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
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问题描述
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:
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