Building Advanced OpenCV3 Projects with Python

Building Advanced OpenCV3 Projects with Python

Building Advanced OpenCV3 Projects with Python

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 3h 30m | 934 MB

Discover how to build advanced OpenCV3 projects with Python

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.

Enhance your skills with real-world example of computer vision by building amazing and interactive application with OpenCV3 and Python 3

What You Will Learn

  • Learn how to perform 3D reconstruction based on Structure from Motion
  • Implementing a street view-like experience with 2D geo-tagged images
  • Real-time head pose estimation and tracking
  • Perform face morphing, averaging, and swapping operations on images.
  • Build an Android selfie camera app with emotion-based selfie filters
  • Perform image stitching to stitch overlapping snaps of landscape images
  • Build an Android App to generate panoramas with HDR and AR capabilities Learn how to detect lanes and segment roads and track vehicles in a driving scene
  • Learn how to make a car learn how to drive itself based on imitation learning
Table of Contents

01 The Course Overview
02 Camera Projection Models
03 Multi-View Stereo
04 Generating Point Clouds
05 2D-to-3D
06 Street View
07 Real-Time Face Detection Based on Eigenfaces
08 3D Head Pose Estimation
09 Detecting Cats and Faces Using Haar Cascades
10 Facial Landmark Detection Using Dlib Library
11 Face Morphology, Averaging, and Swapping
12 Expressions – A Selfie Camera App
13 Image Stitching
14 Aerial Video Montage
15 Marker-Based Augmented Reality
16 Markerless Augmented Reality
17 High-Dynamic Range (HDR) Imaging
18 Building a Panorama App
19 Introduction to Self-Driving Cars
20 Sensors and Measurements
21 Self-Driving Car Architectures
22 Understanding Perception in Self-Driving Cars
23 Learning to Drive Using a CNN
24 Building a Self-Driving Car Based on Imitation Learning