Add depth estimation pipeline
### Feature request

We currently have 2 monocular depth estimation models in the library, namely [DPT](https://huggingface.co/docs/transformers/model_doc/dpt) and [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn).

It would be great to have a pipeline for this task, with the following API:

```
from transformers import pipeline

pipe = pipeline("depth-estimation")
pipe("cats.png")
```

This pipeline could default to the https://huggingface.co/Intel/dpt-large checkpoint. Also check out the [Space](https://huggingface.co/spaces/nielsr/dpt-depth-estimation) that showcases the model.

This can be implemented similar to [other pipelines](https://github.com/huggingface/transformers/tree/main/src/transformers/pipelines). For an example PR that added a pipeline, see https://github.com/huggingface/transformers/pull/11598.

### Motivation

Pipelines are a great way to quickly perform inference with a model for a given task, abstracting away all the complexity.

### Your contribution

I can assist with this, together with @Narsil.
What would be the output like @NielsRogge  ?

My understanding is that depth is just a gray scale image (black = infinitely far, white = infinitely close).


If that's the case It seems really close to `image-segmentation` in the sense that it's generating a new image from the original image, so we should try and reuse as much as possible.

Also maybe we could have something like `image-generation` to try and keep the name generic ? (And have an alias for `depth-estimation` for instance ?)
<|||||>Hi @NielsRogge I would like to add this pipeline. <|||||>Hi @Narsil,

I'm not sure whether we should add this to the existing `image-segmentation` pipeline. Depth estimation is basically pixel regression, rather than pixel classification (the latter is image segmentation). It would be quite confusing to add it there.

Depth estimation is quite a different field, see e.g. https://paperswithcode.com/task/depth-estimation

And hi @nandwalritik, thanks for your interest in this. Feel free to start a draft PR.<|||||>Thanks I will start working on it.<|||||>> I'm not sure whether we should add this to the existing image-segmentation pipeline.

I said we should inspire from it, not reuse it, but I suggested using an `image-generation`one. (Just to be slightly more general)
The output is a grayscale image, right ?