In this recipe, first, we import OpenCV. We then select the first camera it finds (camera(0)). If we were looking for the second camera it finds, then we would increment the camera number (camera(1)). Next, we check whether the camera is available. There can be several reasons why a camera might not be available. First, it could be opened by something else. You could, for example, open the camera in a different application to see whether it is working and this would prevent the Python application from detecting and connecting to the camera. Another common issue is that releasing the camera step in the code does not get executed and the camera needs to be reset. Next, we capture the video frames and present them on the screen until someone presses the Q key. Finally, after someone has exited the application, we release the camera and close the open window.
Artificial Intelligence for IoT Cookbook
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Artificial Intelligence for IoT Cookbook
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Overview of this book
Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users’ lives easier. With this AI cookbook, you’ll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications.
Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You’ll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you’ll learn how to deploy models and improve their performance with ease.
By the end of this book, you’ll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.
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Current Title:
Artificial Intelligence for IoT Cookbook
Table of Contents (11 chapters)
Preface
Setting Up the IoT and AI Environment
Free Chapter
Handling Data
Machine Learning for IoT
Deep Learning for Predictive Maintenance
Anomaly Detection
Computer Vision
NLP and Bots for Self-Ordering Kiosks
Optimizing with Microcontrollers and Pipelines
Deploying to the Edge
Customer Reviews