Building Advanced OpenCV3 Projects with Python

Discover how to build advanced OpenCV3 projects with Python

About This Video

• Practical end-to-end projects covering an important computer vision problem
• Step-by-step guide to creating computer vision applications
• Program advanced computer vision applications in Python using different features of the OpenCV library

In Detail

OpenCV is a native cross-platform C++ library for Computer Vision, Machine Learning, and image processing. It is increasingly being adopted for development in Python.

This course features some trending applications of vision and deep learning and will help you master these techniques. You will learn how to retrieve structure from motion (sfm) and you will also see how we can build an application to capture 2D images and join them dynamically to achieve street views by capturing camera projection angles and relative image positions. You will also learn how to track your head in 3D in real-time, and perform facial recognition against a goldenset. You will also build an app to capture facial emotions based on a CovNet.

Next, you'll generate panoramas using image stitching and we extend this concept by generating a map based on the trajectory of ISS. You'll also learn to build an application to capture beautiful panoramas and also achieve AR effects. You then delve into one of the most trending domains of computer vision: autonomous cars. You'll learn about various architectures and develop the skills to detect lanes, and segment and track vehicles in traffic.You will be using Carla, which is a open driving simulator by Intel, for your project to train a car learn how to drive itself using an end-to-end model.

By the end of this course you will have learned to perform 3D reconstruction by stitching multiple 2D images and recovering camera projection angles. You will also have learned to capture facial landmark points and recognize emotion in images, including in real time. You will also have learned to generate a panorama of a scene and augment a camera view with virtual objects. You will be familiar with the field of self-driving cars and its history, and will have trained a car to drive itself in a simulator.