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Creating a Photo Mosaic with Excel and Python

This lecture examines the entire process of generating a photo mosaic from an algorithmic perspective. The input image is reduced to a set of representative colors matching the number of tiles through K-means clustering, then the Hungarian algorithm is applied to optimally match each color with a tile in a 1:1 correspondence. Through this, you will understand how image processing and combinatorial optimization algorithms combine to create a single visual output.

17 learners are taking this course

Level Basic

Course period Unlimited

  • hjk1000
Python
Python
Excel
Excel
Python
Python
Excel
Excel

What you will gain after the course

  • Understanding Image Data

  • Clustering Technique - KMEANS Algorithm Practice

  • 1:1 Matching Technique - Hungarian Algorithm Practice

After analyzing the input image to understand the overall color distribution, K-means clustering is applied to simplify the image into representative colors matching the number of tiles. Then, the difference between each tile and representative color is defined as a cost, and the Hungarian algorithm is used to optimally match colors and tiles in a 1:1 correspondence. Through this process, a photo mosaic is completed where color information and tiles are harmoniously combined, allowing for an intuitive understanding of the connection between image processing and optimization algorithms.

Recommended for
these people

Who is this course right for?

  • Computer Vision & Image Processing Beginner

  • Developers and students interested in algorithms and optimization

  • Learners interested in data-driven visualization and media art

Need to know before starting?

  • Excel Basics

  • Python Basics

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4.7

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Hello

I am an office worker from a non-major background who is studying deep learning diligently.

I would like to share with you the things I've felt and learned while studying.

Thank you.

Curriculum

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6 lectures ∙ (2hr 10min)

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