Efficient numpy euclidean distance calculation for each element

Question

I have a numpy array of the shape 512x512 and a center point within this range. Now i want to fill the array with the euclidean distance of the center point to the array elements.

For example: Center=[10,10] -> array[0,0]=sqrt((0-10)^2+(0-10)^2)...

I already have working code:

def calc_euclidean_distance(center, point):
   return np.sqrt((center[0] - point[0]) ** 2 + (center[1] - point[1]) ** 2)

target = np.zeros([512, 512])
center = np.array([10, 376]) #example

for iterrow, row in enumerate(target):
   for itercol, i in enumerate(row):
      target[iterrow, itercol] = self.calc_euclidean_distance(center, [iterrow, itercol])

But this is really slow and also probably very "un-pythonic". I can not come up with a better way and now asking for education.

orlp · Accepted Answer · 2021-07-03 13:46:47Z

4

I believe that this does what you want:

w, h = (512, 512)
cx, cy = (10, 376)
xcoords, ycoords = np.meshgrid(np.arange(w), np.arange(h))
target = np.sqrt((xcoords - cx)**2 + (ycoords - cy)**2)

answered Jul 3, 2021 at 13:46

orlp

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1 Comment

Simon Over a year ago

Works great and is (obviously) a great speed up. Did not know about np.meshgrid. Very helpful thank you

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