Tensorflow圖像處理

時間 2019-12-04

標籤 tensorflow 圖像處理简体版

原文原文鏈接

Tensorflow圖像處理

主要內容以下：python

加載圖像
圖像格式
圖像轉換爲TFRecord格式
讀取TFRecord文件
圖像處理
數據讀取方式Dataset API

一.加載圖像

Tensorflow對圖像文件的加載和對二進制文件的加載相同,只是圖像的內容須要解碼.這裏介紹經常使用的兩種方式：第一種是把圖片看做一個圖片直接讀進來,獲取圖片的原始數據,再進行解碼;如使用tf.gfile.FastGFile()讀取圖像文件,而後,利用tf.image.decode_jpeg()或"tf.image.decode_pgn()"進行解碼.代碼以下：web

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)
    
    # 圖像顯示
    plt.figure(1)
    
    # 顯示圖像矩陣
    print(session.run(img_data))
    plt.imshow(img_data.eval())

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第一種方法不太適合讀取批量數據,批量讀取能夠採用另外一種方法,這種方法他圖像當作一個文件,用隊列的方式讀取.在Tensorflow中,隊列不只是一種數據結構,更提供了多線程機制.首先,使用tf.train.string_input_producer找到所需文件,並將其加載到一個隊列中,而後使用tf.WholeFileReader()把完整的文件加載到內存中,用read從隊列中讀取圖像文件,最後用tf.image.decode_jpeg()或"tf.image.decode_png()"進行解碼.代碼以下：數組

import tensorflow as tf

path="./data/image/cat/cat.jpg"

# 建立輸入隊列
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:
    # 協同啓動的線程
    coord=tf.train.Coordinator()
    # 啓動線程運行隊列
    threads=tf.train.start_queue_runners(sess=session,coord=coord)
    print(session.run(image))
    
    # 中止全部的線程
    coord.request_stop()
    coord.join(threads)

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二.圖像格式

圖像中的重要信息經過某種恰當的文件格式存儲.在使用圖像時,不一樣的格式可用於解決不一樣的問題.Tensorflow支持多種圖像格式,經常使用的包括JPEG,PNG,TFRecord圖像格式session

1.JPEG和PNG

Tensorflow支持JPEG,PNG兩種圖像格式,這裏以這兩種爲表明,是由於將其餘格式轉換爲這兩種格式很是容易.JPEG格式圖像使用tf.image.decode_jpeg()解碼,PNG格式圖像使用tf.image.decode_png解碼數據結構

2.TFRecord

對於大量的圖像數據,Tensorflow採用TFRecord格式,將圖像數據和圖像的標籤數據以二進制形式無壓縮方式存儲在TFRecord文件中,這樣其能夠被快速載入內存,方便移動,複製和處理.TFRecord文件中的數據都是經過tf.train.ExampleProtocol Buffer的格式存儲的多線程

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中包含了一個從屬性名稱到取值的字典.其中屬性名稱爲一個字符串,屬性的取值能夠爲字符串(BytesList),實數列表(FloatList)或者整數列表(Int64List).BytesList存放圖像數據,Int64List存放圖像的標籤對應的整數dom

三.圖像轉換爲TFRecord文件

把圖像轉換成TFRecord格式的文件就是把圖像數據和圖像標籤的數據按tf.train.Example數據結構存儲成二進制文件的過程.假設咱們已經按圖像標籤吧若干張貓的圖像和若干張狗的圖片分別存在瞭如下目錄："./data/image/cat/「和」./data/image/dog/",如下代碼能夠把這些圖片轉換成TFRecord文件cat_dog.tfrecord,存放在"./data"目錄下svg

import os
import tensorflow as tf
from PIL import Image  # 處理圖像包
import numpy as np


folder="./data/image/"
savefolder="./data/"

# 設定圖像類別標籤,標籤名稱和對應目錄名相同
label={"cat","dog"}

# 生成的文件
writer=tf.python_io.TFRecordWriter(savefolder+"cat_dog.tfrecords")

# 記錄圖像的個數
count=0

for index,name in enumerate(label):
    folder_path=folder+name+"/"
    for img_name in os.listdir(folder_path):
        # 每個圖片的完整訪問路徑
        img_path=folder_path+img_name
        img=Image.open(img_path)
        img=img.resize((128,128))
        # 將圖片轉化爲二進制格式
        img_raw=img.tobytes()
        
        # example對象對label和image數據進行封裝
        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]))
        }))
        
        # 序列化爲字符串
        writer.write(example.SerializeToString())
        count=count+1
writer.close()

四.讀取TFRecord文件

讀取TFRecord文件首先採用輸入生成器(tf.train.string_input_producer)將其加載到一個隊列,而後讀出(tf.TFRecordReader)並將(tf.parse_single_example)Example對象拆解爲圖像數據和標籤數據,最後對圖像數據解碼並保持成JPG文件函數

# 定義數據流隊列
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對象
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()
    
    # 線程協調器
    threads=tf.train.start_queue_runners(coord=coord)
    # 以多線程的方式啓動對隊列的處理
    for i in range(count):
        # 在會話中取出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()
    # 等待全部進程結束
    coord.join(threads)

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 [[ 95 100 106]
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  ...
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[[[23 28 48]
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  [59 62 81]
  ...
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  ...
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 ...

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 [[16 17 12]
  [22 23 18]
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from IPython.display import Image

Image(filename="./data/image/cat/cat.jpg",width=600)

五.圖像處理序列

利用tf.imageAPI,該API包含了不少圖像處理的函數.代碼基於一個600*600大小的貓的圖像ui

1.調整大小

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.裁剪和填充圖像

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.對角線翻轉圖像

with tf.Session() as session:
    transposed=tf.image.transpose_image(img_data)
    plt.imshow(transposed.eval())
    plt.show()

4.色彩調整

with tf.Session() as session:
    adjusted=tf.image.random_brightness(img_data,max_delta=0.5)
    plt.imshow(adjusted.eval())
    plt.show()

5.色調飽和度

with tf.Session() as session:
    adjusted=tf.image.adjust_hue(img_data,0.1)
    plt.imshow(adjusted.eval())
    plt.show()

六.數據讀取方式–Dataset API

1.構建Dataset

tf.data.Dataset.from_tensor_slices()

利用tf.data.Dataset.from_tensor_slices()從一個或多個tf.Tensor對象中構建一個dataset,其tf.Tensor對象中包括數組,矩陣,字典,元組等.具體實例以下：

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的轉換(transformations)

datasets支持任何結構,當使用Dataset.map(),Dataset.flat_map(),以及Dataset.filter()進行轉換時,它們會對每一個元素應用一個函數,元素結構決定了函數的參數

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迭代器須要在使用前進行iterator.initializer的操做，雖然不方便，但支持參數化，可使用一個或多個 tf.placeholder() 在初始化迭代器時佔位：
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

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