How to crop a 3D image when the point of interest lies on the edge of the image?
1
I have a 3D image of size 512*512*30. I also have a csv file with the points of interest stored in it. I want to crop a 3D volume of size 32*32*16 around the point of interest with the point at its center. Ive written the following to achieve this:
block = [32, 32, 16]
img = imageio.volread('path\to\tiff\file')
x, y, z = 191, 303, 17
img_block = img_block[x - int(block[0] / 2):x + int(block[0] / 2),
y - int(block[1] / 2):y + int(block[1] / 2),
z - int(block[2] / 2):z + int(block[2] / 2)]
This works in the above case but fails when I have an x,y,z point on the edge for example at z = 28 I get an out of bound error, which is expected.
How do I avoid this problem and ensure smooth copping?
Thanks
python numpy image-processing
asked Nov 13 at 16:59
StuckInPhD
82132141
add a comment |
1
I have a 3D image of size 512*512*30. I also have a csv file with the points of interest stored in it. I want to crop a 3D volume of size 32*32*16 around the point of interest with the point at its center. Ive written the following to achieve this:
block = [32, 32, 16]
img = imageio.volread('path\to\tiff\file')
x, y, z = 191, 303, 17
img_block = img_block[x - int(block[0] / 2):x + int(block[0] / 2),
y - int(block[1] / 2):y + int(block[1] / 2),
z - int(block[2] / 2):z + int(block[2] / 2)]
This works in the above case but fails when I have an x,y,z point on the edge for example at z = 28 I get an out of bound error, which is expected.
How do I avoid this problem and ensure smooth copping?
Thanks
python numpy image-processing
asked Nov 13 at 16:59
StuckInPhD
82132141
The answer depends a little on what you want to do with the outliers. Typical ways to deal with the issue are called "bounds-checking". You can pad your array with zeros, mirror at the border or just discard values. What works best for you?
– Dschoni
Nov 13 at 17:02
1
Furthermore: If one point is in the center, the size should be rather odd than even.
– Dschoni
Nov 13 at 17:06
add a comment |
1
1
1
I have a 3D image of size 512*512*30. I also have a csv file with the points of interest stored in it. I want to crop a 3D volume of size 32*32*16 around the point of interest with the point at its center. Ive written the following to achieve this:
block = [32, 32, 16]
img = imageio.volread('path\to\tiff\file')
x, y, z = 191, 303, 17
img_block = img_block[x - int(block[0] / 2):x + int(block[0] / 2),
y - int(block[1] / 2):y + int(block[1] / 2),
z - int(block[2] / 2):z + int(block[2] / 2)]
This works in the above case but fails when I have an x,y,z point on the edge for example at z = 28 I get an out of bound error, which is expected.
How do I avoid this problem and ensure smooth copping?
Thanks
python numpy image-processing
asked Nov 13 at 16:59
StuckInPhD
82132141
I have a 3D image of size 512*512*30. I also have a csv file with the points of interest stored in it. I want to crop a 3D volume of size 32*32*16 around the point of interest with the point at its center. Ive written the following to achieve this:
block = [32, 32, 16]
img = imageio.volread('path\to\tiff\file')
x, y, z = 191, 303, 17
img_block = img_block[x - int(block[0] / 2):x + int(block[0] / 2),
y - int(block[1] / 2):y + int(block[1] / 2),
z - int(block[2] / 2):z + int(block[2] / 2)]
This works in the above case but fails when I have an x,y,z point on the edge for example at z = 28 I get an out of bound error, which is expected.
How do I avoid this problem and ensure smooth copping?
Thanks
python numpy image-processing
python numpy image-processing
asked Nov 13 at 16:59
StuckInPhD
82132141
asked Nov 13 at 16:59
StuckInPhD
82132141
asked Nov 13 at 16:59
StuckInPhD
82132141
asked Nov 13 at 16:59
StuckInPhD
82132141
asked Nov 13 at 16:59
StuckInPhD
82132141
82132141
The answer depends a little on what you want to do with the outliers. Typical ways to deal with the issue are called "bounds-checking". You can pad your array with zeros, mirror at the border or just discard values. What works best for you?
– Dschoni
Nov 13 at 17:02
1
Furthermore: If one point is in the center, the size should be rather odd than even.
– Dschoni
Nov 13 at 17:06
add a comment |
The answer depends a little on what you want to do with the outliers. Typical ways to deal with the issue are called "bounds-checking". You can pad your array with zeros, mirror at the border or just discard values. What works best for you?
– Dschoni
Nov 13 at 17:02
1
Furthermore: If one point is in the center, the size should be rather odd than even.
– Dschoni
Nov 13 at 17:06
The answer depends a little on what you want to do with the outliers. Typical ways to deal with the issue are called "bounds-checking". You can pad your array with zeros, mirror at the border or just discard values. What works best for you?
– Dschoni
Nov 13 at 17:02
The answer depends a little on what you want to do with the outliers. Typical ways to deal with the issue are called "bounds-checking". You can pad your array with zeros, mirror at the border or just discard values. What works best for you?
– Dschoni
Nov 13 at 17:02
1
1
Furthermore: If one point is in the center, the size should be rather odd than even.
– Dschoni
Nov 13 at 17:06
Furthermore: If one point is in the center, the size should be rather odd than even.
– Dschoni
Nov 13 at 17:06
add a comment |
1 Answer
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The answer for padding could look like this:
import numpy as np
point = [0,1,2]
img = imageio.volread('path\to\tiff\file')
block = [32,32,16]
img_x, img_y, img_z = img.shape
img_padded = np.pad(img, block, 'constant', constant_values=0) #lookup np.pad for other padding options
img_block = img_padded[point[0]-block[0]/2:point[0]+block[0]/2...]
answered Nov 13 at 17:15
Dschoni
1,2391944
Thanks, Ive tried padding as well. But wouldnt padding before cropping change the location of x,y,z?
– StuckInPhD
Nov 13 at 17:24
This answer worked for me, but I would like to add that I would add the pad amount to my coordinate, for example, if I only padded the z-dimension usingnpad = ((0, 0), (0, 0), (10, 10))andimg_padded = np.pad(img_block, pad_width=npad, mode='constant', constant_values=0). The this would add 10 rows before and 10 rows after in the z-dimension. So I would add 10 to the z-coordinate.
– StuckInPhD
Nov 14 at 11:52
I'm adding once again thatnp.padhas more options to deal with the padded area. E.g. you can mirror or clamp the value on the edges, which makes sense if you deal with e.g. image reconstruction (to avoid abrupt changes in intensity) but doesn't make much sense for visualisation.
– Dschoni
Nov 15 at 13:29
add a comment |
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1 Answer
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1 Answer
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0
The answer for padding could look like this:
import numpy as np
point = [0,1,2]
img = imageio.volread('path\to\tiff\file')
block = [32,32,16]
img_x, img_y, img_z = img.shape
img_padded = np.pad(img, block, 'constant', constant_values=0) #lookup np.pad for other padding options
img_block = img_padded[point[0]-block[0]/2:point[0]+block[0]/2...]
answered Nov 13 at 17:15
Dschoni
1,2391944
Thanks, Ive tried padding as well. But wouldnt padding before cropping change the location of x,y,z?
– StuckInPhD
Nov 13 at 17:24
This answer worked for me, but I would like to add that I would add the pad amount to my coordinate, for example, if I only padded the z-dimension usingnpad = ((0, 0), (0, 0), (10, 10))andimg_padded = np.pad(img_block, pad_width=npad, mode='constant', constant_values=0). The this would add 10 rows before and 10 rows after in the z-dimension. So I would add 10 to the z-coordinate.
– StuckInPhD
Nov 14 at 11:52
I'm adding once again thatnp.padhas more options to deal with the padded area. E.g. you can mirror or clamp the value on the edges, which makes sense if you deal with e.g. image reconstruction (to avoid abrupt changes in intensity) but doesn't make much sense for visualisation.
– Dschoni
Nov 15 at 13:29
add a comment |
0
The answer for padding could look like this:
import numpy as np
point = [0,1,2]
img = imageio.volread('path\to\tiff\file')
block = [32,32,16]
img_x, img_y, img_z = img.shape
img_padded = np.pad(img, block, 'constant', constant_values=0) #lookup np.pad for other padding options
img_block = img_padded[point[0]-block[0]/2:point[0]+block[0]/2...]
answered Nov 13 at 17:15
Dschoni
1,2391944
Thanks, Ive tried padding as well. But wouldnt padding before cropping change the location of x,y,z?
– StuckInPhD
Nov 13 at 17:24
This answer worked for me, but I would like to add that I would add the pad amount to my coordinate, for example, if I only padded the z-dimension usingnpad = ((0, 0), (0, 0), (10, 10))andimg_padded = np.pad(img_block, pad_width=npad, mode='constant', constant_values=0). The this would add 10 rows before and 10 rows after in the z-dimension. So I would add 10 to the z-coordinate.
– StuckInPhD
Nov 14 at 11:52
I'm adding once again thatnp.padhas more options to deal with the padded area. E.g. you can mirror or clamp the value on the edges, which makes sense if you deal with e.g. image reconstruction (to avoid abrupt changes in intensity) but doesn't make much sense for visualisation.
– Dschoni
Nov 15 at 13:29
add a comment |
0
0
0
The answer for padding could look like this:
import numpy as np
point = [0,1,2]
img = imageio.volread('path\to\tiff\file')
block = [32,32,16]
img_x, img_y, img_z = img.shape
img_padded = np.pad(img, block, 'constant', constant_values=0) #lookup np.pad for other padding options
img_block = img_padded[point[0]-block[0]/2:point[0]+block[0]/2...]
answered Nov 13 at 17:15
Dschoni
1,2391944
The answer for padding could look like this:
import numpy as np
point = [0,1,2]
img = imageio.volread('path\to\tiff\file')
block = [32,32,16]
img_x, img_y, img_z = img.shape
img_padded = np.pad(img, block, 'constant', constant_values=0) #lookup np.pad for other padding options
img_block = img_padded[point[0]-block[0]/2:point[0]+block[0]/2...]
answered Nov 13 at 17:15
Dschoni
1,2391944
answered Nov 13 at 17:15
Dschoni
1,2391944
answered Nov 13 at 17:15
Dschoni
1,2391944
answered Nov 13 at 17:15
Dschoni
1,2391944
1,2391944
Thanks, Ive tried padding as well. But wouldnt padding before cropping change the location of x,y,z?
– StuckInPhD
Nov 13 at 17:24
This answer worked for me, but I would like to add that I would add the pad amount to my coordinate, for example, if I only padded the z-dimension usingnpad = ((0, 0), (0, 0), (10, 10))andimg_padded = np.pad(img_block, pad_width=npad, mode='constant', constant_values=0). The this would add 10 rows before and 10 rows after in the z-dimension. So I would add 10 to the z-coordinate.
– StuckInPhD
Nov 14 at 11:52
I'm adding once again thatnp.padhas more options to deal with the padded area. E.g. you can mirror or clamp the value on the edges, which makes sense if you deal with e.g. image reconstruction (to avoid abrupt changes in intensity) but doesn't make much sense for visualisation.
– Dschoni
Nov 15 at 13:29
add a comment |
Thanks, Ive tried padding as well. But wouldnt padding before cropping change the location of x,y,z?
– StuckInPhD
Nov 13 at 17:24
This answer worked for me, but I would like to add that I would add the pad amount to my coordinate, for example, if I only padded the z-dimension usingnpad = ((0, 0), (0, 0), (10, 10))andimg_padded = np.pad(img_block, pad_width=npad, mode='constant', constant_values=0). The this would add 10 rows before and 10 rows after in the z-dimension. So I would add 10 to the z-coordinate.
– StuckInPhD
Nov 14 at 11:52
I'm adding once again thatnp.padhas more options to deal with the padded area. E.g. you can mirror or clamp the value on the edges, which makes sense if you deal with e.g. image reconstruction (to avoid abrupt changes in intensity) but doesn't make much sense for visualisation.
– Dschoni
Nov 15 at 13:29
Thanks, Ive tried padding as well. But wouldnt padding before cropping change the location of x,y,z?
– StuckInPhD
Nov 13 at 17:24
Thanks, Ive tried padding as well. But wouldnt padding before cropping change the location of x,y,z?
– StuckInPhD
Nov 13 at 17:24
This answer worked for me, but I would like to add that I would add the pad amount to my coordinate, for example, if I only padded the z-dimension using
npad = ((0, 0), (0, 0), (10, 10)) and img_padded = np.pad(img_block, pad_width=npad, mode='constant', constant_values=0). The this would add 10 rows before and 10 rows after in the z-dimension. So I would add 10 to the z-coordinate.– StuckInPhD
Nov 14 at 11:52
This answer worked for me, but I would like to add that I would add the pad amount to my coordinate, for example, if I only padded the z-dimension using
npad = ((0, 0), (0, 0), (10, 10)) and img_padded = np.pad(img_block, pad_width=npad, mode='constant', constant_values=0). The this would add 10 rows before and 10 rows after in the z-dimension. So I would add 10 to the z-coordinate.– StuckInPhD
Nov 14 at 11:52
I'm adding once again that
np.pad has more options to deal with the padded area. E.g. you can mirror or clamp the value on the edges, which makes sense if you deal with e.g. image reconstruction (to avoid abrupt changes in intensity) but doesn't make much sense for visualisation.– Dschoni
Nov 15 at 13:29
I'm adding once again that
np.pad has more options to deal with the padded area. E.g. you can mirror or clamp the value on the edges, which makes sense if you deal with e.g. image reconstruction (to avoid abrupt changes in intensity) but doesn't make much sense for visualisation.– Dschoni
Nov 15 at 13:29
add a comment |
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The answer depends a little on what you want to do with the outliers. Typical ways to deal with the issue are called "bounds-checking". You can pad your array with zeros, mirror at the border or just discard values. What works best for you?
– Dschoni
Nov 13 at 17:02
1
Furthermore: If one point is in the center, the size should be rather odd than even.
– Dschoni
Nov 13 at 17:06