OpenCV Tutorial Project

A comprehensive Python project demonstrating various OpenCV (Open Source Computer Vision Library) capabilities, from basic image operations to real-time face detection.

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📋 Table of Contents

• Overview
• Features
• Project Structure
• Prerequisites
• Installation
• Usage
• Modules & Sections
• Troubleshooting
• Resources
• License

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🎯 Overview

This project provides hands-on examples of OpenCV functionality in Python, covering:

• Basic image processing (read, display, resize, crop, rotate, flip)
• Drawing shapes and adding text overlays
• Real-time webcam capture and processing
• Image filtering and blurring techniques
• Edge detection and thresholding
• Contour detection and shape recognition
• Real-time face, eye, and smile detection using Haar Cascades

Perfect for beginners learning computer vision and intermediate developers looking for reference implementations.

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✨ Features

Core Image Operations

• ✅ Read and write images from disk
• ✅ Convert between color spaces (BGR ↔ Grayscale)
• ✅ Resize, crop, rotate, and flip images
• ✅ Interactive image processing with user input

Drawing & Visualization

• ✅ Draw lines, rectangles, and circles
• ✅ Add custom text with various fonts and colors
• ✅ Overlay graphics on images

Advanced Processing

• ✅ Gaussian and Median blur filters
• ✅ Custom kernel-based image sharpening
• ✅ Canny edge detection
• ✅ Binary thresholding with Otsu's method
• ✅ Bitwise operations (AND, OR, XOR, NOT)

Computer Vision

• ✅ Contour detection and visualization
• ✅ Automated shape classification (Triangle, Square, Circle, etc.)
• ✅ Real-time face detection using Haar Cascades
• ✅ Eye and smile detection within detected faces
• ✅ Region of Interest (ROI) based processing

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📁 Project Structure

D:\Desktop\OpenCV\
│
├── opencv.py                          # Main tutorial script (11 sections)
├── face_detection.py                  # Standalone face detection demo
├── face_eye_smile.py                  # Face, eye, and smile detection
│
├── requirements.txt                   # Python dependencies
├── README.MD                          # This file
│
├── haarcascade_frontalcatface.xml    # Haar Cascade for cat face detection
├── haarcascade_eye.xml               # Haar Cascade for eye detection
├── haarcascade_smile.xml             # Haar Cascade for smile detection
├── eye.xml                           # Additional eye detection cascade
│
└── venv/                             # Virtual environment (Python packages)

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🔧 Prerequisites

• Python: 3.7 or higher
• Operating System: Windows, macOS, or Linux
• Webcam: Required for real-time detection features (optional for image processing)
• Image Files: Sample images (e.g., Arnab.jpg, monalisha.png, OIP.jpg) for testing

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📦 Installation

1. Clone or Download the Project

cd D:\Desktop\OpenCV

2. Create a Virtual Environment

Windows (PowerShell):

python -m venv venv
.\venv\Scripts\Activate.ps1

macOS/Linux:

python3 -m venv venv
source venv/bin/activate

Note: If PowerShell blocks script execution, run:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

3. Install Dependencies

pip install --upgrade pip
pip install -r requirements.txt

This will install:

• opencv-python - OpenCV library for Python
• numpy - Numerical computing library (required by OpenCV)

4. Verify Installation

python -c "import cv2; print('OpenCV version:', cv2.__version__)"

Expected output: OpenCV version: 4.x.x

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🚀 Usage

Running the Main Tutorial

The opencv.py file contains 11 comprehensive sections. Run it with:

python opencv.py

Interactive Sections:

• Some sections prompt for user input (e.g., image paths, actions)
• Webcam sections require pressing q to quit
• Each image display waits for any key press to continue

Running Face Detection Demos

Basic Face Detection:

python face_detection.py

Face, Eye & Smile Detection:

python face_eye_smile.py

Controls:

• Press q to quit the webcam window
• Ensure your webcam is not being used by another application

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📚 Modules & Sections

opencv.py - Complete Tutorial (11 Sections)

Section	Description	Key Functions
1. Basic Image Operations	Read, display, convert to grayscale, resize, save	cv2.imread(), cv2.cvtColor(), cv2.resize(), cv2.imwrite()
2. Interactive Processing	User-driven image operations	Input handling, conditional processing
3. Image Transformations	Resize, crop, rotate, flip operations	cv2.warpAffine(), cv2.flip(), cv2.getRotationMatrix2D()
4. Drawing Shapes & Text	Lines, rectangles, circles, text overlays	cv2.line(), cv2.rectangle(), cv2.circle(), cv2.putText()
5. Webcam Capture	Real-time video feed from camera	cv2.VideoCapture(), frame reading loop
6. Image Filtering	Gaussian blur, median blur, sharpening	cv2.GaussianBlur(), cv2.medianBlur(), cv2.filter2D()
7. Edge Detection	Canny algorithm, binary thresholding	cv2.Canny(), cv2.threshold()
8. Bitwise Operations	Logical operations on images	cv2.bitwise_and(), cv2.bitwise_or(), cv2.bitwise_xor(), cv2.bitwise_not()
9. Contour Detection	Finding and drawing object contours	cv2.findContours(), cv2.drawContours()
10. Shape Detection	Automated shape classification	cv2.approxPolyDP(), cv2.contourArea(), cv2.minEnclosingCircle()
11. Face/Eye/Smile Detection	Real-time detection with Haar Cascades	cv2.CascadeClassifier(), detectMultiScale()

face_detection.py - Standalone Face Detection

Simple webcam-based face detection using Haar Cascades. Features:

• Loads cascade classifier
• Captures webcam feed
• Detects faces in real-time
• Draws rectangles around detected faces

face_eye_smile.py - Advanced Facial Feature Detection

Enhanced detection including:

• Face detection
• Eye detection within face regions (ROI)
• Smile detection within face regions
• Text labels for detected features

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🛠️ Troubleshooting

Common Issues & Solutions

1. Import Error: ModuleNotFoundError: No module named 'cv2'

Solution:

# Activate virtual environment first
.\venv\Scripts\Activate.ps1  # Windows
source venv/bin/activate      # macOS/Linux

# Install OpenCV
pip install opencv-python

2. Webcam Not Opening ("Error: Could not open webcam")

Causes & Solutions:

• Camera in use: Close other applications using the webcam (Zoom, Teams, etc.)
• Permission denied: Grant camera permissions in system settings
• Wrong camera index: Try changing cv2.VideoCapture(0) to cv2.VideoCapture(1) or cv2.VideoCapture(2)

3. Cascade Classifier Not Loading (cascade.empty() returns True)

Solution:

# Verify file exists
import os
print(os.path.exists(r"D:\Desktop\OpenCV\haarcascade_frontalcatface.xml"))

# Use absolute paths with raw strings
cascade = cv2.CascadeClassifier(r"D:\Desktop\OpenCV\haarcascade_frontalcatface.xml")

4. Image Not Found (image is None)

Solution:

• Check file path is correct (use raw strings: r"D:\path\to\image.jpg")
• Verify image file exists in the specified location
• Ensure image format is supported (JPG, PNG, BMP, TIFF)

5. cv2.error: (-215:Assertion failed) !_src.empty()

Cause: Trying to process an empty/invalid image

Solution:

# Always check if image loaded successfully
image = cv2.imread("image.jpg")
if image is None:
    print("Failed to load image")
else:
    # Process image
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

6. Windows Not Closing After cv2.imshow()

Solution:

# Always include waitKey and destroyAllWindows
cv2.imshow("Window", image)
cv2.waitKey(0)  # Wait for key press
cv2.destroyAllWindows()  # Clean up

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📖 Resources

OpenCV Documentation

• Official Docs: https://docs.opencv.org/
• Python Tutorials: https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html
• API Reference: https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html

Haar Cascade Files

• Pre-trained Cascades: https://github.com/opencv/opencv/tree/master/data/haarcascades
• Common cascades:
  • haarcascade_frontalface_default.xml - Human face detection
  • haarcascade_eye.xml - Eye detection
  • haarcascade_smile.xml - Smile detection

Learning Resources

• OpenCV Course: https://opencv.org/courses/
• PyImageSearch: https://pyimagesearch.com/
• Real Python - OpenCV: https://realpython.com/opencv-python/

Community

• Stack Overflow: Tag [opencv] [python]
• OpenCV Forum: https://forum.opencv.org/

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🎓 Key Concepts Explained

Color Spaces

• BGR: OpenCV's default (Blue, Green, Red) - opposite of RGB
• Grayscale: Single channel, values 0-255
• HSV: Hue, Saturation, Value - useful for color-based segmentation

Image Coordinates

(0,0) -------- x (width) -------->
  |
  |
  y (height)
  |
  |
  v

Haar Cascades

Pre-trained classifiers using Haar-like features for object detection:

• Fast: Real-time performance
• Trained: Uses machine learning on thousands of images
• Scale-invariant: Detects objects at different sizes
• Parameters:
  • scaleFactor: How much image size is reduced at each scale (e.g., 1.1 = 10%)
  • minNeighbors: Minimum neighbors for valid detection (higher = fewer false positives)

Region of Interest (ROI)

Selecting a specific area of an image for focused processing:

# Extract face region
roi = image[y:y+h, x:x+w]

# Process only the ROI
eyes = eye_cascade.detectMultiScale(roi)

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🔐 License

This project is provided for educational purposes. OpenCV itself is released under the Apache 2 License.

Haar Cascade Files: Provided by OpenCV (Apache 2 License)

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👤 Author

Arnab

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🤝 Contributing

Feel free to:

• Add more examples
• Improve documentation
• Fix bugs
• Suggest new features

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📝 Notes

• Image Paths: Update paths in scripts to match your local file structure
• Webcam Index: Default is 0; change if you have multiple cameras
• Performance: Real-time detection performance depends on CPU speed and image resolution
• Cascade Selection: Use haarcascade_frontalface_default.xml for human face detection instead of cat faces

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🚦 Quick Start Checklist

• Python 3.7+ installed
• Virtual environment created and activated
• Dependencies installed (pip install -r requirements.txt)
• OpenCV version verified
• Sample images available (or update paths)
• Webcam connected and functional
• Cascade XML files in project directory

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Happy Coding! 🎉

Explore the fascinating world of computer vision with OpenCV!