pdf_parse_by_txt_v2.py

import time

from loguru import logger

from magic_pdf.layout.layout_sort import get_bboxes_layout
from magic_pdf.libs.convert_utils import dict_to_list
from magic_pdf.libs.hash_utils import compute_md5
from magic_pdf.libs.commons import fitz, get_delta_time
from magic_pdf.model.magic_model import MagicModel
from magic_pdf.pre_proc.construct_page_dict import ocr_construct_page_component_v2
from magic_pdf.pre_proc.cut_image import ocr_cut_image_and_table
from magic_pdf.pre_proc.ocr_detect_all_bboxes import ocr_prepare_bboxes_for_layout_split
from magic_pdf.pre_proc.ocr_dict_merge import (
    sort_blocks_by_layout,
    fill_spans_in_blocks,
    fix_block_spans,
)
from magic_pdf.libs.ocr_content_type import ContentType
from magic_pdf.pre_proc.ocr_span_list_modify import (
    remove_overlaps_min_spans,
    get_qa_need_list_v2,
)
from magic_pdf.pre_proc.equations_replace import (
    combine_chars_to_pymudict,
    remove_chars_in_text_blocks,
    replace_equations_in_textblock,
)
from magic_pdf.pre_proc.equations_replace import (
    combine_chars_to_pymudict,
    remove_chars_in_text_blocks,
    replace_equations_in_textblock,
)
from magic_pdf.pre_proc.citationmarker_remove import remove_citation_marker
from magic_pdf.libs.math import float_equal
from magic_pdf.para.para_split_v2 import para_split

def txt_spans_extract(pdf_page, inline_equations, interline_equations):
    text_raw_blocks = pdf_page.get_text("dict", flags=fitz.TEXTFLAGS_TEXT)["blocks"]
    char_level_text_blocks = pdf_page.get_text("rawdict", flags=fitz.TEXTFLAGS_TEXT)[
        "blocks"
    ]
    text_blocks = combine_chars_to_pymudict(text_raw_blocks, char_level_text_blocks)
    text_blocks = replace_equations_in_textblock(
        text_blocks, inline_equations, interline_equations
    )
    text_blocks = remove_citation_marker(text_blocks)
    text_blocks = remove_chars_in_text_blocks(text_blocks)
    spans = []
    for v in text_blocks:
        for line in v["lines"]:
            for span in line["spans"]:
                bbox = span["bbox"]
                if float_equal(bbox[0], bbox[2]) or float_equal(bbox[1], bbox[3]):
                    continue
                spans.append(
                    {
                        "bbox": list(span["bbox"]),
                        "content": span["text"],
                        "type": ContentType.Text,
                    }
                )
    return spans


def replace_text_span(pymu_spans, ocr_spans):
    return list(filter(lambda x: x["type"] != ContentType.Text, ocr_spans)) + pymu_spans


def parse_pdf_by_txt(
    pdf_bytes,
    model_list,
    imageWriter,
    start_page_id=0,
    end_page_id=None,
    debug_mode=False,
):
    pdf_bytes_md5 = compute_md5(pdf_bytes)
    pdf_docs = fitz.open("pdf", pdf_bytes)

    """初始化空的pdf_info_dict"""
    pdf_info_dict = {}

    """用model_list和docs对象初始化magic_model"""
    magic_model = MagicModel(model_list, pdf_docs)

    """根据输入的起始范围解析pdf"""
    end_page_id = end_page_id if end_page_id else len(pdf_docs) - 1

    """初始化启动时间"""
    start_time = time.time()

    for page_id in range(start_page_id, end_page_id + 1):

        """debug时输出每页解析的耗时"""
        if debug_mode:
            time_now = time.time()
            logger.info(
                f"page_id: {page_id}, last_page_cost_time: {get_delta_time(start_time)}"
            )
            start_time = time_now

        """从magic_model对象中获取后面会用到的区块信息"""
        img_blocks = magic_model.get_imgs(page_id)
        table_blocks = magic_model.get_tables(page_id)
        discarded_blocks = magic_model.get_discarded(page_id)
        text_blocks = magic_model.get_text_blocks(page_id)
        title_blocks = magic_model.get_title_blocks(page_id)
        inline_equations, interline_equations, interline_equation_blocks = (
            magic_model.get_equations(page_id)
        )

        page_w, page_h = magic_model.get_page_size(page_id)

        """将所有区块的bbox整理到一起"""
        all_bboxes = ocr_prepare_bboxes_for_layout_split(
            img_blocks,
            table_blocks,
            discarded_blocks,
            text_blocks,
            title_blocks,
            interline_equation_blocks,
            page_w,
            page_h,
        )

        """根据区块信息计算layout"""
        page_boundry = [0, 0, page_w, page_h]
        layout_bboxes, layout_tree = get_bboxes_layout(
            all_bboxes, page_boundry, page_id
        )

        """根据layout顺序，对当前页面所有需要留下的block进行排序"""
        sorted_blocks = sort_blocks_by_layout(all_bboxes, layout_bboxes)

        """ocr 中文本类的 span 用 pymu spans 替换！"""
        ocr_spans = magic_model.get_all_spans(page_id)
        pymu_spans = txt_spans_extract(
            pdf_docs[page_id], inline_equations, interline_equations
        )
        spans = replace_text_span(pymu_spans, ocr_spans)

        """删除重叠spans中较小的那些"""
        spans, dropped_spans_by_span_overlap = remove_overlaps_min_spans(spans)
        """对image和table截图"""
        spans = ocr_cut_image_and_table(
            spans, pdf_docs[page_id], page_id, pdf_bytes_md5, imageWriter
        )

        """将span填入排好序的blocks中"""
        block_with_spans = fill_spans_in_blocks(sorted_blocks, spans)

        """对block进行fix操作"""
        fix_blocks = fix_block_spans(block_with_spans, img_blocks, table_blocks)

        """获取QA需要外置的list"""
        images, tables, interline_equations = get_qa_need_list_v2(fix_blocks)

        """构造pdf_info_dict"""
        page_info = ocr_construct_page_component_v2(
            fix_blocks,
            layout_bboxes,
            page_id,
            page_w,
            page_h,
            layout_tree,
            images,
            tables,
            interline_equations,
            discarded_blocks,
        )
        pdf_info_dict[f"page_{page_id}"] = page_info

    """分段"""
    try:
        para_split(pdf_info_dict, debug_mode=debug_mode)
    except Exception as e:
        logger.exception(e)
        raise e

    """dict转list"""
    pdf_info_list = dict_to_list(pdf_info_dict)
    new_pdf_info_dict = {
        "pdf_info": pdf_info_list,
    }

    return new_pdf_info_dict


if __name__ == "__main__":
    if 1:
        import fitz
        import json

        with open("/opt/data/pdf/20240418/25536-00.pdf", "rb") as f:
            pdf_bytes = f.read()
        pdf_docs = fitz.open("pdf", pdf_bytes)

        with open("/opt/data/pdf/20240418/25536-00.json") as f:
            model_list = json.loads(f.readline())

        magic_model = MagicModel(model_list, pdf_docs)
        for i in range(7):
            print(magic_model.get_imgs(i))

        for page_no, page in enumerate(pdf_docs):
            inline_equations, interline_equations, interline_equation_blocks = (
                magic_model.get_equations(page_no)
            )

            text_raw_blocks = page.get_text("dict", flags=fitz.TEXTFLAGS_TEXT)["blocks"]
            char_level_text_blocks = page.get_text(
                "rawdict", flags=fitz.TEXTFLAGS_TEXT
            )["blocks"]
            text_blocks = combine_chars_to_pymudict(
                text_raw_blocks, char_level_text_blocks
            )
            text_blocks = replace_equations_in_textblock(
                text_blocks, inline_equations, interline_equations
            )
            text_blocks = remove_citation_marker(text_blocks)

            text_blocks = remove_chars_in_text_blocks(text_blocks)