[WIP]feature: image filter

ms_agent/tools/docling/doc_loader.py

@@ -7,14 +7,18 @@
 from docling.datamodel.base_models import InputFormat
 from docling.datamodel.document import ConversionResult
 from docling.datamodel.pipeline_options import PdfPipelineOptions
-from docling.document_converter import DocumentConverter, PdfFormatOption
+from docling.document_converter import (DocumentConverter, PdfFormatOption,
+                                        PowerpointFormatOption,
+                                        WordFormatOption)
 from docling.models.document_picture_classifier import \
     DocumentPictureClassifier
 from docling.models.layout_model import LayoutModel
 from docling.models.table_structure_model import TableStructureModel
 from docling_core.types import DoclingDocument
 from docling_core.types.doc import DocItem
 from ms_agent.tools.docling.doc_postprocess import PostProcess
+from ms_agent.tools.docling.enrich_pipeline import (DocPipelineOptions,
+                                                    EnrichDocPipeline)
 from ms_agent.tools.docling.patches import (download_models_ms,
                                             download_models_pic_classifier_ms,
                                             html_handle_figure,
@@ -55,10 +59,30 @@ def __init__(self, verbose: bool = False):
         pdf_pipeline_options.images_scale = 2.0
         pdf_pipeline_options.accelerator_options = accelerator_options  # type: ignore
 
+        word_pipeline_options = DocPipelineOptions()
+        word_pipeline_options.do_picture_classification = True
+        word_pipeline_options.do_code_enrichment = False
+        word_pipeline_options.do_formula_enrichment = False
+        word_pipeline_options.accelerator_options = accelerator_options  # type: ignore
+
+        ppt_pipeline_options = DocPipelineOptions()
+        ppt_pipeline_options.do_picture_classification = True
+        ppt_pipeline_options.do_code_enrichment = False
+        ppt_pipeline_options.do_formula_enrichment = False
+        ppt_pipeline_options.accelerator_options = accelerator_options  # type: ignore
+
         self._converter = DocumentConverter(
             format_options={
                 InputFormat.PDF:
-                PdfFormatOption(pipeline_options=pdf_pipeline_options)
+                PdfFormatOption(pipeline_options=pdf_pipeline_options),
+                InputFormat.DOCX:
+                WordFormatOption(
+                    pipeline_cls=EnrichDocPipeline,
+                    pipeline_options=word_pipeline_options),
+                InputFormat.PPTX:
+                PowerpointFormatOption(
+                    pipeline_cls=EnrichDocPipeline,
+                    pipeline_options=ppt_pipeline_options)
             })
 
     @staticmethod

ms_agent/tools/docling/doc_postprocess.py

@@ -22,4 +22,14 @@ def filter(doc: DoclingDocument) -> Union[DoclingDocument, None]:
                 if pic_item.image.size.height * pic_item.image.size.width < PostProcess.MIN_PICTURE_SIZE:
                     pic_item.image = None  # Remove image if too small
 
+            if hasattr(pic_item, 'annotations') and pic_item.annotations:
+                pic_classes = getattr(pic_item.annotations[0],
+                                      'predicted_classes', None)
+                pic_class = pic_classes[
+                    0].class_name if pic_classes else None  # Get the first predicted class if available
+                if pic_class is not None and pic_class.lower() in [
+                        'qr_code', 'logo', 'icon'
+                ]:
+                    pic_item.image = None
+
         return doc

ms_agent/tools/docling/enrich_pipeline.py

@@ -0,0 +1,132 @@
+# yapf: disable
+from pathlib import Path
+from typing import Iterable, List, Optional, Union
+
+import numpy as np
+from docling.datamodel.accelerator_options import AcceleratorOptions
+from docling.datamodel.pipeline_options import PipelineOptions
+from docling.datamodel.settings import settings
+from docling.models.code_formula_model import (CodeFormulaModel,
+                                               CodeFormulaModelOptions)
+from docling.models.document_picture_classifier import (
+    DocumentPictureClassifier, DocumentPictureClassifierOptions)
+from docling.pipeline.simple_pipeline import SimplePipeline
+from docling_core.types.doc import (DoclingDocument, NodeItem,
+                                    PictureClassificationClass,
+                                    PictureClassificationData, PictureItem)
+from PIL import Image
+
+
+class DocPipelineOptions(PipelineOptions):
+    """Options for processing Word and PPT documents in the pipeline."""
+
+    artifacts_path: Optional[Union[Path, str]] = None
+    do_picture_classification: bool = False  # True: classify pictures in documents
+    do_code_enrichment: bool = False  # True: perform code OCR
+    do_formula_enrichment: bool = False  # True: perform formula OCR, return Latex code
+
+
+class EnrichDocumentPictureClassifier(DocumentPictureClassifier):
+    """
+    Specializes DocumentPictureClassifier for robust pipeline processing.
+
+    This classifier is designed to handle document formats like Word and PPT where
+    images might be missing or invalid. It overrides the default behavior to
+    skip elements with unreadable images instead of raising an error, thus
+    preventing the entire processing pipeline from halting.
+    """
+
+    def __init__(self, enabled: bool, artifacts_path: Optional[Path],
+                 options: DocumentPictureClassifierOptions,
+                 accelerator_options: AcceleratorOptions):
+        super().__init__(enabled, artifacts_path, options, accelerator_options)
+
+    def __call__(
+        self,
+        doc: DoclingDocument,
+        element_batch: Iterable[NodeItem],
+    ) -> Iterable[NodeItem]:
+        """
+        This method iterates through a batch of elements, extracts their images,
+        and applies the picture classification model. Unlike the base class
+        implementation, it gracefully handles cases where an image cannot be
+        retrieved (i.e., `get_image()` returns None) by skipping that element.
+        This ensures that a single faulty item does not stop the entire batch
+        processing.
+        """
+        if not self.enabled:
+            for element in element_batch:
+                yield element
+            return
+
+        images: List[Union[Image.Image, np.ndarray]] = []
+        elements_with_images: List[PictureItem] = []
+        element_batch_list: List[PictureItem] = list(element_batch)
+        for el in element_batch_list:
+            assert isinstance(
+                el, PictureItem), f'Element {el} is not a PictureItem'
+            img = el.get_image(doc)
+            if img is not None:
+                images.append(img)
+                elements_with_images.append(el)
+
+        if images:
+            outputs = self.document_picture_classifier.predict(images)
+            for element, output in zip(elements_with_images, outputs):
+                element.annotations.append(
+                    PictureClassificationData(
+                        provenance='DocumentPictureClassifier',
+                        predicted_classes=[
+                            PictureClassificationClass(
+                                class_name=pred[0],
+                                confidence=pred[1],
+                            ) for pred in output
+                        ],
+                    ))
+
+        for element in element_batch_list:
+            yield element
+
+
+class EnrichDocPipeline(SimplePipeline):
+    """Pipeline for enriching Word and PPT documents with additional processing steps."""
+
+    def __init__(self, pipeline_options: DocPipelineOptions):
+        super().__init__(pipeline_options)
+
+        artifacts_path: Optional[Path] = None
+        if pipeline_options.artifacts_path is not None:
+            artifacts_path = Path(pipeline_options.artifacts_path).expanduser()
+        elif settings.artifacts_path is not None:
+            artifacts_path = Path(settings.artifacts_path).expanduser()
+
+        if artifacts_path is not None and not artifacts_path.is_dir():
+            raise RuntimeError(
+                f'The value of {artifacts_path=} is not valid. '
+                'When defined, it must point to a folder containing all models required by the pipeline.'
+            )
+
+        self.enrichment_pipe = [
+            # Code Formula Enrichment Model
+            CodeFormulaModel(
+                enabled=pipeline_options.do_code_enrichment
+                or pipeline_options.do_formula_enrichment,
+                artifacts_path=artifacts_path,
+                options=CodeFormulaModelOptions(
+                    do_code_enrichment=pipeline_options.do_code_enrichment,
+                    do_formula_enrichment=pipeline_options.do_formula_enrichment,
+                ),
+                accelerator_options=pipeline_options.accelerator_options,
+            ),
+            # Document Picture Classifier
+            EnrichDocumentPictureClassifier(
+                enabled=pipeline_options.do_picture_classification,
+                artifacts_path=artifacts_path,
+                options=DocumentPictureClassifierOptions(),
+                accelerator_options=pipeline_options.accelerator_options,
+            )
+        ]
+
+    @classmethod
+    def get_default_options(cls) -> DocPipelineOptions:
+        return DocPipelineOptions()