Use Custom Vision API to build an object detection project in C#

Learn how to use a basic Windows application that uses the Computer Vision API to create an object detection project. After it's created, you can add tagged regions, upload images, train the project, obtain the project's default prediction endpoint URL, and use the endpoint to programmatically test an image. Use this open-source example as a template for building your own app for Windows by using the Custom Vision API.

Prerequisites

Get the Custom Vision SDK and samples

To build this example, you need the Custom Vision SDK NuGet Packages:

You can download the images along with the C# Samples.

Get the training and prediction keys

To get the keys used in this example, visit the Custom Vision web page and select the gear icon in the upper right. In the Accounts section, copy the values from the Training Key and Prediction Key fields.

Image of the keys UI

Step 1: Create a console application

In this step, you create a console application and prepare the training key and the images needed for the example:

  1. Start Visual Studio 2015, Community Edition.
  2. Create a new console application.
  3. Add references to the two nuget packages:

    • Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training
    • Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction
  4. Replace the contents of Program.cs with the code that follows.

using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction;
using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training;
using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Threading;

namespace SampleObjectDetection
{
    class Program
    {
        static void Main(string[] args)
        {
            // Add your training key from the settings page of the portal
            string trainingKey = "<your key here>";

            // Create the Api, passing in the training key
            TrainingApi trainingApi = new TrainingApi() { ApiKey = trainingKey };
        }        
    }
}

Step 2: Create a Custom Vision Service project

To create a new Custom Vision Service project, add the following code to the end of your Main() method.

    // Find the object detection domain
    var domains = trainingApi.GetDomains();
    var objDetectionDomain = domains.FirstOrDefault(d => d.Type == "ObjectDetection");

    // Create a new project
    Console.WriteLine("Creating new project:");
    var project = trainingApi.CreateProject("My New Project", null, objDetectionDomain.Id);

Step 3: Add tags to your project

To add tags to your project, insert the following code after the call to CreateProject():

    // Make two tags in the new project
    var forkTag = trainingApi.CreateTag(project.Id, "fork");
    var scissorsTag = trainingApi.CreateTag(project.Id, "scissors");

Step 4: Upload images to the project

For object detection projects we need to identify the region of the object using normalized coordinates and a tag. To add the images and tagged regions, insert the following code at the end of the Main() method:

    Dictionary<string, double[]> fileToRegionMap = new Dictionary<string, double[]>()
    {
        // FileName, Left, Top, Width, Height
        {"scissors_1", new double[] { 0.4007353, 0.194068655, 0.259803921, 0.6617647 } },
        {"scissors_2", new double[] { 0.426470578, 0.185898721, 0.172794119, 0.5539216 } },
        {"scissors_3", new double[] { 0.289215684, 0.259428144, 0.403186262, 0.421568632 } },
        {"scissors_4", new double[] { 0.343137264, 0.105833367, 0.332107842, 0.8055556 } },
        {"scissors_5", new double[] { 0.3125, 0.09766343, 0.435049027, 0.71405226 } },
        {"scissors_6", new double[] { 0.379901975, 0.24308826, 0.32107842, 0.5718954 } },
        {"scissors_7", new double[] { 0.341911763, 0.20714055, 0.3137255, 0.6356209 } },
        {"scissors_8", new double[] { 0.231617644, 0.08459154, 0.504901946, 0.8480392 } },
        {"scissors_9", new double[] { 0.170343131, 0.332957536, 0.767156839, 0.403594762 } },
        {"scissors_10", new double[] { 0.204656869, 0.120539248, 0.5245098, 0.743464053 } },
        {"scissors_11", new double[] { 0.05514706, 0.159754932, 0.799019635, 0.730392158 } },
        {"scissors_12", new double[] { 0.265931368, 0.169558853, 0.5061275, 0.606209159 } },
        {"scissors_13", new double[] { 0.241421565, 0.184264734, 0.448529422, 0.6830065 } },
        {"scissors_14", new double[] { 0.05759804, 0.05027781, 0.75, 0.882352948 } },
        {"scissors_15", new double[] { 0.191176474, 0.169558853, 0.6936275, 0.6748366 } },
        {"scissors_16", new double[] { 0.1004902, 0.279036, 0.6911765, 0.477124184 } },
        {"scissors_17", new double[] { 0.2720588, 0.131977156, 0.4987745, 0.6911765 } },
        {"scissors_18", new double[] { 0.180147052, 0.112369314, 0.6262255, 0.6666667 } },
        {"scissors_19", new double[] { 0.333333343, 0.0274019931, 0.443627447, 0.852941155 } },
        {"scissors_20", new double[] { 0.158088237, 0.04047389, 0.6691176, 0.843137264 } },
        {"fork_1", new double[] { 0.145833328, 0.3509314, 0.5894608, 0.238562092 } },
        {"fork_2", new double[] { 0.294117659, 0.216944471, 0.534313738, 0.5980392 } },
        {"fork_3", new double[] { 0.09191177, 0.0682516545, 0.757352948, 0.6143791 } },
        {"fork_4", new double[] { 0.254901975, 0.185898721, 0.5232843, 0.594771266 } },
        {"fork_5", new double[] { 0.2365196, 0.128709182, 0.5845588, 0.71405226 } },
        {"fork_6", new double[] { 0.115196079, 0.133611143, 0.676470637, 0.6993464 } },
        {"fork_7", new double[] { 0.164215669, 0.31008172, 0.767156839, 0.410130739 } },
        {"fork_8", new double[] { 0.118872553, 0.318251669, 0.817401946, 0.225490168 } },
        {"fork_9", new double[] { 0.18259804, 0.2136765, 0.6335784, 0.643790841 } },
        {"fork_10", new double[] { 0.05269608, 0.282303959, 0.8088235, 0.452614367 } },
        {"fork_11", new double[] { 0.05759804, 0.0894935, 0.9007353, 0.3251634 } },
        {"fork_12", new double[] { 0.3345588, 0.07315363, 0.375, 0.9150327 } },
        {"fork_13", new double[] { 0.269607842, 0.194068655, 0.4093137, 0.6732026 } },
        {"fork_14", new double[] { 0.143382356, 0.218578458, 0.7977941, 0.295751631 } },
        {"fork_15", new double[] { 0.19240196, 0.0633497, 0.5710784, 0.8398692 } },
        {"fork_16", new double[] { 0.140931368, 0.480016381, 0.6838235, 0.240196079 } },
        {"fork_17", new double[] { 0.305147052, 0.2512582, 0.4791667, 0.5408496 } },
        {"fork_18", new double[] { 0.234068632, 0.445702642, 0.6127451, 0.344771236 } },
        {"fork_19", new double[] { 0.219362751, 0.141781077, 0.5919118, 0.6683006 } },
        {"fork_20", new double[] { 0.180147052, 0.239820287, 0.6887255, 0.235294119 } }
    };

    // Add all images for fork
    var imagePath = Path.Combine("Images", "fork");
    var imageFileEntries = new List<ImageFileCreateEntry>();
    foreach (var fileName in Directory.EnumerateFiles(imagePath))
    {
        var region = fileToRegionMap[Path.GetFileNameWithoutExtension(fileName)];
        imageFileEntries.Add(new ImageFileCreateEntry(fileName, File.ReadAllBytes(fileName), null, new List<Region>(new Region[] { new Region(forkTag.Id, region[0], region[1], region[2], region[3]) })));
    }
    trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFileEntries));

    // Add all images for scissors
    imagePath = Path.Combine("Images", "scissors");
    imageFileEntries = new List<ImageFileCreateEntry>();
    foreach (var fileName in Directory.EnumerateFiles(imagePath))
    {
        var region = fileToRegionMap[Path.GetFileNameWithoutExtension(fileName)];
        imageFileEntries.Add(new ImageFileCreateEntry(fileName, File.ReadAllBytes(fileName), null, new List<Region>(new Region[] { new Region(scissorsTag.Id, region[0], region[1], region[2], region[3]) })));
    }
    trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFileEntries));

Step 5: Train the project

Now that you've added tags and images to the project, you can train it:

  1. Insert the following code at the end of Main(). This creates the first iteration in the project.
  2. Mark this iteration as the default iteration.
    // Now there are images with tags start training the project
    Console.WriteLine("\tTraining");
    var iteration = trainingApi.TrainProject(project.Id);

    // The returned iteration will be in progress, and can be queried periodically to see when it has completed
    while (iteration.Status != "Completed")
    {
        Thread.Sleep(1000);

        // Re-query the iteration to get its updated status
        iteration = trainingApi.GetIteration(project.Id, iteration.Id);
    }

    // The iteration is now trained. Make it the default project endpoint
    iteration.IsDefault = true;
    trainingApi.UpdateIteration(project.Id, iteration.Id, iteration);
    Console.WriteLine("Done!\n");

Step 6: Get and use the default prediction endpoint

You're now ready to use the model for prediction:

  1. Obtain the endpoint associated with the default iteration by inserting the following code at the end of Main().
  2. Send a test image to the project by using that endpoint.
    // Now there is a trained endpoint, it can be used to make a prediction

    // Add your prediction key from the settings page of the portal
    // The prediction key is used in place of the training key when making predictions
    string predictionKey = "<your key here>";

    // Create a prediction endpoint, passing in the obtained prediction key
    PredictionEndpoint endpoint = new PredictionEndpoint() { ApiKey = predictionKey };

    // Make a prediction against the new project
    Console.WriteLine("Making a prediction:");
    var imageFile = Path.Combine("Images", "test", "test_image.jpg");
    using (var stream = File.OpenRead(imageFile))
    {
        var result = endpoint.PredictImage(project.Id, File.OpenRead(imageFile));

        // Loop over each prediction and write out the results
        foreach (var c in result.Predictions)
        {
            Console.WriteLine($"\t{c.TagName}: {c.Probability:P1} [ {c.BoundingBox.Left}, {c.BoundingBox.Top}, {c.BoundingBox.Width}, {c.BoundingBox.Height} ]");
        }
    }

Step 7: Run the example

Build and run the solution. The prediction results appear on the console.