Features:

  •  New Editor Mode ‘SymbolRecognizerVR’
  •  Simple drawing tool for 2D and 3D input
  •  Multiple parameters exposed to tweak Neural Network
  • Recognize patterns in 2D (screen space) or 3D (world space)
  • Testing neural network tool
  • Simple visualization of neural network learning progress
  • Tutorial inside the plugin
  • Demo showcasing a ‘Spell Caster’ and ‘Painter’ gameplay scenarios

Code Modules:

  •  SymbolRecognizerVR (Runtime)
  •  SymbolRecognizerVREditor (Editor)

Number of Blueprints: 15

Number of C++ Classes: 35

Network Replicated: No

Documentation: Inside the plugin as a tutorial displayed in the details panel.

Example Project: SrVRDemo

特征:

  •  新编辑器模式’SymbolRecognizerVR’
  •  用于2D和3D输入的简单绘图工具
  •  向tweak神经网络公开的多个参数
  • 识别2d（屏幕空间）或3d（世界空间）中的模式
  • 测试神经网络工具
  • 神经网络学习进度的简单可视化
  • 插件内的教程
  • 演示”施法者”和”画家”游戏场景

代码模块:

  •  SymbolRecognizerVR(运行时)
  •  SymbolRecognizerVREditor（编辑器）

蓝图数目:15

C++类数:35

网络复制：没有

文档：插件内部作为详细信息面板中显示的教程。

示例项目: SrVRDemo










WARNING: Refresh Asset registry if saved profiles doesn't show up after engine update. In content browser find Plugins/SymbolRecognizerVR/Runtime and rename the folder Runtime to RuntimeTemp and rename it back to Runtime.

The tool provides an environment for recording patterns that can be recognized during game, e.g. unlock doors or cast a spell if the drawing is accurate enough.

Showcase Video

Demo Project Video: Link , SpellCaster2Hands

Tutorial Video: Playlist – 1. Editor, 2. Runtime, 3. Game

Demo Project: SrVrDemo ( Link to older version)

Discord: SymbolRecognizer

The plugin is divided into four sections that guide users through the process of creating a neural network:

  1. Profile – This section allows users to create an asset to store their neural network and settings.
  2. Record – Users can record 2D/3D inputs to train their neural network with the help of this simple tool.
  3. Train – This section enables users to feed their neural network with previously gathered input data.
  4. Test – Users can select a trained neural network and draw a pattern to test its accuracy against 'symbols' that were fed into the network.

Once a profile with working neural network is complete, users can do a simple gameplay setup in blueprints or code. The idea is to record the user's input in-game as Vector2D for mouse or Vector3D for motion controller, and then identify which learned pattern matches the user's drawing.

All of the gameplay functionality is exposed to the blueprints by a global object 'SrVRRecognizer' and a basic implementation may follow those steps:

  1. Grab SrVRRecognizer and call the function 'StartRecording' when the TRIGGER button is pressed.
  2. Check a flag 'IsRecording' in Tick and if true, call 'RecordData' passing motion controller world location.
  3. Call 'StopRecording' when TRIGGER button is released.
  4. Call 'GetAccuracyList' once recording has stopped, which gives ratio (0-1) for each trained symbol. If the accuracy for an expected symbol is high enough (close to 1), then cast a spell (spawn emitter?)
  5. Call ResetRecording once you are done with current recording and want to do a test for a new one.

Note: Even though there are simple examples in the tool showing how to paint in 3D using Niagara or 2D onto a mesh with CanvasRenderTarget, the plugin is not about visualizing a drawing effect in-game and it's up to the user to add any visual effects if necessary. The core feature is about recording player's input as Vector 2D/3D and testing it against trained neural network.

警告：如果保存的配置文件在引擎更新后未显示，请刷新资源注册表。 在内容浏览器中查找 插件/符号识别/运行时间 并将文件夹运行时重命名为RuntimeTemp和 将其重命名回运行时.

该工具提供了一个记录模式的环境，可以在游戏中识别，例如解锁门或施法，如果绘图足够准确。

展览短片

演示项目视频: 连结 , 施法者2手

教程视频: 播放列表 – 1. 编辑, 2. 运行时间, 3. 游戏

示范项目: SrVrDemo ( 连结 至旧版本)

不和谐: 符号识别器

该插件分为四个部分，指导用户完成创建神经网络的过程:

  1. 配置文件-此部分允许用户创建一个资产来存储他们的神经网络和设置。
  2. 记录-用户可以记录2D/3D输入，以训练他们的神经网络与这个简单的工具的帮助。
  3. 训练-此部分使用户能够为他们的神经网络提供以前收集的输入数据。
  4. 测试-用户可以选择一个训练有素的神经网络并绘制一个模式，以测试其与输入到网络中的”符号”的准确性。

使用工作神经网络的配置文件完成后，用户可以在蓝图或代码中进行简单的游戏设置。 我们的想法是将用户在游戏中的输入记录为鼠标的Vector2D或运动控制器的Vector3D，然后识别哪个学习的模式与用户的绘图相匹配。

所有游戏性功能都由全局对象公开给蓝图Srvr识别器 一个基本的实现可以遵循这些步骤:

  1. 抓斗/抓斗 Srvr识别器 并调用函数’开始记录‘当触发按钮被按下时。
  2. 检查旗帜’[医]分类“打勾，如果是真的，打电话”录录玫搂鲁‘通过运动控制器世界位置。
  3. 呼叫’停止记录‘当触发按钮被释放。
  4. 呼叫’[医]脱氧核糖核酸‘一旦记录停止，这给出了每个训练符号的比率（0-1）。 如果预期符号的精度足够高（接近1），则施放法术（产卵发射器？)
  5. 打电话 重新计算 一旦你完成了当前的录音，并想做一个新的测试。

注意：尽管该工具中有简单的示例显示了如何使用Niagara或2d在具有CanvasRenderTarget的网格体上以3d形式绘制，但该插件并不是关于在游戏中可视化绘图效果，并且 核心功能是将玩家的输入记录为矢量2d/3d，并根据训练好的神经网络进行测试。

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