Raspberry Pi Computer Vision Programming: Design and implement computer vision applications with Raspberry Pi, OpenCV, and Python 3, 2nd Edition PDF Free Download



Perform a wide variety of computer vision tasks such as image processing and manipulation, feature and object detection, and image restoration to build real-life computer vision applications

Key Features

  • Explore the potential of computer vision with Raspberry Pi and Python programming
  • Perform computer vision tasks such as image processing and manipulation using OpenCV and Raspberry Pi
  • Discover easy-to-follow examples and screenshots to implement popular computer vision techniques and applications

Book Description

Raspberry Pi is one of the popular single-board computers of our generation. All the major image processing and computer vision algorithms and operations can be implemented easily with OpenCV on Raspberry Pi. This updated second edition is packed with cutting-edge examples and new topics, and covers the latest versions of key technologies such as Python 3, Raspberry Pi, and OpenCV. This book will equip you with the skills required to successfully design and implement your own OpenCV, Raspberry Pi, and Python-based computer vision projects.

At the start, you’ll learn the basics of Python 3, and the fundamentals of single-board computers and NumPy. Next, you’ll discover how to install OpenCV 4 for Python 3 on Raspberry Pi, before covering major techniques and algorithms in image processing, manipulation, and computer vision. By working through the steps in each chapter, you’ll understand essential OpenCV features. Later sections will take you through creating graphical user interface (GUI) apps with GPIO and OpenCV. You’ll also learn to use the new computer vision library, Mahotas, to perform various image processing operations. Finally, you’ll explore the Jupyter Notebook and how to set up a Windows computer and Ubuntu for computer vision.

By the end of this book, you’ll be able to confidently build and deploy computer vision apps.

What you will learn

  • Set up a Raspberry Pi for computer vision applications
  • Perform basic image processing with libraries such as NumPy, Matplotlib, and OpenCV
  • Demonstrate arithmetical, logical, and other operations on images
  • Work with a USB webcam and the Raspberry Pi Camera Module
  • Implement low-pass and high-pass filters and understand their applications in image processing
  • Cover advanced techniques such as histogram equalization and morphological transformations
  • Create GUI apps with Python 3 and OpenCV
  • Perform machine learning with K-means clustering and image quantization

Who this book is for

This book is for beginners as well as experienced Raspberry Pi and Python 3 enthusiasts who are looking to explore the amazing world of computer vision. Working knowledge of the Python 3 programming language is assumed.

Table of Contents

Title Page
Copyright and Credits
About Packt
Table of Contents
Chapter 1: Introduction to Computer Vision and the Raspberry Pi
Understanding computer vision
Single-board computers
The Beagleboard family
ASUS Tinkerboard
Intel boards
Raspberry Pi
Raspberry Pi models
OSes for Raspberry Pi
Setting up Raspbian on a Raspberry Pi
Downloading the necessary software
Preparing the microSD card manually
Booting up the Raspberry Pi for the first time
Connecting various RPi board models to the internet
Updating the RPi
Chapter 2: Preparing the Raspberry Pi for Computer Vision
Remotely logging into the RPi with SSH
Remote desktop access
Installing OpenCV on an RPi board
Heatsinks and overclocking RPi 4B
Chapter 3: Introduction to Python Programming
Technical requirements
Understanding Python 3
Python on RPi and Raspberry Pi OS
Python 3 IDEs on Raspberry Pi OS
Working with Python 3 in interactive mode
The basics of Python 3 programming
The SciPy ecosystem
The basics of NumPy
RPi GPIO programming with Python 3
LED programming with GPIO
Push-button programming with GPIO
Chapter 4: Getting Started with Computer Vision
Technical requirements
Exploring image datasets
Working with images using OpenCV
Using matplotlib to visualize images
Drawing geometric shapes with OpenCV and NumPy
Working with a GUI
Event handling and a primitive paint application
Working with a USB webcam
Capturing images with the webcam
Timelapse photography
Webcam video recording
Capturing images with the webcam using Python and OpenCV
Live videos with the webcam using Python and OpenCV
Webcam resolution
FPS of the webcam
Saving webcam videos
Playing back the video with OpenCV
The Pi camera module
Capturing images and videos with the raspistill and raspivid utilities
Using picamera with Python 3
Using the RPi camera module and Python 3 to record videos
Chapter 5: Basics of Image Processing
Technical requirements
Retrieving image properties
Basic operations on images
Splitting the image into channels
Adding a border to an image
Arithmetic operations on images
Blending and transitioning images
Multiplying images by a constant and one another
Creating a negative of an image
Bitwise logical operations on images
Chapter 6: Colorspaces, Transformations, and Thresholding
Technical requirements
Colorspaces and converting them
HSV colorspace
Tracking in real time based on color
Performing transformation operations on images
The translation, rotation, and affine transformation of images
Perspective transformation of images
Thresholding images
Otsu’s binarization method
Adaptive thresholding
Chapter 7: Let’s Make Some Noise
Technical requirements
Introducing noise to an image
Working with kernels
2D convolution with the signal processing module in SciPy
Filtering and blurring with OpenCV
2D convolution filtering
Low-pass filtering
Chapter 8: High-Pass Filters and Feature Detection
Technical requirements
Exploring high-pass filters
Working with the Canny edge detector
Finding circles and lines with Hough transforms
Harris corner detection
Chapter 9: Image Restoration, Segmentation, and Depth Maps
Technical requirements
Restoring damaged images using inpainting
Segmenting images
Mean shift algorithm segmentation
K-means clustering and image quantization
Comparison of k-means and the mean shift algorithm
Disparity maps and depth estimation
Chapter 10: Histograms, Contours, and Morphological Transformations
Technical requirements
Computing and visualizing histograms
Histogram equalization
Visualizing image contours
Applying morphological transformations to images
Chapter 11: Real-Life Applications of Computer Vision
Technical requirements
Implementing the Max RGB filter
Implementing background subtraction
Computing the optical flow
Detecting and tracking motion
Detecting barcodes in images
Implementing the chroma key effect
Chapter 12: Working with Mahotas and Jupyter
Technical requirements
Processing images with Mahotas
Reading images and built-in images
Thresholding images
The distance transform
Combining Mahotas and OpenCV
Other popular image processing libraries
Exploring the Jupyter Notebook for Python 3 programming
Chapter 13: Appendix
Technical requirements
Performance measurement and the management of OpenCV
Reusing a Raspbian OS microSD card
Formatting the SD card using the SD card formatter
The Disk Management utility in Windows
Tour of the raspi-config command-line utility
Installation and the environment setup on Windows, Debian, and Ubuntu
Python implementations and Python distributions
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Publisher‏:‎Packt Publishing (June 29, 2020)
Paperback‏:‎306 pages
Item Weight‏:‎1.16 pounds
Dimensions‏:‎7.5 x 0.69 x 9.25 inches

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