Add ignore_decode_errors option to Image feature for robust decoding #7612
Opened by ArjunJagdale (open)

This PR implements support for robust image decoding in the `Image` feature, as discussed in issue #7612.

## 🔧 What was added
- A new boolean field: `ignore_decode_errors` (default: `False`)
- If set to `True`, any exceptions during decoding will be caught, and `None` will be returned instead of raising an error

```python
features = Features({
    "image": Image(decode=True, ignore_decode_errors=True),
})
````

This enables robust iteration over potentially corrupted datasets — especially useful when streaming datasets like WebDataset or image-heavy public sets where sample corruption is common.

## 🧪 Behavior

* If `ignore_decode_errors=False` (default), decoding behaves exactly as before
* If `True`, decoding errors are caught, and a warning is emitted:

  ```
  [Image.decode_example] Skipped corrupted image: ...
  ```

## 🧵 Linked issue

Closes #7612

Let me know if you'd like a follow-up test PR. Happy to write one!

Comments:
cc @lhoestq

I think splitting the error handling for the main image decoding process and the metadata decoding process is possibly a bit nicer, as some images do render correctly, but their metadata might be invalid and cause the pipeline to fail, which I've encountered recently as in #7668.

The [`decode_image`](https://docs.pytorch.org/vision/main/generated/torchvision.io.decode_image.html) function in `torchvision` handles similar cases by using the `apply_exif_orientation` flag to turn off the exif metadata processing entirely.

> I think splitting the error handling for the main image decoding process and the metadata decoding process is possibly a bit nicer, as some images do render correctly, but their metadata might be invalid and cause the pipeline to fail, which I've encountered recently as in #7668.
> The [`decode_image`](https://docs.pytorch.org/vision/main/generated/torchvision.io.decode_image.html) function in `torchvision` handles similar cases by using the `apply_exif_orientation` flag to turn off the exif metadata processing entirely.
 
 @lhoestq & @Seas0 — that makes total sense.
 
Currently, if EXIF metadata like `.getexif()` fails (due to malformed tags), the whole image gets dropped even if it renders correctly — not ideal.
 
To address this, I'm planning to split the EXIF handling into a separate `try/except` block, like:
```python
try:
    exif = image.getexif()
    if exif.get(PIL.Image.ExifTags.Base.Orientation) is not None:
        image = PIL.ImageOps.exif_transpose(image)
except Exception as exif_err:
    if self.ignore_decode_errors:
        warnings.warn(f"[Image.decode_example] Skipped EXIF metadata: {exif_err}")
    else:
        raise
```

So that, Valid but EXIF-broken images will still be returned & EXIF failures will be skipped only if ignore_decode_errors=True. 

Sounds good??

With the recent EXIF decoding isolation logic added, this PR now fully addresses:

- ✅ #7612 – Robust iteration over corrupt samples (especially useful in `.streaming=True`)
- ✅ #7632 – Graceful handling of invalid image files when using `cast_column(..., Image(...))`
- ✅ #7668 – Broken EXIF metadata no longer crashes decoding; images are returned if usable

All decoding errors (including `.getexif()` and image file loading) are now skipped with a warning when `ignore_decode_errors=True`. This enables safe, scalable image preprocessing pipelines.