How to use interpn?

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I am trying to use interpn (in python using Scipy) to replicate results from Matlab using interp3. However, I am struggling to structure my arguments. I tried the following line: 

f = interpn(blur_maps, fx, fy, pyr_level)

Where blur maps is a 600 x 800 x 7 representing a grayscale image at seven levels of blur, fx and fy are indices of the seven maps. Both fx and fy are 2d arrays. pyr_level is a 2d array that contains values from 1 to 7 representing the blur map to be interpolated.

My question is since I incorrectly arranged the arguments, how can I arrange them in a way that works? I tried to look up examples but I didn't see anything similar. Here is an example of the data I am trying to interpolate:

import numpy as np
import cv2, math
from scipy.interpolate import interpn

levels = 7

img_path = '/Users/alimahdi/Desktop/i4.jpg'
img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2GRAY)
row, col = img.shape

x_range = np.arange(0, col)
y_range = np.arange(0, row)
fx, fy = np.meshgrid(x_range, y_range)

e = np.exp(np.sqrt(fx ** 2 + fy ** 2))
pyr_level = 7 * (e - np.min(e)) / (np.max(e) - np.min(e))


blur_maps = np.zeros((row, col, levels))
blur_maps[:, :, 0] = img

for i in range(levels - 1):
    img = cv2.pyrDown(img)
    r, c = img.shape

    tmp = img
    for j in range(int(math.log(row / r, 2))):
        tmp = cv2.pyrUp(tmp)

    blur_maps[:, :, i + 1] = tmp

pixelGrid = [np.arange(x) for x in blur_maps.shape]
interpPoints = np.array([fx.flatten(), fy.flatten(), pyr_level.flatten()])
interpValues = interpn(pixelGrid, blur_maps, interpPoints.T)
finalValues = np.reshape(interpValues, fx.shape)

I am now getting the following error: ValueError: One of the requested xi is out of bounds in dimension 0 I do know that the problem is in interpPoints but I am not sure how to fix it. Any suggestions?

scipy interpolation
1 Answers

The documentation for scipy.interpolate.interpn states that the first argument is a grid of the data you are interpolating over (which is just the integers of the pixel numbers), second argument is data (blur_maps) and third arguments is the interpolation points in the form (npoints, ndims). So you would have to do something like:

import scipy.interpolate

pixelGrid = [np.arange(x) for x in blur_maps.shape] # create grid of pixel numbers as per the docs
interpPoints = np.array([fx.flatten(), fy.flatten(), pyr_level.flatten()]) 

# interpolate
interpValues = scipy.interpolate.interpn(pixelGrid, blur_maps, interpPoints.T)

# now reshape the output array to get in the original format you wanted
finalValues = np.reshape(interpValues, fx.shape)
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