CS-GO Gaming Aimbot with YOLOv7 | YOLOv8

Develop a CS:GO gaming aimbot using YOLOv7. Gain insights into object detection principles with YOLO.

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Counter-Strike: Global Offensive (CS: GO) is one of the most popular first-person shooter games in the world. Since its release in 2012, it has garnered a massive player base and has become a cornerstone of the esports scene. In such a competitive environment, players are constantly looking for ways to improve their performance. This has led to the development of various tools and cheats, one of the most controversial being aimbots. An aimbot is a type of cheating software that automatically aims at enemies, giving the user a significant advantage.

The Ethics and Legality of Aimbots

Before diving into the technical details of creating an aimbot using YOLO (You Only Look Once) models, it's important to acknowledge the ethical and legal implications. Using aimbots is considered cheating and is against the terms of service of CS: GO. It undermines fair play and can lead to bans or legal consequences. This article is for educational purposes only, to explore the capabilities of computer vision and machine learning, and not to promote or facilitate cheating in games.

YOLOv7 and YOLOv8: An Overview

YOLO is a state-of-the-art, real-time object detection system. YOLOv7 and YOLOv8 are the latest iterations, improving on the speed and accuracy of their predecessors. YOLOv7 brought significant improvements in terms of efficiency and speed, while YOLOv8 further enhances these aspects with better accuracy and even faster inference times. These models can be trained to detect various objects, including player characters in CS: GO, making them suitable for developing an aimbot.

Setting Up the Environment

To create an aimbot using YOLO models, you need a development environment equipped with the necessary tools and libraries. Here’s a basic setup:

Python: The primary language for implementing YOLO models.
OpenCV: A library for real-time computer vision.
PyTorch or TensorFlow: Frameworks for deep learning.
YOLOv7/YOLOv8 Weights and Configuration Files: Pre-trained models or custom-trained models for player detection.
CS: GO: The game itself, running in windowed mode for easier access to the screen content.

Capturing the Game Screen

First, you need to capture the game screen in real-time. This can be done using libraries like mss or pygetwindow combined with OpenCV. Here’s an example:

python
import cv2
import numpy as np
import mss

sct = mss.mss()

monitor = {"top": 0, "left": 0, "width": 1920, "height": 1080}

while True:
    screenshot = np.array(sct.grab(monitor))
    frame = cv2.cvtColor(screenshot, cv2.COLOR_BGRA2BGR)
    cv2.imshow("Screen", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()

This code captures the entire screen and displays it in a window. Adjust the monitor dictionary to match your screen resolution.

Integrating YOLO for Object Detection

Next, integrate the YOLO model to detect players. Load the pre-trained YOLOv7 or YOLOv8 model and run inference on the captured frames.

Loading the Model

Here’s how to load a YOLOv7 model using PyTorch:

python
import torch

model = torch.hub.load('ultralytics/yolov7', 'yolov7')
model.eval()

Running Inference

To run inference on each frame, convert the frame to a format compatible with the model and perform detection:

python
results = model(frame)
detections = results.xyxy[0].cpu().numpy()  # bounding boxes

Drawing Bounding Boxes

Draw the detected bounding boxes on the frame:

python
for x1, y1, x2, y2, conf, cls in detections:
    cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
    label = f"{conf:.2f}"
    cv2.putText(frame, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

Implementing the Aimbot Logic

Once you have detected the players, the next step is to implement the aimbot logic. This involves calculating the center of the detected bounding box and moving the mouse cursor to that position.

Calculating the Target Position

Calculate the center of the bounding box:

python
target_x = int((x1 + x2) / 2)
target_y = int((y1 + y2) / 2)

Moving the Mouse Cursor

Use a library like pyautogui to move the mouse cursor:

python
import pyautogui

pyautogui.moveTo(target_x, target_y)
pyautogui.click()  # Simulate a mouse click

Putting It All Together

Combine all these steps into a single script that captures the screen, runs object detection, and moves the mouse cursor to aim at detected players:

python
import cv2
import numpy as np
import mss
import torch
import pyautogui

# Load YOLO model
model = torch.hub.load('ultralytics/yolov7', 'yolov7')
model.eval()

# Screen capture setup
sct = mss.mss()
monitor = {"top": 0, "left": 0, "width": 1920, "height": 1080}

while True:
    # Capture screen
    screenshot = np.array(sct.grab(monitor))
    frame = cv2.cvtColor(screenshot, cv2.COLOR_BGRA2BGR)

    # Run YOLO inference
    results = model(frame)
    detections = results.xyxy[0].cpu().numpy()  # Bounding boxes

    # Process detections
    for x1, y1, x2, y2, conf, cls in detections:
        if conf > 0.5:  # Confidence threshold
            target_x = int((x1 + x2) / 2)
            target_y = int((y1 + y2) / 2)
            
            # Move mouse cursor
            pyautogui.moveTo(target_x, target_y)
            pyautogui.click()  # Simulate a mouse click

            # Draw bounding box for visualization
            cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
            label = f"{conf:.2f}"
            cv2.putText(frame, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Display the frame
    cv2.imshow("Screen", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()

Conclusion

Creating an aimbot for CS: GO using YOLOv7 or YOLOv8 involves capturing the game screen, detecting players with a YOLO model, and moving the mouse cursor to aim automatically. While the technical challenge can be intriguing, it's crucial to remember that using such cheats in actual gameplay is unethical and can lead to severe consequences. This article aims to showcase the power of computer vision and deep learning, encouraging responsible use of these technologies in a manner that respects the integrity of competitive gaming.

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CS-GO Gaming Aimbot with YOLOv7 | YOLOv8