MinerU/magic_pdf/model/magic_model.py

import enum

from magic_pdf.config.model_block_type import ModelBlockTypeEnum
from magic_pdf.config.ocr_content_type import CategoryId, ContentType
from magic_pdf.data.dataset import Dataset
from magic_pdf.libs.boxbase import (_is_in, bbox_distance, bbox_relative_pos,
                                    calculate_iou)
from magic_pdf.libs.coordinate_transform import get_scale_ratio
from magic_pdf.pre_proc.remove_bbox_overlap import _remove_overlap_between_bbox

CAPATION_OVERLAP_AREA_RATIO = 0.6
MERGE_BOX_OVERLAP_AREA_RATIO = 1.1


class PosRelationEnum(enum.Enum):
    LEFT = 'left'
    RIGHT = 'right'
    UP = 'up'
    BOTTOM = 'bottom'
    ALL = 'all'


class MagicModel:
    """每个函数没有得到元素的时候返回空list."""

    def __fix_axis(self):
        for model_page_info in self.__model_list:
            need_remove_list = []
            page_no = model_page_info['page_info']['page_no']
            horizontal_scale_ratio, vertical_scale_ratio = get_scale_ratio(
                model_page_info, self.__docs.get_page(page_no)
            )
            layout_dets = model_page_info['layout_dets']
            for layout_det in layout_dets:

                if layout_det.get('bbox') is not None:
                    # 兼容直接输出bbox的模型数据,如paddle
                    x0, y0, x1, y1 = layout_det['bbox']
                else:
                    # 兼容直接输出poly的模型数据，如xxx
                    x0, y0, _, _, x1, y1, _, _ = layout_det['poly']

                bbox = [
                    int(x0 / horizontal_scale_ratio),
                    int(y0 / vertical_scale_ratio),
                    int(x1 / horizontal_scale_ratio),
                    int(y1 / vertical_scale_ratio),
                ]
                layout_det['bbox'] = bbox
                # 删除高度或者宽度小于等于0的spans
                if bbox[2] - bbox[0] <= 0 or bbox[3] - bbox[1] <= 0:
                    need_remove_list.append(layout_det)
            for need_remove in need_remove_list:
                layout_dets.remove(need_remove)

    def __fix_by_remove_low_confidence(self):
        for model_page_info in self.__model_list:
            need_remove_list = []
            layout_dets = model_page_info['layout_dets']
            for layout_det in layout_dets:
                if layout_det['score'] <= 0.05:
                    need_remove_list.append(layout_det)
                else:
                    continue
            for need_remove in need_remove_list:
                layout_dets.remove(need_remove)

    def __fix_by_remove_high_iou_and_low_confidence(self):
        for model_page_info in self.__model_list:
            need_remove_list = []
            layout_dets = model_page_info['layout_dets']
            for layout_det1 in layout_dets:
                for layout_det2 in layout_dets:
                    if layout_det1 == layout_det2:
                        continue
                    if layout_det1['category_id'] in [
                        0,
                        1,
                        2,
                        3,
                        4,
                        5,
                        6,
                        7,
                        8,
                        9,
                    ] and layout_det2['category_id'] in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]:
                        if (
                            calculate_iou(layout_det1['bbox'], layout_det2['bbox'])
                            > 0.9
                        ):
                            if layout_det1['score'] < layout_det2['score']:
                                layout_det_need_remove = layout_det1
                            else:
                                layout_det_need_remove = layout_det2

                            if layout_det_need_remove not in need_remove_list:
                                need_remove_list.append(layout_det_need_remove)
                        else:
                            continue
                    else:
                        continue
            for need_remove in need_remove_list:
                layout_dets.remove(need_remove)

    def __init__(self, model_list: list, docs: Dataset):
        self.__model_list = model_list
        self.__docs = docs
        """为所有模型数据添加bbox信息(缩放，poly->bbox)"""
        self.__fix_axis()
        """删除置信度特别低的模型数据(<0.05),提高质量"""
        self.__fix_by_remove_low_confidence()
        """删除高iou(>0.9)数据中置信度较低的那个"""
        self.__fix_by_remove_high_iou_and_low_confidence()
        self.__fix_footnote()

    def _bbox_distance(self, bbox1, bbox2):
        left, right, bottom, top = bbox_relative_pos(bbox1, bbox2)
        flags = [left, right, bottom, top]
        count = sum([1 if v else 0 for v in flags])
        if count > 1:
            return float('inf')
        if left or right:
            l1 = bbox1[3] - bbox1[1]
            l2 = bbox2[3] - bbox2[1]
        else:
            l1 = bbox1[2] - bbox1[0]
            l2 = bbox2[2] - bbox2[0]

        if l2 > l1 and (l2 - l1) / l1 > 0.3:
            return float('inf')

        return bbox_distance(bbox1, bbox2)

    def __fix_footnote(self):
        # 3: figure, 5: table, 7: footnote
        for model_page_info in self.__model_list:
            footnotes = []
            figures = []
            tables = []

            for obj in model_page_info['layout_dets']:
                if obj['category_id'] == 7:
                    footnotes.append(obj)
                elif obj['category_id'] == 3:
                    figures.append(obj)
                elif obj['category_id'] == 5:
                    tables.append(obj)
                if len(footnotes) * len(figures) == 0:
                    continue
            dis_figure_footnote = {}
            dis_table_footnote = {}

            for i in range(len(footnotes)):
                for j in range(len(figures)):
                    pos_flag_count = sum(
                        list(
                            map(
                                lambda x: 1 if x else 0,
                                bbox_relative_pos(
                                    footnotes[i]['bbox'], figures[j]['bbox']
                                ),
                            )
                        )
                    )
                    if pos_flag_count > 1:
                        continue
                    dis_figure_footnote[i] = min(
                        self._bbox_distance(figures[j]['bbox'], footnotes[i]['bbox']),
                        dis_figure_footnote.get(i, float('inf')),
                    )
            for i in range(len(footnotes)):
                for j in range(len(tables)):
                    pos_flag_count = sum(
                        list(
                            map(
                                lambda x: 1 if x else 0,
                                bbox_relative_pos(
                                    footnotes[i]['bbox'], tables[j]['bbox']
                                ),
                            )
                        )
                    )
                    if pos_flag_count > 1:
                        continue

                    dis_table_footnote[i] = min(
                        self._bbox_distance(tables[j]['bbox'], footnotes[i]['bbox']),
                        dis_table_footnote.get(i, float('inf')),
                    )
            for i in range(len(footnotes)):
                if i not in dis_figure_footnote:
                    continue
                if dis_table_footnote.get(i, float('inf')) > dis_figure_footnote[i]:
                    footnotes[i]['category_id'] = CategoryId.ImageFootnote

    def __reduct_overlap(self, bboxes):
        N = len(bboxes)
        keep = [True] * N
        for i in range(N):
            for j in range(N):
                if i == j:
                    continue
                if _is_in(bboxes[i]['bbox'], bboxes[j]['bbox']):
                    keep[i] = False
        return [bboxes[i] for i in range(N) if keep[i]]

    def __tie_up_category_by_distance_v2(
        self,
        page_no: int,
        subject_category_id: int,
        object_category_id: int,
        priority_pos: PosRelationEnum,
    ):
        """_summary_

        Args:
            page_no (int): _description_
            subject_category_id (int): _description_
            object_category_id (int): _description_
            priority_pos (PosRelationEnum): _description_

        Returns:
            _type_: _description_
        """
        AXIS_MULPLICITY = 0.5
        subjects = self.__reduct_overlap(
            list(
                map(
                    lambda x: {'bbox': x['bbox'], 'score': x['score']},
                    filter(
                        lambda x: x['category_id'] == subject_category_id,
                        self.__model_list[page_no]['layout_dets'],
                    ),
                )
            )
        )

        objects = self.__reduct_overlap(
            list(
                map(
                    lambda x: {'bbox': x['bbox'], 'score': x['score']},
                    filter(
                        lambda x: x['category_id'] == object_category_id,
                        self.__model_list[page_no]['layout_dets'],
                    ),
                )
            )
        )
        M = len(objects)

        subjects.sort(key=lambda x: x['bbox'][0] ** 2 + x['bbox'][1] ** 2)
        objects.sort(key=lambda x: x['bbox'][0] ** 2 + x['bbox'][1] ** 2)

        sub_obj_map_h = {i: [] for i in range(len(subjects))}

        dis_by_directions = {
            'top': [[-1, float('inf')]] * M,
            'bottom': [[-1, float('inf')]] * M,
            'left': [[-1, float('inf')]] * M,
            'right': [[-1, float('inf')]] * M,
        }

        for i, obj in enumerate(objects):
            l_x_axis, l_y_axis = (
                obj['bbox'][2] - obj['bbox'][0],
                obj['bbox'][3] - obj['bbox'][1],
            )
            axis_unit = min(l_x_axis, l_y_axis)
            for j, sub in enumerate(subjects):

                bbox1, bbox2, _ = _remove_overlap_between_bbox(
                    objects[i]['bbox'], subjects[j]['bbox']
                )
                left, right, bottom, top = bbox_relative_pos(bbox1, bbox2)
                flags = [left, right, bottom, top]
                if sum([1 if v else 0 for v in flags]) > 1:
                    continue

                if left:
                    if dis_by_directions['left'][i][1] > bbox_distance(
                        obj['bbox'], sub['bbox']
                    ):
                        dis_by_directions['left'][i] = [
                            j,
                            bbox_distance(obj['bbox'], sub['bbox']),
                        ]
                if right:
                    if dis_by_directions['right'][i][1] > bbox_distance(
                        obj['bbox'], sub['bbox']
                    ):
                        dis_by_directions['right'][i] = [
                            j,
                            bbox_distance(obj['bbox'], sub['bbox']),
                        ]
                if bottom:
                    if dis_by_directions['bottom'][i][1] > bbox_distance(
                        obj['bbox'], sub['bbox']
                    ):
                        dis_by_directions['bottom'][i] = [
                            j,
                            bbox_distance(obj['bbox'], sub['bbox']),
                        ]
                if top:
                    if dis_by_directions['top'][i][1] > bbox_distance(
                        obj['bbox'], sub['bbox']
                    ):
                        dis_by_directions['top'][i] = [
                            j,
                            bbox_distance(obj['bbox'], sub['bbox']),
                        ]

            if (
                dis_by_directions['top'][i][1] != float('inf')
                and dis_by_directions['bottom'][i][1] != float('inf')
                and priority_pos in (PosRelationEnum.BOTTOM, PosRelationEnum.UP)
            ):
                RATIO = 3
                if (
                    abs(
                        dis_by_directions['top'][i][1]
                        - dis_by_directions['bottom'][i][1]
                    )
                    < RATIO * axis_unit
                ):

                    if priority_pos == PosRelationEnum.BOTTOM:
                        sub_obj_map_h[dis_by_directions['bottom'][i][0]].append(i)
                    else:
                        sub_obj_map_h[dis_by_directions['top'][i][0]].append(i)
                    continue

            if dis_by_directions['left'][i][1] != float('inf') or dis_by_directions[
                'right'
            ][i][1] != float('inf'):
                if dis_by_directions['left'][i][1] != float(
                    'inf'
                ) and dis_by_directions['right'][i][1] != float('inf'):
                    if AXIS_MULPLICITY * axis_unit >= abs(
                        dis_by_directions['left'][i][1]
                        - dis_by_directions['right'][i][1]
                    ):
                        left_sub_bbox = subjects[dis_by_directions['left'][i][0]][
                            'bbox'
                        ]
                        right_sub_bbox = subjects[dis_by_directions['right'][i][0]][
                            'bbox'
                        ]

                        left_sub_bbox_y_axis = left_sub_bbox[3] - left_sub_bbox[1]
                        right_sub_bbox_y_axis = right_sub_bbox[3] - right_sub_bbox[1]

                        if (
                            abs(left_sub_bbox_y_axis - l_y_axis)
                            + dis_by_directions['left'][i][0]
                            > abs(right_sub_bbox_y_axis - l_y_axis)
                            + dis_by_directions['right'][i][0]
                        ):
                            left_or_right = dis_by_directions['right'][i]
                        else:
                            left_or_right = dis_by_directions['left'][i]
                    else:
                        left_or_right = dis_by_directions['left'][i]
                        if left_or_right[1] > dis_by_directions['right'][i][1]:
                            left_or_right = dis_by_directions['right'][i]
                else:
                    left_or_right = dis_by_directions['left'][i]
                    if left_or_right[1] == float('inf'):
                        left_or_right = dis_by_directions['right'][i]
            else:
                left_or_right = [-1, float('inf')]

            if dis_by_directions['top'][i][1] != float('inf') or dis_by_directions[
                'bottom'
            ][i][1] != float('inf'):
                if dis_by_directions['top'][i][1] != float('inf') and dis_by_directions[
                    'bottom'
                ][i][1] != float('inf'):
                    if AXIS_MULPLICITY * axis_unit >= abs(
                        dis_by_directions['top'][i][1]
                        - dis_by_directions['bottom'][i][1]
                    ):
                        top_bottom = subjects[dis_by_directions['bottom'][i][0]]['bbox']
                        bottom_top = subjects[dis_by_directions['top'][i][0]]['bbox']

                        top_bottom_x_axis = top_bottom[2] - top_bottom[0]
                        bottom_top_x_axis = bottom_top[2] - bottom_top[0]
                        if (
                            abs(top_bottom_x_axis - l_x_axis)
                            + dis_by_directions['bottom'][i][1]
                            > abs(bottom_top_x_axis - l_x_axis)
                            + dis_by_directions['top'][i][1]
                        ):
                            top_or_bottom = dis_by_directions['top'][i]
                        else:
                            top_or_bottom = dis_by_directions['bottom'][i]
                    else:
                        top_or_bottom = dis_by_directions['top'][i]
                        if top_or_bottom[1] > dis_by_directions['bottom'][i][1]:
                            top_or_bottom = dis_by_directions['bottom'][i]
                else:
                    top_or_bottom = dis_by_directions['top'][i]
                    if top_or_bottom[1] == float('inf'):
                        top_or_bottom = dis_by_directions['bottom'][i]
            else:
                top_or_bottom = [-1, float('inf')]

            if left_or_right[1] != float('inf') or top_or_bottom[1] != float('inf'):
                if left_or_right[1] != float('inf') and top_or_bottom[1] != float(
                    'inf'
                ):
                    if AXIS_MULPLICITY * axis_unit >= abs(
                        left_or_right[1] - top_or_bottom[1]
                    ):
                        y_axis_bbox = subjects[left_or_right[0]]['bbox']
                        x_axis_bbox = subjects[top_or_bottom[0]]['bbox']

                        if (
                            abs((x_axis_bbox[2] - x_axis_bbox[0]) - l_x_axis) / l_x_axis
                            > abs((y_axis_bbox[3] - y_axis_bbox[1]) - l_y_axis)
                            / l_y_axis
                        ):
                            sub_obj_map_h[left_or_right[0]].append(i)
                        else:
                            sub_obj_map_h[top_or_bottom[0]].append(i)
                    else:
                        if left_or_right[1] > top_or_bottom[1]:
                            sub_obj_map_h[top_or_bottom[0]].append(i)
                        else:
                            sub_obj_map_h[left_or_right[0]].append(i)
                else:
                    if left_or_right[1] != float('inf'):
                        sub_obj_map_h[left_or_right[0]].append(i)
                    else:
                        sub_obj_map_h[top_or_bottom[0]].append(i)
        ret = []
        for i in sub_obj_map_h.keys():
            ret.append(
                {
                    'sub_bbox': {
                        'bbox': subjects[i]['bbox'],
                        'score': subjects[i]['score'],
                    },
                    'obj_bboxes': [
                        {'score': objects[j]['score'], 'bbox': objects[j]['bbox']}
                        for j in sub_obj_map_h[i]
                    ],
                    'sub_idx': i,
                }
            )
        return ret


    def __tie_up_category_by_distance_v3(
        self,
        page_no: int,
        subject_category_id: int,
        object_category_id: int,
        priority_pos: PosRelationEnum,
    ):
        subjects = self.__reduct_overlap(
            list(
                map(
                    lambda x: {'bbox': x['bbox'], 'score': x['score']},
                    filter(
                        lambda x: x['category_id'] == subject_category_id,
                        self.__model_list[page_no]['layout_dets'],
                    ),
                )
            )
        )
        objects = self.__reduct_overlap(
            list(
                map(
                    lambda x: {'bbox': x['bbox'], 'score': x['score']},
                    filter(
                        lambda x: x['category_id'] == object_category_id,
                        self.__model_list[page_no]['layout_dets'],
                    ),
                )
            )
        )

        ret = []
        N, M = len(subjects), len(objects)
        subjects.sort(key=lambda x: x['bbox'][0] ** 2 + x['bbox'][1] ** 2)
        objects.sort(key=lambda x: x['bbox'][0] ** 2 + x['bbox'][1] ** 2)

        OBJ_IDX_OFFSET = 10000
        SUB_BIT_KIND, OBJ_BIT_KIND = 0, 1

        all_boxes_with_idx = [(i, SUB_BIT_KIND, sub['bbox'][0], sub['bbox'][1]) for i, sub in enumerate(subjects)] + [(i + OBJ_IDX_OFFSET , OBJ_BIT_KIND, obj['bbox'][0], obj['bbox'][1]) for i, obj in enumerate(objects)]
        seen_idx = set()
        seen_sub_idx = set()

        while N > len(seen_sub_idx):
            candidates = []
            for idx, kind, x0, y0 in all_boxes_with_idx:
                if idx in seen_idx:
                    continue
                candidates.append((idx, kind, x0, y0))

            if len(candidates) == 0:
                break
            left_x = min([v[2] for v in candidates])
            top_y =  min([v[3] for v in candidates])

            candidates.sort(key=lambda x: (x[2]-left_x) ** 2 + (x[3] - top_y) ** 2)


            fst_idx, fst_kind, left_x, top_y = candidates[0]
            candidates.sort(key=lambda x: (x[2] - left_x) ** 2 + (x[3] - top_y)**2)
            nxt = None

            for i in range(1, len(candidates)):
                if candidates[i][1] ^ fst_kind == 1:
                    nxt = candidates[i]
                    break
            if nxt is None:
                break

            if fst_kind == SUB_BIT_KIND:
                sub_idx, obj_idx = fst_idx, nxt[0] - OBJ_IDX_OFFSET

            else:
                sub_idx, obj_idx = nxt[0], fst_idx - OBJ_IDX_OFFSET

            pair_dis = bbox_distance(subjects[sub_idx]['bbox'], objects[obj_idx]['bbox'])
            nearest_dis = float('inf')
            for i in range(N):
                if i in seen_idx or i == sub_idx:continue
                nearest_dis = min(nearest_dis, bbox_distance(subjects[i]['bbox'], objects[obj_idx]['bbox']))

            if pair_dis >= 3*nearest_dis:
                seen_idx.add(sub_idx)
                continue

            seen_idx.add(sub_idx)
            seen_idx.add(obj_idx + OBJ_IDX_OFFSET)
            seen_sub_idx.add(sub_idx)

            ret.append(
                {
                    'sub_bbox': {
                        'bbox': subjects[sub_idx]['bbox'],
                        'score': subjects[sub_idx]['score'],
                    },
                    'obj_bboxes': [
                        {'score': objects[obj_idx]['score'], 'bbox': objects[obj_idx]['bbox']}
                    ],
                    'sub_idx': sub_idx,
                }
            )

        for i in range(len(objects)):
            j = i + OBJ_IDX_OFFSET
            if j in seen_idx:
                continue
            seen_idx.add(j)
            nearest_dis, nearest_sub_idx = float('inf'), -1
            for k in range(len(subjects)):
                dis = bbox_distance(objects[i]['bbox'], subjects[k]['bbox'])
                if dis < nearest_dis:
                    nearest_dis = dis
                    nearest_sub_idx = k

            for k in range(len(subjects)):
                if k != nearest_sub_idx: continue
                if k in seen_sub_idx:
                    for kk in range(len(ret)):
                        if ret[kk]['sub_idx'] == k:
                            ret[kk]['obj_bboxes'].append({'score': objects[i]['score'], 'bbox': objects[i]['bbox']})
                            break
                else:
                    ret.append(
                        {
                            'sub_bbox': {
                                'bbox': subjects[k]['bbox'],
                                'score': subjects[k]['score'],
                            },
                            'obj_bboxes': [
                                {'score': objects[i]['score'], 'bbox': objects[i]['bbox']}
                            ],
                            'sub_idx': k,
                        }
                    )
                seen_sub_idx.add(k)
                seen_idx.add(k)


        for i in range(len(subjects)):
            if i in seen_sub_idx:
                continue
            ret.append(
                {
                    'sub_bbox': {
                        'bbox': subjects[i]['bbox'],
                        'score': subjects[i]['score'],
                    },
                    'obj_bboxes': [],
                    'sub_idx': i,
                }
            )


        return ret


    def get_imgs_v2(self, page_no: int):
        with_captions = self.__tie_up_category_by_distance_v3(
            page_no, 3, 4, PosRelationEnum.BOTTOM
        )
        with_footnotes = self.__tie_up_category_by_distance_v3(
            page_no, 3, CategoryId.ImageFootnote, PosRelationEnum.ALL
        )
        ret = []
        for v in with_captions:
            record = {
                'image_body': v['sub_bbox'],
                'image_caption_list': v['obj_bboxes'],
            }
            filter_idx = v['sub_idx']
            d = next(filter(lambda x: x['sub_idx'] == filter_idx, with_footnotes))
            record['image_footnote_list'] = d['obj_bboxes']
            ret.append(record)
        return ret

    def get_tables_v2(self, page_no: int) -> list:
        with_captions = self.__tie_up_category_by_distance_v3(
            page_no, 5, 6, PosRelationEnum.UP
        )
        with_footnotes = self.__tie_up_category_by_distance_v3(
            page_no, 5, 7, PosRelationEnum.ALL
        )
        ret = []
        for v in with_captions:
            record = {
                'table_body': v['sub_bbox'],
                'table_caption_list': v['obj_bboxes'],
            }
            filter_idx = v['sub_idx']
            d = next(filter(lambda x: x['sub_idx'] == filter_idx, with_footnotes))
            record['table_footnote_list'] = d['obj_bboxes']
            ret.append(record)
        return ret

    def get_imgs(self, page_no: int):
        return self.get_imgs_v2(page_no)

    def get_tables(
        self, page_no: int
    ) -> list:  # 3个坐标， caption, table主体，table-note
        return self.get_tables_v2(page_no)

    def get_equations(self, page_no: int) -> list:  # 有坐标，也有字
        inline_equations = self.__get_blocks_by_type(
            ModelBlockTypeEnum.EMBEDDING.value, page_no, ['latex']
        )
        interline_equations = self.__get_blocks_by_type(
            ModelBlockTypeEnum.ISOLATED.value, page_no, ['latex']
        )
        interline_equations_blocks = self.__get_blocks_by_type(
            ModelBlockTypeEnum.ISOLATE_FORMULA.value, page_no
        )
        return inline_equations, interline_equations, interline_equations_blocks

    def get_discarded(self, page_no: int) -> list:  # 自研模型，只有坐标
        blocks = self.__get_blocks_by_type(ModelBlockTypeEnum.ABANDON.value, page_no)
        return blocks

    def get_text_blocks(self, page_no: int) -> list:  # 自研模型搞的，只有坐标，没有字
        blocks = self.__get_blocks_by_type(ModelBlockTypeEnum.PLAIN_TEXT.value, page_no)
        return blocks

    def get_title_blocks(self, page_no: int) -> list:  # 自研模型，只有坐标，没字
        blocks = self.__get_blocks_by_type(ModelBlockTypeEnum.TITLE.value, page_no)
        return blocks

    def get_ocr_text(self, page_no: int) -> list:  # paddle 搞的，有字也有坐标
        text_spans = []
        model_page_info = self.__model_list[page_no]
        layout_dets = model_page_info['layout_dets']
        for layout_det in layout_dets:
            if layout_det['category_id'] == '15':
                span = {
                    'bbox': layout_det['bbox'],
                    'content': layout_det['text'],
                }
                text_spans.append(span)
        return text_spans

    def get_all_spans(self, page_no: int) -> list:

        def remove_duplicate_spans(spans):
            new_spans = []
            for span in spans:
                if not any(span == existing_span for existing_span in new_spans):
                    new_spans.append(span)
            return new_spans

        all_spans = []
        model_page_info = self.__model_list[page_no]
        layout_dets = model_page_info['layout_dets']
        allow_category_id_list = [3, 5, 13, 14, 15]
        """当成span拼接的"""
        #  3: 'image', # 图片
        #  5: 'table',       # 表格
        #  13: 'inline_equation',     # 行内公式
        #  14: 'interline_equation',      # 行间公式
        #  15: 'text',      # ocr识别文本
        for layout_det in layout_dets:
            category_id = layout_det['category_id']
            if category_id in allow_category_id_list:
                span = {'bbox': layout_det['bbox'], 'score': layout_det['score']}
                if category_id == 3:
                    span['type'] = ContentType.Image
                elif category_id == 5:
                    # 获取table模型结果
                    latex = layout_det.get('latex', None)
                    html = layout_det.get('html', None)
                    if latex:
                        span['latex'] = latex
                    elif html:
                        span['html'] = html
                    span['type'] = ContentType.Table
                elif category_id == 13:
                    span['content'] = layout_det['latex']
                    span['type'] = ContentType.InlineEquation
                elif category_id == 14:
                    span['content'] = layout_det['latex']
                    span['type'] = ContentType.InterlineEquation
                elif category_id == 15:
                    span['content'] = layout_det['text']
                    span['type'] = ContentType.Text
                all_spans.append(span)
        return remove_duplicate_spans(all_spans)

    def get_page_size(self, page_no: int):  # 获取页面宽高
        # 获取当前页的page对象
        page = self.__docs.get_page(page_no).get_page_info()
        # 获取当前页的宽高
        page_w = page.w
        page_h = page.h
        return page_w, page_h

    def __get_blocks_by_type(
        self, type: int, page_no: int, extra_col: list[str] = []
    ) -> list:
        blocks = []
        for page_dict in self.__model_list:
            layout_dets = page_dict.get('layout_dets', [])
            page_info = page_dict.get('page_info', {})
            page_number = page_info.get('page_no', -1)
            if page_no != page_number:
                continue
            for item in layout_dets:
                category_id = item.get('category_id', -1)
                bbox = item.get('bbox', None)

                if category_id == type:
                    block = {
                        'bbox': bbox,
                        'score': item.get('score'),
                    }
                    for col in extra_col:
                        block[col] = item.get(col, None)
                    blocks.append(block)
        return blocks

    def get_model_list(self, page_no):
        return self.__model_list[page_no]