TensorFLow 讀取圖片1:初探四種從檔案讀取的方式
阿新 • • 發佈:2019-01-30
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)