Lilly Tech Systems
Learn OpenCV
Master the world's most popular computer vision library. Build image processing pipelines, detect objects, analyze video streams, and deploy vision applications — all with Python and OpenCV.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is OpenCV, its history, architecture, and how it powers computer vision applications worldwide.
2. Installation
Install OpenCV with pip, conda, or from source. Configure GPU support and verify your setup.
3. Image Processing
Read, write, transform, filter, and manipulate images. Color spaces, thresholding, and morphological operations.
4. Object Detection
Detect faces, objects, and features using Haar cascades, HOG, template matching, and DNN modules.
5. Video Processing
Capture, process, and analyze video streams. Background subtraction, optical flow, and real-time tracking.
6. Best Practices
Performance optimization, production deployment patterns, GPU acceleration, and common pitfalls to avoid.
What You'll Learn
By the end of this course, you'll be able to:
Process Images
Load, transform, filter, and enhance images using OpenCV's powerful image processing pipeline.
Detect Objects
Build face detection, object recognition, and feature matching systems for real-world applications.
Analyze Video
Process video streams in real-time with background subtraction, motion detection, and tracking.
Deploy CV Apps
Build production-ready computer vision applications with optimized performance and GPU acceleration.