while (1) {clear; docker ps; sleep 5}
import base64 base64.b64encode(frame)
This is a sample showing how to deploy a Custom Vision model to a Raspberry Pi 3 device running Azure IoT Edge. This solution is made of 3 modules:
- Camera capture - this module captures the video stream from a USB camera, sends the frames for analysis to the custom vision module and shares the output of this analysis to the edgeHub. This module is written in python and uses OpenCV to read the video feed.
- Custom vision - it is a web service over HTTP running locally that takes in images and classifies them based on a custom model built via the Custom Vision website. This module has been exported from the Custom Vision website and slightly modified to run on a ARM architecture. You can modify it by updating the model.pb and label.txt files to update the model.
- SenseHat display - this module gets messages from the edgeHub and blinks the raspberry Pi's senseHat according to the tags specified in the inputs messages. This module is written in python and requires a SenseHat to work.
1- Update the module.json files of the 3 modules above to point to your own Azure Container Registry
2- Build the full solution by running the Build IoT Edge Solution command from the Azure IoT Edge extension in VS Code.
You can run this solution on either of the following hardware:
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Raspberry Pi 3: Set up Azure IoT Edge on a Raspberry Pi 3 (instructions) with a SenseHat and use the arm32v7 module tags.
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Simulated Azure IoT Edge device (such as a PC): Set up Azure IoT Edge (instructions on Windows, instructions on Linux) and use the amd64 module tags.