CF

OpenCV 3 by Example

3h 53m 58s
English
Paid

OpenCV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open-source libraries, aiding developers in constructing comprehensive projects on image processing, motion detection, and image segmentation. Whether you are completely new to Computer Vision or have a basic understanding of it, this video guide will help you grasp the fundamental OpenCV concepts and algorithms through impressive real-world examples and projects.

Getting Started with OpenCV

Beginning with the installation of OpenCV on your system and a basic understanding of image processing, we quickly transition to creating optical flow video analysis or text recognition in complex scenes. This course will introduce you to commonly-used Computer Vision techniques, empowering you to build your own OpenCV projects from scratch. By the end of this video, you will be familiar with the basics of OpenCV, such as matrix operations, filters, and histograms, as well as more advanced concepts like segmentation, machine learning, complex video analysis, and text recognition.

Course Style and Approach

This video course is a practical tutorial filled with many tips and is closely focused on developing Computer Vision applications with OpenCV. Beginning with the fundamentals, the course gradually increases in complexity with each chapter. Sample applications are developed throughout the course, which you can execute and use in your projects.

What You'll Learn

  • Install OpenCV 3 on your operating system
  • Create the necessary CMake scripts to compile the C++ application and manage its dependencies
  • Familiarize yourself with Computer Vision workflows and understand the basic image matrix format and filters
  • Understand segmentation and feature extraction techniques
  • Remove backgrounds from a static scene to identify moving objects for video surveillance
  • Track different objects in a live video using various techniques
  • Utilize the new OpenCV functions for text detection and recognition with Tesseract

About the Author: Packt Publishing

Packt Publishing thumbnail

Packt Publishing is a UK-based technical publisher that has been releasing books, video courses, and subscription content for software developers since 2004. The Packt catalog is one of the broadest in the industry — covering essentially every active programming language, framework, and infrastructure tool — and operates as both a book publisher and a video-course platform.

The CourseFlix listing carries three Packt courses spanning topics typical of the broader Packt catalog: OpenCV 3 by Example, Learning Salt (DevOps configuration management), and Advanced HTML5 Game Development. Material is paid and aimed at developers picking up specific technologies through structured video walkthroughs.

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#1: The Course Overview
All Course Lessons (49)
#Lesson TitleDurationAccess
1
The Course Overview Demo
05:49
2
Getting Started with OpenCV
04:56
3
What Can You Do with OpenCV?
12:09
4
Installing OpenCV
10:17
5
Basic CMakeConfiguration and Creating a Library
04:04
6
Managing Dependencies
03:16
7
Making the Script More Complex
03:42
8
Images and Matrices
02:33
9
Reading/Writing Images
05:07
10
Reading Videos and Cameras
03:09
11
Other Basic Object Types
02:03
12
Basic Matrix Operations, Data Persistence, and Storage
04:39
13
The OpenCVUser Interface and a Basic GUI
05:25
14
The Graphical User Interface with QT
01:49
15
Adding Slider and Mouse Events to Our Interfaces
04:37
16
Adding Buttons to a User Interface
03:56
17
OpenGL Support
04:37
18
Generating a CMakeScript File
01:59
19
Creating the Graphical User Interface
02:25
20
Drawing a Histogram
04:38
21
Image Color Equalization
02:57
22
Lomography Effect
04:18
23
The CartoonizeEffect
04:56
24
Isolating Objects in a Scene
02:22
25
Creating an Application for AOI
01:49
26
Preprocessing the Input Image
09:17
27
Segmenting Our Input Image
11:19
28
Introducing Machine Learning Concepts
07:05
29
Computer Vision and the Machine Learning Workflow
02:46
30
Automatic Object Inspection Classification Example
02:21
31
Feature Extraction
11:25
32
Understanding HaarCascades
04:32
33
What Are Integral Images
02:57
34
Overlaying a Facemask in a Live Video
04:26
35
Get Your Sunglasses On
03:23
36
Tracking Your Nose, Mouth, and Ears
01:32
37
Background Subtraction
04:13
38
Frame Differencing
02:53
39
Morphological Image processing
03:22
40
Other Morphological Operators
04:18
41
Tracking Objects of a Specific Color
03:18
42
Building an Interactive Object Tracker
05:56
43
Detecting Points Using the Harris Corner Detector
03:28
44
Shi-Tomasi Corner Detector
02:24
45
Feature-Based Tracking
08:21
46
Introducing Optical Character Recognition
02:41
47
The Preprocessing Step
10:00
48
Installing Tesseract OCR on Your Operating System
06:22
49
Using Tesseract OCR Library
08:07
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Frequently asked questions

What prerequisites are needed to enroll in this course?
Prospective students should have a basic understanding of programming concepts and some familiarity with computer vision principles. The course starts with installation and basic configurations, so prior experience with OpenCV isn't required, but knowledge of CMake and general programming will be beneficial.
What types of projects will I build in this course?
Throughout the course, you'll work on projects including optical flow video analysis, text recognition in complex scenes, and interactive object tracking. These projects will help you apply OpenCV techniques such as matrix operations, filters, and histograms, as well as advanced concepts like segmentation and machine learning.
Who is the target audience for this course?
The course is designed for developers and enthusiasts who are interested in real-time computer vision and image processing using OpenCV. Whether you are new to these topics or have some basic knowledge, the course offers practical examples to build your expertise in developing computer vision applications.
How does this course compare in depth to other OpenCV courses?
This course offers a practical, project-based approach to learning OpenCV. It begins with foundational concepts and progresses to more complex topics, such as machine learning and video analysis. It provides a comprehensive learning experience by focusing on real-world applications and examples.
What specific tools or platforms are covered in this course?
The course extensively covers OpenCV and its integration with tools like CMake for configuration and Tesseract OCR for text recognition. It also includes lessons on GUI development with QT, using OpenGL for graphical support, and employing machine learning workflows within computer vision projects.
What topics are not covered in this course?
The course does not cover advanced deep learning techniques or the use of OpenCV with frameworks like TensorFlow or PyTorch. It focuses on traditional computer vision methods and practical applications using the OpenCV library and its associated tools.
What is the expected time commitment for completing the course?
The course consists of 49 lessons, with a gradual increase in complexity. While the total runtime is not specified, students should allocate time for both watching the content and working on practical projects. A commitment of several weeks is advised, depending on the individual's pace and familiarity with the material.