Программирование компьютерного зрения с Raspberry Pi.

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
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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