TensorFLow 读取图片 初探TensorFLow从文件读取图片的四种方式

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()

import tensorflow as tf
import os
import matplotlib.pyplot as plt
def file_name(file_dir):  #来自
  for root, dirs, files in os.walk(file_dir): #模块os中的walk()函数遍历文件夹下所有的文件
    print(root) #当前目录路径 
    print(dirs) #当前路径下所有子目录 
    print(files) #当前路径下所有非目录子文件 

def file_name2(file_dir):  #特定类型的文件
  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')


#以下参考 (十图详解TensorFlow数据读取机制)
#path2 = tf.train.match_filenames_once(path)
file_queue = tf.train.string_input_producer(path, shuffle=True, num_epochs=2) #创建输入队列 
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() #协同启动的线程 
#  threads = tf.train.start_queue_runners(sess=sess, coord=coord) #启动线程运行队列 
#  coord.request_stop() #停止所有的线程 
#  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()

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()