NumPy - 使用强度值矩阵进行图像（矩阵）阈值处理。 [英] NumPy - Image (matrix) thresholding using an intensity value matrix.

问题描述

我需要在灰度图像中分割出异常。在算法的某个地方，我计算一个矩阵，其中包含我需要设置为零的已知像素强度。我该怎么做？

I need to segment out anomalies in a greyscale image. In a certain place in my algorithm, I compute a matrix that contains the known pixel intensities that I need to set to zero. How would I do this?

例如：

计算出的像素强度：
（数组（[94,95,96， 97,98,99,100,101,102,103,104,105,106，
107,108,109,110,111,112,113,114,115,116,117,118,119，
120,121,122,123,124,125,126,127,128,129,130​​,131,132，
133,134,135,136,137,138,139,140， 141,142,143,144,145，
146,147,148,149,150,151]），）


图片大小（480,640）：

印刷它给出：
[[86 90 97 ...，142 152 157]

[85 89 97 ...，145 154 158]

[83 87 95 ...，154 158 159]

...，

[130 134 139 ...，156 154 154]

[130 134 140 ...，154 153 152]

[130 134 141 ...，154 153 152]]

For example:
The computed pixel intensities: (array([ 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151]),)

The picture is of size (480,640) :
Printed it gives:
[[ 86 90 97 ..., 142 152 157]
[ 85 89 97 ..., 145 154 158]
[ 83 87 95 ..., 154 158 159]
...,
[130 134 139 ..., 156 154 154]
[130 134 140 ..., 154 153 152]
[130 134 141 ..., 154 153 152]]

我意识到，对于每个像素，我都可以通过强度矩阵。然而，这将太昂贵。 NumPy专家我需要你的帮助！

I realize that for each pixel I could go through the intensity matrix. This would, however, be too expensive. NumPy experts I need your help!

推荐答案

要将图像数组中所有像素设置为零，其值为91到151，包容性，使用：

To set to zero all pixels in an image array which have values from 91 to 151, inclusive, use:

import numpy as np
newimage = np.where(np.logical_and(91<=oldimage, oldimage<=151), 0, oldimage)

将图像数组中的所有像素设置为零其值属于某个数组 vct ，请使用：

To set to zero all pixels in an image array whose values belong to some array vct, use:

newimage = np.where(np.in1d(oldimage, vct).reshape(oldimage.shape), 0, oldimage)

< h3>示例

假设我们有一个 oldimage ，就像这样：

In [12]: oldimage
Out[12]: 
array([[0, 1, 2],
       [3, 4, 5]])

我们有一个名为的数字列表vct ：

In [13]: vct
Out[13]: array([3, 5])

让我们将所有像素设为零n oldimage 也在 vct ：

Let's set to zero all pixels in oldimage that are also in vct:

In [14]: newimage = np.where(np.in1d(oldimage, vct).reshape(oldimage.shape), 0, oldimage)

In [15]: newimage
Out[15]: 
array([[0, 1, 2],
       [0, 4, 0]])

这篇关于NumPy - 使用强度值矩阵进行图像（矩阵）阈值处理。的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！