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dnfm-auto's Introduction

基于yolov5模型实现布万家自动化搬砖

项目根目录的demo_video.pm4为演示视频

demo_video2gif

2024年07月30日,跑图效率不及预期,最佳情况也需要2分30秒+,实用性不高,暂不考虑继续更新

代码已经全部开源,根目录放入yolo转ncnn的权重文件.bin与.param文件,即可运行

标注工具

Label Studio Documentation — Quick start guide for Label Studio

启动方式:

 label-studio start

yolov5所需分类

['Gate' # 门,'Hero' # 玩家人物,'Item' # 掉落物品,'Mark' # 箭头标记,'Monster' # 怪物,'Monster_Fake' # 怪物尸体]

pt转ncnn步骤

yolov5根目录
python export.py --weights best.pt --img 460 --batch 1 --train
python -m onnxsim best.onnx best-sim.onnx
使用官方转化工具
./onnx2ncnn ./model/best-sim.onnx model/best.param model/best.bin

已实现功能

  • 识别图像中人物、怪物、材料、门等物体
  • 自动寻路、过图
  • 固定人物攻击逻辑
  • 根据怪物数量攻击逻辑
  • 识别狮子头房间
  • 开局使用buff技能
  • 拾取材料等掉落物(支持粉装掉落识别)
  • 自动再次挑战

待优化

  1. 寻路箭头在脚底时,移动方向有误 (已优化)
  2. 大量怪物贴脸围殴时,需要尝试触发后撤步脱离
  3. 效率较低,需要配置人物固定房间、固定打法 (已优化,配置奶妈,鬼泣固定打法)
  4. 模型识别不够精确,训练集数量过少
本项目不参与商业用途,仅供学习参考
如果有好的建议和方案,欢迎找我讨论,绿色泡泡:yosaaqwq (注明添加来源,否则不通过)

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