Why is OpenCV switching the shape when resizing?
Loading an image and then resizing it leads to the image being transposed:
import cv2
image = cv2.imread("example.png", cv2.IMREAD_GRAYSCALE)
print (image.shape)
print (cv2.resize(image, dsize=image.shape).shape)
With output:
(337, 458)
(458, 337)
Why do height and width change by using the resize operation? To be more precisely, why does the resize operator expect the dsize argument to be in the order of (width, height), while the shape attribute is in the order (height, width)?
opencv
asked Nov 13 '18 at 9:32
Jens HumrichJens Humrich
554
add a comment |
Loading an image and then resizing it leads to the image being transposed:
import cv2
image = cv2.imread("example.png", cv2.IMREAD_GRAYSCALE)
print (image.shape)
print (cv2.resize(image, dsize=image.shape).shape)
With output:
(337, 458)
(458, 337)
Why do height and width change by using the resize operation? To be more precisely, why does the resize operator expect the dsize argument to be in the order of (width, height), while the shape attribute is in the order (height, width)?
opencv
asked Nov 13 '18 at 9:32
Jens HumrichJens Humrich
554
add a comment |
Loading an image and then resizing it leads to the image being transposed:
import cv2
image = cv2.imread("example.png", cv2.IMREAD_GRAYSCALE)
print (image.shape)
print (cv2.resize(image, dsize=image.shape).shape)
With output:
(337, 458)
(458, 337)
Why do height and width change by using the resize operation? To be more precisely, why does the resize operator expect the dsize argument to be in the order of (width, height), while the shape attribute is in the order (height, width)?
opencv
asked Nov 13 '18 at 9:32
Jens HumrichJens Humrich
554
Loading an image and then resizing it leads to the image being transposed:
import cv2
image = cv2.imread("example.png", cv2.IMREAD_GRAYSCALE)
print (image.shape)
print (cv2.resize(image, dsize=image.shape).shape)
With output:
(337, 458)
(458, 337)
Why do height and width change by using the resize operation? To be more precisely, why does the resize operator expect the dsize argument to be in the order of (width, height), while the shape attribute is in the order (height, width)?
opencv
opencv
asked Nov 13 '18 at 9:32
Jens HumrichJens Humrich
554
asked Nov 13 '18 at 9:32
Jens HumrichJens Humrich
554
edited Nov 13 '18 at 19:49
Jens Humrich
asked Nov 13 '18 at 9:32
Jens HumrichJens Humrich
554
asked Nov 13 '18 at 9:32
Jens HumrichJens Humrich
554
asked Nov 13 '18 at 9:32
Jens HumrichJens Humrich
554
554
add a comment |
add a comment |
1 Answer
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You told it to! Is the simple answer.
When you do:
print(image.shape)
it prints the rows and the columns, i.e. the height and the width.
When you resize it, you must tell it the new width and height, not the new height and the width.
answered Nov 13 '18 at 11:32
Mark SetchellMark Setchell
86.7k674172
Thanks, that is indeed true. Can you add an explanation, why this (apparent) inconsistency was chosen?
– Jens Humrich
Nov 13 '18 at 19:51
I think it's becauseimage.shapeis Python/NumPy thing and NumPy arrays are indexed by row first, then by column. That is called "row major" orderung. OpenCV comes at it from the other way and generally indexes by column first, probably because it has its origins in the C-programming world. I'll add any links I can find if I come across a better explanation - as can anyone else.
– Mark Setchell
Nov 13 '18 at 20:36
add a comment |
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1 Answer
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You told it to! Is the simple answer.
When you do:
print(image.shape)
it prints the rows and the columns, i.e. the height and the width.
When you resize it, you must tell it the new width and height, not the new height and the width.
answered Nov 13 '18 at 11:32
Mark SetchellMark Setchell
86.7k674172
Thanks, that is indeed true. Can you add an explanation, why this (apparent) inconsistency was chosen?
– Jens Humrich
Nov 13 '18 at 19:51
I think it's becauseimage.shapeis Python/NumPy thing and NumPy arrays are indexed by row first, then by column. That is called "row major" orderung. OpenCV comes at it from the other way and generally indexes by column first, probably because it has its origins in the C-programming world. I'll add any links I can find if I come across a better explanation - as can anyone else.
– Mark Setchell
Nov 13 '18 at 20:36
add a comment |
You told it to! Is the simple answer.
When you do:
print(image.shape)
it prints the rows and the columns, i.e. the height and the width.
When you resize it, you must tell it the new width and height, not the new height and the width.
answered Nov 13 '18 at 11:32
Mark SetchellMark Setchell
86.7k674172
Thanks, that is indeed true. Can you add an explanation, why this (apparent) inconsistency was chosen?
– Jens Humrich
Nov 13 '18 at 19:51
I think it's becauseimage.shapeis Python/NumPy thing and NumPy arrays are indexed by row first, then by column. That is called "row major" orderung. OpenCV comes at it from the other way and generally indexes by column first, probably because it has its origins in the C-programming world. I'll add any links I can find if I come across a better explanation - as can anyone else.
– Mark Setchell
Nov 13 '18 at 20:36
add a comment |
You told it to! Is the simple answer.
When you do:
print(image.shape)
it prints the rows and the columns, i.e. the height and the width.
When you resize it, you must tell it the new width and height, not the new height and the width.
answered Nov 13 '18 at 11:32
Mark SetchellMark Setchell
86.7k674172
You told it to! Is the simple answer.
When you do:
print(image.shape)
it prints the rows and the columns, i.e. the height and the width.
When you resize it, you must tell it the new width and height, not the new height and the width.
answered Nov 13 '18 at 11:32
Mark SetchellMark Setchell
86.7k674172
answered Nov 13 '18 at 11:32
Mark SetchellMark Setchell
86.7k674172
answered Nov 13 '18 at 11:32
Mark SetchellMark Setchell
86.7k674172
answered Nov 13 '18 at 11:32
Mark SetchellMark Setchell
86.7k674172
86.7k674172
Thanks, that is indeed true. Can you add an explanation, why this (apparent) inconsistency was chosen?
– Jens Humrich
Nov 13 '18 at 19:51
I think it's becauseimage.shapeis Python/NumPy thing and NumPy arrays are indexed by row first, then by column. That is called "row major" orderung. OpenCV comes at it from the other way and generally indexes by column first, probably because it has its origins in the C-programming world. I'll add any links I can find if I come across a better explanation - as can anyone else.
– Mark Setchell
Nov 13 '18 at 20:36
add a comment |
Thanks, that is indeed true. Can you add an explanation, why this (apparent) inconsistency was chosen?
– Jens Humrich
Nov 13 '18 at 19:51
I think it's becauseimage.shapeis Python/NumPy thing and NumPy arrays are indexed by row first, then by column. That is called "row major" orderung. OpenCV comes at it from the other way and generally indexes by column first, probably because it has its origins in the C-programming world. I'll add any links I can find if I come across a better explanation - as can anyone else.
– Mark Setchell
Nov 13 '18 at 20:36
Thanks, that is indeed true. Can you add an explanation, why this (apparent) inconsistency was chosen?
– Jens Humrich
Nov 13 '18 at 19:51
Thanks, that is indeed true. Can you add an explanation, why this (apparent) inconsistency was chosen?
– Jens Humrich
Nov 13 '18 at 19:51
I think it's because
image.shape is Python/NumPy thing and NumPy arrays are indexed by row first, then by column. That is called "row major" orderung. OpenCV comes at it from the other way and generally indexes by column first, probably because it has its origins in the C-programming world. I'll add any links I can find if I come across a better explanation - as can anyone else.– Mark Setchell
Nov 13 '18 at 20:36
I think it's because
image.shape is Python/NumPy thing and NumPy arrays are indexed by row first, then by column. That is called "row major" orderung. OpenCV comes at it from the other way and generally indexes by column first, probably because it has its origins in the C-programming world. I'll add any links I can find if I come across a better explanation - as can anyone else.– Mark Setchell
Nov 13 '18 at 20:36
add a comment |
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