取图片,将图片数据读取为bytes
            image_data = tf.gfile.FastGFile(filenames[i], 'rb').read()
            #获取图片的高和宽
            height, width = image_reader.read_image_dims(sess, image_data)
            #获取路径中的图片名称
            img_name = os.path.basename(filenames[i])
            if split_name == "test":
              #需要将图片名称转换为二进制
              example = image_to_tfexample(
                split_name,image_data, b'jpg', height, width, img_name.encode())
              tfrecord_writer.write(example.SerializeToString())
            else:
              #获取图片的类别
              class_name = img_name.split(".")[0]
              label_id = label_name_to_id[class_name]
              example = image_to_tfexample(
                split_name,image_data, b'jpg', height, width, label_id)
              tfrecord_writer.write(example.SerializeToString())
        sys.stdout.write('\n')
        sys.stdout.flush()

#生成tfrecord文件
def generate_tfreocrd():
  #获取目录下所有的猫和狗图片的路径
  cat_img_paths,dog_img_paths = _get_dateset_imgPaths(dataset_dir_path,"train")
  #打乱路径列表的顺序
  np.random.shuffle(cat_img_paths)
  np.random.shuffle(dog_img_paths)
  #计算不同类别验证集所占的图片数量
  cat_val_num = int(len(cat_img_paths) * val_size)
  dog_val_num = int(len(dog_img_paths) * val_size)
  #将所有的图片路径分为训练集和验证集
  train_img_paths = cat_img_paths[cat_val_num:]
  val_img_paths = cat_img_paths[:cat_val_num]
  train_img_paths.extend(dog_img_paths[dog_val_num:])
  val_img_paths.extend(dog_img_paths[:dog_val_num])
  #打乱训练集和验证集的顺序
  np.random.shuffle(train_img_paths)
  np.random.shuffle(val_img_paths)
  #将训练集保存为tfrecord文件
  _convert_tfrecord_dataset("train",train_img_paths,label_name_to_num,dataset_dir_path,"catVSdog",2)
  #将验证集保存为tfrecord文件
  _convert_tfrecord_dataset("val",val_img_paths,label_name_to_num,dataset_dir_path,"catVSdog",1)

 #创建一个tfrecord读文件对象
  reader = tf.TFRecordReader
    keys_to_feature = {
      "image/encoded":tf.FixedLenFeature((),tf.string,default_value=""),
      "image/format":tf.FixedLenFeature((),tf.string,default_value="jpg"),
     "image/label":tf.FixedLenFeature([],tf.int64,default_value=tf.zeros([],tf.int64))
    }
    items_to_handles = {
      "image":slim.tfexample_decoder.Image(),
      "label":slim.tfexample_decoder.Tensor("image/label")
    }
    items_to_descriptions = {
      "image":"a 3-channel RGB image",
      "img_name":"a image label"
    }
    #创建一个tfrecoder解析对象
    decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_feature,items_to_handles)
    #读取所有的tfrecord文件,创建数据集
    dataset = slim.dataset.Dataset(
      data_sources = tfrecord_paths,
      decoder = decoder,
      reader = reader,
      num_readers = 4,
      num_samples = num_imgs,
      num_classes = num_classes,
      labels_to_name = labels_to_name,
      items_to_descriptions = items_to_descriptions
    )

def load_batch(split_name,dataset,batch_size,height,width):
  data_provider = slim.dataset_data_provider.DatasetDataProvider(
    dataset,
    common_queue_capacity = 24 + 3 * batch_size,
    common_queue_min = 24
  )
    raw_image,img_label = data_provider.get(["image","label"])
    #Perform the correct preprocessing for this image depending if it is training or evaluating
    image = preprocess_image(raw_image, height, width,True)
    #As for the raw images, we just do a simple reshape to batch it up
    raw_image = tf.expand_dims(raw_image, 0)
    raw_image = tf.image.resize_nearest_neighbor(raw_image, [height, width])
    raw_image = tf.squeeze(raw_image)
    #获取一个batch数据
    images,raw_image,labels = tf.train.batch(
      [image,raw_image,img_label],
      batch_size=batch_size,
      num_threads=4,
      capacity=4*batch_size,
      allow_smaller_final_batch=True
    )
    return images,raw_image,labels

#读取tfrecord文件
def read_tfrecord():
  #从tfreocrd文件中读取数据
  train_dataset = get_dataset_by_tfrecords("train",dataset_dir_path,"catVSdog",2,label_num_to_name)
  images,raw_images,labels = load_batch("train",train_dataset,batch_size,227,227)
  with tf.Session() as sess:
    threads = tf.train.start_queue_runners(sess)
    for i in range(6):
      train_img,train_label = sess.run([raw_images,labels])
      plt.subplot(2,3,i+1)
      plt.imshow(np.array(train_img[0]))
      plt.title("image label:%s"%str(label_num_to_name[train_label[0]]))
    plt.show()