I have a numpy array with shape (140, 23, 2) being 140 frames, 23 objects, and x,y locations. The data has been generated by a GAN and when I animate the movement it's very jittery. I want to smooth it by converting the coordinates for each object so every odd number index to be the mid-point between the even numbered indices either side of it. e.g.

x[1] = (x[0] x[2]) / 2

x[3] = (x[2] x[4]) / 2

Below is my code:

def smooth_coordinates(df):
    # df shape is (140, 23, 2)
    # iterate through each object (23)
    for j in range(len(df[0])):
        # iterate through 140 frames
        for i in range(len(df)):
            # if it's an even number and index allows at least 1 index after it
            if (i%2 != 0) and (i < (len(df[0])-2)):
                df[i][j][0] = ( (df[i-1][j][0] df[i 1][j][0]) /2 )
                df[i][j][1] = ( (df[i-1][j][1] df[i 1][j][1]) /2 )
    return df

Aside from it being very inefficient my input df and output df are identical. Any suggestions for how to achieve this more efficiently?

CodePudding user response：

import numpy as np

a = np.random.randint(100, size= [140, 23, 2])  # input array
b = a.copy()

i = np.ogrid[1: a.shape[0]-1: 2]    # odd indicies
i

>>> [  1,   3,   5,   7,   9,  11,  13,  15,  17,  19,  21,  23,  25,
      27,  29,  31,  33,  35,  37,  39,  41,  43,  45,  47,  49,  51,
      53,  55,  57,  59,  61,  63,  65,  67,  69,  71,  73,  75,  77,
      79,  81,  83,  85,  87,  89,  91,  93,  95,  97,  99, 101, 103,
     105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129,
     131, 133, 135, 137]

(a == b).all()                  # testing for equality

>>> True

a[i] = (a[i-1]   a[i 1]) / 2    # averaging positions across frames

(a == b).all()                  # testing for equality again

>>> False