E Book Real-Time IoT Imaging with Deep Neural Networks - Using Java on the Raspberry Pi 4

E Book Real-Time IoT Imaging with Deep Neural Networks - Using Java on the Raspberry Pi 4





E Book Real-Time IoT Imaging with Deep Neural Networks - Using Java on the Raspberry Pi 4






Author(s): Nicolas Modrzyk
Publisher: Apress, Year: 2020
ISBN: 9781484257210,9781484257227

Introduction 

My grandfather, of Polish origins, used to cook, make, build, plant, grow, and create everything with his own two hands. He also loved to share with adults and children alike how he was doing all these creative tasks. As a child, I was always impressed by his “If I don’t have it, I will try to make it myself” attitude. 
He put in the time and the effort to make something original all the time. One of the cutest memories I have is of us walking in the garden in the cold winter. We saw some birds looking for tiny pieces of food. We tried to give them French bread and grains, but they would not, or maybe could not, get to them because of the freezing winter cold. So, he proposed we build a wooden house for the birds so they could come and eat the grains while being protected from the winds. And so we did. We spent the day grabbing dry wood around the nearby river and cleaning, drying, sawing, polishing, and assembling it so it would eventually look like the house in Figure I-1



E Book Real-Time IoT Imaging with Deep Neural Networks - Using Java on the Raspberry Pi 4



This book is not actually about building bird houses, but it is about Do things yourself or, in fact, come up with ideas and shaping Them in something beneficial to others (as opposed to birds).
With the Internet of Things (IoT), you can use existing development Kit to enable you to create something in a short time. In fact, the technology is great Companies have provided so many of these development kits that it's so hard To choose one and start.
These kits, although easy to use, create a dependence on the entire group The environment, like working without one is difficult. Avoidance of It is almost impossible unless you redesign and rebuild everything for you Created from scratch.
Speaking of "zero", a few years ago, Arduino came out. It is small Italian-made integrated panel that you can easily connect to sensors, Rotors, motors and wireless networks. You can use Arduino easily Write the custom logic needed to interact with all development groups

Already mentioned (Figure I-2).




E Book Real-Time IoT Imaging with Deep Neural Networks - Using Java on the Raspberry Pi 4
E Book Real-Time IoT Imaging with Deep Neural Networks - Using Java on the Raspberry Pi 4



Table of contents :


Table of contents
About the author
About technical commentator
Thanks and appreciation
Introduction
Chapter one: starting
Visual Studio Code Primer
Run the first Java application
Import basic Java packages
Lesson debugging lesson
Add a breakpoint
Implementing step-by-step code
Resumption of implementation
Watch an expression
Change a variable value
Wrapping things
Chapter two: uncovering things in video streams
Going Sepia: OpenCV Java Primer
Few files make things easier ...
OpenCV Primer 2: Upload, resize and add photos
Simple addition
Weighted addition
Back to sepia
Find Marcel: detect primer objects
Finding cat faces in pictures using a workbook
What is the advantage?
Where in the world is Marcel?
Search for cat faces in pictures using the Yolo Neural Network
Chapter 3: Seeing on Raspberry Pi 4
Bring berries to life
the shopping
Download the operating system
Create a bootable SD card
Connect the cables
First boot
Find berries using nmap
Easily set up SSH
Set up Visual Studio Studio for remote use
Java setting OpenJDK
An alternative to setting up the Java SDK
Check the OpenCV / Java template
Implementing portal clones
Download the zip file
Using Maven
Install Visual Java Java Pack Pack remotely
Run the first OpenCV example
Runs on Linux or VM with AWS instead
Live video capture
Play a video
Chapter Four: Analysis of video streams on Raspberry Bay
Filters app overview
Apply basic filters
Gray filter
Edge Keep Filter
Sage
Correction (again)
Incorporate filters
Instagram-like filters app
Color map
the lesson
dark brown
Carton
Pencil effect
Object detection procedure
Remove background
Disclosure of features
Detection by color
Har revealed
Transparent overlay on detection
Detect template matching
Yolo revealed
Chapter Five: Vision and Home Automation
Rhasspy message flow
MQTT message queues
Installing mosquitoes
Comparison of other MQTT brokers
MQTT messages at the command line
MQTT messages in Java
Set up dependencies
Send the basic MQTT message
Rhasspy message simulation
JSON Fun
Listen to basic MQTT messages
Listen to MQTT JSON messages
Sound and Rhasspy setting
Prepare the speaker
Install the Docker
Install Rhasspy with Docker
Start the Rhasspy console
Rhasspy controller
The first voice commands
First command, full sentence
Speak section and try your intention
Accurately controlled intentions
Optional words
Adding alternatives
Make destinations with slots more readable
Select reusable slots
Settings: Get that intention into the queue
Settings: Wake-Up Word
Create a goal of distinction
Detect objects and sound in real time
Simple setup: origami + sound
Origami prepare real-time video analysis
Create a Yolo filter
Run video analysis alone
Integration with sound
index



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