Get an introduction to the field of computer vision and learn how OpenCV fits into the world of image processing and machine learning. You will also learn how to set up OpenCV in Python and understand the various capabilities of the library.

Learn how to manipulate and process images using OpenCV, from reading and displaying images to performing simple operations like resizing, rotating, and transforming images.

Dive into more advanced image processing techniques such as filtering, edge detection, histogram equalization, and colour space transformations.

Discover how to detect key features in images and match them across multiple images. You’ll learn about different feature detectors like SIFT, SURF, and ORB, and how they are applied to real-world computer vision problems.

Learn how to detect objects in images and video streams using algorithms such as Haar Cascades and HOG, and track objects over time in real-time using trackers like KLT and MIL.

Explore the principles and techniques behind face detection and recognition. This module covers how to implement face recognition systems using OpenCV and machine learning algorithms.

Learn techniques for segmenting images into distinct regions or objects. You will explore thresholding methods, contour detection, and advanced segmentation algorithms like the watershed algorithm.

Integrate deep learning models into OpenCV workflows. This module introduces neural networks and how to use pre-trained models for tasks like image classification and object detection using OpenCV and Python.