static async Task WorkOnProject(string trainingEndpoint, string trainingKey, string projectName, string COCOInstancesFilePathOrUrl, IList <string> categories, uint numberOfImages, bool isDetectionModel, bool train)
{
Console.Write($"\nLoading, parsing and preparing {COCOInstancesFilePathOrUrl}...");
if (numberOfImages == 0 || numberOfImages > customVisionImageLimit)
{
numberOfImages = 50000;
}
var data = await COCODatasetFactory.LoadFromCOCOAnnotationJSONFileAsync(COCOInstancesFilePathOrUrl, categories, numberOfImages);
Console.WriteLine("done.");
Console.WriteLine("Prepared COCO dataset with {0} categories and {1} images.\n", data.categoriesAndIds.Count, data.traningSet.Count);
Console.Write($"Initializing Custom Vision Project {COCOInstancesFilePathOrUrl}...");
ProjectTraningConfiguration trainingConfig = await ProjectTraningConfiguration.CreateProjectAsync(trainingEndpoint, trainingKey, projectName, true, isDetectionModel);
Console.WriteLine("done.");
Console.Write($"\nLoading traning images to Custom Vision Project...");
CustomVisionProjectTraning traning = new CustomVisionProjectTraning(trainingConfig);
int ignoredImages = await traning.LoadWithCOCO(data, isDetectionModel);
if (ignoredImages == 0)
{
Console.WriteLine("done.");
}
else
{
Console.WriteLine($"done. Ignored {ignoredImages} image(s).");
}
if (train)
{
Console.Write($"\nTraning Custom Vision Project...");
await traning.Train();
Console.WriteLine("done.");
}
else
{
Console.WriteLine("Traning is not requested.");
}
}
public async Task <int> LoadWithCOCO(COCODatasetFactory imageSet, bool isDetectionModel)
{
var imageUrlBatches = new List <List <ImageUrlCreateEntry> >();
var imageUrlEntries = new List <ImageUrlCreateEntry>();
var tagsInBatch = new List <Guid>();
Dictionary <int, Guid> categoriesToTags = new Dictionary <int, Guid>();
int imagesIgnored = 0;
foreach (var cat in imageSet.categoriesAndIds.Keys)
{
string name = imageSet.categoriesAndIds[cat];
var tags = configuration.tagList.Where(tag => tag.Name == name);
if (tags.Any())
{
categoriesToTags.Add(cat, tags.First().Id);
}
else
{
var tag = await configuration.AddTagAsync(name);
categoriesToTags.Add(cat, tag.Id);
}
}
for (int i = 0; i < imageSet.traningSet.Values.Count; i++)
{
var image = imageSet.traningSet.Values.ElementAt(i);
List <Region> regions = null;
IList <Guid> tags = null;
if (isDetectionModel)
{
regions = new List <Region>();
foreach (var bb in image.bounding_boxes)
{
Region region = new Region();
region.TagId = categoriesToTags[bb.category_id];
if (!tagsInBatch.Contains(region.TagId))
{
tagsInBatch.Add(region.TagId);
}
// normalized bounding box
region.Left = bb.bboxArray[0] / image.imageWidth;
region.Width = bb.bboxArray[2] / image.imageWidth;
region.Top = bb.bboxArray[1] / image.imageHeight;
region.Height = bb.bboxArray[3] / image.imageHeight;
//x1, x2, y1, y2
//[bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
regions.Add(region);
}
if (regions.Count == 0)
{
continue;
}
}
else
{
tags = image.bounding_boxes.Select(bb => categoriesToTags[bb.category_id]).ToList();
if (tags.Count == 0)
{
continue;
}
foreach (var t in tags)
{
if (!tagsInBatch.Contains(t))
{
tagsInBatch.Add(t);
}
}
}
// Traning batch cannot have more than 20 different tags (across all regions)
bool tooManyTags = false;
if (tagsInBatch.Count > 19)
{
//If more than 20 tags, "rewind" one image back, we will add this image into the next batch
i--;
tooManyTags = true;
}
if (!tooManyTags) // if not too many tags with this image included, add this image
{
ImageUrlCreateEntry entry =
isDetectionModel ? new ImageUrlCreateEntry(image.coco_url, null, regions) :
new ImageUrlCreateEntry(image.coco_url, tags);
imageUrlEntries.Add(entry);
}
// 64 is maximim batch size for Custom Vision, 20 is maximum number of distinct tags per batch
// If exceeds, add this batch to the list, and start a new batch
if (imageUrlEntries.Count > 63 || tooManyTags)
{
imageUrlBatches.Add(imageUrlEntries);
tagsInBatch.Clear();
imageUrlEntries = new List <ImageUrlCreateEntry>();
}
}
if (imageUrlEntries.Count > 0)
{
imageUrlBatches.Add(imageUrlEntries);
}
foreach (var batch in imageUrlBatches)
{
if (batch.Count == 0)
{
continue;
}
var createImagesResult = await configuration.trainingApi.CreateImagesFromUrlsWithHttpMessagesAsync(configuration.project.Id,
new ImageUrlCreateBatch(batch));
if (createImagesResult.Response.IsSuccessStatusCode)
{
if (!createImagesResult.Body.IsBatchSuccessful)
{
//Maybe it's actually OK or we submitted duplicates (OKDuplicate Status)
var notOKImages = createImagesResult.Body.Images.Where(im => (im.Status != "OKDuplicate" && im.Status != "OK" &&
im.Status != "ErrorTagLimitExceed")); // Custom Vision may decline an image with too many regions, but reports it as too many tags
if (notOKImages != null && notOKImages.Count() > 0)
{
var message = await createImagesResult.Response.Content.ReadAsStringAsync();
throw new Exception($"Failed to create a trainig image batch (batch is not successful). Result:\n {message}");
}
else
{
var tooManyTags = createImagesResult.Body.Images.Where(im => (im.Status == "ErrorTagLimitExceed"));
if (tooManyTags != null)
{
imagesIgnored += tooManyTags.Count();
}
}
}
}
else
{
var message = createImagesResult.Response.Content.ReadAsStringAsync();
throw new Exception($"Failed to create a trainig image batch ({createImagesResult.Response.ReasonPhrase}).");
}
}
return(imagesIgnored);
}
