This repo contains the code necessary for the actual ML models and their wrappers for ease-of-use. It's designed to only have the classes I'll import in more complex pipelines, so it can be installed via pip without all the overhead I may add in repos like dbdie_info_extraction (WIP).
It is very much a WIP repo. I write it whenever I'm feeling inspired, thus its development is slow and erratic, ngl, but you are free to expand on it according to the licence's permissions and restrictions.
It is recommended that you have the dbdie_ml and dbdie_api repos at the same folder level, in some path of your choice. Also, the code's now being developed in Linux, so it's best to use it in Linux or in Windows' WSL.
If you want the functionalities the API provides (including the use of the PostgreSQL database), you should navigate to the dbdie_api repo either by CLI or using a code editor like VS Code, and you should install the dbdie_ml dependency.
pip install ../dbdie_mlFor your convenience, there's also a Makefile available that should make this process more straightforward.
make venv # creates the .venv folder in the repo's directory
source .venv/bin/activate
make install # installs external dependencies and the dbdie_ml package
make api # runs the API on localhostYou can explore the code for yourself without the API just by installing the package on a venv of your choice.
pip install <path_to_dbdie_ml_fd>Cropper: Crops images according to its settings. Used to avoid hardcoding settings in the code.CropperSwarm: Chain ofCroppersthat can be run in sequence.InfoExtractor: Extracts information of an image using multipleIEModels.IEModel: ML model for the extraction of a particular information type, such as perks, addons, etc.FullMatchOut: Extracted match information.
You must set an env var DBDIE_MAIN_FD that points to a folder which will contain the DBDIE folder structure.
You can create this structure using the make folders command.
Data folder
<DBDIE_MAIN_FD>
└── data
├── crops
│ ├── _old_versions
│ │ ├── ...
│ │ └── 0
│ │ ├── addons__killer
│ │ ├── ...
│ │ └── status__surv
│ ├── addons__killer
│ ├── addons__surv
│ ├── character__killer
│ ├── character__surv
│ ├── item__killer
│ ├── item__surv
│ ├── offering__killer
│ ├── offering__surv
│ ├── perks__killer
│ ├── perks__surv
│ ├── player__killer
│ ├── player__surv
│ ├── points
│ ├── prestige
│ └── status__surv
├── img
│ ├── _old_versions
│ │ ├── ...
│ │ └── 0
│ │ ├── cropped
│ │ └── pending
│ ├── cropped
│ └── pending
└── labels
├── _old_versions
│ ├── ...
│ └── 0
│ └── labels
│ ├── addons__killer
│ ├── ...
│ └── status__surv
├── label_ref
└── labels
├── character__killer
├── character__surv
├── perks__killer
├── perks__surv
└── ...Inference folder (WIP)
<DBDIE_MAIN_FD>
└── inference
├── crops
│ ├── addons__killer
│ └── ...
├── img
│ ├── cropped
│ └── pending
└── labels
├── label_ref
└── labels
├── character__killer
└── ...(WIP)
If you just want to know how do I crop the endcard screenshot and the Pytorch architectures I use, check out the following files:
- dbdie_ml/crop_settings.py
- dbdie_ml/models/custom.py
If you have any images I can use to further train my models with data different than my own, feel free to reach out and help contribute to this project. Images shared will be used solely for the experimentation and training of this project, won't be uploaded to this or any other place.
You can take the credit in this README if you want to. Also, the user who shared them can at any time revoke their use, and thus the images will be promptly deleted from the ML database.
No labels are needed but they are welcome nonetheless.
I chose to link all the code I develop to a GPL-3.0 license. You can see its details in the LICENSE file, but I find it easier to read its summary here.
Disclaimer: This is a personal project and I have no affiliation with BHVR and/or Dead By Daylight's team.