private async Task <ImagePrediction> AnalyzeProductImageAsync(Guid modelId)
{
ImagePrediction result = null;
try
{
var iteractions = await trainingApi.GetIterationsAsync(modelId);
var latestTrainedIteraction = iteractions.Where(i => i.Status == "Completed").OrderByDescending(i => i.TrainedAt.Value).FirstOrDefault();
if (latestTrainedIteraction == null)
{
throw new Exception("This project doesn't have any trained models yet.");
}
if (CurrentInputProductImage?.ImageUrl != null)
{
result = await CustomVisionServiceHelper.PredictImageUrlWithRetryAsync(predictionApi, modelId, new ImageUrl(CurrentInputProductImage.ImageUrl), latestTrainedIteraction.Id);
}
else if (CurrentInputProductImage?.GetImageStreamCallback != null)
{
result = await CustomVisionServiceHelper.PredictImageWithRetryAsync(predictionApi, modelId, CurrentInputProductImage.GetImageStreamCallback, latestTrainedIteraction.Id);
}
}
catch (Exception ex)
{
await Util.GenericApiCallExceptionHandler(ex, "Custom Vision error analyzing product image");
}
return(result);
}
public async Task <VisualAlertScenarioData> ExportOnnxProject(Project project)
{
// get latest iteration
IList <Iteration> iterations = await trainingApi.GetIterationsAsync(project.Id);
Iteration latestTrainedIteration = iterations.Where(i => i.Status == "Completed").OrderByDescending(i => i.TrainedAt.Value).FirstOrDefault();
// export iteration - get download url
Export exportProject = null;
if (latestTrainedIteration != null && latestTrainedIteration.Exportable)
{
// get project's download Url for the particular platform Windows (ONNX) model
exportProject = await CustomVisionServiceHelper.ExportIteration(trainingApi, project.Id, latestTrainedIteration.Id);
}
if (string.IsNullOrEmpty(exportProject?.DownloadUri))
{
throw new ArgumentNullException("Download Uri");
}
// download onnx model
Guid newModelId = Guid.NewGuid();
StorageFolder onnxProjectDataFolder = await VisualAlertDataLoader.GetOnnxModelStorageFolderAsync();
StorageFile file = await onnxProjectDataFolder.CreateFileAsync($"{newModelId}.onnx", CreationCollisionOption.ReplaceExisting);
bool success = await Util.UnzipModelFileAsync(exportProject.DownloadUri, file);
if (!success)
{
await file.DeleteAsync();
return(null);
}
return(new VisualAlertScenarioData
{
Id = newModelId,
Name = project.Name,
ExportDate = DateTime.UtcNow,
FileName = file.Name,
FilePath = file.Path
});
}
private async Task ExportProject(Platform currentPlatform)
{
CustomVisionProjectType customVisionProjectType = CustomVisionServiceHelper.ObjectDetectionDomainGuidList.Contains(this.CurrentProject.Settings.DomainId)
? CustomVisionProjectType.ObjectDetection : CustomVisionProjectType.Classification;
bool success = false;
try
{
this.shareStatusTextBlock.Text = "Exporting model...";
this.shareStatusPanelDescription.Visibility = Visibility.Collapsed;
this.closeFlyoutBtn.Visibility = Visibility.Visible;
this.projectShareProgressRing.IsActive = true;
// get latest iteration of the project
if (this.LatestTrainedIteration == null)
{
await this.LoadLatestIterationInCurrentProject();
}
if (LatestTrainedIteration != null && LatestTrainedIteration.Exportable)
{
// get project's download Url for the particular platform
// Windows (ONNX) model: export latest version of the ONNX model (1.2)
Export exportProject = await CustomVisionServiceHelper.ExportIteration(trainingApi, this.CurrentProject.Id, LatestTrainedIteration.Id);
success = await ExportOnnxProject(exportProject, customVisionProjectType);
}
}
catch (Exception ex)
{
await Util.GenericApiCallExceptionHandler(ex, "We couldn't export the model at this time.");
}
finally
{
this.projectShareProgressRing.IsActive = false;
this.closeFlyoutBtn.Visibility = Visibility.Collapsed;
this.shareStatusTextBlock.Text = success
? "The project was exported successfully."
: "Something went wrong and we couldn't export the model. Sorry :(";
}
}
private async void UpdateResults(ImageAnalyzer img)
{
this.searchErrorTextBlock.Visibility = Visibility.Collapsed;
Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction.Models.ImagePrediction result = null;
var currentProjectViewModel = (ProjectViewModel)this.projectsComboBox.SelectedValue;
var currentProject = ((ProjectViewModel)this.projectsComboBox.SelectedValue).Model;
var trainingApi = this.userProvidedTrainingApi;
var predictionApi = this.userProvidedPredictionApi;
try
{
var iteractions = await trainingApi.GetIterationsAsync(currentProject.Id);
var latestTrainedIteraction = iteractions.Where(i => i.Status == "Completed").OrderByDescending(i => i.TrainedAt.Value).FirstOrDefault();
if (latestTrainedIteraction == null)
{
throw new Exception("This project doesn't have any trained models yet. Please train it, or wait until training completes if one is in progress.");
}
if (img.ImageUrl != null)
{
result = await CustomVisionServiceHelper.PredictImageUrlWithRetryAsync(predictionApi, currentProject.Id, new Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction.Models.ImageUrl(img.ImageUrl), latestTrainedIteraction.Id);
}
else
{
result = await CustomVisionServiceHelper.PredictImageWithRetryAsync(predictionApi, currentProject.Id, img.GetImageStreamCallback, latestTrainedIteraction.Id);
}
}
catch (Exception ex)
{
await Util.GenericApiCallExceptionHandler(ex, "Error");
}
this.progressRing.IsActive = false;
this.resultsDetails.Visibility = Visibility.Visible;
var matches = result?.Predictions?.Where(r => Math.Round(r.Probability * 100) > 0);
if (matches == null || !matches.Any())
{
this.searchErrorTextBlock.Visibility = Visibility.Visible;
}
else
{
if (!currentProjectViewModel.IsObjectDetection)
{
this.resultsGridView.ItemsSource = matches.Select(t => new { Tag = t.TagName, Probability = string.Format("{0}%", Math.Round(t.Probability * 100)) });
}
else
{
this.resultsDetails.Visibility = Visibility.Collapsed;
this.currentDetectedObjects = matches.Where(m => m.Probability >= 0.6);
ShowObjectDetectionBoxes(this.currentDetectedObjects);
}
}
if (result?.Predictions != null && !currentProjectViewModel.IsObjectDetection)
{
this.activeLearningButton.Opacity = 1;
this.PredictionDataForRetraining.Clear();
this.PredictionDataForRetraining.AddRange(result.Predictions.Select(
t => new ActiveLearningTagViewModel
{
PredictionResultId = result.Id,
TagId = t.TagId,
TagName = t.TagName,
HasTag = Math.Round(t.Probability * 100) > 0
}));
}
else
{
this.activeLearningButton.Opacity = 0;
}
}
private async void UpdateResults(ImageAnalyzer img)
{
Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction.Models.ImagePrediction result = null;
var currentProjectViewModel = (ProjectViewModel)this.projectsComboBox.SelectedValue;
var currentProject = ((ProjectViewModel)this.projectsComboBox.SelectedValue).Model;
var trainingApi = this.userProvidedTrainingApi;
var predictionApi = this.userProvidedPredictionApi;
try
{
var iteractions = await trainingApi.GetIterationsAsync(currentProject.Id);
var latestTrainedIteraction = iteractions.Where(i => i.Status == "Completed").OrderByDescending(i => i.TrainedAt.Value).FirstOrDefault();
if (latestTrainedIteraction == null)
{
throw new Exception("This project doesn't have any trained models yet. Please train it, or wait until training completes if one is in progress.");
}
if (img.ImageUrl != null)
{
result = await CustomVisionServiceHelper.PredictImageUrlWithRetryAsync(predictionApi, currentProject.Id, new Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction.Models.ImageUrl(img.ImageUrl), latestTrainedIteraction.Id);
}
else
{
result = await CustomVisionServiceHelper.PredictImageWithRetryAsync(predictionApi, currentProject.Id, img.GetImageStreamCallback, latestTrainedIteraction.Id);
}
}
catch (Exception ex)
{
await Util.GenericApiCallExceptionHandler(ex, "Error");
}
this.progressRing.IsActive = false;
var matches = result?.Predictions?.Where(r => Math.Round(r.Probability * 100) > 0);
if (!currentProjectViewModel.IsObjectDetection)
{
//show image classification matches
OverlayPresenter.MatchInfo = new MatchOverlayInfo(matches);
}
else
{
//show detected objects
OverlayPresenter.ObjectInfo = matches.Where(m => m.Probability >= 0.6).Select(i => new PredictedObjectOverlayInfo(i)).ToList();
}
if (result?.Predictions != null && !currentProjectViewModel.IsObjectDetection)
{
this.activeLearningButton.Opacity = 1;
this.PredictionDataForRetraining.Clear();
this.PredictionDataForRetraining.AddRange(result.Predictions.Select(t => new ActiveLearningTagViewModel
{
PredictionResultId = result.Id,
TagId = t.TagId,
TagName = t.TagName,
HasTag = Math.Round(t.Probability * 100) > 0
}));
}
else
{
this.activeLearningButton.Opacity = 0;
}
}
