private async void btnTrain_Click(object sender, RoutedEventArgs e)
{
//btnTrain.IsEnabled = false;
//var someTask = Task.Factory.StartNew(() => Train());
//await someTask;
//btnTrain.IsEnabled = true;
// Now there are images with tags start training the project
Log("Training");
var iteration = await trainingApi.TrainProjectAsync(project.Id);
// 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);
}
// The iteration is now trained. Make it the default project endpoint
iteration.IsDefault = true;
trainingApi.UpdateIteration(project.Id, iteration.Id, iteration);
Log("Done Training!");
}
private void buttonTrain_Click(object sender, EventArgs e)
{
if (trainingApi.GetIterations(project.Id).Count < 10)
{
labelStatus.Text = "TRAINING";
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);
labelStatus.Text = "Done!";
}
else
{
MessageBox.Show("The number of iterations is 10");
}
}
private static void TrainTheModel(Project project)
{
try
{
var trainingKey = ConfigurationManager.AppSettings["CustomVision_TrainingKey"];
var trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
var iteration = trainingApi.TrainProject(project.Id);
Console.WriteLine($"\tWaiting for training process finishes");
// The returned iteration will be in progress, and can be queried periodically to see when it has completed
while (iteration.Status == "Training")
{
Console.WriteLine($"\t...");
Thread.Sleep(1000);
// Re-query the iteration to get it's updated status
iteration = trainingApi.GetIteration(project.Id, iteration.Id);
}
Console.WriteLine($"\tUpdating default iteration");
// The iteration is now trained. Make it the default project endpoint
iteration.IsDefault = true;
trainingApi.UpdateIteration(project.Id, iteration.Id, iteration);
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
/// <summary>
/// Train a Model given a set of trainings
/// </summary>
/// <param name="project"></param>
/// <param name="trainings"></param>
/// <param name="token"></param>
/// <returns></returns>
public void TrainModel(ref ProjectModel project, IList <Models.Training> trainings)
{
// Save all Training Tasks to be performed
//var tagCreationTasks = (from training in trainings
// from tag in training.Tags
// select TrainingApi.CreateTagAsync(project.Id, tag.ToString(), cancellationToken: token)).
// Cast<Task>().ToList();
var tagNames = trainings.Select(t => t.TagName).Distinct();
var tagNameToId = new Dictionary <string, Guid>();
foreach (var tagName in tagNames)
{
var tagModel = TrainingApi.CreateTag(project.Id, tagName);
tagNameToId.Add(tagName, tagModel.Id);
}
// Execute Trainings
//await Task.WhenAny(tagCreationTasks);
foreach (var training in trainings)
{
TrainingApi.CreateImagesFromData(project.Id, training.ImageFileStream,
new List <string> {
tagNameToId[training.TagName].ToString()
});
}
//var imageCreationTasks = (from training in trainings
// from tag in training.Tags
// select TrainingApi.CreateImagesFromDataAsync(project.Id, training.ImageFileStream, new List<string> { tag.ToString() }, cancellationToken: token)).Cast<Task>().ToList();
//await Task.WhenAny(imageCreationTasks);
var iterationModel = TrainingApi.TrainProject(project.Id);
// The returned iteration will be in progress, and can be queried periodically to see when it has completed
while (iterationModel.Status == "Training")
{
Thread.Sleep(1000);
// Re-query the iteration to get it's updated status
iterationModel = TrainingApi.GetIteration(project.Id, iterationModel.Id);
}
iterationModel.IsDefault = true;
// Complete Training
TrainingApi.UpdateIteration(project.Id, iterationModel.Id, iterationModel);
}
private static void TrainClassifier(Guid projectId, TrainingApi trainingApi)
{
Console.WriteLine("Training classifier");
var iteration = trainingApi.TrainProject(projectId);
while (iteration.Status == "Training")
{
Thread.Sleep(1000);
iteration = trainingApi.GetIteration(projectId, iteration.Id);
}
// Make the newly trained version the default for RESTful API requests
iteration.IsDefault = true;
trainingApi.UpdateIteration(projectId, iteration.Id, iteration);
Console.WriteLine("Training complete. Press any key to exit.");
}
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 static async Task TrainProject(TrainingApi trainingApi, Guid projectId)
{
var iteration = await trainingApi.TrainProjectAsync(projectId);
while (iteration.Status == "Training")
{
Console.Clear();
Console.WriteLine("Training in progress...");
Thread.Sleep(1000);
iteration = await trainingApi.GetIterationAsync(projectId, iteration.Id);
}
iteration.IsDefault = true;
trainingApi.UpdateIteration(projectId, iteration.Id, iteration);
Console.WriteLine();
Console.WriteLine("Project successfully trained... Press any key to continue");
Console.ReadLine();
}
public int Execute()
{
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = _trainingKey
};
Console.Write("\tTraining");
Iteration iteration = trainingApi.TrainProject(_projectId);
while (iteration.Status == "Training")
{
Thread.Sleep(1000);
Console.Write(".");
iteration = trainingApi.GetIteration(_projectId, iteration.Id);
}
iteration.IsDefault = true;
trainingApi.UpdateIteration(_projectId, iteration.Id, iteration);
Console.WriteLine("done!");
return(0);
}
static void Main(string[] args)
{
// Add your training & prediction key from the settings page of the portal
string trainingKey = "<your training key here>";
string predictionKey = "<your prediction key here>";
// Create the Api, passing in the training key
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
// 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);
// Make two tags in the new project
var forkTag = trainingApi.CreateTag(project.Id, "fork");
var scissorsTag = trainingApi.CreateTag(project.Id, "scissors");
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));
// 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 == "Training")
{
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");
// Now there is a trained endpoint, it can be used to make a prediction
// 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} ]");
}
}
Console.ReadKey();
}
public static async Task TrainAsync(ParsingOptions options)
{
// Create the Api, passing in the training key
var trainingApi = new TrainingApi {
ApiKey = options.TrainingKey
};
if (options.Delete)
{
try
{
await DeleteImagesAndTagsAsync(options, trainingApi);
Console.WriteLine("Images and tags successfully deleted.");
}
catch
{
}
return;
}
var fullFolder = Path.GetFullPath(options.Folder);
if (!Directory.Exists(fullFolder))
{
Console.WriteLine($"Error: folder \"{fullFolder}\" does not exist.");
Console.WriteLine(string.Empty);
return;
}
try
{
await DeleteImagesAndTagsAsync(options, trainingApi);
// Creates the string for resized images.
string resizeString = null;
if (options.Width.GetValueOrDefault() > 0)
{
resizeString += $"width={options.Width}&";
}
if (options.Height.GetValueOrDefault() > 0)
{
resizeString += $"height={options.Height}&";
}
if (!string.IsNullOrWhiteSpace(resizeString))
{
resizeString += "crop=auto&scale=both";
}
foreach (var dir in Directory.EnumerateDirectories(fullFolder).Where(f => !Path.GetFileName(f).StartsWith("!")))
{
var tagName = Path.GetFileName(dir).ToLower();
Console.WriteLine($"\nCreating tag '{tagName}'...");
var tag = await trainingApi.CreateTagAsync(options.ProjectId, tagName);
var images = Directory.EnumerateFiles(dir, "*.*", SearchOption.AllDirectories)
.Where(s => s.EndsWith(".jpg", StringComparison.InvariantCultureIgnoreCase) ||
s.EndsWith(".jpeg", StringComparison.InvariantCultureIgnoreCase) ||
s.EndsWith(".png", StringComparison.InvariantCultureIgnoreCase) ||
s.EndsWith(".bmp", StringComparison.InvariantCultureIgnoreCase));
foreach (var image in images)
{
var imageName = Path.GetFileName(image);
Console.WriteLine($"Uploading image {imageName}...");
// Resizes the image before sending it to the service.
using (var input = new MemoryStream(File.ReadAllBytes(image)))
{
if (!string.IsNullOrWhiteSpace(resizeString))
{
using (var output = new MemoryStream())
{
ImageBuilder.Current.Build(input, output, new ResizeSettings(resizeString));
output.Position = 0;
await trainingApi.CreateImagesFromDataAsync(options.ProjectId, output, new List <string>() { tag.Id.ToString() });
}
}
else
{
await trainingApi.CreateImagesFromDataAsync(options.ProjectId, input, new List <string>() { tag.Id.ToString() });
}
}
}
}
// Now there are images with tags start training the project
Console.WriteLine("\nTraining...");
var iteration = await trainingApi.TrainProjectAsync(options.ProjectId);
// The returned iteration will be in progress, and can be queried periodically to see when it has completed
while (iteration.Status == "Training")
{
await Task.Delay(1000);
// Re-query the iteration to get it's updated status
iteration = trainingApi.GetIteration(options.ProjectId, iteration.Id);
}
// The iteration is now trained. Make it the default project endpoint
iteration.IsDefault = true;
trainingApi.UpdateIteration(options.ProjectId, iteration.Id, iteration);
Console.WriteLine("Training completed.\n");
}
catch (Exception ex)
{
Console.WriteLine($"\nUnexpected error: {ex.GetBaseException()?.Message}.\n");
}
}
public static async Task TrainAsync(ParsingOptions options)
{
// Create the Api, passing in the training key
var trainingApi = new TrainingApi {
ApiKey = options.TrainingKey
};
CognitiveServiceTrainerStorage storageImages = new CognitiveServiceTrainerStorage();
if (options.Delete)
{
try
{
await DeleteImagesAndTagsAsync(options, trainingApi);
storageImages.DeleteDB();
Console.WriteLine("Images and tags successfully deleted.");
}
catch
{
}
if (string.IsNullOrEmpty(options.Folder))
{
return;
}
}
var fullFolder = Path.GetFullPath(options.Folder);
if (!Directory.Exists(fullFolder))
{
Console.WriteLine($"Error: folder \"{fullFolder}\" does not exist.");
Console.WriteLine(string.Empty);
return;
}
try
{
//await DeleteImagesAndTagsAsync(options, trainingApi);
foreach (var dir in Directory.EnumerateDirectories(fullFolder).Where(f => !Path.GetFileName(f).StartsWith("!")))
{
var tagName = Path.GetFileName(dir).ToLower();
Console.WriteLine($"\nCheck latest images uploaded '{tagName}'...");
IList <Microsoft.Cognitive.CustomVision.Training.Models.Image> imagesAlreadyExists = new List <Microsoft.Cognitive.CustomVision.Training.Models.Image>();
IList <Microsoft.Cognitive.CustomVision.Training.Models.Image> imagesAlreadyExistsTmp = new List <Microsoft.Cognitive.CustomVision.Training.Models.Image>();
int skip = 0;
while ((imagesAlreadyExistsTmp = await trainingApi.GetTaggedImagesAsync(options.ProjectId, tagIds: new[] { tagName }, take: 50, skip: skip)).Any())
{
skip += 50;
foreach (var item in imagesAlreadyExistsTmp)
{
imagesAlreadyExists.Add(item);
}
}
Console.WriteLine($"\nCreating tag '{tagName}'...");
var tagExist = storageImages.FindTag(tagName, options.ProjectId);
Tag tag = null;
if (tagExist == null)
{
tag = await trainingApi.CreateTagAsync(options.ProjectId, tagName);
storageImages.InsertTag(new Storage.Collections.StorageTag {
IdCustomVision = tag.Id, TagName = tag.Name, ProjectId = options.ProjectId
});
}
else
{
if (trainingApi.GetTag(tagExist.ProjectId, tagExist.IdCustomVision) == null)
{
await trainingApi.DeleteTagAsync(options.ProjectId, tagExist.IdCustomVision);
tag = await trainingApi.CreateTagAsync(options.ProjectId, tagName);
storageImages.InsertTag(new Storage.Collections.StorageTag {
IdCustomVision = tag.Id, TagName = tag.Name, ProjectId = options.ProjectId
});
}
else
{
tag = new Tag(tagExist.IdCustomVision, tagName);
}
}
var images = Directory.EnumerateFiles(dir, "*.*", SearchOption.AllDirectories)
.Where(s => s.EndsWith(".jpg", StringComparison.InvariantCultureIgnoreCase) ||
s.EndsWith(".jpeg", StringComparison.InvariantCultureIgnoreCase) ||
s.EndsWith(".png", StringComparison.InvariantCultureIgnoreCase) ||
s.EndsWith(".bmp", StringComparison.InvariantCultureIgnoreCase)).ToList();
List <ImageDto> tempImages = new List <ImageDto>();
for (int i = 0; i < images.Count; i++)
{
Stream imageToUpload = null;
string image = images.ElementAt(i);
var imageName = Path.GetFileName(image);
var storageImage = storageImages.FindImage(image, options.ProjectId);
if (storageImage == null || !imagesAlreadyExists.Any(x => x.Id == storageImage.IdCustomVision))
{
// Resizes the image before sending it to the service.
using (var input = new MemoryStream(File.ReadAllBytes(image)))
{
if (options.Width.GetValueOrDefault() > 0 || options.Height.GetValueOrDefault() > 0)
{
imageToUpload = await ResizeImageAsync(input, options.Width.GetValueOrDefault(), options.Height.GetValueOrDefault());
}
else
{
imageToUpload = input;
}
tempImages.Add(new ImageDto
{
FullName = image,
FileName = imageName,
Content = imageToUpload.ToByteArray(),
Tag = tag
});
imageToUpload.Dispose();
}
}
else
{
Console.WriteLine($"Image already exist {imageName}...");
}
//Persist batch images
if (tempImages.Count % 32 == 0 && tempImages.Any() || (i == images.Count - 1))
{
await UploadImagesAsync(tempImages);
tempImages.Clear();
tempImages.Capacity = 0;
}
}
}
// Now there are images with tags start training the project
Console.WriteLine("\nTraining...");
var iteration = await trainingApi.TrainProjectAsync(options.ProjectId);
// The returned iteration will be in progress, and can be queried periodically to see when it has completed
while (iteration.Status == "Training")
{
await Task.Delay(1000);
// Re-query the iteration to get it's updated status
iteration = trainingApi.GetIteration(options.ProjectId, iteration.Id);
}
// The iteration is now trained. Make it the default project endpoint
iteration.IsDefault = true;
trainingApi.UpdateIteration(options.ProjectId, iteration.Id, iteration);
Console.WriteLine("Training completed.\n");
}
catch (Exception ex)
{
Console.WriteLine($"\nUnexpected error: {ex.GetBaseException()?.Message}.\n");
}
async Task UploadImagesAsync(List <ImageDto> images)
{
ImageFileCreateBatch imageFileCreateBatch = new ImageFileCreateBatch();
imageFileCreateBatch.Images = new List <ImageFileCreateEntry>();
foreach (var img in images)
{
imageFileCreateBatch.Images.Add(new ImageFileCreateEntry
{
Name = img.FullName,
TagIds = new [] { img.Tag.Id },
Contents = img.Content
});
Console.WriteLine($"Uploading image {img.FileName}...");
}
await Policy
.Handle <Exception>()
.WaitAndRetryAsync(retries)
.ExecuteAsync(async() =>
{
ImageCreateSummary reponseCognitiveService = await trainingApi.CreateImagesFromFilesAsync(options.ProjectId, imageFileCreateBatch);
if (reponseCognitiveService.Images != null)
{
for (int i = 0; i < reponseCognitiveService.Images.Count; i++)
{
var img = reponseCognitiveService.Images.ElementAt(i);
// https://docs.microsoft.com/en-us/rest/api/cognitiveservices/customvisiontraining/createimagesfrompredictions/createimagesfrompredictions
if ((img.Status == "OK" || img.Status == "OKDuplicate") && img.Image != null)
{
var uploadedImage = img.Image;
var tagsToStore = uploadedImage.Tags?
.Select(x => new Storage.Collections.StorageImageTag()
{
Created = x.Created,
TagId = x.TagId
}).ToList();
storageImages.InsertImage(new Storage.Collections.StorageImage()
{
ProjectId = options.ProjectId,
FullFileName = imageFileCreateBatch.Images.ElementAt(i).Name,
IdCustomVision = uploadedImage.Id,
ImageUri = uploadedImage.ImageUri,
Created = uploadedImage.Created,
Height = uploadedImage.Height,
Tags = tagsToStore,
ThumbnailUri = uploadedImage.ThumbnailUri,
Width = uploadedImage.Width
});
}
else
{
Console.WriteLine($"API bad response: {img.Status }");
throw new InvalidOperationException($"API bad response: {img.Status }");
}
}
}
});
await Task.Delay(500);
}
}
public static HttpResponseMessage Run([HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = nameof(TrainClassifier))]
HttpRequestMessage req, TraceWriter log)
{
using (var analytic = new AnalyticService(new RequestTelemetry
{
Name = nameof(TrainClassifier)
}))
{
try
{
var allTags = new List <string>();
var json = req.Content.ReadAsStringAsync().Result;
var j = JObject.Parse(json);
var gameId = (string)j["gameId"];
var imageUrls = j["imageUrls"].ToObject <List <string> >();
var tags = (JArray)j["tags"];
var game = CosmosDataService.Instance.GetItemAsync <Game>(gameId).Result;
var api = new TrainingApi(new TrainingApiCredentials(ConfigManager.Instance.CustomVisionTrainingKey));
ProjectModel project = null;
//Get the existing project for this game if there is one
if (!string.IsNullOrEmpty(game.CustomVisionProjectId))
{
try
{
project = api.GetProject(Guid.Parse(game.CustomVisionProjectId));
}
catch (Exception) { }
}
//Otherwise create a new project and associate it with the game
if (project == null)
{
project = api.CreateProject(game.Name, game.Id);
game.CustomVisionProjectId = project.Id.ToString();
CosmosDataService.Instance.UpdateItemAsync <Game>(game).Wait();
}
//Generate tag models for training
var tagModels = new List <ImageTagModel>();
foreach (string tag in tags)
{
var model = api.CreateTag(project.Id, tag.Trim());
tagModels.Add(model);
}
//Batch the image urls that were sent up from Azure Storage (blob)
var batch = new ImageUrlCreateBatch(tagModels.Select(m => m.Id).ToList(), imageUrls);
var summary = api.CreateImagesFromUrls(project.Id, batch);
if (!summary.IsBatchSuccessful)
{
return(req.CreateErrorResponse(HttpStatusCode.BadRequest, "Image batch was unsuccessful"));
}
//Traing the classifier and generate a new iteration, that we'll set as the default
var iteration = api.TrainProject(project.Id);
while (iteration.Status == "Training")
{
Thread.Sleep(1000);
iteration = api.GetIteration(project.Id, iteration.Id);
}
iteration.IsDefault = true;
api.UpdateIteration(project.Id, iteration.Id, iteration);
return(req.CreateResponse(HttpStatusCode.OK, true));
}
catch (Exception e)
{
analytic.TrackException(e);
return(req.CreateErrorResponse(HttpStatusCode.BadRequest, e));
}
}
}
static void Main(string[] args)
{
// Agregue su clave de entrenamiento desde la página de configuración del portale
string trainingKey = "8f15a89a49a44979bf478d9391a45cd6";
// Crea el Api, pasando la clave de entrenamiento
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
// Create a new project
Console.WriteLine("Creating new project:");
var project = trainingApi.CreateProject("My New Project");
// Crea un nuevo proyecto
var hemlockTag = trainingApi.CreateTag(project.Id, "Hemlock");
var japaneseCherryTag = trainingApi.CreateTag(project.Id, "Japanese Cherry");
// Add some images to the tags
Console.WriteLine("\tUploading images");
LoadImagesFromDisk();
// Las imágenes se pueden cargar de a una por vez
foreach (var image in hemlockImages)
{
using (var stream = new MemoryStream(File.ReadAllBytes(image)))
{
trainingApi.CreateImagesFromData(project.Id, stream, new List <string>()
{
hemlockTag.Id.ToString()
});
}
}
// O subido en un solo lote
var imageFiles = japaneseCherryImages.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFiles, new List <Guid>()
{
japaneseCherryTag.Id
}));
// Ahora hay imágenes con etiquetas que comienzan a entrenar el proyecto
Console.WriteLine("\tTraining");
var iteration = trainingApi.TrainProject(project.Id);
// La iteración devuelta estará en progreso, y se puede consultar periódicamente para ver cuándo se completó
while (iteration.Status == "Training")
{
Thread.Sleep(1000);
// Vuelva a consultar la iteración para obtener su estado actualizado
iteration = trainingApi.GetIteration(project.Id, iteration.Id);
}
// La iteración ahora está entrenada. Convertirlo en el punto final predeterminado del proyecto
iteration.IsDefault = true;
trainingApi.UpdateIteration(project.Id, iteration.Id, iteration);
Console.WriteLine("Done!\n");
// Ahora hay un punto final entrenado, se puede usar para hacer una predicción
// Agregue su clave de predicción desde la página de configuración del portal
// La clave de predicción se usa en lugar de la clave de entrenamiento cuando se hacen predicciones
string predictionKey = "559018cc3d434cef8095da2e8b8dd30c";
// Crear un punto final de predicción, pasando la clave de predicción obtenida
PredictionEndpoint endpoint = new PredictionEndpoint()
{
ApiKey = predictionKey
};
// Hacer una predicción contra el nuevo proyecto
Console.WriteLine("Making a prediction:");
var result = endpoint.PredictImage(project.Id, testImage);
// Pasa el cursor sobre cada predicción y escribe los resultados
foreach (var c in result.Predictions)
{
Console.WriteLine($"\t{c.Tag}: {c.Probability:P1}");
}
Console.ReadKey();
}
/// <summary>
/// 更新Iteration信息
/// </summary>
/// <param name="projectId"></param>
/// <param name="iterationId"></param>
/// <param name="iteration"></param>
public void UpdateIteration(Guid projectId, Guid iterationId, IterationModel iteration)
{
trainingApi.UpdateIteration(projectId, iterationId, iteration);
}
async public static Task <HttpResponseMessage> Run([HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = nameof(TrainClassifier))]
HttpRequestMessage req, TraceWriter log)
{
using (var analytic = new AnalyticService(new RequestTelemetry
{
Name = nameof(TrainClassifier)
}))
{
try
{
var allTags = new List <string>();
var json = req.Content.ReadAsStringAsync().Result;
var j = JObject.Parse(json);
var gameId = (string)j["gameId"];
var imageUrls = j["imageUrls"].ToObject <List <string> >();
var tags = (JArray)j["tags"];
var game = CosmosDataService.Instance.GetItemAsync <Game>(gameId).Result;
TrainingApi api = new TrainingApi {
ApiKey = ConfigManager.Instance.CustomVisionTrainingKey
};
Project project = null;
//Get the existing project for this game if there is one
if (!string.IsNullOrEmpty(game.CustomVisionProjectId))
{
try { project = api.GetProject(Guid.Parse(game.CustomVisionProjectId)); }
catch (Exception) { }
}
//Otherwise create a new project and associate it with the game
if (project == null)
{
project = api.CreateProject($"{game.Name}_{DateTime.Now.ToString()}_{Guid.NewGuid().ToString()}", game.Id);
game.CustomVisionProjectId = project.Id.ToString();
CosmosDataService.Instance.UpdateItemAsync <Game>(game).Wait();
}
var tagItems = tags.Select(t => api.CreateTag(project.Id, t.ToString().Trim()));
var entries = imageUrls.Select(u => new ImageUrlCreateEntry(u)).ToList();
//Batch the image urls that were sent up from Azure Storage (blob)
var batch = new ImageUrlCreateBatch(entries, tagItems.Select(t => t.Id).ToList());
var summary = api.CreateImagesFromUrls(project.Id, batch);
//if(!summary.IsBatchSuccessful)
// return req.CreateErrorResponse(HttpStatusCode.BadRequest, "Image batch was unsuccessful");
//Traing the classifier and generate a new iteration, that we'll set as the default
var iteration = api.TrainProject(project.Id);
while (iteration.Status == "Training")
{
Thread.Sleep(1000);
iteration = api.GetIteration(project.Id, iteration.Id);
}
iteration.IsDefault = true;
api.UpdateIteration(project.Id, iteration.Id, iteration);
var data = new Event("Training classifier");
data.Add("project", project.Name);
data.Add("iteration", iteration.Id);
await EventHubService.Instance.SendEvent(data);
return(req.CreateResponse(HttpStatusCode.OK, true));
}
catch (Exception e)
{
analytic.TrackException(e);
var baseException = e.GetBaseException();
var operationException = baseException as HttpOperationException;
var reason = baseException.Message;
if (operationException != null)
{
var jobj = JObject.Parse(operationException.Response.Content);
var code = jobj.GetValue("Code");
if (code != null && !string.IsNullOrWhiteSpace(code.ToString()))
{
reason = code.ToString();
}
}
return(req.CreateErrorResponse(HttpStatusCode.BadRequest, reason));
}
}
}
static void Main(string[] args)
{
string trainingKey = GetTrainingKey(configTrainingKey, args);
TrainingApi trainingApi = new TrainingApi {
ApiKey = trainingKey
};
Console.WriteLine("Creating new project:");
var project = trainingApi.CreateProject("Bike Type");
var MbikesTag = trainingApi.CreateTag(project.Id, "Mountain");
var RbikesTag = trainingApi.CreateTag(project.Id, "Racing");
Console.WriteLine("\tUploading images");
LoadImages();
foreach (var image in bikesImages)
{
trainingApi.CreateImagesFromData(project.Id, image, new List <string>()
{
MbikesTag.Id.ToString()
});
}
foreach (var image in rBikesImages)
{
trainingApi.CreateImagesFromData(project.Id, image, new List <string>()
{
RbikesTag.Id.ToString()
});
}
trainingApi.CreateImagesFromData(project.Id, testImage, new List <string>()
{
MbikesTag.Id.ToString()
});
Console.WriteLine("\tTraining");
var iteration = trainingApi.TrainProject(project.Id);
while (iteration.Status.Equals("Training"))
{
Thread.Sleep(1000);
iteration = trainingApi.GetIteration(project.Id, iteration.Id);
}
iteration.IsDefault = true;
trainingApi.UpdateIteration(project.Id, iteration.Id, iteration);
Console.WriteLine("Done!\n");
var predictionKey = GetPredictionKey(configPredictionKey, args);
PredictionEndpoint endpoint = new PredictionEndpoint {
ApiKey = predictionKey
};
Console.WriteLine("Making a prediction:");
var result = endpoint.PredictImage(project.Id, testImage);
foreach (var c in result.Predictions)
{
Console.WriteLine($"\t{c.Tag}: {c.Probability:P1}");
}
Console.ReadKey();
}
static void Main(string[] args)
{
// Add your training key from the settings page of the portal
string trainingKey = "d34ef84918894544889d7136f32d4e67";
// Create the Api, passing in the training key
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
// Getting Project or create project
Console.WriteLine("Getting project");
var project = trainingApi.GetProject(new Guid("06d4aeff-fb9d-453f-8912-5b2e89d1f1d4"));
//var project = trainingApi.CreateProject("MurdersWeapons");
// Make two tags in the new project
var batTag = trainingApi.CreateTag(project.Id, "Bat");
var candleTag = trainingApi.CreateTag(project.Id, "Candle");
var flashlightTag = trainingApi.CreateTag(project.Id, "Flashlight");
var gitarTag = trainingApi.CreateTag(project.Id, "Gitar");
var hammerTag = trainingApi.CreateTag(project.Id, "Hammer");
var screwdriverTag = trainingApi.CreateTag(project.Id, "Screwdriver");
// Add some images to the tags
Console.WriteLine("\tUploading images");
LoadImagesFromDisk();
var imageFiles = batImages.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFiles, new List <Guid>()
{
batTag.Id
}));
imageFiles = candleImages.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFiles, new List <Guid>()
{
candleTag.Id
}));
imageFiles = flashlightImages.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFiles, new List <Guid>()
{
flashlightTag.Id
}));
imageFiles = gitarImages.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFiles, new List <Guid>()
{
gitarTag.Id
}));
imageFiles = hammerImages.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFiles, new List <Guid>()
{
hammerTag.Id
}));
imageFiles = screwdriverImages.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFiles, new List <Guid>()
{
screwdriverTag.Id
}));
// 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 == "Training")
{
Thread.Sleep(1000);
// Re-query the iteration to get it's 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");
Console.ReadKey();
}
public void Main(string[] args)
{
// Add your training & prediction key from the settings page of the portal
string trainingKey = "d7ba782c8051443c8557ff464418949f";
//string predictionKey = "<your prediction key here>";
// Create the Api, passing in the training key
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
// Create a new project
Console.WriteLine("Creating new project:");
//var project = trainingApi.CreateProject("My New Project");
// Make two tags in the new project
var hemlockTag = trainingApi.CreateTag(project.Id, "Hemlock");
var japaneseCherryTag = trainingApi.CreateTag(project.Id, "Japanese Cherry");
// Add some images to the tags
Console.WriteLine("\tUploading images");
LoadImagesFromDisk();
// Images can be uploaded one at a time
foreach (var image in hemlockImages)
{
using (var stream = new MemoryStream(File.ReadAllBytes(image)))
{
trainingApi.CreateImagesFromData(project.Id, stream, new List <string>()
{
hemlockTag.Id.ToString()
});
}
}
// Or uploaded in a single batch
var imageFiles = japaneseCherryImages.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFiles, new List <Guid>()
{
japaneseCherryTag.Id
}));
// 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 == "Training")
{
Thread.Sleep(1000);
// Re-query the iteration to get it's 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");
// Now there is a trained endpoint, it can be used to make a prediction
// Create a prediction endpoint, passing in obtained prediction key
PredictionEndpoint endpoint = new PredictionEndpoint()
{
ApiKey = predictionKey
};
// 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.TagName}: {c.Probability:P1}");
}
Console.ReadKey();
}
static void Main(string[] args)
{
string chaveClassificacao = "<Chave de treinamento>";
string idProjeto = "<ID Projeto>";
// Crio o cliente para o Azure Custom View
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = chaveClassificacao
};
Console.WriteLine("Iniciando as tags.");
// Crio a tag shitzu
Tag shitzuTag = trainingApi.CreateTag(new Guid(idProjeto), "Shitzu");
// Crio a tag poodle
Tag poodleTag = trainingApi.CreateTag(new Guid(idProjeto), "Poodle");
// Carrego as imagens de shitzu
string[] imagensShitzu = Directory.GetFiles(Path.Combine("Amostras", "Shitzu")).ToArray();
// Carrego as imagens de poodle
string[] imagensPoodle = Directory.GetFiles(Path.Combine("Amostras", "Poodle")).ToArray();
Console.WriteLine("Cadastrando as imagens de shitzu.");
// Cadastro das imagens de shitzu
foreach (var imagem in imagensShitzu)
{
using (var stream = new MemoryStream(File.ReadAllBytes(imagem)))
{
trainingApi.CreateImagesFromData(new Guid(idProjeto), stream, new List <string>()
{
shitzuTag.Id.ToString()
});
}
}
Console.WriteLine("Cadastrando as imagens de poodle.");
// Cadastro das imagens de poodle
foreach (var imagem in imagensPoodle)
{
using (var stream = new MemoryStream(File.ReadAllBytes(imagem)))
{
trainingApi.CreateImagesFromData(new Guid(idProjeto), stream, new List <string>()
{
poodleTag.Id.ToString()
});
}
}
// Inicio o treinamento com as imagens cadastradas.
Iteration interacao = trainingApi.TrainProject(new Guid(idProjeto));
Console.WriteLine("Treinando.");
// Verifico periodicamente até concluir
while (interacao.Status == "Training")
{
Thread.Sleep(1000);
// Verifico novamente o status
interacao = trainingApi.GetIteration(new Guid(idProjeto), interacao.Id);
}
// Agora que o treinamento está concluído, configuro como o endpoint padrão.
interacao.IsDefault = true;
trainingApi.UpdateIteration(new Guid(idProjeto), interacao.Id, interacao);
Console.WriteLine("Finalizado.");
}
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("<your key here>", 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("Car Assessment");
// Make two tags in the new project
var WriteOffTag = trainingApi.CreateTag(project.Id, "WriteOff");
var DentTag = trainingApi.CreateTag(project.Id, "Dent");
// Add some images to the tags
Console.WriteLine("\\tUploading images");
LoadImagesFromDisk();
// Images can be uploaded one at a time
foreach (var image in WriteOffImages)
{
trainingApi.CreateImagesFromData(project.Id, image, new List <string> ()
{
WriteOffTag.Id.ToString()
});
}
// Or uploaded in a single batch
trainingApi.CreateImagesFromData(project.Id, DentImages, new List <Guid> ()
{
DentTag.Id
});
// 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 == "Training")
{
Thread.Sleep(1000);
// Re-query the iteration to get it's 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");
// 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($"\\t{c.Tag}: {c.Probability:P1}");
}
Console.ReadKey();
}
public async Task Main()
{
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = CustomVisionAPIKey
};
trainingApi.HttpClient.Timeout = TimeSpan.FromMinutes(TimeoutMins);
if (!QuickTest)
{
Console.WriteLine($"Creating Custom Vision Project: {ProjectName}");
var project = trainingApi.CreateProject(ProjectName);
Console.WriteLine($"Scanning subfolders within: {ImagePath}");
List <Tag> allTags = new List <Tag>();
foreach (var folder in Directory.EnumerateDirectories(ImagePath))
{
string folderName = Path.GetFileName(folder);
var tagNames = folderName.Contains(",") ? folderName.Split(',').Select(t => t.Trim()).ToArray() : new string[] { folderName };
// Create tag for each comma separated value in subfolder name
foreach (var tag in tagNames)
{
// Check we've not already created this tag from another subfolder
if (!allTags.Any(t => t.Name.Equals(tag)))
{
Console.WriteLine($"Creating Tag: {tag}");
var imageTag = trainingApi.CreateTag(project.Id, tag);
allTags.Add(imageTag);
}
}
}
foreach (var currentFolder in Directory.EnumerateDirectories(ImagePath))
{
string folderName = Path.GetFileName(currentFolder);
var tagNames = folderName.Contains(",") ? folderName.Split(',').Select(t => t.Trim()).ToArray() : new string[] { folderName };
try
{
// Load the images to be uploaded from disk into memory
Console.WriteLine($"Uploading: {ImagePath}\\{folderName} images...");
var images = Directory.GetFiles(currentFolder).ToList();
var folderTags = allTags.Where(t => tagNames.Contains(t.Name)).Select(t => t.Id).ToList();
var imageFiles = images.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img), folderTags)).ToList();
var imageBatch = new ImageFileCreateBatch(imageFiles);
var summary = await trainingApi.CreateImagesFromFilesAsync(project.Id, new ImageFileCreateBatch(imageFiles));
// List any images that didn't make it
foreach (var imageResult in summary.Images.Where(i => !i.Status.Equals("OK")))
{
Console.WriteLine($"{ImagePath}\\{folderName}\\{imageResult.SourceUrl}: {imageResult.Status}");
}
Console.WriteLine($"Uploaded {summary.Images.Where(i => i.Status.Equals("OK")).Count()}/{images.Count()} images successfully from {ImagePath}\\{folderName}");
}
catch (Exception exp)
{
Console.WriteLine($"Error processing {currentFolder}: {exp.Source}:{exp.Message}");
}
}
try
{
// Train CV model and set iteration to the default
Console.WriteLine($"Training model");
var iteration = trainingApi.TrainProject(project.Id);
while (iteration.Status.Equals("Training"))
{
Thread.Sleep(1000);
iteration = trainingApi.GetIteration(project.Id, iteration.Id);
Console.WriteLine($"Model status: {iteration.Status}");
}
if (iteration.Status.Equals("Completed"))
{
iteration.IsDefault = true;
trainingApi.UpdateIteration(project.Id, iteration.Id, iteration);
Console.WriteLine($"Iteration: {iteration.Id} set as default");
}
else
{
Console.WriteLine($"Iteration status: {iteration.Status}");
}
}
catch (Exception exp)
{
Console.WriteLine($"Error training model (check you have at least 5 images per tag and 2 tags)");
Console.WriteLine($"Error {exp.Source}: {exp.Message}");
}
}
else
{
// Quick test existing (trained) model
Console.WriteLine($"Custom Vision Quick test: {ProjectName} with image {ImagePath}");
// Retrieve CV project
var projects = trainingApi.GetProjects();
var project = projects.Where(p => p.Name.Equals(ProjectName)).FirstOrDefault();
if (project == null)
{
Console.WriteLine($"Can't find Custom Vision Project: {ProjectName}");
return;
}
// Read test image
if (!File.Exists(ImagePath))
{
Console.WriteLine($"Can't find image: {ImagePath}");
return;
}
var image = new MemoryStream(File.ReadAllBytes(ImagePath));
// Get the default iteration to test against and check results
var iterations = trainingApi.GetIterations(project.Id);
var defaultIteration = iterations.Where(i => i.IsDefault == true).FirstOrDefault();
if (defaultIteration == null)
{
Console.WriteLine($"No default iteration has been set");
return;
}
var result = trainingApi.QuickTestImage(project.Id, image, defaultIteration.Id);
foreach (var prediction in result.Predictions)
{
Console.WriteLine($"Tag: {prediction.Tag} Probability: {prediction.Probability}");
}
}
// fin
stopwatch.Stop();
Console.WriteLine($"Done.");
Console.WriteLine($"Total time: {stopwatch.Elapsed}");
}
static void Main(string[] args)
{
// Add your training key from the settings page of the portal
string trainingKey = "YOUR TRAINING KEY";
// Create the Api, passing in the training key
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
// 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("CSharp Office", null, objDetectionDomain.Id);
//var project = trainingApi.GetProject();
using (StreamReader r = new StreamReader("Office.json"))
{
string json = r.ReadToEnd();
Console.Write(json);
var data = JObject.Parse(json);
//Export Tags from code
var inputTags = data["inputTags"].ToString().Split(',');
foreach (string t in inputTags)
{
Console.WriteLine(t);
trainingApi.CreateTag(project.Id, t);
}
// Get all tagsIDs created on custom vision and add into a dictionary
Dictionary <string, Guid> imgtags = new Dictionary <string, Guid>();
foreach (Tag t in trainingApi.GetTags(project.Id))
{
Console.WriteLine(t.Name + " - " + t.Id);
imgtags.Add(t.Name, t.Id);
}
// Create Image TagIDs with normalized points
Dictionary <string, double[]> imgtagdic = new Dictionary <string, double[]>();
foreach (var a in data["visitedFrames"])
{
Console.WriteLine(a);
try {
foreach (var key in data["frames"][a.ToString()])
{
double x1 = Convert.ToDouble(key["x1"].ToString());
double y1 = Convert.ToDouble(key["y1"].ToString());
double x2 = Convert.ToDouble(key["x2"].ToString());
double y2 = Convert.ToDouble(key["y2"].ToString());
int h = Convert.ToInt32(key["height"].ToString());
int w = Convert.ToInt32(key["width"].ToString());
double tleft = (double)x1 / (double)w;
double ttop = (double)y1 / (double)h;
double twidth = (double)(x2 - x1) / (double)w;
double theight = (double)(y2 - y1) / (double)h;
try {
string tag = key["tags"][0].ToString();
// Defining UniqueID per tags in photo below
imgtagdic.Add(imgtags[tag].ToString() + "," + a.ToString() + ',' + key["name"].ToString() + tag, new double[] { tleft, ttop, twidth, theight });
}
catch {
Console.WriteLine("An Error occured on imtagdic");
}
}
}
catch {
Console.WriteLine("An Error occured on json parsing");
}
}
// Add all images for fork
var imagePath = Path.Combine("", "Office");
string[] allphotos = Directory.GetFiles(imagePath);
var imageFileEntries = new List <ImageFileCreateEntry>();
foreach (var key in imgtagdic)
{
Guid tagguid = Guid.Parse(key.Key.Split(',')[0]);
var fileName = allphotos[Convert.ToInt32(key.Key.Split(',')[1])];
imageFileEntries.Add(new ImageFileCreateEntry(fileName, File.ReadAllBytes(fileName), null, new List <Region>(new Region[] { new Region(tagguid, key.Value[0], key.Value[1], key.Value[2], key.Value[3]) })));
//
//Tried the add list of tags
//List<Guid> listtags = new List<Guid> { tagguid };
//imageFileEntries.Add(new ImageFileCreateEntry(fileName, File.ReadAllBytes(fileName), listtags, new List<Region>(new Region[] { new Region(tagguid, key.Value[0], key.Value[1], key.Value[2], key.Value[3]) })));
}
Console.WriteLine("\tUpload has started!");
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFileEntries));
Console.WriteLine("\tUpload is done!");
// 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");
}
}
public Boolean Train(string tagName, string description = null)
{
// Add your training & prediction key from the settings page of the portal
string trainingKey = "6308b3b62b344e3f8e4170c4728deed2";
// Create the Api, passing in the training key
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
var project = trainingApi.GetProjects().First(f => f.Name == "WA-SE-AI");
// Make two tags in the new project
Tag trainTag;
try
{
trainTag = trainingApi.GetTags(project.Id).First(f => f.Name == tagName);
}
catch (Exception)
{
trainTag = trainingApi.CreateTag(project.Id, tagName, description);
}
// Add some images to the tags
Console.WriteLine("Start load image into memory");
List <string> TrainImages = LoadImagesFromDisk(tagName);
if (TrainImages != null)
{
Console.WriteLine("Uploading " + TrainImages.Count + "images");
var trainImageFiles = TrainImages.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(trainImageFiles, new List <Guid>()
{
trainTag.Id
}));
// 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 == "Training")
{
Thread.Sleep(1000);
// Re-query the iteration to get it's 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("Training Done!\n");
return(true);
}
else
{
Console.WriteLine("No image found!\n");
return(true);
}
}
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();
}
static void Main(string[] args)
{
// Add your training and prediction key from the settings page of the portal
string trainingKey = "<add your training key here>";
string predictionKey = "<add your prediction key here>";
// Create the Api, passing in the training key
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
// Create a new project
Console.WriteLine("Creating new project:");
var project = trainingApi.CreateProject("My First Project");
// Make two tags in the new project
var hemlockTag = trainingApi.CreateTag(project.Id, "Hemlock");
var japaneseCherryTag = trainingApi.CreateTag(project.Id, "Japanese Cherry");
// Add some images to the tags
Console.WriteLine("\\tUploading images");
LoadImagesFromDisk();
// Images are then uploaded
foreach (var image in hemlockImages)
{
trainingApi.CreateImagesFromData(project.Id, image, new List <string>()
{
hemlockTag.Id.ToString()
});
}
foreach (var image in japaneseCherryImages)
{
trainingApi.CreateImagesFromData(project.Id, image, new List <string>()
{
japaneseCherryTag.Id.ToString()
});
}
// 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 == "Training")
{
Thread.Sleep(1000);
// Re-query the iteration to get it's 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");
// Now there is a trained endpoint, it can be used to make a prediction
PredictionEndpoint endpoint = new PredictionEndpoint()
{
ApiKey = predictionKey
};
// 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.TagName}: {c.Probability:P1}");
}
Console.ReadKey();
}
static void Main(string[] args)
{
// Add your training & prediction key from the settings page of the portal
string trainingKey = "d7ba782c8051443c8557ff464418949f";
// Create the Api, passing in the training key
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
// Create a new project
Console.WriteLine("Creating new project:");
var project = trainingApi.GetProject(new Guid("65d4860d-036d-4e2a-97af-50c3afaec972"));
var sourceDirectory = System.Configuration.ConfigurationSettings.AppSettings["ImageSourece"];
var imageDirectories = Directory.GetDirectories(sourceDirectory).ToList();
foreach (var directory in imageDirectories)
{
var diseaseTags = directory.Split(new string[] { "___" }, StringSplitOptions.None);
if (diseaseTags.Count() < 2)
{
continue;
}
var diseaseTag = diseaseTags[1].Replace('_', ' ');
if (diseaseTag == "healthy")
{
continue;
}
var plantName = diseaseTags[0].Substring(diseaseTags[0].LastIndexOf('\\') + 1).Replace('_', ' ');
var imagefiles = Directory.GetFiles(Path.Combine(sourceDirectory, directory)).ToList();
var imagePerTag = 190;
var tagName = string.Format("{0} {1}", plantName, diseaseTag);
if (trainingApi.GetTags(project.Id).Any(t => t.Name == tagName))
{
continue;
}
var tag = trainingApi.CreateTag(project.Id, tagName);
var randomFiles = GetRandomFiles(imagefiles, imagePerTag);
//max upload size 64 in a batch
var batchStartIndex = 0;
var batchsize = 64;
while (batchStartIndex < imagePerTag)
{
var itemsCountToTake = (imagePerTag - batchStartIndex >= batchsize) ? batchsize : imagePerTag - batchStartIndex;
var batchImages = randomFiles.Skip(batchStartIndex).Take(itemsCountToTake);
var imageFilesToUpload = batchImages.Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFilesToUpload, new List <Guid>()
{
tag.Id
}));
batchStartIndex += itemsCountToTake;
}
}
// 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 == "Training")
{
System.Threading.Thread.Sleep(1000);
// Re-query the iteration to get it's 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");
Console.ReadKey();
}
static void Main(string[] args)
{
var keys = GetApiKeys();
var trainingApi = new TrainingApi {
ApiKey = keys.TrainingKey
};
var predictionEndpoint = new PredictionEndpoint {
ApiKey = keys.PredictionKey
};
var projects = trainingApi.GetProjects();
var herbProject = projects.FirstOrDefault(p => p.Name == "Herbs");
Console.WriteLine("Press 1 to predict and 2 to train:");
var pathChoice = Console.ReadLine();
if ("1".Equals(pathChoice))
{
Console.WriteLine("Press 1 to predict on a URL or 2 to predict on a local file:");
var predictType = Console.ReadLine();
if ("1".Equals(predictType))
{
Console.WriteLine("Input the URL to an image to test:");
var imageUrl = Console.ReadLine();
if (herbProject != null)
{
var result = predictionEndpoint.PredictImageUrl(herbProject.Id, new Microsoft.Cognitive.CustomVision.Prediction.Models.ImageUrl(imageUrl));
PrintResults(result);
}
}
else
{
Console.WriteLine("Input path to image to test:");
var imagePath = Console.ReadLine();
if (!File.Exists(imagePath))
{
Console.WriteLine("File does not exist. Press enter to exit.");
Console.ReadLine();
return;
}
Console.WriteLine("Image predictions:");
var imageFile = File.OpenRead(imagePath);
if (herbProject != null)
{
var result = predictionEndpoint.PredictImage(herbProject.Id, imageFile);
PrintResults(result);
}
else
{
Console.WriteLine("Project doesn't exist.");
}
}
Console.ReadLine();
}
else
{
Console.WriteLine("Input path to image to train model with:");
var imagePath = Console.ReadLine();
Console.WriteLine("What tag would you give this image? Rosemary, cilantro, or basil?");
var imageTag = Console.ReadLine();
var capitilizedTag = char.ToUpper(imageTag.First()) + imageTag.Substring(1).ToLower();
if (!File.Exists(imagePath))
{
Console.WriteLine("File does not exist. Press enter to exit.");
Console.ReadLine();
return;
}
var imageFile = File.OpenRead(imagePath);
var tags = trainingApi.GetTags(herbProject.Id);
var matchedTag = tags.Tags.FirstOrDefault(t => t.Name == capitilizedTag);
var memoryStream = new MemoryStream();
imageFile.CopyTo(memoryStream);
var fileCreateEntry = new ImageFileCreateEntry(imageFile.Name, memoryStream.ToArray());
var fileCreateBatch = new ImageFileCreateBatch {
Images = new List <ImageFileCreateEntry> {
fileCreateEntry
}, TagIds = new List <Guid> {
matchedTag.Id
}
};
var result = trainingApi.CreateImagesFromFiles(herbProject.Id, fileCreateBatch);
var resultImage = result.Images.FirstOrDefault();
switch (resultImage.Status)
{
case "OKDuplicate":
Console.WriteLine("Image is already used for training. Please use another to train with");
Console.ReadLine();
break;
default:
break;
}
var iteration = trainingApi.TrainProject(herbProject.Id);
while (iteration.Status != "Completed")
{
System.Threading.Thread.Sleep(1000);
iteration = trainingApi.GetIteration(herbProject.Id, iteration.Id);
}
iteration.IsDefault = true;
trainingApi.UpdateIteration(herbProject.Id, iteration.Id, iteration);
Console.WriteLine("Done training!");
Console.ReadLine();
}
}
public static async Task <HttpResponseMessage> Run([HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = null)] HttpRequestMessage req, TraceWriter log)
{
try
{
var allTags = new List <string>();
var json = req.Content.ReadAsStringAsync().Result;
var jobj = JObject.Parse(json);
var tags = (JArray)jobj["tags"];
var term = jobj["term"].ToString();
var projectId = jobj["projectId"].ToString();
var trainingKey = jobj["trainingKey"].ToString();
var offset = 0;
if (jobj["offset"] != null)
{
offset = (int)jobj["offset"];
}
var imageUrls = await SearchForImages(term, offset);
var api = new TrainingApi(new TrainingApiCredentials(trainingKey));
var project = api.GetProject(Guid.Parse(projectId));
var tagModels = new List <ImageTagModel>();
var existingTags = api.GetTags(project.Id);
foreach (string tag in tags)
{
ImageTagModel model = existingTags.Tags.SingleOrDefault(t => t.Name == tag);
if (model == null)
{
model = api.CreateTag(project.Id, tag.Trim());
}
tagModels.Add(model);
}
var batch = new ImageUrlCreateBatch(tagModels.Select(m => m.Id).ToList(), imageUrls);
var summary = api.CreateImagesFromUrls(project.Id, batch);
//if(!summary.IsBatchSuccessful)
// return req.CreateErrorResponse(HttpStatusCode.BadRequest, "Image batch was unsuccessful");
//Traing the classifier and generate a new iteration, that we'll set as the default
var iteration = api.TrainProject(project.Id);
while (iteration.Status == "Training")
{
Thread.Sleep(1000);
iteration = api.GetIteration(project.Id, iteration.Id);
}
iteration.IsDefault = true;
api.UpdateIteration(project.Id, iteration.Id, iteration);
return(req.CreateResponse(HttpStatusCode.OK, iteration.Id));
}
catch (Exception e)
{
var exception = e.GetBaseException();
return(req.CreateErrorResponse(HttpStatusCode.BadRequest, exception.Message));
}
async Task <List <string> > SearchForImages(string term, int offset)
{
var client = new HttpClient();
client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", "c2adf0e5c057447ea9e0f50cc5202251");
var uri = $"https://api.cognitive.microsoft.com/bing/v7.0/images/search?count=50&q={term}&offset={offset}";
var json = await client.GetStringAsync(uri);
var jobj = JObject.Parse(json);
var arr = (JArray)jobj["value"];
var list = new List <string>();
foreach (var result in arr)
{
list.Add(result["contentUrl"].ToString());
}
return(list);
}
}