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{
  "pipeline": [
    {
      "type": "readers.ept",
      "filename": "https://pixel8austin.storage.googleapis.com/lidar/ept.json",
      "bounds": "([621208.2565613358, 621408.2565613358], [3347979.1272665653, 3348179.1272665653])"
    },
    {
        "type":"filters.normal",
        "knn":8

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from numba import cuda

@cuda.jit(device=True)
def _gpu_average(arr):
    out = 0.0
    for i in range(arr.shape[0]):
        for j in range(arr.shape[1]):
            out = out + arr[i,j]
    return out / arr.size

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import numpy as np
import numba

@numba.stencil(neighborhood=((-1, 1), (-1, 1)))
def _average(arr):
    return np.mean(arr[-1:2, -1:2])
            

@numba.jit(nopython=True, parallel=True)
def averaging_filter(arr):

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def reconstruct(pyr):
    img = pyr[-1]
    for resid in reversed(pyr[:-1]):
        up = np.stack([cv2.pyrUp(layer) for layer in img])
        nr, nc = resid.shape[1:]
        img = up[:,:nr,:nc] + resid
    return img

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import numpy as np
import cv2

def next_pyramid_layer(img):
    down = np.stack([cv2.pyrDown(layer) for layer in img])
    up = np.stack([cv2.pyrUp(layer) for layer in down])
    nr, nc = img.shape[1:]
    resid = img - up[:,:nr,:nc]
    return img - up[:,:nr,:nc], down

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