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https://github.com/loisglld/waldoko
Where's Waldo solved using a signal processing approach
https://github.com/loisglld/waldoko
Last synced: about 1 month ago
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Where's Waldo solved using a signal processing approach
- Host: GitHub
- URL: https://github.com/loisglld/waldoko
- Owner: loisglld
- Created: 2024-02-12T12:40:26.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-02-16T16:16:05.000Z (10 months ago)
- Last Synced: 2024-08-22T14:45:30.913Z (4 months ago)
- Language: Jupyter Notebook
- Size: 41.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
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README
# Waldoko
_Waldoko_ is funny side project I imagined during my 2D signal processing class. I wanted to crack the famous game "Where's Waldo" using image processing techniques. The idea is to use a pattern matching algorithm to find Waldo in the image.
> Why "Waldoko"? Because it's a mix between "Waldo" and "doko" (Japanese for "where").
## Quick Overview
The project is divided into two main parts:
1. **Pattern Building**: We need to build a pattern from the image of Waldo. This pattern will be used to find Waldo in every other images.
So from the very start, from learning how to open a picture in Python, to the final pattern, we will go through the following steps: - Open the image - Convert it to grayscale - Detect the edges - Find the contours - Draw the bounding box - Extract the pattern
> It _was_ the plan, but I'm lazy and I've done it manually. Check the 1st ipynb for more details.
> I'm letting room for improvement here, but I'm not sure I'll ever come back to this project.
2. **Pattern Matching**: We will use the pattern to find Waldo in the other images. I used the phase correlation algorithm to find the pattern in the images. The phase correlation is a technique used to find the translation between two images. It's often used in image registration, but it can also be used to find a pattern in an image.
## Results
I've tested the algorithm on a few images and it worked pretty well but to be honest, a good old CNN would have done the job better. But it was fun to try something different.
