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OpenCV for Python Developers

via LinkedIn Learning

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Overview

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Learn how to harness the image-processing power of OpenCV to develop Python scripts that manipulate photos, create custom video streams, and even perform object and face tracking.

Syllabus

Introduction
  • Image processing with OpenCV
  • What you should know
  • How to use the exercise files
1. 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
2. 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
3. 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
4. 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

Taught by

Patrick W. Crawford

Reviews

4.7 rating at LinkedIn Learning based on 156 ratings

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