图表富集

警告 本示例仅演示如何开发新的富集模型。它并不运行实际的图片分类模型。

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import logging
from collections.abc import Iterable
from pathlib import Path
from typing import Any

import logging from collections.abc import Iterable from pathlib import Path from typing import Any

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from docling_core.types.doc import (
    DoclingDocument,
    NodeItem,
    PictureClassificationClass,
    PictureClassificationData,
    PictureItem,
)

from docling_core.types.doc import ( DoclingDocument, NodeItem, PictureClassificationClass, PictureClassificationData, PictureItem, )

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from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.models.base_model import BaseEnrichmentModel
from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline

from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import PdfPipelineOptions from docling.document_converter import DocumentConverter, PdfFormatOption from docling.models.base_model import BaseEnrichmentModel from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline

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class ExamplePictureClassifierPipelineOptions(PdfPipelineOptions):
    do_picture_classifer: bool = True

class ExamplePictureClassifierPipelineOptions(PdfPipelineOptions): do_picture_classifer: bool = True

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class ExamplePictureClassifierEnrichmentModel(BaseEnrichmentModel):
    def __init__(self, enabled: bool):
        self.enabled = enabled

    def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
        return self.enabled and isinstance(element, PictureItem)

    def __call__(
        self, doc: DoclingDocument, element_batch: Iterable[NodeItem]
    ) -> Iterable[Any]:
        if not self.enabled:
            return

        for element in element_batch:
            assert isinstance(element, PictureItem)

            # uncomment this to interactively visualize the image
            # element.get_image(doc).show()

            element.annotations.append(
                PictureClassificationData(
                    provenance="example_classifier-0.0.1",
                    predicted_classes=[
                        PictureClassificationClass(class_name="dummy", confidence=0.42)
                    ],
                )
            )

            yield element

class ExamplePictureClassifierEnrichmentModel(BaseEnrichmentModel): def __init__(self, enabled: bool): self.enabled = enabled def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool: return self.enabled and isinstance(element, PictureItem) def __call__( self, doc: DoclingDocument, element_batch: Iterable[NodeItem] ) -> Iterable[Any]: if not self.enabled: return for element in element_batch: assert isinstance(element, PictureItem) # uncomment this to interactively visualize the image # element.get_image(doc).show() element.annotations.append( PictureClassificationData( provenance="example_classifier-0.0.1", predicted_classes=[ PictureClassificationClass(class_name="dummy", confidence=0.42) ], ) ) yield element

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class ExamplePictureClassifierPipeline(StandardPdfPipeline):
    def __init__(self, pipeline_options: ExamplePictureClassifierPipelineOptions):
        super().__init__(pipeline_options)
        self.pipeline_options: ExamplePictureClassifierPipeline

        self.enrichment_pipe = [
            ExamplePictureClassifierEnrichmentModel(
                enabled=pipeline_options.do_picture_classifer
            )
        ]

    @classmethod
    def get_default_options(cls) -> ExamplePictureClassifierPipelineOptions:
        return ExamplePictureClassifierPipelineOptions()

class ExamplePictureClassifierPipeline(StandardPdfPipeline): def __init__(self, pipeline_options: ExamplePictureClassifierPipelineOptions): super().__init__(pipeline_options) self.pipeline_options: ExamplePictureClassifierPipeline self.enrichment_pipe = [ ExamplePictureClassifierEnrichmentModel( enabled=pipeline_options.do_picture_classifer ) ] @classmethod def get_default_options(cls) -> ExamplePictureClassifierPipelineOptions: return ExamplePictureClassifierPipelineOptions()

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def main():
    logging.basicConfig(level=logging.INFO)

    input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")

    pipeline_options = ExamplePictureClassifierPipelineOptions()
    pipeline_options.images_scale = 2.0
    pipeline_options.generate_picture_images = True

    doc_converter = DocumentConverter(
        format_options={
            InputFormat.PDF: PdfFormatOption(
                pipeline_cls=ExamplePictureClassifierPipeline,
                pipeline_options=pipeline_options,
            )
        }
    )
    result = doc_converter.convert(input_doc_path)

    for element, _level in result.document.iterate_items():
        if isinstance(element, PictureItem):
            print(
                f"The model populated the `data` portion of picture {element.self_ref}:\n{element.annotations}"
            )

def main(): logging.basicConfig(level=logging.INFO) input_doc_path = Path("./tests/data/pdf/2206.01062.pdf") pipeline_options = ExamplePictureClassifierPipelineOptions() pipeline_options.images_scale = 2.0 pipeline_options.generate_picture_images = True doc_converter = DocumentConverter( format_options={ InputFormat.PDF: PdfFormatOption( pipeline_cls=ExamplePictureClassifierPipeline, pipeline_options=pipeline_options, ) } ) result = doc_converter.convert(input_doc_path) for element, _level in result.document.iterate_items(): if isinstance(element, PictureItem): print( f"The model populated the `data` portion of picture {element.self_ref}:\n{element.annotations}" )

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if __name__ == "__main__":
    main()

if __name__ == "__main__": main()