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4
.gitignore
vendored
4
.gitignore
vendored
@ -17,7 +17,3 @@ utils/__pycache__
|
||||
models/__pycache__
|
||||
venv
|
||||
*.old
|
||||
elite
|
||||
!Robloxmodel.pt
|
||||
!rust.pt
|
||||
!rust.engine
|
||||
@ -1,24 +0,0 @@
|
||||
# Conda: A Package and Environment Manager
|
||||
|
||||
## What is Conda?
|
||||
|
||||
Conda is an open-source package management system and environment management system. It was created for Python programs, but it can package and distribute software for any language.
|
||||
|
||||
## Key Features of Conda
|
||||
|
||||
1. **Package Management**: Conda helps you manage and keep track of packages in your projects. It can install packages from the Conda package repository and other sources.
|
||||
|
||||
2. **Environment Management**: Conda allows you to create separate environments containing files, packages, and their dependencies that will not interfere with each other. This can be extremely useful when working on projects with different requirements.
|
||||
|
||||
3. **Cross-Platform**: Conda is a cross-platform tool, which means it works on Windows, macOS, and Linux.
|
||||
|
||||
4. **Language Agnostic**: Originally, Conda was created for Python. Now, it can handle packages from any language, which is a big advantage over pip, which is Python-specific.
|
||||
|
||||
## Benefits of Using Conda
|
||||
|
||||
- **Simplicity**: Conda simplifies package management and deployment.
|
||||
- **Reproducibility**: Conda allows you to share your environments with others, which helps in reproducing research.
|
||||
- **Isolation**: With Conda, you can easily create isolated environments to separate different projects.
|
||||
- **Wide Package Support**: Conda supports a wide array of packages, and it's not limited to Python.
|
||||
|
||||
In conclusion, Conda is a powerful tool for managing packages and environments, making it easier to manage projects and their dependencies.
|
||||
@ -1,48 +0,0 @@
|
||||
```markdown
|
||||
# Installing Miniconda
|
||||
|
||||
Follow these steps to install Miniconda on your system:
|
||||
|
||||
## Step 1: Download Miniconda
|
||||
|
||||
First, download the appropriate Miniconda installer for your system from the [official Miniconda website](https://docs.conda.io/en/latest/miniconda.html).
|
||||
|
||||
## Step 2: Run the Installer
|
||||
|
||||
- **Windows**: Double click the `.exe` file and follow the instructions.
|
||||
- **macOS and Linux**: Open a terminal, navigate to the directory where you downloaded the installer, and run the following command:
|
||||
```
|
||||
|
||||
```bash
|
||||
bash Miniconda3-latest-Linux-x86_64.sh
|
||||
```
|
||||
|
||||
Replace `Miniconda3-latest-Linux-x86_64.sh` with the name of the file you downloaded.
|
||||
|
||||
## Step 3: Follow the Prompts
|
||||
|
||||
The installer will prompt you to review the license agreement, choose the install location, and optionally allow the installer to initialize Miniconda3 by appending it to your `PATH`.
|
||||
|
||||
## Step 4: Verify the Installation
|
||||
|
||||
To verify that the installation was successful, open a new terminal window and type:
|
||||
|
||||
```bash
|
||||
conda list
|
||||
```
|
||||
|
||||
If Miniconda has been installed and added to your `PATH`, this should display a list of installed packages.
|
||||
|
||||
## Step 5: Update Conda to the Latest Version
|
||||
|
||||
It's a good practice to make sure you're running the latest version of Conda. You can update it by running:
|
||||
|
||||
```bash
|
||||
conda update conda
|
||||
```
|
||||
|
||||
That's it! You have successfully installed Miniconda on your system.
|
||||
|
||||
Now when you open up a terminal you should see a prompt and (base) to indicate no conda environment is active.
|
||||
|
||||
|
||||
@ -1,41 +0,0 @@
|
||||
```markdown
|
||||
# Creating a New Conda Environment with Python 3.11
|
||||
|
||||
Follow these steps to create a new Conda environment with Python 3.11:
|
||||
|
||||
## Step 1: Open a Terminal
|
||||
|
||||
Open a terminal window. This could be Git Bash, Terminal on macOS, or Command Prompt on Windows.
|
||||
|
||||
## Step 2: Create a New Conda Environment
|
||||
|
||||
To create a new Conda environment with Python 3.11, use the following command:
|
||||
```
|
||||
|
||||
```bash
|
||||
conda create --name RootKit python=3.11
|
||||
```
|
||||
|
||||
In this command, `RootKit` is the name of the new environment, and `python=3.11` specifies that we want Python 3.11 in this environment.
|
||||
|
||||
## Step 3: Activate the New Environment
|
||||
|
||||
After creating the new environment, you need to activate it using the following command:
|
||||
|
||||
```bash
|
||||
conda activate RootKit
|
||||
```
|
||||
|
||||
Now, `RootKit` is your active environment.
|
||||
|
||||
## Step 4: Verify Python Version
|
||||
|
||||
To verify that the correct version of Python is installed in your new environment, use the following command:
|
||||
|
||||
```bash
|
||||
python --version
|
||||
```
|
||||
|
||||
This should return `Python 3.11.x`.
|
||||
|
||||
That's it! You have successfully created a new Conda environment with Python 3.11.
|
||||
@ -1,46 +0,0 @@
|
||||
```markdown
|
||||
# Cloning a GitHub Repository
|
||||
|
||||
Cloning a GitHub repository creates a local copy of the remote repo. This allows you to save all files from the repository on your local computer. Here's how you can do it:
|
||||
|
||||
## Step 1: Copy the Repository URL
|
||||
|
||||
Navigate to the main page of the repository on GitHub and click the "Code" button. Then click the "copy to clipboard" button to copy the repository URL.
|
||||
|
||||
## Step 2: Open a Terminal
|
||||
|
||||
Open a terminal window on your computer. If you're using Windows, you can use Git Bash or Command Prompt. On macOS, you can use the Terminal app.
|
||||
|
||||
## Step 3: Navigate to the Directory
|
||||
|
||||
Navigate to the directory where you want to clone the repository using the `cd` (change directory) command. For example:
|
||||
```
|
||||
|
||||
```bash
|
||||
cd /path/to/your/directory
|
||||
```
|
||||
|
||||
## Step 4: Clone the Repository
|
||||
|
||||
Now, run the `git clone` command followed by the URL of the repository that you copied in step 1:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/RootKit-Org/AI-Aimbot.git
|
||||
```
|
||||
|
||||
Replace `https://github.com/RootKit-Org/AI-Aimbot.git` with the URL you copied.
|
||||
|
||||
## Step 5: Verify the Cloning Process
|
||||
|
||||
Navigate into the cloned repository and list its files to verify that the cloning process was successful:
|
||||
|
||||
```bash
|
||||
cd AI-Aimbot
|
||||
ls
|
||||
```
|
||||
|
||||
Replace `AI-Aimbot` with the name of your repository if you called it something else. The `ls` command will list all the files in the directory.
|
||||
|
||||
That's it! You have successfully cloned a GitHub repository to your local machine.
|
||||
|
||||
By cloning the repo, any later changes you can git pull.
|
||||
@ -1,39 +0,0 @@
|
||||
```markdown
|
||||
# Installing Requirements
|
||||
|
||||
Follow these steps to install all the requirements to your system:
|
||||
|
||||
## Step 1: Activate your environment:
|
||||
|
||||
## Step 2: Only if you have an NVIDIA graphics card - Download and Install CUDA:
|
||||
|
||||
Nvidia CUDA Toolkit 11.8 [DOWNLOAD HERE](https://developer.nvidia.com/cuda-11-8-0-download-archive)
|
||||
|
||||
## Step 3: Install PYTORCH:
|
||||
|
||||
- For NVIDIA GPU:
|
||||
|
||||
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
|
||||
|
||||
- For AMD or CPU only:
|
||||
|
||||
pip install torch torchvision torchaudio
|
||||
|
||||
## Step 4: Install requirements.txt:
|
||||
|
||||
pip install -r requirements.txt
|
||||
|
||||
## Step 5: Install additional modules:
|
||||
|
||||
Because you are using Conda, you need to install additional requirements in your environment.
|
||||
|
||||
pip install -r Conda/additionalRequirements.txt
|
||||
|
||||
## Step 6: Test your installation:
|
||||
|
||||
To test your installation, run the following command:
|
||||
|
||||
python main.py
|
||||
|
||||
You should now have a working AI AIMBOT. If you want to use the fastest version continue the installation steps on the RootKit AI Aimbot README.md
|
||||
```
|
||||
@ -1,20 +0,0 @@
|
||||
argilla
|
||||
bettercam
|
||||
datasets
|
||||
fastapi
|
||||
langchainplus-sdk
|
||||
langsmith
|
||||
markdownlit
|
||||
onnx
|
||||
onnxruntime
|
||||
opencv-python
|
||||
panel
|
||||
pygetwindow
|
||||
sentence-transformers
|
||||
streamlit
|
||||
streamlit-camera-input-live
|
||||
streamlit-extras
|
||||
streamlit-faker
|
||||
streamlit-image-coordinates
|
||||
streamlit-keyup
|
||||
transformers
|
||||
35
README.md
35
README.md
@ -2,12 +2,8 @@
|
||||
|
||||
|
||||
|
||||
[](https://makeapullrequest.com)
|
||||
[](http://makeapullrequest.com)
|
||||
|
||||
|
||||
Want to make your own bot? Then use the [Starter Code Pack](https://github.com/RootKit-Org/AI-Aimbot-Starter-Code)!
|
||||
--
|
||||
--
|
||||
## 🙌 Welcome Aboard!
|
||||
We're a charity on a mission to educate and certify the upcoming wave of developers in the world of Computer Engineering 🌍. Need assistance? Hop into our [Discord](https://discord.gg/rootkitorg) and toss your questions at `@Wonder` in the *#ai-aimbot channel* (be sure to stick to this channel or face the consequences! 😬). Type away your query and include `@Wonder` in there.
|
||||
|
||||
@ -39,7 +35,7 @@ Intended for educational use 🎓, our aim is to highlight the vulnerability of
|
||||
- 🛑 Is it a `pip is not recognized...` error? [WATCH THIS!](https://youtu.be/zWYvRS7DtOg)
|
||||
3. Fire up `PowerShell` or `Command Prompt` on Windows 🔍.
|
||||
4. To install `PyTorch`, select the appropriate command based on your GPU.
|
||||
- Nvidia `pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118`
|
||||
- Nvidia `pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118`
|
||||
- AMD or CPU `pip install torch torchvision torchaudio`
|
||||
5. 📦 Run the command below to install the required Open Source packages:
|
||||
```
|
||||
@ -89,33 +85,30 @@ Follow these sparkly steps to get your TensorRT ready for action! 🛠️✨
|
||||
5. **CUDNN Installation** 🧩
|
||||
Click to install [CUDNN 📥](https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.6/local_installers/11.x/cudnn-windows-x86_64-8.9.6.50_cuda11-archive.zip/). You'll need a Nvidia account to proceed. Don't worry it's free.
|
||||
|
||||
6. **Unzip and Relocate** 📁➡️
|
||||
Open the .zip CuDNN file and move all the folders/files to where the CUDA Toolkit is on your machine, usually at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8`.
|
||||
|
||||
7. **Get TensorRT 8.6 GA** 🔽
|
||||
6. **Get TensorRT 8.6 GA** 🔽
|
||||
Fetch [`TensorRT 8.6 GA 🛒`](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/zip/TensorRT-8.6.1.6.Windows10.x86_64.cuda-11.8.zip).
|
||||
|
||||
8. **Unzip and Relocate** 📁➡️
|
||||
7. **Unzip and Relocate** 📁➡️
|
||||
Open the .zip TensorRT file and move all the folders/files to where the CUDA Toolkit is on your machine, usually at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8`.
|
||||
|
||||
9. **Python TensorRT Installation** 🎡
|
||||
8. **Python TensorRT Installation** 🎡
|
||||
Once you have all the files copied over, you should have a folder at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\python`. If you do, good, then run the following command to install TensorRT in python.
|
||||
```
|
||||
pip install "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\python\tensorrt-8.6.1-cp311-none-win_amd64.whl"
|
||||
pip install C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\python\tensorrt-8.6.1-cp311-none-win_amd64.whl
|
||||
```
|
||||
🚨 If the following steps didn't work, don't stress out! 😅 The labeling of the files corresponds with the Python version you have installed on your machine. We're not looking for the 'lean' or 'dispatch' versions. 🔍 Just locate the correct file and replace the path with your new one. 🔄 You've got this! 💪
|
||||
|
||||
10. **Set Your Environmental Variables** 🌎
|
||||
9. **Set Your Environmental Variables** 🌎
|
||||
Add these paths to your environment:
|
||||
- `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib`
|
||||
- `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\libnvvp`
|
||||
- `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin`
|
||||
|
||||
11. **Download Pre-trained Models** 🤖
|
||||
10. **Download Pre-trained Models** 🤖
|
||||
You can use one of the .engine models we supply. But if it doesn't work, then you will need to re-export it. Grab the `.pt` file here for the model you want. We recommend `yolov5s.py` or `yolov5m.py` [HERE 🔗](https://github.com/ultralytics/yolov5/releases/tag/v7.0).
|
||||
|
||||
12. **Run the Export Script** 🏃♂️💻
|
||||
Time to execute `export.py` with the following command. Patience is key; it might look frozen, but it's just concentrating hard! Can take up to 20 minutes.
|
||||
11. **Run the Export Script** 🏃♂️💻
|
||||
Time to execute `export.py` with the following command. Patience is key; it might look frozen, but it's just concentrating hard! Can take up to 20 mintues.
|
||||
|
||||
```
|
||||
python .\export.py --weights ./yolov5s.pt --include engine --half --imgsz 320 320 --device 0
|
||||
@ -131,11 +124,7 @@ If you've followed these steps, you should be all set with TensorRT! ⚙️🚀
|
||||
*Default settings are generally great for most scenarios. Check out the comments in the code for more insights. 🔍 The configuration settings are now located in the `config.py` file!<br>
|
||||
**CAPS_LOCK is the default for flipping the switch on the autoaim superpower! ⚙️ 🎯**
|
||||
|
||||
`useMask` - Set to `True` or `False` to turn on and off 🎭
|
||||
|
||||
`maskWidth` - The width of the mask to use. Only used when `useMask` is `True` 📐
|
||||
|
||||
`maskHeight` - The height of the mask to use. Only used when `useMask` is `True` 📐
|
||||
`aaRightShift` - Might need a tweak in 3rd person games like Fortnite and New World. 🎮 Typically, a setting of `100` or `150` should hit the mark. 🎯👌
|
||||
|
||||
`aaQuitKey` - The go-to key is `q`, but if it clashes with your game style, swap it out! ⌨️♻️
|
||||
|
||||
@ -185,7 +174,7 @@ Show off your work or new models via Pull Requests in `customScripts` or `custom
|
||||
|
||||
## 🌠 Future Ideas
|
||||
|
||||
- [x] Mask Player to avoid false positives
|
||||
- [ ] Mask Player to avoid false positives
|
||||
|
||||
|
||||
Happy Coding and Aiming! 🎉👾
|
||||
10
config.py
10
config.py
@ -2,11 +2,9 @@
|
||||
screenShotHeight = 320
|
||||
screenShotWidth = 320
|
||||
|
||||
# Use "left" or "right" for the mask side depending on where the interfering object is, useful for 3rd player models or large guns
|
||||
useMask = False
|
||||
maskSide = "left"
|
||||
maskWidth = 80
|
||||
maskHeight = 200
|
||||
# For use in games that are 3rd person and character model interferes with the autoaim
|
||||
# EXAMPLE: Fortnite and New World
|
||||
aaRightShift = 0
|
||||
|
||||
# Autoaim mouse movement amplifier
|
||||
aaMovementAmp = .4
|
||||
@ -33,4 +31,4 @@ centerOfScreen = True
|
||||
# 1 - CPU
|
||||
# 2 - AMD
|
||||
# 3 - NVIDIA
|
||||
onnxChoice = 1
|
||||
onnxChoice = 3
|
||||
@ -1,14 +0,0 @@
|
||||
# Explain your model
|
||||
Rust dataset. 6k images - 10/10/80 split. Included weights file - best.pt
|
||||
|
||||
Tell the community about your model
|
||||
- What data was it trained on?
|
||||
- Rust Images
|
||||
- How much data was it trained on?
|
||||
- 6k Images
|
||||
- How many models do you have?
|
||||
- 1
|
||||
- Are they for pytorch, onnx, tensorrt, something else?
|
||||
- tensorrt
|
||||
- Any set up info
|
||||
|
||||
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|
Before Width: | Height: | Size: 238 KiB |
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@ -1,7 +1,7 @@
|
||||
# Performance optimizations
|
||||
|
||||
This version aimes to achieve the best performance possible on AMD hardware.
|
||||
To achieve this, the script acts more as an aim assist instead of a full fledged aimbot.
|
||||
To achieve this, the script acts more as an aim assist insted of a full fledged aimbot.
|
||||
The user will still need to do most on the aim
|
||||
|
||||
Changes that have been made:
|
||||
|
||||
@ -1,180 +0,0 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
import cv2
|
||||
import time
|
||||
import win32api
|
||||
import win32con
|
||||
import pandas as pd
|
||||
import gc
|
||||
from utils.general import (cv2, non_max_suppression, xyxy2xywh)
|
||||
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import aaMovementAmp, useMask, maskWidth, maskHeight, aaQuitKey, screenShotHeight, confidence, headshot_mode, cpsDisplay, visuals, centerOfScreen
|
||||
import gameSelection
|
||||
|
||||
def main():
|
||||
# External Function for running the game selection menu (gameSelection.py)
|
||||
camera, cWidth, cHeight = gameSelection.gameSelection()
|
||||
|
||||
# Used for forcing garbage collection
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Loading Yolo5 Small AI Model, for better results use yolov5m or yolov5l
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5s',
|
||||
pretrained=True, force_reload=True)
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
|
||||
if torch.cuda.is_available():
|
||||
model.half()
|
||||
|
||||
# Used for colors drawn on bounding boxes
|
||||
COLORS = np.random.uniform(0, 255, size=(1500, 3))
|
||||
|
||||
# Main loop Quit if Q is pressed
|
||||
last_mid_coord = None
|
||||
with torch.no_grad():
|
||||
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
|
||||
|
||||
# Getting Frame
|
||||
npImg = np.array(camera.get_latest_frame())
|
||||
|
||||
from config import maskSide # "temporary" workaround for bad syntax
|
||||
if useMask:
|
||||
maskSide = maskSide.lower()
|
||||
if maskSide == "right":
|
||||
npImg[-maskHeight:, -maskWidth:, :] = 0
|
||||
elif maskSide == "left":
|
||||
npImg[-maskHeight:, :maskWidth, :] = 0
|
||||
else:
|
||||
raise Exception('ERROR: Invalid maskSide! Please use "left" or "right"')
|
||||
|
||||
# Normalizing Data
|
||||
im = torch.from_numpy(npImg)
|
||||
if im.shape[2] == 4:
|
||||
# If the image has an alpha channel, remove it
|
||||
im = im[:, :, :3,]
|
||||
|
||||
im = torch.movedim(im, 2, 0)
|
||||
if torch.cuda.is_available():
|
||||
im = im.half()
|
||||
im /= 255
|
||||
if len(im.shape) == 3:
|
||||
im = im[None]
|
||||
|
||||
# Detecting all the objects
|
||||
results = model(im, size=screenShotHeight)
|
||||
|
||||
# Suppressing results that dont meet thresholds
|
||||
pred = non_max_suppression(
|
||||
results, confidence, confidence, 0, False, max_det=1000)
|
||||
|
||||
# Converting output to usable cords
|
||||
targets = []
|
||||
for i, det in enumerate(pred):
|
||||
s = ""
|
||||
gn = torch.tensor(im.shape)[[0, 0, 0, 0]]
|
||||
if len(det):
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}, " # add to string
|
||||
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
|
||||
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
|
||||
|
||||
targets = pd.DataFrame(
|
||||
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
|
||||
|
||||
center_screen = [cWidth, cHeight]
|
||||
|
||||
# If there are people in the center bounding box
|
||||
if len(targets) > 0:
|
||||
if (centerOfScreen):
|
||||
# Compute the distance from the center
|
||||
targets["dist_from_center"] = np.sqrt((targets.current_mid_x - center_screen[0])**2 + (targets.current_mid_y - center_screen[1])**2)
|
||||
|
||||
# Sort the data frame by distance from center
|
||||
targets = targets.sort_values("dist_from_center")
|
||||
|
||||
# Get the last persons mid coordinate if it exists
|
||||
if last_mid_coord:
|
||||
targets['last_mid_x'] = last_mid_coord[0]
|
||||
targets['last_mid_y'] = last_mid_coord[1]
|
||||
# Take distance between current person mid coordinate and last person mid coordinate
|
||||
targets['dist'] = np.linalg.norm(
|
||||
targets.iloc[:, [0, 1]].values - targets.iloc[:, [4, 5]], axis=1)
|
||||
targets.sort_values(by="dist", ascending=False)
|
||||
|
||||
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
|
||||
xMid = targets.iloc[0].current_mid_x
|
||||
yMid = targets.iloc[0].current_mid_y
|
||||
|
||||
box_height = targets.iloc[0].height
|
||||
if headshot_mode:
|
||||
headshot_offset = box_height * 0.38
|
||||
else:
|
||||
headshot_offset = box_height * 0.2
|
||||
|
||||
mouseMove = [xMid - cWidth, (yMid - headshot_offset) - cHeight]
|
||||
|
||||
# Moving the mouse
|
||||
if win32api.GetKeyState(0x14):
|
||||
win32api.mouse_event(win32con.MOUSEEVENTF_MOVE, int(
|
||||
mouseMove[0] * aaMovementAmp), int(mouseMove[1] * aaMovementAmp), 0, 0)
|
||||
last_mid_coord = [xMid, yMid]
|
||||
|
||||
else:
|
||||
last_mid_coord = None
|
||||
|
||||
# See what the bot sees
|
||||
if visuals:
|
||||
# Loops over every item identified and draws a bounding box
|
||||
for i in range(0, len(targets)):
|
||||
halfW = round(targets["width"][i] / 2)
|
||||
halfH = round(targets["height"][i] / 2)
|
||||
midX = targets['current_mid_x'][i]
|
||||
midY = targets['current_mid_y'][i]
|
||||
(startX, startY, endX, endY) = int(
|
||||
midX + halfW), int(midY + halfH), int(midX - halfW), int(midY - halfH)
|
||||
|
||||
idx = 0
|
||||
|
||||
# draw the bounding box and label on the frame
|
||||
label = "{}: {:.2f}%".format(
|
||||
"Human", targets["confidence"][i] * 100)
|
||||
cv2.rectangle(npImg, (startX, startY), (endX, endY),
|
||||
COLORS[idx], 2)
|
||||
y = startY - 15 if startY - 15 > 15 else startY + 15
|
||||
cv2.putText(npImg, label, (startX, y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
|
||||
|
||||
# Forced garbage cleanup every second
|
||||
count += 1
|
||||
if (time.time() - sTime) > 1:
|
||||
if cpsDisplay:
|
||||
print("CPS: {}".format(count))
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Uncomment if you keep running into memory issues
|
||||
# gc.collect(generation=0)
|
||||
|
||||
# See visually what the Aimbot sees
|
||||
if visuals:
|
||||
cv2.imshow('Live Feed', npImg)
|
||||
if (cv2.waitKey(1) & 0xFF) == ord('q'):
|
||||
exit()
|
||||
camera.stop()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
main()
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exception(e)
|
||||
print("ERROR: " + str(e))
|
||||
print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")
|
||||
@ -1,203 +0,0 @@
|
||||
import onnxruntime as ort
|
||||
import numpy as np
|
||||
import gc
|
||||
import numpy as np
|
||||
import cv2
|
||||
import time
|
||||
import win32api
|
||||
import win32con
|
||||
import pandas as pd
|
||||
from utils.general import (cv2, non_max_suppression, xyxy2xywh)
|
||||
from mouse_driver.MouseMove import mouse_move as ghub_move
|
||||
import torch
|
||||
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import aaMovementAmp, useMask, maskHeight, maskWidth, aaQuitKey, confidence, headshot_mode, cpsDisplay, visuals, onnxChoice, centerOfScreen
|
||||
import gameSelection
|
||||
|
||||
def main():
|
||||
# External Function for running the game selection menu (gameSelection.py)
|
||||
camera, cWidth, cHeight = gameSelection.gameSelection()
|
||||
|
||||
# Used for forcing garbage collection
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Choosing the correct ONNX Provider based on config.py
|
||||
onnxProvider = ""
|
||||
if onnxChoice == 1:
|
||||
onnxProvider = "CPUExecutionProvider"
|
||||
elif onnxChoice == 2:
|
||||
onnxProvider = "DmlExecutionProvider"
|
||||
elif onnxChoice == 3:
|
||||
import cupy as cp
|
||||
onnxProvider = "CUDAExecutionProvider"
|
||||
|
||||
so = ort.SessionOptions()
|
||||
so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
|
||||
ort_sess = ort.InferenceSession('RRRR.onnx', sess_options=so, providers=[
|
||||
onnxProvider])
|
||||
|
||||
# Used for colors drawn on bounding boxes
|
||||
COLORS = np.random.uniform(0, 255, size=(1500, 3))
|
||||
|
||||
# Main loop Quit if Q is pressed
|
||||
last_mid_coord = None
|
||||
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
|
||||
|
||||
# Getting Frame
|
||||
npImg = np.array(camera.get_latest_frame())
|
||||
|
||||
from config import maskSide # "temporary" workaround for bad syntax
|
||||
if useMask:
|
||||
maskSide = maskSide.lower()
|
||||
if maskSide == "right":
|
||||
npImg[-maskHeight:, -maskWidth:, :] = 0
|
||||
elif maskSide == "left":
|
||||
npImg[-maskHeight:, :maskWidth, :] = 0
|
||||
else:
|
||||
raise Exception('ERROR: Invalid maskSide! Please use "left" or "right"')
|
||||
|
||||
# If Nvidia, do this
|
||||
if onnxChoice == 3:
|
||||
# Normalizing Data
|
||||
im = torch.from_numpy(npImg).to('cuda')
|
||||
if im.shape[2] == 4:
|
||||
# If the image has an alpha channel, remove it
|
||||
im = im[:, :, :3,]
|
||||
|
||||
im = torch.movedim(im, 2, 0)
|
||||
im = im.half()
|
||||
im /= 255
|
||||
if len(im.shape) == 3:
|
||||
im = im[None]
|
||||
# If AMD or CPU, do this
|
||||
else:
|
||||
# Normalizing Data
|
||||
im = np.array([npImg])
|
||||
if im.shape[3] == 4:
|
||||
# If the image has an alpha channel, remove it
|
||||
im = im[:, :, :, :3]
|
||||
im = im / 255
|
||||
im = im.astype(np.half)
|
||||
im = np.moveaxis(im, 3, 1)
|
||||
|
||||
# If Nvidia, do this
|
||||
if onnxChoice == 3:
|
||||
outputs = ort_sess.run(None, {'images': cp.asnumpy(im)})
|
||||
# If AMD or CPU, do this
|
||||
else:
|
||||
outputs = ort_sess.run(None, {'images': np.array(im)})
|
||||
|
||||
im = torch.from_numpy(outputs[0]).to('cpu')
|
||||
|
||||
pred = non_max_suppression(
|
||||
im, confidence, confidence, 0, False, max_det=10)
|
||||
|
||||
targets = []
|
||||
for i, det in enumerate(pred):
|
||||
s = ""
|
||||
gn = torch.tensor(im.shape)[[0, 0, 0, 0]]
|
||||
if len(det):
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {int(c)}, " # add to string
|
||||
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
|
||||
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
|
||||
|
||||
targets = pd.DataFrame(
|
||||
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
|
||||
|
||||
center_screen = [cWidth, cHeight]
|
||||
|
||||
# If there are people in the center bounding box
|
||||
if len(targets) > 0:
|
||||
if (centerOfScreen):
|
||||
# Compute the distance from the center
|
||||
targets["dist_from_center"] = np.sqrt((targets.current_mid_x - center_screen[0])**2 + (targets.current_mid_y - center_screen[1])**2)
|
||||
|
||||
# Sort the data frame by distance from center
|
||||
targets = targets.sort_values("dist_from_center")
|
||||
|
||||
# Get the last persons mid coordinate if it exists
|
||||
if last_mid_coord:
|
||||
targets['last_mid_x'] = last_mid_coord[0]
|
||||
targets['last_mid_y'] = last_mid_coord[1]
|
||||
# Take distance between current person mid coordinate and last person mid coordinate
|
||||
targets['dist'] = np.linalg.norm(
|
||||
targets.iloc[:, [0, 1]].values - targets.iloc[:, [4, 5]], axis=1)
|
||||
targets.sort_values(by="dist", ascending=False)
|
||||
|
||||
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
|
||||
xMid = targets.iloc[0].current_mid_x
|
||||
yMid = targets.iloc[0].current_mid_y
|
||||
|
||||
box_height = targets.iloc[0].height
|
||||
if headshot_mode:
|
||||
headshot_offset = box_height * 0.38
|
||||
else:
|
||||
headshot_offset = box_height * 0.2
|
||||
|
||||
mouseMove = [xMid - cWidth, (yMid - headshot_offset) - cHeight]
|
||||
# Moving the mouse
|
||||
#imagine recalculating everything to find out you have a drop in replacement
|
||||
if win32api.GetKeyState(0x02) < 0:
|
||||
ghub_move(mouseMove[0],mouseMove[1])
|
||||
last_mid_coord = [xMid, yMid]
|
||||
|
||||
else:
|
||||
last_mid_coord = None
|
||||
|
||||
# See what the bot sees
|
||||
if visuals:
|
||||
# Loops over every item identified and draws a bounding box
|
||||
for i in range(0, len(targets)):
|
||||
halfW = round(targets["width"][i] / 2)
|
||||
halfH = round(targets["height"][i] / 2)
|
||||
midX = targets['current_mid_x'][i]
|
||||
midY = targets['current_mid_y'][i]
|
||||
(startX, startY, endX, endY) = int(midX + halfW), int(midY +
|
||||
halfH), int(midX - halfW), int(midY - halfH)
|
||||
|
||||
idx = 0
|
||||
# draw the bounding box and label on the frame
|
||||
label = "{}: {:.2f}%".format(
|
||||
"Character", targets["confidence"][i] * 100)
|
||||
cv2.rectangle(npImg, (startX, startY), (endX, endY),
|
||||
COLORS[idx], 2)
|
||||
y = startY - 15 if startY - 15 > 15 else startY + 15
|
||||
cv2.putText(npImg, label, (startX, y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
|
||||
|
||||
|
||||
# Forced garbage cleanup every second
|
||||
count += 1
|
||||
if (time.time() - sTime) > 1:
|
||||
if cpsDisplay:
|
||||
print("CPS: {}".format(count))
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Uncomment if you keep running into memory issues
|
||||
# gc.collect(generation=0)
|
||||
|
||||
# See visually what the Aimbot sees
|
||||
if visuals:
|
||||
cv2.imshow('Live Feed', npImg)
|
||||
if (cv2.waitKey(1) & 0xFF) == ord('q'):
|
||||
exit()
|
||||
camera.stop()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
main()
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exception(e)
|
||||
print("ERROR: " + str(e))
|
||||
print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")
|
||||
@ -1,172 +0,0 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
import cv2
|
||||
import time
|
||||
import win32api
|
||||
import win32con
|
||||
import pandas as pd
|
||||
from utils.general import (cv2, non_max_suppression, xyxy2xywh)
|
||||
from models.common import DetectMultiBackend
|
||||
from mouse_driver.MouseMove import mouse_move as ghub_move
|
||||
import cupy as cp
|
||||
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import aaMovementAmp, useMask, maskHeight, maskWidth, aaQuitKey, confidence, headshot_mode, cpsDisplay, visuals, centerOfScreen, screenShotWidth
|
||||
import gameSelection
|
||||
|
||||
def main():
|
||||
# External Function for running the game selection menu (gameSelection.py)
|
||||
camera, cWidth, cHeight = gameSelection.gameSelection()
|
||||
|
||||
# Used for forcing garbage collection
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Loading Yolo5 Small AI Model
|
||||
model = DetectMultiBackend('RRRR320half.engine', device=torch.device(
|
||||
'cuda'), dnn=False, data='', fp16=True)
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
|
||||
# Used for colors drawn on bounding boxes
|
||||
COLORS = np.random.uniform(0, 255, size=(1500, 3))
|
||||
|
||||
# Main loop Quit if exit key is pressed
|
||||
last_mid_coord = None
|
||||
with torch.no_grad():
|
||||
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
|
||||
|
||||
npImg = cp.array([camera.get_latest_frame()])
|
||||
if npImg.shape[3] == 4:
|
||||
# If the image has an alpha channel, remove it
|
||||
npImg = npImg[:, :, :, :3]
|
||||
|
||||
from config import maskSide # "temporary" workaround for bad syntax
|
||||
if useMask:
|
||||
maskSide = maskSide.lower()
|
||||
if maskSide == "right":
|
||||
npImg[:, -maskHeight:, -maskWidth:, :] = 0
|
||||
elif maskSide == "left":
|
||||
npImg[:, -maskHeight:, :maskWidth, :] = 0
|
||||
else:
|
||||
raise Exception('ERROR: Invalid maskSide! Please use "left" or "right"')
|
||||
|
||||
im = npImg / 255
|
||||
im = im.astype(cp.half)
|
||||
|
||||
im = cp.moveaxis(im, 3, 1)
|
||||
im = torch.from_numpy(cp.asnumpy(im)).to('cuda')
|
||||
|
||||
# Detecting all the objects
|
||||
results = model(im)
|
||||
|
||||
pred = non_max_suppression(
|
||||
results, confidence, confidence, 0, False, max_det=2)
|
||||
|
||||
targets = []
|
||||
for i, det in enumerate(pred):
|
||||
s = ""
|
||||
gn = torch.tensor(im.shape)[[0, 0, 0, 0]]
|
||||
if len(det):
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}, " # add to string
|
||||
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
|
||||
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
|
||||
|
||||
targets = pd.DataFrame(
|
||||
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
|
||||
|
||||
center_screen = [cWidth, cHeight]
|
||||
|
||||
# If there are people in the center bounding box
|
||||
if len(targets) > 0:
|
||||
if (centerOfScreen):
|
||||
# Compute the distance from the center
|
||||
targets["dist_from_center"] = np.sqrt((targets.current_mid_x - center_screen[0])**2 + (targets.current_mid_y - center_screen[1])**2)
|
||||
|
||||
# Sort the data frame by distance from center
|
||||
targets = targets.sort_values("dist_from_center")
|
||||
|
||||
# Get the last persons mid coordinate if it exists
|
||||
if last_mid_coord:
|
||||
targets['last_mid_x'] = last_mid_coord[0]
|
||||
targets['last_mid_y'] = last_mid_coord[1]
|
||||
# Take distance between current person mid coordinate and last person mid coordinate
|
||||
targets['dist'] = np.linalg.norm(
|
||||
targets.iloc[:, [0, 1]].values - targets.iloc[:, [4, 5]], axis=1)
|
||||
targets.sort_values(by="dist", ascending=False)
|
||||
|
||||
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
|
||||
xMid = targets.iloc[0].current_mid_x
|
||||
yMid = targets.iloc[0].current_mid_y
|
||||
|
||||
box_height = targets.iloc[0].height
|
||||
if headshot_mode:
|
||||
headshot_offset = box_height * 0.38
|
||||
else:
|
||||
headshot_offset = box_height * 0.2
|
||||
|
||||
mouseMove = [xMid - cWidth, (yMid - headshot_offset) - cHeight]
|
||||
|
||||
if win32api.GetKeyState(0x91):# Moving the mouse
|
||||
if win32api.GetKeyState(0x02) < 0 or win32api.GetKeyState(0x01) < 0:
|
||||
ghub_move(mouseMove[0],mouseMove[1])
|
||||
time.sleep(0.01)
|
||||
last_mid_coord = [xMid, yMid]
|
||||
|
||||
else:
|
||||
last_mid_coord = None
|
||||
|
||||
# See what the bot sees
|
||||
if visuals:
|
||||
npImg = cp.asnumpy(npImg[0])
|
||||
# Loops over every item identified and draws a bounding box
|
||||
for i in range(0, len(targets)):
|
||||
halfW = round(targets["width"][i] / 2)
|
||||
halfH = round(targets["height"][i] / 2)
|
||||
midX = targets['current_mid_x'][i]
|
||||
midY = targets['current_mid_y'][i]
|
||||
(startX, startY, endX, endY) = int(
|
||||
midX + halfW), int(midY + halfH), int(midX - halfW), int(midY - halfH)
|
||||
|
||||
idx = 0
|
||||
# draw the bounding box and label on the frame
|
||||
label = "{}: {:.2f}%".format(
|
||||
"Character", targets["confidence"][i] * 100)
|
||||
cv2.rectangle(npImg, (startX, startY), (endX, endY),
|
||||
COLORS[idx], 2)
|
||||
y = startY - 15 if startY - 15 > 15 else startY + 15
|
||||
cv2.putText(npImg, label, (startX, y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
|
||||
|
||||
# Forced garbage cleanup every second
|
||||
count += 1
|
||||
if (time.time() - sTime) > 1:
|
||||
if cpsDisplay:
|
||||
print("CPS: {}".format(count))
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Uncomment if you keep running into memory issues
|
||||
# gc.collect(generation=0)
|
||||
|
||||
# See visually what the Aimbot sees
|
||||
if visuals:
|
||||
cv2.imshow('Live Feed', npImg)
|
||||
if (cv2.waitKey(1) & 0xFF) == ord('q'):
|
||||
exit()
|
||||
camera.stop()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
main()
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exception(e)
|
||||
print("ERROR: " + str(e))
|
||||
print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")
|
||||
@ -1,5 +0,0 @@
|
||||
# Explain your model
|
||||
|
||||
switched aimkey to RMB
|
||||
|
||||
added scrollock as a toggle key
|
||||
@ -1,188 +0,0 @@
|
||||
from unittest import result
|
||||
import torch
|
||||
import numpy as np
|
||||
import cv2
|
||||
import time
|
||||
import win32api
|
||||
import win32con
|
||||
import pandas as pd
|
||||
from utils.general import (cv2, non_max_suppression, xyxy2xywh)
|
||||
from models.common import DetectMultiBackend
|
||||
import cupy as cp
|
||||
import socket
|
||||
|
||||
ip = '' # raspberry board ip
|
||||
port = 50123 # raspberry port
|
||||
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||||
print(f'Connecting to {ip}:{port}...')
|
||||
try:
|
||||
client.connect((ip, port))
|
||||
except TimeoutError as e:
|
||||
print(f'ERROR: Could not connect. {e}')
|
||||
client.close()
|
||||
exit(1)
|
||||
|
||||
def moveafy(x, y):
|
||||
x = int(np.floor(x))
|
||||
y = int(np.floor(y))
|
||||
|
||||
if x != 0 or y != 0:
|
||||
command = (f'M{x},{y}\r')
|
||||
client.sendall(command.encode())
|
||||
get_response()
|
||||
|
||||
def get_response():
|
||||
return f'Socket: {client.recv(4).decode()}'
|
||||
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import aaMovementAmp, useMask, maskHeight, maskWidth, aaQuitKey, confidence, headshot_mode, cpsDisplay, visuals, centerOfScreen
|
||||
import gameSelection
|
||||
|
||||
def main():
|
||||
# External Function for running the game selection menu (gameSelection.py)
|
||||
camera, cWidth, cHeight = gameSelection.gameSelection()
|
||||
|
||||
# Used for forcing garbage collection
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Loading Yolo5 Small AI Model
|
||||
model = DetectMultiBackend('afyfort.engine', device=torch.device('cuda'), dnn=False, data='', fp16=True)
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
|
||||
# Used for colors drawn on bounding boxes
|
||||
COLORS = np.random.uniform(0, 255, size=(1500, 3))
|
||||
|
||||
# Main loop Quit if Q is pressed
|
||||
last_mid_coord = None
|
||||
with torch.no_grad():
|
||||
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
|
||||
|
||||
npImg = cp.array([camera.get_latest_frame()])
|
||||
if npImg.shape[3] == 4:
|
||||
# If the image has an alpha channel, remove it
|
||||
npImg = npImg[:, :, :, :3]
|
||||
|
||||
if useMask:
|
||||
npImg[:, -maskHeight:, :maskWidth, :] = 0
|
||||
|
||||
im = npImg / 255
|
||||
im = im.astype(cp.half)
|
||||
|
||||
im = cp.moveaxis(im, 3, 1)
|
||||
im = torch.from_numpy(cp.asnumpy(im)).to('cuda')
|
||||
|
||||
# Detecting all the objects
|
||||
results = model(im)
|
||||
|
||||
pred = non_max_suppression(
|
||||
results, confidence, confidence, 0, False, max_det=10)
|
||||
|
||||
targets = []
|
||||
for i, det in enumerate(pred):
|
||||
s = ""
|
||||
gn = torch.tensor(im.shape)[[0, 0, 0, 0]]
|
||||
if len(det):
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}, " # add to string
|
||||
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
|
||||
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
|
||||
|
||||
targets = pd.DataFrame(
|
||||
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
|
||||
|
||||
center_screen = [cWidth, cHeight]
|
||||
|
||||
# If there are people in the center bounding box
|
||||
if len(targets) > 0:
|
||||
if (centerOfScreen):
|
||||
# Compute the distance from the center
|
||||
targets["dist_from_center"] = np.sqrt((targets.current_mid_x - center_screen[0])**2 + (targets.current_mid_y - center_screen[1])**2)
|
||||
|
||||
# Sort the data frame by distance from center
|
||||
targets = targets.sort_values("dist_from_center")
|
||||
|
||||
# Get the last persons mid coordinate if it exists
|
||||
if last_mid_coord:
|
||||
targets['last_mid_x'] = last_mid_coord[0]
|
||||
targets['last_mid_y'] = last_mid_coord[1]
|
||||
# Take distance between current person mid coordinate and last person mid coordinate
|
||||
targets['dist'] = np.linalg.norm(
|
||||
targets.iloc[:, [0, 1]].values - targets.iloc[:, [4, 5]], axis=1)
|
||||
targets.sort_values(by="dist", ascending=False)
|
||||
|
||||
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
|
||||
xMid = targets.iloc[0].current_mid_x
|
||||
yMid = targets.iloc[0].current_mid_y
|
||||
|
||||
box_height = targets.iloc[0].height
|
||||
if headshot_mode:
|
||||
headshot_offset = box_height * 0.38
|
||||
else:
|
||||
headshot_offset = box_height * 0.2
|
||||
|
||||
mouseMove = [xMid - cWidth, (yMid - headshot_offset) - cHeight]
|
||||
|
||||
# Moving the mouse
|
||||
if win32api.GetAsyncKeyState(0x02) < 0:
|
||||
# win32api.mouse_event(win32con.MOUSEEVENTF_MOVE, int(mouseMove[0] * aaMovementAmp), int(mouseMove[1] * aaMovementAmp), 0, 0)
|
||||
moveafy(int(mouseMove[0] * aaMovementAmp), int(mouseMove[1] * aaMovementAmp))
|
||||
last_mid_coord = [xMid, yMid]
|
||||
|
||||
else:
|
||||
last_mid_coord = None
|
||||
|
||||
# See what the bot sees
|
||||
if visuals:
|
||||
npImg = cp.asnumpy(npImg[0])
|
||||
# Loops over every item identified and draws a bounding box
|
||||
for i in range(0, len(targets)):
|
||||
halfW = round(targets["width"][i] / 2)
|
||||
halfH = round(targets["height"][i] / 2)
|
||||
midX = targets['current_mid_x'][i]
|
||||
midY = targets['current_mid_y'][i]
|
||||
(startX, startY, endX, endY) = int(
|
||||
midX + halfW), int(midY + halfH), int(midX - halfW), int(midY - halfH)
|
||||
|
||||
idx = 0
|
||||
# draw the bounding box and label on the frame
|
||||
label = "{}: {:.2f}%".format(
|
||||
"Human", targets["confidence"][i] * 100)
|
||||
cv2.rectangle(npImg, (startX, startY), (endX, endY),
|
||||
COLORS[idx], 2)
|
||||
y = startY - 15 if startY - 15 > 15 else startY + 15
|
||||
cv2.putText(npImg, label, (startX, y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
|
||||
|
||||
# Forced garbage cleanup every second
|
||||
count += 1
|
||||
if (time.time() - sTime) > 1:
|
||||
if cpsDisplay:
|
||||
print("CPS: {}".format(count))
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Uncomment if you keep running into memory issues
|
||||
# gc.collect(generation=0)
|
||||
|
||||
# See visually what the Aimbot sees
|
||||
if visuals:
|
||||
cv2.imshow('Live Feed', npImg)
|
||||
if (cv2.waitKey(1) & 0xFF) == ord('q'):
|
||||
exit()
|
||||
camera.stop()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
main()
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exception(e)
|
||||
print(str(e))
|
||||
print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")
|
||||
@ -1,180 +0,0 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
import cv2
|
||||
import time
|
||||
import win32api
|
||||
import win32con
|
||||
import pandas as pd
|
||||
import gc
|
||||
from utils.general import (cv2, non_max_suppression, xyxy2xywh)
|
||||
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import aaMovementAmp, useMask, maskWidth, maskHeight, aaQuitKey, screenShotHeight, confidence, headshot_mode, cpsDisplay, visuals, centerOfScreen
|
||||
import gameSelection
|
||||
|
||||
def main():
|
||||
# External Function for running the game selection menu (gameSelection.py)
|
||||
camera, cWidth, cHeight = gameSelection.gameSelection()
|
||||
|
||||
# Used for forcing garbage collection
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Loading Yolo5 Small AI Model, for better results use yolov5m or yolov5l
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5s',
|
||||
pretrained=True, force_reload=True)
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
|
||||
if torch.cuda.is_available():
|
||||
model.half()
|
||||
|
||||
# Used for colors drawn on bounding boxes
|
||||
COLORS = np.random.uniform(0, 255, size=(1500, 3))
|
||||
|
||||
# Main loop Quit if Q is pressed
|
||||
last_mid_coord = None
|
||||
with torch.no_grad():
|
||||
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
|
||||
|
||||
# Getting Frame
|
||||
npImg = np.array(camera.get_latest_frame())
|
||||
|
||||
from config import maskSide # "temporary" workaround for bad syntax
|
||||
if useMask:
|
||||
maskSide = maskSide.lower()
|
||||
if maskSide == "right":
|
||||
npImg[-maskHeight:, -maskWidth:, :] = 0
|
||||
elif maskSide == "left":
|
||||
npImg[-maskHeight:, :maskWidth, :] = 0
|
||||
else:
|
||||
raise Exception('ERROR: Invalid maskSide! Please use "left" or "right"')
|
||||
|
||||
# Normalizing Data
|
||||
im = torch.from_numpy(npImg)
|
||||
if im.shape[2] == 4:
|
||||
# If the image has an alpha channel, remove it
|
||||
im = im[:, :, :3,]
|
||||
|
||||
im = torch.movedim(im, 2, 0)
|
||||
if torch.cuda.is_available():
|
||||
im = im.half()
|
||||
im /= 255
|
||||
if len(im.shape) == 3:
|
||||
im = im[None]
|
||||
|
||||
# Detecting all the objects
|
||||
results = model(im, size=screenShotHeight)
|
||||
|
||||
# Suppressing results that dont meet thresholds
|
||||
pred = non_max_suppression(
|
||||
results, confidence, confidence, 0, False, max_det=1000)
|
||||
|
||||
# Converting output to usable cords
|
||||
targets = []
|
||||
for i, det in enumerate(pred):
|
||||
s = ""
|
||||
gn = torch.tensor(im.shape)[[0, 0, 0, 0]]
|
||||
if len(det):
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}, " # add to string
|
||||
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
|
||||
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
|
||||
|
||||
targets = pd.DataFrame(
|
||||
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
|
||||
|
||||
center_screen = [cWidth, cHeight]
|
||||
|
||||
# If there are people in the center bounding box
|
||||
if len(targets) > 0:
|
||||
if (centerOfScreen):
|
||||
# Compute the distance from the center
|
||||
targets["dist_from_center"] = np.sqrt((targets.current_mid_x - center_screen[0])**2 + (targets.current_mid_y - center_screen[1])**2)
|
||||
|
||||
# Sort the data frame by distance from center
|
||||
targets = targets.sort_values("dist_from_center")
|
||||
|
||||
# Get the last persons mid coordinate if it exists
|
||||
if last_mid_coord:
|
||||
targets['last_mid_x'] = last_mid_coord[0]
|
||||
targets['last_mid_y'] = last_mid_coord[1]
|
||||
# Take distance between current person mid coordinate and last person mid coordinate
|
||||
targets['dist'] = np.linalg.norm(
|
||||
targets.iloc[:, [0, 1]].values - targets.iloc[:, [4, 5]], axis=1)
|
||||
targets.sort_values(by="dist", ascending=False)
|
||||
|
||||
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
|
||||
xMid = targets.iloc[0].current_mid_x
|
||||
yMid = targets.iloc[0].current_mid_y
|
||||
|
||||
box_height = targets.iloc[0].height
|
||||
if headshot_mode:
|
||||
headshot_offset = box_height * 0.38
|
||||
else:
|
||||
headshot_offset = box_height * 0.2
|
||||
|
||||
mouseMove = [xMid - cWidth, (yMid - headshot_offset) - cHeight]
|
||||
|
||||
# Moving the mouse
|
||||
if win32api.GetKeyState(0x14):
|
||||
win32api.mouse_event(win32con.MOUSEEVENTF_MOVE, int(
|
||||
mouseMove[0] * aaMovementAmp), int(mouseMove[1] * aaMovementAmp), 0, 0)
|
||||
last_mid_coord = [xMid, yMid]
|
||||
|
||||
else:
|
||||
last_mid_coord = None
|
||||
|
||||
# See what the bot sees
|
||||
if visuals:
|
||||
# Loops over every item identified and draws a bounding box
|
||||
for i in range(0, len(targets)):
|
||||
halfW = round(targets["width"][i] / 2)
|
||||
halfH = round(targets["height"][i] / 2)
|
||||
midX = targets['current_mid_x'][i]
|
||||
midY = targets['current_mid_y'][i]
|
||||
(startX, startY, endX, endY) = int(
|
||||
midX + halfW), int(midY + halfH), int(midX - halfW), int(midY - halfH)
|
||||
|
||||
idx = 0
|
||||
|
||||
# draw the bounding box and label on the frame
|
||||
label = "{}: {:.2f}%".format(
|
||||
"Human", targets["confidence"][i] * 100)
|
||||
cv2.rectangle(npImg, (startX, startY), (endX, endY),
|
||||
COLORS[idx], 2)
|
||||
y = startY - 15 if startY - 15 > 15 else startY + 15
|
||||
cv2.putText(npImg, label, (startX, y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
|
||||
|
||||
# Forced garbage cleanup every second
|
||||
count += 1
|
||||
if (time.time() - sTime) > 1:
|
||||
if cpsDisplay:
|
||||
print("CPS: {}".format(count))
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Uncomment if you keep running into memory issues
|
||||
# gc.collect(generation=0)
|
||||
|
||||
# See visually what the Aimbot sees
|
||||
if visuals:
|
||||
cv2.imshow('Live Feed', npImg)
|
||||
if (cv2.waitKey(1) & 0xFF) == ord('q'):
|
||||
exit()
|
||||
camera.stop()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
main()
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exception(e)
|
||||
print("ERROR: " + str(e))
|
||||
print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")
|
||||
@ -1,203 +0,0 @@
|
||||
import onnxruntime as ort
|
||||
import numpy as np
|
||||
import gc
|
||||
import numpy as np
|
||||
import cv2
|
||||
import time
|
||||
import win32api
|
||||
import win32con
|
||||
import pandas as pd
|
||||
from utils.general import (cv2, non_max_suppression, xyxy2xywh)
|
||||
from mouse_driver.MouseMove import mouse_move as ghub_move
|
||||
import torch
|
||||
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import aaMovementAmp, useMask, maskHeight, maskWidth, aaQuitKey, confidence, headshot_mode, cpsDisplay, visuals, onnxChoice, centerOfScreen
|
||||
import gameSelection
|
||||
|
||||
def main():
|
||||
# External Function for running the game selection menu (gameSelection.py)
|
||||
camera, cWidth, cHeight = gameSelection.gameSelection()
|
||||
|
||||
# Used for forcing garbage collection
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Choosing the correct ONNX Provider based on config.py
|
||||
onnxProvider = ""
|
||||
if onnxChoice == 1:
|
||||
onnxProvider = "CPUExecutionProvider"
|
||||
elif onnxChoice == 2:
|
||||
onnxProvider = "DmlExecutionProvider"
|
||||
elif onnxChoice == 3:
|
||||
import cupy as cp
|
||||
onnxProvider = "CUDAExecutionProvider"
|
||||
|
||||
so = ort.SessionOptions()
|
||||
so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
|
||||
ort_sess = ort.InferenceSession('RRRR.onnx', sess_options=so, providers=[
|
||||
onnxProvider])
|
||||
|
||||
# Used for colors drawn on bounding boxes
|
||||
COLORS = np.random.uniform(0, 255, size=(1500, 3))
|
||||
|
||||
# Main loop Quit if Q is pressed
|
||||
last_mid_coord = None
|
||||
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
|
||||
|
||||
# Getting Frame
|
||||
npImg = np.array(camera.get_latest_frame())
|
||||
|
||||
from config import maskSide # "temporary" workaround for bad syntax
|
||||
if useMask:
|
||||
maskSide = maskSide.lower()
|
||||
if maskSide == "right":
|
||||
npImg[-maskHeight:, -maskWidth:, :] = 0
|
||||
elif maskSide == "left":
|
||||
npImg[-maskHeight:, :maskWidth, :] = 0
|
||||
else:
|
||||
raise Exception('ERROR: Invalid maskSide! Please use "left" or "right"')
|
||||
|
||||
# If Nvidia, do this
|
||||
if onnxChoice == 3:
|
||||
# Normalizing Data
|
||||
im = torch.from_numpy(npImg).to('cuda')
|
||||
if im.shape[2] == 4:
|
||||
# If the image has an alpha channel, remove it
|
||||
im = im[:, :, :3,]
|
||||
|
||||
im = torch.movedim(im, 2, 0)
|
||||
im = im.half()
|
||||
im /= 255
|
||||
if len(im.shape) == 3:
|
||||
im = im[None]
|
||||
# If AMD or CPU, do this
|
||||
else:
|
||||
# Normalizing Data
|
||||
im = np.array([npImg])
|
||||
if im.shape[3] == 4:
|
||||
# If the image has an alpha channel, remove it
|
||||
im = im[:, :, :, :3]
|
||||
im = im / 255
|
||||
im = im.astype(np.half)
|
||||
im = np.moveaxis(im, 3, 1)
|
||||
|
||||
# If Nvidia, do this
|
||||
if onnxChoice == 3:
|
||||
outputs = ort_sess.run(None, {'images': cp.asnumpy(im)})
|
||||
# If AMD or CPU, do this
|
||||
else:
|
||||
outputs = ort_sess.run(None, {'images': np.array(im)})
|
||||
|
||||
im = torch.from_numpy(outputs[0]).to('cpu')
|
||||
|
||||
pred = non_max_suppression(
|
||||
im, confidence, confidence, 0, False, max_det=10)
|
||||
|
||||
targets = []
|
||||
for i, det in enumerate(pred):
|
||||
s = ""
|
||||
gn = torch.tensor(im.shape)[[0, 0, 0, 0]]
|
||||
if len(det):
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {int(c)}, " # add to string
|
||||
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
|
||||
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
|
||||
|
||||
targets = pd.DataFrame(
|
||||
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
|
||||
|
||||
center_screen = [cWidth, cHeight]
|
||||
|
||||
# If there are people in the center bounding box
|
||||
if len(targets) > 0:
|
||||
if (centerOfScreen):
|
||||
# Compute the distance from the center
|
||||
targets["dist_from_center"] = np.sqrt((targets.current_mid_x - center_screen[0])**2 + (targets.current_mid_y - center_screen[1])**2)
|
||||
|
||||
# Sort the data frame by distance from center
|
||||
targets = targets.sort_values("dist_from_center")
|
||||
|
||||
# Get the last persons mid coordinate if it exists
|
||||
if last_mid_coord:
|
||||
targets['last_mid_x'] = last_mid_coord[0]
|
||||
targets['last_mid_y'] = last_mid_coord[1]
|
||||
# Take distance between current person mid coordinate and last person mid coordinate
|
||||
targets['dist'] = np.linalg.norm(
|
||||
targets.iloc[:, [0, 1]].values - targets.iloc[:, [4, 5]], axis=1)
|
||||
targets.sort_values(by="dist", ascending=False)
|
||||
|
||||
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
|
||||
xMid = targets.iloc[0].current_mid_x
|
||||
yMid = targets.iloc[0].current_mid_y
|
||||
|
||||
box_height = targets.iloc[0].height
|
||||
if headshot_mode:
|
||||
headshot_offset = box_height * 0.38
|
||||
else:
|
||||
headshot_offset = box_height * 0.2
|
||||
|
||||
mouseMove = [xMid - cWidth, (yMid - headshot_offset) - cHeight]
|
||||
# Moving the mouse
|
||||
#imagine recalculating everything to find out you have a drop in replacement
|
||||
if win32api.GetKeyState(0x02) < 0:
|
||||
ghub_move(mouseMove[0],mouseMove[1])
|
||||
last_mid_coord = [xMid, yMid]
|
||||
|
||||
else:
|
||||
last_mid_coord = None
|
||||
|
||||
# See what the bot sees
|
||||
if visuals:
|
||||
# Loops over every item identified and draws a bounding box
|
||||
for i in range(0, len(targets)):
|
||||
halfW = round(targets["width"][i] / 2)
|
||||
halfH = round(targets["height"][i] / 2)
|
||||
midX = targets['current_mid_x'][i]
|
||||
midY = targets['current_mid_y'][i]
|
||||
(startX, startY, endX, endY) = int(midX + halfW), int(midY +
|
||||
halfH), int(midX - halfW), int(midY - halfH)
|
||||
|
||||
idx = 0
|
||||
# draw the bounding box and label on the frame
|
||||
label = "{}: {:.2f}%".format(
|
||||
"Character", targets["confidence"][i] * 100)
|
||||
cv2.rectangle(npImg, (startX, startY), (endX, endY),
|
||||
COLORS[idx], 2)
|
||||
y = startY - 15 if startY - 15 > 15 else startY + 15
|
||||
cv2.putText(npImg, label, (startX, y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
|
||||
|
||||
|
||||
# Forced garbage cleanup every second
|
||||
count += 1
|
||||
if (time.time() - sTime) > 1:
|
||||
if cpsDisplay:
|
||||
print("CPS: {}".format(count))
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Uncomment if you keep running into memory issues
|
||||
# gc.collect(generation=0)
|
||||
|
||||
# See visually what the Aimbot sees
|
||||
if visuals:
|
||||
cv2.imshow('Live Feed', npImg)
|
||||
if (cv2.waitKey(1) & 0xFF) == ord('q'):
|
||||
exit()
|
||||
camera.stop()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
main()
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exception(e)
|
||||
print("ERROR: " + str(e))
|
||||
print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")
|
||||
@ -1,172 +0,0 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
import cv2
|
||||
import time
|
||||
import win32api
|
||||
import win32con
|
||||
import pandas as pd
|
||||
from utils.general import (cv2, non_max_suppression, xyxy2xywh)
|
||||
from models.common import DetectMultiBackend
|
||||
from mouse_driver.MouseMove import mouse_move as ghub_move
|
||||
import cupy as cp
|
||||
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import aaMovementAmp, useMask, maskHeight, maskWidth, aaQuitKey, confidence, headshot_mode, cpsDisplay, visuals, centerOfScreen, screenShotWidth
|
||||
import gameSelection
|
||||
|
||||
def main():
|
||||
# External Function for running the game selection menu (gameSelection.py)
|
||||
camera, cWidth, cHeight = gameSelection.gameSelection()
|
||||
|
||||
# Used for forcing garbage collection
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Loading Yolo5 Small AI Model
|
||||
model = DetectMultiBackend('RRRR320half.engine', device=torch.device(
|
||||
'cuda'), dnn=False, data='', fp16=True)
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
|
||||
# Used for colors drawn on bounding boxes
|
||||
COLORS = np.random.uniform(0, 255, size=(1500, 3))
|
||||
|
||||
# Main loop Quit if exit key is pressed
|
||||
last_mid_coord = None
|
||||
with torch.no_grad():
|
||||
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
|
||||
|
||||
npImg = cp.array([camera.get_latest_frame()])
|
||||
if npImg.shape[3] == 4:
|
||||
# If the image has an alpha channel, remove it
|
||||
npImg = npImg[:, :, :, :3]
|
||||
|
||||
from config import maskSide # "temporary" workaround for bad syntax
|
||||
if useMask:
|
||||
maskSide = maskSide.lower()
|
||||
if maskSide == "right":
|
||||
npImg[:, -maskHeight:, -maskWidth:, :] = 0
|
||||
elif maskSide == "left":
|
||||
npImg[:, -maskHeight:, :maskWidth, :] = 0
|
||||
else:
|
||||
raise Exception('ERROR: Invalid maskSide! Please use "left" or "right"')
|
||||
|
||||
im = npImg / 255
|
||||
im = im.astype(cp.half)
|
||||
|
||||
im = cp.moveaxis(im, 3, 1)
|
||||
im = torch.from_numpy(cp.asnumpy(im)).to('cuda')
|
||||
|
||||
# Detecting all the objects
|
||||
results = model(im)
|
||||
|
||||
pred = non_max_suppression(
|
||||
results, confidence, confidence, 0, False, max_det=2)
|
||||
|
||||
targets = []
|
||||
for i, det in enumerate(pred):
|
||||
s = ""
|
||||
gn = torch.tensor(im.shape)[[0, 0, 0, 0]]
|
||||
if len(det):
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}, " # add to string
|
||||
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
|
||||
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
|
||||
|
||||
targets = pd.DataFrame(
|
||||
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
|
||||
|
||||
center_screen = [cWidth, cHeight]
|
||||
|
||||
# If there are people in the center bounding box
|
||||
if len(targets) > 0:
|
||||
if (centerOfScreen):
|
||||
# Compute the distance from the center
|
||||
targets["dist_from_center"] = np.sqrt((targets.current_mid_x - center_screen[0])**2 + (targets.current_mid_y - center_screen[1])**2)
|
||||
|
||||
# Sort the data frame by distance from center
|
||||
targets = targets.sort_values("dist_from_center")
|
||||
|
||||
# Get the last persons mid coordinate if it exists
|
||||
if last_mid_coord:
|
||||
targets['last_mid_x'] = last_mid_coord[0]
|
||||
targets['last_mid_y'] = last_mid_coord[1]
|
||||
# Take distance between current person mid coordinate and last person mid coordinate
|
||||
targets['dist'] = np.linalg.norm(
|
||||
targets.iloc[:, [0, 1]].values - targets.iloc[:, [4, 5]], axis=1)
|
||||
targets.sort_values(by="dist", ascending=False)
|
||||
|
||||
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
|
||||
xMid = targets.iloc[0].current_mid_x
|
||||
yMid = targets.iloc[0].current_mid_y
|
||||
|
||||
box_height = targets.iloc[0].height
|
||||
if headshot_mode:
|
||||
headshot_offset = box_height * 0.38
|
||||
else:
|
||||
headshot_offset = box_height * 0.2
|
||||
|
||||
mouseMove = [xMid - cWidth, (yMid - headshot_offset) - cHeight]
|
||||
|
||||
if win32api.GetKeyState(0x91):# Moving the mouse
|
||||
if win32api.GetKeyState(0x02) < 0 or win32api.GetKeyState(0x01) < 0:
|
||||
ghub_move(mouseMove[0],mouseMove[1])
|
||||
time.sleep(0.01)
|
||||
last_mid_coord = [xMid, yMid]
|
||||
|
||||
else:
|
||||
last_mid_coord = None
|
||||
|
||||
# See what the bot sees
|
||||
if visuals:
|
||||
npImg = cp.asnumpy(npImg[0])
|
||||
# Loops over every item identified and draws a bounding box
|
||||
for i in range(0, len(targets)):
|
||||
halfW = round(targets["width"][i] / 2)
|
||||
halfH = round(targets["height"][i] / 2)
|
||||
midX = targets['current_mid_x'][i]
|
||||
midY = targets['current_mid_y'][i]
|
||||
(startX, startY, endX, endY) = int(
|
||||
midX + halfW), int(midY + halfH), int(midX - halfW), int(midY - halfH)
|
||||
|
||||
idx = 0
|
||||
# draw the bounding box and label on the frame
|
||||
label = "{}: {:.2f}%".format(
|
||||
"Character", targets["confidence"][i] * 100)
|
||||
cv2.rectangle(npImg, (startX, startY), (endX, endY),
|
||||
COLORS[idx], 2)
|
||||
y = startY - 15 if startY - 15 > 15 else startY + 15
|
||||
cv2.putText(npImg, label, (startX, y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
|
||||
|
||||
# Forced garbage cleanup every second
|
||||
count += 1
|
||||
if (time.time() - sTime) > 1:
|
||||
if cpsDisplay:
|
||||
print("CPS: {}".format(count))
|
||||
count = 0
|
||||
sTime = time.time()
|
||||
|
||||
# Uncomment if you keep running into memory issues
|
||||
# gc.collect(generation=0)
|
||||
|
||||
# See visually what the Aimbot sees
|
||||
if visuals:
|
||||
cv2.imshow('Live Feed', npImg)
|
||||
if (cv2.waitKey(1) & 0xFF) == ord('q'):
|
||||
exit()
|
||||
camera.stop()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
main()
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exception(e)
|
||||
print("ERROR: " + str(e))
|
||||
print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")
|
||||
237
export.py
237
export.py
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
|
||||
|
||||
@ -77,25 +77,6 @@ from utils.torch_utils import select_device, smart_inference_mode
|
||||
MACOS = platform.system() == 'Darwin' # macOS environment
|
||||
|
||||
|
||||
class iOSModel(torch.nn.Module):
|
||||
|
||||
def __init__(self, model, im):
|
||||
super().__init__()
|
||||
b, c, h, w = im.shape # batch, channel, height, width
|
||||
self.model = model
|
||||
self.nc = model.nc # number of classes
|
||||
if w == h:
|
||||
self.normalize = 1. / w
|
||||
else:
|
||||
self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]) # broadcast (slower, smaller)
|
||||
# np = model(im)[0].shape[1] # number of points
|
||||
# self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]).expand(np, 4) # explicit (faster, larger)
|
||||
|
||||
def forward(self, x):
|
||||
xywh, conf, cls = self.model(x)[0].squeeze().split((4, 1, self.nc), 1)
|
||||
return cls * conf, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4)
|
||||
|
||||
|
||||
def export_formats():
|
||||
# YOLOv5 export formats
|
||||
x = [
|
||||
@ -110,7 +91,7 @@ def export_formats():
|
||||
['TensorFlow Lite', 'tflite', '.tflite', True, False],
|
||||
['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
|
||||
['TensorFlow.js', 'tfjs', '_web_model', False, False],
|
||||
['PaddlePaddle', 'paddle', '_paddle_model', True, True], ]
|
||||
['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
|
||||
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
|
||||
|
||||
|
||||
@ -155,7 +136,7 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
|
||||
import onnx
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
|
||||
f = str(file.with_suffix('.onnx'))
|
||||
f = file.with_suffix('.onnx')
|
||||
|
||||
output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0']
|
||||
if dynamic:
|
||||
@ -205,68 +186,23 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
|
||||
|
||||
|
||||
@try_export
|
||||
def export_openvino(file, metadata, half, int8, data, prefix=colorstr('OpenVINO:')):
|
||||
def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
|
||||
# YOLOv5 OpenVINO export
|
||||
check_requirements('openvino-dev>=2023.0') # requires openvino-dev: https://pypi.org/project/openvino-dev/
|
||||
import openvino.runtime as ov # noqa
|
||||
from openvino.tools import mo # noqa
|
||||
check_requirements('openvino-dev') # requires openvino-dev: https://pypi.org/project/openvino-dev/
|
||||
import openvino.inference_engine as ie
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with openvino {ov.__version__}...')
|
||||
f = str(file).replace(file.suffix, f'_openvino_model{os.sep}')
|
||||
f_onnx = file.with_suffix('.onnx')
|
||||
f_ov = str(Path(f) / file.with_suffix('.xml').name)
|
||||
if int8:
|
||||
check_requirements('nncf>=2.4.0') # requires at least version 2.4.0 to use the post-training quantization
|
||||
import nncf
|
||||
import numpy as np
|
||||
from openvino.runtime import Core
|
||||
LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
|
||||
f = str(file).replace('.pt', f'_openvino_model{os.sep}')
|
||||
|
||||
from utils.dataloaders import create_dataloader
|
||||
core = Core()
|
||||
onnx_model = core.read_model(f_onnx) # export
|
||||
|
||||
def prepare_input_tensor(image: np.ndarray):
|
||||
input_tensor = image.astype(np.float32) # uint8 to fp16/32
|
||||
input_tensor /= 255.0 # 0 - 255 to 0.0 - 1.0
|
||||
|
||||
if input_tensor.ndim == 3:
|
||||
input_tensor = np.expand_dims(input_tensor, 0)
|
||||
return input_tensor
|
||||
|
||||
def gen_dataloader(yaml_path, task='train', imgsz=640, workers=4):
|
||||
data_yaml = check_yaml(yaml_path)
|
||||
data = check_dataset(data_yaml)
|
||||
dataloader = create_dataloader(data[task],
|
||||
imgsz=imgsz,
|
||||
batch_size=1,
|
||||
stride=32,
|
||||
pad=0.5,
|
||||
single_cls=False,
|
||||
rect=False,
|
||||
workers=workers)[0]
|
||||
return dataloader
|
||||
|
||||
# noqa: F811
|
||||
|
||||
def transform_fn(data_item):
|
||||
"""
|
||||
Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
|
||||
Parameters:
|
||||
data_item: Tuple with data item produced by DataLoader during iteration
|
||||
Returns:
|
||||
input_tensor: Input data for quantization
|
||||
"""
|
||||
img = data_item[0].numpy()
|
||||
input_tensor = prepare_input_tensor(img)
|
||||
return input_tensor
|
||||
|
||||
ds = gen_dataloader(data)
|
||||
quantization_dataset = nncf.Dataset(ds, transform_fn)
|
||||
ov_model = nncf.quantize(onnx_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED)
|
||||
else:
|
||||
ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework='onnx', compress_to_fp16=half) # export
|
||||
|
||||
ov.serialize(ov_model, f_ov) # save
|
||||
args = [
|
||||
'mo',
|
||||
'--input_model',
|
||||
str(file.with_suffix('.onnx')),
|
||||
'--output_dir',
|
||||
f,
|
||||
'--data_type',
|
||||
('FP16' if half else 'FP32'),]
|
||||
subprocess.run(args, check=True, env=os.environ) # export
|
||||
yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml
|
||||
return f, None
|
||||
|
||||
@ -287,7 +223,7 @@ def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')):
|
||||
|
||||
|
||||
@try_export
|
||||
def export_coreml(model, im, file, int8, half, nms, prefix=colorstr('CoreML:')):
|
||||
def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
|
||||
# YOLOv5 CoreML export
|
||||
check_requirements('coremltools')
|
||||
import coremltools as ct
|
||||
@ -295,8 +231,6 @@ def export_coreml(model, im, file, int8, half, nms, prefix=colorstr('CoreML:')):
|
||||
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
|
||||
f = file.with_suffix('.mlmodel')
|
||||
|
||||
if nms:
|
||||
model = iOSModel(model, im)
|
||||
ts = torch.jit.trace(model, im, strict=False) # TorchScript model
|
||||
ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
|
||||
bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
|
||||
@ -501,7 +435,7 @@ def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
|
||||
'10',
|
||||
'--out_dir',
|
||||
str(file.parent),
|
||||
f_tfl, ], check=True)
|
||||
f_tfl,], check=True)
|
||||
return f, None
|
||||
|
||||
|
||||
@ -522,7 +456,7 @@ def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')):
|
||||
'--quantize_uint8' if int8 else '',
|
||||
'--output_node_names=Identity,Identity_1,Identity_2,Identity_3',
|
||||
str(f_pb),
|
||||
str(f), ]
|
||||
str(f),]
|
||||
subprocess.run([arg for arg in args if arg], check=True)
|
||||
|
||||
json = Path(f_json).read_text()
|
||||
@ -572,129 +506,6 @@ def add_tflite_metadata(file, metadata, num_outputs):
|
||||
tmp_file.unlink()
|
||||
|
||||
|
||||
def pipeline_coreml(model, im, file, names, y, prefix=colorstr('CoreML Pipeline:')):
|
||||
# YOLOv5 CoreML pipeline
|
||||
import coremltools as ct
|
||||
from PIL import Image
|
||||
|
||||
print(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
|
||||
batch_size, ch, h, w = list(im.shape) # BCHW
|
||||
t = time.time()
|
||||
|
||||
# YOLOv5 Output shapes
|
||||
spec = model.get_spec()
|
||||
out0, out1 = iter(spec.description.output)
|
||||
if platform.system() == 'Darwin':
|
||||
img = Image.new('RGB', (w, h)) # img(192 width, 320 height)
|
||||
# img = torch.zeros((*opt.img_size, 3)).numpy() # img size(320,192,3) iDetection
|
||||
out = model.predict({'image': img})
|
||||
out0_shape, out1_shape = out[out0.name].shape, out[out1.name].shape
|
||||
else: # linux and windows can not run model.predict(), get sizes from pytorch output y
|
||||
s = tuple(y[0].shape)
|
||||
out0_shape, out1_shape = (s[1], s[2] - 5), (s[1], 4) # (3780, 80), (3780, 4)
|
||||
|
||||
# Checks
|
||||
nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height
|
||||
na, nc = out0_shape
|
||||
# na, nc = out0.type.multiArrayType.shape # number anchors, classes
|
||||
assert len(names) == nc, f'{len(names)} names found for nc={nc}' # check
|
||||
|
||||
# Define output shapes (missing)
|
||||
out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80)
|
||||
out1.type.multiArrayType.shape[:] = out1_shape # (3780, 4)
|
||||
# spec.neuralNetwork.preprocessing[0].featureName = '0'
|
||||
|
||||
# Flexible input shapes
|
||||
# from coremltools.models.neural_network import flexible_shape_utils
|
||||
# s = [] # shapes
|
||||
# s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192))
|
||||
# s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384)) # (height, width)
|
||||
# flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s)
|
||||
# r = flexible_shape_utils.NeuralNetworkImageSizeRange() # shape ranges
|
||||
# r.add_height_range((192, 640))
|
||||
# r.add_width_range((192, 640))
|
||||
# flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r)
|
||||
|
||||
# Print
|
||||
print(spec.description)
|
||||
|
||||
# Model from spec
|
||||
model = ct.models.MLModel(spec)
|
||||
|
||||
# 3. Create NMS protobuf
|
||||
nms_spec = ct.proto.Model_pb2.Model()
|
||||
nms_spec.specificationVersion = 5
|
||||
for i in range(2):
|
||||
decoder_output = model._spec.description.output[i].SerializeToString()
|
||||
nms_spec.description.input.add()
|
||||
nms_spec.description.input[i].ParseFromString(decoder_output)
|
||||
nms_spec.description.output.add()
|
||||
nms_spec.description.output[i].ParseFromString(decoder_output)
|
||||
|
||||
nms_spec.description.output[0].name = 'confidence'
|
||||
nms_spec.description.output[1].name = 'coordinates'
|
||||
|
||||
output_sizes = [nc, 4]
|
||||
for i in range(2):
|
||||
ma_type = nms_spec.description.output[i].type.multiArrayType
|
||||
ma_type.shapeRange.sizeRanges.add()
|
||||
ma_type.shapeRange.sizeRanges[0].lowerBound = 0
|
||||
ma_type.shapeRange.sizeRanges[0].upperBound = -1
|
||||
ma_type.shapeRange.sizeRanges.add()
|
||||
ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i]
|
||||
ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i]
|
||||
del ma_type.shape[:]
|
||||
|
||||
nms = nms_spec.nonMaximumSuppression
|
||||
nms.confidenceInputFeatureName = out0.name # 1x507x80
|
||||
nms.coordinatesInputFeatureName = out1.name # 1x507x4
|
||||
nms.confidenceOutputFeatureName = 'confidence'
|
||||
nms.coordinatesOutputFeatureName = 'coordinates'
|
||||
nms.iouThresholdInputFeatureName = 'iouThreshold'
|
||||
nms.confidenceThresholdInputFeatureName = 'confidenceThreshold'
|
||||
nms.iouThreshold = 0.45
|
||||
nms.confidenceThreshold = 0.25
|
||||
nms.pickTop.perClass = True
|
||||
nms.stringClassLabels.vector.extend(names.values())
|
||||
nms_model = ct.models.MLModel(nms_spec)
|
||||
|
||||
# 4. Pipeline models together
|
||||
pipeline = ct.models.pipeline.Pipeline(input_features=[('image', ct.models.datatypes.Array(3, ny, nx)),
|
||||
('iouThreshold', ct.models.datatypes.Double()),
|
||||
('confidenceThreshold', ct.models.datatypes.Double())],
|
||||
output_features=['confidence', 'coordinates'])
|
||||
pipeline.add_model(model)
|
||||
pipeline.add_model(nms_model)
|
||||
|
||||
# Correct datatypes
|
||||
pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString())
|
||||
pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString())
|
||||
pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString())
|
||||
|
||||
# Update metadata
|
||||
pipeline.spec.specificationVersion = 5
|
||||
pipeline.spec.description.metadata.versionString = 'https://github.com/ultralytics/yolov5'
|
||||
pipeline.spec.description.metadata.shortDescription = 'https://github.com/ultralytics/yolov5'
|
||||
pipeline.spec.description.metadata.author = 'glenn.jocher@ultralytics.com'
|
||||
pipeline.spec.description.metadata.license = 'https://github.com/ultralytics/yolov5/blob/master/LICENSE'
|
||||
pipeline.spec.description.metadata.userDefined.update({
|
||||
'classes': ','.join(names.values()),
|
||||
'iou_threshold': str(nms.iouThreshold),
|
||||
'confidence_threshold': str(nms.confidenceThreshold)})
|
||||
|
||||
# Save the model
|
||||
f = file.with_suffix('.mlmodel') # filename
|
||||
model = ct.models.MLModel(pipeline.spec)
|
||||
model.input_description['image'] = 'Input image'
|
||||
model.input_description['iouThreshold'] = f'(optional) IOU Threshold override (default: {nms.iouThreshold})'
|
||||
model.input_description['confidenceThreshold'] = \
|
||||
f'(optional) Confidence Threshold override (default: {nms.confidenceThreshold})'
|
||||
model.output_description['confidence'] = 'Boxes × Class confidence (see user-defined metadata "classes")'
|
||||
model.output_description['coordinates'] = 'Boxes × [x, y, width, height] (relative to image size)'
|
||||
model.save(f) # pipelined
|
||||
print(f'{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)')
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
|
||||
@ -771,11 +582,9 @@ def run(
|
||||
if onnx or xml: # OpenVINO requires ONNX
|
||||
f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
|
||||
if xml: # OpenVINO
|
||||
f[3], _ = export_openvino(file, metadata, half, int8, data)
|
||||
f[3], _ = export_openvino(file, metadata, half)
|
||||
if coreml: # CoreML
|
||||
f[4], ct_model = export_coreml(model, im, file, int8, half, nms)
|
||||
if nms:
|
||||
pipeline_coreml(ct_model, im, file, model.names, y)
|
||||
f[4], _ = export_coreml(model, im, file, int8, half)
|
||||
if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
|
||||
assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
|
||||
assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.'
|
||||
@ -831,7 +640,7 @@ def parse_opt(known=False):
|
||||
parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
|
||||
parser.add_argument('--keras', action='store_true', help='TF: use Keras')
|
||||
parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
|
||||
parser.add_argument('--int8', action='store_true', help='CoreML/TF/OpenVINO INT8 quantization')
|
||||
parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
|
||||
parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
|
||||
parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
|
||||
parser.add_argument('--opset', type=int, default=17, help='ONNX: opset version')
|
||||
|
||||
@ -1,14 +1,13 @@
|
||||
import pygetwindow
|
||||
import time
|
||||
import bettercam
|
||||
from typing import Union
|
||||
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import screenShotHeight, screenShotWidth
|
||||
from config import aaRightShift, screenShotHeight, screenShotWidth
|
||||
|
||||
def gameSelection() -> (bettercam.BetterCam, int, Union[int, None]):
|
||||
def gameSelection() -> (bettercam.BetterCam, int, int | None):
|
||||
# Selecting the correct game window
|
||||
try:
|
||||
videoGameWindows = pygetwindow.getAllWindows()
|
||||
@ -56,8 +55,18 @@ def gameSelection() -> (bettercam.BetterCam, int, Union[int, None]):
|
||||
return None
|
||||
print("Successfully activated the game window...")
|
||||
|
||||
# Setting up the screen shots
|
||||
sctArea: dict[str, int] = {"mon": 1, "top": videoGameWindow.top + (videoGameWindow.height - screenShotHeight) // 2,
|
||||
"left": aaRightShift + ((videoGameWindow.left + videoGameWindow.right) // 2) - (screenShotWidth // 2),
|
||||
"width": screenShotWidth,
|
||||
"height": screenShotHeight}
|
||||
|
||||
#! Uncomment if you want to view the entire screen
|
||||
# sctArea = {"mon": 1, "top": 0, "left": 0, "width": 1920, "height": 1080}
|
||||
|
||||
# Starting screenshoting engine
|
||||
left = ((videoGameWindow.left + videoGameWindow.right) // 2) - (screenShotWidth // 2)
|
||||
left = aaRightShift + \
|
||||
((videoGameWindow.left + videoGameWindow.right) // 2) - (screenShotWidth // 2)
|
||||
top = videoGameWindow.top + \
|
||||
(videoGameWindow.height - screenShotHeight) // 2
|
||||
right, bottom = left + screenShotWidth, top + screenShotHeight
|
||||
@ -65,10 +74,8 @@ def gameSelection() -> (bettercam.BetterCam, int, Union[int, None]):
|
||||
region: tuple = (left, top, right, bottom)
|
||||
|
||||
# Calculating the center Autoaim box
|
||||
cWidth: int = screenShotWidth // 2
|
||||
cHeight: int = screenShotHeight // 2
|
||||
|
||||
print(region)
|
||||
cWidth: int = sctArea["width"] / 2
|
||||
cHeight: int = sctArea["height"] / 2
|
||||
|
||||
camera = bettercam.create(region=region, output_color="BGRA", max_buffer_len=512)
|
||||
if camera is None:
|
||||
|
||||
BIN
imgs/console.jpg
BIN
imgs/console.jpg
Binary file not shown.
|
Before Width: | Height: | Size: 3.8 KiB |
16
main.py
16
main.py
@ -11,7 +11,7 @@ from utils.general import (cv2, non_max_suppression, xyxy2xywh)
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import aaMovementAmp, useMask, maskWidth, maskHeight, aaQuitKey, screenShotHeight, confidence, headshot_mode, cpsDisplay, visuals, centerOfScreen
|
||||
from config import aaMovementAmp, aaRightShift, aaQuitKey, screenShotHeight, confidence, headshot_mode, cpsDisplay, visuals, centerOfScreen
|
||||
import gameSelection
|
||||
|
||||
def main():
|
||||
@ -41,16 +41,6 @@ def main():
|
||||
# Getting Frame
|
||||
npImg = np.array(camera.get_latest_frame())
|
||||
|
||||
from config import maskSide # "temporary" workaround for bad syntax
|
||||
if useMask:
|
||||
maskSide = maskSide.lower()
|
||||
if maskSide == "right":
|
||||
npImg[-maskHeight:, -maskWidth:, :] = 0
|
||||
elif maskSide == "left":
|
||||
npImg[-maskHeight:, :maskWidth, :] = 0
|
||||
else:
|
||||
raise Exception('ERROR: Invalid maskSide! Please use "left" or "right"')
|
||||
|
||||
# Normalizing Data
|
||||
im = torch.from_numpy(npImg)
|
||||
if im.shape[2] == 4:
|
||||
@ -109,7 +99,7 @@ def main():
|
||||
targets.sort_values(by="dist", ascending=False)
|
||||
|
||||
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
|
||||
xMid = targets.iloc[0].current_mid_x
|
||||
xMid = targets.iloc[0].current_mid_x + aaRightShift
|
||||
yMid = targets.iloc[0].current_mid_y
|
||||
|
||||
box_height = targets.iloc[0].height
|
||||
@ -176,5 +166,5 @@ if __name__ == "__main__":
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exception(e)
|
||||
print("ERROR: " + str(e))
|
||||
print(str(e))
|
||||
print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")
|
||||
18
main_onnx.py
18
main_onnx.py
@ -1,5 +1,6 @@
|
||||
import onnxruntime as ort
|
||||
import numpy as np
|
||||
import cupy as cp
|
||||
import gc
|
||||
import numpy as np
|
||||
import cv2
|
||||
@ -13,7 +14,7 @@ import torch
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import aaMovementAmp, useMask, maskHeight, maskWidth, aaQuitKey, confidence, headshot_mode, cpsDisplay, visuals, onnxChoice, centerOfScreen
|
||||
from config import aaMovementAmp, aaRightShift, aaQuitKey, confidence, headshot_mode, cpsDisplay, visuals, onnxChoice, centerOfScreen
|
||||
import gameSelection
|
||||
|
||||
def main():
|
||||
@ -31,7 +32,6 @@ def main():
|
||||
elif onnxChoice == 2:
|
||||
onnxProvider = "DmlExecutionProvider"
|
||||
elif onnxChoice == 3:
|
||||
import cupy as cp
|
||||
onnxProvider = "CUDAExecutionProvider"
|
||||
|
||||
so = ort.SessionOptions()
|
||||
@ -49,16 +49,6 @@ def main():
|
||||
# Getting Frame
|
||||
npImg = np.array(camera.get_latest_frame())
|
||||
|
||||
from config import maskSide # "temporary" workaround for bad syntax
|
||||
if useMask:
|
||||
maskSide = maskSide.lower()
|
||||
if maskSide == "right":
|
||||
npImg[-maskHeight:, -maskWidth:, :] = 0
|
||||
elif maskSide == "left":
|
||||
npImg[-maskHeight:, :maskWidth, :] = 0
|
||||
else:
|
||||
raise Exception('ERROR: Invalid maskSide! Please use "left" or "right"')
|
||||
|
||||
# If Nvidia, do this
|
||||
if onnxChoice == 3:
|
||||
# Normalizing Data
|
||||
@ -132,7 +122,7 @@ def main():
|
||||
targets.sort_values(by="dist", ascending=False)
|
||||
|
||||
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
|
||||
xMid = targets.iloc[0].current_mid_x
|
||||
xMid = targets.iloc[0].current_mid_x + aaRightShift
|
||||
yMid = targets.iloc[0].current_mid_y
|
||||
|
||||
box_height = targets.iloc[0].height
|
||||
@ -199,5 +189,5 @@ if __name__ == "__main__":
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exception(e)
|
||||
print("ERROR: " + str(e))
|
||||
print(str(e))
|
||||
print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")
|
||||
@ -1,3 +1,4 @@
|
||||
from unittest import result
|
||||
import torch
|
||||
import numpy as np
|
||||
import cv2
|
||||
@ -12,7 +13,7 @@ import cupy as cp
|
||||
# Could be do with
|
||||
# from config import *
|
||||
# But we are writing it out for clarity for new devs
|
||||
from config import aaMovementAmp, useMask, maskHeight, maskWidth, aaQuitKey, confidence, headshot_mode, cpsDisplay, visuals, centerOfScreen, screenShotWidth
|
||||
from config import aaMovementAmp, aaRightShift, aaQuitKey, confidence, headshot_mode, cpsDisplay, visuals, centerOfScreen
|
||||
import gameSelection
|
||||
|
||||
def main():
|
||||
@ -31,7 +32,7 @@ def main():
|
||||
# Used for colors drawn on bounding boxes
|
||||
COLORS = np.random.uniform(0, 255, size=(1500, 3))
|
||||
|
||||
# Main loop Quit if exit key is pressed
|
||||
# Main loop Quit if Q is pressed
|
||||
last_mid_coord = None
|
||||
with torch.no_grad():
|
||||
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
|
||||
@ -40,17 +41,6 @@ def main():
|
||||
if npImg.shape[3] == 4:
|
||||
# If the image has an alpha channel, remove it
|
||||
npImg = npImg[:, :, :, :3]
|
||||
|
||||
from config import maskSide # "temporary" workaround for bad syntax
|
||||
if useMask:
|
||||
maskSide = maskSide.lower()
|
||||
if maskSide == "right":
|
||||
npImg[:, -maskHeight:, -maskWidth:, :] = 0
|
||||
elif maskSide == "left":
|
||||
npImg[:, -maskHeight:, :maskWidth, :] = 0
|
||||
else:
|
||||
raise Exception('ERROR: Invalid maskSide! Please use "left" or "right"')
|
||||
|
||||
im = npImg / 255
|
||||
im = im.astype(cp.half)
|
||||
|
||||
@ -100,7 +90,7 @@ def main():
|
||||
targets.sort_values(by="dist", ascending=False)
|
||||
|
||||
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
|
||||
xMid = targets.iloc[0].current_mid_x
|
||||
xMid = targets.iloc[0].current_mid_x + aaRightShift
|
||||
yMid = targets.iloc[0].current_mid_y
|
||||
|
||||
box_height = targets.iloc[0].height
|
||||
@ -167,5 +157,5 @@ if __name__ == "__main__":
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exception(e)
|
||||
print("ERROR: " + str(e))
|
||||
print(str(e))
|
||||
print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Common modules
|
||||
"""
|
||||
@ -24,24 +24,12 @@ import torch.nn as nn
|
||||
from PIL import Image
|
||||
from torch.cuda import amp
|
||||
|
||||
# Import 'ultralytics' package or install if if missing
|
||||
try:
|
||||
import ultralytics
|
||||
|
||||
assert hasattr(ultralytics, '__version__') # verify package is not directory
|
||||
except (ImportError, AssertionError):
|
||||
import os
|
||||
|
||||
os.system('pip install -U ultralytics')
|
||||
import ultralytics
|
||||
|
||||
from ultralytics.utils.plotting import Annotator, colors, save_one_box
|
||||
|
||||
from utils import TryExcept
|
||||
from utils.dataloaders import exif_transpose, letterbox
|
||||
from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
|
||||
increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
|
||||
xyxy2xywh, yaml_load)
|
||||
from utils.plots import Annotator, colors, save_one_box
|
||||
from utils.torch_utils import copy_attr, smart_inference_mode
|
||||
|
||||
|
||||
@ -345,7 +333,7 @@ class DetectMultiBackend(nn.Module):
|
||||
super().__init__()
|
||||
w = str(weights[0] if isinstance(weights, list) else weights)
|
||||
pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
|
||||
fp16 &= pt or jit or onnx or engine or triton # FP16
|
||||
fp16 &= pt or jit or onnx or engine # FP16
|
||||
nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)
|
||||
stride = 32 # default stride
|
||||
cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA
|
||||
@ -365,8 +353,7 @@ class DetectMultiBackend(nn.Module):
|
||||
model.half() if fp16 else model.float()
|
||||
if extra_files['config.txt']: # load metadata dict
|
||||
d = json.loads(extra_files['config.txt'],
|
||||
object_hook=lambda d: {
|
||||
int(k) if k.isdigit() else k: v
|
||||
object_hook=lambda d: {int(k) if k.isdigit() else k: v
|
||||
for k, v in d.items()})
|
||||
stride, names = int(d['stride']), d['names']
|
||||
elif dnn: # ONNX OpenCV DNN
|
||||
@ -385,18 +372,18 @@ class DetectMultiBackend(nn.Module):
|
||||
stride, names = int(meta['stride']), eval(meta['names'])
|
||||
elif xml: # OpenVINO
|
||||
LOGGER.info(f'Loading {w} for OpenVINO inference...')
|
||||
check_requirements('openvino>=2023.0') # requires openvino-dev: https://pypi.org/project/openvino-dev/
|
||||
check_requirements('openvino') # requires openvino-dev: https://pypi.org/project/openvino-dev/
|
||||
from openvino.runtime import Core, Layout, get_batch
|
||||
core = Core()
|
||||
ie = Core()
|
||||
if not Path(w).is_file(): # if not *.xml
|
||||
w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir
|
||||
ov_model = core.read_model(model=w, weights=Path(w).with_suffix('.bin'))
|
||||
if ov_model.get_parameters()[0].get_layout().empty:
|
||||
ov_model.get_parameters()[0].set_layout(Layout('NCHW'))
|
||||
batch_dim = get_batch(ov_model)
|
||||
network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
|
||||
if network.get_parameters()[0].get_layout().empty:
|
||||
network.get_parameters()[0].set_layout(Layout('NCHW'))
|
||||
batch_dim = get_batch(network)
|
||||
if batch_dim.is_static:
|
||||
batch_size = batch_dim.get_length()
|
||||
ov_compiled_model = core.compile_model(ov_model, device_name='AUTO') # AUTO selects best available device
|
||||
executable_network = ie.compile_model(network, device_name='CPU') # device_name="MYRIAD" for Intel NCS2
|
||||
stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata
|
||||
elif engine: # TensorRT
|
||||
LOGGER.info(f'Loading {w} for TensorRT inference...')
|
||||
@ -536,7 +523,7 @@ class DetectMultiBackend(nn.Module):
|
||||
y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
|
||||
elif self.xml: # OpenVINO
|
||||
im = im.cpu().numpy() # FP32
|
||||
y = list(self.ov_compiled_model(im).values())
|
||||
y = list(self.executable_network([im]).values())
|
||||
elif self.engine: # TensorRT
|
||||
if self.dynamic and im.shape != self.bindings['images'].shape:
|
||||
i = self.model.get_binding_index('images')
|
||||
@ -553,7 +540,7 @@ class DetectMultiBackend(nn.Module):
|
||||
elif self.coreml: # CoreML
|
||||
im = im.cpu().numpy()
|
||||
im = Image.fromarray((im[0] * 255).astype('uint8'))
|
||||
# im = im.resize((192, 320), Image.BILINEAR)
|
||||
# im = im.resize((192, 320), Image.ANTIALIAS)
|
||||
y = self.model.predict({'image': im}) # coordinates are xywh normalized
|
||||
if 'confidence' in y:
|
||||
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Experimental modules
|
||||
"""
|
||||
@ -87,11 +87,11 @@ def attempt_load(weights, device=None, inplace=True, fuse=True):
|
||||
|
||||
model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode
|
||||
|
||||
# Module updates
|
||||
# Module compatibility updates
|
||||
for m in model.modules():
|
||||
t = type(m)
|
||||
if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
|
||||
m.inplace = inplace
|
||||
m.inplace = inplace # torch 1.7.0 compatibility
|
||||
if t is Detect and not isinstance(m.anchor_grid, list):
|
||||
delattr(m, 'anchor_grid')
|
||||
setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Default anchors for COCO data
|
||||
|
||||
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
TensorFlow, Keras and TFLite versions of YOLOv5
|
||||
Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
|
||||
@ -310,7 +310,7 @@ class TFDetect(keras.layers.Layer):
|
||||
y = tf.concat([xy, wh, tf.sigmoid(y[..., 4:5 + self.nc]), y[..., 5 + self.nc:]], -1)
|
||||
z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))
|
||||
|
||||
return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1), )
|
||||
return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),)
|
||||
|
||||
@staticmethod
|
||||
def _make_grid(nx=20, ny=20):
|
||||
@ -486,7 +486,7 @@ class TFModel:
|
||||
iou_thres,
|
||||
conf_thres,
|
||||
clip_boxes=False)
|
||||
return (nms, )
|
||||
return (nms,)
|
||||
return x # output [1,6300,85] = [xywh, conf, class0, class1, ...]
|
||||
# x = x[0] # [x(1,6300,85), ...] to x(6300,85)
|
||||
# xywh = x[..., :4] # x(6300,4) boxes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
YOLO-specific modules
|
||||
|
||||
@ -21,8 +21,8 @@ if str(ROOT) not in sys.path:
|
||||
if platform.system() != 'Windows':
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from models.common import * # noqa
|
||||
from models.experimental import * # noqa
|
||||
from models.common import *
|
||||
from models.experimental import *
|
||||
from utils.autoanchor import check_anchor_order
|
||||
from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
|
||||
from utils.plots import feature_visualization
|
||||
@ -76,7 +76,7 @@ class Detect(nn.Module):
|
||||
y = torch.cat((xy, wh, conf), 4)
|
||||
z.append(y.view(bs, self.na * nx * ny, self.no))
|
||||
|
||||
return x if self.training else (torch.cat(z, 1), ) if self.export else (torch.cat(z, 1), x)
|
||||
return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
|
||||
|
||||
def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
|
||||
d = self.anchors[i].device
|
||||
@ -126,7 +126,7 @@ class BaseModel(nn.Module):
|
||||
|
||||
def _profile_one_layer(self, m, x, dt):
|
||||
c = m == self.model[-1] # is final layer, copy input as inplace fix
|
||||
o = thop.profile(m, inputs=(x.copy() if c else x, ), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
|
||||
o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
|
||||
t = time_sync()
|
||||
for _ in range(10):
|
||||
m(x.copy() if c else x)
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -13,4 +13,4 @@ ipython
|
||||
psutil
|
||||
dxcam
|
||||
onnxruntime_directml
|
||||
bettercam
|
||||
git+https://github.com/RootKit-Org/BetterCam
|
||||
@ -330,7 +330,7 @@ def classify_albumentations(
|
||||
if vflip > 0:
|
||||
T += [A.VerticalFlip(p=vflip)]
|
||||
if jitter > 0:
|
||||
color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, saturation, 0 hue
|
||||
color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue
|
||||
T += [A.ColorJitter(*color_jitter, 0)]
|
||||
else: # Use fixed crop for eval set (reproducibility)
|
||||
T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
|
||||
@ -68,7 +68,7 @@ Run information streams from your environment to the W&B cloud console as you tr
|
||||
You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started.
|
||||
|
||||
<details open>
|
||||
<h3> 1: Train and Log Evaluation simultaneously </h3>
|
||||
<h3> 1: Train and Log Evaluation simultaneousy </h3>
|
||||
This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b>
|
||||
Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
|
||||
so no images will be uploaded from your system more than once.
|
||||
@ -102,7 +102,7 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma
|
||||
</details>
|
||||
|
||||
<h3> 4: Save model checkpoints as artifacts </h3>
|
||||
To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base command, where `n` represents checkpoint interval.
|
||||
To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
|
||||
You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged
|
||||
|
||||
<details>
|
||||
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Reference in New Issue
Block a user