初探TensorFLow从文件读取图片的四种方式

import matplotlib.pyplot as pltimport tensorflow as tfimport numpy as npprint(tf.__version__)image_raw = tf.gfile.FastGFile('test/a.jpg','rb').read()  #bytesimg = 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 tfimport osimport matplotlib.pyplot as pltdef file_name(file_dir):  #来自http://www.vevb.com/article/134543.htm  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http://www.vevb.com/article/134547.htm (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 pltimport tensorflow as tfimport numpy as npprint(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()