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Apex-CV-YOLO-v8-Aim-Assist-Bot

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Download Latest Version (.zip)

- - data.yaml
- best_8n.pt
- best_8s.pt
- - DELETE.me
- - DELETE.me
- - DELETE.me
- split.py
- __init__.py
- dxshot.cp310-win_amd64.pyd
- dxshot.cp311-win_amd64.pyd
- dxshot.cp37-win_amd64.pyd
- dxshot.cp38-win_amd64.pyd
- dxshot.cp39-win_amd64.pyd
- utils.py
addenv.cmd
Ape-xCV.bat
args_.py
capture.py
draw.py
listen.py
main.py
predict.py
README.md
requirements.txt
train8n.py
train8n40.py
train8s.py
train8s40.py
trt.py
yolov8n.pt
yolov8s.pt

# Installation Guide

1. Get the code

git clone https://github.com/Ape-xCV/Apex-CV-YOLO-v8-Aim-Assist-Bot

Downloads the entire project code from GitHub to your computer.

cd Apex-CV-YOLO-v8-Aim-Assist-Bot

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install tensorrt-8.6.1-cp310-none-win_amd64.whl

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install tensorrt-8.6.1-cp310-none-win_amd64.whl

Installs the package published on PyPI directly — no need to clone the source.

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# Apex CV YOLO v8 Aim Assist Bot

- Due to the widespread adoption of controller aim assist on PC gaming...

## Introduction

- It is the aim of this project to provide a quality object detection model.
- Object detection is a technique used in **computer vision** for the identification and localization of objects within an image or a video.
- This project is based on: [Franklin-Zhang0/Yolo-v8-Apex-Aim-assist](https://github.com/Franklin-Zhang0/Yolo-v8-Apex-Aim-assist).
- It is safe to use if you follow the best practices.
- This project is regularly maintained. Better models are made available as more training is created. It is a slowly but surely endeavour.

## Features and Requirements

Features:

* [x] Faster screen capture with _dxshot_
* [x] Faster CPU NMS (Non Maximum Supression) with _NumPy_
* [x] Optional GPU NMS (Non Maximum Supression) with _**TensorRT** efficientNMSPlugin_
* [x] Class targeting `Ally`, `Enemy`, `Tag`
* [x] Humanized mouse control with PID (Proportional-Integral-Derivative)

Requirements:

* [x] NVIDIA **Turing** / **Ampere** / **Ada_Lovelace** GPU

## Benchmarks

<details>
<summary>Test system:</summary>

- OS: Windows 10 Enterprise 1803 (OS build 17134)
    - CPU: Intel Core i7 3770K @ 4.0 GHz
    - GPU: NVIDIA GeForce RTX 2070_8G / 3080_12G
    - RAM: 16G DDR3 @ 2133 MHz
    - Monitor resolution: 1920 x 1080
    - In-game resolution: 1920 x 1080
    - In-game FPS: RTSS async locked @ 72 FPS
</details>

| GPU          | imgsz         | apex_8s.pt | apex_8s.trt | Precision |
| ------------ | ------------- | ---------- | ----------- | --------- |
| RTX 2070_8G  | 640/<br>1080p | 51/35 FPS  | 72/50 FPS   | FP16/32   |
| RTX 3080_12G | 640/<br>1080p | 53 FPS     | 72 FPS      | FP32      |

<details>
<summary>Video settings:</summary>

- Aspect Ratio               16:9
    - Resolution                 1920 x 1080
    - Brightness                 50%
    - Field of View (FOV)        90
    - FOV Ability Scaling        Enabled
    - Sprint View Shake          Normal
    - V-Sync                     Disabled
    - NVidia Reflex              Enabled+Boost
    - Adaptive Resolution FPS    60
    - Adaptive Supersampling     Disabled
    - Anti-aliasing              TSAA
    - Texture Streaming Budget   Ultra (8GB VRAM)
    - Texture Filtering          Anisotropic 16X
    - Ambient Occlusion Quality  High
    - Sun Shadow Coverage        Low
    - Sun Shadow Detail          Low
    - Spot Shadow Detail         Low
    - Volumetric Lighting        Enabled
    - Dynamic Spot Shadows       Enabled
    - Model Detail               High
    - Effects Detail             High
    - Impact Marks               Disabled
    - Ragdolls                   Low
</details>

### 0. Disclaimer

- This guide has been tested twice, and each time on a fresh install of Windows.
    - Every detail matters. If you are having issues, you are not following the guide.

### 1. Environment set up in Windows

- Version checklist:

| CUDA   | cuDNN | TensorRT | PyTorch |
    | :----: | :---: | :------: | :-----: |
    | 12.1.0 | 8.9.0 | 8.6.1.6  | 2.1.2   |

- Extract `Apex-CV-YOLO-v8-Aim-Assist-Bot-main.zip` to **C:\TEMP\Ape-xCV**

- Install `Visual Studio 2019 Build Tools`.
    - Download from: [`OneDrive`](https://1drv.ms/u/s!Ap42eSVvSYEggSzWcPLIUR0X81Uc) or [`Microsoft website`](https://visualstudio.microsoft.com/vs/older-downloads/).
    - On `Individual components` tab:
        - ✅ MSVC v142 - VS 2019 C++ x64/x86 build tools (Latest)
        - ✅ C++ CMake tools for Windows
        - ✅ Windows 10 SDK (10.0.19041.0)
    - ➡️ Install

- Install `CUDA 12.1.0` from: [`NVIDIA website`](https://developer.nvidia.com/cuda-12-1-0-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exe_local).
    - ✅ I understand, and wish to continue the installation regardless.

- Install `cuDNN 8.9.0`.
    - Register for the [`NVIDIA developer program`](https://developer.nvidia.com/login).
        - Go to the cuDNN download site:[`cuDNN download archive`](https://developer.nvidia.com/rdp/cudnn-archive).
        - Click `Download cuDNN v8.9.0 (April 11th, 2023), for CUDA 12.x`.
        - Download `Local Installer for Windows (Zip)`.
    - Unzip `cudnn-windows-x86_64-8.9.0.131_cuda12-archive.zip`.
    - Copy all three folders (`bin`,`include`,`lib`) and paste <_overwrite_> them into `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1`

- Install `Python 3.10.0 (64-bit)` from: [`Python website`](https://www.python.org/ftp/python/3.10.0/python-3.10.0-amd64.exe).
    - ❌ Install launcher for all users
    - ✅ Add Python 3.10 to PATH
    - ➡️ Install Now
        - `🔰 Disable path length limit`

- Install `python requirements`.
``` shell
cd /D C:\TEMP\Ape-xCV
pip install numpy==1.23.1
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt
```

### 2.1 Usage

#### ⚠️ **Lock your in-game FPS. Give your GPU some slack. It is needed for object detection.** ⚠️
- Do not use V-Sync to lock your FPS. V-Sync introduces input lag.
    - Use NVIDIA Control Panel.
    - OR
    - Use RTSS.

- Set in-game mouse sensitivity to **3.50**.
    - The PID control (`Kp`, `Ki`, `Kd`) values in `args_.py` come already fine-tuned.
    - If the mouse moves too fast, **EAC will flag your account** and you will be banned on the next ban wave.
        - So, don't mess with the PID. Change your mouse DPI instead.

- SHIFT
    - Hold to **lock on target**.
- LEFT_LOCK
    - Enabled when pressing `'1'` or `'2'`. Disabled when pressing `'G'`.
    - Use to **lock on target while firing** automatic weapons.
- RIGHT_LOCK
    - You need to change your ADS from **toggle** to **hold**.
    - Use CURSOR_RIGHT to toggle **lock on target while scoping**.
- AUTO_FIRE
    - Use CURSOR_UP to toggle **auto fire non-automatic weapons** while locking on target.
- HOME
    - 💀 Terminate script.

- Load the Firing Range and give this script a go!
    - 🐵 Run `Ape-xCV.bat`.

### 2.2 Best pratices

- To summarize:
    - ✅ Use default PID. Set in-game mouse sensitivity to **3.50**.
    - ❌ No AUTO_FIRE with automatic weapons.

### 3.1 TensorRT (.engine)

- Install `TensorRT`.
    - Go to the TensorRT download site: [NVIDIA TensorRT 8.x Download](https://developer.nvidia.com/nvidia-tensorrt-8x-download).
    - Download `TensorRT 8.6 GA for Windows 10 and CUDA 12.0 and 12.1 ZIP Package` from: [NVIDIA website](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/zip/TensorRT-8.6.1.6.Windows10.x86_64.cuda-12.0.zip).
    - Extract `TensorRT-8.6.1.6.Windows10.x86_64.cuda-12.0.zip` to **C:\TEMP**
    - Press **_[Win+R]_** and enter **cmd** to open a _Command Prompt_. Then input:
    ```shell
    cd /D C:\TEMP\Ape-xCV
    addenv C:\TEMP\TensorRT-8.6.1.6\lib
    ```
    - TensorRT was added to PATH. Close that _Command Prompt_ and open a new one. Then input:
    ```shell
    cd /D C:\TEMP\TensorRT-8.6.1.6\python
    pip install tensorrt-8.6.1-cp310-none-win_amd64.whl
    ```

- To export `best_8s.pt` to `best_8s.engine`:
    - Press **_[Win+R]_** and enter **cmd** to open a _Command Prompt_. Then input:
    ```shell
    set CUDA_MODULE_LOADING=LAZY
    cd /D C:\TEMP\Ape-xCV\MODEL
    yolo export model=best_8s.pt format=engine opset=12 workspace=7
    ```

- Install Notepad++ from: [`Notepad++ website`](https://notepad-plus-plus.org/downloads/).
- Open `C:\TEMP\Ape-xCV\args_.py` with Notepad++.
```shell
def arg_init(args):
    ...
    args.add_argument("--model", type=str,
                    default="/best_8s.pt", help="model path")
```

- Do not change the identation! In --model change `best_8s.pt` to `best_8s.engine`
- Save `args_.py`.
    - 🐵 Run `Ape-xCV.bat`.

### 3.2 TensorRT (.trt)

- If `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin\zlibwapi.dll` is missing.
    - Copy `C:\Program Files\NVIDIA Corporation\Nsight Systems 2023.1.2\host-windows-x64\zlib.dll` to **C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin** and then rename it to **zlibwapi.dll**

- To export `best_8s.pt` to `best_8s.trt`:
    - Press **_[Win+R]_** and enter **cmd** to open a _Command Prompt_. Then input:
    ```shell
    set CUDA_MODULE_LOADING=LAZY
    cd /D C:\TEMP\Ape-xCV\MODEL
    yolo export model=best_8s.pt format=onnx opset=12
    ```
    - If RTX 20 Series (FP16):
    ```shell
    C:\TEMP\TensorRT-8.6.1.6\bin\trtexec.exe --onnx=best_8s.onnx --saveEngine=best_8s.trt --buildOnly --workspace=7168 --fp16
    ```
    - If RTX 30 Series (FP32):
    ```shell
    C:\TEMP\TensorRT-8.6.1.6\bin\trtexec.exe --onnx=best_8s.onnx --saveEngine=best_8s.trt --buildOnly --workspace=7168
    ```

- Open `C:\TEMP\Ape-xCV\args_.py` with Notepad++.
```shell
def arg_init(args):
    ...
    args.add_argument("--model", type=str,
                    default="/best_8s.pt", help="model path")
```

- In --model change `best_8s.pt` to `best_8s.trt`
- Save `args_.py`.
    - 🐵 Run `Ape-xCV.bat`.

### 3.3 TensorRT with GPU NMS (.trt)

- Cons:
    - ❌ No speed increase.
    - ❌ IoU (Intersection over Union) threshold is hardcoded into engine, ignoring `args_.py`.

- Download: [Linaom1214/TensorRT-For-YOLO-Series](https://github.com/Linaom1214/TensorRT-For-YOLO-Series).
- Extract `TensorRT-For-YOLO-Series-main.zip` to **C:\TEMP**
- Rename `C:\TEMP\TensorRT-For-YOLO-Series-main` to `C:\TEMP\Linaom1214`
- To export `best_8s.onnx` to `best_8s_e2e.trt`:
    - Press **_[Win+R]_** and enter **cmd** to open a _Command Prompt_. Then input:
    ```shell
    set CUDA_MODULE_LOADING=LAZY
    cd /D C:\TEMP\Linaom1214
    ```
    - If RTX 20 Series (FP16):
    ```shell
    python export.py -o C:/TEMP/Ape-xCV/MODEL/best_8s.onnx -e C:/TEMP/Ape-xCV/MODEL/best_8s_e2e.trt -p fp16 -w 7 --end2end --conf_thres 0.6 --iou_thres 0.8 --v8
    ```
    - If RTX 30 Series (FP32):
    ```shell
    python export.py -o C:/TEMP/Ape-xCV/MODEL/best_8s.onnx -e C:/TEMP/Ape-xCV/MODEL/best_8s_e2e.trt -p fp32 -w 7 --end2end --conf_thres 0.6 --iou_thres 0.8 --v8
    ```

- Open `C:\TEMP\Ape-xCV\args_.py` with Notepad++.
```shell
def arg_init(args):
    ...
    args.add_argument("--model", type=str,
                    default="/best_8s.pt", help="model path")
    args.add_argument("--end2end", type=bool,
                    default=False, help="use TensorRT efficientNMSPlugin")
```

- In --model change `best_8s.pt` to `best_8s_e2e.trt`
- In --end2end change `False` to `True`
- Save `args_.py`.
    - 🐵 Run `Ape-xCV.bat`.

### 4. args_.py

- Open `C:\TEMP\Ape-xCV\args_.py` with Notepad++.
```shell
def arg_init(args):
    ...
    args.add_argument("--classes", type=int,
                    default=[1,2], help="classes to be detected TensorRT(.trt); can be expanded but needs to be an array. "
                    "0 represents 'Ally', "
                    "1 represents 'Enemy', "
                    "2 represents 'Tag'... "
                    "Change default accordingly if your dataset changes")
    args.add_argument("--target_index", type=int,
                    default=1, help="class to be targeted PyTorch(.pt)")
    args.add_argument("--half", type=bool,
                    default=True, help="use FP16 to predict PyTorch(.pt)")
    args.add_argument("--iou", type=float,
                    default=0.8, help="predict intersection over union")  # 0.8 is recommended
    args.add_argument("--conf", type=float,
                    default=0.6, help="predict confidence")  # 0.6+ is recommended
    screen_height = win32api.GetSystemMetrics(1)
    args.add_argument("--crop_size", type=float,
                    default=640/screen_height, help="the portion to detect from the screen. 1/3 for 1440P or 1/2 for 1080P, imgsz/screen_height=direct")
    args.add_argument("--wait", type=float, default=0, help="wait time")
    args.add_argument("--verbose", type=bool, default=False, help="predict verbose")
    args.add_argument("--draw_boxes", type=bool,
                    default=False, help="outline detected target, borderless window")
```

- Until you understand how NMS works, do not change `--iou`.
- `--crop_size` "the portion to detect from the screen". Will be scaled down to 640x640 for input. The default **640/screen_height** is the best value.
- `--draw_boxes` "outline detected target, borderless window". Set to `True` in the Firing Range only.

# ❤️ Sponsor

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| :----: | :------------: |
| [![](https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif)](https://www.paypal.com/donate/?hosted_button_id=QCSCYPD8HTATY) | [![](http://api.qrserver.com/v1/create-qr-code/?data=LKLYRUadyHitp5B7aB56yfBYSuG2UnuLEz&size=100x100)](https://quickref.me/emoji)<br>LKLYRUadyHitp5B7aB56yfBYSuG2UnuLEz |

### 5. Train your own model

- Download starter dataset from: [`OneDrive`](https://1drv.ms/u/s!Ap42eSVvSYEggShTC0z5f0AsYCEe).
- Extract **apex.zip** to `C:\TEMP\Ape-xCV\datasets\apex`
- Press **_[Win+R]_** and enter **cmd** to open a _Command Prompt_. Then input:
```shell
cd /D C:\TEMP\Ape-xCV
python train8s40.py
```

- This will **train** your YOLO v**8 s**mall model for **40** epochs with images and labels from `C:\TEMP\Ape-xCV\datasets\apex` and save it to `C:\TEMP\Ape-xCV\runs\detect\train\weights\best.pt`.

- You can add your own images (640x640); and create the labels with: [`developer0hye/Yolo_Label`](https://github.com/developer0hye/Yolo_Label).
    - Copy those into `C:\TEMP\Ape-xCV\SPLIT\input`
    - Press **_[Win+R]_** and enter **cmd** to open a _Command Prompt_. Then input:
    ```shell
    cd /D C:\TEMP\Ape-xCV\SPLIT
    python split.py
    ```
    - Your images and labels are now split into `train` and `valid`.
    - Browse `C:\TEMP\Ape-xCV\datasets\apex` and distribute them accordingly.