Learn OPENCV with Real Code Examples

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).

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

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

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

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

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

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

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

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