Amazon Rekognition Concepts
Amazon Rekognition Demonstrations
Amazon Rekognition Course Review
The course is part of these learning paths
In this course, you'll learn about Amazon Rekognition, a service that enables you to easily and quickly integrate computer vision features directly into your own applications. At its core, Amazon Rekognition provides an API that you submit images and/or videos to. You then instruct the Rekognition service to perform a specific analysis on the media. The analysis can be anything from detecting faces within an image to extracting labels from a video in an asynchronous manner.
We’ll provide in-depth reviews of the image, video, and collection based API sets. Amazon Rekognition makes it easy to add image and video analysis to your applications. You simply submit your image or video to the Rekognition service, and the service will then identify objects, people, text, scenes, emotions, and activities. Additionally the service can be used to moderate content, by detecting inappropriate or objectionable content.
We finish the course with a couple of demonstrations:
- In the 1st demonstration we will show you how to use Amazon Rekognition together with S3 and CloudFront for hosting, to build a simple facial analysis static web application.
- In the 2nd demonstration we will show you how to use the AWSCLI to implement an object and feature detection system that sends an email when certain features are detected within an image.
Both demonstrations will highlight the capabilities of the Amazon Rekognition and the different approaches you can adopt to interface with the service.
The intended audience for this course includes:
- Data scientists interested in mining information from images and/or video
- Machine Learning enthusiasts with an interest in computer vision
- Developers interested in learning how to integrate image and video analysis into their own applications
- Anyone interested in learning how Amazon Rekognition works
By completing this course, you will:
- Understand what Amazon Rekognition is and what it offers
- Understand the benefits of using the Amazon Rekognition service
- Understand how to use Amazon Rekognition APIs to process both images and videos
- Understand how to use Collections and the storage based API set
- Understand business use cases and scenarios that can benefit from using the Amazon Rekognition service
- Be able to architect and integrate Amazon Rekognition into your own applications
The following prerequisites will be both useful and helpful for this course:
- General development and coding experience
- AWS S3 and IAM security experience (for the demonstrations)
The agenda for the remainder of this course is as follows:
- We’ll discuss what Amazon Rekognition is and when and why you might consider using it
- We’ll review the Amazon Rekognition service and provide an in-depth review of each of its features
- We’ll discuss benefits and business use cases that can be empowered by leveraging Amazon Rekognition
- We’ll provide some example business use cases and scenarios that utilise the Rekognition service
- Finally - We’ll present 2 demonstrations that highlight how to integrate with the Rekognition service
If you have thoughts or suggestions for this course, please contact Cloud Academy at email@example.com.
Welcome back. In this lecture, we'll go through the various methods that you can use to work with the Amazon Rekognition service the first of which is via the AWS Web Console. The Amazon Rekognition Console is feature-rich and comes with several pre-configured demos. The pre-configured demos can be used to get familiarized with the service very quickly.
For example, the object and scene detection demo allows you to upload an image and have it perform object and feature extraction, the feature detections of which are provided in a formatted view in the right-hand side. Additionally, the raw request and response messages are available for consultation to help with understanding the details of calling the particular API operation, in this case, the detect-labels API.
Amazon Rekognition makes it very easy to integrate image and video analysis into your own applications. You can integrate the Amazon Rekognition service into your own applications by leveraging the appropriate SDK. Amazon provides SDKs for all the common software languages as can be seen on this slice.
As with most AWS services, the Amazon Rekognition service can be used via the AWS CLI. The AWS CLI supports all recognition API operations as shown here. Let's walk through a quick example using the AWS CLI. In this scenario, a video camera is set up to detect movement in front of a home. Whenever movement is detected, a picture is taken and uploaded into an S3 bucket.
We then invoke the Rekognition detect-labels command using the AWS CLI to determine if someone is present. If confirmed, an email is sent to alert the homeowner. Breaking down the structure of the command, we first call the detect-labels command to perform feature extraction on the picture stored in S3. The response is then filtered searching for labels of interest, in this case, people, person and/or human.
If any of these labels exist, we send an email off by piping the filtered output into a Bash script that then creates and sends an email. Later on in our course, we'll provide a demonstration of this scenario using the AWS CLI.
That concludes our lecture on the different methods of interfacing with the Rekognition service. In the next lecture, we'll review a few business use cases where Rekognition can be used. Go ahead and close this lecture and we'll see you shortly in the next one.
Jeremy is a Content Lead Architect and DevOps SME here at Cloud Academy where he specializes in developing DevOps technical training documentation.
He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 25+ years. In recent times, Jeremy has been focused on DevOps, Cloud (AWS, Azure, GCP), Security, Kubernetes, and Machine Learning.
Jeremy holds professional certifications for AWS, Azure, GCP, Terraform, Kubernetes (CKA, CKAD, CKS).