Cat Face Detector ฅ⁠^⁠•⁠ﻌ⁠•⁠^⁠ฅ

A beginner-friendly computer vision project that detects cat faces in images and videos using OpenCV and Haar Cascades.

📋 Table of Contents

• Overview
• Features
• Installation
• Usage
• Project Structure
• How It Works
• Results
• Future Improvements
• Contributing
• License

🎯 Overview

This project demonstrates real-time cat face detection using computer vision techniques. Built as a beginner-friendly introduction to OpenCV, it can detect cat faces in:

• Static images
• Video files
• Live webcam feeds

Perfect for learning the fundamentals of object detection and creating fun content with your pets!

✨ Features

• ✅ Real-time detection in video streams
• ✅ Batch processing of images and videos
• ✅ Webcam support for live demonstrations
• ✅ Automatic video saving with detections
• ✅ Detection statistics (frames processed, cats detected)
• ✅ Easy-to-use command-line interface

🚀 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Step 1: Clone the Repository

git clone https://github.com/Ph0enix22/cat-detector.git
cd cat-detector

Step 2: Create Virtual Environment (Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# Mac/Linux
python3 -m venv venv
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Download Haar Cascade File

Download the haarcascade_frontalcatface.xml file from the OpenCV GitHub repository and place it in the project root directory.

💻 Usage

Detect Cats in an Image

python cat_detector.py --input path/to/cat_image.jpg --mode image

Detect Cats in a Video

python cat_detector.py --input path/to/cat_video.mp4 --mode video

Use Webcam for Live Detection

python cat_detector.py --input 0 --mode video

Advanced Options

# Custom output directory
python cat_detector.py --input video.mp4 --mode video --output my_outputs

# Use different cascade file
python cat_detector.py --input video.mp4 --cascade custom_cascade.xml

# Preview only (don't save output)
python cat_detector.py --input 0 --mode video --no-save

All Command-Line Arguments

Argument	Short	Description	Default
--input	-i	Input image/video path, or 0 for webcam	Required
--mode	-m	Detection mode: 'image' or 'video'	video
--cascade	-c	Path to Haar cascade XML file	haarcascade_frontalcatface.xml
--output	-o	Output directory	outputs
--no-save	-	Don't save output (preview only)	False

📁 Project Structure

cat-detector/
│
├── cat_detector.py                 # Main detection script
├── haarcascade_frontalcatface.xml  # Haar cascade model
├── requirements.txt                # Python dependencies
├── README.md                       # Project documentation
├── .gitignore                      # Git ignore rules
├── LICENSE                         # Project license
│
├── videos/                      # Input videos (cat footage)
│   ├── cat_video1.mp4
│   └── kitten_video1.mp4
│
├── outputs/                     # Detected outputs (images + videos)
│   ├── detected_20251227_233739.mp4
│   └── detected_cat1.jpeg
│
└── screenshots/                 # Original cat images before detection
│   ├── cat1.jpeg
│   └── cat2.jpeg
└── demo/                           # Demo assets (GIFs, live detection screenshots, screen recordings)
    ├── demo_detection.gif
    └── demo_recording.mp4

🔍 How It Works

1. Haar Cascade Classifiers

This project uses Haar Cascade Classifiers, a machine learning-based approach for object detection:

• Pre-trained on thousands of positive (cat faces) and negative (non-cat) images
• Uses multiple stages of weak classifiers to identify features
• Fast and efficient for real-time detection
• Works best with frontal face views

2. Detection Pipeline

Input (Image/Video)
    ↓
Convert to Grayscale
    ↓
Apply Haar Cascade Detector
    ↓
Find Regions of Interest (ROI)
    ↓
Draw Bounding Boxes
    ↓
Output Annotated Image/Video

3. Key Parameters

• scaleFactor (1.3): How much the image size is reduced at each scale
• minNeighbors (10): How many neighbors each candidate rectangle should have
• minSize (75x75): Minimum object size to detect

📊 Results

Detection Statistics (Example)

Total frames processed: 450
Total cat detections: 412
Average detections per frame: 0.92
Detection accuracy: ~90% on frontal faces

Performance

• Processing Speed: ~30 FPS on modern laptops
• Best Results: Well-lit environments with frontal face views
• Limitations: Reduced accuracy with side profiles or poor lighting

🎥 Demo

Live Webcam Detection (GIF)

Image Detection

🚧 Future Improvements

Short-term

• Add size-based classification (cat vs kitten)
• Implement confidence scores
• Create a simple GUI with Tkinter
• Add support for multiple cascade types

Long-term

• Upgrade to YOLO for better accuracy
• Train custom model on specific pets
• Deploy as web app with Streamlit
• Add pet activity tracking over time

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• OpenCV for the computer vision library
• OpenCV Haar Cascade models for the pre-trained classifier
• My cats, for patiently (and not so patiently) posing for the camera
• My siblings, for helping with recordings, testing, and feedback

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🐦‍🔥 Author

SMJ

• GitHub: Ph0enix22
• LinkedIn: Syeda Midhath Javeria
• Email: syedamidhath159@gmail.com

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⭐ If you found this project helpful, please consider giving it a star!

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🎓 Learning Resources

New to computer vision? Check these out:

• OpenCV Python Tutorials
• PyImageSearch
• Sentdex OpenCV Tutorial

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Made with ❤️ by SMJ (for beginners in computer vision)