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

Related tags

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。
Owner
Fabian
No Bio
Fabian
GitHub Repository
Pytorch implementation of Depth-conditioned Dynamic Message Propagation forMonocular 3D Object Detection

DDMP-3D Pytorch implementation of Depth-conditioned Dynamic Message Propagation forMonocular 3D Object Detection, a paper on CVPR2021. Instroduction T

Li Wang 32 Nov 09, 2022
Weakly supervised medical named entity classification

Trove Trove is a research framework for building weakly supervised (bio)medical named entity recognition (NER) and other entity attribute classifiers

60 Nov 18, 2022
Implementation of TabTransformer, attention network for tabular data, in Pytorch

Tab Transformer Implementation of Tab Transformer, attention network for tabular data, in Pytorch. This simple architecture came within a hair's bread

Phil Wang 420 Jan 05, 2023
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
Baselines for TrajNet++

TrajNet++ : The Trajectory Forecasting Framework PyTorch implementation of Human Trajectory Forecasting in Crowds: A Deep Learning Perspective TrajNet

VITA lab at EPFL 183 Jan 05, 2023
State of the Art Neural Networks for Generative Deep Learning

pyradox-generative State of the Art Neural Networks for Generative Deep Learning Table of Contents pyradox-generative Table of Contents Installation U

Ritvik Rastogi 8 Sep 29, 2022
Machine-in-the-Loop Rewriting for Creative Image Captioning

Machine-in-the-Loop Rewriting for Creative Image Captioning Data Annotated sources of data used in the paper: Data Source URL Mohammed et al. Link Gor

Vishakh P 6 Jul 24, 2022
The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

Hierarchical Token Semantic Audio Transformer Introduction The Code Repository for "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound

Knut(Ke) Chen 134 Jan 01, 2023
Supplementary code for TISMIR paper "Sliding-Window Pitch-Class Histograms as a Means of Modeling Musical Form"

Sliding-Window Pitch-Class Histograms as a Means of Modeling Musical Form This is supplementary code for the TISMIR paper Sliding-Window Pitch-Class H

1 Nov 27, 2021
A naive ROS interface for visualDet3D.

YOLO3D ROS Node This repo contains a Monocular 3D detection Ros node. Base on https://github.com/Owen-Liuyuxuan/visualDet3D All parameters are exposed

Yuxuan Liu 19 Oct 08, 2022
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2020

Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2020

Phillip Lippe 1.1k Jan 07, 2023
Discord Multi Tool that focuses on design and easy usage

Multi-Tool-v1.0 Discord Multi Tool that focuses on design and easy usage Delete webhook Block all friends Spam webhook Modify webhook Webhook info Tok

Lodi#0001 24 May 23, 2022
Efficient and intelligent interactive segmentation annotation software

Efficient and intelligent interactive segmentation annotation software

294 Dec 30, 2022
Official code of CVPR 2021's PLOP: Learning without Forgetting for Continual Semantic Segmentation

PLOP: Learning without Forgetting for Continual Semantic Segmentation This repository contains all of our code. It is a modified version of Cermelli e

Arthur Douillard 116 Dec 14, 2022
This is a repository for a No-Code object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operating systems.

OpenVINO Inference API This is a repository for an object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operati

BMW TechOffice MUNICH 68 Nov 24, 2022
DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

English | 简体中文 Introduction DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks Reference Pat

CV Newbie 28 Dec 13, 2022
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features

PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features Overview This repository is the Pytorch implementation of PRIN/SPRIN: On Extracting P

Yang You 17 Mar 02, 2022
Companion code for the paper Theoretical characterization of uncertainty in high-dimensional linear classification

Companion code for the paper Theoretical characterization of uncertainty in high-dimensional linear classification Usage The required packages are lis

0 Feb 07, 2022
yolov5 deepsort 行人 车辆 跟踪 检测 计数

yolov5 deepsort 行人 车辆 跟踪 检测 计数 实现了 出/入 分别计数。 默认是 南/北 方向检测，若要检测不同位置和方向，可在 main.py 文件第13行和21行，修改2个polygon的点。 默认检测类别：行人、自行车、小汽车、摩托车、公交车、卡车。 检测类别可在 detect

554 Dec 30, 2022
Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022

PyCRE Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022 Dependencies This project is developed

<a href=[email protected]"> 7 May 06, 2022