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 itβs 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
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Color models- HSL model, HSV model, RGB model
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Image loading
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Image thresholding
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Blob detection
Motion Detection
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Capture video from video
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Capture video from video file
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Background subtraction
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Saving video
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Colorspace based tracking
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Camshift algorithm
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Optical flow based tracking
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Face detection
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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
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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
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