OpenCV for Python Developers
- Offered byLinkedin Learning
OpenCV for Python Developers at Linkedin Learning Overview
Duration | 3 hours |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
OpenCV for Python Developers at Linkedin Learning Highlights
- Earn a sharable certificate
- 1 exercise file
- 4 quizzes
- Access on tablet and phone
OpenCV for Python Developers at Linkedin Learning Course details
- Computer Vision
- OpenCV
- OpenCV is a toolkit for advanced image recognition
- It is among the most popular professional tools used for facial recognition and is being used in a wide variety of security, marketing, and photography applications
- This course offers Python developers a detailed introduction to OpenCV, starting with installing and configuring Mac, Windows, or Linux development environment along with Python 3
- Learn about the data and image types unique to OpenCV, and find out how to manipulate pixels and images
- Learn how to leverage the image-processing power of OpenCV using methods like template matching and pre-train machine learning models to identify and recognize features
OpenCV for Python Developers at Linkedin Learning Curriculum
Introduction
Image processing with OpenCV
What you should know
How to use the exercise files
Install and Configure OpenCV
Python and OpenCV
Using virtual environments
Install on Mac OS
Install on Windows
Install on Linux: Prerequisites
Install on Linux: Compile OpenCV
Using OpenCV with Google Colab
Test the install
Basic Image Operations
Get started with OpenCV and Python
Get started with OpenCV and Python: Google Collab
Access and understand pixel data
Data types and structures
Image types and color channels
Pixel manipulations and filtering
Blur, dilation, and erosion
Scale and rotate images
Use video inputs
Create custom interfaces
Challenge: Create a simple drawing app
Solution: Create a simple drawing app
Object Detection
Segmentation and binary images
Simple thresholding
Adaptive thresholding
Skin detection
Introduction to contours
Contour object detection
Area, perimeter, center, and curvature
Canny edge detection
Object detection overview
Challenge: Assign object ID and attributes
Solution: Assign object ID and attributes
Face and Feature Detection
Overview of face and feature detection
Introduction to template matching
Application of template matching
Haar cascading
Face detection
Challenge: Eye detection
Solution: Eye detection
Conclusion
Additional techniques
Next steps
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