Overview
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Find out how to write and execute computer vision applications on the Raspberry Pi 4.
Syllabus
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Introduction
- Getting started with computer vision
- What you should know
- Using the exercise files
- Introducing the Raspberry Pi 4
- Setting up the environment
- Using the Thonny IDE
- Introducing OpenCV
- NumPy array operations
- Running a simple image processing example
- Theory of convolution
- Convolution in OpenCV
- Computing image gradients
- Forming histograms of gradients (HOGs)
- Computing HOGs in OpenCV
- Understanding Support Vector Machines (SVMs)
- Detecting objects with HOGs and SVMs
- Introducing neural networks
- Training neural networks
- Creating neural networks in OpenCV
- Classifying irises with a neural network
- Introducing convolutional neural networks (CNNs)
- Creating CNNs with Keras
- Training CNNs with TensorFlow
- Executing models with TensorFlow Lite
- Recognizing objects with the Raspberry Pi
- 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
- Next steps
