init

.gitignore

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+build/
+log/images/

.vscode/settings.json

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+{
+    "cmake.sourceDirectory": "${workspaceFolder}/src",
+    "files.associations": {
+        "cstdlib": "cpp",
+        "algorithm": "cpp",
+        "cmath": "cpp",
+        "array": "cpp",
+        "atomic": "cpp",
+        "bit": "cpp",
+        "cctype": "cpp",
+        "charconv": "cpp",
+        "chrono": "cpp",
+        "clocale": "cpp",
+        "compare": "cpp",
+        "complex": "cpp",
+        "concepts": "cpp",
+        "cstddef": "cpp",
+        "cstdint": "cpp",
+        "cstdio": "cpp",
+        "cstring": "cpp",
+        "ctime": "cpp",
+        "cwchar": "cpp",
+        "deque": "cpp",
+        "exception": "cpp",
+        "format": "cpp",
+        "forward_list": "cpp",
+        "fstream": "cpp",
+        "functional": "cpp",
+        "initializer_list": "cpp",
+        "iomanip": "cpp",
+        "ios": "cpp",
+        "iosfwd": "cpp",
+        "iostream": "cpp",
+        "istream": "cpp",
+        "iterator": "cpp",
+        "limits": "cpp",
+        "list": "cpp",
+        "locale": "cpp",
+        "map": "cpp",
+        "memory": "cpp",
+        "mutex": "cpp",
+        "new": "cpp",
+        "optional": "cpp",
+        "ostream": "cpp",
+        "queue": "cpp",
+        "random": "cpp",
+        "ratio": "cpp",
+        "set": "cpp",
+        "sstream": "cpp",
+        "stdexcept": "cpp",
+        "stop_token": "cpp",
+        "streambuf": "cpp",
+        "string": "cpp",
+        "system_error": "cpp",
+        "thread": "cpp",
+        "tuple": "cpp",
+        "type_traits": "cpp",
+        "typeinfo": "cpp",
+        "unordered_map": "cpp",
+        "utility": "cpp",
+        "vector": "cpp",
+        "xfacet": "cpp",
+        "xhash": "cpp",
+        "xiosbase": "cpp",
+        "xlocale": "cpp",
+        "xlocbuf": "cpp",
+        "xlocinfo": "cpp",
+        "xlocmes": "cpp",
+        "xlocmon": "cpp",
+        "xlocnum": "cpp",
+        "xloctime": "cpp",
+        "xmemory": "cpp",
+        "xstddef": "cpp",
+        "xstring": "cpp",
+        "xtr1common": "cpp",
+        "xtree": "cpp",
+        "xutility": "cpp"
+    }
+}

README.md

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+# HuTao Fisher
+
+胡桃老婆过生日说她钓鱼不得法，还得自己跳下水抓，我听了心想怎么能亏待老婆呢？于是就写了这个全自动的HuTao Fisher，老婆想钓鱼直接站到水边拿起鱼竿打开钓鱼辅助工具，鱼就自己上钩啦！(x)
+
+---
+
+## 简介 Intro
+
+借鉴7eu7d7的工作[genshin-auto-fish](https://github.com/7eu7d7/genshin_auto_fish)的思路以及部分实现，在此基础上用C++重构了整体代码，并且进行了一些修改，做了一点微小的工作：(x)
+
+- 重构抛竿判定：将其抽象为一三分类器（过近，过远，刚好）。并且在判定抛竿的部分通过一定的近似消除摄像机高度的影响。
+- 删除了强化学习力度控制（因为我不会）
+- 加入了对渊下宫鳐鱼的支持
+- 将识别网络替换为[nanodet](https://github.com/RangiLyu/nanodet)，使用[ncnn](https://github.com/Tencent/ncnn)进行推理，并进一步调试，并在自己的钓鱼数据集上达到了0.668的mAP
+- 优化了一些错误情况的处理，使整体鲁棒性加强
+- 实现多分辨率适配。（通过将截屏缩放到一1024\*576的中间层进行处理，因此原则上分辨率高于1024\*576即可正常运行）
+
+---
+
+Refer to the work [genshin-auto-fish](https://github.com/7eu7d7/genshin_auto_fish) of 7eu7d7. On this basis, I refactored the whole code with C++, and made some changes, and did a little work:
+
+- Refactored the casting rod judgment: abstract it into a trinary classifier (too close, too far, ok). In addition, the effect of camera height is eliminated by a certain approximation.
+
+- Removed the reinforcement learning power control (because I don't know how to perform it)
+
+- Now support rays in Enkanomiya
+
+- Replace the recognition network with [nanodet](https://github.com/RangiLyu/nanodet) and adjust, then infer with [ncnn](https://github.com/Tencent/ncnn), achieved 0.668 mAP on my own dataset
+
+- Optimized the handling of some error cases and enhance the overall robustness
+
+- Achieve multi-resolution adaptation. (By scaling the screen to a 1024\*576 intermediate layer for processing, so in theory if you have a resolution higher than 1024\*576 the fisher can work properly)
+
+
+
+
+## 使用说明 User's Guide
+
+先运行HutaoFisher.exe再运行游戏。
+
+可在运行后的命令行界面依次设置是否使用GPU推理，是否记录全部图像（错误时的图像始终会被输出，但其他图像记录可被关闭），是否记录抛竿数据以帮助优化抛竿判定（此项若为是，则抛竿失败后需Alt+Tab切出原神输入错误原因）
+
+在全屏模式下运行游戏，并且显示器长宽比应为16:9（重要！）
+
+在启动钓鱼器之前应手动走到钓鱼点，选择鱼竿并在游戏中进入钓鱼。
+
+按Alt+A进入钓鱼，Alt+X退出钓鱼。
+
+提示音依照音阶顺序依次为：
+
+1. 扫描钓鱼点寻找鱼
+2. 选择待钓的鱼
+3. 选择鱼饵
+4. 准备抛竿
+5. 检测咬钩
+6. 控制力度条
+7. 钓鱼成功
+
+这也是单次钓鱼时程序的运行顺序。
+
+钓鱼器出现严重错误时会发出低音蜂鸣并尝试从头开始钓鱼，若钓鱼过程中程序运行连续出现3次严重错误则钓鱼器会自动终止，需要重新按Alt+A以开始钓鱼。
+
+可能有用的图像与记录存放在log文件夹中。
+
+---
+
+Run HutaoFisher.exe before launch the game.
+
+You can set whether to use the GPU inference, whether to record all images (image will always be logged when error occurs, but other image record can be turned off), whether to save casting rod data to help optimize rod decision (if this is true, for cast rod failure press Alt + Tab to switch away from the Genshin game and input error reason)
+
+Run the game in full screen mode with a 16:9 aspect ratio (important!).
+
+Before starting the fisher, you should manually walk to the fishing place, select a fishing rod and enter fishing in the game.
+
+Press Alt+A to enter fishing and Alt+X to exit fishing.
+
+The beeps are in order of scale:
+
+1. Scan fishing spots for fish
+
+2. Choose the fish to catch
+
+3. Choose bait
+
+4. Prepare to cast the rod
+
+5. Test the bite
+
+6. Control strength bar
+
+7. Successful fishing
+
+This is also the order which the program runs on a single fishing process.
+
+When a serious error occurs, the fisher will beep lowly and try to start fishing anew. If there are three successive serious errors occured during the fishing process, the fisher will automatically stop, and you need to press Alt+A again to restart fishing.
+
+Probably useful images and data are stored in the log folder.
+
+
+
+## 代码与编译 Code and Compilation
+
+老实说，我没学过软件工程，所以我也不知道要怎么合理地构建一个C++项目（
+
+所以不推荐从源代码构建程序：直接下载Release压缩包即可解压即用。
+
+源代码位于src文件夹中。用cmake构建可能可以得到程序。
+
+lib文件夹中包含了预编译的ncnn库，若成功编译，要运行程序应将ncnn.dll复制到编译得到的exe文件处。
+
+另外程序还使用了opencv库，若要进行编译还得在电脑上预先安装opencv（或者自己改cmake配置以使用预编译库？实在不想做了orz）（真相是作者装完opencv以后才搞明白了一点如何链接预编译库）
+
+不过fishing.cpp里面包含了一些注释掉的整体调试用的函数，main.cpp里也提供了调试的入口，如果你成功跑通了编译，可以利用这些函数做进一步的调试。
+
+至于数据集与网络训练的过程，没什么特色，代码也基本是一坨屎山（想到哪里写哪里），再考虑到挨mhy铁拳的风险，就不开源了(?)
+
+---
+
+To be honest, I haven't studied software engineering, so I don't know how to build a C++ project correctly.
+
+So it's not recommended to build from source code: just download the Release package, unzip and use it.
+
+The source code is located in the src folder. It may be possible to get a executive program by building with cmake.
+
+The lib folder contains the pre-compiled ncnn library. To run the program, copy ncnn.dll to the compiled exe file.
+
+In addition, the program uses the OpenCV library. To compile, you have to install OpencV on your computer in advance (or change the cmake configuration to use a precompiled library? I'm so tired to do that ORZ) (the fact is that the author didn't figure out how to link a precompiled library until after installing OpencV)
+
+However, fishing.cpp contains some annotated functions for overall debugging, and main.cpp also provides a debug entry. If you achieve the compilation, they may help in further debugging.
+
+As for the data set and network training process, there is no highlight, and the code is basically a mountain of shit (write as I think), and considering the risk of being beaten by mihoyo, so please let me not to open the source code (?)