基于ajjnn的手绘图案识别

对应MC版本1.21.3。与ajjnn数据包冲突。

ajjnn：https://github.com/AjjMC/ajjnn

手绘图像的转换：Ehan写的

神经网络模型：ajjnn

引用：卡儿的数学库 - 整数排序、整数除法

　

要求

1. 图案必须一笔画完。
2. 在上一个图案解析完成后再画下一个。
3. 图案不要画得太大。
4. 画的时候不要仰头也不要低头。

　

命令

安装 ajjnn：function ajjnn:__install
卸载 ajjnn：function ajjnn:__uninstall
获取画笔：give @s minecraft:ink_sac[minecraft:custom_data={ajjnn:{brush:1b}},minecraft:consumable={consume_seconds:2147483647},minecraft:custom_name='[{"text":"Brush","color":"green","italic":false},{"text":" (Right Click on Canvas)","color":"gray"}]']

载入demo_digits模型：function ajjnn:__load {model:"demo_digits"}
模型说明：仅数字。输出为int型0~9。

载入demo_letters模型：function ajjnn:__load {model:"demo_letters"}
模型说明：仅大写字母。输出为字符串型大写字母。

载入demo_balanced模型：function ajjnn:__load {model:"demo_balanced"}
模型说明：支持大小写字母和数字。无论是数字还是字母，输出都为字符串型，大写字母全支持，其中小写字母仅包括 a b d e f g h n q r t

这三个模型都只支持印刷体

执行这个函数可手动检测输入图案：function ajjnn:_forward
若图案是玩家手绘出来的，则图案会自动存储在路径：storage ajjnn:data input
图案存储在这个路径可供用户手动检测

获得输出：data get storage ajjnn:temp decoded

检测玩家是否画完：scoreboard players get #ajnn_RUNNING int
分数为1就是画完了。

手动输入图案：

data modify storage ajjnn:data input set value [\
\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d\
\
]

写成这种排版是为了说明，输入的列表里存储的是一个28*28的画板，画板里有784个像素点，每个像素点的取值范围是0~1，表示图案在这一点的透明度，0是完全透明，1是完全不透明，以此类推。

注意，输入的图案要尽量上下左右居中。

比如这是一个输入了字母"G"的图案的范例：

data modify storage ajjnn:data input set value [\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,1d,1d,1d,1d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,1d,1d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,1d,1d,1d,1d,0d,0d,0d,0d,1d,1d,1d,1d,1d,1d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,1d,1d,0d,1d,1d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,1d,1d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,\
0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d,0d\
]

　

以下为ajjnn的说明：

　

　

A Neural Network Deployment Library for Minecraft Mapmaking

AVAILABLE ON 1.21.3

CLICK HERE TO DOWNLOAD

Please report any bugs in the issues section.

Handwritten digit and letter classification models.

Overview

This datapack allows mapmakers to deploy neural networks of arbitrary widths and depths in Minecraft. It functions as a black box that performs inference dynamically for a given model, without requiring any modifications. A Python script is provided, converting trained PyTorch models to standalone mcfunction files used to load them into the game.

Installing

After this folder has been added to the "datapacks" folder of a Minecraft world, /reload needs to be run in-game. A list of the datapack's commands is available via /function ajjnn:__help. By convention, all functions run directly by the mapmaker start with two underscores. Functions starting with a single underscore are aliases that do not give any feedback messages in the chat. These are meant to be used by the mapmaker as part of their own map's datapack. Any functions not listed here are internal and are not meant to be used.

Function	Description
/function ajjnn:__copyright	Displays datapack copyright information
/function ajjnn:__demo/kit	Gives demo kit
/function ajjnn:__demo/place_canvas	Places or relocates demo canvas
/function ajjnn:__demo/remove_canvas	Removes demo canvas
/function ajjnn:__forward	Performs forward pass
/function ajjnn:__help	Displays datapack command list
/function ajjnn:__load	Loads PyTorch model into Minecraft
/function ajjnn:__install	Installs datapack
/function ajjnn:__manual	Displays datapack manual link
/function ajjnn:__uninstall	Uninstalls datapack
/function ajjgui:__version	Displays datapack version
/function ajjnn:__view	Displays model architecture
/function ajjnn:_forward	Runs /function ajjnn:__forward without feedback
/function ajjnn:_load	Runs /function ajjnn:__load without feedback

The datapack can be installed by running /function ajjnn:__install. It can be uninstalled using /function ajjnn:__uninstall, which removes all data associated with it from the world.

Converting Models

The datapack is limited to neural networks trained in PyTorch using torch.nn.Sequential. The following layers are supported at the moment:

Datapack Layer	Pytorch Layer Based On
Hard Sigmoid	torch.nn.HardSigmoid
Linear	torch.nn.Linear
ReLU	torch.nn.ReLU

The provided Python script convert.py maps the PyTorch layers on the right to the datapack layers on the left. Dropout layers torch.nn.Dropout, used during the training process, are skipped, and an argmax function can be optionally applied by the script in the last layer, useful for classification models. The model parameters are rounded to a three decimal point precision to be compatible with the datapack's floating point arithmetic. Due to the large number of command executions involved, the number of input features and network width cannot exceed 784. However, there is no limit to network depth. The number of ticks a forward pass takes increases with both the width and the depth of the network but not with the number of input features. All converted models are automatically stored in ./data/ajjnn/functions/models/ as <model name>.mcfunction, where the name can be specified. They can then be loaded with /function ajjnn:__load {model:<model name>}.

Available Demos

To test the datapack, some simple neural networks for handwritten character classification have been trained on the EMNIST dataset and converted to their respective mcfunction files. These are demo_digits.mcfunction (10 classes, 96% accuracy), demo_letters.mcfunction (27 classes, 86% accuracy) and demo_balanced.mcfunction (47 classes, 80% accuracy). The models can be loaded by specifying their name (e.g., /function ajjnn:__load {model:"demo_digits"}).

The neural networks receive an input vector of 784 features, which take the values 0 (background) or 1 (character) in a flattened 28x28-pixel image. Along with these demos, a canvas is provided that allows the user to draw characters through raycasting. The canvas can be placed or relocated with /function ajjnn:__demo/place_canvas and removed with /function ajjnn:__demo/remove_canvas. A brush-eraser kit can be obtained with /function ajjnn:__demo/kit. White is used for pixels not drawn and black for drawn pixels. The drawable area of the canvas is restricted to 20x20 pixels to indicate the expected character size as the dataset had been padded. On top of the canvas, there is a gray arrow facing south, showing the upward direction when drawing characters. As part of this demo setup, inference is performed as the user is drawing, giving real-time feedback on their actionbar.

Running Models

Data Storage NBT Tag	Description	Type
ajjnn:data input	Model input	Double List
ajjnn:data name	Model name	String
ajjnn:data output	Model output	Any
ajjnn:data parameters	Number of model parameters	Int
ajjnn:data sequence	Model layers	Compound List
ajjnn:data status	Model status	Byte

The currently loaded model's architecture and parameters are stored in the ajjnn:data sequence NBT tag. Mapmakers can set the input ajjnn:data input, perform a forward pass with /function ajjnn:__forward and retrieve the output ajjnn:data output. The status of the model is determined by the ajjnn:data status NBT tag. If this value is set to 0b, the model is idle and can be used. If it is set to 1b, the model is running and cannot be used. Once the output has been calculated, this value is set to 2b for a single tick and then back to 0b.

Copyright

Copyright © 2023 - 2025 Ajj (https://github.com/AjjMC/ajjnn)