PaddleX/paddlex/inference/pipelines_new/layout_parsing/pipeline.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Any, Dict, Optional
import os, sys
import numpy as np
import cv2
from ..base import BasePipeline
from .utils import get_sub_regions_ocr_res
from ..components import convert_points_to_boxes
from .result import LayoutParsingResult
from ....utils import logging
from ...utils.pp_option import PaddlePredictorOption
from ...common.reader import ReadImage
from ...common.batch_sampler import ImageBatchSampler
from ..ocr.result import OCRResult
from ..doc_preprocessor.result import DocPreprocessorResult

# [TODO] 待更新models_new到models
from ...models_new.object_detection.result import DetResult


class LayoutParsingPipeline(BasePipeline):
    """Layout Parsing Pipeline"""

    entities = ["layout_parsing"]

    def __init__(
        self,
        config: Dict,
        device: str = None,
        pp_option: PaddlePredictorOption = None,
        use_hpip: bool = False,
        hpi_params: Optional[Dict[str, Any]] = None,
    ) -> None:
        """Initializes the layout parsing pipeline.

        Args:
            config (Dict): Configuration dictionary containing various settings.
            device (str, optional): Device to run the predictions on. Defaults to None.
            pp_option (PaddlePredictorOption, optional): PaddlePredictor options. Defaults to None.
            use_hpip (bool, optional): Whether to use high-performance inference (hpip) for prediction. Defaults to False.
            hpi_params (Optional[Dict[str, Any]], optional): HPIP parameters. Defaults to None.
        """

        super().__init__(
            device=device, pp_option=pp_option, use_hpip=use_hpip, hpi_params=hpi_params
        )

        self.inintial_predictor(config)

        self.batch_sampler = ImageBatchSampler(batch_size=1)

        self.img_reader = ReadImage(format="BGR")

    def set_used_models_flag(self, config: Dict) -> None:
        """
        Set the flags for which models to use based on the configuration.

        Args:
            config (Dict): A dictionary containing configuration settings.

        Returns:
            None
        """
        pipeline_name = config["pipeline_name"]

        self.pipeline_name = pipeline_name

        self.use_doc_preprocessor = False
        self.use_general_ocr = False
        self.use_seal_recognition = False
        self.use_table_recognition = False

        if "use_doc_preprocessor" in config:
            self.use_doc_preprocessor = config["use_doc_preprocessor"]

        if "use_general_ocr" in config:
            self.use_general_ocr = config["use_general_ocr"]

        if "use_seal_recognition" in config:
            self.use_seal_recognition = config["use_seal_recognition"]

        if "use_table_recognition" in config:
            self.use_table_recognition = config["use_table_recognition"]

    def inintial_predictor(self, config: Dict) -> None:
        """Initializes the predictor based on the provided configuration.

        Args:
            config (Dict): A dictionary containing the configuration for the predictor.

        Returns:
            None
        """

        self.set_used_models_flag(config)

        layout_det_config = config["SubModules"]["LayoutDetection"]
        self.layout_det_model = self.create_model(layout_det_config)

        if self.use_doc_preprocessor:
            doc_preprocessor_config = config["SubPipelines"]["DocPreprocessor"]
            self.doc_preprocessor_pipeline = self.create_pipeline(
                doc_preprocessor_config
            )

        if self.use_general_ocr or self.use_table_recognition:
            general_ocr_config = config["SubPipelines"]["GeneralOCR"]
            self.general_ocr_pipeline = self.create_pipeline(general_ocr_config)

        if self.use_seal_recognition:
            seal_recognition_config = config["SubPipelines"]["SealRecognition"]
            self.seal_recognition_pipeline = self.create_pipeline(
                seal_recognition_config
            )

        if self.use_table_recognition:
            table_recognition_config = config["SubPipelines"]["TableRecognition"]
            self.table_recognition_pipeline = self.create_pipeline(
                table_recognition_config
            )

        return

    def get_text_paragraphs_ocr_res(
        self, overall_ocr_res: OCRResult, layout_det_res: DetResult
    ) -> OCRResult:
        """
        Retrieves the OCR results for text paragraphs, excluding those of formulas, tables, and seals.

        Args:
            overall_ocr_res (OCRResult): The overall OCR result containing text information.
            layout_det_res (DetResult): The detection result containing the layout information of the document.

        Returns:
            OCRResult: The OCR result for text paragraphs after excluding formulas, tables, and seals.
        """
        object_boxes = []
        for box_info in layout_det_res["boxes"]:
            if box_info["label"].lower() in ["formula", "table", "seal"]:
                object_boxes.append(box_info["coordinate"])
        object_boxes = np.array(object_boxes)
        return get_sub_regions_ocr_res(overall_ocr_res, object_boxes, flag_within=False)

    def check_input_params_valid(self, input_params: Dict) -> bool:
        """
        Check if the input parameters are valid based on the initialized models.

        Args:
            input_params (Dict): A dictionary containing input parameters.

        Returns:
            bool: True if all required models are initialized according to input parameters, False otherwise.
        """

        if input_params["use_doc_preprocessor"] and not self.use_doc_preprocessor:
            logging.error(
                "Set use_doc_preprocessor, but the models for doc preprocessor are not initialized."
            )
            return False

        if input_params["use_general_ocr"] and not self.use_general_ocr:
            logging.error(
                "Set use_general_ocr, but the models for general OCR are not initialized."
            )
            return False

        if input_params["use_seal_recognition"] and not self.use_seal_recognition:
            logging.error(
                "Set use_seal_recognition, but the models for seal recognition are not initialized."
            )
            return False

        if input_params["use_table_recognition"] and not self.use_table_recognition:
            logging.error(
                "Set use_table_recognition, but the models for table recognition are not initialized."
            )
            return False

        return True

    def predict_doc_preprocessor_res(
        self, image_array: np.ndarray, input_params: dict
    ) -> tuple[DocPreprocessorResult, np.ndarray]:
        """
        Preprocess the document image based on input parameters.

        Args:
            image_array (np.ndarray): The input image array.
            input_params (dict): Dictionary containing preprocessing parameters.

        Returns:
            tuple[DocPreprocessorResult, np.ndarray]: A tuple containing the preprocessing
                                              result dictionary and the processed image array.
        """
        if input_params["use_doc_preprocessor"]:
            use_doc_orientation_classify = input_params["use_doc_orientation_classify"]
            use_doc_unwarping = input_params["use_doc_unwarping"]
            doc_preprocessor_res = next(
                self.doc_preprocessor_pipeline(
                    image_array,
                    use_doc_orientation_classify=use_doc_orientation_classify,
                    use_doc_unwarping=use_doc_unwarping,
                )
            )
            doc_preprocessor_image = doc_preprocessor_res["output_img"]
        else:
            doc_preprocessor_res = {}
            doc_preprocessor_image = image_array
        return doc_preprocessor_res, doc_preprocessor_image

    def predict_overall_ocr_res(self, image_array: np.ndarray) -> OCRResult:
        """
        Predict the overall OCR result for the given image array.

        Args:
            image_array (np.ndarray): The input image array to perform OCR on.

        Returns:
            OCRResult: The predicted OCR result with updated dt_boxes.
        """
        overall_ocr_res = next(self.general_ocr_pipeline(image_array))
        dt_boxes = convert_points_to_boxes(overall_ocr_res["dt_polys"])
        overall_ocr_res["dt_boxes"] = dt_boxes
        return overall_ocr_res

    def predict(
        self,
        input: str | list[str] | np.ndarray | list[np.ndarray],
        use_doc_orientation_classify: bool = False,
        use_doc_unwarping: bool = False,
        use_general_ocr: bool = True,
        use_seal_recognition: bool = True,
        use_table_recognition: bool = True,
        **kwargs
    ) -> LayoutParsingResult:
        """
        This function predicts the layout parsing result for the given input.

        Args:
            input (str | list[str] | np.ndarray | list[np.ndarray]): The input image(s) or pdf(s) to be processed.
            use_doc_orientation_classify (bool): Whether to use document orientation classification.
            use_doc_unwarping (bool): Whether to use document unwarping.
            use_general_ocr (bool): Whether to use general OCR.
            use_seal_recognition (bool): Whether to use seal recognition.
            use_table_recognition (bool): Whether to use table recognition.
            **kwargs: Additional keyword arguments.

        Returns:
            LayoutParsingResult: The predicted layout parsing result.
        """

        input_params = {
            "use_doc_preprocessor": self.use_doc_preprocessor,
            "use_doc_orientation_classify": use_doc_orientation_classify,
            "use_doc_unwarping": use_doc_unwarping,
            "use_general_ocr": use_general_ocr,
            "use_seal_recognition": use_seal_recognition,
            "use_table_recognition": use_table_recognition,
        }

        if use_doc_orientation_classify or use_doc_unwarping:
            input_params["use_doc_preprocessor"] = True
        else:
            input_params["use_doc_preprocessor"] = False

        if not self.check_input_params_valid(input_params):
            yield None

        for img_id, batch_data in enumerate(self.batch_sampler(input)):
            image_array = self.img_reader(batch_data)[0]
            img_id += 1

            doc_preprocessor_res, doc_preprocessor_image = (
                self.predict_doc_preprocessor_res(image_array, input_params)
            )

            layout_det_res = next(self.layout_det_model(doc_preprocessor_image))

            if input_params["use_general_ocr"] or input_params["use_table_recognition"]:
                overall_ocr_res = self.predict_overall_ocr_res(doc_preprocessor_image)
            else:
                overall_ocr_res = {}

            if input_params["use_general_ocr"]:
                text_paragraphs_ocr_res = self.get_text_paragraphs_ocr_res(
                    overall_ocr_res, layout_det_res
                )
            else:
                text_paragraphs_ocr_res = {}

            if input_params["use_table_recognition"]:
                table_res_list = next(
                    self.table_recognition_pipeline(
                        doc_preprocessor_image,
                        use_layout_detection=False,
                        use_doc_orientation_classify=False,
                        use_doc_unwarping=False,
                        overall_ocr_res=overall_ocr_res,
                        layout_det_res=layout_det_res,
                    )
                )
                table_res_list = table_res_list["table_res_list"]
            else:
                table_res_list = []

            if input_params["use_seal_recognition"]:
                seal_res_list = next(
                    self.seal_recognition_pipeline(
                        doc_preprocessor_image,
                        use_layout_detection=False,
                        use_doc_orientation_classify=False,
                        use_doc_unwarping=False,
                        layout_det_res=layout_det_res,
                    )
                )
                seal_res_list = seal_res_list["seal_res_list"]
            else:
                seal_res_list = []

            single_img_res = {
                "layout_det_res": layout_det_res,
                "doc_preprocessor_res": doc_preprocessor_res,
                "overall_ocr_res": overall_ocr_res,
                "text_paragraphs_ocr_res": text_paragraphs_ocr_res,
                "table_res_list": table_res_list,
                "seal_res_list": seal_res_list,
                "input_params": input_params,
                "img_id": img_id,
            }
            yield LayoutParsingResult(single_img_res)