public async Task <IList <PredictionViewModel> > Predict(byte[] image)
{
var trainingCredentials = new TrainingApiCredentials(APIKeys.TrainingAPIKey);
var trainingApi = new TrainingApi(trainingCredentials);
var predictionEndpointCredentials = new PredictionEndpointCredentials(APIKeys.PredictionAPIKey);
var predictionEndpoint = new PredictionEndpoint(predictionEndpointCredentials);
var projects = await trainingApi.GetProjectsAsync();
var stream = new System.IO.MemoryStream(image);
var results = await predictionEndpoint.PredictImageAsync(projects[0].Id, stream);
var predictions = new List <PredictionViewModel>();
foreach (var result in results.Predictions)
{
predictions.Add(new PredictionViewModel()
{
Tag = result.Tag, Prediction = result.Probability
});
}
return(predictions);
}
/// <summary>
/// Initialize Model from preloaded Data
/// </summary>
/// <param name="token"></param>
/// <returns></returns>
public void InitializeModel()
{
if (string.IsNullOrWhiteSpace(_trainingKey))
{
throw new Exception("Call SetTrainingKey before training the Model");
}
var trainingCredentials = new TrainingApiCredentials(_trainingKey);
TrainingApi = new TrainingApi(trainingCredentials);
// Check if project already exists
var projects = TrainingApi.GetProjects();
var duplicate = projects.FirstOrDefault(p => p.Name == _projectName);
if (duplicate != null)
{
TrainingApi.DeleteProject(duplicate.Id);
}
// Create a new CV Project
var project = TrainingApi.CreateProject(_projectName);
// Save Project Guid
ProjectGuid = project.Id;
// Retrieve Model Initialization Data from Folder "Training"
var trainings = RetrieveTrainings();
TrainModel(ref project, trainings);
}
public void CreateNewProject()
{
var credentials = new TrainingApiCredentials(Key);
var trainingApi = new TrainingApi(credentials);
// Create the Api, passing in a credentials object that contains the training key
var project = CreateProject(trainingApi);
TrainProject(trainingApi, project);
}
public static bool IsValidCard(string url)
{
try
{
string trainingKey = Constants.CustomVisionTrainingAPIKey;
// Create the Api, passing in a credentials object that contains the training key
TrainingApiCredentials trainingCredentials = new TrainingApiCredentials(trainingKey);
TrainingApi trainingApi = new TrainingApi(trainingCredentials);
var projects = trainingApi.GetProjects();
ProjectModel project = projects.FirstOrDefault(e => e.Name == "IDVision");
// Get the prediction key, which is used in place of the training key when making predictions
var account = trainingApi.GetAccountInfo();
var predictionKey = account.Keys.PredictionKeys.PrimaryKey;
// Create a prediction endpoint, passing in a prediction credentials object that contains the obtained prediction key
PredictionEndpointCredentials predictionEndpointCredentials = new PredictionEndpointCredentials(predictionKey);
PredictionEndpoint endpoint = new PredictionEndpoint(predictionEndpointCredentials);
byte[] byteData = Utilities.Utilities.GetImagesAsByteArrayFromUri(url);
MemoryStream stream = new MemoryStream(byteData);
// Make a prediction against the new project
var predictionResult = endpoint.PredictImage(project.Id, stream);
// Loop over each prediction and write out the results
foreach (var c in predictionResult.Predictions)
{
if ((c.Probability * 100) > 80)
{
return(true);
}
else
{
return(false);
}
}
}
catch (Exception ex)
{
throw ex;
}
return(false);
}
static int Main(string[] args)
{
Console.WriteLine("Custom Vision Image Trainer V1.0");
if (args.Length < 4 || args.Contains("-?"))
{
Console.WriteLine("Usage: customvisiontrainer.exe {custom vision account key} {custom vision project} {source image uri} {image tag(s)}");
return(1);
}
// Create the Api, passing in a credentials object that contains the training key
TrainingApiCredentials trainingCredentials = new TrainingApiCredentials(args[0]);
TrainingApi trainingApi = new TrainingApi(trainingCredentials);
// Get a reference to the project and create a tag
var project = trainingApi.GetProjects().First(proj => String.Equals(proj.Name, args[1], StringComparison.OrdinalIgnoreCase));
// Create the specified tag(s), if it doesn't already exist
var tags = args[3].Split(';')
.Select(tag =>
{
var tagObject = trainingApi.GetTags(project.Id).Tags.FirstOrDefault(projTag => String.Equals(projTag.Name, tag, StringComparison.OrdinalIgnoreCase));
if (tagObject == null)
{
tagObject = trainingApi.CreateTag(project.Id, tag);
}
return(tagObject);
})
.ToList();
// Enumerate the list of images from the specified root uri
var storageUri = new UriBuilder(args[2]);
var path = storageUri.Path;
storageUri.Path = "";
var blobClient = new CloudBlobClient(storageUri.Uri);
var pathSegments = path.Split(new char[] { '/' }, StringSplitOptions.RemoveEmptyEntries);
var container = blobClient.GetContainerReference(pathSegments[0]);
var blobUris = container.ListBlobs(String.Join("/", pathSegments.Skip(1)))
.Select(blobItem => blobItem.Uri)
.ToList();
// Upload the images directly to the custom vision project
var images = new ImageUrlCreateBatch(tags.Select(tag => tag.Id).ToList(),
blobUris.Select(blobUri => blobUri.ToString()).ToList());
trainingApi.CreateImagesFromUrlsAsync(project.Id, images, new CancellationTokenSource(60000).Token).Wait();
return(0);
}
static void Main()
{
Console.OutputEncoding = System.Text.Encoding.UTF8;
TrainingApiCredentials trainingCredentials = new TrainingApiCredentials("API_KEY_HERE");
TrainingApi trainingApi = new TrainingApi(trainingCredentials);
var project = trainingApi.GetProjects()[1];
foreach (string f in food)
{
FoodClassifier g = new FoodClassifier(f, trainingApi, project);
System.Threading.Thread t = new System.Threading.Thread(g.Classify);
t.Start();
System.Threading.Thread.Sleep(10000);
}
Console.Write("\nPress Enter to exit ");
Console.ReadLine();
}
private static void Train()
{
string trainingKey = "fdf8998652a44b5ea1380439f25d71ca";
string projectKey = "INat-ImageClassifier";
string trainPath = @"c:\data\Images\train\";
TrainingApiCredentials trainingCredentials = new TrainingApiCredentials(trainingKey);
TrainingApi trainingApi = new TrainingApi(trainingCredentials);
var project = trainingApi.CreateProject(projectKey);
var files = new DirectoryInfo(trainPath).GetFiles("*", SearchOption.AllDirectories);
foreach (var image in files)
{
if (tagCount >= 50)
{
break;
}
List <string> tags = new List <string>();
string tagName = image.Directory.Name.Replace("_", " ");
var tagdata = trainingApi.GetTags(project.Id);
CreateTags(trainingApi, project, tags, tagName, tagdata);
var imagestream = new MemoryStream(File.ReadAllBytes(image.FullName));
Console.WriteLine("Sending image " + image.Name + " To Custom Vision AI for training...");
trainingApi.CreateImagesFromData(project.Id, imagestream, tags);
}
var iteration = trainingApi.TrainProject(project.Id);
while (iteration.Status == "Training")
{
Thread.Sleep(100);
iteration = trainingApi.GetIteration(project.Id, iteration.Id);
Console.WriteLine("Training...");
}
iteration.IsDefault = true;
trainingApi.UpdateIteration(project.Id, iteration.Id, iteration);
}
private void ConfirmButton_Click(object sender, RoutedEventArgs e)
{
if (String.IsNullOrEmpty(TrainingKeyTextBox.Text))
{
tipTextBlock.Text = "请输入Training Key";
return;
}
else
{
try
{
TrainingApiCredentials trainingCredentials = new TrainingApiCredentials(TrainingKeyTextBox.Text.Trim());
TrainingApi trainingApi = new TrainingApi(trainingCredentials);
MainWindow.currentWindow.SetTrainingAPi(trainingApi);
MainWindow.currentWindow.GetProjects();
MainWindow.currentWindow.StartMainPage();
}
catch
{
tipTextBlock.Text = "Training Key验证失败";
return;
}
}
}
static void Main(string[] args)
{
// You can either add your training key here, pass it on the command line, or type it in when the program runs
string trainingKey = GetTrainingKey(trainingKeyString, args);
// Create the Api, passing in a credentials object that contains the training key
TrainingApiCredentials trainingCredentials = new TrainingApiCredentials(trainingKey);
TrainingApi trainingApi = new TrainingApi(trainingCredentials);
// Create a new project
Console.WriteLine("Creating new project:");
var project = trainingApi.CreateProject(projectName);
// Create some tags, you need at least two
var tag1 = trainingApi.CreateTag(project.Id, "tag1");
var tag2 = trainingApi.CreateTag(project.Id, "tag2");
// Add some images to the tags
Console.Write("\n\tProcessing images");
// Upload using the path to the images, a reference to the training API, a ference to your project and a tag
UploadImages(@"..\..\..\Images\1", trainingApi, project, new List <string>()
{
tag1.Id.ToString()
});
UploadImages(@"..\..\..\Images\1", trainingApi, project, new List <string>()
{
tag2.Id.ToString()
});
// Or uploaded in a single batch
//trainingApi.CreateImagesFromData(project.Id, japaneseCherryImages, new List<Guid>() { japaneseCherryTag.Id });
// Now there are images with tags start training the project
Console.WriteLine("\tStarting training");
IterationModel iteration = null;
try
{
iteration = trainingApi.TrainProject(project.Id);
}
catch (Exception e)
{
Console.WriteLine($"Trainig could not be completed. Error: {e.Message}");
}
if (iteration != null)
{
// The returned iteration will be in progress, and can be queried periodically to see when it has completed
while (iteration.Status == "Training")
{
Thread.Sleep(1000);
// Re-query the iteration to get it's updated status
iteration = trainingApi.GetIteration(project.Id, iteration.Id);
}
Console.WriteLine($"\tFinished training iteration {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");
// Now there is a trained endpoint, it can be used to make a prediction
// Get the prediction key, which is used in place of the training key when making predictions
//var account = trainingApi.GetAccountInfo();
//var predictionKey = account.Keys.PredictionKeys.PrimaryKey;
//// Create a prediction endpoint, passing in a prediction credentials object that contains the obtained prediction key
//PredictionEndpointCredentials predictionEndpointCredentials = new PredictionEndpointCredentials(predictionKey);
//PredictionEndpoint endpoint = new PredictionEndpoint(predictionEndpointCredentials);
//// Make a prediction against the new project
//Console.WriteLine("Making a prediction:");
//var result = endpoint.PredictImage(project.Id, testImage);
//// Loop over each prediction and write out the results
//foreach (var c in result.Predictions)
//{
// Console.WriteLine($"\t{c.Tag}: {c.Probability:P1}");
//}
}
Console.ReadKey();
}
