从numpy数组中分离最大/最小标记的补丁 [英] Isolate greatest/smallest labeled patches from numpy array

问题描述

我有一个大的numpy数组，并用scipy中的连接组件进行了标记.现在，我想创建此数组的子集，仅保留大小最大或最小的标签. 两个极端当然可以发生几次.

import numpy
from scipy import ndimage
....
# Loaded in my image file here. To big to paste
....
s = ndimage.generate_binary_structure(2,2) # iterate structure
labeled_array, numpatches = ndimage.label(array,s) # labeling
# get the area (nr. of pixels) of each labeled patch
sizes = ndimage.sum(array,labeled_array,range(1,numpatches+1)) 

# To get the indices of all the min/max patches. Is this the correct label id?
map = numpy.where(sizes==sizes.max()) 
mip = numpy.where(sizes==sizes.min())

# This here doesn't work! Now i want to create a copy of the array and fill only those cells
# inside the largest, respecitively the smallest labeled patches with values
feature = numpy.zeros_like(array, dtype=int)
feature[labeled_array == map] = 1

有人可以给我提示如何继续前进吗?

解决方案

以下是完整代码:

import numpy
from scipy import ndimage

array = numpy.zeros((100, 100), dtype=np.uint8)
x = np.random.randint(0, 100, 2000)
y = np.random.randint(0, 100, 2000)
array[x, y] = 1

pl.imshow(array, cmap="gray", interpolation="nearest")

s = ndimage.generate_binary_structure(2,2) # iterate structure
labeled_array, numpatches = ndimage.label(array,s) # labeling

sizes = ndimage.sum(array,labeled_array,range(1,numpatches+1)) 
# To get the indices of all the min/max patches. Is this the correct label id?
map = numpy.where(sizes==sizes.max())[0] + 1 
mip = numpy.where(sizes==sizes.min())[0] + 1

# inside the largest, respecitively the smallest labeled patches with values
max_index = np.zeros(numpatches + 1, np.uint8)
max_index[map] = 1
max_feature = max_index[labeled_array]

min_index = np.zeros(numpatches + 1, np.uint8)
min_index[mip] = 1
min_feature = min_index[labeled_array]

注意:

• numpy.where返回一个元组
• 标签1的大小为sizes [0]，因此您需要在numpy.where的结果中加1.
• 要获取具有多个标签的遮罩数组，可以使用labeled_array作为标签遮罩数组的索引.

结果:

i have a large numpy array and labeled it with the connected component labeling in scipy. Now i want to create subsets of this array, where only the biggest or smallest labels in size are left. Both extrema can of course occur several times.

import numpy
from scipy import ndimage
....
# Loaded in my image file here. To big to paste
....
s = ndimage.generate_binary_structure(2,2) # iterate structure
labeled_array, numpatches = ndimage.label(array,s) # labeling
# get the area (nr. of pixels) of each labeled patch
sizes = ndimage.sum(array,labeled_array,range(1,numpatches+1)) 

# To get the indices of all the min/max patches. Is this the correct label id?
map = numpy.where(sizes==sizes.max()) 
mip = numpy.where(sizes==sizes.min())

# This here doesn't work! Now i want to create a copy of the array and fill only those cells
# inside the largest, respecitively the smallest labeled patches with values
feature = numpy.zeros_like(array, dtype=int)
feature[labeled_array == map] = 1

Someone can give me hint how to move on?

解决方案

Here is the full code:

import numpy
from scipy import ndimage

array = numpy.zeros((100, 100), dtype=np.uint8)
x = np.random.randint(0, 100, 2000)
y = np.random.randint(0, 100, 2000)
array[x, y] = 1

pl.imshow(array, cmap="gray", interpolation="nearest")

s = ndimage.generate_binary_structure(2,2) # iterate structure
labeled_array, numpatches = ndimage.label(array,s) # labeling

sizes = ndimage.sum(array,labeled_array,range(1,numpatches+1)) 
# To get the indices of all the min/max patches. Is this the correct label id?
map = numpy.where(sizes==sizes.max())[0] + 1 
mip = numpy.where(sizes==sizes.min())[0] + 1

# inside the largest, respecitively the smallest labeled patches with values
max_index = np.zeros(numpatches + 1, np.uint8)
max_index[map] = 1
max_feature = max_index[labeled_array]

min_index = np.zeros(numpatches + 1, np.uint8)
min_index[mip] = 1
min_feature = min_index[labeled_array]

Notes:

• numpy.where returns a tuple
• the size of label 1 is sizes[0], so you need to add 1 to the result of numpy.where
• To get a mask array with multiple labels, you can use labeled_array as the index of a label mask array.

The results:

这篇关于从numpy数组中分离最大/最小标记的补丁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！