tf.extract_image_patches用法

最新推荐文章于 2022-10-26 09:41:32 发布

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最新推荐文章于 2022-10-26 09:41:32 发布

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原文链接：https://stackoverflow.com/questions/40731433/understanding-tf-extract-image-patches-for-extracting-patches-from-an-image?newreg=2f7aa12c4b6d4870a979698318d457a4

tf.extract_image_patches: 提取图片中的一小块区域

表达式如下：

extract_image_patches(
images,
ksizes,
strides,
rates,
padding,
name=None
)

image:输入图像的tesnsor，必须是[batch, in_rows, in_cols, depth]类型

ksize:滑动窗口的大小，长度必须大于四

strides:每块patch区域之间中心点之间的距离，必须是: [1, stride_rows, stride_cols, 1].

rates:在原始图像的一块patch中，隔多少像素点，取一个有效像素点，必须是[1, rate_rows, rate_cols, 1]

padding:有两个取值，“VALID”或者“SAME”，“VALID”表示所取的patch区域必须完全包含在原始图像中."SAME"表示

可以取超出原始图像的部分，这一部分进行0填充。

for example:

import tensorflow as tf
 
n = 10
# images is a 1 x 10 x 10 x 1 array that contains the numbers 1 through 100 in order
images = [[[[x * n + y + 1] for y in range(n)] for x in range(n)]]
 
# We generate four outputs as follows:
# 1. 3x3 patches with stride length 5
# 2. Same as above, but the rate is increased to 2
# 3. 4x4 patches with stride length 7; only one patch should be generated
# 4. Same as above, but with padding set to 'SAME'
with tf.Session() as sess:
  print tf.extract_image_patches(images=images, ksizes=[1, 3, 3, 1], strides=[1, 5, 5, 1], rates=[1, 1, 1, 1], padding='VALID').eval(), '\n\n'
  print tf.extract_image_patches(images=images, ksizes=[1, 3, 3, 1], strides=[1, 5, 5, 1], rates=[1, 2, 2, 1], padding='VALID').eval(), '\n\n'
  print tf.extract_image_patches(images=images, ksizes=[1, 4, 4, 1], strides=[1, 7, 7, 1], rates=[1, 1, 1, 1], padding='VALID').eval(), '\n\n'
  print tf.extract_image_patches(images=images, ksizes=[1, 4, 4, 1], strides=[1, 7, 7, 1], rates=[1, 1, 1, 1], padding='SAME').eval()

Output:

[[[[ 1  2  3 11 12 13 21 22 23]
   [ 6  7  8 16 17 18 26 27 28]]
 
  [[51 52 53 61 62 63 71 72 73]
   [56 57 58 66 67 68 76 77 78]]]]
 
 
[[[[  1   3   5  21  23  25  41  43  45]
   [  6   8  10  26  28  30  46  48  50]]
 
  [[ 51  53  55  71  73  75  91  93  95]
   [ 56  58  60  76  78  80  96  98 100]]]]
 
 
[[[[ 1  2  3  4 11 12 13 14 21 22 23 24 31 32 33 34]]]]
 
 
[[[[  1   2   3   4  11  12  13  14  21  22  23  24  31  32  33  34]
   [  8   9  10   0  18  19  20   0  28  29  30   0  38  39  40   0]]
 
  [[ 71  72  73  74  81  82  83  84  91  92  93  94   0   0   0   0]
   [ 78  79  80   0  88  89  90   0  98  99 100   0   0   0   0   0]]]]

第一个输出可以看成是这样取值

 *  *  *  4  5  *  *  *  9 10 
 *  *  * 14 15  *  *  * 19 20 
 *  *  * 24 25  *  *  * 29 30 
31 32 33 34 35 36 37 38 39 40 
41 42 43 44 45 46 47 48 49 50 
 *  *  * 54 55  *  *  * 59 60 
 *  *  * 64 65  *  *  * 69 70 
 *  *  * 74 75  *  *  * 79 80 
81 82 83 84 85 86 87 88 89 90 
91 92 93 94 95 96 97 98 99 100