Video Analytics using OpenCV and Python Shells

Video Analytics using OpenCV and Python Shells, available at $19.99, has an average rating of 3.9, with 92 lectures, based on 97 reviews, and has 24907 subscribers.

You will learn about Understand and learn how to perform video analysis with OpenCV. Learn Color Models, Image Loading and Image Thresholding Master Open CV and Object Detection This course is ideal for individuals who are Students and professionals who are interested in learning robotics and biometrics. or People who are very keen about learning Video analytics It is particularly useful for Students and professionals who are interested in learning robotics and biometrics. or People who are very keen about learning Video analytics.

Enroll now: Video Analytics using OpenCV and Python Shells

Summary

Title: Video Analytics using OpenCV and Python Shells

Price: $19.99

Average Rating: 3.9

Number of Lectures: 92

Number of Published Lectures: 92

Number of Curriculum Items: 92

Number of Published Curriculum Objects: 92

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Understand and learn how to perform video analysis with OpenCV.
Learn Color Models, Image Loading and Image Thresholding
Master Open CV and Object Detection

Who Should Attend

Students and professionals who are interested in learning robotics and biometrics.
People who are very keen about learning Video analytics

Target Audiences

Students and professionals who are interested in learning robotics and biometrics.
People who are very keen about learning Video analytics

OpenCV (Open Source Computer Vision Library) is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, C, Python and Java interfaces. Computer vision applications and technology are blowing up right now! With several apps and industries making amazing use of the technology. Through this training we shall understand and learn how to perform video analysis with OpenCV.

The training will include the following;

Object detection

Color models- HSL model, HSV model, RGB model
Image loading
Image thresholding
Blob detection

Motion Detection

Capture video from video
Capture video from video file
Background subtraction
Saving video
Colorspace based tracking
Camshift algorithm
Optical flow based tracking
Face detection
Contour based tracking

The goal behind providing this open CV training program is to make individuals proficient with the use of open CV library that is commonly used for developing computer vision applications. Open CV is a cross-platform library that is normally used for developing real-time computer vision applications that use visual aesthetics, image processing, video capture, etc. The training abs and bridging the gap between the industry demand and the individual skillset by providing this industry-ready training on an open CV. With the help of this training program, participants would be able to understand the concepts of open CV and can implement their knowledge in the industry easily.

With the help of this training program or open CV, individuals would be able to understand the core concepts of open CV and camp detail about the course as per their eagerness to learn new things. The quality and output of the training depend on the efforts that a participant will put in practicing and revising the skills and concepts taught in the training program. Many schools will learn throughout the training like data science, data visualization, video analytics, Open CV, Python, linear algebra, probability, business analytics, predictive analysis, random forest, etc. once an individual becomes proficient with the use of these technologies there ample of opportunities that are open for the individuals as the market is demanding these skills to a great extent and ready to offer opportunities like data analyst, business analyst, statistician, quality engineer, quality manager, consultant, software test engineer, statistical analyst, team leader opportunities that are open for the individuals.

Course Curriculum

Chapter 1: OpenCV for Beginners

Lecture 1: Introduction to OpenCV

Lecture 2: About OpenCV Installation

Lecture 3: First Example of OpenCV

Lecture 4: Reading and Saving Image

Lecture 5: Image Color Transition

Lecture 6: Image Translation

Lecture 7: Image Rotation

Lecture 8: Image Scaling

Lecture 9: Various Transformations

Lecture 10: Image Wrapping

Lecture 11: Image Wrapping Continue

Lecture 12: Accessing Web Cam

Lecture 13: Keyboard Inputs

Lecture 14: Carbonizing Using Webcam

Lecture 15: Face Datasets

Lecture 16: Face Datasets Continue

Lecture 17: Face Recognition and Output

Chapter 2: Video Analytics using OpenCV and Python Shells

Lecture 1: Introduction to Video Analytics

Lecture 2: Purpose of BGR Model

Lecture 3: Importance of HSL Model

Lecture 4: Learning about HSV Color Model

Lecture 5: Process on Image Loading

Lecture 6: Program for Image Loading

Lecture 7: Concept of Image Thresholding

Lecture 8: Modules for Image Thresholding

Lecture 9: Program For Adapter Thresholding

Lecture 10: Understanding OpenCV Library

Lecture 11: Object Detection and Tracking

Lecture 12: Tracking Approach using Object Detection

Lecture 13: Learning Capturing Video from Camera

Lecture 14: Capturing Video from File

Lecture 15: Learning to Save Video

Lecture 16: Example Code for Saving Video

Lecture 17: Knowing Blob Detection

Lecture 18: Simple Blob Detector

Lecture 19: Tracking Using Color Spaces

Lecture 20: Examples for Tracking Using Color Spaces

Lecture 21: Smoothing Images for Clear Detection

Lecture 22: Functions and Coding for Smoothing Images

Lecture 23: Understanding Contour Detection

Lecture 24: Learning about Camshift Algorithm

Lecture 25: Initializing the Video Capture Object

Lecture 26: Optical Flow Algorithm

Lecture 27: Program of Optical Flow Algorithm

Lecture 28: Face Detection and Tracking

Lecture 29: Cascade clarifier Inbuilt Function

Chapter 3: Face Detection Using OpenCV and Python

Lecture 1: Introduction of Project

Lecture 2: Installation

Lecture 3: Face Detection Image Part 1

Lecture 4: Face Detection Image Part 2

Lecture 5: Face Detection URL Part 1

Lecture 6: Face Detection URL Part 2

Lecture 7: Face Detection Video

Lecture 8: Real time Face Detection

Lecture 9: Real time Face Detection Web Camera

Lecture 10: Face-Smile

Lecture 11: Eyes Detection

Lecture 12: Eyes Detection Continue

Chapter 4: Project on OpenCV – Hand Gesture

Lecture 1: Introduction to Project

Lecture 2: Installation of Tools and Libraries

Lecture 3: Importing Libraries

Lecture 4: Coding

Lecture 5: Image loading Segmentation

Lecture 6: Contouring Thresholding

Lecture 7: Hand Gesture Code Libraries Importing

Lecture 8: Hand Background Function

Lecture 9: Hand Segmentation Function Part 1

Lecture 10: Hand Segmentation Function Part 2

Lecture 11: Hand Segmentation Function Part 3

Lecture 12: Hand Segmentation Function Part 4

Lecture 13: Hand Segmentation Function Part 5

Lecture 14: Hand Segmentation Function Part 6

Lecture 15: Hand Segmentation Function Part 7

Lecture 16: Hand Segmentation Function Part 8

Lecture 17: Hand Segmentation Function Part 9

Lecture 18: Function Calling to Execute

Lecture 19: Function Calling to Execute Continue

Lecture 20: Function Calling and Final Execution Logic

Lecture 21: Logic to Automate Web Browser with Hand

Lecture 22: Logic to Automate Web Browser with Hand Continue

Lecture 23: Showing Output of the Code

Chapter 5: Face Recognition using OpenCV

Lecture 1: Introduction of Project

Lecture 2: Edge Detection

Lecture 3: Canny Edge Detection

Lecture 4: Canny Edge Detection Continue

Lecture 5: Creating Dataset

Lecture 6: Creating Dataset Continue

Lecture 7: Training Classifier using Dataset

Lecture 8: Training Classifier using Dataset Continue

Lecture 9: Face and Eyes Detection and Recognition Part 1

Lecture 10: Face and Eyes Detection and Recognition Part 2

Lecture 11: Face and Eyes Detection and Recognition Part 3

Instructors

EDUCBA Bridging the Gap
Learn real world skills online

Rating Distribution

1 stars: 11 votes
2 stars: 11 votes
3 stars: 19 votes
4 stars: 26 votes
5 stars: 30 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu