python dataset.shape_浅谈tensorflow中Dataset图片的批量读取及维度的操作详解

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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