Question

I have a python list of points (x/y coordinates):

   [(200, 245), (344, 248), (125, 34), ...]

It represents a contour on a 2d plane. I would like to use some numpy/scipy algorithms for smoothing, interpolation etc. They normally require numpy array as input. For example scipy.ndimage.interpolation.zoom.

What is the simplest way to get the right numpy array from my list of points?

EDIT: I added the word “image” to my question, hope it is clear now, I am really sorry, if it was somehow misleading. Example of what I meant (points to binary image array).

Input:

[(0, 0), (2, 0), (2, 1)]

Output:

[[0, 0, 1],
 [1, 0, 1]]

Rounding the accepted answer here is the working sample:

import numpy as np

coordinates = [(0, 0), (2, 0), (2, 1)]

x, y = [i[0] for i in coordinates], [i[1] for i in coordinates]
max_x, max_y = max(x), max(y)

image = np.zeros((max_y + 1, max_x + 1))

for i in range(len(coordinates)):
    image[max_y - y[i], x[i]] = 1

Answer 1

Ah, better now, so you do have all the points you want to fill… then its very simple:

image = np.zeros((max_x, max_y))
image[coordinates] = 1

You could create an array first, but its not necessary.