Azure Custom Vision: Object Recognition in .NET Applications

Hey, there! In this tutorial, we'll learn how to call an Azure Custom Vision object recognition model from a .NET application.

For example, we will recognize these objects in an image: hats, faces, guitars, and bottles.

Note: This tutorial shows the process of creating the Custom Vision model: Object detection with Azure Custom Vision.

Source code of this example: GitHub - Azure Object Recognition.

Custom Vision API

As a first point, in our .NET project, we must install the following NuGet packages:

• Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training
• Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction

These packages will allow us to call our Custom Vision model using their access and information methods.

CustomVision.cs class

With the NuGet packages installed, we can create a new class, and here as a first instance, we will need to establish the variables that will allow us to refer to our specific Azure Custom Vision project:

private string Key = "YOUR_CUSTOMVISION_KEY";
private string Endpoint = "YOUR_ENDPOINT";
private string ProjectID = "YOUR_PROJECT_ID";
private string publishedModelName = "YOUR_MODEL_NAME";
private double minProbability = 0.75;

These values can be found in the general settings of your project:

With these attributes, we can generate the respective endpoint for the authentication of our project and finally define a DetectObjects method, sending as a parameter the path of the image to be analyzed and receiving as a response the JSON with the detected objects.

This would be the source code of the entire C# class:

using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction;
using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction.Models;
using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training;
using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models;
using ApiKeyServiceClientCredentials = Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction.ApiKeyServiceClientCredentials;

namespace CognitiveServices;

public class CustomVision
{
    private string Key = "YOUR_CUSTOMVISION_KEY";
    private string Endpoint = "YOUR_ENDPOINT";
    private string ProjectID = "YOUR_PROJECT_ID";
    private string publishedModelName = "YOUR_MODEL_NAME";
    private double minProbability = 0.75;

    private CustomVisionTrainingClient trainingApi;
    private CustomVisionPredictionClient predictionApi;
    private Project project;

    public CustomVision()
    {
        trainingApi = AuthenticateTraining(Endpoint, Key);
        predictionApi = AuthenticatePrediction(Endpoint, Key);
        project = trainingApi.GetProject(new Guid(ProjectID));
    }

    private CustomVisionTrainingClient AuthenticateTraining(string endpoint, string trainingKey)
    {
        return new CustomVisionTrainingClient(new Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.ApiKeyServiceClientCredentials(trainingKey))
        {
            Endpoint = endpoint
        };
    }

    private CustomVisionPredictionClient AuthenticatePrediction(string endpoint, string predictionKey)
    {
        return new CustomVisionPredictionClient(new ApiKeyServiceClientCredentials(predictionKey))
        {
            Endpoint = endpoint
        };
    }

    public List<PredictionModel> DetectObjects(string imageFile)
    {
        using (var stream = File.Open(imageFile, FileMode.Open, FileAccess.Read, FileShare.ReadWrite))
        {
            var result = predictionApi.DetectImage(project.Id, publishedModelName, stream);
            return result.Predictions.Where(x => x.Probability > minProbability).ToList();
        }
    }
}

Program.cs / Call example

In a new class, we can define a collection of type List<PredictionModel> to store the recognized objects of a particular image after calling the previously defined DetectObjects method.

This would be the source code of this new class:

using CognitiveServices;

var customVision = new CustomVision();
var imagePath = "image.jpg";

var objects = customVision.DetectObjects(imagePath);

Console.WriteLine("Object - Probability - Position(X,Y)");
foreach (var obj in objects)
{
    Console.WriteLine("{0}: {1} - ({2},{3})",
        obj.TagName,
        obj.Probability,
        obj.BoundingBox.Left,
        obj.BoundingBox.Top);
}

And this is the result in the console for the previous example:

Thanks for reading!

If you have any questions or ideas in mind, it will be a pleasure to be in communication with you and together exchange knowledge with each other.

See you on Twitter / esDanielGomez.com.