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pythonnumpyscipyinterpolation
Harshit Kumar
Harshit Kumar

Reputation: 12837

Interpolate 2d numpy array to change shape

I've a numpy array of shape (960, 2652), I want to change its size to (1000, 1600) using linear/cubic interpolation.

>>> print(arr.shape)
(960, 2652)

I've check this and this answer, which recommends to use scipy.interpolate.interp2d, but what should I provide as x and y?

from scipy import interpolate
f = interpolate.interp2d(x, y, arr, kind='cubic')

Upvotes: 0

Views: 2239

Answers (3)

ChaosPredictor
ChaosPredictor

Reputation: 4051

for new version of spicy (>1.10)

import numpy as np
import spipy as sp

def inter_image(image, size_out):    
    x_size = image.shape[0]
    y_size = image.shape[1]

    x = np.linspace(0, 1, x_size)
    y = np.linspace(0, 1, y_size)
    f = sp.interpolate.RegularGridInterpolator((y, x), image)

    x = np.linspace(0, 1, size_out[0])
    y = np.linspace(0, 1, size_out[1])
    xg, yg = np.meshgrid(x, y)

    return  f((yg, xg))

Upvotes: 0

MKo
MKo

Reputation: 91

skimage.transform.resize is a very convenient way to do this:

import numpy as np
from skimage.transform import resize
import matplotlib.pyplot as plt
from scipy import misc
    
arr = misc.face(gray=True)

dim1, dim2 = 1000, 1600
    
arr2= resize(arr,(dim1,dim2),order=3)  #order = 3 for cubic spline
    
print(arr2.shape) 

plt.figure()
plt.imshow(arr)
plt.figure()
plt.imshow(arr2)

Upvotes: 2

Nils Werner
Nils Werner

Reputation: 36729

The datapoints on the old domain, i.e.

import numpy as np
from scipy import interpolate
from scipy import misc
import matplotlib.pyplot as plt

arr = misc.face(gray=True)

x = np.linspace(0, 1, arr.shape[0])
y = np.linspace(0, 1, arr.shape[1])
f = interpolate.interp2d(y, x, arr, kind='cubic')

x2 = np.linspace(0, 1, 1000)
y2 = np.linspace(0, 1, 1600)
arr2 = f(y2, x2)

arr.shape # (768, 1024)
arr2.shape # (1000, 1600)

plt.figure()
plt.imshow(arr)
plt.figure()
plt.imshow(arr2)

Upvotes: 4

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