OpenCV for Python Developers

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.

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