Learning Path: Opencv: Master Image Processing With Opencv 3

Develop interactive computer vision applications with the popular C++ libraries of OpenCV 3

What you'll learn

Scan an image with pointers and neighbor access

Represent colors with hue, saturation, and brightness

Retrieve similar images using the histogram comparison

Calibrate the camera from different image observations

Detect people and objects in images using machine learning techniques

Reconstruct a 3D scene from images

Add the miniaturizing tilt-shift effect

Load videos and store them

Requirements

Basic programming knowledge on C++ is needed.

Description

OpenCV 3 is a native cross-platform C++ Library for computer vision, machine learning, and image processing. Computer vision applications are the latest buzz of recent time! Big brands such as Microsoft, Apple, Google, Facebook, and Apple are increasingly making use of computer vision for object, pattern, image, and face recognition. This has led to a very high demand for computer vision expertise. So, if you're interested to know how to use the OpenCV library to build computer vision applications, then go for this Learning Path.



Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.


The highlights of this Learning Path are:

Dive into the essentials of OpenCV and build your own projects
Learn how to apply complex visual effects to images
Reconstruct a 3D scene from images
Master the fundamental concepts in computer vision and image processing

Let’s take a quick look at your learning journey. This Learning Path helps you to get started with the OpenCV library and shows you how to install and deploy it to write effective computer vision applications following good programming practices. You will learn how to read and display images. You will then be introduced to the basic OpenCV data structures.


Further, you will start a new project and see how to load an image file and show it. Next, you'll find out how to handle keyboard events in our display window. In the next project, you will jump into interactively adjusting image brightness. You will then learn to add a miniaturizing tilt-shift effect and how to blur images. In the final project, you will learn to apply Instagram-like color ambiance filters to images.


By the end of this Learning Path, you will be able to build computer vision applications that make the most of OpenCV 3.



Meet Your Experts:



We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:




Robert Laganiere is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the co-author of several scientific publications and patents in content-based video analysis, visual surveillance, driver-assistance, object detection, and tracking. Robert authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development, published by McGraw Hill in 2001. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of startups companies such as Cognivue Corp, iWatchlife, and Tempo Analytics.AdiShavit is an experienced software architect and has been an OpenCV user since it was in early beta back in 2000. Since then he has been using it pretty much continuously to build systems and products ranging from embedded, vehicle, and mobile apps to desktops and large, distributed cloud-based servers and services. His specialty is in computer vision, image processing, and machine learning with an emphasis on real-time applications. He specializes in cross-platform, high performance software combined with a high production-quality maintainable code base. He builds many products, apps, and services that leverage OpenCV.

Overview

Section 1: OpenCV 3 – Getting Started with Image Processing

Lecture 1 The Course Overview

Lecture 2 Installing the OpenCV Library

Lecture 3 Loading, Displaying, and Saving Images

Lecture 4 Exploring the cv::Mat Data Structure

Lecture 5 Defining Regions of Interest

Lecture 6 Accessing Pixel Values

Lecture 7 Scanning an Image with Pointers

Lecture 8 Scanning an Image with Iterators

Lecture 9 Writing Efficient Image-Scanning Loops

Lecture 10 Scanning an Image with Neighbor Access

Lecture 11 Performing Simple Image Arithmetic

Lecture 12 Remapping an Image

Lecture 13 Comparing Colors Using the Strategy Design Pattern

Lecture 14 Segmenting an Image with the GrabCut Algorithm

Lecture 15 Converting Color Representations

Lecture 16 Representing Colors with Hue, Saturation, and Brightness

Lecture 17 Computing an Image Histogram

Lecture 18 Applying Look-Up Tables to Modify the Image's Appearance

Lecture 19 Equalizing the Image Histogram

Lecture 20 Backprojecting a Histogram to Detect Specific Image Content

Lecture 21 Using the Mean Shift Algorithm to Find an Object

Lecture 22 Retrieving Similar Images Using Histogram Comparison

Lecture 23 Counting Pixels with Integral Images

Section 2: OpenCV 3 – Advanced Image Detection and Reconstruction

Lecture 24 The Course Overview

Lecture 25 Detecting Corners in an Image

Lecture 26 Detecting Features Quickly

Lecture 27 Detecting Scale-Invariant Features

Lecture 28 Detecting FAST Features at Multiple Scales

Lecture 29 Matching Local Templates

Lecture 30 Describing and Matching Local Intensity Patterns

Lecture 31 Matching Keypoints with Binary Descriptors

Lecture 32 Computing the Fundamental Matrix of an Image Pair

Lecture 33 Matching Images Using Random Sample Consensus

Lecture 34 Computing a Homography Between Two Images

Lecture 35 Detecting a Planar Target in Images

Lecture 36 Recognizing Faces Using Nearest Neighbors

Lecture 37 Finding Objects and Faces with a Cascade of Haar Features

Lecture 38 Detecting Objects and People with Support Vector Machines

Section 3: OpenCV 3 Projects for Photo Filtering

Lecture 39 The Course Overview

Lecture 40 Building OpenCV

Lecture 41 Creating a New Project

Lecture 42 Loading Images

Lecture 43 Showing Images

Lecture 44 Keyboard Events

Lecture 45 Understanding Brightness and Contrast

Lecture 46 Adjusting Brightness and Contrast with OpenCV

Lecture 47 Interactive Image Adjustment

Lecture 48 Storing Images

Lecture 49 Miniature Faking

Lecture 50 Blurring Images

Lecture 51 Compositing Images

Lecture 52 The Miniature Effect via Partial Blurring

Lecture 53 Handling Mouse Events

Lecture 54 Color Filters

Lecture 55 Remapping Colors

Lecture 56 Processing Video

Lecture 57 Recoloring Video

Lecture 58 Saving Video

This learning path is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers who wish to be introduced to the concepts of computer vision programming.