Hands-On GPU Computing with Python: Explore the capabilities of GPUs for solving high performance computational problems

Title Page
Copyright and Credits
	Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
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Contributors
	About the author
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Preface
	Who this book is for
	What this book covers
	To get the most out of this book
		Download the example code files
		Download the color images
		Code in Action
		Conventions used
	Get in touch
		Reviews
Introducing CUDA and Getting Started with CUDA
	Technical requirements
	Introducing CUDA
		Parallel processing 
		Introducing GPU architecture and CUDA
		CUDA architecture
	CUDA applications
	CUDA development environment
		CUDA-supported GPU
		NVIDIA graphics card driver
		Standard C compiler
		CUDA development kit
	Installing the CUDA toolkit on all operating systems
		Windows
		Linux 
		Mac
	A basic program in CUDA C
		Steps for creating a CUDA C program on Windows 
		 Steps for creating a CUDA C program on Ubuntu
	Summary
	Questions
Parallel Programming using CUDA C
	Technical requirements
	CUDA program structure
		Two-variable addition program in CUDA C
		A kernel call
		Configuring kernel parameters
		CUDA API functions
		Passing parameters to CUDA functions
			Passing parameters by value
			Passing parameters by reference
	Executing threads on a device
	Accessing GPU device properties from CUDA programs
		General device properties
		Memory-related properties
		Thread-related properties
	Vector operations in CUDA 
		Two-vector addition program
		Comparing latency between the CPU and the GPU code 
		Elementwise squaring of vectors in CUDA
	Parallel communication patterns
		Map
		Gather
		Scatter
		Stencil
		Transpose 
	Summary
	Questions
Threads, Synchronization, and Memory
	Technical requirements
	Threads
	Memory architecture
		Global memory
		Local memory and registers
		Cache memory
	Thread synchronization
		Shared memory
		Atomic operations
	Constant memory
	Texture memory
	Dot product and matrix multiplication example
		Dot product
		Matrix multiplication
	Summary
	Questions
Advanced Concepts in CUDA
	Technical requirements
	Performance measurement of CUDA programs
		CUDA Events
		The Nvidia Visual Profiler
	Error handling in CUDA 
		Error handling from within the code
		Debugging tools
	Performance improvement of CUDA programs
		Using an optimum number of blocks and threads
		Maximizing arithmetic efficiency
		Using coalesced or strided memory access
		Avoiding thread divergence
		Using page-locked host memory
	CUDA streams
		Using multiple CUDA streams
	Acceleration of sorting algorithms using CUDA
		Enumeration or rank sort algorithms
	Image processing using CUDA
		Histogram calculation on the GPU using CUDA
	Summary
	Questions
Getting Started with OpenCV with CUDA Support
	Technical requirements
	Introduction to image processing and computer vision
	Introduction to OpenCV
	Installation of OpenCV with CUDA support
		Installation of OpenCV on Windows
			Using pre-built binaries
			Building libraries from source
		Installation of OpenCV with CUDA support on Linux
	Working with images in OpenCV
		Image representation inside OpenCV
		Reading and displaying an image
			Reading and displaying a color image
		Creating images using OpenCV
			Drawing shapes on the blank image
				Drawing a line
				Drawing a rectangle
				Drawing a circle
				Drawing an ellipse
				Writing text on an image
		Saving an image to a file
	Working with videos in OpenCV
		Working with video stored on a computer
		Working with videos from a webcam
		Saving video to a disk
	Basic computer vision applications using the OpenCV CUDA module
		Introduction to the OpenCV CUDA module
		Arithmetic and logical operations on images
			Addition of two images
			Subtracting two images
			Image blending
			Image inversion
		Changing the color space of an image
		Image thresholding
	Performance comparison of OpenCV applications with and without CUDA support
	Summary
	Questions
Basic Computer Vision Operations Using OpenCV and CUDA
	Technical requirements
	Accessing the individual pixel intensities of an image
	Histogram calculation and equalization in OpenCV
		Histogram equalization
			Grayscale images
			Color image
	Geometric transformation on images
		Image resizing
		Image translation and rotation
	Filtering operations on images
		Convolution operations on an image
		Low pass filtering on an image
			Averaging filters
			Gaussian filters
			Median filtering
		High-pass filtering on an image
			Sobel filters
			Scharr filters
			Laplacian filters
	Morphological operations on images
	Summary
	Questions
Object Detection and Tracking Using OpenCV and CUDA
	Technical requirements
	Introduction to object detection and tracking
		Applications of object detection and tracking
		Challenges in object detection
	Object detection and tracking based on color
		Blue object detection and tracking
	Object detection and tracking based on shape
		Canny edge detection
		Straight line detection using Hough transform
		Circle detection 
	Key-point detectors and descriptors
		Features from Accelerated Segment Test (FAST) feature detector
		Oriented FAST and Rotated BRIEF (ORB) feature detection
		Speeded up robust feature detection and matching
	Object detection using Haar cascades
		Face detection using Haar cascades
			From video
		Eye detection using Haar cascades
	Object tracking using background subtraction
		Mixture of Gaussian (MoG) method
		GMG for background subtraction
	Summary
	Questions
Introduction to the Jetson TX1 Development Board and Installing OpenCV on Jetson TX1
	Technical requirements
	Introduction to Jetson TX1
		Important features of the Jetson TX1
		Applications of Jetson TX1
	Installation of JetPack on Jetson TX1
		Basic requirements for installation
		Steps for installation
	Summary
	Questions
Deploying Computer Vision Applications on Jetson TX1
	Technical requirements
	Device properties of Jetson TX1 GPU
	Basic CUDA program on Jetson TX1
	Image processing on Jetson TX1 
		Compiling OpenCV with CUDA support (if necessary)
		Reading and displaying images
		Image addition
		Image thresholding
		Image filtering on Jetson TX1
	Interfacing cameras with Jetson TX1
		Reading and displaying video from onboard camera
	Advanced applications on Jetson TX1
		Face detection using Haar cascades
		Eye detection using Haar cascades
		Background subtraction using Mixture of Gaussian (MoG)
	Computer vision using Python and OpenCV on Jetson TX1
	Summary
	Questions
Getting Started with PyCUDA
	Technical requirements
	Introduction to Python programming language
	Introduction to the PyCUDA module
	Installing PyCUDA on Windows
		Steps to check PyCUDA installation
	Installing PyCUDA on Ubuntu
		Steps to check the PyCUDA installation
	Summary
	Questions
Working with PyCUDA
	Technical requirements
	Writing the first program in PyCUDA 
		A kernel call
	Accessing GPU device properties from PyCUDA program
	Thread and block execution in PyCUDA
	Basic programming concepts in PyCUDA 
		Adding two numbers in PyCUDA 
		Simplifying the addition program using driver class
	Measuring performance of PyCUDA programs using CUDA events
		CUDA events
		Measuring performance of PyCUDA using large array addition   
	Complex programs in PyCUDA
		Element-wise squaring of a matrix in PyCUDA
			Simple kernel invocation with multidimensional threads
			Using inout with the kernel invocation
			Using gpuarray class
		Dot product using GPU array
		Matrix multiplication
	Advanced kernel functions in PyCUDA
		Element-wise kernel in PyCUDA
		Reduction kernel 
		Scan kernel 
	Summary
	Questions
Basic Computer Vision Applications Using PyCUDA
	Technical requirements
	Histogram calculation in PyCUDA
		Using atomic operations
		Using shared memory
	Basic computer vision operations using PyCUDA
		Color space conversion in PyCUDA
			BGR to gray conversion on an image
			BGR to gray conversion on a webcam video
		Image addition in PyCUDA
		Image inversion in PyCUDA using gpuarray
	Summary
	Questions
Assessments
	Chapter 1
	Chapter 2
	Chapter 3
	Chapter 4
	Chapter 5
	Chapter 6
	Chapter 7
	Chapter 8
	Chapter 9
	Chapter 10
	Chapter 11
	Chapter 12
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