DeadEye 辅助瞄准系统 / DeadEye Auto Aiming System

DeadEye 辅助瞄准系统是一款高性能的图像辅助瞄准工具，旨在通过图像处理和目标追踪技术提高射击精度。该系统通过分析屏幕截图，检测并追踪目标，最后通过模拟操作实现辅助瞄准和自动扳机功能。

The DeadEye Aiming Assist System is a high-performance image aiming tool designed to improve shooting accuracy through image processing and target tracking technology. The system analyzes screen captures to detect and track targets, and ultimately facilitates aiming and triggers actions through simulated operations.

特性

• 高速截图: 使用 ScreenShotHelper 类快速截取屏幕图像，并自动适配不同的屏幕分辨率。
• 目标检测: 目标检测 YoloDetector 类继承于 DetectModule 基类，基于 Yolov8 目标检测神经网络，支持基于.pt权重文件的直接检测或使用 TensorRT 部署后的 .onnx /.trt 模型进行精确的目标检测（目前只测试了 .pt 模型，确认可以正常使用）。
• 目标追踪: 结合匈牙利算法和卡尔曼滤波优化目标追踪的精确性和稳定性。
• 辅助瞄准: 辅助瞄准模块 DeadEyeAutoAimingModule 类继承于 AutoAimModule 基类，利用 PID 控制算法实现平滑的辅助瞄准。
• 易于扩展: 用户可以根据需求定制或扩展自己的目标检测模块或辅助瞄准模块。
• 多语言UI: 具有简单直观的用户界面,支持英文和简体中文两种语言,用户可以方便地切换语言或者增加新的语言支持。

Features

• High-speed Screenshot: Uses the ScreenShotHelper class to quickly capture screen images and automatically adapts to different screen resolutions.
• Target Detection: The target detection class YoloDetector is derived from the base class DetectModule. It is based on the Yolov8 target detection neural network and supports direct detection using .pt weight files or precise target detection using deployed TensorRT models with .onnx or .trt formats (currently, only the .pt model has been tested and confirmed to work properly).
• Target Tracking: Combines the Hungarian algorithm and Kalman filter to optimize the accuracy and stability of target tracking.
• Assist Aiming: The DeadEyeAutoAimingModule class, inheriting from the AutoAimModule base class, uses PID control algorithms to achieve smooth assist aiming.
• Easy to Extend: Users can customize or extend their own target detection modules or assist aiming modules according to their needs.
• Multi-language UI: Features a simple and intuitive user interface that supports both English and Simplified Chinese. Users can easily switch between languages or add support for new languages.

技术路线

截图模块 (ScreenShotHelper) / Screenshot Module (ScreenShotHelper)

• 自动分辨率计算: 自动检测屏幕分辨率并调整截图尺寸适应不同大小的窗口需求。
• 高速截图: 采用 dxcam 或 mss 实现高速截图功能。

目标检测模块 (DetectModule)

• YoloDetector 类: 可以使用 .pt 模型进行直接检测，也支持使用 .onnx / .trt 模型的基于 TensorRT 的部署方式进行高效检测。

目标追踪

• 目标类 (Target): 基于检测结果，使用匈牙利算法进行帧间目标匹配和编号，此外使用卡尔曼滤波算法对目标位置进行预测和优化，以实现平滑的追踪效果。

瞄准模块 (AutoAimModule)

• DeadEyeAutoAimingModule: 基础的辅助瞄准模块类，实现了核心的瞄准功能。
• 定制扩展: 允许用户继承或修改基类，创建个性化的辅助瞄准模块。

Technological Path

Screenshot Module (ScreenShotHelper)

• Automatic Resolution Calculation: Automatically detects screen resolution and adjusts the screenshot size to accommodate different window sizes.
• High-Speed Screenshots: Uses advanced libraries like dxcam or mss to capture high-speed screenshots with minimal performance impact.

Target Detection Module (YoloDetector)

• Model Compatibility: Supports direct detection using .pt model files, offering flexibility in model training and deployment.
• Efficient Detection: Incorporates .onnx models with TensorRT for efficient and accurate target detection, optimizing for lower latency and higher throughput.

Target Tracking

• Inter-Frame Matching: Utilizes the Hungarian algorithm for consistent target matching across frames, ensuring reliable tracking.
• Position Prediction and Optimization: Employs Kalman filter algorithms to predict and optimize target positions, resulting in smoother tracking and better performance.

Aiming Module (DeadEyeAutoAimingModule)

• Core Aiming Functions: Implements the fundamental features necessary for assisted aiming, such as target locking and trajectory adjustment.
• Customization and Extension: Designed with extensibility in mind, allowing users to inherit from or modify the base class to create customized aiming modules tailored to specific requirements.

使用指南

运行程序

1. 执行 main.py 文件启动程序，操作UI或使用快捷键进行识别系统的暂停、继续或者开启自动瞄准、自动扳机等。
2. 程序运行中可以使用以下默认快捷键：
   • P: 暂停/继续程序
   • O: 完全结束程序
   • 鼠标左键: 按下时开启自动瞄准

Usage Guide

Running the Program

1. Execute the main.py file to start the program. Use the UI or hotkeys to pause, resume, or enable features like auto-aiming and auto-triggering.
2. While the program is running, you can use the following default hotkeys:
   • P: Pause/Resume the program
   • O: Completely exit the program
   • Left Mouse Button: Enable auto-aiming while pressed

效果演示

以下动图展示了此项目可提供的辅助瞄准效果：

Demonstration

The following animation demonstrates the aiming assist effect provided by this project:

注意事项

• 注1：此项目是从之前损坏的项目代码中整合而来，尚未进行完整的重新测试。可能存在未知的错误或问题，请在使用时注意。
• 注2：项目仅供学习和测试用途，严禁用于游戏作弊或任何违反游戏使用规则的行为。
• 注3：2024.3.12，项目已修复了绝大多数错误，并且更换目标检测模块至 Yolov8 版本，使用 .pt 权重文件进行目标检测并且辅助瞄准测试无误，暂未测试 .onnx 及 .trt 权重文件的推理流程。

Precautions

• Note 1: This project is integrated from previously damaged project code and has not been completely retested. There may be unknown errors or issues, so please be cautious when using it.
• Note 2: The project is for learning and testing purposes only, and it is strictly forbidden to use it for game cheating or any behavior that violates game usage rules.
• Note 3: As of March 12, 2024, the project has fixed most of the errors and has updated the target detection module to the Yolov8 version. It now uses the .pt weight file for target detection, and the assisted aiming has been tested without issues. The inference process for .onnx and .trt weight files has not been tested yet.

常见问题

1. TensorRT类

   FileNotFoundError: Could not find: nvinfer.dll. Is it on your PATH?

   FileNotFoundError: Could not find: cudnn64_8.dll. Is it on your PATH?

   Could not find module 'C:\...\nvinfer_plugin.dll

   解决方式在 TensorRTEngine.py 开头注释中给出。

2. dxcam类

   AttributeError: module 'dxcam' has no attribute 'output_res'. Did you mean: 'output_info'?

   请使用以下代码替换 dxcam 中的 __init__ 文件

   import weakref
   import time
   from dxcam.dxcam import DXCamera, Output, Device
   from dxcam.util.io import (
      enum_dxgi_adapters,
      get_output_metadata,
   )
   
   
   class Singleton(type):
      _instances = {}
   
      def __call__(cls, *args, **kwargs):
         if cls not in cls._instances:
               cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
         else:
               print(f"Only 1 instance of {cls.__name__} is allowed.")
   
         return cls._instances[cls]
   
   
   class DXFactory(metaclass=Singleton):
   
      _camera_instances = weakref.WeakValueDictionary()
   
      def __init__(self) -> None:
         p_adapters = enum_dxgi_adapters()
         self.devices, self.outputs = [], []
         for p_adapter in p_adapters:
               device = Device(p_adapter)
               p_outputs = device.enum_outputs()
               if len(p_outputs) != 0:
                  self.devices.append(device)
                  self.outputs.append([Output(p_output) for p_output in p_outputs])
         self.output_metadata = get_output_metadata()
   
      def create(
         self,
         device_idx: int = 0,
         output_idx: int = None,
         region: tuple = None,
         output_color: str = "RGB",
         max_buffer_len: int = 64,
      ):
         device = self.devices[device_idx]
         if output_idx is None:
               # Select Primary Output
               output_idx = [
                  idx
                  for idx, metadata in enumerate(
                     self.output_metadata.get(output.devicename)
                     for output in self.outputs[device_idx]
                  )
                  if metadata[1]
               ][0]
         instance_key = (device_idx, output_idx)
         if instance_key in self._camera_instances:
               print(
                  "".join(
                     (
                           f"You already created a DXCamera Instance for Device {device_idx}--Output {output_idx}!\n",
                           "Returning the existed instance...\n",
                           "To change capture parameters you can manually delete the old object using `del obj`.",
                     )
                  )
               )
               return self._camera_instances[instance_key]
   
         output = self.outputs[device_idx][output_idx]
         output.update_desc()
         camera = DXCamera(
               output=output,
               device=device,
               region=region,
               output_color=output_color,
               max_buffer_len=max_buffer_len,
         )
         self._camera_instances[instance_key] = camera
         time.sleep(0.1)  # Fix for https://github.com/ra1nty/DXcam/issues/31
         return camera
   
      def device_info(self) -> str:
         ret = ""
         for idx, device in enumerate(self.devices):
               ret += f"Device[{idx}]:{device}\n"
         return ret
   
      def output_info(self) -> str:
         ret = ""
         for didx, outputs in enumerate(self.outputs):
               for idx, output in enumerate(outputs):
                  ret += f"Device[{didx}] Output[{idx}]: "
                  ret += f"Res:{output.resolution} Rot:{output.rotation_angle}"
                  ret += f" Primary:{self.output_metadata.get(output.devicename)[1]}\n"
         return ret
   
      def output_res(self):
         res = []
         for didx, outputs in enumerate(self.outputs):
               for idx, output in enumerate(outputs):
                  res.append(output.resolution)
         return res
   
      def clean_up(self):
         for _, camera in self._camera_instances.items():
               camera.release()
   
   
   __factory = DXFactory()
   
   
   def create(
      device_idx: int = 0,
      output_idx: int = None,
      region: tuple = None,
      output_color: str = "RGB",
      max_buffer_len: int = 64,
   ):
      return __factory.create(
         device_idx=device_idx,
         output_idx=output_idx,
         region=region,
         output_color=output_color,
         max_buffer_len=max_buffer_len,
      )
   
   
   def device_info():
      return __factory.device_info()
   
   
   def output_info():
      return __factory.output_info()
   
   def output_res():
      return __factory.output_res()

Frequently Asked Questions

1. TensorRT related problems

   FileNotFoundError: Could not find: nvinfer.dll. Is it on your PATH?

   FileNotFoundError: Could not find: cudnn64_8.dll. Is it on your PATH?

   Could not find module 'C:\...\nvinfer_plugin.dll

   The solution is provided in the comments at the beginning of TensorRTEngine.py.

2. DxCam related problems

   'AttributeError: module 'dxcam' has no attribute 'output_res'. Did you mean: 'output_info'?'

   Please replace the __init__ file in dxcam with the above python code.

使用须知

欢迎您使用本仓库中的文件。在使用本仓库中的任何文件之前，请仔细阅读以下使用须知：

1. 版权声明：本仓库中的原创性文件所有权利均归仓库所有者所有，未经许可不得用于商业用途。

2. 引用与借鉴：如果您在自己的项目中借鉴、引用或使用了本仓库中的任何文件，请务必在您的项目文档（如README文件）中明确注明出处，并提供指向本仓库的链接。建议的引用格式如下：

   本项目中使用了 DeadEyeAutoAimingSystem 仓库中的部分文件，特此表示感谢。

3. 再次分发：如果您需要再次分发本仓库中的文件，请确保在分发时包含本使用须知，并明确注明原始仓库的链接。

4. 问题反馈：如果您在使用本仓库中的文件时遇到任何问题，欢迎通过 Issue 或 Pull Request 的方式反馈给我们。我们会尽快处理并给予回复。

Usage Notice

Welcome to use the files in this repository. Before using any files in this repository, please read the following usage notice carefully:

1. Copyright Statement: The original files in this repository are owned by the repository owner. Commercial use is not allowed without permission.

2. Citation and Reference: If you reference, cite, or use any files from this repository in your own project, please make sure to clearly indicate the source in your project documentation (such as the README file) and provide a link to this repository. The recommended citation format is as follows:

   This project uses some files from the DeadEyeAutoAimingSystem repository. We would like to express our gratitude.

3. Redistribution: If you need to redistribute the files from this repository, please ensure that you include this usage notice and clearly state the link to the original repository when distributing.

4. Feedback: If you encounter any problems while using the files in this repository, please feel free to provide feedback to us through Issues or Pull Requests. We will handle and respond as soon as possible.

参考项目

在开发本项目的过程中,我参考了以下优秀的开源项目。在此对这些项目的贡献者表示感谢!

• YOLOv7: YOLOv7 的官方实现。
• Ultralytics Assets: Ultralytics 的资源库,包含各种 YOLO 模型的训练数据、权重文件和配置文件。
• TensorRT-For-YOLO-Series: 一个全面的 TensorRT 部署 YOLO 系列模型的项目,帮助我优化了模型的推理性能。

Reference Projects

During the development of this project, I referenced the following excellent open-source projects. I would like to express our gratitude to the contributors of these projects!

• YOLOv7: The official implementation of YOLOv7.
• Ultralytics Assets: The resource repository of Ultralytics, containing training data, weight files, and configuration files for various YOLO models.
• TensorRT-For-YOLO-Series: A comprehensive project for deploying YOLO series models with TensorRT, which helped us optimize the inference performance of the models.

开源许可

本项目采用 GNU Affero General Public License v3.0 (AGPLv3) 开源许可证。在使用、修改或分发本项目代码时,你必须同意并遵守 AGPLv3 的所有条款。

请查看 LICENSE 文件以获取完整的许可证文本。

Open Source License

This project is licensed under the GNU Affero General Public License v3.0 (AGPLv3). By using, modifying, or distributing the code in this project, you agree to comply with all the terms and conditions of AGPLv3.

Please refer to the LICENSE file for the full text of the license.