FPSAutomaticAiming——基于YOLOV5的FPS类游戏自动瞄准AI

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Deep LearningFPSAutomaticAiming
Overview

FPSAutomaticAiming——基于YOLOV5的FPS类游戏自动瞄准AI

声明:

本项目仅限于学习交流，不可用于非法用途，包括但不限于：用于游戏外挂等，使用本项目产生的任何后果与本人无关！

简介

本项目基于yolov5,实现了一款FPS类游戏（CF、CSGO等）的自瞄AI，本项目旨在使用现有网络结构实现一个完整的落地项目，仅供人工智能自动控制等方面的学习研究，不可用于非法用途！！！

环境配置

1.软件环境
使用conda导入yolo.yaml。

name: yolo
channels:
- pytorch
- conda-forge
- https://mirrors.ustc.edu.cn/anaconda/pkgs/main
- https://mirrors.ustc.edu.cn/anaconda/pkgs/free
- https://mirrors.bfsu.edu.cn/anaconda/pkgs/msys2
- https://mirrors.bfsu.edu.cn/anaconda/pkgs/pro
- https://mirrors.bfsu.edu.cn/anaconda/pkgs/r
- https://mirrors.bfsu.edu.cn/anaconda/pkgs/free
- https://mirrors.bfsu.edu.cn/anaconda/pkgs/main
- defaults
dependencies:
- absl-py=0.13.0=py38haa95532_0
- aiohttp=3.7.4=py38h2bbff1b_1
- async-timeout=3.0.1=py38haa95532_0
- attrs=21.2.0=pyhd3eb1b0_0
- blas=1.0=mkl
- blinker=1.4=py38haa95532_0
- bottleneck=1.3.2=py38h2a96729_1
- brotli=1.0.9=ha925a31_2
- brotlipy=0.7.0=py38h2bbff1b_1003
- ca-certificates=2021.5.30=h5b45459_0
- cachetools=4.2.2=pyhd3eb1b0_0
- certifi=2021.5.30=py38haa244fe_0
- cffi=1.14.6=py38h2bbff1b_0
- chardet=3.0.4=py38haa95532_1003
- click=8.0.1=pyhd3eb1b0_0
- cryptography=3.4.7=py38h71e12ea_0
- cudatoolkit=10.2.89=h74a9793_1
- cycler=0.10.0=py38_0
- fonttools=4.25.0=pyhd3eb1b0_0
- freetype=2.10.4=hd328e21_0
- google-auth=1.33.0=pyhd3eb1b0_0
- google-auth-oauthlib=0.4.1=py_2
- grpcio=1.35.0=py38hc60d5dd_0
- icc_rt=2019.0.0=h0cc432a_1
- icu=58.2=ha925a31_3
- idna=2.10=pyhd3eb1b0_0
- importlib-metadata=3.10.0=py38haa95532_0
- intel-openmp=2021.3.0=haa95532_3372
- jpeg=9b=hb83a4c4_2
- kiwisolver=1.3.1=py38hd77b12b_0
- libpng=1.6.37=h2a8f88b_0
- libprotobuf=3.17.2=h23ce68f_1
- libtiff=4.2.0=hd0e1b90_0
- libuv=1.40.0=he774522_0
- lz4-c=1.9.3=h2bbff1b_1
- markdown=3.3.4=py38haa95532_0
- matplotlib=3.4.2=py38haa95532_0
- matplotlib-base=3.4.2=py38h49ac443_0
- mkl=2021.3.0=haa95532_524
- mkl-service=2.4.0=py38h2bbff1b_0
- mkl_fft=1.3.0=py38h277e83a_2
- mkl_random=1.2.2=py38hf11a4ad_0
- msys2-conda-epoch=20160418=1
- multidict=5.1.0=py38h2bbff1b_2
- munkres=1.1.4=py_0
- ninja=1.7.2=0
- numexpr=2.7.3=py38hb80d3ca_1
- numpy=1.20.3=py38ha4e8547_0
- numpy-base=1.20.3=py38hc2deb75_0
- oauthlib=3.1.1=pyhd3eb1b0_0
- olefile=0.46=py_0
- openssl=1.1.1k=h8ffe710_1
- pandas=1.3.1=py38h6214cd6_0
- pillow=8.3.1=py38h4fa10fc_0
- pip=21.0.1=py38haa95532_0
- protobuf=3.17.2=py38hd77b12b_0
- pyasn1=0.4.8=py_0
- pyasn1-modules=0.2.8=py_0
- pycparser=2.20=py_2
- pyjwt=2.1.0=py38haa95532_0
- pyopenssl=20.0.1=pyhd3eb1b0_1
- pyparsing=2.4.7=pyhd3eb1b0_0
- pyqt=5.9.2=py38ha925a31_4
- pysocks=1.7.1=py38haa95532_0
- python=3.8.11=h6244533_1
- python-dateutil=2.8.2=pyhd3eb1b0_0
- python-mss=6.1.0=pyhd3deb0d_0
- python_abi=3.8=2_cp38
- pytorch=1.9.0=py3.8_cuda10.2_cudnn7_0
- pytz=2021.1=pyhd3eb1b0_0
- pyyaml=5.4.1=py38h2bbff1b_1
- qt=5.9.7=vc14h73c81de_0
- requests=2.25.1=pyhd3eb1b0_0
- requests-oauthlib=1.3.0=py_0
- rsa=4.7.2=pyhd3eb1b0_1
- scipy=1.6.2=py38h66253e8_1
- seaborn=0.11.2=pyhd3eb1b0_0
- setuptools=52.0.0=py38haa95532_0
- sip=4.19.13=py38ha925a31_0
- six=1.16.0=pyhd3eb1b0_0
- sqlite=3.36.0=h2bbff1b_0
- tensorboard=2.5.0=py_0
- tensorboard-plugin-wit=1.6.0=py_0
- tk=8.6.10=he774522_0
- torchaudio=0.9.0=py38
- torchvision=0.10.0=py38_cu102
- tornado=6.1=py38h2bbff1b_0
- tqdm=4.62.1=pyhd3eb1b0_1
- typing-extensions=3.10.0.0=hd3eb1b0_0
- typing_extensions=3.10.0.0=pyh06a4308_0
- urllib3=1.26.6=pyhd3eb1b0_1
- vc=14.2=h21ff451_1
- vs2015_runtime=14.27.29016=h5e58377_2
- werkzeug=1.0.1=pyhd3eb1b0_0
- wheel=0.37.0=pyhd3eb1b0_0
- win_inet_pton=1.1.0=py38haa95532_0
- wincertstore=0.2=py38_0
- xz=5.2.5=h62dcd97_0
- yaml=0.2.5=he774522_0
- yarl=1.6.3=py38h2bbff1b_0
- zipp=3.5.0=pyhd3eb1b0_0
- zlib=1.2.11=h62dcd97_4
- zstd=1.4.9=h19a0ad4_0
- pip:
  - colorama==0.4.4
  - mouseinfo==0.1.3
  - opencv-python==4.5.3.56
  - polygon3==3.0.9.1
  - pyautogui==0.9.53
  - pygetwindow==0.0.9
  - pymsgbox==1.0.9
  - pyperclip==1.8.2
  - pyrect==0.1.4
  - pyscreeze==0.1.27
  - pytweening==1.0.3
  - tensorboard-data-server==0.6.1
  - thop==0.0.31-2005241907
prefix: D:\Miniconda3\envs\yolo

2.硬件环境

本项目中控制鼠标移动时使用了“易键鼠”。（也可以自行修改相关代码，使用pyautogui，pywin32等库来控制键盘鼠标）

使用方法

1.训练模型。

  • 本项目的训练方法请查看yolov5相关文档。

2.使用。

  • 启动前在utils/CFUtils.py文件中修改屏幕分辨率，检测框范围等参数。
  • 如需更换模型，请在CFdetect.py文件中修改模型位置。
  • 修改好相关参数后直接运行Main.py启动本项目。
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