Smartish Edge Camera – Azure IoT Hub

The SmartEdgeCameraAzureIoTService application uses the same You Only Look Once(YOLOV5) + ML.Net + Open Neural Network Exchange(ONNX) plumbing as the SmartEdgeCameraAzureStorageService.

If there is an object with a label in the PredictionLabelsOfInterest list, a tally of each of the different object classes is sent to an Azure IoT Hub.

"Application": {
  "DeviceID": "",
  "ImageTimerDue": "0.00:00:15",
  "ImageTimerPeriod": "0.00:00:30",

  "ImageCameraFilepath": "ImageCamera.jpg",

  "YoloV5ModelPath": "YoloV5/yolov5s.onnx",

  "PredicitionScoreThreshold": 0.7,
  "PredictionLabelsOfInterest": [
    "person"
  ],
}

The Azure IoT hub can configured via a Shared Access Signature(SAS) device policy connection string or the Azure IoT Hub Device Provisioning Service(DPS)

Cars and bicycles in my backyard with no object(s) of interest
SmartEdgeCameraAzureIoTService no object(s) of interest
Cars and bicycles in my backyard with one object of interest
SmartEdgeCameraAzureIoTService one object of interest
Azure IoT Explorer Telemetry with one object of interest

After the You Only Look Once(YOLOV5)+ML.Net+Open Neural Network Exchange(ONNX) plumbing has loaded a timer with a configurable due time and period is started. Using some Language Integrated Query (LINQ) code any predictions with a score < PredictionScoreThreshold are discarded, then the list of predictions is checked to see if there are any in the PredictionLabelsOfInterest. If there are any matching predictions a count of the instances of each class is generated with more LINQ code.

private async void ImageUpdateTimerCallback(object state)
{
	DateTime requestAtUtc = DateTime.UtcNow;

	// Just incase - stop code being called while photo already in progress
	if (_cameraBusy)
	{
		return;
	}
	_cameraBusy = true;

	_logger.LogInformation("Image processing start");

	try
	{
#if CAMERA_RASPBERRY_PI
		RaspberryPIImageCapture();
#endif
#if CAMERA_SECURITY
		SecurityCameraImageCapture();
#endif
		List<YoloPrediction> predictions;

		using (Image image = Image.FromFile(_applicationSettings.ImageCameraFilepath))
		{
			_logger.LogTrace("Prediction start");
			predictions = _scorer.Predict(image);
			_logger.LogTrace("Prediction done");
		}

		if (_logger.IsEnabled(LogLevel.Trace))
		{
			_logger.LogTrace("Predictions {0}", predictions.Select(p => new { p.Label.Name, p.Score }));
		}

		var predictionsOfInterest = predictions.Where(p => p.Score > _applicationSettings.PredicitionScoreThreshold)
										.Select(c => c.Label.Name)
										.Intersect(_applicationSettings.PredictionLabelsOfInterest, StringComparer.OrdinalIgnoreCase);

		if (predictionsOfInterest.Any())
		{
			if (_logger.IsEnabled(LogLevel.Trace))
			{
				_logger.LogTrace("Predictions of interest {0}", predictionsOfInterest.ToList());
			}

			var predictionsTally = predictions.GroupBy(p => p.Label.Name)
									.Select(p => new
									{
										Label = p.Key,
										Count = p.Count()
									});

			if (_logger.IsEnabled(LogLevel.Information))
			{
				_logger.LogInformation("Predictions tally {0}", predictionsTally.ToList());
			}

			JObject telemetryDataPoint = new JObject();

			foreach (var predictionTally in predictionsTally)
			{
				telemetryDataPoint.Add(predictionTally.Label, predictionTally.Count);
			}

			using (Message message = new Message(Encoding.ASCII.GetBytes(JsonConvert.SerializeObject(telemetryDataPoint))))
			{
				message.Properties.Add("iothub-creation-time-utc", requestAtUtc.ToString("s", CultureInfo.InvariantCulture));

				await _deviceClient.SendEventAsync(message);
			}
		}
	}
	catch (Exception ex)
	{
		_logger.LogError(ex, "Camera image download, post processing, telemetry failed");
	}
	finally
	{
		_cameraBusy = false;
	}

	TimeSpan duration = DateTime.UtcNow - requestAtUtc;

	_logger.LogInformation("Image processing done {0:f2} sec", duration.TotalSeconds);
}

The list of prediction class counts is used to populate a Newtonsoft JObject which serialised to generate a Java Script Object Notation(JSON) payload for an Azure IoT Hub message.

The test-rig consisted of a Unv ADZK-10 Security Camera, Power over Ethernet(PoE) and my HP Prodesk 400G4 DM (i7-8700T)

2 thoughts on “Smartish Edge Camera – Azure IoT Hub

  1. Pingback: Smartish Edge Camera – Azure IoT Central Part 2 | devMobile's blog

  2. Pingback: Smartish Edge Camera – Azure IoT Image Upload | devMobile's blog

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