# Nearest neighbour interpolation
**URL:** https://discourse.vtk.org/t/nearest-neighbour-interpolation/6081
**Category:** Support
**Created:** [June 25, 2021, 7:14pm UTC](https://discourse.vtk.org/t/nearest-neighbour-interpolation/6081 "2021-06-25T19:14:29Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@nfisdnf986](https://discourse.vtk.org/u/nfisdnf986)
#### Post date: [June 25, 2021, 7:14pm UTC](https://discourse.vtk.org/t/nearest-neighbour-interpolation/6081/1 "2021-06-25T19:14:29Z")
I seem to be getting a linear interpolation output even when trying to use Nearest neighbour.
**Code:**
```auto
#!/usr/bin/env python3
import vtk
from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk
import numpy as np
def vtkToNumpy(data):
np_arr = vtk_to_numpy(data.GetPointData().GetScalars())
dims = data.GetDimensions()
numpy_data = np_arr.reshape(dims[2], dims[1], dims[0])
numpy_data = numpy_data.transpose(2, 1,0)
return numpy_data
def numpyToVTK(data):
flat_data_array = data.transpose(2,1,0).flatten()
vtk_data_array = numpy_to_vtk(flat_data_array)
vtk_data = numpy_to_vtk(num_array=vtk_data_array, deep=True, array_type=vtk.VTK_UNSIGNED_CHAR)
img = vtk.vtkImageData()
img.GetPointData().SetScalars(vtk_data)
img.SetDimensions(data.shape)
img.SetOrigin([0, 0, 0])
img.SetSpacing([1, 1, 1])
return img
def main():
img = np.zeros((5, 5, 5), dtype=np.uint16)
img[1:4, 1:4, 1:3] = 5
img[2:4, 2:4, 4:9] = 11
vtkImg = numpyToVTK(img)
resample = vtk.vtkImageResample()
resample.SetInputData(vtkImg)
resample.SetInterpolationModeToNearestNeighbor()
resample.InterpolateOff()
resample.SetBackgroundColor([0, 0, 0, 0])
resample.SetBackgroundLevel(0)
resample.SetOutputSpacing([0.5, 0.5, 0.5])
resample.Update()
resampleNpImg = vtkToNumpy(resample.GetOutput())
print("\nOriginal size", vtkImg.GetDimensions())
print("Original center", vtkImg.GetOrigin())
print("Original spacing", vtkImg.GetSpacing())
print("\nResampled size", resample.GetOutput().GetDimensions())
print("Resampled center", resample.GetOutput().GetCenter())
print("Resampled spacing", resample.GetOutput().GetSpacing())
print("\noriginalImg", np.unique(img), "\n\n", img)
print("\nresampleNpImg", np.unique(resampleNpImg), "\n\n", resampleNpImg)
if __name__ == " __main__":
main()
```
**Output:**
```auto
Original size (5, 5, 5)
Original center (0.0, 0.0, 0.0)
Original spacing (1.0, 1.0, 1.0)
Resampled size (9, 9, 9)
Resampled center (2.0, 2.0, 2.0)
Resampled spacing (0.5, 0.5, 0.5)
originalImg [0 5 11]
[[[0 0 0 0 0]
[0 0 0 0 0]
[0 0 0 0 0]
[0 0 0 0 0]
[0 0 0 0 0]]
[[0 0 0 0 0]
[0 5 5 0 0]
[0 5 5 0 0]
[0 5 5 0 0]
[0 0 0 0 0]]
[[0 0 0 0 0]
[0 5 5 0 0]
[0 5 5 0 11]
[0 5 5 0 11]
[0 0 0 0 0]]
[[0 0 0 0 0]
[0 5 5 0 0]
[0 5 5 0 11]
[0 5 5 0 11]
[0 0 0 0 0]]
[[0 0 0 0 0]
[0 0 0 0 0]
[0 0 0 0 0]
[0 0 0 0 0]
[0 0 0 0 0]]]
resampleNpImg [0 1 3 5 6 11]
[[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]
[[0 0 0 0 0 0 0 0 0]
[0 1 1 1 1 1 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 1 1 1 1 1 0 0 0]
[0 0 0 0 0 0 0 0 0]]
[[0 0 0 0 0 0 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 3 5 5 5 3 0 0 0]
[0 3 5 5 5 3 0 0 0]
[0 3 5 5 5 3 0 0 0]
[0 3 5 5 5 3 0 0 0]
[0 3 5 5 5 3 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 0 0 0 0 0 0 0 0]]
[[0 0 0 0 0 0 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 3 5 5 5 3 0 0 0]
[0 3 5 5 5 3 0 1 3]
[0 3 5 5 5 3 0 3 6]
[0 3 5 5 5 3 0 3 6]
[0 3 5 5 5 3 0 3 6]
[0 1 3 3 3 1 0 1 3]
[0 0 0 0 0 0 0 0 0]]
[[0 0 0 0 0 0 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 3 5 5 5 3 0 0 0]
[0 3 5 5 5 3 0 3 6]
[0 3 5 5 5 3 0 6 11]
[0 3 5 5 5 3 0 6 11]
[0 3 5 5 5 3 0 6 11]
[0 1 3 3 3 1 0 3 6]
[0 0 0 0 0 0 0 0 0]]
[[0 0 0 0 0 0 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 3 5 5 5 3 0 0 0]
[0 3 5 5 5 3 0 3 6]
[0 3 5 5 5 3 0 6 11]
[0 3 5 5 5 3 0 6 11]
[0 3 5 5 5 3 0 6 11]
[0 1 3 3 3 1 0 3 6]
[0 0 0 0 0 0 0 0 0]]
[[0 0 0 0 0 0 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 3 5 5 5 3 0 0 0]
[0 3 5 5 5 3 0 3 6]
[0 3 5 5 5 3 0 6 11]
[0 3 5 5 5 3 0 6 11]
[0 3 5 5 5 3 0 6 11]
[0 1 3 3 3 1 0 3 6]
[0 0 0 0 0 0 0 0 0]]
[[0 0 0 0 0 0 0 0 0]
[0 1 1 1 1 1 0 0 0]
[0 1 3 3 3 1 0 0 0]
[0 1 3 3 3 1 0 1 3]
[0 1 3 3 3 1 0 3 6]
[0 1 3 3 3 1 0 3 6]
[0 1 3 3 3 1 0 3 6]
[0 1 1 1 1 1 0 1 3]
[0 0 0 0 0 0 0 0 0]]
[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]]
```
The output should only contain _5_s and _11_s with Nearest neighbour method. Tried using _InterpolateOff_ too, but same result. Are there any other flags I need to set to get an output with only _[5, 11]_?
---
### Author:  [@dgobbi](https://discourse.vtk.org/u/dgobbi)
#### Post date: [June 25, 2021, 8:10pm UTC](https://discourse.vtk.org/t/nearest-neighbour-interpolation/6081/2 "2021-06-25T20:10:16Z")
Thanks for the test script. I have confirmed that this “unwanted interpolation” occurs in the master branch. It doesn’t seem to occur in the release branch.
Until I’ve tracked down and fixed this problem, you can use this alternative method for controlling the interpolation:
```python
interp = vtk.vtkImageInterpolator()
interp.SetInterpolationModeToNearest()
resample = vtk.vtkImageResample()
resample.SetInputData(vtkImg)
#resample.SetInterpolationModeToNearestNeighbor()
#resample.InterpolateOff()
# use external interpolator
resample.SetInterpolator(interp)
```