How to color match images using Python

How to use Python to color match images

Introduction:
In modern society, image processing has been widely used in many fields, such as movie special effects, medicine Image diagnosis, etc. Among them, image color matching is an important technology, which can make the colors between different pictures consistent, thereby improving user experience. This article will introduce how to use Python to color match images, and explain it in detail through code examples.

1. Install dependent libraries

Before we begin, we need to ensure that the Python environment has been installed and the PIL library (Python Imaging Library) has been installed. If the PIL library is not installed, you can install it through the following command:

pip install pillow

2. Read the image data

First, we need to read the data of the image to be matched and the reference image, and add Convert it into a data structure that can be manipulated. Suppose we have two pictures: image.jpg is the picture to be matched, reference.jpg is the reference picture, the code example is as follows:

from PIL import Image

def read_image(filename):
    image = Image.open(filename)
    data = list(image.getdata())
    width, height = image.size
    return data, width, height

image_data, image_width, image_height = read_image('image.jpg')
reference_data, reference_width, reference_height = read_image('reference.jpg')

3. Calculate each The average and standard deviation of each channel

In order to achieve color matching, we need to calculate the average and standard deviation of each channel of the image to be matched and the reference image. The code example is as follows:

import numpy as np

def calculate_mean_std(data):
    pixels = np.array(data, dtype=np.float32)
    mean = np.mean(pixels, axis=0)
    std = np.std(pixels, axis=0)
    return mean, std

image_mean, image_std = calculate_mean_std(image_data)
reference_mean, reference_std = calculate_mean_std(reference_data)

4. Color matching

With the mean and standard deviation of each channel, we can use the following formula for color matching:

matched_data = (image_data - image_mean) / image_std * reference_std + reference_mean

The code example is as follows:

def match_color(data, mean, std, reference_mean, reference_std):
    matched_data = np.array(data, dtype=np.float32)
    matched_data = (matched_data - mean) / std * reference_std + reference_mean
    matched_data = matched_data.clip(0, 255)
    return list(matched_data.astype(np.uint8))

matched_image_data = match_color(image_data, image_mean, image_std, reference_mean, reference_std)

5. Save the matched image

Finally, we save the matched image data as a new image file. The code example is as follows:

def save_image(data, width, height, filename):
    image = Image.new('RGB', (width, height))
    image.putdata(data)
    image.save(filename)

save_image(matched_image_data, image_width, image_height, 'matched_image.jpg')

Conclusion:
Through the above steps, we have learned how to use Python to color match images. This technology has wide applications in image processing, design and other fields, and can effectively improve the quality and consistency of images. I hope this article is helpful to you, and you are welcome to try it and apply it to actual projects.

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