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Cleaned up Readme and Main.py for v1 release

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README.md
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# Base Undetectable Aimbot [![Pull Requests Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat)](http://makeapullrequest.com)
Watch the video! - Coming Soon # Ultimate Aimbot
**Adhere to our GNU licence or else we WILL come after you legally.**<br />
- free to use, sell, profit from, litterally anything you want to do with it
- **credit MUST be given to RootKit for the underlying base code**
Watch the tutorial video! - Coming Soon<br />
Watch the shorts video! - https://youtu.be/EEgspHlU_H0
Join teh Discord - https://discord.gg/rootkit Join teh Discord - https://discord.gg/rootkit
## V2 - Coming soon
We have already finished the V2 bot BUT will be releasing it on the 6th. Patreons subs will get it right now (https://www.patreon.com/rootkit)!
V2 bot runs about 5-8x faster. Additionally V2 will now need detectron so it will run on more computers.
## Current Stats ## Current Stats
This bot's speed is VERY dependent on your hardware. We will update the model it uses for detection later with a faster one. This bot's speed is VERY dependent on your hardware. We will update the model it uses for detection later with a faster one.
@ -30,7 +41,7 @@ ANYTHING dealing with Machine Learning can be funky with your computer. So if yo
4. To install `PyTorch` go to this website, https://pytorch.org/get-started/locally/, and Select the stable build, your OS, Pip, Python and CUDA 11.3. Then select the text that is generated and run that command. 4. To install `PyTorch` go to this website, https://pytorch.org/get-started/locally/, and Select the stable build, your OS, Pip, Python and CUDA 11.3. Then select the text that is generated and run that command.
5. To install `detectron2` go to this website, https://detectron2.readthedocs.io/en/latest/tutorials/install.html and follow the instructions. They don't officially support Windows but it will work on Windows. 5. To install `detectron2` go to this website, https://detectron2.readthedocs.io/en/latest/tutorials/install.html and follow the instructions. **They don't officially support Windows but it will work on Windows**. You will either need to install it on the WSL or you will need the underlying compiler.
6. Copy and past the commands below into your terminal. This will install the Open Source packages needed to run the program. 6. Copy and past the commands below into your terminal. This will install the Open Source packages needed to run the program.
``` ```
@ -41,7 +52,6 @@ pip install opencv-python
pip install mss pip install mss
pip install numpy pip install numpy
``` ```
***IF YOU GET THE FOLLOWING ERROR `pip is not recognized as an internal or external command, operable program, or batch file` Ask someone in the discord to help you out. We will be releasing a video about this soon since all the videos available for it are garbage.***
### Run ### Run
If you have python and the packages you are good to go. Load up any game on your MAIN monitor and load into a game. If you have python and the packages you are good to go. Load up any game on your MAIN monitor and load into a game.
@ -56,5 +66,8 @@ If you have python and the packages you are good to go. Load up any game on your
5. Type `python main.py`, press enter and that is it! 5. Type `python main.py`, press enter and that is it!
## Community Based
We are a community based nonprofit. We are always open to pull requests on any of our repos. You will always be given credit for all of you work. Depending on what you contribute, we will give you any revenue earned on your contributions 💰💰💰!
**We are always looking for new Volunteers to join our Champions! **We are always looking for new Volunteers to join our Champions!
If you have any ideas for videos or programs, let us know!** If you have any ideas for videos or programs, let us know!**

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main.py
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import detectron2
import pyautogui import pyautogui
import gc import gc
import pydirectinput import pydirectinput
@ -15,47 +14,49 @@ from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog from detectron2.data import MetadataCatalog, DatasetCatalog
# Window title to go after and the height of the screenshots def main():
videoGameWindowTitle = "Counter-Strike" # Window title to go after and the height of the screenshots
screenShotHeight = 250 videoGameWindowTitle = "Counter-Strike"
videoGameWindowTitle = "Valorant"
screenShotHeight = 250
# How big the Autoaim box should be around the center of the screen # How big the Autoaim box should be around the center of the screen
aaDetectionBox = 300 aaDetectionBox = 300
# Autoaim speed # Autoaim speed
aaMovementAmp = 2 aaMovementAmp = 2
# 0 will point center mass, 40 will point around the head in CSGO # 0 will point center mass, 40 will point around the head in CSGO
aaAimExtraVertical = 40 aaAimExtraVertical = 40
# Loading up the object detection model # Loading up the object detection model
cfg = get_cfg() cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml")) cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml") cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml")
predictor = DefaultPredictor(cfg) predictor = DefaultPredictor(cfg)
# Selecting the correct game window # Selecting the correct game window
videoGameWindows = pyautogui.getWindowsWithTitle(videoGameWindowTitle) videoGameWindows = pyautogui.getWindowsWithTitle(videoGameWindowTitle)
videoGameWindow = videoGameWindows[0] videoGameWindow = videoGameWindows[0]
# Select that Window # Select that Window
videoGameWindow.activate() videoGameWindow.activate()
# Setting up the screen shots # Setting up the screen shots
sctArea = {"mon": 1, "top": videoGameWindow.top + round((videoGameWindow.height - screenShotHeight) / 2), "left": videoGameWindow.left, "width": videoGameWindow.width, "height": screenShotHeight} sctArea = {"mon": 1, "top": videoGameWindow.top + round((videoGameWindow.height - screenShotHeight) / 2), "left": videoGameWindow.left, "width": videoGameWindow.width, "height": screenShotHeight}
sct = mss.mss() sct = mss.mss()
# Calculating the center Autoaim box # Calculating the center Autoaim box
cWidth = sctArea["width"] / 2 cWidth = sctArea["width"] / 2
cHeight = sctArea["height"] / 2 cHeight = sctArea["height"] / 2
# Used for forcing garbage collection # Used for forcing garbage collection
count = 0 count = 0
sTime = time.time() sTime = time.time()
# Main loop # Main loop
while True: while True:
# Getting screenshop, making into np.array and dropping alpha dimention. # Getting screenshop, making into np.array and dropping alpha dimention.
npImg = np.delete(np.array(sct.grab(sctArea)), 3, axis=2) npImg = np.delete(np.array(sct.grab(sctArea)), 3, axis=2)
@ -90,9 +91,12 @@ while True:
sTime = time.time() sTime = time.time()
gc.collect(generation=0) gc.collect(generation=0)
# Uncomment to see visually what the Aimbot sees #! Uncomment to see visually what the Aimbot sees
# v = Visualizer(npImg[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2) # v = Visualizer(npImg[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
# out = v.draw_instance_predictions(predictions["instances"].to("cpu")) # out = v.draw_instance_predictions(predictions["instances"].to("cpu"))
# cv2.imshow('sample image',out.get_image()[:, :, ::-1]) # cv2.imshow('sample image',out.get_image()[:, :, ::-1])
# if (cv2.waitKey(1) & 0xFF) == ord('q'): # if (cv2.waitKey(1) & 0xFF) == ord('q'):
# exit() # exit()
if __name__ == "__main__":
main()