Discrepancy in `nyu_depth_v2` dataset
State: open

### Describe the bug

I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-side comparison,

![image](https://user-images.githubusercontent.com/36858976/214381162-1d9582c2-6750-4114-a01a-61ca1cd5f872.png)

I tried to find the origin of this issue but sadly as I mentioned in tensorflow/datasets/issues/4674, the download link from `fast-depth` doesn't work anymore hence couldn't verify if the error originated there or during porting data from there to HF.

Hi @sayakpaul, as you worked on huggingface/datasets/issues/5255, if you still have access to that data could you please share the data or perhaps checkout this issue?


### Steps to reproduce the bug

This [notebook](https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing#scrollTo=UEW7QSh0jf0i) from @sayakpaul  could be used to generate depth maps and actual ground truths could be checked from this  [dataset](https://www.kaggle.com/datasets/awsaf49/nyuv2-bts-dataset) from BTS repo.

> Note: BTS dataset has only 36K data compared to the train-test 50K. They sampled the data as adjacent frames look quite the same

### Expected behavior

Expected depth maps should be smooth rather than discrete/clipped.

### Environment info

- `datasets` version: 2.8.1.dev0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5

Ccing @dwofk (the author of `fast-depth`). 

Thanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed. 

If you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :) 

Good catch ! Ideally it would be nice to have the datasets in the raw form, this way users can choose whatever processing they want to apply

> Ccing @dwofk (the author of `fast-depth`).
> 
> Thanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed.
> 
> If you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :)

@sayakpaul I would love to create a PR on this. As this will be my first PR here, some guidance would be helpful.

Need a bit of advice on the dataset, there are three publicly available datasets. Which one should I consider for PR?
1. [BTS](https://github.com/cleinc/bts): Containst train/test: 36K/654 data, dtype = `uint16` hence more precise
2. [DenseDepth](https://github.com/ialhashim/DenseDepth) It contains train/test: 50K/654 data, dtype = `uint8` hence less precise
3. [Official](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html#raw_parts):  Size is big 400GB+, requires **MatLab** code for fixing **projection** and **sync**, DataType: `pgm` and `dump` hence can't be used directly.

cc: @lhoestq



I think BTS. Repositories like https://github.com/vinvino02/GLPDepth usually use BTS. Also, just for clarity, the PR will be to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. Once we have worked it out, we can update the following things:

* https://github.com/huggingface/blog/pull/718
* https://huggingface.co/docs/datasets/main/en/depth_estimation

Don't worry about it if it seems overwhelming. We will work it out together :) 

@lhoestq what do you think? 

@sayakpaul If I get this right I have to,
1. Create a PR on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2
2. Create a PR on https://github.com/huggingface/blog
3. Create a PR on https://github.com/huggingface/datasets to update https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx

The last two are low-hanging fruits. Don't worry about them. 

Yup opening a PR to use BTS on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 sounds good :) Thanks for the help !

Finally, I have found the origin of the **discretized depth map**. When I first loaded the datasets from HF I noticed it was 30GB but in DenseDepth data is only 4GB with dtype=uint8. This means data from fast-depth (before loading to HF) must have high precision. So when I tried to dig deeper by directly loading depth_map from `h5py`, I found depth_map from `h5py`  came with `float32`. But when the data is processed in HF with `datasets.Image()` it was directly converted to `uint8` from `float32`  hence the **discretized** depth map.
https://github.com/huggingface/datasets/blob/c78559cacbb0ca6e0bc8bfc313cc0359f8c23ead/src/datasets/features/image.py#L91-L93

## Solutions:

#### 1. Array2D
Use `Array2D` feature with `float32` for depth_map 

* Code:
```py
Features({'depth_map': Array2D(shape=(480, 640), dtype='float32')})
```
* Pros:
No precision loss.

* Cons:
As depth_map is saved as Array I think it can't be visuzlied in [hf.co/dataset](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) page like segmentation mask.

#### 2. Uint16
Use `uint16` as dtype for Image in `_h5_loader` for saving depth maps and accept `uint16` dtype in `datasets.Image()` feature.

* Code
```py
depth = np.array(h5f["depth"])
depth /= 10.0 # [0, max_depth] -> [0, 1]
depth *= (2**16 -1) # transform from [0, 1] -> [0, 2^16 - 1]
depth = depth.astype('uint16')
```
* Pros:
    * We can visualize depth map in hf.co/datasets page like segmentation mask.
    * No need for post-processing.

* Cons:
     * We need to make two change
          * Modify `_h5_loader` in https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 to convert depth_map from `float32` to `uint16`.
          * Make sure `datasets.Image()` converts `np.ndarray` to `uint16` checking max value
     * Precision loss due to `float32` to `uint16`
     * Post-processing required for depth_map to transform from `[0, 2^16 - 1]` to `[0, max_depth]`  before feeding them to model.

Thanks so much for digging into this. 

Since the second solution entails changes to core datatypes in `datasets`, I think it's better to go with the first solution. 

@lhoestq WDYT?

@sayakpaul Yes, Solution 1 requires minimal change and provides no precision loss. But I think support for `uint16` image would be a great addition as many datasets come with `uint16` image. For example [UW-Madison GI Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation) dataset, here the image itself comes with `uint16` dtype rather than mask. So, saving `uint16` image with `uint8` will result in precision loss.

Perhaps we can adapt solution 1 for this issue and Add support for `uint16` image separately?

Using Array2D makes it not practical to use to train a model - in `transformers` we expect an image type.

There is a pull request to support more precision than uint8 in Image() here: https://github.com/huggingface/datasets/pull/5365/files

we can probably merge it today and do a release right away

Fantastic, @lhoestq! 

@awsaf49 then let's wait for the PR to get merged and then take the next steps? 

Sure

The PR adds support for uint16 which is ok for BTS if I understand correctly, would it be ok for you ?

If the main issue with the current version of NYU we have on the Hub is related to the precision loss stemming from `Image()`, I'd prefer if `Image()` supported float32 as well. 

I also prefer `float32` as it offers more precision. But I'm not sure if we'll be able to visualize image with `float32` precision.

We could have a separate loading for the float32 one using Array2D, but I feel like it's less convenient to use due to the amount of disk space and because it's not an Image() type. That's why I think uint16 is a better solution for users

A bit confused here, If https://github.com/huggingface/datasets/pull/5365 gets merged won't this issue will be resolved automatically?

Yes in theory :)

actually float32 also seems to work in this PR (it just doesn't work for multi-channel)

In that case, a new PR isn't necessary, right?

Yep. I just tested from the PR and it works:
```python
>>> train_dataset = load_dataset("sayakpaul/nyu_depth_v2", split="train", streaming=True)                                                         
Downloading readme: 100%|██████████████████| 8.71k/8.71k [00:00<00:00, 3.60MB/s]
>>> next(iter(train_dataset))
{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=640x480 at 0x1382ED7F0>,
 'depth_map': <PIL.TiffImagePlugin.TiffImageFile image mode=F size=640x480 at 0x1382EDF28>}
>>> x = next(iter(train_dataset))
>>> np.asarray(x["depth_map"])                                             
array([[0.       , 0.       , 0.       , ..., 0.       , 0.       ,
        0.       ],
       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,
        0.       ],
       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,
        0.       ],
       ...,
       [0.       , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,
        0.       ],
       [0.       , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,
        0.       ],
       [0.       , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,
        0.       ]], dtype=float32)
```

Great! the case is closed! This issue has been solved and I have to say, it was quite the thrill ride. I felt like Sherlock Holmes, solving a mystery and finding the bug🕵️‍♂️. But in all seriousness, it was a pleasure working on this issue and I'm glad we could get to the bottom of it.

On another note, should I consider closing the issue? I think we still need to make updates on https://github.com/huggingface/blog and https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx

Haha thanks Mr Holmes :p

maybe let's close this issue when we're done updating the blog post and the documentation

@awsaf49 thank you for your hard work! 

I am a little unsure why the other links need to be updated, though. They all rely on datasets internally. 

I think depth_map still shows discretized version. It would be nice to have corrected one.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/depth_est_target_viz.png" width = 300>

Also, I think we need to make some changes in the code to visualize depth_map as it is `float32` . `plot.imshow()` supports either [0, 1] +  float32 or [0. 255]  + uint8

Oh yes! Do you want to start with the fixes? Please feel free to say no but I wanted to make sure your contributions are reflected properly in our doc and the blog :)

Yes I think that would be nice :)

I'll make the changes tomorrow. I hope it's okay...