# -*- coding: utf-8 -*-
"""
Created on Fri Jun 15 10:01:56 2018
@author: itdp03
"""
import tensorflow as tf
import glob
from itertools import groupby
from collections import defaultdict
from PIL import Image
import os
IMAGE_WIDTH = 256
IMAGE_HEIGHT = 256
IMAGE_CHANNEL = 1
sess = tf.InteractiveSession()
#查找符合一定规则的所有文件，并将文件名以lis形式返回。
image_filenames = glob.glob("./Image/*/*.jpg")#获取所用文件路径
#用list类型初始化training和testing数据集，用defaultdict的好处是为字典中不存在的键提供默认值
training_dataset = defaultdict(list)
testing_dataset = defaultdict(list)
#将品种名从文件名中切分出，image_filename_with_breed是一个迭代器，用list(image_filename_with_breed)将其转换为list，其中的元素类似于：('n02085620-Chihuahua', './imagenet-dogs/n02085620-Chihuahua/n02085620_10131.jpg')。
image_filename_with_breed = list(map(lambda filename: (filename.split("\\")[-2], filename), image_filenames))
## Group each image by the breed which is the 0th element in the tuple returned above
#groupby后得到的是一个迭代器，每个元素的形式为：
#('n02085620-Chihuahua', <itertools._grouper at 0xd5892e8>)，其中第1个元素为种类；第2个元素代表该类的文件，这两个元素也分别对应for循环里的dog_breed和breed_images。
for dog_breed, breed_images in groupby(image_filename_with_breed,
lambda x: x[0]):
#enumerate的作用是列举breed_images中的所有元素，可同时返回索引和元素，i和breed_image
#的最后一个值分别是：168、('n02116738-African_hunting_dog', './imagenet-dogs/
#n02116738-African_hunting_dog/n02116738_9924.jpg')
for i, breed_image in enumerate(breed_images):
#因为breed_images是按类分别存储的，所以下面是将大约20%的数据作为测试集，大约80%的
#数据作为训练集。
#testing_dataset和training_dataset是两个字典，testing_dataset中
#的第一个元素是 'n02085620-Chihuahua': ['./imagenet-dogs/n02085620-Chihuahua/
#n02085620_10074.jpg', './imagenet-dogs/n02085620-Chihuahua/
#n02085620_11140.jpg',.....]
if i % 5 == 0:
testing_dataset[dog_breed].append(breed_image[1])
else:
training_dataset[dog_breed].append(breed_image[1])
# 测试每种类型下的测试集是否至少包含了18%的数据
breed_training_count = len(training_dataset[dog_breed])
breed_testing_count = len(testing_dataset[dog_breed])
#round(，n)函数：浮点数x的四舍五入值，n：小数点后n位。
assert round(breed_testing_count /
(breed_training_count + breed_testing_count),
2) > 0.18, "Not enough testing images."
"""
Fill a TFRecords file with the images found in `dataset` and include their category.
Parameters
----------
dataset : dict(list)
Dictionary with each key being a label for the list of image filenames of its value.
record_location : str
Location to store the TFRecord output.
"""
def write_records_file(dataset, record_location):
if not os.path.exists(record_location):
print("目录 %s 不存在，自动创建中..." % (record_location))
os.makedirs(record_location)
writer = None
# Enumerating the dataset because the current index is used to breakup the files if they get over 100
# images to avoid a slowdown in writing.
current_index = 0
#遍历每一种类型的所有文件
for breed, images_filenames in dataset.items():
#遍历每一个文件
for image_filename in images_filenames:
if current_index % 100 == 0:
if writer:
writer.close()
#创建tensorflow record的文件名
record_filename = "{record_location}-{current_index}.tfrecords".format( #print('{名字}今天{动作}'.format(名字='陈某某',动作='拍视频'))#通过关键字
record_location=record_location,
current_index=current_index)
writer = tf.python_io.TFRecordWriter(record_filename)
current_index += 1
#使用Image.open读取图像比tf.read_file的速度快10倍，建议使用Image.open
image = Image.open(image_filename)
#test--hzz
#grayscale_image = tf.image.rgb_to_grayscale(image)
image = image.resize((IMAGE_WIDTH, IMAGE_HEIGHT))
image_bytes = image.tobytes() # 将图片转成二进制
# Instead of using the label as a string, it'd be more efficient to turn it into either an
# integer index or a one-hot encoded rank one tensor.
#将表示种类的字符串转换为python默认的utf-8格式，防止有问题
image_label = breed.encode("utf-8")
image_name = image_filename.split("\\")[-1].encode("utf-8")
print(image_label)
## 创建一个 example protocol buffer 。
# 其中，feature={
# 'label':
# tf.train.Feature(bytes_list=tf.train.BytesList(value=[image_label])),
# 'image':
# tf.train.Feature(bytes_list=tf.train.BytesList(value=[image_bytes]))
# })是创建1个属性
example = tf.train.Example(
features=tf.train.Features(feature={
'label':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[image_label])),
'name':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[image_name])),
'image':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[image_bytes]))
}))
#SerializeToString()将文件序列化为二进制字符串
writer.write(example.SerializeToString())
writer.close()
#分别将测试数据和训练数据写入tensorflow record，分别保存在文件夹./output/testing-images/和./output/
#training-images/下面。
write_records_file(training_dataset, "./training-image/training-image")
write_records_file(testing_dataset, "./testing-image/testing-image")
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