AWS Panorama is a machine learning appliance and SDK, which enables you to add computer vision (CV) to your on-premises cameras or on new AWS Panorama enabled cameras. AWS Panorama gives you the ability to make real-time decisions to improve your operations, by giving you compute power at the edge.
This repository contains sample applications for AWS Panorama, and Test Utility which allows running Panorama applications in simulation environment without real Panorama appliance device.
Test Utility is a set of python libraries and commandline commands, which allows you to test-run Panorama applications without Panorama appliance device. With Test Utility, you can start running sample applications and developing your own Panorama applications before preparing real Panorama appliance. Sample applications in this repository also use Test Utility.
For more about the Test Utility and its current capabilities, please refer to Introducing AWS Panorama Test Utility document.
To set up your environment for Test Utility, please refer to Test Utility environment setup.
To know how to use Test Utility, please refer to How to use Test Utility.
| Application | Description | Framework | Usecase | Complexity | Model | Python Version |
|---|---|---|---|---|---|---|
| People Counter | 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 | Download | 3.8 |
| Car Detector and Tracker | This is a sample computer vision application that can detect and track cars | Tensorflow | Object Detection | Medium | Download | 3.7 |
| Pose estimation | This is a sample computer vision application that can detect people and estimate pose of them | MXNet | Pose estimation | Advanced | yolo3_mobilenet1.0_coco, simple_pose_resnet152_v1d | 3.7 |
| Object Detection Tensorflow SSD (TF37_opengpu) | This example shows how to run a TF SSD Mobilenet Model using Tensorflow | Tensorflow (Open GPU) | Object Detection (BYO Container) | Advanced | N/A | 3.7 |
| Object Detection PyTorch Yolov5s (PT37_opengpu) | This example shows how to run your own YoloV5s model using PyTorch | PyTorch (Open GPU) | Object Detection (BYO Container) | Advanced | N/A | 3.7 |
| Object Detection ONNX Runtime Yolov5s (ONNX_opengpu) | This example shows how to run your own YoloV5s model using ONNX Runtime | ONNX Runtime (Open GPU) | Object Detection (BYO Container) | Advanced | N/A | 3.8 |
| Object Detection with Yolov5 ONNX model optimized for TensorRT (ONNX2TRT_opengpu) | This sample shows how to run a Yolov5 ONNX model optimized for TensorRT | TensorRT Runtime (Open GPU) | Object Detection (BYO Container) | Advanced | N/A | 3.6 |
| Object Detection using TensorRT network definition APIs (TRTPT36_opengpu) | This example shows how to get infernece from a YoloV6s model optimized using TensorRT Network definition API's | TensorRT (OpenGPU) | Object Detection (BYO Container) | Advanced | N/A | 3.6 |
| Inbound networking | This sample explains how to enable inbound networking port on Panorama device, and how to run a simple HTTP server wihtin a Panorama application. | N/A | Network | Easy | N/A | 3.7 |
| MOT Analysis | This sample shows how to build end to end multi object tracking solution using pretrained YOLOX model, kinesis video upstream by gstreamer and dashboard | PyTorch | Object Tracking | Advanced | YOLOX | 3.7 |
| Kinesis Video Streams | This sample shows how to build an application to push multiple video streams from Panoram to Amazon Kinesis Video Streams service with AWS IoT. | N/A | Media | Advanced | N/A | 3.8 |
Step 1 : Go to aws-panorama-samples/samples and open your choice of project Step 2 : Open the .ipynb notebook and follow the instructions in the notebook Step 3 : To make any changes, change the corresponding node package.json or the graph.json in the application folder
For more information, check out the documentation for the AWS Panorama DX CLI here
List of tools for ease of development of panorama. Please see details at corresponding tool page.
We use AWS Panorama Samples GitHub issues for tracking questions, bugs, and feature requests.
This library is licensed under the MIT-0 License.