message Example {
 Features features = 1;
};

message Features{
 map<string,Feature> featrue = 1;
};

message Feature{
  oneof kind{
    BytesList bytes_list = 1;
    FloatList float_list = 2;
    Int64List int64_list = 3;
  }
};

import os 
import tensorflow as tf 
from PIL import Image 
import matplotlib.pyplot as plt 

cwd='/home/ruyiwei/Documents/Iris&Contact/'
classes={'iris','contact'} 
writer= tf.python_io.TFRecordWriter("iris_contact.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((512,80))
    img_raw=img.tobytes()
    #plt.imshow(img) # if you want to check you image,please delete '#'
    #plt.show()
    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()

def read_and_decode(filename): # read iris_contact.tfrecords
  filename_queue = tf.train.string_input_producer([filename])# create a queue

  reader = tf.TFRecordReader()
  _, serialized_example = reader.read(filename_queue)#return file_name and file
  features = tf.parse_single_example(serialized_example,
                    features={
                      'label': tf.FixedLenFeature([], tf.int64),
                      'img_raw' : tf.FixedLenFeature([], tf.string),
                    })#return image and label

  img = tf.decode_raw(features['img_raw'], tf.uint8)
  img = tf.reshape(img, [512, 80, 3]) #reshape image to 512*80*3
  img = tf.cast(img, tf.float32) * (1. / 255) - 0.5 #throw img tensor
  label = tf.cast(features['label'], tf.int32) #throw label tensor
  return img, label

filename_queue = tf.train.string_input_producer(["iris_contact.tfrecords"]) 
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)  #return file and file_name
features = tf.parse_single_example(serialized_example,
                  features={
                    'label': tf.FixedLenFeature([], tf.int64),
                    'img_raw' : tf.FixedLenFeature([], tf.string),
                  }) 
image = tf.decode_raw(features['img_raw'], tf.uint8)
image = tf.reshape(image, [512, 80, 3])
label = tf.cast(features['label'], tf.int32)
with tf.Session() as sess: 
  init_op = tf.initialize_all_variables()
  sess.run(init_op)
  coord=tf.train.Coordinator()
  threads= tf.train.start_queue_runners(coord=coord)
  for i in range(20):
    example, l = sess.run([image,label])#take out image and label
    img=Image.fromarray(example, 'RGB')
    img.save(cwd+str(i)+'_''Label_'+str(l)+'.jpg')#save image
    print(example, l)
  coord.request_stop()
  coord.join(threads)