System information

• OS version/distro: Windows 10.0.17134
• .NET Version (eg., dotnet --info): .NET Core 2.2.202

Issue

• What did you do?
  My intention was to implement object detection in video. Please see attached code sample and explanation below.
• What happened?
  I wasn't able to find proper API methods for configuring MLContext pipeline.
• What did you expect?
  I expected it should be possible to pass individual in-memory representation of image(as a Bitmap object for example) into pipeline.

Source code / logs

Let's pretend we have a task to run object detection model on frames coming from video stream.
It seems natural to pass 1 frame one by one as they arriving from camera.

I can't find such possibility with current MLContext pipeline and API available.

Here's example pipeline from machinelearning-samples :

            var pipeline = mlContext.Transforms.LoadImages(outputColumnName: "image", imageFolder: imagesFolder, inputColumnName: nameof(ImageNetData.ImagePath))
                            .Append(mlContext.Transforms.ResizeImages(outputColumnName: "image", imageWidth: ImageNetSettings.imageWidth, imageHeight: ImageNetSettings.imageHeight, inputColumnName: "image"))
                            .Append(mlContext.Transforms.ExtractPixels(outputColumnName: "image"))
                            .Append(mlContext.Transforms.ApplyOnnxModel(modelFile: modelLocation, outputColumnNames: new[] { TinyYoloModelSettings.ModelOutput }, inputColumnNames: new[] { TinyYoloModelSettings.ModelInput }));

Having LoadImages extension method overload accepting in-memory image representation (Bitmap or whatever) would definetly solve a problem:

public static ImageLoadingEstimator LoadImages(this TransformsCatalog catalog, params Bitmap[] images);

Am I missing some way implement this without storing frames onto the disk? Or does it mean ML.NET wasn't designed for realtime video processing?

Thanks in advance.