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Daniel Romaniuk
In this one hour long project-based course, you will learn the basics of reading, analyzing and writing video using Python, NumPy and OpenCV. We will be working with a short documentary film which is in the public domain, so we are free to download and manipulate it. We will read in the video data frame-by-frame, examine the contents and modify them from Python.
Read more
In this one hour long project-based course, you will learn the basics of reading, analyzing and writing video using Python, NumPy and OpenCV. We will be working with a short documentary film which is in the public domain, so we are free to download and manipulate it. We will read in the video data frame-by-frame, examine the contents and modify them from Python.
Read more
In this one hour long project-based course, you will learn the basics of reading, analyzing and writing video using Python, NumPy and OpenCV. We will be working with a short documentary film which is in the public domain, so we are free to download and manipulate it. We will read in the video data frame-by-frame, examine the contents and modify them from Python.
By the end of this project, you will have analyzed a video at the pixel level, modified its contents and created a new video file
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/cx3i9q/analyzing
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What's inside
Syllabus
Project Overview
In this one hour long project-based course, you will learn the basics of reading, analyzing and writing video using Python, NumPy and OpenCV. We will be working with a short documentary film which is in the public domain, so we are free to download and manipulate it. We will read in the video data frame-by-frame, examine the contents and modify them from Python. By the end of this project, you will have analyzed a video at the pixel level, modified its contents and created a new video file.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Highly relevant for career growth as this course teaches tools and knowledge that are in high demand in industry
Provides hands-on practice with Python, NumPy, and OpenCV, industry-standard tools for video analysis
Taught by experienced instructors Daniel Romaniuk, who has a strong reputation for his work in video analysis
Suitable for learners new to video analysis, as it starts with the basics and gradually introduces advanced concepts
Save this course
Save Analyzing Video with OpenCV and NumPy to your list so you can find it easily later:
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Reviews summary
Opencv and numpy: video analysis
According to students, this course, Analyzing Video with OpenCV and NumPy, offers clear instruction and engaging assignments that make the basics of video analysis accessible to learners. The course also serves as a useful tool for those interested in exploring OpenCV and NumPy. However, some learners wish that the course material was more thorough and the course platform more accessible.
Makes learning video analysis enjoyable
"Great course"
Easy to follow explanations
"Well explained and easy to follow every step, Thanks :)"
"Clear and understandable, some comments could be a bit more detailed"
"Fast and directly, clear, a great bases for your video projects"
Difficulties accessing material
"My name is not shown on certificate"
"I do not recommend taking this type of course, take one and pass it, however after a few days I have tried to review the material, and my surprise is that it asks me to pay again to be able to review the material"
Course lacks depth and comprehensiveness
"somehow helpful , but it is too limited knowledge"
"Its basic content for sure, not sure why its branded as an intermediate project"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Analyzing Video with OpenCV and NumPy with these
activities:
Review Python Basics
Show steps
Refresh your Python programming skills to ensure a strong foundation for the course content on video analysis.
Browse courses on
Python Basics
Show steps
-
Review basic Python syntax, data types, and control structures.
-
Practice writing simple Python scripts to perform basic operations.
Resource Compilation: Video Analysis Tools and Techniques
Show steps
Bolster your understanding by compiling a comprehensive list of resources, including tools, libraries, and tutorials, related to video analysis.
Browse courses on
Video Analysis
Show steps
-
Conduct research to identify useful resources related to video analysis with Python, NumPy, and OpenCV.
-
Organize the resources into categories, such as libraries, tutorials, sample code, and documentation.
-
Create a document or online repository to share your compilation with others.
-
Update and maintain your compilation as you discover new resources.
Video Analysis Coding Exercises
Show steps
Reinforce your understanding of video processing techniques by working through coding exercises that focus on analyzing and modifying video data.
Browse courses on
Video Analysis
Show steps
-
Import the necessary libraries (Python, NumPy, OpenCV).
-
Load the video file into a NumPy array.
-
Loop through the video frames.
-
Perform operations on each frame, such as color correction, image filtering, or object detection.
-
Save the modified video file.
Two other activities
Expand to see all activities and additional details
Show all five activities
Peer Review: Video Analysis Projects
Show steps
Enhance your project development and critical thinking skills by participating in peer review sessions where you exchange feedback on video analysis projects.
Browse courses on
Peer Review
Show steps
-
Form small groups with fellow learners enrolled in the online course.
-
Share your video analysis projects with your group for review.
-
Provide constructive feedback on each other's projects, focusing on strengths, areas for improvement, and suggestions.
-
Incorporate feedback into your own project to enhance its quality.
-
Facilitate discussions on best practices and innovative approaches in video analysis.
Data Visualization Project: Video Analysis
Show steps
Solidify your learning by creating a data visualization project that showcases your ability to extract meaningful insights from video data.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset or video footage for your visualization project.
-
Extract relevant data from the video using Python, NumPy, and OpenCV.
-
Analyze and process the data to identify patterns or trends.
-
Design and create data visualizations using libraries such as Matplotlib, Seaborn, or Plotly.
-
Present your findings and insights in a clear and engaging manner.
Review Python Basics
Show steps
Refresh your Python programming skills to ensure a strong foundation for the course content on video analysis.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review basic Python syntax, data types, and control structures.
- Practice writing simple Python scripts to perform basic operations.
Resource Compilation: Video Analysis Tools and Techniques
Show steps
Bolster your understanding by compiling a comprehensive list of resources, including tools, libraries, and tutorials, related to video analysis.
Browse courses on
Video Analysis
Show steps
Show steps
Show steps
- Conduct research to identify useful resources related to video analysis with Python, NumPy, and OpenCV.
- Organize the resources into categories, such as libraries, tutorials, sample code, and documentation.
- Create a document or online repository to share your compilation with others.
- Update and maintain your compilation as you discover new resources.
Video Analysis Coding Exercises
Show steps
Reinforce your understanding of video processing techniques by working through coding exercises that focus on analyzing and modifying video data.
Browse courses on
Video Analysis
Show steps
Show steps
Show steps
- Import the necessary libraries (Python, NumPy, OpenCV).
- Load the video file into a NumPy array.
- Loop through the video frames.
- Perform operations on each frame, such as color correction, image filtering, or object detection.
- Save the modified video file.
Two other activities
Expand to see all activities and additional details
Show all five activities
Peer Review: Video Analysis Projects
Show steps
Enhance your project development and critical thinking skills by participating in peer review sessions where you exchange feedback on video analysis projects.
Browse courses on
Peer Review
Show steps
Show steps
Show steps
- Form small groups with fellow learners enrolled in the online course.
- Share your video analysis projects with your group for review.
- Provide constructive feedback on each other's projects, focusing on strengths, areas for improvement, and suggestions.
- Incorporate feedback into your own project to enhance its quality.
- Facilitate discussions on best practices and innovative approaches in video analysis.
Data Visualization Project: Video Analysis
Show steps
Solidify your learning by creating a data visualization project that showcases your ability to extract meaningful insights from video data.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset or video footage for your visualization project.
- Extract relevant data from the video using Python, NumPy, and OpenCV.
- Analyze and process the data to identify patterns or trends.
- Design and create data visualizations using libraries such as Matplotlib, Seaborn, or Plotly.
- Present your findings and insights in a clear and engaging manner.
Career center
Learners who complete Analyzing Video with OpenCV and NumPy will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve various problems. This course on analyzing videos with OpenCV and NumPy provides a strong foundation for Machine Learning Engineers who work with video data. By learning how to read, analyze, and write video data, Machine Learning Engineers can develop more effective and accurate machine learning models for various applications.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems that can interpret and understand visual information. This course on analyzing videos with OpenCV and NumPy provides a strong foundation for Computer Vision Engineers who work with video data. By learning how to read, analyze, and write video data, Computer Vision Engineers can develop more effective and accurate computer vision systems for various applications.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course on analyzing videos with OpenCV and NumPy provides a strong foundation for Data Scientists who work with video data. By learning how to read, analyze, and write video data, Data Scientists can develop more effective and accurate models for extracting insights from video data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course on analyzing videos with OpenCV and NumPy provides a strong foundation for Software Engineers who work with video data. By learning how to read, analyze, and write video data, Software Engineers can develop more effective and efficient software systems for various applications.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. This course on analyzing videos with OpenCV and NumPy provides a strong foundation for Data Analysts who work with video data. By learning how to read, analyze, and write video data, Data Analysts can develop more effective and accurate data analysis models for various applications.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for technical products and services. This course on analyzing videos with OpenCV and NumPy may be useful for Technical Writers who work with video data. By learning how to read, analyze, and write video data, Technical Writers can develop more effective and user-friendly documentation for various applications.
Customer Success Manager
Customer Success Manager
Customer Success Managers ensure that customers are satisfied with products and services. This course on analyzing videos with OpenCV and NumPy may be useful for Customer Success Managers who work with video data. By learning how to read, analyze, and write video data, Customer Success Managers can develop more effective and efficient customer support strategies for various applications.
User Experience Designer
User Experience Designer
User Experience Designers design and develop user interfaces for products and services. This course on analyzing videos with OpenCV and NumPy may be useful for User Experience Designers who work with video data. By learning how to read, analyze, and write video data, User Experience Designers can develop more effective and user-friendly user interfaces for various applications.
Interaction Designer
Interaction Designer
Interaction Designers design and develop interactive experiences for products and services. This course on analyzing videos with OpenCV and NumPy may be useful for Interaction Designers who work with video data. By learning how to read, analyze, and write video data, Interaction Designers can develop more effective and user-friendly interactive experiences for various applications.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. This course on analyzing videos with OpenCV and NumPy may be useful for Marketing Managers who work with video data. By learning how to read, analyze, and write video data, Marketing Managers can develop more effective and engaging marketing campaigns for various applications.
Motion Graphics Designer
Motion Graphics Designer
Motion Graphics Designers create and animate graphic elements for various applications. This course on analyzing videos with OpenCV and NumPy may be useful for Motion Graphics Designers who work with video data. By learning how to read, analyze, and write video data, Motion Graphics Designers can develop more effective and visually appealing motion graphics for various applications.
Project Manager
Project Manager
Project Managers plan, execute, and manage projects to achieve specific goals. This course on analyzing videos with OpenCV and NumPy may be useful for Project Managers who work with video data. By learning how to read, analyze, and write video data, Project Managers can develop more effective and efficient project plans for various applications.
Sales Manager
Sales Manager
Sales Managers lead and manage sales teams to achieve sales goals. This course on analyzing videos with OpenCV and NumPy may be useful for Sales Managers who work with video data. By learning how to read, analyze, and write video data, Sales Managers can develop more effective and efficient sales strategies for various applications.
Business Analyst
Business Analyst
Business Analysts identify and analyze business needs and develop solutions to improve business processes. This course on analyzing videos with OpenCV and NumPy may be useful for Business Analysts who work with video data. By learning how to read, analyze, and write video data, Business Analysts can develop more effective and efficient business solutions for various applications.
Product Manager
Product Manager
Product Managers define and manage the development of new products and features. This course on analyzing videos with OpenCV and NumPy may be useful for Product Managers who work with video data. By learning how to read, analyze, and write video data, Product Managers can develop more effective and user-friendly products for various applications.
For more career information including salaries, visit:
OpenCourser.com/course/cx3i9q/analyzing
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Analyzing Video with OpenCV and NumPy.
This classic textbook provides a comprehensive overview of computer vision theory and algorithms. It covers a wide range of topics, including image formation, feature detection, and object recognition. While it is more advanced than the course material, it valuable resource for gaining a deeper understanding of the underlying principles of computer vision.
This textbook provides a comprehensive overview of computer vision theory and algorithms. It covers a wide range of topics, including image formation, feature detection, and object recognition. While it is more advanced than the course material, it provides a solid foundation for understanding the underlying principles of computer vision.
Save
This comprehensive textbook provides a solid foundation in computer vision algorithms and their applications. It covers a wide range of topics, including image processing, feature detection and matching, object recognition, and video analysis. While it is more advanced than the course material, it can serve as a valuable reference for further exploration.
Provides a comprehensive introduction to digital image processing techniques. It covers topics such as image enhancement, compression, and analysis. While it is more advanced than the course material, it provides a solid foundation for understanding the underlying principles of image processing.
This cookbook provides a collection of practical recipes for solving common computer vision problems using OpenCV. It covers a wide range of topics, including image processing, object detection, and motion tracking. It valuable resource for applying the concepts covered in the course to real-world projects.
While this book focuses on deep learning for computer vision, it provides valuable insights into the latest advancements in image and video analysis. It covers topics such as convolutional neural networks, object detection, and image segmentation. Understanding these concepts can enhance your ability to apply advanced techniques to video analysis projects.
While this book focuses on image processing using MATLAB, it provides valuable insights into the underlying algorithms and concepts that are also applicable to Python-based video analysis. It covers topics such as image enhancement, segmentation, and object recognition.
This beginner-friendly book provides a step-by-step introduction to OpenCV and computer vision concepts. It covers the basics of image processing, object detection, and motion tracking. It good starting point for those who are new to the field and want to gain a solid foundation.
While not directly related to computer vision, this book provides a comprehensive introduction to machine learning concepts and algorithms using Python. Understanding these concepts can enhance your ability to analyze and interpret the results of video analysis.
This best-selling book provides a comprehensive and beginner-friendly introduction to Python programming. It covers essential topics such as data structures, control flow, and object-oriented programming. Understanding these concepts is essential for effectively working with Python for video analysis.
While not directly related to computer vision, this book provides a comprehensive overview of Python for data analysis. It covers essential topics such as data manipulation, visualization, and statistical modeling. Understanding these concepts can enhance your ability to analyze and interpret the data generated from video analysis.
This concise reference provides quick access to essential Python syntax and functions. It valuable resource for quickly looking up Python commands and syntax while working on video analysis projects.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cx3i9q/analyzing
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Daniel Romaniuk
Language
English
Good to know
Highly relevant for career growth as this course teaches tools and knowledge that are in high demand in industry
Provides hands-on practice with Python, NumPy, and OpenCV, industry-standard tools for video analysis
Taught by experienced instructors Daniel Romaniuk, who has a strong reputation for his work in video analysis
Suitable for learners new to video analysis, as it starts with the basics and gradually introduces advanced concepts
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