public ImageClassificationResult Classify(MemoryStream image)
{
// TODO: the classification depends on System.Drawing which needs some libraries to be installed in mac/linux
// There are alternatives to Bitmap, like ImageSharp, but would that work with ML.NET ????
// https://www.hanselman.com/blog/HowDoYouUseSystemDrawingInNETCore.aspx
// Can install with: brew install mono-libgdiplus
// Convert to Bitmap
Bitmap bitmapImage = (Bitmap)Image.FromStream(image);
// Set the specific image data into the ImageInputData type used in the DataView
ImageInputData imageInputData = new ImageInputData {
Image = bitmapImage
};
// Predict code for provided image
ImageLabelPredictions imageLabelPredictions = _predictionEnginePool.Predict(imageInputData);
// Predict the image's label (The one with highest probability)
float[] probabilities = imageLabelPredictions.PredictedLabels;
var maxProbability = probabilities.Max();
var maxProbabilityIndex = probabilities.AsSpan().IndexOf(maxProbability);
return(new ImageClassificationResult()
{
Label = _labels[maxProbabilityIndex],
Probability = maxProbability
});
}
public async Task <IActionResult> ClassifyImage(IFormFile imageFile)
{
if (imageFile.Length == 0)
{
return(BadRequest());
}
var imageMemoryStream = new MemoryStream();
await imageFile.CopyToAsync(imageMemoryStream);
// Check that the image is valid.
byte[] imageData = imageMemoryStream.ToArray();
if (!imageData.IsValidImage())
{
return(StatusCode(StatusCodes.Status415UnsupportedMediaType));
}
// Convert to Image.
Image image = Image.FromStream(imageMemoryStream);
// Convert to Bitmap.
Bitmap bitmapImage = (Bitmap)image;
_logger.LogInformation("Start processing image...");
// Measure execution time.
var watch = System.Diagnostics.Stopwatch.StartNew();
// Set the specific image data into the ImageInputData type used in the DataView.
var imageInputData = new ImageInputData {
Image = bitmapImage
};
// Predict code for provided image.
ImageLabelPredictions imageLabelPredictions = _predictionEnginePool.Predict(imageInputData);
// Stop measuring time.
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
_logger.LogInformation($"Image processed in {elapsedMs} miliseconds");
// Predict the image's label (The one with highest probability).
ImagePredictedLabelWithProbability imageBestLabelPrediction
= FindBestLabelWithProbability(imageLabelPredictions, imageInputData);
return(Ok(imageBestLabelPrediction));
}
private ImagePredictedLabelWithProbability FindBestLabelWithProbability(ImageLabelPredictions imageLabelPredictions, ImageInputData imageInputData)
{
// Read TF model's labels (labels.txt) to classify the image across those labels.
var labels = ReadLabels(_labelsFilePath);
float[] probabilities = imageLabelPredictions.PredictedLabels;
// Set a single label as predicted or even none if probabilities were lower than 70%.
var imageBestLabelPrediction = new ImagePredictedLabelWithProbability()
{
ImageId = imageInputData.GetHashCode().ToString(), //This ID is not really needed, it could come from the application itself, etc.
};
(imageBestLabelPrediction.PredictedLabel, imageBestLabelPrediction.Probability) = GetBestLabel(labels, probabilities);
return(imageBestLabelPrediction);
}
private ImagePredictedLabelWithProbability FindBestLabelWithProbability(ImageLabelPredictions imageLabelPredictions, ImageInputData imageInputData)
{
//Read TF model's labels (labels.txt) to classify the image across those labels
var labels = ReadLabels(_labelsFilePath);
float[] probabilities = imageLabelPredictions.PredictedLabels;
//Set a single label as predicted or even none if probabilities were lower than 70%
var imageBestLabelPrediction = new ImagePredictedLabelWithProbability()
{
ImagePath = imageInputData.ImagePath,
};
(imageBestLabelPrediction.PredictedLabel, imageBestLabelPrediction.Probability) = GetBestLabel(labels, probabilities);
return(imageBestLabelPrediction);
}
public async Task <IActionResult> ClassifyImage(IFormFile imageFile)
{
if (imageFile.Length == 0)
{
return(BadRequest());
}
//Save the temp image file into the temp-folder
IImageFileWriter imageWriter = new ImageFileWriter();
string fileName = await imageWriter.UploadImageAsync(imageFile, _tempImagesFolderPath);
string imageFilePath = Path.Combine(_tempImagesFolderPath, fileName);
//Convert image stream to byte[]
//
//byte[] imageData = null;
//MemoryStream image = new MemoryStream();
//await imageFile.CopyToAsync(image);
//imageData = image.ToArray();
//if (!imageData.IsValidImage())
// return StatusCode(StatusCodes.Status415UnsupportedMediaType);
_logger.LogInformation($"Start processing image...");
//Measure execution time
var watch = System.Diagnostics.Stopwatch.StartNew();
//Set the specific image data
var imageInputData = new ImageInputData {
ImagePath = imageFilePath
};
//Predict code for provided image
ImageLabelPredictions imageLabelPredictions = _predictionEnginePool.Predict(imageInputData);
//Stop measuring time
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
_logger.LogInformation($"Image processed in {elapsedMs} miliseconds");
//try
//{
// // DELETE FILE WHEN CLOSED
// using (FileStream fs = new FileStream(imageFilePath, FileMode.Open, FileAccess.Read, FileShare.Read, 4096, FileOptions.DeleteOnClose))
// {
// // temp file exists
// fs.Close();
// }
//}
//catch (Exception e)
//{
// throw e;
//}
//Predict the image's label (The one with highest probability)
ImagePredictedLabelWithProbability imageBestLabelPrediction
= FindBestLabelWithProbability(imageLabelPredictions, imageInputData);
//return new ObjectResult(result);
return(Ok(imageBestLabelPrediction));
}
