private void radioButtonDetection_CheckedChanged(object sender, EventArgs e)
{
if (radioButtonDetection.Checked)
{
currentPredictionMode = PredictionMode.Detection;
project = trainingApi.GetProject(new Guid("e1ec7b7e-ac74-4959-9c8b-4d48d25a7668"));
buttonClear.Enabled = isPredicting ? false : true;
}
pictureBox1.Invalidate();
}
private string connectToAnalyze()
{
// custom vision
TrainingApi trainingAPI = new TrainingApi()
{
ApiKey = "1360135bcfaa45d5b6d69dc06dcb04e4"
};
Guid _projectkey = new Guid("ea8591bc-bff1-47d6-b46d-64938d2d8b58");
project = trainingAPI.GetProject(_projectkey);
PredictionEndpoint endpoint = new PredictionEndpoint()
{
ApiKey = "65125a9d5a774acaaeaa0f9705404307"
};
// 28bf5d1c-1025-4d3f-a7d7-4dfd4b65f4a1 iteration key
ArrayList lists = new ArrayList();
byte[] imgArr = GetFileAsByteArray(App._file.AbsolutePath);
System.IO.MemoryStream stream = new System.IO.MemoryStream(imgArr);
var result = endpoint.PredictImage(project.Id, stream, new Guid("28bf5d1c-1025-4d3f-a7d7-4dfd4b65f4a1"));
foreach (var prediction in result.Predictions)
{
lists.Add($"{prediction.Tag}");
}
return(lists.Get(0).ToString());
}
public Form1()
{
InitializeComponent();
trainingApi = new TrainingApi()
{
ApiKey = trainingKey
};
currentPredictionMode = PredictionMode.Detection;
project = trainingApi.GetProject(new Guid("e1ec7b7e-ac74-4959-9c8b-4d48d25a7668"));
// Create a prediction endpoint, passing in obtained prediction key
endpoint = new PredictionEndpoint()
{
ApiKey = predictionKey
};
labelInfo.Text = textBoxUrl.Text = "";
percentLevel = trackBar1.Value;
selectedItemIndex = -1;
isPredicting = true;
textBoxTag.Enabled = buttonAddTag.Enabled = buttonSend.Enabled = buttonClear.Enabled = false;
labelStatus.Text = "PREDICTING";
treeListToUpload = new List <TreeData>();
treeListDetection = new List <TreeData>();
treeListClassification = new List <TreeData>();
}
static void Main(string[] args)
{
var options = Options.InitializeOptions <TrainingOptions>(args);
if (options == null)
{
return;
}
var trainingApi = new TrainingApi()
{
ApiKey = options.Trainingkey
};
if (options.BaseUri != null)
{
Console.WriteLine($"The default base uri is {trainingApi.BaseUri}. Changed it to {options.BaseUri}.");
trainingApi.BaseUri = options.BaseUri;
}
ProjectInfo project;
if (options.ProjectId != Guid.Empty)
{
if (!trainingApi.CheckIfProjectExists(options.ProjectId))
{
Console.WriteLine($"Cannot get the project with id {options.ProjectId}. Please check if the Guid is right.");
return;
}
project = new ProjectInfo(trainingApi.GetProject(options.ProjectId));
}
else
{
Console.WriteLine("ProjectId is not provided. Creating a new project...");
var projectInfo = ProjectInfo.ReadProjectInfo(options.WorkDir + options.ProjectInfoFileName);
project = trainingApi.CreateProjectAsync(projectInfo).Result;
}
Console.WriteLine($"Project Id: {project.Id}");
if (options.ImageSource != "existing")
{
var uploadResult = trainingApi.ReadAndUploadImagesAsync(project, options, options.AllowedTagNames).Result;
if (!uploadResult.IsBatchSuccessful)
{
Console.WriteLine("No image uploaded successfully. Please check if those images have been uploaded before.");
return;
}
}
trainingApi.TrainProject(project.Id);
Console.WriteLine("The classifier is now training. You can check its status in the web portal.\nPress any key to exit.");
Console.ReadKey();
}
static void Main(string[] args)
{
var options = Options.InitializeOptions <ImagesDownloadOptions>(args);
if (options == null)
{
return;
}
var trainingApi = new TrainingApi()
{
ApiKey = options.Trainingkey
};
if (options.BaseUri != null)
{
Console.WriteLine($"The default base uri is {trainingApi.BaseUri}. Changed it to {options.BaseUri}.");
trainingApi.BaseUri = options.BaseUri;
}
string errorMessage;
if (!options.ValidateWithTrainingApi(trainingApi, out errorMessage))
{
Console.WriteLine(errorMessage);
return;
}
var imageGetter = new ImagesLoaderFromProject(trainingApi, options.ProjectId, options.IterationId, options.AllowedTagNames);
var images = imageGetter.LoadImages();
var imageFileWriter = ImageFilesWriterGenerator.GenerateImageFileWriter(options);
imageFileWriter.WriteImagesToDisk(images);
var project = new ProjectInfo(trainingApi.GetProject(options.ProjectId));
project.WriteProjectInfo(options.WorkDir + options.ProjectInfoFileName).Wait();
}
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));
}
}
}
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)
{
// 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();
}
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();
}
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);
}
}