downsample a multiband image represented as a 3d numpy array (bands, height, width)

downsample-multiband-arr.py

MASKED_VAL = -1  # Will depend on the band data (in my case it was always positive)

width = im.shape[-1]
height = im.shape[-2]

b = math.ceil(max(width, height)/factor)
dest_dim = b * factor
width_pad = dest_dim - width
height_pad = dest_dim - height

# Pad the array to a dimention that divides by the factor)
im = np.pad(im,
            ((0,0),
             (height_pad // 2, math.ceil(height_pad / 2)),
             (width_pad // 2, math.ceil(width_pad / 2))),
            mode="constant", constant_values=(MASKED_VAL))

# Ignore the padded values when calculating the mean value for downsampling
im = im.reshape(im.shape[0], -1, factor, b, factor)
im = np.ma.masked_where(im == MASKED_VAL, im)
im[im == MASKED_VAL] = 0

# Downsampling can use other aggregation functions, i.e. sum / max / min / median
im = im.mean((-1, -3)) / factor