protected override bool TagSelector(TrainingModels.Tag tag, SkiVideoEntity video)
{
if (tag == null || video == null)
{
return(false);
}
else
{
return(tag.Name == video.RopeLengthM.ToString());
}
}
protected override bool TagSelector(TrainingModels.Tag tag, SkiVideoEntity video)
{
if (tag == null || video == null)
{
return(false);
}
else
{
return(tag.Name == video.Skier.Trim());
}
}
protected abstract bool TagSelector(TrainingModels.Tag tag, SkiVideoEntity video);
private TrainingModels.Tag CreateTag(SkiVideoEntity video, string tagName)
{
TrainingModels.Tag tag = trainingApi.CreateTag(ProjectId, tagName);
tags.Add(tag);
return(tag);
}
public object Process()
{
if (!Directory.Exists(Options.Folder))
{
Console.WriteLine($"Folder not found: {Options.Folder}");
return(null);
}
var tagDirectories = Directory.GetDirectories(Options.Folder);
if (tagDirectories.Length == 0)
{
Console.WriteLine($"Not subfolders found in {Options.Folder}. Subfolders are required to define tags and associated images.");
return(null);
}
var cropRect = new Rectangle();
if (!string.IsNullOrEmpty(Options.CropArea))
{
var dims = Options.CropArea.Split(",");
try
{
var x = int.Parse(dims[0]);
var y = int.Parse(dims[1]);
var w = int.Parse(dims[2]);
var h = int.Parse(dims[3]);
cropRect = new Rectangle(x, y, w, h);
}
catch
{
Console.WriteLine($"Unable to parse {Options.CropArea} into a rectangle. Required format is: x,y,width, height");
return(new object());
}
Console.WriteLine($"Crop area is [{cropRect.Left},{cropRect.Top}], {cropRect.Width}x{cropRect.Height}");
}
else
{
Console.WriteLine("No cropping will be performed.");
}
Console.WriteLine();
var settings = JsonConvert.DeserializeObject <Settings>(File.ReadAllText(Program.SETTINGS_FILE));
var trainingApi = CvService.AuthenticateTraining(settings.CvTrainingEndpoint, settings.CvTrainingKey, settings.CvPredictionKey);
// Explore subdirectories
var totalFileCount = 0;
Console.WriteLine($"Base folder: {Options.Folder}");
foreach (var directory in tagDirectories)
{
var files = Directory.GetFiles(directory);
totalFileCount += files.Length;
Console.WriteLine($" /{directory.Split(Path.DirectorySeparatorChar).Last()}, {files.Length} image(s)");
}
Console.WriteLine(" --------");
Console.WriteLine($" {totalFileCount} image(s) in total");
if (totalFileCount == 0)
{
Console.WriteLine("Nothing to do...");
}
else if (Options.IsInteractive)
{
Console.WriteLine();
Console.WriteLine(Program.CONFIRM_PHRASE);
Console.ReadLine();
}
else
{
Console.WriteLine();
}
var project = trainingApi.GetProject(Options.ProjectId);
Console.WriteLine($"Target project is '{project.Name}'");
Console.WriteLine();
if (totalFileCount == 0)
{
return(new object());
}
// Go through each subdirectory
var fileCountFormat = string.Empty;
for (int i = 0; i < totalFileCount.ToString().Length; i++)
{
fileCountFormat += "0";
}
var fileCount = 1;
foreach (var directory in tagDirectories)
{
var targetTagName = directory.Split(Path.DirectorySeparatorChar).Last();
Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models.Tag targetTag = null;
try
{
targetTag = trainingApi.GetTags(Options.ProjectId).SingleOrDefault(t => t.Name == targetTagName);
// If a tag with this name does not already exist, create it
if (targetTag == null)
{
if (targetTagName.ToLowerInvariant() == "negative")
{
targetTag = trainingApi.CreateTag(Options.ProjectId, targetTagName, type: "Negative");
}
else
{
targetTag = trainingApi.CreateTag(Options.ProjectId, targetTagName);
}
Console.WriteLine($"Created tag '{targetTagName}' (id={targetTag.Id})");
}
else
{
Console.WriteLine($"Reusing existing tag '{targetTag.Name}' (id={targetTag.Id})");
}
}
catch (CustomVisionErrorException ex)
{
Console.WriteLine($"Error: {ex.DetailedMessage()}");
return(new object());
}
Console.WriteLine();
if (cropRect == Rectangle.Empty)
{
fileCount = UploadFolder(trainingApi, fileCount, directory, targetTag);
}
else
{
// Crops into temporary files
//foreach (var file in Directory.GetFiles(directory))
//{
// Console.Write($"[{fileCount++.ToString(fileCountFormat)}/{totalFileCount}] Processing {Path.Combine(directory, file)}...");
// using (var image = SixLabors.ImageSharp.Image.Load(file))
// {
// image.Mutate(x => x.Crop(cropRect));
// using (var stream = new MemoryStream())
// {
// //image.Save(stream, new JpegEncoder() { Quality = 100 });
// var tmpFile = Path.GetTempFileName();
// try
// {
// var res = trainingApi.CreateImagesFromData(Options.ProjectId, stream, new List<Guid>() { targetTag.Id });
// var errorCount = PrintBatchErrors(res);
// }
// catch (CustomVisionErrorException ex) { Console.WriteLine($"Error: {ex.DetailedMessage()}"); }
// }
// }
//}
var tempDir = Path.Combine(Path.GetTempPath(), targetTagName);
if (Directory.Exists(tempDir) && Options.IsInteractive)
{
Console.WriteLine($"Temporary folder {tempDir} already exists.");
Console.WriteLine(Program.CONFIRM_PHRASE);
Console.ReadLine();
}
else
{
Directory.CreateDirectory(tempDir);
Console.Write($"Created temporary folder {tempDir}");
}
foreach (var file in Directory.GetFiles(directory))
{
Console.Write($"Cropping {Path.Combine(directory.Split(Path.DirectorySeparatorChar).Last(), file)}...");
using (var image = SixLabors.ImageSharp.Image.Load(file))
{
image.Mutate(x => x.Crop(cropRect));
image.Save(Path.Combine(tempDir, Path.GetFileName(file)), new JpegEncoder()
{
Quality = 100
});
}
Console.WriteLine("ok");
}
fileCount = UploadFolder(trainingApi, fileCount, tempDir, targetTag);
Directory.Delete(tempDir, true);
Console.WriteLine($"Deleted {tempDir}");
}
}
return(new object());
}
private int UploadFolder(CustomVisionTrainingClient trainingApi, int fileCount, string directory, Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models.Tag targetTag)
{
Console.WriteLine($"Batch uploading content from {directory}....");
var imageFiles = Directory.GetFiles(directory).Select(img => new ImageFileCreateEntry(Path.GetFileName(img), File.ReadAllBytes(img))).ToList();
try
{
while (imageFiles.Any())
{
var batchSize = Math.Min(imageFiles.Count, BATCH_SIZE);
var ifcb = new ImageFileCreateBatch(imageFiles.Take(batchSize).ToList(), new List <Guid>()
{
targetTag.Id
});
var res = trainingApi.CreateImagesFromFiles(Options.ProjectId, ifcb);
var errorCount = PrintBatchErrors(res);
imageFiles.RemoveRange(0, batchSize);
fileCount += batchSize;
Console.WriteLine($" {batchSize} files uploaded with {errorCount} errors ({fileCount} total)");
}
}
catch (CustomVisionErrorException ex) { Console.WriteLine($"Error: {ex.DetailedMessage()}"); }
fileCount += imageFiles.Count;
return(fileCount);
}
public async Task <bool> UploadImageAsync(Stream data, string fileName, string tagName)
{
CustomVisionTrainingClient trainingApi = AuthenticateTraining(_configuration.TrainingEndpoint, _configuration.TrainingKey);
Guid projectIdGuid = Guid.Parse(_configuration.ProjectId);
Project project = await trainingApi.GetProjectAsync(projectIdGuid);
IList <Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models.Tag> tags = await trainingApi.GetTagsAsync(projectIdGuid);
Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models.Tag existedTag = tags.SingleOrDefault(t => t.Name == tagName);
if (existedTag == null)
{
existedTag = trainingApi.CreateTag(projectIdGuid, tagName);
}
ImageCreateSummary result = trainingApi.CreateImagesFromData(projectIdGuid, data, new List <Guid>()
{
existedTag.Id
});
return(result.IsBatchSuccessful);
}