Bilinear Interpolation to Resize an Image

# Bilinear Interpolation to Resize an Image

Bilinear interpolation can be used to resize an image, in particular to make it larger.  See my video at https://youtu.be/mxTUZW4CR_w for the the details.

Here is the test image that I process in that video:

Here is the code:

```'''
use bilinear interpolation to resize an image
'''

import numpy as np
from PIL import Image

def resizeImage(name) :
img1 = Image.open(name)

old = np.asarray(img1)    # convert to Numpy array
rows, cols, layers = old.shape
new = np.zeros( (2*rows - 1, 2*cols - 1, layers) )
print("original dimensions:", old.shape)

for layer in range(3) :
new[:, :, layer] = resizeLayer(old[:, :, layer])

# convert the values to unsigned, 8-bit integers
new = new.astype(np.uint8)
print("     new dimensions:", new.shape)

img2 = Image.fromarray(new)  # convert back to Image
newName = "big-" + name
img2.save(newName)

def resizeLayer(old) :
rows, cols = old.shape

rNew = 2*rows - 1
cNew = 2*cols - 1
new = np.zeros((rNew, cNew))

# move old points
new[0:rNew:2, 0:cNew:2] = old[0:rows, 0:cols]

''' alternative approach
# something like this would be necessary in languages
#   that don't support slicing
new = np.zeros( (2*rows - 1, 2*cols - 1) )
for r in range(rows) :
for c in range(cols) :
new[2*r, 2*c] = old[r,c]

rows, cols = new.shape
'''

# produce vertical values
new[1:rNew:2, :] = (new[0:rNew-1:2, :] + new[2:rNew:2, :]) / 2
''' alternative approach
for r in range(1, rows, 2) :
for c in range(0, cols, 2) :
# top + bottom
new[r,c] = ( new[r-1,c] + new[r+1,c] ) // 2
'''

# produce horizontal values
new[:, 1:cNew:2] = (new[:, 0:cNew-1:2] + new[:, 2:cNew:2]) / 2
''' alternative approach
for r in range(0, rows, 2) :
for c in range(1, cols, 2) :
# left + right
new[r,c] = ( new[r,c-1] + new[r,c+1] ) // 2
'''

# produce center values
new[1:rNew:2, 1:cNew:2] = (new[0:rNew-2:2, 0:cNew-2:2] +
new[0:rNew-2:2, 2:cNew:2] +
new[2:rNew:2, 0:cNew-2:2] +
new[2:rNew:2, 2:cNew:2] ) / 4
''' alternative approach
for r in range(1, rows, 2) :
for c in range(1, cols, 2) :
# top + bottom + left + right
new[r,c] = ( new[r-1,c] + new[r+1,c] + new[r,c-1] + new[r,c+1] ) // 4
'''

return new

####################  main  ####################
'''
test = np.array([[1, 3,  5],
[3, 5,  7],
[5, 7,  9],
[7, 9, 11]])
print(test)
test = resizeLayer(test)
print()
print(test)
'''

filename = 'book_fausett_small.jpg'
resizeImage(filename)
```