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by Ashwin Pajankar
Raspberry Pi Computer Vision Programming - Second Edition
Raspberry Pi Computer Vision Programming
Second Edition
Why subscribe?
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Code in Action
Download the color images
Conventions used
Get in touch
Reviews
Chapter 1: Introduction to Computer Vision and the Raspberry Pi
Understanding computer vision
OpenCV
Single-board computers
The Beagleboard family
ASUS Tinkerboard
NVIDIA Jetson
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
Summary
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
Summary
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
Matplotlib
RPi GPIO programming with Python 3
LED programming with GPIO
Push-button programming with GPIO
Summary
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
Summary
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
Summary
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
Scaling
The translation, rotation, and affine transformation of images
Perspective transformation of images
Thresholding images
Otsu's binarization method
Adaptive thresholding
Summary
Chapter 7: Let's Make Some Noise
Technical requirements
Noise
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
Summary
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
Exercise
Summary
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
Summary
Chapter 10: Histograms, Contours, and Morphological Transformations
Technical requirements
Computing and visualizing histograms
Histogram equalization
Visualizing image contours
Applying morphological transformations to images
Summary
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
Summary
Chapter 12: Working with Mahotas and Jupyter
Technical requirements
Processing images with Mahotas
Reading images and built-in images
Thresholding images
The distance transform
Colorspace
Combining Mahotas and OpenCV
Other popular image processing libraries
Exploring the Jupyter Notebook for Python 3 programming
Summary
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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