Apply 3d luts cube files into an image using python

Applying 3D LUTs cube files into an image using Python can be achieved in different ways. In this article, we will explore three different options to solve this problem.

Option 1: Using OpenCV

OpenCV is a popular computer vision library that provides various image processing functions. To apply 3D LUTs cube files using OpenCV, we can utilize the LUT function.


import cv2
import numpy as np

def apply_lut(image, lut_file):
    lut = np.loadtxt(lut_file)
    result = cv2.LUT(image, lut)
    return result

image = cv2.imread('input_image.jpg')
output = apply_lut(image, 'lut.cube')
cv2.imwrite('output_image.jpg', output)

In this code snippet, we first load the input image using cv2.imread. Then, we define a function apply_lut that takes the image and the path to the LUT file as inputs. Inside the function, we load the LUT file using np.loadtxt and apply it to the image using cv2.LUT. Finally, we save the output image using cv2.imwrite.

Option 2: Using PIL (Python Imaging Library)

PIL is another popular library for image processing in Python. We can use the ImageOps module from PIL to apply 3D LUTs cube files.


from PIL import Image, ImageOps

def apply_lut(image, lut_file):
    lut = ImageOps.load_lut(lut_file)
    result = ImageOps.colorize(image, lut)
    return result

image = Image.open('input_image.jpg')
output = apply_lut(image, 'lut.cube')
output.save('output_image.jpg')

In this code snippet, we import the necessary modules from PIL. Then, we define a function apply_lut that takes the image and the path to the LUT file as inputs. Inside the function, we load the LUT file using ImageOps.load_lut and apply it to the image using ImageOps.colorize. Finally, we save the output image using output.save.

Option 3: Using NumPy

If you prefer a more low-level approach, you can use NumPy to apply 3D LUTs cube files. This option requires a bit more manual processing of the image data.


import numpy as np

def apply_lut(image, lut_file):
    lut = np.loadtxt(lut_file)
    result = lut[image.astype(int)]
    return result.astype(np.uint8)

image = np.array(Image.open('input_image.jpg'))
output = apply_lut(image, 'lut.cube')
Image.fromarray(output).save('output_image.jpg')

In this code snippet, we first convert the input image to a NumPy array using np.array. Then, we define a function apply_lut that takes the image and the path to the LUT file as inputs. Inside the function, we load the LUT file using np.loadtxt and apply it to the image by indexing the LUT array with the image array. Finally, we convert the output back to an image using Image.fromarray and save it using output.save.

Among these three options, the best choice depends on your specific requirements and preferences. If you are already using OpenCV or PIL in your project, it might be more convenient to use the corresponding libraries. However, if you prefer a more low-level approach or have specific performance considerations, using NumPy directly can provide more flexibility.

Rate this post

8 Responses

  1. Option 3: Using NumPy seems like the obvious choice, but I wonder if its actually the most efficient one.

    1. I used NumPy for a similar task and it worked like a charm. Its widely known for its efficiency, so Id say its a safe bet. But hey, theres always room for debate. Give it a try and see for yourself!

  2. Wow, I never thought applying 3D LUTs using Python could be this versatile! Option 2 using PIL seems super efficient.

    1. I completely agree! Option 2 using PIL is definitely the way to go. Its fast, efficient, and delivers outstanding results. Who knew Python could be this versatile? Kudos to the developer for such a brilliant solution!

Leave a Reply

Your email address will not be published. Required fields are marked *

Table of Contents