ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
Author: NielsRogge
State: open
Labels: name: bug

## Describe the bug

When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.

## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image

dataset = load_dataset("hf-internal-testing/example-documents")

# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")

new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```

## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:

```
import datasets

feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```

## Actual results

```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```


Comments: @mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:

```
from datasets import Dataset, Features, Image, Value
import pandas as pd
import requests
import PIL

# we need to define the features ourselves
features = Features({
    'a': Value(dtype='int32'),
    'b': Image(),
})

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = PIL.Image.open(requests.get(url, stream=True).raw)

df = pd.DataFrame({"a": [1, 2], 
                   "b": [image, image]})

dataset = Dataset.from_pandas(df, features=features) 
```
results in 

```
ArrowInvalid: ('Could not convert <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F7991A15C10> with type JpegImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column b with type object')
```

Will the PR linked above also fix that?

I would expect this to work, but it doesn't. Shouldn't be too hard to fix tho (in a subsequent PR).

Hi @mariosasko just wanted to check in if there is a PR to follow for this. I was looking to create a demo app using this. If it's not working I can just use byte encoded images in the dataset which are not displayed. 

Hi @darraghdog! No PR yet, but I plan to fix this before the next release.

I was just pointed here by @mariosasko, meanwhile I found a workaround using `encode_example` like so:

```
from datasets import load_from_disk, Dataset
DATASET_PATH = "/hf/m4-master/data/cm4/cm4-10000-v0.1"
ds1 = load_from_disk(DATASET_PATH)
ds2 = Dataset.from_dict(mapping={k: [] for k in ds1[99].keys()},
                       features=ds1.features
)
for i in range(2):
    # could add several representative items here
    row = ds1[99]
    row_encoded = ds2.features.encode_example(row)
    ds2 = ds2.add_item(row_encoded)
```

Hmm, interesting. If I create the dataset on the fly:

```
from datasets import load_from_disk, Dataset
DATASET_PATH = "/hf/m4-master/data/cm4/cm4-10000-v0.1"
ds1 = load_from_disk(DATASET_PATH)
ds2 = Dataset.from_dict(mapping={k: [v]*2 for k, v in ds1[99].items()},
                        features=ds1.features)
```

it doesn't fail with the error in the OP, as `from_dict` performs `encode_batch`.

However if I try to use this dataset it fails now with:

```
Traceback (most recent call last):
  File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 524, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/fingerprint.py", line 480, in wrapper
    out = func(self, *args, **kwargs)
  File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2775, in _map_single
    batch = apply_function_on_filtered_inputs(
  File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2655, in apply_function_on_filtered_inputs
    processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
  File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2347, in decorated
    result = f(decorated_item, *args, **kwargs)
  File "debug_leak2.py", line 235, in split_pack_and_pad
    images.append(image_transform(image.convert("RGB")))
AttributeError: 'dict' object has no attribute 'convert'
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "debug_leak2.py", line 418, in <module>
    train_loader, val_loader = get_dataloaders()
  File "debug_leak2.py", line 348, in get_dataloaders
    dataset = dataset.map(mapper, batch_size=32, batched=True, remove_columns=dataset.column_names, num_proc=4)
  File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2500, in map
    transformed_shards[index] = async_result.get()
  File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/multiprocess/pool.py", line 771, in get
    raise self._value
AttributeError: 'dict' object has no attribute 'convert'
```

but if I create that same dataset one item at a time as in the previous comment's code snippet it doesn't fail.

The features of this dataset are set to:

```
{'texts': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 
'images': Sequence(feature=Image(decode=True, id=None), length=-1, id=None)}
```

> @mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:
> 
> ```
> from datasets import Dataset, Features, Image, Value
> import pandas as pd
> import requests
> import PIL
> 
> # we need to define the features ourselves
> features = Features({
>     'a': Value(dtype='int32'),
>     'b': Image(),
> })
> 
> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
> image = PIL.Image.open(requests.get(url, stream=True).raw)
> 
> df = pd.DataFrame({"a": [1, 2], 
>                    "b": [image, image]})
> 
> dataset = Dataset.from_pandas(df, features=features) 
> ```
> 
> results in
> 
> ```
> ArrowInvalid: ('Could not convert <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F7991A15C10> with type JpegImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column b with type object')
> ```
> 
> Will the PR linked above also fix that?

It looks like the problem still exists.
Any news ? Any good workaround ?

Thank you

There is a workaround: 
Create a loader python scrypt and upload the dataset to huggingface.

Here is an example how to do that:

https://huggingface.co/datasets/jamescalam/image-text-demo/tree/main

and Here are videos with explanations:

https://www.youtube.com/watch?v=lqK4ocAKveE and https://www.youtube.com/watch?v=ODdKC30dT8c

cc @mariosasko gentle ping for a fix :)

Any update on this? I'm still facing this issure. Any workaround?

I was facing the same issue. Downgrading datasets from 2.11.0 to 2.4.0 solved the issue. 

> Any update on this? I'm still facing this issure. Any workaround?

I was able to resolve my issue with a quick workaround: 

```
from collections import defaultdict
from datasets import Dataset
  
data = defaultdict(list)
for idx in tqdm(range( len(dataloader)),desc="Captioning..."):
    img = dataloader[idx]
    data['image'].append(img)
    data['text'].append(f"{img_{idx}})
  
dataset = Dataset.from_dict(data)
dataset = dataset.filter(lambda example: example['image'] is not None)
dataset = dataset.filter(lambda example: example['text'] is not None)
  
dataset.push_to_hub(path-to-repo', private=False)
```

Hope it helps!
Happy coding

> > Any update on this? I'm still facing this issure. Any workaround?
> 
> I was able to resolve my issue with a quick workaround:
> 
> ```
> from collections import defaultdict
> from datasets import Dataset
>   
> data = defaultdict(list)
> for idx in tqdm(range( len(dataloader)),desc="Captioning..."):
>     img = dataloader[idx]
>     data['image'].append(img)
>     data['text'].append(f"{img_{idx}})
>   
> dataset = Dataset.from_dict(data)
> dataset = dataset.filter(lambda example: example['image'] is not None)
> dataset = dataset.filter(lambda example: example['text'] is not None)
>   
> dataset.push_to_hub(path-to-repo', private=False)
> ```
> 
> Hope it helps! Happy coding

It works!! 

> 

how did this work,  how to use this script or where to paste it?

I had a similar issue to @NielsRogge where I was unable to create a dataset from a Pandas DataFrame containing PIL.Images.

I found another workaround that works in this case which involves converting the DataFrame to a python dictionary, and then creating a dataset from said python dictionary.

This is a generic example of my workaround. The example assumes that you have your data in a Pandas DataFrame variable called "dataframe" plus a dictionary of your data's features in a variable called "features".
```
import datasets

dictionary = dataframe.to_dict(orient='list')
dataset = datasets.Dataset.from_dict(dictionary, features=features)
```

cc @mariosasko this issue has been open for 2 years, would be great to resolve it :)

I have the same issue, my current workaround is saving the dataframe to a csv and then loading the dataset from the csv. Would also appreciate it a fix :)

> data = defaultdict(list)

awesome, it really works~

I found something that can be used as solution.

I have the same problem when I've try to load the images from a pamdas dataset

If you have all on a pandas dataset try 
Dataset.from_dict( your_df.reset_index(drop=True).to_dict(orient='list'), split=set_your_split)

And this avoid the error