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https://github.com/woctezuma/download-steam-banners

Retrieve Steam games with similar store banners, with MobileNet v1.
https://github.com/woctezuma/download-steam-banners

discovery game games steam steam-game steam-games

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Retrieve Steam games with similar store banners, with MobileNet v1.

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# Download Steam Banners



[![Build status][build-image]][build]

[![Code coverage][codecov-image]][codecov]

[![Code Quality][codacy-image]][codacy]



This repository contains Python code to retrieve Steam games with similar store banners.



![Similar banners with cosine similarity and average pooling](https://github.com/woctezuma/download-steam-banners/wiki/img/LVUG4Gb.png)



## Requirements



-   Install the latest version of [Python 3.X](https://www.python.org/downloads/). For CNTK, you will need Python 3.6.

-   Install the required packages:



```bash

pip install -r requirements.txt

```



On Windows, the most recent version of OpenCV can also be downloaded from [this repository](https://www.lfd.uci.edu/~gohlke/pythonlibs/). To install it:



```bash

pip install opencv_python-4.0.1-cp36-cp36m-win_amd64.whl

```



## Data



A data snapshot from February 2019 is available in [`download-steam-banners-data/`](https://github.com/woctezuma/download-steam-banners-data).



Otherwise, you would have to:

1.   download app details with [`steam-api`](https://github.com/woctezuma/steam-api),

2.   parse app details to find the banner URL of each game,

3.   download the banners with [`download_steam_banners.py`](download_steam_banners.py).



Alternatively, run [this IPython notebook][download_steam_banners]. 

[![Open In Colab][colab-badge]][download_steam_banners]

A list of of appIDs (tied to games) is first downloaded via [SteamSpy API](https://github.com/woctezuma/steamspypi).

Then banner URLs are directly inferred from appIDs, without relying on app details.



## Usage



Store banners are resized to 128x128 with [`batch_resize_images.py`](batch_resize_images.py).



To retrieve Steam games with similar store banners, image features are:

1.   extracted by [a neural net](https://keras.io/applications/#models-for-image-classification-with-weights-trained-on-imagenet) with [`build_feature_index.py`](build_feature_index.py),

2.   either concatenated, or merged via a pooling process (average or maximum pooling),

3.   compared based on cosine similarity or Minkowski distance with [`retrieve_similar_features.py`](retrieve_similar_features.py).



Alternatively:

-   [image hashes](https://github.com/JohannesBuchner/imagehash) could be computed with `build_search_index.py` and `retrieve_similar_banners.py`,

-   different image features could be computed, e.g. [ORB descriptors](https://docs.opencv.org/master/dc/dc3/tutorial_py_matcher.html) with `build_feature_index.py` and `retrieve_similar_features.py`.



## Caveat



Fill-in the path to `steam-api/data/appdetails/` in `download_steam_banners.py`.

If you use [PyCharm](https://www.jetbrains.com/pycharm/) on Windows, you could just mention your Windows username as follows:



```python

def get_user_name():

    user_name = 'Woctezuma' # <--- here

    return user_name

```



## Results



### Similar games



Results obtained with a neural net ([MobileNet](https://github.com/keras-team/keras-applications/blob/master/keras_applications/mobilenet.py)) are shown [on the Wiki](https://github.com/woctezuma/download-steam-banners/wiki).



Results obtained with alternative methods are not shown:

-   image hashes are mostly useful to detect duplicates,

-   ORB descriptors seem less relevant than the features obtained with MobileNet. 



An in-depth commentary is provided [on the Wiki](https://github.com/woctezuma/download-steam-banners/wiki/Commentary).

Overall, I would suggest to match features with:

-   cosine similarity, to avoid having to deal with weird matches of feature vectors with a norm close to zero,

-   either [concatenation](https://github.com/woctezuma/download-steam-banners/wiki/top_100_cosine_similarity) or [average pooling](https://github.com/woctezuma/download-steam-banners/wiki/top_100_cosine_similarity_with_average_pooling): with concatenation, the banner layout greatly constrains the matching.



### Unique games



It is possible to highlight games with *unique* store banners, by applying a threshold to similarity values output by the algorithm.

This is done in [`find_unique_games.py`](find_unique_games.py):

-   cosine similarity is used to compare features,

-   a game is *unique* if the similarity score between a query game and its most similar game (other than itself) is lower than or equal to an arbitrary threshold.



Results are shown [here](https://github.com/woctezuma/download-steam-banners/wiki/Unique_Games).



## References



-   [`match-steam-banners`](https://github.com/woctezuma/match-steam-banners): retrieve Steam games with similar store **banners** using MobileNet v3,

-   [`download-steam-screenshots`](https://github.com/woctezuma/download-steam-screenshots): retrieve Steam games with similar store **screenshots**,

-   [Zhang, Richard, et al. "The unreasonable effectiveness of deep features as a perceptual metric." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.](https://github.com/richzhang/PerceptualSimilarity)

-   [Image hashes](https://github.com/JohannesBuchner/imagehash)

-   [Another Github repository about image similarity](https://github.com/ankonzoid/artificio)



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