Tensorflow图像处理
主要内容如下：
- 加载图像
- 图像格式
- 图像转换为TFRecord格式
- 读取TFRecord文件
- 图像处理
- 数据读取方式Dataset API
一.加载图像
Tensorflow对图像文件的加载和对二进制文件的加载相同,只是图像的内容需要解码.这里介绍常用的两种方式：第一种是把图片看作一个图片直接读进来,获取图片的原始数据,再进行解码;如使用tf.gfile.FastGFile()读取图像文件,然后,利用tf.image.decode_jpeg()或"tf.image.decode_pgn()"进行解码.代码如下：
import matplotlib.pyplot as plt
import tensorflow as tf
%matplotlib inline
image_raw_data_jpg=tf.gfile.FastGFile("./data/image/cat/cat.jpg","rb").read()
with tf.Session() as session:
img_data=tf.image.decode_jpeg(image_raw_data_jpg)
# 图像显示
plt.figure(1)
# 显示图像矩阵
print(session.run(img_data))
plt.imshow(img_data.eval())
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第一种方法不太适合读取批量数据,批量读取可以采用另一种方法,这种方法他图像看成一个文件,用队列的方式读取.在Tensorflow中,队列不仅是一种数据结构,更提供了多线程机制.首先,使用tf.train.string_input_producer找到所需文件,并将其加载到一个队列中,然后使用tf.WholeFileReader()把完整的文件加载到内存中,用read从队列中读取图像文件,最后用tf.image.decode_jpeg()或"tf.image.decode_png()"进行解码.代码如下：
import tensorflow as tf
path="./data/image/cat/cat.jpg"
# 创建输入队列
file_queue=tf.train.string_input_producer([path])
image_reader=tf.WholeFileReader()
_,image=image_reader.read(file_queue)
image=tf.image.decode_jpeg(image)
with tf.Session() as session:
# 协同启动的线程
coord=tf.train.Coordinator()
# 启动线程运行队列
threads=tf.train.start_queue_runners(sess=session,coord=coord)
print(session.run(image))
# 停止所有的线程
coord.request_stop()
coord.join(threads)
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二.图像格式
图像中的重要信息通过某种恰当的文件格式存储.在使用图像时,不同的格式可用于解决不同的问题.Tensorflow支持多种图像格式,常用的包括JPEG,PNG,TFRecord图像格式
1.JPEG和PNG
Tensorflow支持JPEG,PNG两种图像格式,这里以这两种为代表,是因为将其他格式转换为这两种格式非常容易.JPEG格式图像使用tf.image.decode_jpeg()解码,PNG格式图像使用tf.image.decode_png解码
2.TFRecord
对于大量的图像数据,Tensorflow采用TFRecord格式,将图像数据和图像的标签数据以二进制形式无压缩方式存储在TFRecord文件中,这样其可以被快速载入内存,方便移动,复制和处理.TFRecord文件中的数据都是通过tf.train.ExampleProtocol Buffer的格式存储的
message Example{
Features features=1;
};
message Features{
map<string,Feature> feature=1;
};
message Feature{
oneof kind{
ByteList bytes_list=1;
FloatList float_list=2;
Int64List int64_list=3;
}
};
tf.train.Example中包含了一个从属性名称到取值的字典.其中属性名称为一个字符串,属性的取值可以为字符串(BytesList),实数列表(FloatList)或者整数列表(Int64List).BytesList存放图像数据,Int64List存放图像的标签对应的整数
三.图像转换为TFRecord文件
把图像转换成TFRecord格式的文件就是把图像数据和图像标签的数据按tf.train.Example数据结构存储成二进制文件的过程.假设我们已经按图像标签吧若干张猫的图像和若干张狗的图片分别存在了以下目录："./data/image/cat/“和”./data/image/dog/",以下代码可以把这些图片转换成TFRecord文件cat_dog.tfrecord,存放在"./data"目录下
import os
import tensorflow as tf
from PIL import Image # 处理图像包
import numpy as np
folder="./data/image/"
savefolder="./data/"
# 设定图像类别标签,标签名称和对应目录名相同
label={"cat","dog"}
# 生成的文件
writer=tf.python_io.TFRecordWriter(savefolder+"cat_dog.tfrecords")
# 记录图像的个数
count=0
for index,name in enumerate(label):
folder_path=folder+name+"/"
for img_name in os.listdir(folder_path):
# 每一个图片的完整访问路径
img_path=folder_path+img_name
img=Image.open(img_path)
img=img.resize((128,128))
# 将图片转化为二进制格式
img_raw=img.tobytes()
# example对象对label和image数据进行封装
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]))
}))
# 序列化为字符串
writer.write(example.SerializeToString())
count=count+1
writer.close()
四.读取TFRecord文件
读取TFRecord文件首先采用输入生成器(tf.train.string_input_producer)将其加载到一个队列,然后读出(tf.TFRecordReader)并将(tf.parse_single_example)Example对象拆解为图像数据和标签数据,最后对图像数据解码并保持成JPG文件
# 定义数据流队列
filename_queue=tf.train.string_input_producer([savefolder+"cat_dog.tfrecords"])
reader=tf.TFRecordReader()
# 返回文件名和文件
_,serialized_example=reader.read(filename_queue)
features=tf.parse_single_example(serialized_example,
features={
"label":tf.FixedLenFeature([],tf.int64),
"img_raw":tf.FixedLenFeature([],tf.string)
})
# 取出包含image和label的feature对象
image=tf.decode_raw(features["img_raw"],tf.uint8)
image=tf.reshape(image,[128,128,3])
label=tf.cast(features["label"],tf.int32)
with tf.Session() as session:
init_op=tf.initialize_all_variables()
session.run(init_op)
coord=tf.train.Coordinator()
# 线程协调器
threads=tf.train.start_queue_runners(coord=coord)
# 以多线程的方式启动对队列的处理
for i in range(count):
# 在会话中取出image和label
example,l=session.run([image,label])
img=Image.fromarray(example,"RGB")
img.save(folder+str(i)+"_label_"+str(l)+".jpg")
print(example,l)
coord.request_stop()
# 等待所有进程结束
coord.join(threads)
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[[[123 121 109]
[133 131 119]
[ 87 80 70]
...
[122 108 97]
[155 141 130]
[136 122 111]]
[[ 95 93 81]
[ 96 92 81]
[ 79 72 62]
...
[136 122 111]
[136 122 111]
[111 97 86]]
[[ 95 91 80]
[ 48 44 33]
[104 97 87]
...
[ 55 41 30]
[162 148 137]
[130 116 105]]
...
[[114 100 91]
[ 46 32 23]
[ 97 83 74]
...
[151 163 143]
[ 73 82 63]
[114 121 103]]
[[180 166 157]
[151 137 128]
[ 92 78 69]
...
[152 164 144]
[ 50 59 40]
[130 139 120]]
[[107 93 84]
[ 14 0 0]
[198 184 175]
...
[ 77 90 70]
[100 109 90]
[100 109 90]]] 1
[[[255 255 255]
[255 255 255]
[255 255 255]
...
[255 255 255]
[255 255 255]
[255 255 255]]
[[255 255 255]
[255 255 255]
[255 255 255]
...
[255 255 255]
[255 255 255]
[255 255 255]]
[[255 255 255]
[255 255 255]
[255 255 255]
...
[255 255 255]
[255 255 255]
[255 255 255]]
...
[[255 255 255]
[255 255 255]
[255 255 255]
...
[253 255 254]
[253 255 254]
[253 255 254]]
[[255 255 255]
[255 255 255]
[255 255 255]
...
[255 255 255]
[255 255 255]
[255 255 255]]
[[255 255 255]
[255 255 255]
[255 255 255]
...
[255 255 255]
[255 255 255]
[255 255 255]]] 1
from IPython.display import Image
Image(filename="./data/image/cat/cat.jpg",width=600)
五.图像处理序列
利用tf.imageAPI,该API包含了很多图像处理的函数.代码基于一个600*600大小的猫的图像
1.调整大小
image_raw_data=tf.gfile.FastGFile("./data/image/cat/cat.jpg","rb").read()
img_data=tf.image.decode_jpeg(image_raw_data)
with tf.Session() as session:
resized=tf.image.resize_images(img_data,[300,300],method=0)
cat=np.asarray(resized.eval(),dtype="uint8")
plt.imshow(cat)
plt.show()
2.裁剪和填充图像
with tf.Session() as session:
croped=tf.image.resize_image_with_crop_or_pad(img_data,300,300)
padded=tf.image.resize_image_with_crop_or_pad(img_data,3000,3000)
plt.imshow(croped.eval())
plt.show()
plt.imshow(padded.eval())
plt.show()
3.对角线翻转图像
with tf.Session() as session:
transposed=tf.image.transpose_image(img_data)
plt.imshow(transposed.eval())
plt.show()
4.色彩调整
with tf.Session() as session:
adjusted=tf.image.random_brightness(img_data,max_delta=0.5)
plt.imshow(adjusted.eval())
plt.show()
5.色调饱和度
with tf.Session() as session:
adjusted=tf.image.adjust_hue(img_data,0.1)
plt.imshow(adjusted.eval())
plt.show()
六.数据读取方式–Dataset API
1.构建Dataset
tf.data.Dataset.from_tensor_slices()
利用tf.data.Dataset.from_tensor_slices()从一个或多个tf.Tensor对象中构建一个dataset,其tf.Tensor对象中包括数组,矩阵,字典,元组等.具体实例如下：
import tensorflow as tf
import numpy as np
arry1=np.array([1.0,2.0,3.0,4.0,5.0])
dataset=tf.data.Dataset.from_tensor_slices(arry1)
iterator=dataset.make_one_shot_iterator()
one_element=iterator.get_next()
with tf.Session() as session:
for i in range(len(arry1)):
print(session.run(one_element))
1.0
2.0
3.0
4.0
5.0
Dataset的转换(transformations)
datasets支持任何结构,当使用Dataset.map(),Dataset.flat_map(),以及Dataset.filter()进行转换时,它们会对每个元素应用一个函数,元素结构决定了函数的参数
import tensorflow as tf
import numpy as np
a1=np.array([1.0,2.0,3.0,4.0,5.0])
dataset=tf.data.Dataset.from_tensor_slices(a1)
dataset=dataset.map(lambda x:x**2)
iterator=dataset.make_one_shot_iterator()
one_element=iterator.get_next()
with tf.Session() as session:
for i in range(len(a1)):
print(session.run(one_element))
1.0
4.0
9.0
16.0
25.0
dataset = tf.data.Dataset.range(100)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
sess=tf.Session()
for i in range(100):
value = sess.run(next_element)
assert i == value
#initializable迭代器需要在使用前进行iterator.initializer的操作，虽然不方便，但支持参数化，可以使用一个或多个 tf.placeholder() 在初始化迭代器时占位：
max_value = tf.placeholder(tf.int64, shape=[])
dataset = tf.data.Dataset.range(max_value)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
dataset = tf.data.Dataset.range(5)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
result=tf.add(next_element,next_element)
session.run(iterator.initializer)
print(session.run(result))
print(session.run(result))
print(session.run(result))
print(session.run(result))
print(session.run(result))
try:
session.run(result)
except tf.errors.OutOfRangeError:
print("End of dataset")
0
2
4
6
8
End of dataset
import cv2
def _read_py_function