Introduction to AWS Panorama

AWS Panorama is a service that enables you to run computer vision applications at the edge. By using the AWS Panorama Appliance with your existing network cameras, you can run applications that use machine learning to collect data from video streams, output video with text and graphical overlays, and interact with other AWS services.

AWS Panorama Computer Vision Examples

This repository provides some examples to get you kick-started on building applications for AWS Panorama.

Resources

AWS Panorama Documentation

Please refer to README file in each folder for more specific instructions.

How to Run a Sample

We recommend using a Sagemaker Jupyter Notebook Instance
- Go to your AWS Sagemaker console
- Click Notebook Instances -> Create
- Choose the instance type (These were built on P2 instances)
- Choose the Volume size in GB (5 GB should be enough)
- Create Notebook Instance
Once your notebook instance is created, click the name of your Notebook instance
- Go to Permissions and encryption
- Click on the IAM role arn
- In permissions, attach the following policies
  - AWSLambdaFullAccess
  - IAMFullAccess
  - AmazonS3FullAccess
  - AWSIoTFullAccess
  - AmazonSageMakerFullAccess
Restart your Notebook Instance and launch Jupyter Lab
Launch a terminal session and do the following
- cd SageMaker
Clone the repository git clone https://github.com/aws-samples/aws-panorama-samples.git
cd aws-panorama-samples
wget https://panorama-starter-kit.s3.amazonaws.com/public/v1/Models/Models.zip
unzip -q Models.zip
sudo rm Models.zip
wget https://panorama-starter-kit.s3.amazonaws.com/public/v1/Models/panorama_sdk.zip
unzip -q panorama_sdk.zip
sudo rm panorama_sdk.zip
cd ..
sudo sh aws-panorama-samples/panorama_sdk/run_after_clone.sh
We suggest using conda_mxnet_latest_p37 kernel for all use cases (Except specified in the individual README)
At this point, the set up is done. You can explore the different applications in each of the folders
Follow the README in the individual examples for information about the use case

List of Samples

For each of the samples below, we include instructions on how to deploy them to the edge Panorama device inside the notebook.

Application	README	Description	Framework	Usecase	Complexity
People Counter	README.md	This is a sample computer vision application that can count the number of people in each frame of a streaming video (Start with this)	MXNet	Object Detection	Easy
Custom Object Detector	README.md	This is a sample computer vision application that showcases how to build your own models using Gluoncv, and then deploy them on the AWS Panorama device	MXNet	Object Detection	Medium
Fall Detection	README.md	This is a sample computer vision application that showcases how to build a fall detection computer vision application,and then deploy them on the AWS Panorama device. This also showcases how to use multiple models for a single usecase on the Panorama device	MXNet	Pose Detection & Object Detection	Advanced
Social Distance Calculation	README.md	This is an advanced use case where we build a sample computer vision application that uses object detection models and some simple math to detect social distancing infractions	MXNet	Object Detection	Advanced
Handwash Detection	README.md	This is a sample computer vision application that showcases how to detect Hand washing using an action detection model	MXNet	Action Detection	Easy
Smoking Detection	README.md	This is a sample computer vision application that showcases how to detect somone Smoking using an action detection model	MXNet	Action Detection	Easy
Image Classification	README.md	This is a sample computer vision application that showcases build a simple image classification model using AWS Panorama	MXNet	Image Classification	Easy
Semantic Segmentation	README.md	This is a sample computer vision application that showcases how to use a Gluoncv Segmentation model and build a segmentation use case	MXNet	Semantic Segmentation	Medium

Getting Help

We use AWS Panorama Samples GitHub issues for tracking questions, bugs, and feature requests.

License

This library is licensed under the MIT-0 License. See the LICENSE file.