private IEnumerable <Image> GetImagesInBatch(Func <int, int, IEnumerable <ImageModel> > getAnImageModelBatch)
{
var tagIdToName = TrainingApi.GetTags(ProjectId, IterationId).Tags.ToDictionary(x => x.Id, x => x.Name);
var batchIndex = 0;
IList <ImageModel> imageModels;
do
{
imageModels = getAnImageModelBatch(ImageDownloadBatchSize, batchIndex * ImageDownloadBatchSize).ToList();
foreach (var imageModel in imageModels)
{
yield return(new Image
{
Id = imageModel.Id,
Path = imageModel.ImageUri,
TagIds = imageModel.Labels?.Select(x => x.TagId).ToList(),
TagNames = imageModel.Labels?.Select(x => tagIdToName[x.TagId]).ToList()
});
}
batchIndex++;
} while (imageModels.Count != 0);
}
private IEnumerable <Guid> GetTagIds(IEnumerable <string> tagNames)
{
var tagNameToDictionary = TrainingApi.GetTags(ProjectId, IterationId).Tags.ToDictionary(x => x.Name, x => x.Id);
return(tagNames?.Select(x =>
{
Guid id;
return tagNameToDictionary.TryGetValue(x, out id) ? id : Guid.Empty;
}).Where(x => x != Guid.Empty).ToList() ?? Enumerable.Empty <Guid>());
}
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);
}
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);
}
public int Execute()
{
string tagName = _tagArgument.Value;
string imagesPath = _pathArgument.Value;
if (!Directory.Exists(imagesPath))
{
return(Util.Fail($"The path '{imagesPath}' does not exist"));
}
string[] trainingImages = Directory.GetFiles(imagesPath);
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = _trainingKey
};
TagList tagList = trainingApi.GetTags(_projectId);
Tag tag = tagList.Tags.FirstOrDefault(t => t.Name == tagName);
if (tag == null)
{
return(Util.Fail($"No tag named {tagName} was found."));
}
List <string> tagIds = new List <string> {
tag.Id.ToString()
};
foreach (var imagePath in trainingImages)
{
Console.Write($"Uploading '{imagePath}'... ");
using (var imageStream = new MemoryStream(File.ReadAllBytes(imagePath)))
trainingApi.CreateImagesFromData(_projectId, imageStream, tagIds);
Console.WriteLine($"done");
}
Console.WriteLine($"\r\nDone uploading {trainingImages.Length} images.");
return(0);
}
public static void CreateImageFromUrls([QueueTrigger("create-image-from-urls")] CreateImageFromUrlsRequest imageCreateRequest, TraceWriter log)
{
log.Info($"C# Queue trigger function processed: {imageCreateRequest}");
// https://docs.microsoft.com/ja-jp/azure/cognitive-services/custom-vision-service/csharp-tutorial
var CV_ProjectId = Environment.GetEnvironmentVariable("CV_ProjectId");
var CV_TrainingKey = Environment.GetEnvironmentVariable("CV_TrainingKey");
//var CV_PredictionKey = Environment.GetEnvironmentVariable("CV_PredictionKey");
var trainingApi = new TrainingApi()
{
ApiKey = CV_TrainingKey
};
var projectId = Guid.Parse(CV_ProjectId);
//var project = new ProjectInfo(trainingApi.GetProject(Guid.Parse(CV_ProjectId)));
var requestTags = imageCreateRequest.Tags.ToList();
var existTags = trainingApi.GetTags(projectId);
var nonExistTagNames = requestTags.Except(existTags.Select(x => x.Name));
var tagIds = existTags.Where(x => requestTags.Contains(x.Name)).Select(x => x.Id).ToList();
nonExistTagNames.ToList().ForEach(tagName =>
{
// XXX 同時に同じタグ名を指定された複数のキューを処理すると、重複してタグ作成してしまう。
// 現状、重複エラー後のリトライで処理
var tag = trainingApi.CreateTag(projectId, tagName);
tagIds.Add(tag.Id);
});
var images = new List <ImageUrlCreateEntry>()
{
new ImageUrlCreateEntry()
{
Url = imageCreateRequest.Url
}
};
trainingApi.CreateImagesFromUrls(Guid.Parse(CV_ProjectId), new ImageUrlCreateBatch(images, tagIds));
}
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();
}
}
static void Main(string[] args)
{
// Create the Api, passing in the training key
string trainingKey = "";
if (String.IsNullOrEmpty(trainingKey))
{
Console.WriteLine("The custom vision training key needs to be set.");
Environment.Exit(1);
}
TrainingApi trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
// Find the object detection project
var domains = trainingApi.GetDomains();
var objDetectionDomain = domains.FirstOrDefault(d => d.Type == "ObjectDetection");
var project = trainingApi.GetProjects().FirstOrDefault(p => p.Settings.DomainId == objDetectionDomain.Id);
string modelPath = Path.Combine(Environment.CurrentDirectory, "..", "model");
string dataPath = Path.Combine(modelPath, "dataset.json");
string imagesPath = Path.Combine(modelPath, "images");
var images = JsonConvert.DeserializeObject <IEnumerable <Image> >(File.ReadAllText(dataPath));
// Create tags, unless they already exist
var existingTags = trainingApi.GetTags(project.Id);
var tagsToImport = images
.SelectMany(i => i.Tags
.Select(t => t.TagName))
.Distinct()
.Where(t => !existingTags.Any(e => string.Compare(e.Name, t, ignoreCase: true)));
if (tagsToImport.Any())
{
foreach (var tag in tagsToImport)
{
Console.WriteLine($"Importing {tag}");
var newTag = trainingApi.CreateTag(project.Id, tag);
existingTags.Add(newTag);
}
}
// Upload images with region data, in batches of 50
while (images.Any())
{
var currentBatch = images.Take(BATCH_SIZE);
var imageFileEntries = new List <ImageFileCreateEntry>();
foreach (var imageRef in currentBatch)
{
var regions = new List <Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models.Region>();
foreach (var region in imageRef.Regions)
{
regions.Add(new Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models.Region()
{
Height = region.Height,
Width = region.Width,
Top = region.Top,
Left = region.Left,
TagId = existingTags.First(t => t.Name == region.TagName).Id
});
}
string imagePath = Path.Combine(imagesPath, string.Concat(imageRef.Id, ".png"));
imageFileEntries.Add(new ImageFileCreateEntry()
{
Name = imagePath,
Contents = File.ReadAllBytes(imagePath),
Regions = regions
});
}
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFileEntries));
images = images.Skip(BATCH_SIZE);
}
Console.WriteLine("Training data upload complete!");
}
/// <summary>
/// 根据Project Id获取所有Tags
/// </summary>
/// <param name="projectId"></param>
/// <returns></returns>
public ImageTagListModel GetAllTags(Guid projectId)
{
ImageTagListModel taglist = trainingApi.GetTags(projectId);
return(taglist);
}
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)
{
// 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");
}
}
private void buttonSend_Click(object sender, EventArgs e)
{
if (pictureBox1.BackgroundImage == null)
{
MessageBox.Show("Missing image or selected regions");
return;
}
if (currentPredictionMode == PredictionMode.Detection)
{
if (treeListToUpload.Count == 0)
{
MessageBox.Show("Missing selected regions");
return;
}
try
{
var imageFileEntries = new List <ImageFileCreateEntry>();
List <Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models.Region> regions = new List <Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models.Region>();
foreach (var item in treeListToUpload)
{
var tagList = trainingApi.GetTags(project.Id);
Tag tag = tagList.FirstOrDefault(x => x.Name.ToLower().Equals(item.TagName.ToLower()));
if (tag == null)
{
tag = trainingApi.CreateTag(project.Id, System.Globalization.CultureInfo.CurrentCulture.TextInfo.ToTitleCase(item.TagName.ToLower()));
}
var region = new double[] { item.BoundingBox.Left, item.BoundingBox.Top, item.BoundingBox.Width, item.BoundingBox.Height };
regions.Add(new Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models.Region(tag.Id, region[0], region[1], region[2], region[3]));
}
imageFileEntries.Add(new ImageFileCreateEntry(null, GetStream(pictureBox1.BackgroundImage).ToArray(), null, regions));
trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFileEntries));
treeListToUpload.Clear();
pictureBox1.Invalidate();
MessageBox.Show("The image is uploaded successfully");
}
catch (Exception ex)
{
labelInfo.Text = ex.ToString();
}
}
else if (currentPredictionMode == PredictionMode.Classification)
{
if (imageTagToUpload == "")
{
MessageBox.Show("Missing tag");
return;
}
try
{
var tagList = trainingApi.GetTags(project.Id);
Tag tag = tagList.FirstOrDefault(x => x.Name.ToLower().Equals(imageTagToUpload.ToLower()));
if (tag == null)
{
tag = trainingApi.CreateTag(project.Id, imageTagToUpload.ToLower());
}
trainingApi.CreateImagesFromData(project.Id, GetStream(pictureBox1.BackgroundImage), new List <string>()
{
tag.Id.ToString()
});
MessageBox.Show("The image is uploaded successfully");
}
catch (Exception ex)
{
labelInfo.Text = ex.ToString();
}
}
}
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);
}
}
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);
}
}