private void MakePredictionRequest(string filepath)
{
CustomVisionTrainingClient trainingClient = new CustomVisionTrainingClient()
{
ApiKey = trainingKey,
Endpoint = SouthCentralUsEndpoint
};
// Create a prediction endpoint, passing in obtained prediction key
CustomVisionPredictionClient endpoint = new CustomVisionPredictionClient()
{
ApiKey = predictionKey,
Endpoint = SouthCentralUsEndpoint
};
var project = trainingClient.GetProjects().FirstOrDefault();
// Make a prediction against the new project
Console.WriteLine("Making a prediction:");
var result = endpoint.PredictImage(project.Id, new MemoryStream(File.ReadAllBytes(filepath)));
// Loop over each prediction and write out the results
foreach (var c in result.Predictions)
{
Console.WriteLine($"\t{c.TagName}: {c.Probability:P1}");
}
}
public async Task <IActionResult> Post(List <IFormFile> img)
{
var requestStream = Request.HttpContext.Items;
const string scue = "https://southcentralus.api.cognitive.microsoft.com";
string trainingKey = "4f473807b7434dd5a1bdb45cb9104b38";
CustomVisionTrainingClient trainingApi = new CustomVisionTrainingClient()
{
ApiKey = trainingKey,
Endpoint = scue
};
// Find the object detection domain
var domains = trainingApi.GetDomainsAsync();
//var objDetectionDomain = domains.FirstOrDefault(d => d.Type == "ObjectDetection");
var project = trainingApi.GetProject(Guid.Parse("b911d77a-ef25-47fd-86ed-87db4500ef7b"));
const string southcentralus = "https://southcentralus.api.cognitive.microsoft.com";
string predictionKey = "a58f3ca5856c491db0b73b87cb1118cf";
CustomVisionPredictionClient endpoint = new CustomVisionPredictionClient()
{
ApiKey = predictionKey,
Endpoint = southcentralus
};
var c = new List <PredictionModel>();
var result = endpoint.PredictImage(Guid.Parse("cbfa66a3-9815-47d6-a389-7438e468ac15"), img[0].OpenReadStream());
ImageUrl imgUrl = new ImageUrl();
imgUrl.Url = "https://http2.mlstatic.com/guitarra-tagima-pr-200-special-pr200-sunburst-D_NQ_NP_894387-MLB26271081482_112017-F.jpg";
var resultImageUrl = endpoint.PredictImageUrl(Guid.Parse("cbfa66a3-9815-47d6-a389-7438e468ac15"), imgUrl);
foreach (var item in result.Predictions.OrderBy(x => x.Probability))
{
var pm = new PredictionModel(Math.Round(item.Probability * 100), item.TagId, item.TagName, item.BoundingBox);
if (pm.Probability > 70)
{
pm.BoundingBox.Top = Convert.ToInt32(pm.BoundingBox.Top * 380);
pm.BoundingBox.Height = Convert.ToInt32(pm.BoundingBox.Height * 380);
pm.BoundingBox.Left = Convert.ToInt32(pm.BoundingBox.Left * 700);
pm.BoundingBox.Width = Convert.ToInt32(pm.BoundingBox.Width * 700);
c.Add(pm);
}
}
return(Ok(c));
}
protected void Button1_Click(object sender, EventArgs e)
{
try
{
//trainmodel();
if (FileUpload1.HasFile)
{
string fileName = Path.GetFileName(FileUpload1.PostedFile.FileName);
FileUpload1.PostedFile.SaveAs(Server.MapPath("~/Images/") + fileName);
//Response.Redirect(Request.Url.AbsoluteUri);
// Create the Api, passing in the training key
//CustomVisionTrainingClient trainingApi = new CustomVisionTrainingClient()
//{
// ApiKey = trainingKey,
// Endpoint = SouthCentralUsEndpoint
//};
//var project = trainingApi.GetProject(projectid);
using (var stream = new MemoryStream(File.ReadAllBytes(Server.MapPath("~/Images/") + fileName)))
{
// Create a prediction endpoint, passing in obtained prediction key
CustomVisionPredictionClient endpoint = new CustomVisionPredictionClient()
{
ApiKey = predictionKey,
Endpoint = SouthCentralUsEndpoint
};
var predictions = new ImagePredictionResultModel();
// Make a prediction against the new project
TextBox1.Text = string.Format("Making a prediction:") + System.Environment.NewLine;
var result = endpoint.PredictImage(projectid, stream);
// Loop over each prediction and write out the results
foreach (var c in result.Predictions)
{
TextBox1.Text += string.Format($"\t{c.TagName}: {c.Probability:P1}" + System.Environment.NewLine);
}
}
}
}
catch (Exception ex)
{
showerror(ex);
//throw ex;
}
}
public async Task <IActionResult> Post(List <IFormFile> img)
{
var requestStream = Request.HttpContext.Items;
const string scue = "https://southcentralus.api.cognitive.microsoft.com";
string trainingKey = "4f473807b7434dd5a1bdb45cb9104b38";
CustomVisionTrainingClient trainingApi = new CustomVisionTrainingClient()
{
ApiKey = trainingKey,
Endpoint = scue
};
// Find the object detection domain
var domains = trainingApi.GetDomainsAsync();
//var objDetectionDomain = domains.FirstOrDefault(d => d.Type == "ObjectDetection");
var project = trainingApi.GetProject(Guid.Parse("b911d77a-ef25-47fd-86ed-87db4500ef7b"));
const string southcentralus = "https://southcentralus.api.cognitive.microsoft.com";
string predictionKey = "a58f3ca5856c491db0b73b87cb1118cf";
CustomVisionPredictionClient endpoint = new CustomVisionPredictionClient()
{
ApiKey = predictionKey,
Endpoint = southcentralus
};
//foreach (var item in img)
//{
// var result = endpoint.PredictImage(project.Id, item.OpenReadStream());
// foreach (var c in result.Predictions)
// {
// //Console.WriteLine($"\t{c.TagName}: {c.Probability:P1} [ {c.BoundingBox.Left}, {c.BoundingBox.Top}, {c.BoundingBox.Width}, {c.BoundingBox.Height} ]");
// return Ok(c);
// }
//}
var result = endpoint.PredictImage(project.Id, img[0].OpenReadStream());
//var a = Math.Round(result.Predictions[0].Probability, 5);
//var b = Math.Round(result.Predictions[1].Probability, 5);
//var c = result.Predictions[0];
//var d = result.Predictions[1];
return(Ok(result.Predictions));
}
public async void objectdetect()
{
try
{
//string testImageFile = @"D:\Pictures\test_img1.jpg";
//string subscriptionKey = ConfigurationManager.AppSettings["subscriptionKey"].ToString();
byte[] data = System.Convert.FromBase64String(Request.Form["formfield"]);
if (data.Length > 0)
{
using (Stream s = new MemoryStream(data))
{
// Create a prediction endpoint, passing in obtained prediction key
CustomVisionPredictionClient endpoint = new CustomVisionPredictionClient()
{
ApiKey = predictionKey,
Endpoint = SouthCentralUsEndpoint
};
// Make a prediction against the new project
TextBox1.Text = string.Format("Making a prediction:") + System.Environment.NewLine;
var result = endpoint.PredictImage(projectid, s);
// Loop over each prediction and write out the results
foreach (var c in result.Predictions)
{
TextBox1.Text += string.Format($"\t{c.TagName}: {c.Probability:P1}" + System.Environment.NewLine);
}
}
}
}
catch (Exception ex)
{
showerror(ex);
//throw ex;
}
}
public static string PredictImage(Stream imageStream)
{
if (busy)
{
App.Log.WriteInfo("Skip image while robot is in process");
return(null);
}
App.Log.WriteInfo(String.Format("ComputerVision call. images size {0}", imageStream.Length));
ImagePrediction results = endpoint.PredictImage(projectId, imageStream);
PredictionModel model = results.Predictions.OrderByDescending <PredictionModel, double>(m => m.Probability).First <PredictionModel>();
App.Log.WriteInfo(String.Format("ComputerVision recognize an images as {0} with probability {1}", model.TagName, model.Probability));
if (model.Probability > predictionThreshold)
{
DoRobotAction(model.TagName);
return(model.TagName);
}
else
{
return(null);
}
}
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
CustomVisionTrainingClient trainingApi = new CustomVisionTrainingClient()
{
ApiKey = trainingKey,
Endpoint = SouthCentralUsEndpoint
};
// 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
CustomVisionPredictionClient endpoint = new CustomVisionPredictionClient()
{
ApiKey = predictionKey,
Endpoint = SouthCentralUsEndpoint
};
// 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();
}
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
CustomVisionTrainingClient trainingApi = new CustomVisionTrainingClient()
{
ApiKey = trainingKey,
Endpoint = SouthCentralUsEndpoint
};
// 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
CustomVisionPredictionClient endpoint = new CustomVisionPredictionClient()
{
ApiKey = predictionKey,
Endpoint = SouthCentralUsEndpoint
};
// 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();
}
private async Task MessageReceivedAsync(IDialogContext context, IAwaitable <IMessageActivity> result)
{
/* When MessageReceivedAsync is called, it's passed an IAwaitable<IMessageActivity>. To get the message,
* await the result. */
var message = await result as Activity;
if (message.Attachments.Count > 0)
{
// Create a prediction endpoint, passing in obtained prediction key
CustomVisionPredictionClient endpoint = new CustomVisionPredictionClient()
{
//Prediction Key from custom vision web site
ApiKey = "6c3cfebc453243f987a3966b781f6faf",
Endpoint = SouthCentralUsEndpoint
};
/* Handle the attachment file from user */
foreach (var file in message.Attachments)
{
byte[] attachmentBytes = new System.Net.WebClient().DownloadData(file.ContentUrl);
MemoryStream attachmentMemoryStream = new MemoryStream(attachmentBytes);
//Id as below is the project ID from custom vision web site
var predictImgResult = endpoint.PredictImage(Guid.Parse("a4fe9af2-e1b9-45d9-84b9-0abc141f32e2"), attachmentMemoryStream);
string replyMsg = string.Empty;
foreach (var c in predictImgResult.Predictions)
{
replyMsg += c.Probability.ToString("0.00") + " could be " + c.TagName + Environment.NewLine;
}
await context.PostAsync("File received!And the prediction result is: " + replyMsg);
}
}
else
{
/* When message return, call Text Analutics service to identify language type */
ITextAnalyticsClient textanalyticsClient = new TextAnalyticsClient(new ApiKeyServiceClientCredentials())
{
Endpoint = ConfigurationManager.AppSettings["TextAnalyticsEndPoint"]
};
var languageDetectResult = textanalyticsClient.DetectLanguageAsync(new BatchInput(
new List <Input>()
{
new Input("id", message.Text)
})).Result;
if (languageDetectResult.Documents != null && languageDetectResult.Documents.Count > 0)
{
//ch-ZN support
if (languageDetectResult.Documents[0].DetectedLanguages.Count(c => c.Name.Equals("Chinese_Simplified")) > 0)
{
await context.Forward(new BasicQnAMakerZNDialog(), AfterAnswerAsync, message, CancellationToken.None);
}
//en-UK support
else
{
await context.Forward(new BasicQnAMakerDialog(), AfterAnswerAsync, message, CancellationToken.None);
if (message.Text.ToLower().Contains("mail") || message.Text.ToLower().Contains("manager"))
{
Mail.SendMail();
}
}
}
//Default support language is en-UK also
else
{
await context.Forward(new BasicQnAMakerDialog(), AfterAnswerAsync, message, CancellationToken.None);
}
}
}