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Announcing two new AWS DeepLens sample projects with step-by-step instructions

We are excited to announce the launch of two new sample projects: “Build a worker safety system” and “Who drinks the most coffee?” for AWS DeepLens. These sample projects provide guided instructions on how to use computer vision to build a complete machine learning application on AWS. The applications span the edge and the cloud, integrating models running on the device with the AWS services on the cloud. The sample projects consist of step-by-step instructions, complete with code and a video tutorial for developers to build the application from scratch.

AWS DeepLens is the world’s first deep learning enabled video camera built to help developers of all skill levels to get started with deep learning. The new (2019) edition of the AWS DeepLens can now be purchased in six countries (USUKGermanyFranceSpainItaly, and Canada), and preordered in Japan. The 2019 edition is easier to set up, and (thanks to Amazon SageMaker Neo) runs machine learning models up to twice as fast as the earlier edition.

To get started with these fully guided sample projects, navigate to the AWS DeepLens management console. On the left navigation, navigate to Recipes to access the latest step-by-step tutorials. Choose a Recipe and follow the instructions provided to build the machine learning application. AWS DeepLens management console is available in Asia Pacific (Tokyo), EU (Frankfurt), and US-East (N. Virginia) Regions.

The following Recipes are available:

1) Build a worker safety system:

Use AWS DeepLens and Amazon Rekognition to build an application that helps identify if a person at a construction site is wearing the right safety gear, in this case, a hard hat. In this Recipe, developers learn to use the face detection model available on AWS DeepLens to detect a face and upload it to S3 for further processing. Developers learn to write a Lambda function that gets triggered on an S3 upload and integrates with Amazon Rekognition to detect if the person is not wearing a helmet. If no helmet is detected, the Lambda function sends a violation log to Amazon CloudWatch and alerts via AWS IoT. Developers also learn to build a web portal that shows the alert live.

2) Who drinks the most coffee?

Learn to build an application that counts the number of cups of coffee that people drink and displays the tally on a leaderboard. This Recipe uses face detection to track the number of people that drink coffee. As part of this Recipe, developers learn to write a Lambda function that gets triggered when a face is detected. Then, Amazon Rekognition is used to detect the presence of a coffee mug, and the face is added to a DynamoDB database that is maintained by (and private to) the developer. The Recipe also features a leaderboard that tracks the number of coffees over time.

If you have any questions regarding Recipes, please reach out to us on AWS DeepLens developer forum. For project inspiration, visit the AWS DeepLens Community Projects to find videos, descriptions, and links to GitHub repos.

Happy building!


About the Author

Jyothi Nookula is a Senior Product Manager for AWS DeepLens. She loves to build products that delight her customers. In her spare time, she loves to paint and host charity fund raisers for her art exhibitions.

 

 

 

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.