Mastering OpenCV for AI-Powered Computer Vision
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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
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