OpenCV (Open Source Computer Vision Library) is an open-source library used for real-time image processing and computer vision applications.

Why Use OpenCV?
Fast image/video processing
Large collection of functions (face detection, object tracking, etc.)
Works with NumPy arrays
Cross-platform support
Integrates with deep learning models (e.g. with TensorFlow, PyTorch)

Installation
bash
pip install opencv-python


Basic Example: Load & Display Image
python
import cv2

img = cv2.imread('image.jpg') Load image
cv2.imshow('Image', img) Display
cv2.waitKey(0) Wait for key press
cv2.destroyAllWindows() Close window


Read from Webcam & Convert to Grayscale
python
cap = cv2.VideoCapture(0)

while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('Grayscale Video', gray)

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

cap.release()
cv2.destroyAllWindows()


Popular Features
Face Detection (Haar Cascades)
Edge Detection (Canny)
Object Tracking
Image Filtering (Blur, Sharpen)
Drawing shapes & text on images

Real-World Use Cases
Facial recognition systems
Surveillance cameras
Self-driving cars
Augmented reality

Summary

Ideal For: Developers working with images/videos or real-time vision apps
Strength: Fast processing, huge toolkit, active community