本文記錄一下TensorFLow的幾種圖片讀取方法，官方文件有較為全面的介紹。

1.使用gfile讀圖片，decode輸出是Tensor，eval後是ndarray

import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np

print(tf.__version__)

image_raw = tf.gfile.FastGFile('test/a.jpg','rb').read()   #bytes
img = tf.image.decode_jpeg(image_raw)  #Tensor
#img2 = tf.image.convert_image_dtype(img, dtype = tf.uint8)
 


with tf.Session() as sess:
    print(type(image_raw)) # bytes
    print(type(img)) # Tensor
    #print(type(img2))

    print(type(img.eval())) # ndarray !!!
    print(img.eval().shape)
    print(img.eval().dtype)

#    print(type(img2.eval()))
#    print(img2.eval().shape)
#    print(img2.eval().dtype)
 

    plt.figure(1)
    plt.imshow(img.eval())
    plt.show()

輸出為：

1.3.0
<class 'bytes'>
<class 'tensorflow.python.framework.ops.Tensor'>
<class 'numpy.ndarray'>
(666, 1000, 3)
uint8
圖片顯示（略）

2.使用WholeFileReader輸入queue，decode輸出是Tensor，eval後是ndarray

import tensorflow as tf
import os
import
 
 matplotlib.pyplot as plt

def file_name(file_dir):   #來自http://blog.csdn.net/lsq2902101015/article/details/51305825
    for root, dirs, files in os.walk(file_dir):  #模組os中的walk()函式遍歷資料夾下所有的檔案
        print(root) #當前目錄路徑  
        print(dirs) #當前路徑下所有子目錄  
        print(files) #當前路徑下所有非目錄子檔案  

def file_name2(file_dir):   #特定型別的檔案
    L=[]   
    for root, dirs, files in os.walk(file_dir):  
        for file in files:  
            if os.path.splitext(file)[1] == '.jpg':   
                L.append(os.path.join(root, file))  
    return L 

path = file_name2('test')


#以下參考http://blog.csdn.net/buptgshengod/article/details/72956846 (十圖詳解TensorFlow資料讀取機制)
#以及http://blog.csdn.net/uestc_c2_403/article/details/74435286

#path2 = tf.train.match_filenames_once(path)
file_queue = tf.train.string_input_producer(path, shuffle=True, num_epochs=2) #建立輸入佇列  
image_reader = tf.WholeFileReader()  
key, image = image_reader.read(file_queue)  
image = tf.image.decode_jpeg(image)  

with tf.Session() as sess:  
#    coord = tf.train.Coordinator() #協同啟動的執行緒  
#    threads = tf.train.start_queue_runners(sess=sess, coord=coord) #啟動執行緒執行佇列  
#    coord.request_stop() #停止所有的執行緒  
#    coord.join(threads)  

    tf.local_variables_initializer().run()
    threads = tf.train.start_queue_runners(sess=sess)

    #print (type(image))  
    #print (type(image.eval()))  
    #print(image.eval().shape)
    for _ in path+path:
        plt.figure
        plt.imshow(image.eval())
        plt.show()

3.使用read_file，decode輸出是Tensor，eval後是ndarray

import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np

print(tf.__version__)

image_value = tf.read_file('test/a.jpg')
img = tf.image.decode_jpeg(image_value, channels=3)

with tf.Session() as sess:
    print(type(image_value)) # bytes
    print(type(img)) # Tensor
    #print(type(img2))

    print(type(img.eval())) # ndarray !!!
    print(img.eval().shape)
    print(img.eval().dtype)

#    print(type(img2.eval()))
#    print(img2.eval().shape)
#    print(img2.eval().dtype)
    plt.figure(1)
    plt.imshow(img.eval())
    plt.show()

輸出是：

1.3.0
<class 'tensorflow.python.framework.ops.Tensor'>
<class 'tensorflow.python.framework.ops.Tensor'>
<class 'numpy.ndarray'>
(666, 1000, 3)
uint8
顯示圖片（略）