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from pathlib import Path
from typing import Iterable, List, Literal, Optional, Tuple, Union
import numpy as np
from docling_core.types.doc import (
DoclingDocument,
NodeItem,
PictureClassificationClass,
PictureClassificationData,
PictureItem,
)
from PIL import Image
from pydantic import BaseModel
from docling.datamodel.pipeline_options import AcceleratorOptions
from docling.models.base_model import BaseEnrichmentModel
from docling.utils.accelerator_utils import decide_device
class DocumentPictureClassifierOptions(BaseModel):
"""
Options for configuring the DocumentPictureClassifier.
Attributes
----------
kind : Literal["document_picture_classifier"]
Identifier for the type of classifier.
"""
kind: Literal["document_picture_classifier"] = "document_picture_classifier"
class DocumentPictureClassifier(BaseEnrichmentModel):
"""
A model for classifying pictures in documents.
This class enriches document pictures with predicted classifications
based on a predefined set of classes.
Attributes
----------
enabled : bool
Whether the classifier is enabled for use.
options : DocumentPictureClassifierOptions
Configuration options for the classifier.
document_picture_classifier : DocumentPictureClassifierPredictor
The underlying prediction model, loaded if the classifier is enabled.
Methods
-------
__init__(enabled, artifacts_path, options, accelerator_options)
Initializes the classifier with specified configurations.
is_processable(doc, element)
Checks if the given element can be processed by the classifier.
__call__(doc, element_batch)
Processes a batch of elements and adds classification annotations.
"""
_model_repo_folder = "ds4sd--DocumentFigureClassifier"
images_scale = 2
def __init__(
self,
enabled: bool,
artifacts_path: Optional[Path],
options: DocumentPictureClassifierOptions,
accelerator_options: AcceleratorOptions,
):
"""
Initializes the DocumentPictureClassifier.
Parameters
----------
enabled : bool
Indicates whether the classifier is enabled.
artifacts_path : Optional[Union[Path, str]],
Path to the directory containing model artifacts.
options : DocumentPictureClassifierOptions
Configuration options for the classifier.
accelerator_options : AcceleratorOptions
Options for configuring the device and parallelism.
"""
self.enabled = enabled
self.options = options
if self.enabled:
device = decide_device(accelerator_options.device)
from docling_ibm_models.document_figure_classifier_model.document_figure_classifier_predictor import (
DocumentFigureClassifierPredictor,
)
if artifacts_path is None:
artifacts_path = self.download_models()
else:
artifacts_path = artifacts_path / self._model_repo_folder
self.document_picture_classifier = DocumentFigureClassifierPredictor(
artifacts_path=str(artifacts_path),
device=device,
num_threads=accelerator_options.num_threads,
)
@staticmethod
def download_models(
local_dir: Optional[Path] = None, force: bool = False, progress: bool = False
) -> Path:
from huggingface_hub import snapshot_download
from huggingface_hub.utils import disable_progress_bars
if not progress:
disable_progress_bars()
download_path = snapshot_download(
repo_id="ds4sd/DocumentFigureClassifier",
force_download=force,
local_dir=local_dir,
revision="v1.0.0",
)
return Path(download_path)
def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
"""
Determines if the given element can be processed by the classifier.
Parameters
----------
doc : DoclingDocument
The document containing the element.
element : NodeItem
The element to be checked.
Returns
-------
bool
True if the element is a PictureItem and processing is enabled; False otherwise.
"""
return self.enabled and isinstance(element, PictureItem)
def __call__(
self,
doc: DoclingDocument,
element_batch: Iterable[NodeItem],
) -> Iterable[NodeItem]:
"""
Processes a batch of elements and enriches them with classification predictions.
Parameters
----------
doc : DoclingDocument
The document containing the elements to be processed.
element_batch : Iterable[NodeItem]
A batch of pictures to classify.
Returns
-------
Iterable[NodeItem]
An iterable of NodeItem objects after processing. The field
'data.classification' is added containing the classification for each picture.
"""
if not self.enabled:
for element in element_batch:
yield element
return
images: List[Union[Image.Image, np.ndarray]] = []
elements: List[PictureItem] = []
for el in element_batch:
assert isinstance(el, PictureItem)
elements.append(el)
img = el.get_image(doc)
assert img is not None
images.append(img)
outputs = self.document_picture_classifier.predict(images)
for element, output in zip(elements, outputs):
element.annotations.append(
PictureClassificationData(
provenance="DocumentPictureClassifier",
predicted_classes=[
PictureClassificationClass(
class_name=pred[0],
confidence=pred[1],
)
for pred in output
],
)
)
yield element
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