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Co-authored-by: MorMundHS-MA <MorMundHS-MA@users.noreply.github.com> Co-authored-by: Luca Schilling <lucaschilling96@web.de>
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| # NeuralNetworks | ||
| [](https://github.com/psf/black) | ||
| <p align="center"> | ||
| <a href="https://github.com/ContentAutomation"><img src="https://contentautomation.s3.eu-central-1.amazonaws.com/logo.png" alt="Logo" width="150"/></a> | ||
| <br /> | ||
| <br /> | ||
| <a href="http://choosealicense.com/licenses/mit/"><img src="https://img.shields.io/badge/license-MIT-3C93B4.svg?style=flat" alt="MIT License"></a> | ||
| <a href="https://github.com/psf/black"><img src="https://img.shields.io/badge/code%20style-black-000000.svg" alt="Code style: black"></a> | ||
| <br /> | ||
| <a href="https://github.com/tensorflow/tensorflow"><img src="https://img.shields.io/badge/TensorFlow-FF6F00.svg?logo=tensorflow&logoColor=white" alt="Tensorflow"></a> | ||
| <br /> | ||
| <br /> | ||
| <i>Implementation of a Neural Network that can detect whether a video is Ingame or not </i> | ||
| <br /> | ||
| <br /> | ||
| <i><b>Authors</b>: | ||
| <a href="https://github.com/ChristianCoenen">Christian C.</a>, | ||
| <a href="https://github.com/MorMund">Moritz M.</a>, | ||
| <a href="https://github.com/lucaSchilling">Luca S.</a> | ||
| </i> | ||
| </p> | ||
| <hr /> | ||
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| <p align="center"> | ||
| <img src="https://contentautomation.s3.eu-central-1.amazonaws.com/detect_ingame.png" alt="Ingame Detection" width="1200"/></a> | ||
| </p> | ||
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| ## Setup | ||
| This project requires [Poetry](https://python-poetry.org/) to install the required dependencies. | ||
| Check out [this link](https://python-poetry.org/docs/) to install Poetry on your operating system. | ||
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| Make sure you have installed [Python](https://www.python.org/downloads/) 3.8 or higher! Otherwise Step 3 will let you know that you have no compatible Python version installed. | ||
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| 1. Clone/Download this repository | ||
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| *Note: For test models/assets, download [Release v1.0](https://github.com/ContentAutomation/NeuralNetworks/releases/latest)* | ||
| 2. Navigate to the root of the repository | ||
| 3. Run ```poetry install``` to create a virtual environment with Poetry | ||
| 4. Run ```poetry run python src/filename.py``` to run the program. Alternatively you can run ```poetry shell``` followed by ```python src/filename.py``` | ||
| 5. Enjoy :) | ||
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| ## Script Explanations | ||
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| ### video2images.py | ||
| This utility can be used to build the dataset by splitting video files into images. | ||
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| ### predict.py | ||
| This script is used to verify the performance of the trained neural network | ||
| by specifying a path to the model of the trained neural network, | ||
| and a video clip that should be analyzed. | ||
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| ### game_detection.py | ||
| This script is used to train a neural network (e.g. create a model) on a given dataset. | ||
| If enough data is present, the neural network will learn to distinguish Ingame clips from clips | ||
| that are not ingame (e.g. Lobby, Queue, ...) | ||
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| ## Creating a new model for a game | ||
| Let's assume you want to create a new model for the game Dota2. The following steps have to be performed: | ||
| 1. Download clips for Dota2 that are both ingame and not ingame (recommended source: Twitch) | ||
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| HINT: You can download clips manually or by creating a compilation with [TwitchCompilationCreator](https://github.com/ContentAutomation/TwitchCompilationCreator) | ||
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| 2. Split the clips into images via ```video2images.py``` | ||
| 3. Create the following folder structure | ||
| ```bash | ||
| ... | ||
| │ | ||
| └───anyFolderName | ||
| │ | ||
| └───dota2 | ||
| └───nogame | ||
| ``` | ||
| 4. Sort the clips from step 1 into those folders depending on if they are ingame or not | ||
| 5. Create a ```main.py``` file in ```./src/``` to initialize a ```GameDetection``` object, then run it (see example below) | ||
| 6. Test the created model on a few example clips using ```predict.py``` to verify its accuracy | ||
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| NOTE: The number of images in the 'gamename' or 'nogame' folder has to be greater than or equal to the defined batch size | ||
| ```python | ||
| # For more information about the parameters, check out game_detection.py | ||
| m = GameDetection( | ||
| model_name="ResNet50", | ||
| game_name="dota2", | ||
| dataset_path="---PATH TO 'anyFolderName'---", | ||
| input_size=(224, 224), | ||
| batch_size=16, | ||
| save_generated_images=False, | ||
| convert_to_gray=False, | ||
| ) | ||
| m.train(epochs=2) | ||
| ``` |