Reputation: 4419
I have an image that is created by using a bayer filter and the colors are slightly off. I need to multiply RG and B of each pixel by a certain factor ( a different factor for R, G and B each) to get the correct color. I am using the python imaging library and of course writing in python. is there any way to do this efficiently?
Thanks!
Upvotes: 3
Views: 17525
Reputation: 207445
You can do this pretty efficiently and simply using a simple transform matrix with the convert()
function:
#!/usr/bin/env python3
from PIL import Image
# Open image
im = Image.open('tree.jpg')
# Make transform matrix, to multiply R by 1.5, leaving G and B unchanged
Matrix = ( 1.5, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0)
# Apply transform and save
im = im.convert("RGB", Matrix)
im.save('result.png')
Input image:
Result image:
You can read that matrix as saying:
newRed = 1.5*oldRed + 0.0*oldGreen + 0.0*oldGreen + 0.0
newGreen = 0.0*oldRed + 1.0*oldGreen + 0.0*oldBlue + 0.0
newBlue = 0.0*oldRed + 0.0*oldGreen + 1.0*oldBlue + 0.0
Keywords: Python, PIL, Pillow, color matrix, colour matrix, transform, multiply channel, scale channel, separate, separately, individual channels, bands, components, individually, split channels, merge channels, image, image processing.
Upvotes: 7
Reputation: 35269
Here is how to do it:
point
to multiply a channel by factor (1.5
in the example, on the r channel). Heres the code:
import Image
im = Image.open('1_tree.jpg')
im = im.convert('RGB')
r, g, b = im.split()
r = r.point(lambda i: i * 1.5)
out = Image.merge('RGB', (r, g, b))
out.show()
original:
With the red channel multiplied by 1.5
(its a bit redder..):
Upvotes: 10
Reputation: 308140
You can use .split
to split the image into a separate image for each of the three channels, .eval
as suggested by astynax, and Image.merge
to put them back together.
If any of your multiplication factors are greater than 1, be sure to clamp the output to 255.
Upvotes: 1
Reputation: 2491
From documentation:
from PIL import Image
Image.eval(image, function) => image
Applies the function (which should take one argument) to each pixel in the given image. If the image has more than one band, the same function is applied to each band. Note that the function is evaluated once for each possible pixel value, so you cannot use random components or other generators.
Upvotes: 1
Reputation: 18906
As a basic optimization, it may save a little time if you create 3 lookup tables, one each for R, G, and B, to map the input value (0-255) to the output value (0-255). Looking up an array entry is probably faster than multiplying by a decimal value and rounding the result to an integer. Not sure how much faster.
Of course, this assumes that the values should always map the same.
Upvotes: 2