PaddleX/paddlex/inference/pipelines/layout_parsing/result_v2.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 __future__ import annotations

import copy
from pathlib import Path
from typing import Dict

import cv2
import re
import numpy as np
from PIL import Image
from PIL import ImageDraw

from ...common.result import (
    BaseCVResult,
    HtmlMixin,
    JsonMixin,
    MarkdownMixin,
    StrMixin,
    XlsxMixin,
)
from .utils import get_layout_ordering
from .utils import recursive_img_array2path
from .utils import get_show_color


class LayoutParsingResultV2(BaseCVResult, HtmlMixin, XlsxMixin, MarkdownMixin):
    """Layout Parsing Result V2"""

    def __init__(self, data) -> None:
        """Initializes a new instance of the class with the specified data."""
        super().__init__(data)
        HtmlMixin.__init__(self)
        XlsxMixin.__init__(self)
        MarkdownMixin.__init__(self)
        JsonMixin.__init__(self)
        self.already_sorted = False

    def _to_img(self) -> dict[str, np.ndarray]:
        res_img_dict = {}
        model_settings = self["model_settings"]
        if model_settings["use_doc_preprocessor"]:
            res_img_dict.update(**self["doc_preprocessor_res"].img)
        res_img_dict["layout_det_res"] = self["layout_det_res"].img["res"]

        if model_settings["use_general_ocr"] or model_settings["use_table_recognition"]:
            res_img_dict["overall_ocr_res"] = self["overall_ocr_res"].img["ocr_res_img"]

        if model_settings["use_general_ocr"]:
            general_ocr_res = copy.deepcopy(self["overall_ocr_res"])
            general_ocr_res["rec_polys"] = self["text_paragraphs_ocr_res"]["rec_polys"]
            general_ocr_res["rec_texts"] = self["text_paragraphs_ocr_res"]["rec_texts"]
            general_ocr_res["rec_scores"] = self["text_paragraphs_ocr_res"][
                "rec_scores"
            ]
            general_ocr_res["rec_boxes"] = self["text_paragraphs_ocr_res"]["rec_boxes"]
            res_img_dict["text_paragraphs_ocr_res"] = general_ocr_res.img["ocr_res_img"]

        if model_settings["use_table_recognition"] and len(self["table_res_list"]) > 0:
            table_cell_img = Image.fromarray(
                copy.deepcopy(self["doc_preprocessor_res"]["output_img"])
            )
            table_draw = ImageDraw.Draw(table_cell_img)
            rectangle_color = (255, 0, 0)
            for sno in range(len(self["table_res_list"])):
                table_res = self["table_res_list"][sno]
                cell_box_list = table_res["cell_box_list"]
                for box in cell_box_list:
                    x1, y1, x2, y2 = [int(pos) for pos in box]
                    table_draw.rectangle(
                        [x1, y1, x2, y2], outline=rectangle_color, width=2
                    )
            res_img_dict["table_cell_img"] = table_cell_img

        if model_settings["use_seal_recognition"] and len(self["seal_res_list"]) > 0:
            for sno in range(len(self["seal_res_list"])):
                seal_res = self["seal_res_list"][sno]
                seal_region_id = seal_res["seal_region_id"]
                sub_seal_res_dict = seal_res.img
                key = f"seal_res_region{seal_region_id}"
                res_img_dict[key] = sub_seal_res_dict["ocr_res_img"]

        # if (
        #     model_settings["use_formula_recognition"]
        #     and len(self["formula_res_list"]) > 0
        # ):
        #     for sno in range(len(self["formula_res_list"])):
        #         formula_res = self["formula_res_list"][sno]
        #         formula_region_id = formula_res["formula_region_id"]
        #         sub_formula_res_dict = formula_res.img
        #         key = f"formula_res_region{formula_region_id}"
        #         res_img_dict[key] = sub_formula_res_dict["res"]

        return res_img_dict

    def _to_str(self, *args, **kwargs) -> dict[str, str]:
        """Converts the instance's attributes to a dictionary and then to a string.

        Args:
            *args: Additional positional arguments passed to the base class method.
            **kwargs: Additional keyword arguments passed to the base class method.

        Returns:
            Dict[str, str]: A dictionary with the instance's attributes converted to strings.
        """
        data = {}
        data["input_path"] = self["input_path"]
        model_settings = self["model_settings"]
        data["model_settings"] = model_settings
        if self["model_settings"]["use_doc_preprocessor"]:
            data["doc_preprocessor_res"] = self["doc_preprocessor_res"].str["res"]
        data["layout_det_res"] = self["layout_det_res"].str["res"]
        if model_settings["use_general_ocr"] or model_settings["use_table_recognition"]:
            data["overall_ocr_res"] = self["overall_ocr_res"].str["res"]
        if model_settings["use_general_ocr"]:
            general_ocr_res = {}
            general_ocr_res["rec_polys"] = self["text_paragraphs_ocr_res"]["rec_polys"]
            general_ocr_res["rec_texts"] = self["text_paragraphs_ocr_res"]["rec_texts"]
            general_ocr_res["rec_scores"] = self["text_paragraphs_ocr_res"][
                "rec_scores"
            ]
            general_ocr_res["rec_boxes"] = self["text_paragraphs_ocr_res"]["rec_boxes"]
            data["text_paragraphs_ocr_res"] = general_ocr_res
        if model_settings["use_table_recognition"] and len(self["table_res_list"]) > 0:
            data["table_res_list"] = []
            for sno in range(len(self["table_res_list"])):
                table_res = self["table_res_list"][sno]
                data["table_res_list"].append(table_res.str["res"])
        if model_settings["use_seal_recognition"] and len(self["seal_res_list"]) > 0:
            data["seal_res_list"] = []
            for sno in range(len(self["seal_res_list"])):
                seal_res = self["seal_res_list"][sno]
                data["seal_res_list"].append(seal_res.str["res"])
        if (
            model_settings["use_formula_recognition"]
            and len(self["formula_res_list"]) > 0
        ):
            data["formula_res_list"] = []
            for sno in range(len(self["formula_res_list"])):
                formula_res = self["formula_res_list"][sno]
                data["formula_res_list"].append(formula_res.str["res"])

        return JsonMixin._to_str(data, *args, **kwargs)

    def _to_json(self, *args, **kwargs) -> dict[str, str]:
        """
        Converts the object's data to a JSON dictionary.

        Args:
            *args: Positional arguments passed to the JsonMixin._to_json method.
            **kwargs: Keyword arguments passed to the JsonMixin._to_json method.

        Returns:
            Dict[str, str]: A dictionary containing the object's data in JSON format.
        """
        data = {}
        data["input_path"] = self["input_path"]
        model_settings = self["model_settings"]
        data["model_settings"] = model_settings
        if self["model_settings"]["use_doc_preprocessor"]:
            data["doc_preprocessor_res"] = self["doc_preprocessor_res"].json["res"]
        data["layout_det_res"] = self["layout_det_res"].json["res"]
        if model_settings["use_general_ocr"] or model_settings["use_table_recognition"]:
            data["overall_ocr_res"] = self["overall_ocr_res"].json["res"]
        if model_settings["use_general_ocr"]:
            general_ocr_res = {}
            general_ocr_res["rec_polys"] = self["text_paragraphs_ocr_res"]["rec_polys"]
            general_ocr_res["rec_texts"] = self["text_paragraphs_ocr_res"]["rec_texts"]
            general_ocr_res["rec_scores"] = self["text_paragraphs_ocr_res"][
                "rec_scores"
            ]
            general_ocr_res["rec_boxes"] = self["text_paragraphs_ocr_res"]["rec_boxes"]
            data["text_paragraphs_ocr_res"] = general_ocr_res
        if model_settings["use_table_recognition"] and len(self["table_res_list"]) > 0:
            data["table_res_list"] = []
            for sno in range(len(self["table_res_list"])):
                table_res = self["table_res_list"][sno]
                data["table_res_list"].append(table_res.json["res"])
        if model_settings["use_seal_recognition"] and len(self["seal_res_list"]) > 0:
            data["seal_res_list"] = []
            for sno in range(len(self["seal_res_list"])):
                seal_res = self["seal_res_list"][sno]
                data["seal_res_list"].append(seal_res.json["res"])
        if (
            model_settings["use_formula_recognition"]
            and len(self["formula_res_list"]) > 0
        ):
            data["formula_res_list"] = []
            for sno in range(len(self["formula_res_list"])):
                formula_res = self["formula_res_list"][sno]
                data["formula_res_list"].append(formula_res.json["res"])
        return JsonMixin._to_json(data, *args, **kwargs)

    def _to_html(self) -> dict[str, str]:
        """Converts the prediction to its corresponding HTML representation.

        Returns:
            Dict[str, str]: The str type HTML representation result.
        """
        model_settings = self["model_settings"]
        res_html_dict = {}
        if model_settings["use_table_recognition"] and len(self["table_res_list"]) > 0:
            for sno in range(len(self["table_res_list"])):
                table_res = self["table_res_list"][sno]
                table_region_id = table_res["table_region_id"]
                key = f"table_{table_region_id}"
                res_html_dict[key] = table_res.html["pred"]
        return res_html_dict

    def _to_xlsx(self) -> dict[str, str]:
        """Converts the prediction HTML to an XLSX file path.

        Returns:
            Dict[str, str]: The str type XLSX representation result.
        """
        model_settings = self["model_settings"]
        res_xlsx_dict = {}
        if model_settings["use_table_recognition"] and len(self["table_res_list"]) > 0:
            for sno in range(len(self["table_res_list"])):
                table_res = self["table_res_list"][sno]
                table_region_id = table_res["table_region_id"]
                key = f"table_{table_region_id}"
                res_xlsx_dict[key] = table_res.xlsx["pred"]
        return res_xlsx_dict

    def save_to_pdf_order(self, save_path: str) -> None:
        """
        Save the layout ordering to an image file.

        Args:
            save_path (str): The path where the image should be saved.

        Returns:
            None
        """
        input_path = Path(self["input_path"])
        page_index = self["page_index"]
        save_path = Path(save_path)
        if save_path.suffix.lower() not in (".jpg", ".png"):
            if input_path.suffix.lower() == ".pdf":
                save_path = save_path / f"page_{page_index}.jpg"
            else:
                save_path = save_path / f"{input_path.stem}.jpg"
        else:
            save_path = save_path.with_suffix("")

        ordering_image_path = (
            save_path.parent / f"{save_path.stem}_layout_order_res.jpg"
        )

        try:
            image = Image.fromarray(self["doc_preprocessor_res"]["output_img"])
        except OSError as e:
            print(f"Error opening image: {e}")
            return

        draw = ImageDraw.Draw(image, "RGBA")
        parsing_result = self["parsing_res_list"]

        for block in parsing_result:
            if self.already_sorted == False:
                block = get_layout_ordering(
                    block,
                    no_mask_labels=[
                        "text",
                        "formula",
                        "algorithm",
                        "reference",
                        "content",
                        "abstract",
                    ],
                    already_sorted=self.already_sorted,
                )

            sub_blocks = block["sub_blocks"]
            for sub_block in sub_blocks:
                bbox = sub_block["layout_bbox"]
                index = sub_block.get("index", None)
                label = sub_block["sub_label"]
                fill_color = get_show_color(label)
                draw.rectangle(bbox, fill=fill_color)
                if index is not None:
                    text_position = (bbox[2] + 2, bbox[1] - 10)
                    draw.text(text_position, str(index), fill="red")

        self.already_sorted = True

        # Ensure the directory exists and save the image
        ordering_image_path.parent.mkdir(parents=True, exist_ok=True)
        print(f"Saving ordering image to {ordering_image_path}")
        image.save(str(ordering_image_path))

    def _to_markdown(self) -> dict:
        """
        Save the parsing result to a Markdown file.

        Returns:
            Dict
        """
        if self.save_path == None:
            is_save_mk_img = False
        else:
            is_save_mk_img = True
            save_path = Path(self.save_path)

        parsing_result = self["parsing_res_list"]

        for block in parsing_result:
            if self.already_sorted == False:
                block = get_layout_ordering(
                    block,
                    no_mask_labels=[
                        "text",
                        "formula",
                        "algorithm",
                        "reference",
                        "content",
                        "abstract",
                    ],
                    already_sorted=self.already_sorted,
                )
        self.already_sorted == True

        if is_save_mk_img:
            recursive_img_array2path(
                self["parsing_res_list"],
                save_path.parent,
                labels=["img"],
            )

        def _format_data(obj):

            def format_title(content_value):
                content_value = content_value.rstrip(".")
                level = (
                    content_value.count(
                        ".",
                    )
                    + 1
                    if "." in content_value
                    else 1
                )
                return f"{'#' * level} {content_value}".replace("-\n", "").replace(
                    "\n",
                    " ",
                )

            def format_centered_text(key):
                return (
                    f'<div style="text-align: center;">{sub_block[key]}</div>'.replace(
                        "-\n",
                        "",
                    ).replace("\n", " ")
                    + "\n"
                )

            def format_image(label):
                if is_save_mk_img is False:
                    return ""

                img_tags = []
                if "img" in sub_block[label]:
                    img_tags.append(
                        '<div style="text-align: center;"><img src="{}" alt="Image" /></div>'.format(
                            sub_block[label]["img"]
                            .replace("-\n", "")
                            .replace("\n", " "),
                        ),
                    )
                if "image_text" in sub_block[label]:
                    img_tags.append(
                        '<div style="text-align: center;">{}</div>'.format(
                            sub_block[label]["image_text"]
                            .replace("-\n", "")
                            .replace("\n", " "),
                        ),
                    )
                return "\n".join(img_tags)

            def format_reference():
                pattern = r"\s*\[\s*\d+\s*\]\s*"
                res = re.sub(
                    pattern,
                    lambda match: "\n" + match.group(),
                    sub_block["reference"].replace("\n", ""),
                )
                return "\n" + res

            def format_table():
                return "\n" + sub_block["table"]

            handlers = {
                "paragraph_title": lambda: format_title(sub_block["paragraph_title"]),
                "doc_title": lambda: f"# {sub_block['doc_title']}".replace(
                    "-\n",
                    "",
                ).replace("\n", " "),
                "table_title": lambda: format_centered_text("table_title"),
                "figure_title": lambda: format_centered_text("figure_title"),
                "chart_title": lambda: format_centered_text("chart_title"),
                "text": lambda: sub_block["text"]
                .replace("-\n", " ")
                .replace("\n", " "),
                # 'number': lambda: str(sub_block['number']),
                "abstract": lambda: sub_block["abstract"]
                .replace("-\n", " ")
                .replace("\n", " "),
                "content": lambda: sub_block["content"]
                .replace("-\n", " ")
                .replace("\n", " "),
                "image": lambda: format_image("image"),
                "chart": lambda: format_image("chart"),
                "formula": lambda: f"$${sub_block['formula']}$$",
                "table": format_table,
                # "reference": format_reference,
                "reference": lambda: sub_block["reference"],
                "algorithm": lambda: sub_block["algorithm"].strip("\n"),
                "seal": lambda: format_image("seal"),
            }
            parsing_result = obj["parsing_res_list"]
            markdown_content = ""
            for block in parsing_result:  # for each block show ordering results
                sub_blocks = block["sub_blocks"]
                last_label = None
                seg_start_flag = None
                seg_end_flag = None
                for sub_block in sorted(
                    sub_blocks,
                    key=lambda x: x.get("sub_index", 999),
                ):
                    label = sub_block.get("label")
                    seg_start_flag = sub_block.get("seg_start_flag")
                    handler = handlers.get(label)
                    if handler:
                        if (
                            label == last_label == "text"
                            and seg_start_flag == seg_end_flag == False
                        ):
                            markdown_content += " " + handler()
                        else:
                            markdown_content += "\n\n" + handler()
                        last_label = label
                        seg_end_flag = sub_block.get("seg_end_flag")

            return markdown_content

        return _format_data(self)