Learn OPENCV with Real Code Examples
Updated Nov 24, 2025
Explain
OpenCV enables developers to process images and videos to detect objects, track motion, and analyze visual content.
It provides pre-built functions for feature detection, image transformations, filtering, and machine learning integration.
OpenCV supports multiple programming languages including Python, C++, Java, and JavaScript (via OpenCV.js).
Core Features
Image I/O: read/write images and videos
Image processing: filtering, edge detection, color conversion
Feature detection: SIFT, SURF, ORB, keypoints, descriptors
Object detection: Haar cascades, DNN modules
Camera calibration and 3D reconstruction
Basic Concepts Overview
Image: a matrix of pixels
Video: sequence of frames (images)
Contours: shapes or boundaries in images
Keypoints and descriptors: features for recognition
DNN: deep neural network module for advanced vision tasks
Project Structure
main.py - main scripts for image/video tasks
data/ - images, videos, datasets
utils/ - helper functions (filters, transformations)
models/ - pre-trained models and checkpoints
notebooks/ - experimentation and prototyping
Building Workflow
Load or capture an image/video
Preprocess: resize, grayscale, normalize
Detect features or objects
Apply filters or transformations
Display, save, or feed data into ML models
Difficulty Use Cases
Beginner: basic image display, reading, and writing
Intermediate: filtering, edge detection, transformations
Advanced: object detection, tracking, facial recognition
Expert: integrating deep learning models, real-time pipelines
Enterprise: large-scale CV applications for automation or robotics
Comparisons
OpenCV vs Pillow: full CV library vs image I/O/manipulation
OpenCV vs scikit-image: performance and real-time focus
OpenCV vs MediaPipe: general CV vs specialized tracking
OpenCV vs TensorFlow CV modules: traditional vs deep learning
OpenCV vs Dlib: general CV vs facial/shape recognition
Versioning Timeline
1999 – OpenCV started by Intel
2006 – OpenCV 1.0 released
2012 – OpenCV 2.0 with new C++ interface
2015 – OpenCV 3.x with DNN module and GPU support
2023 – OpenCV 5.x with modernized APIs and accelerated performance
Glossary
Image: matrix of pixel values
Frame: single image in a video sequence
Contour: boundary of shapes in images
Keypoint: distinct point used for matching
DNN: deep neural network module