OpenCV for Python Developers
3h 6mIntermediate2023-01-11
Authors
Patrick W. Crawford
Digital Artist, Developer, Blender Enthusiast
Course details
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 your 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. Instructor Patrick W. Crawford also shows how to read video streams as inputs, and create custom real-time video interfaces. Then comes the real power of OpenCV: object, facial, and feature detection. 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.
Skills covered
OpenCVNeural Networks and Deep LearningPythonArtificial Intelligence (AI)Programming LanguagesOpen SourceSoftware DevelopmentDeep Dive (X:Y)
Concepts
0. Introduction
- 01 - Image processing with OpenCV
- 02 - What you should know
- 03 - How to use the exercise files
1. Install and Configure OpenCV
- 04 - Python and OpenCV
- 05 - Using virtual environments
- 06 - Install on Mac OS
- 07 - Install on Windows
- 08 - Install on Linux - Prerequisites
- 09 - Install on Linux - Compile OpenCV
- 10 - Using OpenCV with Google Colab
- 11 - Test the install
2. Basic Image Operations
- 12 - Get started with OpenCV and Python
- 13 - Get started with OpenCV and Python - Google Collab
- 14 - Access and understand pixel data
- 15 - Data types and structures
- 16 - Image types and color channels
- 17 - Pixel manipulations and filtering
- 18 - Blur, dilation, and erosion
- 19 - Scale and rotate images
- 20 - Use video inputs
- 21 - Create custom interfaces
- 22 - Challenge - Create a simple drawing app
- 23 - Solution - Create a simple drawing app
3. Object Detection
- 24 - Segmentation and binary images
- 25 - Simple thresholding
- 26 - Adaptive thresholding
- 27 - Skin detection
- 28 - Introduction to contours
- 29 - Contour object detection
- 30 - Area, perimeter, center, and curvature
- 31 - Canny edge detection
- 32 - Object detection overview
- 33 - Challenge - Assign object ID and attributes
- 34 - Solution - Assign object ID and attributes
4. Face and Feature Detection
- 35 - Overview of face and feature detection
- 36 - Introduction to template matching
- 37 - Application of template matching
- 38 - Haar cascading
- 39 - Face detection
- 40 - Challenge - Eye detection
- 41 - Solution - Eye detection
Conclusion
- 42 - Additional techniques
- 43 - Next steps