Computer Vision on the Raspberry Pi 4

Find out how to write and execute computer vision applications on the Raspberry Pi 4.

Introduction

• Getting started with computer vision
• What you should know
• Using the exercise files

1. Programming Python on the Raspberry Pi 4

• Introducing the Raspberry Pi 4
• Setting up the environment
• Using the Thonny IDE

2. OpenCV on the Raspberry Pi

• Introducing OpenCV
• NumPy array operations
• Running a simple image processing example
• Theory of convolution
• Convolution in OpenCV

3. Object Detection

• Computing image gradients
• Forming histograms of gradients (HOGs)
• Computing HOGs in OpenCV
• Understanding Support Vector Machines (SVMs)
• Detecting objects with HOGs and SVMs

4. Understanding Neural Networks

• Introducing neural networks
• Training neural networks
• Creating neural networks in OpenCV
• Classifying irises with a neural network

5. Convolutional Neural Networks (CNNs)

• Introducing convolutional neural networks (CNNs)
• Creating CNNs with Keras
• Training CNNs with TensorFlow
• Executing models with TensorFlow Lite
• Recognizing objects with the Raspberry Pi

6. The Raspberry Pi HQ Camera

• Introducing the picamera package
• Accessing a Raspberry Pi camera in Python
• Object detection with a Raspberry Pi camera
• Object recognition with a Raspberry Pi camera

Conclusion

• Next steps