Improve flexibility of input types for image classification

**Is your feature request related to a problem? Please describe.**
The image classifier in ML.NET currently accepts input in the form of a byte array (only certain file formats, e.g. png jpeg gif), which is flexible enough for many scenarios. However, it is sometimes the case that the source image originates in a System.Image.Bitmap, OpenCV mat, or other managed/unmanaged memory block not in png/jpeg/gif format. To perform a prediction with this sort of data, it must be converted which takes time and requires allocations for data that already exists.

In a production computer vision environment where image data is streamed in as System.Drawing.Bitmap objects whose RawFormat is MemoryBmp, it is undesirable to dedicate 25-30ms per image to save each Bitmap into a managed byte[] whose RawFormat is jpeg (80ms for png), if it can be avoided by an update to allow more direct consumption of other formats and datatypes.

**Describe the solution you'd like**
Direct consumption of System.Drawing.Bitmap (RawFormat=MemoryBmp) by ML.NET in a way which avoids managed allocations or format conversion would be ideal. Accepting an IntPtr to unmanaged pixel data would be fine as well, with support for bmp format.

**Describe alternatives you've considered**
Various methods to convert Bitmap data into byte[] were tested and are functional, but none achieves the performance desired.

**Additional context**
The general use case for this is for processing of images received via a camera which streams images in as Bitmap objects. Needing to convert this to managed memory in one of several formats is a bottleneck which would ideally be avoidable through the solution described above.