py7dtd - A 7 Days to Die tools collection

https://7daystodie.com/

In this repository are collected tools and scripts for the game 7 Days to Die:

• Tools for the detection of objects/entities
• Scripts for the automatization of actions (mining, crafting, etc.)
• Aim bots
• Passcode cracking
• Block detection

Installing

This software can be installed as a pip module.

Windows

py -m pip install py7dtd

Unix

python -m pip install py7dtd

Usage

There are currently three functions available:

• Auto shooting
• Crack passcode
• Blocks detection

Contributing

Clone the module, create a virtual environment and install it:

git clone git@github.com:tassoneroberto/py7dtd.git
cd py7dtd
py -3.10 -m venv venv
.\venv\Scripts\Activate.ps1
py -m pip install .[ai]
ltt install torch torchvision

Note: if you are not interested in "entities detection" or "aimbot" then you can omit [ai] from the above command and the ltt install torch torchvision command:

py -m pip install .

Development mode

To install the package in edit mode (for developers) specify -e:

py -m pip install -e .[ai]
py -m ltt install torch torchvision

Dependencies for entities detection

Disclaimer: an NVIDIA® GPU card with CUDA® architectures 3.5, 3.7, 5.2, 6.0, 6.1, 7.0 or higher is required. See the list of CUDA®-enabled GPU cards (https://developer.nvidia.com/cuda-gpus).

Install the following dependencies:

• C++ redistributable (https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads)
• NVIDIA® GPU drivers —CUDA® 11.2.1 (https://www.nvidia.com/drivers)
• CUDA® Toolkit 11.1 (https://developer.nvidia.com/cuda-toolkit-archive)
• cuDNN SDK 7.6 (https://developer.nvidia.com/cudnn)

In order to use the module you need a x64 version of Python 3.10.x. You can download it at this page: https://www.python.org/downloads/windows/

You also need to have a trained model for the entities detection in ./dataset/models/.

Training the entity detection model

The dataset used to train the model has to be increased to to improve the reliability of the detection. This can be done using the open source tool labelImg https://github.com/tzutalin/labelImg. The folder dataset needs to have the following structure:

• train
  • annotations
    • classes.txt
    • source1.txt
    • source2.txt
    • source3.txt
    • ...
  • images
    • source1.png
    • source2.png
    • source3.png
    • ...
• validation
  • annotations
    • classes.txt
    • source101.txt
    • source102.txt
    • source103.txt
    • ...
  • images
    • source101.png
    • source102.png
    • source103.png
    • ...

Run the following command to start training the model:

py7dtd_model_training

The following table is listing all the arguments that can be specified:

Notes

• The annotation has to be in the YOLO format.
• The test set has to be a lot smaller than the size of the train set.
• The classes.txt are automatically generated by labelImg and contain the names of the objects to detect.
• You can use a pre-trained model for transfer learning. To do so you have to download the file https://github.com/OlafenwaMoses/ImageAI/releases/download/3.0.0-pretrained/yolov3.pt and put it in ./dataset/models (or specify a different location with the argument --pretrained).

Entities detection

❗ Under development ❗

Simple entities detector using AI (Computer Vision).

The objects detection (trees, zombies, etc.) is done using ImageAI: https://github.com/OlafenwaMoses/ImageAI/

The annotation of the images has been done using the tool labelImg: https://github.com/heartexlabs/labelImg/

Proof of concept

Aim bot

❗ Under development ❗

Simple aim bot capable of:

• Detect zombies/players
• Move the mouse to the target
• Shoot

Usage

py7dtd_auto_shooting --delay 200

Note: Press ESC to interrupt the bot.

Command line arguments

The following table is listing all the arguments that can be specified:

Passcode cracking

❗ Under development ❗

Bruteforce/dictionary attack on chests/doors passcode.

Usage

It is recommended to set the game in window mode with a resolution of 640x480.

Example of a bruteforce attack testing passcodes composed of digits and lowercase characters.

Note: Press ESC to interrupt the bot.

py7dtd_crack_passcode --brute --digits --lower

Get the arguments list with the help function:

py7dtd_crack_passcode --help

Example of a dictionary attack with no limit in tries.

py7dtd_crack_passcode --dict

Note: dictionaries can be found at https://github.com/danielmiessler/SecLists/tree/master/Passwords.

Command line arguments

The available methods are bruteforce attack (--brute) and dictionary attack (--dict).

The following table is listing all the arguments to use for each method:

*At least one of these is required

Blocks detection

❗ Under development ❗

Detection of block, like topsoil and destroyed stone blocks, by taking screenshots of the map.

Usage

It is recommended to set the game in window mode with the highest resolution possible.

Specify the blocks to be identified by passing them as arguments (e.g. --topsoil). Specify an output folder (or keep the default one ./blocks_detection). Run the script and open the game map by pressing M. Press P to take a screenshot of the map and automatically mark in red the specified blocks. The screenshots, with the block marked, will be saved in the output folder.

Note: Press ESC to interrupt the script.

py7dtd_blocks_detection --topsoil --destroyed

Get the arguments list with the help function:

py7dtd_blocks_detection --help

Example of detection of topsoil blocks in the desert biome:

Command line arguments

The following table is listing all the arguments:

*At least one of these is required