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浅谈tensorflow中Dataset图片的批量读取及维度的操作详解

更新时间:2020-08-22 23:30:01 作者:startmvc
三维的读取图片(w,h,c):importtensorflowastfimportglobimportosdef_parse_function(filename):#print(filename)imag

三维的读取图片(w, h, c):


import tensorflow as tf
 
import glob
import os
 
 
def _parse_function(filename):
 # print(filename)
 image_string = tf.read_file(filename)
 image_decoded = tf.image.decode_image(image_string) # (375, 500, 3)
 
 image_resized = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200)
 return image_resized
 
 
 
 
with tf.Session() as sess:
 
 print( sess.run( img ).shape )

读取批量图片的读取图片(b, w, h, c):


import tensorflow as tf
 
import glob
import os
 
'''
 Dataset 批量读取图片
'''
 
def _parse_function(filename):
 # print(filename)
 image_string = tf.read_file(filename)
 image_decoded = tf.image.decode_image(image_string) # (375, 500, 3)
 
 image_decoded = tf.expand_dims(image_decoded, axis=0)
 
 image_resized = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200)
 return image_resized
 
 
 
img = _parse_function('../pascal/VOCdevkit/VOC2012/JPEGImages/2007_000068.jpg')
 
# image_resized = tf.image.resize_image_with_crop_or_pad( tf.truncated_normal((1,220,300,3))*10, 200, 200) 这种四维 形式是可以的
 
with tf.Session() as sess:
 
 print( sess.run( img ).shape ) #直接初始化就可以 ,转换成四维报错误,不知道为什么,若谁想明白,请留言 报错误
 #InvalidArgumentError (see above for traceback): Input shape axis 0 must equal 4, got shape [5]

Databae的操作:


import tensorflow as tf
 
import glob
import os
 
'''
 Dataset 批量读取图片:
 
 原因:
 1. 先定义图片名的list,存放在Dataset中 from_tensor_slices()
 2. 映射函数, 在函数中,对list中的图片进行读取,和resize,细节
 tf.read_file(filename) 返回的是三维的,因为这个每次取出一张图片,放进队列中的,不需要转化为四维
 然后对图片进行resize, 然后每个batch进行访问这个函数 ,所以get_next() 返回的是 [batch, w, h, c ]
 3. 进行shuffle , batch repeat的设置
 
 4. iterator = dataset.make_one_shot_iterator() 设置迭代器
 
 5. iterator.get_next() 获取每个batch的图片
'''
 
def _parse_function(filename):
 # print(filename)
 image_string = tf.read_file(filename)
 image_decoded = tf.image.decode_image(image_string) #(375, 500, 3)
 '''
 Tensor` with type `uint8` with shape `[height, width, num_channels]` for
 BMP, JPEG, and PNG images and shape `[num_frames, height, width, 3]` for
 GIF images.
 '''
 
 # image_resized = tf.image.resize_images(label, [200, 200])
 ''' images 三维,四维的都可以
 images: 4-D Tensor of shape `[batch, height, width, channels]` or
 3-D Tensor of shape `[height, width, channels]`.
 size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
 new size for the images.
 
 '''
 image_resized = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200)
 
 # return tf.squeeze(mage_resized,axis=0)
 return image_resized
 
filenames = glob.glob( os.path.join('../pascal/VOCdevkit/VOC2012/JPEGImages', "*." + 'jpg') )
 
 
dataset = tf.data.Dataset.from_tensor_slices((filenames))
 
dataset = dataset.map(_parse_function)
 
dataset = dataset.shuffle(10).batch(2).repeat(10)
iterator = dataset.make_one_shot_iterator()
 
img = iterator.get_next()
 
with tf.Session() as sess:
 # print( sess.run(img).shape ) #(4, 200, 200, 3)
 for _ in range (10):
 print( sess.run(img).shape )

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