Docling/docling/models/rapid_ocr_model.py

import logging
from collections.abc import Iterable
from pathlib import Path
from typing import Optional, Type

import numpy
from docling_core.types.doc import BoundingBox, CoordOrigin
from docling_core.types.doc.page import BoundingRectangle, TextCell

from docling.datamodel.accelerator_options import AcceleratorDevice, AcceleratorOptions
from docling.datamodel.base_models import Page
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import (
    OcrOptions,
    RapidOcrOptions,
)
from docling.datamodel.settings import settings
from docling.models.base_ocr_model import BaseOcrModel
from docling.utils.accelerator_utils import decide_device
from docling.utils.profiling import TimeRecorder

_log = logging.getLogger(__name__)


class RapidOcrModel(BaseOcrModel):
    def __init__(
        self,
        enabled: bool,
        artifacts_path: Optional[Path],
        options: RapidOcrOptions,
        accelerator_options: AcceleratorOptions,
    ):
        super().__init__(
            enabled=enabled,
            artifacts_path=artifacts_path,
            options=options,
            accelerator_options=accelerator_options,
        )
        self.options: RapidOcrOptions

        self.scale = 3  # multiplier for 72 dpi == 216 dpi.

        if self.enabled:
            try:
                from rapidocr_onnxruntime import RapidOCR  # type: ignore
            except ImportError:
                raise ImportError(
                    "RapidOCR is not installed. Please install it via `pip install rapidocr_onnxruntime` to use this OCR engine. "
                    "Alternatively, Docling has support for other OCR engines. See the documentation."
                )

            # Decide the accelerator devices
            device = decide_device(accelerator_options.device)
            use_cuda = str(AcceleratorDevice.CUDA.value).lower() in device
            use_dml = accelerator_options.device == AcceleratorDevice.AUTO
            intra_op_num_threads = accelerator_options.num_threads

            self.reader = RapidOCR(
                text_score=self.options.text_score,
                cls_use_cuda=use_cuda,
                rec_use_cuda=use_cuda,
                det_use_cuda=use_cuda,
                det_use_dml=use_dml,
                cls_use_dml=use_dml,
                rec_use_dml=use_dml,
                intra_op_num_threads=intra_op_num_threads,
                print_verbose=self.options.print_verbose,
                det_model_path=self.options.det_model_path,
                cls_model_path=self.options.cls_model_path,
                rec_model_path=self.options.rec_model_path,
                rec_keys_path=self.options.rec_keys_path,
            )

    def __call__(
        self, conv_res: ConversionResult, page_batch: Iterable[Page]
    ) -> Iterable[Page]:
        if not self.enabled:
            yield from page_batch
            return

        for page in page_batch:
            assert page._backend is not None
            if not page._backend.is_valid():
                yield page
            else:
                with TimeRecorder(conv_res, "ocr"):
                    ocr_rects = self.get_ocr_rects(page)

                    all_ocr_cells = []
                    for ocr_rect in ocr_rects:
                        # Skip zero area boxes
                        if ocr_rect.area() == 0:
                            continue
                        high_res_image = page._backend.get_page_image(
                            scale=self.scale, cropbox=ocr_rect
                        )
                        im = numpy.array(high_res_image)
                        result, _ = self.reader(
                            im,
                            use_det=self.options.use_det,
                            use_cls=self.options.use_cls,
                            use_rec=self.options.use_rec,
                        )

                        del high_res_image
                        del im

                        if result is not None:
                            cells = [
                                TextCell(
                                    index=ix,
                                    text=line[1],
                                    orig=line[1],
                                    confidence=line[2],
                                    from_ocr=True,
                                    rect=BoundingRectangle.from_bounding_box(
                                        BoundingBox.from_tuple(
                                            coord=(
                                                (line[0][0][0] / self.scale)
                                                + ocr_rect.l,
                                                (line[0][0][1] / self.scale)
                                                + ocr_rect.t,
                                                (line[0][2][0] / self.scale)
                                                + ocr_rect.l,
                                                (line[0][2][1] / self.scale)
                                                + ocr_rect.t,
                                            ),
                                            origin=CoordOrigin.TOPLEFT,
                                        )
                                    ),
                                )
                                for ix, line in enumerate(result)
                            ]
                            all_ocr_cells.extend(cells)

                    # Post-process the cells
                    self.post_process_cells(all_ocr_cells, page)

                # DEBUG code:
                if settings.debug.visualize_ocr:
                    self.draw_ocr_rects_and_cells(conv_res, page, ocr_rects)

                yield page

    @classmethod
    def get_options_type(cls) -> Type[OcrOptions]:
        return RapidOcrOptions