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Bilinear interpolation: Difference between revisions
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i38q =: mp (C38r mp~ n38r)
i38r =: mp (C38r mp~ n38r)
</lang>
=={{header|Python}}==
Of course, it is much faster to use PIL, Pillow or SciPy to resize an image than to rely on this code.
<lang python>#!/bin/python
import numpy as np
from scipy.misc import imread, imshow
from scipy import ndimage
def GetBilinearPixel(imArr, posX, posY):
out = []
#Get integer and fractional parts of numbers
modXi = int(posX)
modYi = int(posY)
modXf = posX - modXi
modYf = posY - modYi
modXiPlusOneLim = min(modXi+1,imArr.shape[1]-1)
modYiPlusOneLim = min(modYi+1,imArr.shape[0]-1)
#Get pixels in four corners
for chan in range(imArr.shape[2]):
bl = imArr[modYi, modXi, chan]
br = imArr[modYi, modXiPlusOneLim, chan]
tl = imArr[modYiPlusOneLim, modXi, chan]
tr = imArr[modYiPlusOneLim, modXiPlusOneLim, chan]
#Calculate interpolation
b = modXf * br + (1. - modXf) * bl
t = modXf * tr + (1. - modXf) * tl
pxf = modYf * t + (1. - modYf) * b
out.append(int(pxf+0.5))
return out
if __name__=="__main__":
im = imread("test.jpg")
enlargedShape = list(map(int, [im.shape[0]*1.6, im.shape[1]*1.6, im.shape[2]]))
enlargedImg = np.empty(enlargedShape, dtype=np.uint8)
rowScale = float(im.shape[0]) / float(enlargedImg.shape[0])
colScale = float(im.shape[1]) / float(enlargedImg.shape[1])
for r in range(enlargedImg.shape[0]):
for c in range(enlargedImg.shape[1]):
orir = r * rowScale #Find position in original image
oric = c * colScale
enlargedImg[r, c] = GetBilinearPixel(im, oric, orir)
imshow(enlargedImg)
</lang>
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