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import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.download_config import DownloadConfig
from ..table import array_cast
from ..utils.file_utils import is_local_path, xopen
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_IMAGE_COMPRESSION_FORMATS: Optional[list[str]] = None
_NATIVE_BYTEORDER = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_VALID_IMAGE_ARRAY_DTPYES = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class Image:
"""Image [`Feature`] to read image data from an image file.
Input: The Image feature accepts as input:
- A `str`: Absolute path to the image file (i.e. random access is allowed).
- A `dict` with the keys:
- `path`: String with relative path of the image file to the archive file.
- `bytes`: Bytes of the image file.
This is useful for archived files with sequential access.
- An `np.ndarray`: NumPy array representing an image.
- A `PIL.Image.Image`: PIL image object.
Args:
mode (`str`, *optional*):
The mode to convert the image to. If `None`, the native mode of the image is used.
decode (`bool`, defaults to `True`):
Whether to decode the image data. If `False`,
returns the underlying dictionary in the format `{"path": image_path, "bytes": image_bytes}`.
Examples:
```py
>>> from datasets import load_dataset, Image
>>> ds = load_dataset("AI-Lab-Makerere/beans", split="train")
>>> ds.features["image"]
Image(decode=True, id=None)
>>> ds[0]["image"]
<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x500 at 0x15E52E7F0>
>>> ds = ds.cast_column('image', Image(decode=False))
{'bytes': None,
'path': '/root/.cache/huggingface/datasets/downloads/extracted/b0a21163f78769a2cf11f58dfc767fb458fc7cea5c05dccc0144a2c0f0bc1292/train/healthy/healthy_train.85.jpg'}
```
"""
mode: Optional[str] = None
decode: bool = True
id: Optional[str] = None
# Automatically constructed
dtype: ClassVar[str] = "PIL.Image.Image"
pa_type: ClassVar[Any] = pa.struct({"bytes": pa.binary(), "path": pa.string()})
_type: str = field(default="Image", init=False, repr=False)
def __call__(self):
return self.pa_type
def encode_example(self, value: Union[str, bytes, bytearray, dict, np.ndarray, "PIL.Image.Image"]) -> dict:
"""Encode example into a format for Arrow.
Args:
value (`str`, `np.ndarray`, `PIL.Image.Image` or `dict`):
Data passed as input to Image feature.
Returns:
`dict` with "path" and "bytes" fields
"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'.")
if isinstance(value, list):
value = np.array(value)
if isinstance(value, str):
return {"path": value, "bytes": None}
elif isinstance(value, (bytes, bytearray)):
return {"path": None, "bytes": value}
elif isinstance(value, np.ndarray):
# convert the image array to PNG/TIFF bytes
return encode_np_array(value)
elif isinstance(value, PIL.Image.Image):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(value)
elif value.get("path") is not None and os.path.isfile(value["path"]):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("path")}
elif value.get("bytes") is not None or value.get("path") is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("bytes"), "path": value.get("path")}
else:
raise ValueError(
f"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."
)
def decode_example(self, value: dict, token_per_repo_id=None) -> "PIL.Image.Image":
"""Decode example image file into image data.
Args:
value (`str` or `dict`):
A string with the absolute image file path, a dictionary with
keys:
- `path`: String with absolute or relative image file path.
- `bytes`: The bytes of the image file.
token_per_repo_id (`dict`, *optional*):
To access and decode
image files from private repositories on the Hub, you can pass
a dictionary repo_id (`str`) -> token (`bool` or `str`).
Returns:
`PIL.Image.Image`
"""
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead.")
if config.PIL_AVAILABLE:
import PIL.Image
import PIL.ImageOps
else:
raise ImportError("To support decoding images, please install 'Pillow'.")
if token_per_repo_id is None:
token_per_repo_id = {}
path, bytes_ = value["path"], value["bytes"]
if bytes_ is None:
if path is None:
raise ValueError(f"An image should have one of 'path' or 'bytes' but both are None in {value}.")
else:
if is_local_path(path):
image = PIL.Image.open(path)
else:
source_url = path.split("::")[-1]
pattern = (
config.HUB_DATASETS_URL
if source_url.startswith(config.HF_ENDPOINT)
else config.HUB_DATASETS_HFFS_URL
)
source_url_fields = string_to_dict(source_url, pattern)
token = (
token_per_repo_id.get(source_url_fields["repo_id"]) if source_url_fields is not None else None
)
download_config = DownloadConfig(token=token)
with xopen(path, "rb", download_config=download_config) as f:
bytes_ = BytesIO(f.read())
image = PIL.Image.open(bytes_)
else:
image = PIL.Image.open(BytesIO(bytes_))
image.load() # to avoid "Too many open files" errors
if image.getexif().get(PIL.Image.ExifTags.Base.Orientation) is not None:
image = PIL.ImageOps.exif_transpose(image)
if self.mode and self.mode != image.mode:
image = image.convert(self.mode)
return image
def flatten(self) -> Union["FeatureType", dict[str, "FeatureType"]]:
"""If in the decodable state, return the feature itself, otherwise flatten the feature into a dictionary."""
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("binary"),
"path": Value("string"),
}
)
def cast_storage(self, storage: Union[pa.StringArray, pa.StructArray, pa.ListArray]) -> pa.StructArray:
"""Cast an Arrow array to the Image arrow storage type.
The Arrow types that can be converted to the Image pyarrow storage type are:
- `pa.string()` - it must contain the "path" data
- `pa.binary()` - it must contain the image bytes
- `pa.struct({"bytes": pa.binary()})`
- `pa.struct({"path": pa.string()})`
- `pa.struct({"bytes": pa.binary(), "path": pa.string()})` - order doesn't matter
- `pa.list(*)` - it must contain the image array data
Args:
storage (`Union[pa.StringArray, pa.StructArray, pa.ListArray]`):
PyArrow array to cast.
Returns:
`pa.StructArray`: Array in the Image arrow storage type, that is
`pa.struct({"bytes": pa.binary(), "path": pa.string()})`.
"""
if pa.types.is_string(storage.type):
bytes_array = pa.array([None] * len(storage), type=pa.binary())
storage = pa.StructArray.from_arrays([bytes_array, storage], ["bytes", "path"], mask=storage.is_null())
elif pa.types.is_binary(storage.type):
path_array = pa.array([None] * len(storage), type=pa.string())
storage = pa.StructArray.from_arrays([storage, path_array], ["bytes", "path"], mask=storage.is_null())
elif pa.types.is_struct(storage.type):
if storage.type.get_field_index("bytes") >= 0:
bytes_array = storage.field("bytes")
else:
bytes_array = pa.array([None] * len(storage), type=pa.binary())
if storage.type.get_field_index("path") >= 0:
path_array = storage.field("path")
else:
path_array = pa.array([None] * len(storage), type=pa.string())
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=storage.is_null())
elif pa.types.is_list(storage.type):
bytes_array = pa.array(
[encode_np_array(np.array(arr))["bytes"] if arr is not None else None for arr in storage.to_pylist()],
type=pa.binary(),
)
path_array = pa.array([None] * len(storage), type=pa.string())
storage = pa.StructArray.from_arrays(
[bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null()
)
return array_cast(storage, self.pa_type)
def embed_storage(self, storage: pa.StructArray) -> pa.StructArray:
"""Embed image files into the Arrow array.
Args:
storage (`pa.StructArray`):
PyArrow array to embed.
Returns:
`pa.StructArray`: Array in the Image arrow storage type, that is
`pa.struct({"bytes": pa.binary(), "path": pa.string()})`.
"""
@no_op_if_value_is_null
def path_to_bytes(path):
with xopen(path, "rb") as f:
bytes_ = f.read()
return bytes_
bytes_array = pa.array(
[
(path_to_bytes(x["path"]) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
],
type=pa.binary(),
)
path_array = pa.array(
[os.path.basename(path) if path is not None else None for path in storage.field("path").to_pylist()],
type=pa.string(),
)
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
return array_cast(storage, self.pa_type)
def list_image_compression_formats() -> list[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'.")
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
_IMAGE_COMPRESSION_FORMATS = list(set(PIL.Image.OPEN.keys()) & set(PIL.Image.SAVE.keys()))
return _IMAGE_COMPRESSION_FORMATS
def image_to_bytes(image: "PIL.Image.Image") -> bytes:
"""Convert a PIL Image object to bytes using native compression if possible, otherwise use PNG/TIFF compression."""
buffer = BytesIO()
if image.format in list_image_compression_formats():
format = image.format
else:
format = "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
image.save(buffer, format=format)
return buffer.getvalue()
def encode_pil_image(image: "PIL.Image.Image") -> dict:
if hasattr(image, "filename") and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(image)}
def encode_np_array(array: np.ndarray) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'.")
dtype = array.dtype
dtype_byteorder = dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
dtype_kind = dtype.kind
dtype_itemsize = dtype.itemsize
dest_dtype = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
if dtype_kind not in ["u", "i"]:
raise TypeError(
f"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."
)
dest_dtype = np.dtype("|u1")
if dtype != dest_dtype:
warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'")
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
dest_dtype = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
dtype_str = dtype_byteorder + dtype_kind + str(dtype_itemsize)
if np.dtype(dtype_str) in _VALID_IMAGE_ARRAY_DTPYES:
dest_dtype = np.dtype(dtype_str)
warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'")
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
f"Cannot downcast dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"
)
image = PIL.Image.fromarray(array.astype(dest_dtype))
return {"path": None, "bytes": image_to_bytes(image)}
def objects_to_list_of_image_dicts(
objs: Union[list[str], list[dict], list[np.ndarray], list["PIL.Image.Image"]],
) -> list[dict]:
"""Encode a list of objects into a format suitable for creating an extension array of type `ImageExtensionType`."""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'.")
if objs:
_, obj = first_non_null_value(objs)
if isinstance(obj, str):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(obj, np.ndarray):
obj_to_image_dict_func = no_op_if_value_is_null(encode_np_array)
return [obj_to_image_dict_func(obj) for obj in objs]
elif isinstance(obj, PIL.Image.Image):
obj_to_image_dict_func = no_op_if_value_is_null(encode_pil_image)
return [obj_to_image_dict_func(obj) for obj in objs]
else:
return objs
else:
return objs
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