Add initial implementation for OpenCV human detection with alarm functionality

- Create main.py for human detection using YOLO model
- Add README.md for project overview and usage instructions
- Include requirements.txt for necessary packages
- Add .gitignore to exclude virtual environment and model files

Author: ket0x4

.gitignore

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+venv/
+*pt

README.md

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+# OpenCV Human Detection
+
+Simple OpenCV script for human decetion and playing alarm sound so I can sleep in peace in work without worrying about to be caught by my boss.
+
+### Download the model
+* [Yolo11](https://docs.ultralytics.com/tr/models/yolo11/#performance-metrics)
+* Open `main.py` and change line 18 to the path of the model you downloaded.
+
+### Usage 
+```shell
+python -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+python main.py
+```
+
+### Requirements
+- Python 3.8-3.11
+- CPU
+- Camera
+- Headphones

alarm.mp3

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+import torch
+import cv2
+import warnings
+import os
+from ultralytics import YOLO
+import pygame
+
+# Suppress the wayland warning
+os.environ["QT_QPA_PLATFORM"] = "xcb"  # Set to "wayland" if needed
+
+# Suppress Qt font warnings
+warnings.filterwarnings("ignore", message="QFont::fromString")
+# Suppress PyTorch FutureWarnings
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+# Enable CPU
+device = 'cpu'
+model = YOLO("yolo11s.pt").to(device)
+
+# Open video file or webcam
+cap = cv2.VideoCapture(0)  # Replace 0 with file path for a video
+
+pygame.mixer.init()
+alarm_sound = pygame.mixer.Sound('alarm.mp3')
+
+alerted = False
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # Resize frame to smaller size for CPU
+    #frame = cv2.resize(frame, (1024, 1024))
+    
+    # Convert image to RGB and to appropriate device and precision
+    img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    img_rgb = torch.from_numpy(img_rgb).permute(2, 0, 1).unsqueeze(0).to(device)
+    img_rgb = img_rgb.float()  # Convert to floating point
+    img_rgb = img_rgb / 255.0  # Normalize tensor to [0.0, 1.0]
+
+    # Run YOLOv9 inference
+    with torch.no_grad():
+        results = model(img_rgb)
+
+    # Check for human detection and play sound once
+    if 0 in results[0].boxes.cls:
+        if not alerted:
+            alarm_sound.play()
+            alerted = True
+    else:
+        alerted = False
+
+    # Convert the results back to BGR format for OpenCV
+    result_img = cv2.cvtColor(results[0].plot(), cv2.COLOR_RGB2BGR)
+    
+    # Scale the result image for display
+    result_img = cv2.resize(result_img, (1280, 860))  # Adjust dimensions as needed
+
+    # Display the output frame
+    cv2.imshow('Uykum geldi', result_img)
+
+    # Exit loop on 'q' press
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release video capture and close windows
+cap.release()
+cv2.destroyAllWindows()

main.py

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+opencv-python
+ultralytics
+torch --index-url https://download.pytorch.org/whl/cpu
+torchaudio --index-url https://download.pytorch.org/whl/cpu
+torchvision --index-url https://download.pytorch.org/whl/cpu