diff --git a/.github/actions/setup-poetry/action.yml b/.github/actions/setup-poetry/action.yml index 853c517..473326d 100644 --- a/.github/actions/setup-poetry/action.yml +++ b/.github/actions/setup-poetry/action.yml @@ -8,7 +8,7 @@ runs: using: 'composite' steps: - name: Install poetry - run: pipx install poetry==1.8.4 + run: pipx install poetry==1.8.5 shell: bash - uses: actions/setup-python@v5 with: diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index bfeeb0d..5cd194e 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -6,7 +6,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ['3.9', '3.10', '3.11', '3.12'] + python-version: ['3.9', '3.10', '3.11', '3.12', '3.13'] steps: - uses: actions/checkout@v4 - uses: ./.github/actions/setup-poetry diff --git a/.gitignore b/.gitignore index 800f262..631b56d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +*~ + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/docling_ibm_models/reading_order/__init__.py b/docling_ibm_models/reading_order/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/docling_ibm_models/reading_order/reading_order_rb.py b/docling_ibm_models/reading_order/reading_order_rb.py new file mode 100644 index 0000000..882bd3a --- /dev/null +++ b/docling_ibm_models/reading_order/reading_order_rb.py @@ -0,0 +1,631 @@ +# +# Copyright IBM Corp. 2024 - 2024 +# SPDX-License-Identifier: MIT +# +import copy +import logging +import os +import re +from collections.abc import Iterable +from typing import Dict, List, Set, Tuple + +from docling_core.types.doc.base import BoundingBox, Size +from docling_core.types.doc.document import RefItem +from docling_core.types.doc.labels import DocItemLabel +from pydantic import BaseModel + + +class PageElement(BoundingBox): + + eps: float = 1.0e-3 + + cid: int + ref: RefItem = RefItem(cref="#") # type: ignore + + text: str = "" + + page_no: int + page_size: Size + + label: DocItemLabel + + def __str__(self): + return f"{self.cid:6.2f}\t{str(self.label):<10}\t{self.l:6.2f}, {self.b:6.2f}, {self.r:6.2f}, {self.t:6.2f}" + + def __lt__(self, other): + if self.page_no == other.page_no: + + if self.overlaps_horizontally(other): + return self.b > other.b + else: + return self.l < other.l + else: + return self.page_no < other.page_no + + def follows_maintext_order(self, rhs) -> bool: + return self.cid + 1 == rhs.cid + + +class ReadingOrderPredictor: + r""" + Rule based reading order for DoclingDocument + """ + + def __init__(self): + self.dilated_page_element = True + + self.initialise() + + def initialise(self): + self.h2i_map: Dict[int, int] = {} + self.i2h_map: Dict[int, int] = {} + + self.l2r_map: Dict[int, int] = {} + self.r2l_map: Dict[int, int] = {} + + self.up_map: Dict[int, List[int]] = {} + self.dn_map: Dict[int, List[int]] = {} + + self.heads: List[int] = [] + + def predict_reading_order( + self, page_elements: List[PageElement] + ) -> List[PageElement]: + + page_nos: Set[int] = set() + + for i, elem in enumerate(page_elements): + page_nos.add(elem.page_no) + + page_to_elems: Dict[int, List[PageElement]] = {} + page_to_headers: Dict[int, List[PageElement]] = {} + page_to_footers: Dict[int, List[PageElement]] = {} + + for page_no in page_nos: + page_to_elems[page_no] = [] + page_to_footers[page_no] = [] + page_to_headers[page_no] = [] + + for i, elem in enumerate(page_elements): + if elem.label == DocItemLabel.PAGE_HEADER: + page_to_headers[elem.page_no].append(elem) + elif elem.label == DocItemLabel.PAGE_FOOTER: + page_to_footers[elem.page_no].append(elem) + else: + page_to_elems[elem.page_no].append(elem) + + # print("headers ....") + for page_no, elems in page_to_headers.items(): + page_to_headers[page_no] = self._predict_page(elems) + + # print("elems ....") + for page_no, elems in page_to_elems.items(): + page_to_elems[page_no] = self._predict_page(elems) + + # print("footers ....") + for page_no, elems in page_to_footers.items(): + page_to_footers[page_no] = self._predict_page(elems) + + sorted_elements = [] + for page_no in page_nos: + sorted_elements.extend(page_to_headers[page_no]) + sorted_elements.extend(page_to_elems[page_no]) + sorted_elements.extend(page_to_footers[page_no]) + + return sorted_elements + + def predict_to_captions( + self, sorted_elements: List[PageElement] + ) -> Dict[int, List[int]]: + + to_captions: Dict[int, List[int]] = {} + + page_nos: Set[int] = set() + for i, elem in enumerate(sorted_elements): + page_nos.add(elem.page_no) + + page_to_elems: Dict[int, List[PageElement]] = {} + for page_no in page_nos: + page_to_elems[page_no] = [] + + for i, elem in enumerate(sorted_elements): + page_to_elems[elem.page_no].append(elem) + + for page_no, elems in page_to_elems.items(): + + page_to_captions = self._find_to_captions( + page_elements=page_to_elems[page_no] + ) + for key, val in page_to_captions.items(): + to_captions[key] = val + + return to_captions + + def predict_to_footnotes( + self, sorted_elements: List[PageElement] + ) -> Dict[int, List[int]]: + + to_footnotes: Dict[int, List[int]] = {} + + page_nos: Set[int] = set() + for i, elem in enumerate(sorted_elements): + page_nos.add(elem.page_no) + + page_to_elems: Dict[int, List[PageElement]] = {} + for page_no in page_nos: + page_to_elems[page_no] = [] + + for i, elem in enumerate(sorted_elements): + page_to_elems[elem.page_no].append(elem) + + for page_no, elems in page_to_elems.items(): + page_to_footnotes = self._find_to_footnotes( + page_elements=page_to_elems[page_no] + ) + for key, val in page_to_footnotes.items(): + to_footnotes[key] = val + + return to_footnotes + + def predict_merges( + self, sorted_elements: List[PageElement] + ) -> Dict[int, List[int]]: + + merges: Dict[int, List[int]] = {} + + curr_ind = -1 + for ind, elem in enumerate(sorted_elements): + + if ind <= curr_ind: + continue + + if elem.label in [DocItemLabel.TEXT]: + + ind_p1 = ind + 1 + while ind_p1 < len(sorted_elements) and sorted_elements[ind_p1] in [ + DocItemLabel.PAGE_HEADER, + DocItemLabel.PAGE_FOOTER, + DocItemLabel.TABLE, + DocItemLabel.PICTURE, + DocItemLabel.CAPTION, + DocItemLabel.FOOTNOTE, + ]: + ind_p1 += 1 + + if ( + ind_p1 < len(sorted_elements) + and sorted_elements[ind_p1].label == elem.label + and ( + elem.page_no != sorted_elements[ind_p1].label + or elem.is_strictly_left_of(sorted_elements[ind_p1]) + ) + ): + + m1 = re.fullmatch(".+([a-z\,\-])(\s*)", elem.text) + m2 = re.fullmatch("(\s*[a-z])(.+)", sorted_elements[ind_p1].text) + + if m1 and m2: + merges[elem.cid] = [sorted_elements[ind_p1].cid] + curr_ind = ind_p1 + + return merges + + def _predict_page(self, page_elements: List[PageElement]) -> List[PageElement]: + r""" + Reorder the output of the + """ + + self.initialise() + + """ + for i, elem in enumerate(page_elements): + print(f"{i:6.2f}\t{str(elem)}") + """ + + for i, elem in enumerate(page_elements): + page_elements[i] = elem.to_bottom_left_origin( # type: ignore + page_height=page_elements[i].page_size.height + ) + + self._init_h2i_map(page_elements) + + self._init_l2r_map(page_elements) + + self._init_ud_maps(page_elements) + + if self.dilated_page_element: + dilated_page_elements: List[PageElement] = copy.deepcopy( + page_elements + ) # deep-copy + dilated_page_elements = self._do_horizontal_dilation( + page_elements, dilated_page_elements + ) + + # redo with dilated provs + self._init_ud_maps(dilated_page_elements) + + self._find_heads(page_elements) + + self._sort_ud_maps(page_elements) + + """ + print(f"heads: {self.heads}") + + print("l2r: ") + for k,v in self.l2r_map.items(): + print(f" -> {k}: {v}") + + print("r2l: ") + for k,v in self.r2l_map.items(): + print(f" -> {k}: {v}") + + print("up: ") + for k,v in self.up_map.items(): + print(f" -> {k}: {v}") + + print("dn: ") + for k,v in self.dn_map.items(): + print(f" -> {k}: {v}") + """ + + order: List[int] = self._find_order(page_elements) + # print(f"order: {order}") + + sorted_elements: List[PageElement] = [] + for ind in order: + sorted_elements.append(page_elements[ind]) + + """ + for i, elem in enumerate(sorted_elements): + print(f"{i:6.2f}\t{str(elem)}") + """ + + return sorted_elements + + def _init_h2i_map(self, page_elems: List[PageElement]): + self.h2i_map = {} + self.i2h_map = {} + + for i, pelem in enumerate(page_elems): + self.h2i_map[pelem.cid] = i + self.i2h_map[i] = pelem.cid + + def _init_l2r_map(self, page_elems: List[PageElement]): + self.l2r_map = {} + self.r2l_map = {} + + # this currently leads to errors ... might be necessary in the future ... + for i, pelem_i in enumerate(page_elems): + for j, pelem_j in enumerate(page_elems): + + if ( + False # pelem_i.follows_maintext_order(pelem_j) + and pelem_i.is_strictly_left_of(pelem_j) + and pelem_i.overlaps_vertically_with_iou(pelem_j, 0.8) + ): + self.l2r_map[i] = j + self.r2l_map[j] = i + + def _init_ud_maps(self, page_elems: List[PageElement]): + self.up_map = {} + self.dn_map = {} + + for i, pelem_i in enumerate(page_elems): + self.up_map[i] = [] + self.dn_map[i] = [] + + for j, pelem_j in enumerate(page_elems): + + if j in self.r2l_map: + i = self.r2l_map[j] + + self.dn_map[i] = [j] + self.up_map[j] = [i] + + continue + + for i, pelem_i in enumerate(page_elems): + + if i == j: + continue + + is_horizontally_connected: bool = False + is_i_just_above_j: bool = pelem_i.overlaps_horizontally( + pelem_j + ) and pelem_i.is_strictly_above(pelem_j) + + for w, pelem_w in enumerate(page_elems): + + if not is_horizontally_connected: + is_horizontally_connected = pelem_w.is_horizontally_connected( + pelem_i, pelem_j + ) + + # ensure there is no other element that is between i and j vertically + if is_i_just_above_j and ( + pelem_i.overlaps_horizontally(pelem_w) + or pelem_j.overlaps_horizontally(pelem_w) + ): + i_above_w: bool = pelem_i.is_strictly_above(pelem_w) + w_above_j: bool = pelem_w.is_strictly_above(pelem_j) + + is_i_just_above_j = not (i_above_w and w_above_j) + + if is_i_just_above_j: + + while i in self.l2r_map: + i = self.l2r_map[i] + + self.dn_map[i].append(j) + self.up_map[j].append(i) + + def _do_horizontal_dilation(self, page_elems, dilated_page_elems): + + for i, pelem_i in enumerate(dilated_page_elems): + + x0 = pelem_i.l + y0 = pelem_i.b + + x1 = pelem_i.r + y1 = pelem_i.t + + if i in self.up_map and len(self.up_map[i]) > 0: + pelem_up = page_elems[self.up_map[i][0]] + + x0 = min(x0, pelem_up.l) + x1 = max(x1, pelem_up.r) + + if i in self.dn_map and len(self.dn_map[i]) > 0: + pelem_dn = page_elems[self.dn_map[i][0]] + + x0 = min(x0, pelem_dn.l) + x1 = max(x1, pelem_dn.r) + + pelem_i.l = x0 + pelem_i.r = x1 + + overlaps_with_rest: bool = False + for j, pelem_j in enumerate(page_elems): + + if i == j: + continue + + if not overlaps_with_rest: + overlaps_with_rest = pelem_j.overlaps(pelem_i) + + # update + if not overlaps_with_rest: + dilated_page_elems[i].l = x0 + dilated_page_elems[i].b = y0 + dilated_page_elems[i].r = x1 + dilated_page_elems[i].t = y1 + + return dilated_page_elems + + def _find_heads(self, page_elems: List[PageElement]): + head_page_elems = [] + for key, vals in self.up_map.items(): + if len(vals) == 0: + head_page_elems.append(page_elems[key]) + + """ + print("before sorting the heads: ") + for l, elem in enumerate(head_page_elems): + print(f"{l}\t{str(elem)}") + """ + + # this will invoke __lt__ from PageElements + head_page_elems = sorted(head_page_elems) + + """ + print("after sorting the heads: ") + for l, elem in enumerate(head_page_elems): + print(f"{l}\t{str(elem)}") + """ + + self.heads = [] + for item in head_page_elems: + self.heads.append(self.h2i_map[item.cid]) + + def _sort_ud_maps(self, provs: List[PageElement]): + for ind_i, vals in self.dn_map.items(): + + child_provs: List[PageElement] = [] + for ind_j in vals: + child_provs.append(provs[ind_j]) + + # this will invoke __lt__ from PageElements + child_provs = sorted(child_provs) + + self.dn_map[ind_i] = [] + for child in child_provs: + self.dn_map[ind_i].append(self.h2i_map[child.cid]) + + def _find_order(self, provs: List[PageElement]): + order: List[int] = [] + + visited: List[bool] = [False for _ in provs] + + for j in self.heads: + + if not visited[j]: + + order.append(j) + visited[j] = True + self._depth_first_search_downwards(j, order, visited) + + if len(order) != len(provs): + logging.error("something went wrong") + + return order + + def _depth_first_search_upwards( + self, j: int, order: List[int], visited: List[bool] + ): + """depth_first_search_upwards""" + + k = j + + inds = self.up_map[j] + for ind in inds: + if not visited[ind]: + return self._depth_first_search_upwards(ind, order, visited) + + return k + + def _depth_first_search_downwards( + self, j: int, order: List[int], visited: List[bool] + ): + """depth_first_search_downwards""" + + inds: List[int] = self.dn_map[j] + + for i in inds: + k: int = self._depth_first_search_upwards(i, order, visited) + + if not visited[k]: + order.append(k) + visited[k] = True + + self._depth_first_search_downwards(k, order, visited) + + def _find_to_captions( + self, page_elements: List[PageElement] + ) -> Dict[int, List[int]]: + + captions: Set[int] = set() + + # caption to picture-item/table-item + from_captions: Dict[int, Tuple[List[int], List[int]]] = {} + + # picture-item/table-item to caption + to_captions: Dict[int, List[int]] = {} + + # init from_captions + for ind, page_element in enumerate(page_elements): + if page_element.label == DocItemLabel.CAPTION: + from_captions[page_element.cid] = ([], []) + + for ind, page_element in enumerate(page_elements): + if page_element.label == DocItemLabel.CAPTION: + ind_m1 = ind - 1 + while ind_m1 >= 0 and page_elements[ind_m1].label in [ + DocItemLabel.TABLE, + DocItemLabel.PICTURE, + ]: + from_captions[page_element.cid][0].append(page_elements[ind_m1].cid) + ind_m1 = ind_m1 - 1 + + ind_p1 = ind + 1 + while ind_p1 < len(page_elements) and page_elements[ind_p1].label in [ + DocItemLabel.TABLE, + DocItemLabel.PICTURE, + ]: + from_captions[page_element.cid][1].append(page_elements[ind_p1].cid) + ind_p1 = ind_p1 + 1 + + """ + for cid_i, to_item in from_captions.items(): + print("from-captions: ", cid_i, ": ", to_item[0], "; ", to_item[1]) + """ + + assigned_cids = set() + for cid_i, to_item in from_captions.items(): + if len(from_captions[cid_i][0]) == 0 and len(from_captions[cid_i][1]) > 0: + for cid_j in from_captions[cid_i][1]: + # To avoid overwriting that to_captions[cid_j] when they exist + if to_captions.get(cid_j) is None: + to_captions[cid_j] = [cid_i] + elif cid_i not in to_captions[cid_j]: + to_captions[cid_j].append(cid_i) + # to_captions[cid_j] = [cid_i] + + assigned_cids.add(cid_j) + + if len(from_captions[cid_i][0]) > 0 and len(from_captions[cid_i][1]) == 0: + for cid_j in from_captions[cid_i][0]: + # To avoid overwriting that to_captions[cid_j] when they exist + if to_captions.get(cid_j) is None: + to_captions[cid_j] = [cid_i] + elif cid_i not in to_captions[cid_j]: + to_captions[cid_j].append(cid_i) + # to_captions[cid_j] = [cid_i] + assigned_cids.add(cid_j) + + for cid_i, to_item in from_captions.items(): + # To avoid changing the size of from_captions[cid_i][0] while iterating... + preceding_to_remove = set() + following_to_remove = set() + + for cid_j in from_captions[cid_i][0]: + if cid_j in assigned_cids: + preceding_to_remove.add(cid_j) + # from_captions[cid_i][0].remove(cid_j) + + for cid_j in from_captions[cid_i][1]: + if cid_j in assigned_cids: + following_to_remove.add(cid_j) + # from_captions[cid_i][1].remove(cid_j) + + for num in preceding_to_remove: + from_captions[cid_i][0].remove(num) + for num in following_to_remove: + from_captions[cid_i][1].remove(num) + + for cid_i, to_item in from_captions.items(): + if len(from_captions[cid_i][0]) == 0 and len(from_captions[cid_i][1]) > 0: + for cid_j in from_captions[cid_i][1]: + to_captions[cid_j] = [cid_i] + assigned_cids.add(cid_j) + + if len(from_captions[cid_i][0]) > 0 and len(from_captions[cid_i][1]) == 0: + for cid_j in from_captions[cid_i][0]: + to_captions[cid_j] = [cid_i] + assigned_cids.add(cid_j) + + """ + for cid_i, to_item in to_captions.items(): + print("to-captions: ", cid_i, ": ", to_item) + """ + + def _remove_overlapping_indexes(mapping): + used = set() + result = {} + for key, values in sorted(mapping.items()): + valid = [ + v + for v in sorted(values, key=lambda v: abs(v - key)) + if v not in used + ] + if valid: + result[key] = [valid[0]] + used.add(valid[0]) + return result + + to_captions = _remove_overlapping_indexes(to_captions) + return to_captions + + def _find_to_footnotes( + self, page_elements: List[PageElement] + ) -> Dict[int, List[int]]: + + to_footnotes: Dict[int, List[int]] = {} + + # Try find captions that precede the table and footnotes that come after the table + for ind, page_element in enumerate(page_elements): + + if page_element.label in [DocItemLabel.TABLE, DocItemLabel.PICTURE]: 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a/tests/test_layout_predictor.py +++ b/tests/test_layout_predictor.py @@ -3,6 +3,7 @@ # SPDX-License-Identifier: MIT # import os +import json from pathlib import Path import torch @@ -71,7 +72,10 @@ def test_layoutpredictor(init: dict): # Predict on the test image for img_fn in init["test_imgs"]: + + true_layout_fn = img_fn+".json" with Image.open(img_fn) as img: + w, h = img.size # Load images as PIL objects @@ -81,8 +85,20 @@ def test_layoutpredictor(init: dict): assert pred["t"] >= 0 and pred["t"] <= h assert pred["r"] >= 0 and pred["r"] <= w assert pred["b"] >= 0 and pred["b"] <= h + assert i + 1 == init["pred_bboxes"] + if os.path.exists(true_layout_fn): + with open(true_layout_fn, "r") as fr: + true_layout = json.load(fr) + + """ + # FIXME: write a simple test to check all objects are found + else: + with open(true_layout_fn, "w") as fw: + fw.write(json.dumps(pred_layout, indent=4)) + """ + # Load images as numpy arrays np_arr = np.asarray(img) for i, pred in enumerate(lpredictor.predict(np_arr)): diff --git a/tests/test_reading_order.py b/tests/test_reading_order.py new file mode 100644 index 0000000..c7b7207 --- /dev/null +++ b/tests/test_reading_order.py @@ -0,0 +1,272 @@ +# +# Copyright IBM Corp. 2024 - 2024 +# SPDX-License-Identifier: MIT +# +import os +import json +import glob +import copy + +import logging + +from pathlib import Path + +import numpy as np +import pytest +from PIL import Image + +from datasets import load_dataset + +from typing import List, Dict +import random + +from docling_ibm_models.reading_order.reading_order_rb import PageElement, ReadingOrderPredictor + +from docling_core.types.doc.document import DoclingDocument, DocItem, RefItem, TextItem, ContentLayer + +# Configure logging +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" +) + +def rank_array(arr): + """Compute ranks, resolving ties by averaging.""" + sorted_indices = sorted(range(len(arr)), key=lambda i: arr[i]) # Sort indices + ranks = [0] * len(arr) # Initialize ranks + + i = 0 + while i < len(arr): + start = i + while i + 1 < len(arr) and arr[sorted_indices[i]] == arr[sorted_indices[i + 1]]: + i += 1 # Handle ties + avg_rank = sum(range(start + 1, i + 2)) / (i - start + 1) # Average rank for ties + for j in range(start, i + 1): + ranks[sorted_indices[j]] = avg_rank + i += 1 + return ranks + +def spearman_rank_correlation(arr1, arr2): + assert len(arr1) == len(arr2), "Arrays must have the same length" + + # Compute ranks + rank1 = rank_array(arr1) + rank2 = rank_array(arr2) + + # Compute rank differences and apply formula + d = [rank1[i] - rank2[i] for i in range(len(arr1))] + d_squared_sum = sum(d_i ** 2 for d_i in d) + + n = len(arr1) + if n > 1: + rho = 1 - (6 * d_squared_sum) / (n * (n**2 - 1)) + else: + rho = 0 + return rho + +def test_readingorder(): + + ro_scores, caption_scores, footnote_scores = [], [], [] + + # Init the reading-order model + romodel = ReadingOrderPredictor() + + ds = load_dataset("ds4sd/docling-dpbench") + for row in ds["test"]: + true_doc = DoclingDocument.model_validate_json(row["GroundTruthDocument"]) + + true_elements: List[PageElement] = [] + pred_elements: List[PageElement] = [] + + to_ref: Dict[int, str] = {} + from_ref: Dict[str, int] = {} + + for item, level in true_doc.iterate_items(included_content_layers={ContentLayer.BODY, ContentLayer.FURNITURE}): + if isinstance(item, DocItem): + for prov in item.prov: + + page_height = true_doc.pages[prov.page_no].size.height + bbox = prov.bbox.to_bottom_left_origin(page_height=page_height) + + text = "" + if isinstance(item, TextItem): + text = item.text + + true_elements.append( + PageElement( + cid=len(true_elements), + ref=item.get_ref(), + text = text, + page_no=prov.page_no, + page_size = true_doc.pages[prov.page_no].size, + label=item.label, + l = bbox.l, + r = bbox.r, + b = bbox.b, + t = bbox.t, + coord_origin = bbox.coord_origin + ) + ) + + to_ref[true_elements[-1].cid] = item.get_ref().cref + from_ref[item.get_ref().cref] = true_elements[-1].cid + + rand_elements = copy.deepcopy(true_elements) + random.shuffle(rand_elements) + + """ + print(f"reading {os.path.basename(filename)}") + for true_elem, rand_elem in zip(true_elements, rand_elements): + print("true: ", str(true_elem), ", rand: ", str(rand_elem)) + """ + + pred_elements = romodel.predict_reading_order(page_elements=rand_elements) + #pred_elements = romodel.predict_page(page_elements=rand_elements) + + assert len(pred_elements)==len(true_elements), f"{len(pred_elements)}!={len(true_elements)}" + + true_cids, pred_cids = [], [] + for true_elem, pred_elem, rand_elem in zip(true_elements, + pred_elements, + rand_elements): + true_cids.append(true_elem.cid) + pred_cids.append(pred_elem.cid) + + score = spearman_rank_correlation(true_cids, pred_cids) + ro_scores.append(score) + + filename = row["document_id"] + + if score == 0: + continue + # Identify special cases ... + if filename in ["doc_906d54a21ef3c7bfac03f4bb613b0c79ef32fdf81b362450c79e98a96f88708a_page_000001.png", + "doc_2cd17a32ee330a239e19c915738df0c27e8ec3635a60a7e16e2a0cf3868d4af3_page_000001.png", + "doc_bcb3dafc35b5e7476fd1b9cd6eccf5eeef936cd5b13ad846a4943f1e7797f4e9_page_000001.png", + "doc_a0edae1fa147c7bb78ebc493743a68ba4372b5ead31f2a2b146c35119462379e_page_000001.png", + "doc_94ba5468fcb6277721947697048846dc0d0551296be3b45f5918ab857d21dcc7_page_000001.png", + "doc_cbb4a13ffd01d9f777fdb939451d6a21cea1b869ee50d79581451e3601df9ec8_page_000001.png"]: + # print(f"{os.path.basename(filename)}: {score}") + assert score>=0.60, f"reading-order score={score}>0.60" + else: + assert score>=0.90, f"reading-order score={score}>0.90 for {filename}" + + + true_to_captions: Dict[int, List[int]] = {} + true_to_footnotes: Dict[int, List[int]] = {} + + total_caption_links = 0 + total_footnote_links = 0 + + for table in true_doc.tables: + table_cid = from_ref[table.get_ref().cref] + + true_to_captions[table_cid] = [] + for caption in table.captions: + caption_cid = from_ref[caption.get_ref().cref] + true_to_captions[table_cid].append(caption_cid) + + total_caption_links += 1 + + true_to_footnotes[table_cid] = [] + for footnote in table.footnotes: + footnote_cid = from_ref[footnote.get_ref().cref] + true_to_footnotes[table_cid].append(footnote_cid) + + total_footnote_links += 1 + + for picture in true_doc.pictures: + picture_cid = from_ref[picture.get_ref().cref] + + true_to_captions[picture_cid] = [] + for caption in picture.captions: + caption_cid = from_ref[caption.get_ref().cref] + true_to_captions[picture_cid].append(caption_cid) + + total_caption_links += 1 + + true_to_footnotes[picture_cid] = [] + for footnote in picture.footnotes: + footnote_cid = from_ref[footnote.get_ref().cref] + true_to_footnotes[picture_cid].append(footnote_cid) + + total_footnote_links += 1 + + if total_caption_links>0: + #print(" *********** ") + + pred_to_captions = romodel.predict_to_captions(sorted_elements=pred_elements) + + """ + for key,val in pred_to_captions.items(): + print(f"pred {key}: {val}") + """ + + score, total = 0.0, 0.0 + for key,val in true_to_captions.items(): + # print(f"true {key}: {val}") + + total += 1.0 + if key in pred_to_captions and pred_to_captions[key]==val: + score += 1.0 + + # print(f"to_captions: {score/total}") + caption_scores.append(score/total) + + if total_footnote_links>0: + # print(" *********** ") + + pred_to_footnotes = romodel.predict_to_footnotes(sorted_elements=pred_elements) + + """ + for key,val in pred_to_footnotes.items(): + print(f"pred {key}: {val}") + """ + + score, total = 0.0, 0.0 + for key,val in true_to_footnotes.items(): + # print(f"true {key}: {val}") + + total += 1.0 + if key in pred_to_footnotes and pred_to_footnotes[key]==val: + score += 1.0 + + # print(f"to_footnotes: {score/total}") + footnote_scores.append(score/total) + + pred_merges = romodel.predict_merges(sorted_elements=pred_elements) + # print("merges: ", pred_merges) + + + mean_ro_score = sum(ro_scores)/len(ro_scores) + mean_cp_score = sum(caption_scores)/len(caption_scores) + mean_ft_score = sum(footnote_scores)/len(footnote_scores) + + assert mean_ro_score>0.95, "mean_ro_score>0.95" + assert mean_cp_score>0.85, "mean_cp_score>0.85" + assert mean_ft_score>0.90, "mean_ft_score>0.90" + + print("\n score(reading): ", mean_ro_score) + print(" score(caption): ", mean_cp_score) + print("score(footnotes): ", mean_ft_score) + + +""" +def test_readingorder_multipage(): + + filename = Path("") + + # Init the reading-order model + romodel = ReadingOrderPredictor() + + true_elements: List[PageElement] = [] + pred_elements: List[PageElement] = [] + + with open(filename, "r") as fr: + data = json.load(fr) + true_elements = [PageElement.model_validate(item) for item in data] + + pred_elements = romodel.predict_reading_order(page_elements=true_elements) + for true_elem, pred_elem in zip(true_elements, pred_elements): + print("true: ", str(true_elem), ", pred: ", str(pred_elem)) +""" +