OpenCV Python from Scratch
We are generating an ever-increasing amount of visual data. Youtube users upload over 500 hours of video every minute, over 100 million posts are made on Instagram every day. There are already over 5 million video cameras with cellular connectivity, and they’re growing at a rate of 40.7%. With the advent of the Metaverse, this data generation is just going to keep growing exponentially.
What you’ll learn
- Work with images and video files using Python & OpenCV.
- Manipulate images to remove noise and fix camera perspective skews.
- Understand color spaces and use thresholding to mask colors.
- Detect lines, contours, and shapes.
- Build 5+ real world projects.
Course Content
- Introduction –> 1 lecture • 6min.
- OpenCV Fundamentals –> 28 lectures • 2hr 51min.
- Projects –> 8 lectures • 2hr 3min.
- Bonus –> 1 lecture • 1min.
Requirements
- Basic Python Programming Skills.
- A PC or Laptop.
- Windows 10/Ubuntu.
We are generating an ever-increasing amount of visual data. Youtube users upload over 500 hours of video every minute, over 100 million posts are made on Instagram every day. There are already over 5 million video cameras with cellular connectivity, and they’re growing at a rate of 40.7%. With the advent of the Metaverse, this data generation is just going to keep growing exponentially.
All this data provides fuel to rapidly grow the already $10 billion computer vision industry, necessitating the need for programmers and developers to learn skills required to work with image and video data.
That’s cool and all, but where does one start? There are thousands and thousands of courses out there. Ones in different programming languages and different vision libraries. Which one should you choose?
As the fastest-growing language, Python seems like the obvious choice of language to leverage the power of existing computer vision libraries. And with easy access to 2,500-plus classic and state-of-the-art computer vision algorithms, OpenCV is a good place to start.
In this course, you’ll take your first steps towards becoming an expert in computer vision! You’ll learn how to use Python and the OpenCV library to analyze images and video data. The course takes a project-based approach. There’s no unnecessary complicated theory fuzz, we get straight to the point: the code and the applications. There are over 5 hours of rich informative content with 5+ real-world projects.
Here’s a quick overview of what you’ll learn:
- Downloading & setting up OpenCV
- Reading saved images, videos and working with live video streams
- Manipulating images on a pixel-level
- Annotating images with text and shapes
- Contour & shape detection
- Basics of color spaces like RGB & HSV
- Thresholding & color detection
- Project 1: Face Detection & Blurring
- Project 2: Image Classification With Tensorflow Keras Neural Networks
- Project 3: Object Detection using Haar-Cascade Classifier
- Project 4: Object Tracking
- Project 5: Using Wrap Transform to change the camera perspective
- Project 6: Motion Estimation using Optical Flow
Requirements
Please ensure that you have the following:
- Basic Python Programming Skills
- Mid to high range PC/ Laptop
- Windows 10/Ubuntu
30 Day Udemy Refund Guarantee
If you are not happy with this course for any reason, you are covered by Udemy’s 30-day no-questions-asked refund guarantee.
Who This Course Is For:
- Students who are taking courses related to Artificial Intelligence
- Python Developers looking to break into computer vision
- Machine Learning Engineers who are working on a vision-related problem statement
- Professionals who want to understand how computer vision works and how they can apply it to real projects
- AI hobbyists that want to tinker with DIY projects using computer vision
- Anyone with basic Python proficiency who wants to learn computer vision