1 | initial version |
Small python-example to illustrate it:
>>> a = np.array([100,100,100,100,50,100,100,100,100]).reshape(3,-1)
>>> cv2.blur(a, (3,3))
array([[78, 89, 78],
[89, 94, 89],
[78, 89, 78]], dtype=int32)
( Note, the border pixels change here as well, since they are interpolated).
No, a kernel size of (1,1) doesn't make sense and will have no effect (then each pixel will just be divided by 1).
cv2.blur(a, (1,1)) array([[100, 100, 100], [100, 50, 100], [100, 100, 100]], dtype=int32)
2 | No.2 Revision |
The middle pixel will be taken into account, i.e. in your example it will change to 94.
Small python-example to illustrate it:
>>> a = np.array([100,100,100,100,50,100,100,100,100]).reshape(3,-1)
>>> cv2.blur(a, (3,3))
array([[78, 89, 78],
[89, 94, 89],
[78, 89, 78]], dtype=int32)
( Note, the border pixels change here as well, since they are interpolated).
No, a kernel size of (1,1) doesn't make sense and will have no effect (then each pixel will just be divided by 1).
>>> cv2.blur(a, (1,1))
array([[100, 100, 100],[100, 50, 100],[100, 100, 100]],dtype=int32)dtype=int32)
3 | No.3 Revision |
The middle pixel will be taken into account, i.e. in your example it will change to 94.
Small python-example to illustrate it:
>>> a = np.array([100,100,100,100,50,100,100,100,100]).reshape(3,-1)
>>> cv2.blur(a, (3,3))
array([[78, 89, 78],
[89, 94, 89],
[78, 89, 78]], dtype=int32)
( Note, (Note, the border pixels change here as well, since they the border-pixels are interpolated).
No, a kernel size of (1,1) doesn't make sense and will have no effect (then each pixel will just be divided by 1).
>>> cv2.blur(a, (1,1))
array([[100, 100, 100],
[100, 50, 100],
[100, 100, 100]], dtype=int32)