I’m a big fan of Azure Custom Vision, and when I talk about Computer Vision I usually share an example on how to tag images from drawings. I had 3 types of drawings
I build a model in Custom Vision and then I show my amazing drawing skills with something like this. The custom CV model identifies this as a fish, so I get to a point where my drawing skills are a good fit for this model.
Note: I think the 1st time we did something with these drawings was in an Insider Dev Tour on 2017 or 2018. This photo is from Microsoft Ignite 2019.
Azure Custom Vision is a super cool tool that allow us to create complex Computer Vision projects without code. And, on top of that we have an additional feature:
Once we train a CV model, we can export the model to be used in different platforms. In example, TensorFlow, ONNX, or as a packaged website in a Docker image.
If we go to the Docker option, we will have also the choice to export to different versions:
- ARM (Raspberry Pi3)
That’s cool. I can download and install the Linux version, and then build and run the project with these 2 commands
docker build -t drawingsit01 . docker run -p 127.0.0.1:80:80 -d drawingsit01
Now I can test this with a tool like Postman. In order to do this, I draw (again) an amazing fish, and then I perform a POST request to my local running docker container, and it was recognized as a fish with a 99% of accuracy.
So, let’s recap for future posts.
- We created and trained a project in Azure Custom Vision.
- We exported the project as a docker image.
- We run the image locally, and tested with an image.
Right now, I think I have everything that I need to create an Azure IoT Module based on the exported Linux module from Custom Vision.
More in next posts !
More posts in my blog ElBruno.com.
More info in https://beacons.ai/elbruno
Azure ☁ IoT
- Install ☁ Azure IoT on Raspberry Pi
- Deploy ☁ Azure Blob Storage on IoT Edge, lessons learned
- Connect to ☁ Azure Blob Storage on IoT Edge using Microsoft Azure Storage Explorer
- Lesson learned and tips on how to install Azure IoT Edge on Ubuntu on a Raspberry Pi
- Azure IoT Explorer, in preview and awesome
- Mapping a local ☁ Azure IoT Edge folder module with an Edge device folder 📁
- Creating a folder 📂 in the docker definition in an ☁ Azure IoT Edge
- Granting access to Raspberry Pi GPIO from an ☁ Azure IoT Edge Module
Create an Azure IoT Module using Raspberry Pi and Grove Sensors
- Raspberry Pi + Grove Sensors, read temperature and humidity values
- Raspberry Pi + Grove Sensors, send temperature and humidity values as telemetry to Azure IoT Hub
- Raspberry Pi + Grove Sensors, create a Azure IoT Module to send temperature and humidity values as telemetry to Azure IoT Hub
- Raspberry Pi + Grove Sensors, publish and use the Azure IoT Module
- Raspberry Pi + Grove Sensors, notes on build and configuration
- Raspberry Pi + Grove Sensors, details on how to send a telemetry message and sample messages
Create an Azure IoT Module from Azure Custom Vision project
- Create and export a Custom Vision Project as Docker image
- Analyze the content of the CV Docker image
- Create and analyze an Azure IoT Module
- Merge the CV project as an Azure IoT Module
- Deploy to an Azure IoT device and test the CV module
- Send telemetry for each analyzed image
- Add digital twin configuration to the Azure IoT module (coming soon)
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