Mastering OpenCV for AI-Powered Computer Vision
web-development.
Table of Contents
- 1. Course Modules:
- 2. Module 1: Introduction to OpenCV
- 3. Module 2: Image Processing Fundamentals
- 4. Module 3: Filtering, Thresholding & Edge Detection
- 5. Module 4: Contours, Shapes & Object Detection
- 6. Module 5: Face Detection and Recognition
- 7. Module 6: Video Processing and Real-Time Vision
- 8. Module 7: Integration with AI Models
- 9. Module 8: Capstone Project – Build a Vision App
- 10. Tools & Technologies Used:
- 11. Target Audience:
- 12. Global Learning Benefits:
Course Modules:
Module 1: Introduction to OpenCV
What is OpenCV and why use it?
Installing OpenCV with Python
Exploring OpenCV functions and use cases
Module 2: Image Processing Fundamentals
Reading, displaying, and saving images
Color models (RGB, BGR, Grayscale, HSV)
Drawing shapes and text on images
Module 3: Filtering, Thresholding & Edge Detection
Image blurring, smoothing, and sharpening
Adaptive thresholding and binary segmentation
Sobel, Laplacian, and Canny edge detection
Module 4: Contours, Shapes & Object Detection
Finding and drawing contours
Shape recognition and object boundary tracking
Real-time object detection logic
Module 5: Face Detection and Recognition
Haar Cascades vs. Deep Learning-based detection
Detecting faces, eyes, and smiles
Face recognition with DNNs
Module 6: Video Processing and Real-Time Vision
Capturing and processing live video streams
Frame-by-frame transformations
Live object and face tracking
Module 7: Integration with AI Models
Using OpenCV with TensorFlow and PyTorch
Running pretrained models with OpenCV DNN
Building intelligent camera systems
Module 8: Capstone Project – Build a Vision App
Choose one:
Real-time face detection with live webcam
Object counter for retail shelf analytics
Motion detector for smart security
Submit app demo, source code, and design breakdown
Tools & Technologies Used:
OpenCV (cv2)
Python
NumPy, Matplotlib
Optional: TensorFlow, PyTorch (for DNN integration)
Target Audience:
Beginners in computer vision
Python developers entering AI
Robotics and hardware hobbyists
Students building portfolio projects in vision AI
Global Learning Benefits:
Learn the industry-standard toolkit for computer vision
Gain hands-on experience with live image and video processing
Build and deploy real-time intelligent vision applications
Prepare for advanced vision applications in robotics, AR, and surveillance
?Master Study NLP Fundamentals: The Foundation of Language Understanding in AI
?Shop our library of over one million titles and learn anytime
?? Learn with our expert tutors
Read Also About Understanding Deployment in the AI Lifecycle
