https://github.com/ace4896/project-sekai-thumbnail-matching-notes

Notes on extracting and matching Project Sekai card thumbnails.
https://github.com/ace4896/project-sekai-thumbnail-matching-notes

opencv project-sekai python sekai

Last synced: 8 days ago
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Notes on extracting and matching Project Sekai card thumbnails.

• Host: GitHub
• URL: https://github.com/ace4896/project-sekai-thumbnail-matching-notes
• Owner: Ace4896
• Created: 2023-06-20T16:16:50.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-10-05T17:36:49.000Z (over 1 year ago)
• Last Synced: 2024-11-07T15:36:52.667Z (about 2 months ago)
• Topics: opencv, project-sekai, python, sekai
• Language: Jupyter Notebook
• Homepage:
• Size: 53 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Project Sekai - Thumbnail Matching Notes

Notes on extracting and matching Project Sekai card thumbnails. 

## Usage

In this folder are various Jupyter notebooks outlining my attempts at extracting and matching card thumbnails:

- **Thumbnail Extraction**

  - [`character-box`](./character-box.ipynb)

    - Getting the character box from a screenshot of the character list

    - This step helps narrow down the search for thumbnails, as it filters out most of the menu controls

  - [`thumbnail-extraction`](./thumbnail-extraction.ipynb)

    - Extracting individual card thumbnails from the character list

- **Thumbnail Matching**

  - [`orb-feature-matching`](./orb-feature-matching.ipynb)

    - This approach involves computing ORB Features and performing brute-force matching of features

    - While this would be the most accurate, it's probably too slow for the usecase in mind (importing of cards from a screenshot)

    - I also haven't figured out a way to measure the "similarity" between the feature descriptors

  - [`hsv-histogram`](./hsv-histogram.ipynb)

    - This approach involves generating normalised colour histograms from HSV channels and comparing similarity

    - The matching speed is good, even with a linear search

    - However, the histograms are quite large, so storage may be an issue when more thumbnails are added

  - [`image-hashing`](./image-hashing.ipynb)

    - This approach involves generating image hashes from the thumbnails and comparing the distance between hashes

    - The matching speed is better than the histogram method, without a big sacrifice in accuracy

    - The hashes are just 64-bit integers, so storage is very simple

To run these notebooks:

- Install Python 3.11 or higher (may work on older versions, but not sure if it'll work)

- [Create a virtual environment and activate it](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#creating-a-virtual-environment)

- Install dependencies: `pip install -r requirements.txt`

- Run `jupyter notebook`