I have a 2D numpy array taken from a segmentation. Therefore, it's an image like the one in the right:
The colours you see means that each value of my array can only have a value in a limit range (e.g., green is 5, orange is 7...). Now I would like to change all the cells that contains a 5 (green) and its y-coordinate is up to a value I want (e.g. only apply the later condition up to row 400). What's the most optimized algorithm to do this?
I guess that you can use something like:
np.where(myarray == 5, myarray, valueIwant)
but I will need to apply the condition for y-index...
CodePudding user response：
Your current example seems to be misaligned with what you want:
a = np.array([1, 1, 2, 2, 3, 3])
np.where(a==2, a, 7)
produces:
array([7, 7, 2, 2, 7, 7])
If you want to replace 2 with some other value:
array([1, 1, 7, 7, 3, 3])
you can do this:
np.where(a==2, 7, a)
or
a[a==2] = 7
To replace only up to a certain value:
sub_array = a[:3]
sub_array[sub_array==2] = 7
a
array([1, 1, 7, 2, 3, 3])