Welcome to the OpenCV Python Examples documentation! This section provides a curated list of practical code snippets and tutorials to help you master computer vision with OpenCV. 🌟

📚 Table of Contents

  1. Image Processing Basics
  2. Object Detection & Tracking
  3. Video Analysis Techniques
  4. Machine Learning Applications
  5. Advanced Image Manipulation

📷 Example Visuals

Here are some visual aids to complement your learning:

OpenCV_Python_Logo
*Figure 1: OpenCV Python Logo - Symbolizing the integration of computer vision and programming*
Canny_Edge_Detection
*Figure 2: Canny Edge Detection - A classic image processing technique*

🧩 Sample Code Snippets

1. Grayscale Conversion

import cv2

# Load an image
image = cv2.imread('input.jpg')

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Display the result
cv2.imshow('Grayscale Image', gray)
cv2.waitKey(0)

👉 Explore more about color space conversion

2. Face Detection

# Load pre-trained face classifier
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Detect faces in an image
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)

# Draw rectangles around detected faces
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)

📸 View face detection demo

🔄 Practical Tips

  • Always use cv2.imshow() to visualize intermediate results
  • For real-time applications, consider using cv2.VideoCapture() with a camera index
  • Check out OpenCV's official Python tutorials for deeper concepts

📖 Related Resources

For further learning, you might want to:

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