From bf76f4e37535baebd703d9aa4a3f959bfa8ef08d Mon Sep 17 00:00:00 2001 From: candyhong <1102229410@qq.com> Date: Mon, 4 Nov 2024 16:44:37 +0800 Subject: [PATCH] support table structure model inference, and table inference concatenated with OCR model. --- configs/table/example.png | Bin 0 -> 35966 bytes configs/table/example_structure.png | Bin 0 -> 331751 bytes mindocr/models/__init__.py | 1 + mindocr/models/table_master.py | 57 ++ mindocr/utils/visualize.py | 2 + requirements.txt | 4 + tools/infer/text/config.py | 30 + tools/infer/text/postprocess.py | 13 + tools/infer/text/predict_table_recognition.py | 118 ++++ tools/infer/text/predict_table_structure.py | 120 ++++ tools/infer/text/preprocess.py | 17 +- tools/infer/text/utils/__init__.py | 2 + tools/infer/text/utils/matcher.py | 614 ++++++++++++++++++ tools/infer/text/{ => utils}/utils.py | 0 14 files changed, 977 insertions(+), 1 deletion(-) create mode 100644 configs/table/example.png create mode 100644 configs/table/example_structure.png create mode 100644 mindocr/models/table_master.py create mode 100644 tools/infer/text/predict_table_recognition.py create mode 100644 tools/infer/text/predict_table_structure.py create mode 100644 tools/infer/text/utils/__init__.py create mode 100644 tools/infer/text/utils/matcher.py rename tools/infer/text/{ => utils}/utils.py (100%) diff --git a/configs/table/example.png b/configs/table/example.png new file mode 100644 index 0000000000000000000000000000000000000000..e7cd5d5c36902079f047095cc70326f5fd4639e0 GIT binary patch literal 35966 zcmaHT2RN2*|M#UrcBGP#l28Z@q)-u(P!Y*a$cT*WQIzaTR+N>M9ZH4Fq6k^pqwHj6 zkN0!;d!Fb2yyJKs$J5VKx7&4}*Li-wpY^?bRh8xGs9C8Af}lIAAbXJ@$ma3CO`A91 zZ{9JEej2+rB=< zRBhk96z@Fk3w{r7??0PrvF9R}3t#A~T|ooZEj^qwMKdlvw)8g|Y@UANJZd~BIpI{d zy!w1bYT|HRYe+?(H9_H3s=tXj&Z>)y7T?b9Et`~P@}0F@zEi3%93e#GsxDJzWg*N$ zE9Y7I&wn5G9sVNiI9bxt($dzp*jMhO_d$lG#^H>doSe2c`%+_1&-$0woXpI#IZ+lJ zuLDoVmDnjLDCp{To6PNzmiA~&I!{JMW+A#<6M6Xc>(})3^zy;G5-+B@S;WP~^_AS& 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zba(&MJFNy*T)@LS8PCs;KlinFI!p6`9~J_=`(CSqwm+QuXZPXh^aH=FF5LUKZvL}Z zoA&eH+0QQ{b7MxX#CyT@`U~tUi{7lt)YAesj6kUHYlcbR;rf_q4fk$yi*!#fuU#y8 z-IN=+@baE4JNp6oe+4xL*>n8Xris01`#7)Aj?q?(`PZ+8tgn;gjzeA6!@Yd{`wHFq zK#Ox9`=9n20{u~6c=z##KxgLz-8MGIEKj8Loj%Xw&-?iNylbo_$1TgG|2(g^>w5`| z0D^0Yq?)QR3ONY!iSo~q!4^a zAn>a#kRCK*j_>uQdfVQ!%O2nPr5zZU&`^djfaQrclNGQ-3Y_CD7CsNW5e`{F&wQzU zOjV07W^8gXXS|Cd#dG+_%7@a-ch{W5&|{_ERscM%tJm#t3ozP|&Et8jeo|ZPMU;X0 z$2+JJK+_&&H|8DM2?;=2.31.0 pycocotools>=2.0.2 setuptools-scm albumentations +beautifulsoup4 +pandas +tablepyxl +lxml diff --git a/tools/infer/text/config.py b/tools/infer/text/config.py index 139bfc063..92d6e47f1 100644 --- a/tools/infer/text/config.py +++ b/tools/infer/text/config.py @@ -166,6 +166,36 @@ def create_parser(): "due to padding or resizing to the same shape.", ) parser.add_argument("--kie_batch_num", type=int, default=8) + parser.add_argument( + "--table_algorithm", + type=str, + default="TABLE_MASTER", + choices=["TABLE_MASTER"], + help="table structure recognition algorithm", + ) + parser.add_argument( + "--table_model_dir", + type=str, + help="directory containing the table structure recognition model checkpoint best.ckpt, " + "or path to a specific checkpoint file.", + ) + parser.add_argument( + "--table_amp_level", + type=str, + default="O2", + choices=["O0", "O1", "O2", "O3"], + help="Auto Mixed Precision level. This setting only works on GPU and Ascend", + ) + parser.add_argument( + "--table_char_dict_path", + type=str, + default="./mindocr/utils/dict/table_master_structure_dict.txt", + help="path to character dictionary for table structure recognition. " + "If None, will pick according to table_algorithm and table_model_dir.", + ) + parser.add_argument( + "--table_max_len", type=int, default=480, help="max length of the input image for table structure recognition." + ) return parser diff --git a/tools/infer/text/postprocess.py b/tools/infer/text/postprocess.py index e3c4d449f..8e86a1cf6 100644 --- a/tools/infer/text/postprocess.py +++ b/tools/infer/text/postprocess.py @@ -81,6 +81,16 @@ def __init__(self, task="det", algo="DB", rec_char_dict_path=None, **kwargs): elif task == "ser": class_path = "mindocr/utils/dict/class_list_xfun.txt" postproc_cfg = dict(name="VQASerTokenLayoutLMPostProcess", class_path=class_path) + elif task == "table": + table_char_dict_path = kwargs.get( + "table_char_dict_path", "mindocr/utils/dict/table_master_structure_dict.txt" + ) + postproc_cfg = dict( + name="TableMasterLabelDecode", + character_dict_path=table_char_dict_path, + merge_no_span_structure=True, + box_shape="pad", + ) postproc_cfg.update(kwargs) self.task = task @@ -142,3 +152,6 @@ def __call__(self, pred, data=None, **kwargs): pred, segment_offset_ids=kwargs.get("segment_offset_ids"), ocr_infos=kwargs.get("ocr_infos") ) return output + elif self.task == "table": + output = self.postprocess(pred, labels=kwargs.get("labels")) + return output diff --git a/tools/infer/text/predict_table_recognition.py b/tools/infer/text/predict_table_recognition.py new file mode 100644 index 000000000..327bf6519 --- /dev/null +++ b/tools/infer/text/predict_table_recognition.py @@ -0,0 +1,118 @@ +""" +Infer table from images with structure model and ocr model. + +Example: + $ python tools/infer/text/predict_table_recognition.py --image_dir {path_to_img} --table_algorithm TABLE_MASTER +""" +import logging +import os +import sys +from typing import Union + +import cv2 +import numpy as np +from config import parse_args +from predict_system import TextSystem +from predict_table_structure import StructureAnalyzer +from utils import TableMasterMatcher, get_image_paths + +from mindocr.utils.logger import set_logger + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../../../"))) + +logger = logging.getLogger("mindocr") + + +class TableAnalyzer: + """ + Model inference class for table structure analysis and match with ocr result. + Example: + >>> args = parse_args() + >>> analyzer = TableAnalyzer(args) + >>> img_path = "path/to/image.jpg" + >>> pred_html, time_prof = analyzer(img_path) + """ + + def __init__(self, args): + self.text_system = TextSystem(args) + self.table_structure = StructureAnalyzer(args) + self.match = TableMasterMatcher() + + def _structure(self, img_or_path: Union[str, np.ndarray], do_visualize: bool = True): + structure_res, elapse = self.table_structure(img_or_path, do_visualize) + return structure_res, elapse + + def _text_ocr(self, img_or_path: Union[str, np.ndarray], do_visualize: bool = True): + boxes, text_scores, time_prof = self.text_system(img_or_path, do_visualize) + if isinstance(img_or_path, str): + img = cv2.imread(img_or_path) + elif isinstance(img_or_path, np.ndarray): + img = img_or_path + else: + raise ValueError("Invalid input type, should be str or np.ndarray.") + h, w = img.shape[:2] + r_boxes = [] + for box in boxes: + x_min = max(0, box[:, 0].min() - 1) + x_max = min(w, box[:, 0].max() + 1) + y_min = max(0, box[:, 1].min() - 1) + y_max = min(h, box[:, 1].max() + 1) + box = [x_min, y_min, x_max, y_max] + r_boxes.append(box) + dt_boxes = np.array(r_boxes) + return dt_boxes, text_scores, time_prof + + def __call__(self, img_or_path: Union[str, np.ndarray], do_visualize: bool = True): + boxes, text_scores, ocr_time_prof = self._text_ocr(img_or_path, do_visualize) + structure_res, struct_time_prof = self._structure(img_or_path, do_visualize) + pred_html = self.match(structure_res, boxes, text_scores) + time_prof = { + "ocr": ocr_time_prof, + "table": struct_time_prof, + } + return pred_html, time_prof + + +def parse_html_table(html_table): + from bs4 import BeautifulSoup + + soup = BeautifulSoup(html_table, "html.parser") + table = soup.find("table") + if not table: + raise ValueError("No table found in the HTML string.") + return table + + +def to_excel(html_table, excel_path): + from tablepyxl import tablepyxl + + table = parse_html_table(html_table) + tablepyxl.document_to_xl(str(table), excel_path) + + +def to_csv(html_table, csv_path): + import pandas as pd + + table = parse_html_table(html_table) + df = pd.read_html(str(table))[0] + df.to_csv(csv_path, index=False) + + +def main(): + args = parse_args() + set_logger(name="mindocr") + analyzer = TableAnalyzer(args) + img_paths = get_image_paths(args.image_dir) + save_dir = args.draw_img_save_dir + for i, img_path in enumerate(img_paths): + logger.info(f"Infering {i+1}/{len(img_paths)}: {img_path}") + pred_html, time_prof = analyzer(img_path, do_visualize=True) + logger.info(f"Time profile: {time_prof}") + img_name = os.path.basename(img_path).rsplit(".", 1)[0] + to_csv(pred_html, os.path.join(save_dir, f"{img_name}.csv")) + logger.info(f"Done! All structure results are saved to {args.draw_img_save_dir}") + + +if __name__ == "__main__": + main() diff --git a/tools/infer/text/predict_table_structure.py b/tools/infer/text/predict_table_structure.py new file mode 100644 index 000000000..3ecb81782 --- /dev/null +++ b/tools/infer/text/predict_table_structure.py @@ -0,0 +1,120 @@ +""" +Infer table structure from images. + +Example: + $ python tools/infer/text/predict_table_structure.py --image_dir {path_to_img} --table_algorithm TABLE_MASTER +""" +import logging +import os +import sys +import time +from typing import Dict, Union + +import numpy as np +from config import parse_args +from postprocess import Postprocessor +from preprocess import Preprocessor +from utils import get_ckpt_file, get_image_paths + +from mindspore import Tensor + +from mindocr.models import build_model +from mindocr.utils.logger import set_logger +from mindocr.utils.visualize import draw_boxes, show_imgs + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../../../"))) + + +algo_to_model_name = { + "TABLE_MASTER": "table_master", +} +logger = logging.getLogger("mindocr") + + +class StructureAnalyzer: + """ + Model inference class for table structure analysis. + Example: + >>> args = parse_args() + >>> analyzer = StructureAnalyzer(args) + >>> img_path = "path/to/image.jpg" + >>> (structure_str_list, bbox_list), elapsed_time = analyzer(img_path) + """ + + def __init__(self, args): + ckpt_dir = args.table_model_dir + if ckpt_dir is None: + pretrained = True + ckpt_load_path = None + else: + ckpt_load_path = get_ckpt_file(ckpt_dir) + pretrained = False + if args.table_algorithm not in algo_to_model_name: + raise ValueError( + f"Invalid table algorithm {args.table_algorithm}. " + f"Supported table algorithms are {list(algo_to_model_name.keys())}" + ) + model_name = algo_to_model_name[args.table_algorithm] + + self.model = build_model( + model_name, pretrained=pretrained, ckpt_load_path=ckpt_load_path, amp_level=args.table_amp_level + ) + self.model.set_train(False) + self.preprocess = Preprocessor(task="table", table_max_len=args.table_max_len) + self.postprocess = Postprocessor(task="table", table_char_dict_path=args.table_char_dict_path) + self.vis_dir = args.draw_img_save_dir + os.makedirs(self.vis_dir, exist_ok=True) + + def __call__( + self, + img_or_path: Union[str, np.ndarray, Dict], + do_visualize: bool = True, + ): + """ + Perform model inference. + Args: + img_or_path (Union[str, np.ndarray, Dict]): Input image or image path. + do_visualize (bool): Whether to visualize the result. + Returns: + Structure string list, bounding box list, and elapsed time. + """ + time_profile = {} + start_time = time.time() + data = self.preprocess(img_or_path) + input_np = data["image"] + if len(input_np.shape) == 3: + input_np = Tensor(np.expand_dims(input_np, axis=0)) + + net_pred = self.model(input_np) + shape_list = np.expand_dims(data["shape"], axis=0) + post_result = self.postprocess(net_pred, labels=[shape_list]) + structure_str_list = post_result["structure_batch_list"][0][0] + structure_str_list = ["", "", ""] + structure_str_list + ["
", "", ""] + bbox_list = post_result["bbox_batch_list"][0] + elapse = time.time() - start_time + time_profile["structure"] = elapse + + if do_visualize: + vst = time.time() + img_name = os.path.basename(data.get("img_path", "input.png")).rsplit(".", 1)[0] + save_path = os.path.join(self.vis_dir, img_name + "_structure.png") + structure_vis = draw_boxes(img_or_path, bbox_list, draw_type="rectangle") + show_imgs([structure_vis], show=False, save_path=save_path) + time_profile["vis"] = time.time() - vst + return (structure_str_list, bbox_list), time_profile + + +def main(): + args = parse_args() + set_logger(name="mindocr") + analyzer = StructureAnalyzer(args) + img_paths = get_image_paths(args.image_dir) + for i, img_path in enumerate(img_paths): + logger.info(f"Inferring {i+1}/{len(img_paths)}: {img_path}") + _ = analyzer(img_path, do_visualize=True) + logger.info(f"Done! All structure results are saved to {args.draw_img_save_dir}") + + +if __name__ == "__main__": + main() diff --git a/tools/infer/text/preprocess.py b/tools/infer/text/preprocess.py index 42cb67fea..f19d11138 100644 --- a/tools/infer/text/preprocess.py +++ b/tools/infer/text/preprocess.py @@ -187,7 +187,22 @@ def __init__(self, task="det", algo="DB", **kwargs): }, {"ToCHWImage": None}, ] - + elif task == "table": + table_max_len = kwargs.get("table_max_len", 480) + pipeline = [ + {"DecodeImage": {"img_mode": "RGB", "keep_ori": True, "to_float32": False}}, + {"ResizeTableImage": {"max_len": table_max_len}}, + {"PaddingTableImage": {"size": [table_max_len, table_max_len]}}, + { + "TableImageNorm": { + "std": [0.5, 0.5, 0.5], + "mean": [0.5, 0.5, 0.5], + "scale": "1./255.", + "order": "hwc", + } + }, + {"ToCHWImage": None}, + ] self.pipeline = pipeline self.transforms = create_transforms(pipeline) diff --git a/tools/infer/text/utils/__init__.py b/tools/infer/text/utils/__init__.py new file mode 100644 index 000000000..1e474566d --- /dev/null +++ b/tools/infer/text/utils/__init__.py @@ -0,0 +1,2 @@ +from .matcher import Matcher, TableMasterMatcher +from .utils import * diff --git a/tools/infer/text/utils/matcher.py b/tools/infer/text/utils/matcher.py new file mode 100644 index 000000000..41a77470f --- /dev/null +++ b/tools/infer/text/utils/matcher.py @@ -0,0 +1,614 @@ +import copy +import glob +import math +import os +import pickle +import re + +import numpy as np +from shapely.geometry import MultiPoint, Polygon + + +def remove_empty_bboxes(bboxes): + """ + Remove empty bboxes. + """ + return np.array([bbox for bbox in bboxes if np.sum(bbox) != 0.0]) + + +def xywh2xyxy(bboxes): + """ + Convert bboxes from xywh to xyxy format. + """ + if len(bboxes.shape) == 1: + new_bboxes = np.empty_like(bboxes) + new_bboxes[0] = bboxes[0] - bboxes[2] / 2 + new_bboxes[1] = bboxes[1] - bboxes[3] / 2 + new_bboxes[2] = bboxes[0] + bboxes[2] / 2 + new_bboxes[3] = bboxes[1] + bboxes[3] / 2 + return new_bboxes + if len(bboxes.shape) == 2: + new_bboxes = np.empty_like(bboxes) + new_bboxes[:, 0] = bboxes[:, 0] - bboxes[:, 2] / 2 + new_bboxes[:, 1] = bboxes[:, 1] - bboxes[:, 3] / 2 + new_bboxes[:, 2] = bboxes[:, 0] + bboxes[:, 2] / 2 + new_bboxes[:, 3] = bboxes[:, 1] + bboxes[:, 3] / 2 + return new_bboxes + raise ValueError + + +def xyxy2xywh(bboxes): + """ + Convert bboxes from xyxy to xywh format. + """ + if len(bboxes.shape) == 1: + new_bboxes = np.empty_like(bboxes) + new_bboxes[0] = bboxes[0] + (bboxes[2] - bboxes[0]) / 2 + new_bboxes[1] = bboxes[1] + (bboxes[3] - bboxes[1]) / 2 + new_bboxes[2] = bboxes[2] - bboxes[0] + new_bboxes[3] = bboxes[3] - bboxes[1] + return new_bboxes + if len(bboxes.shape) == 2: + new_bboxes = np.empty_like(bboxes) + new_bboxes[:, 0] = bboxes[:, 0] + (bboxes[:, 2] - bboxes[:, 0]) / 2 + new_bboxes[:, 1] = bboxes[:, 1] + (bboxes[:, 3] - bboxes[:, 1]) / 2 + new_bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0] + new_bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1] + return new_bboxes + raise ValueError + + +def pickle_load(path, prefix="end2end"): + if os.path.isfile(path): + with open(path, "rb") as f: + return pickle.load(f) + if os.path.isdir(path): + data = {} + search_path = os.path.join(path, f"{prefix}_*.pkl") + for pkl in glob.glob(search_path): + with open(pkl, "rb") as f: + data.update(pickle.load(f)) + return data + raise ValueError(f"Path '{path}' is neither a file nor a directory.") + + +def convert_coord(xyxy): + new_bbox = np.zeros([4, 2], dtype=np.float32) + new_bbox[0, 0], new_bbox[0, 1] = xyxy[0], xyxy[1] + new_bbox[1, 0], new_bbox[1, 1] = xyxy[2], xyxy[1] + new_bbox[2, 0], new_bbox[2, 1] = xyxy[2], xyxy[3] + new_bbox[3, 0], new_bbox[3, 1] = xyxy[0], xyxy[3] + return new_bbox + + +def cal_iou(bbox1, bbox2): + bbox1_poly = Polygon(bbox1).convex_hull + bbox2_poly = Polygon(bbox2).convex_hull + union_poly = np.concatenate((bbox1, bbox2)) + + if not bbox1_poly.intersects(bbox2_poly): + return 0.0 + inter_area = bbox1_poly.intersection(bbox2_poly).area + union_area = MultiPoint(union_poly).convex_hull.area + if union_area == 0: + return 0.0 + return inter_area / union_area + + +def cal_distance(p1, p2): + delta_x = p1[0] - p2[0] + delta_y = p1[1] - p2[1] + return math.sqrt((delta_x**2) + (delta_y**2)) + + +def is_inside(center_point, corner_point): + """ + Check if center_point inside the bbox(corner_point) or not. + Args: + center_point (tuple): Center point (x, y). + corner_point (tuple): Bounding box corners ((x1, y1), (x2, y2)). + Returns: + bool: True if the point is inside the bounding box, False otherwise. + """ + return ( + corner_point[0][0] <= center_point[0] <= corner_point[1][0] + and corner_point[0][1] <= center_point[1] <= corner_point[1][1] + ) + + +def find_no_match(match_list, all_end2end_nums, match_type="end2end"): + """ + Find out no match end2end bbox in previous match list. + Args: + match_list (List[Tuple[int, int]]): List of matching pairs. + all_end2end_nums (int): Total number of end2end_xywh. + match_type (str): 'end2end' corresponding to idx 0, 'master' corresponding to idx 1. + Returns: + List[int]: List of indices with no match. + """ + idx_map = {"end2end": 0, "master": 1} + if match_type not in idx_map: + raise ValueError("Invalid type. Expected 'end2end' or 'master'.") + idx = idx_map[match_type] + + matched_bbox_indices = {m[idx] for m in match_list} + return [n for n in range(all_end2end_nums) if n not in matched_bbox_indices] + + +def is_abs_lower_than_threshold(this_bbox, target_bbox, threshold=3): + # Only consider y axis, for grouping in row. + return abs(this_bbox[1] - target_bbox[1]) < threshold + + +def sort_line_bbox(g, bg): + """ + Sorted the bbox in the same line(group) + compare coord 'x' value, where 'y' value is closed in the same group. + """ + combined = sorted(zip(g, bg), key=lambda item: item[1][0]) + g_sorted, bg_sorted = zip(*combined) + return list(g_sorted), list(bg_sorted) + + +def flatten(sorted_groups, sorted_bbox_groups): + idxs = [] + bboxes = [] + for group, bbox_group in zip(sorted_groups, sorted_bbox_groups): + for g, bg in zip(group, bbox_group): + idxs.append(g) + bboxes.append(bg) + return idxs, bboxes + + +def sort_bbox(end2end_xywh_bboxes, no_match_end2end_indexes): + """ + This function will group the render end2end bboxes in row. + """ + groups = [] + bbox_groups = [] + for index, this_bbox in zip(no_match_end2end_indexes, end2end_xywh_bboxes): + for g, bg in zip(groups, bbox_groups): + if is_abs_lower_than_threshold(this_bbox, bg[0]): + g.append(index) + bg.append(this_bbox) + break + else: + groups.append([index]) + bbox_groups.append([this_bbox]) + # sorted bboxes in a group + sorted_groups_bbox_pairs = sorted( + (sort_line_bbox(g, bg) for g, bg in zip(groups, bbox_groups)), key=lambda x: x[1][0][1] + ) + sorted_groups, sorted_bbox_groups = zip(*sorted_groups_bbox_pairs) + # flatten, get final result + end2end_sorted_idx_list, end2end_sorted_bbox_list = flatten(sorted_groups, sorted_bbox_groups) + return ( + end2end_sorted_idx_list, + end2end_sorted_bbox_list, + list(sorted_groups), + list(sorted_bbox_groups), + ) + + +def get_bboxes_list(end2end_result, structure_master_result): + """ + Get bbox(xyxy and xywh) list from end2end and structure master result. + """ + end2end_xyxy_bboxes = [] + end2end_xywh_bboxes = [] + for item in end2end_result: + bbox = item["bbox"] + end2end_xyxy_bboxes.append(bbox) + end2end_xywh_bboxes.append(xyxy2xywh(bbox)) + end2end_xyxy_bboxes = np.array(end2end_xyxy_bboxes) + end2end_xywh_bboxes = np.array(end2end_xywh_bboxes) + + structure_master_xyxy_bboxes = remove_empty_bboxes(structure_master_result["bbox"]) + structure_master_xywh_bboxes = xyxy2xywh(structure_master_xyxy_bboxes) + + return ( + end2end_xyxy_bboxes, + end2end_xywh_bboxes, + structure_master_xywh_bboxes, + structure_master_xyxy_bboxes, + ) + + +def center_rule_match(end2end_xywh_bboxes, structure_master_xyxy_bboxes): + """ + Judge end2end Bbox's center point is inside structure master Bbox or not, if yes, return the match pairs. + """ + match_pairs_list = [] + for i, end2end_xywh in enumerate(end2end_xywh_bboxes): + center_point_end2end = (end2end_xywh[0], end2end_xywh[1]) + for j, master_xyxy in enumerate(structure_master_xyxy_bboxes): + corner_point_master = ((master_xyxy[0], master_xyxy[1]), (master_xyxy[2], master_xyxy[3])) + if is_inside(center_point_end2end, corner_point_master): + match_pairs_list.append([i, j]) + return match_pairs_list + + +def iou_rule_match(end2end_xyxy_bboxes, end2end_xyxy_indexes, structure_master_xyxy_bboxes): + """ + Use iou to find matching list, choose max iou value bbox as match pair. + """ + match_pair_list = [] + for end2end_index, end2end_bbox in zip(end2end_xyxy_indexes, end2end_xyxy_bboxes): + max_iou = 0 + max_match = None + end2end_4xy = convert_coord(end2end_bbox) + for j, master_bbox in enumerate(structure_master_xyxy_bboxes): + master_4xy = convert_coord(master_bbox) + iou = cal_iou(end2end_4xy, master_4xy) + if iou > max_iou: + max_match = [end2end_index, j] + max_iou = iou + + if max_match: + match_pair_list.append(max_match) + return match_pair_list + + +def distance_rule_match(end2end_indexes, end2end_bboxes, master_indexes, master_bboxes): + """ + Match end2end bounding boxes with master bounding boxes based on the minimum distance. + """ + min_match_list = [] + for j, master_bbox in zip(master_indexes, master_bboxes): + min_distance = np.inf + min_match = [0, 0] + master_point = (master_bbox[0], master_bbox[1]) + for i, end2end_bbox in zip(end2end_indexes, end2end_bboxes): + end2end_point = (end2end_bbox[0], end2end_bbox[1]) + dist = cal_distance(master_point, end2end_point) + if dist < min_distance: + min_match = [i, j] + min_distance = dist + min_match_list.append(min_match) + return min_match_list + + +def extra_match(no_match_end2end_indexes, master_bbox_nums): + """ + Create virtual master bboxes and match them with the no match end2end indexes. + """ + return [[no_match_end2end_indexes[i], i + master_bbox_nums] for i in range(len(no_match_end2end_indexes))] + + +def get_match_dict(match_list): + """ + Convert match_list to a dict, where key is master bbox's index, value is end2end bbox index. + """ + match_dict = {} + for end2end_index, master_index in match_list: + match_dict.setdefault(master_index, []).append(end2end_index) + return match_dict + + +def reduce_repeat_bb(text_list, break_token): + """ + convert ['Local', 'government', 'unit'] to ['Local government unit'] + """ + if all(text.startswith("") and text.endswith("") for text in text_list): + new_text_list = [text[3:-4] for text in text_list] + return [f"{break_token.join(new_text_list)}"] + return text_list + + +def get_match_text_dict(match_dict, end2end_info, break_token=" "): + match_text_dict = {} + for master_index, end2end_index_list in match_dict.items(): + text_list = [end2end_info[end2end_index]["text"] for end2end_index in end2end_index_list] + text_list = reduce_repeat_bb(text_list, break_token) + text = break_token.join(text_list) + match_text_dict[master_index] = text + return match_text_dict + + +def merge_span_token(master_token_list): + """ + Merge the span style token (row span or col span). + """ + new_master_token_list = [] + pointer = 0 + if master_token_list[-1] != "": + master_token_list.append("") + while pointer < len(master_token_list) and master_token_list[pointer] != "": + try: + if master_token_list[pointer] == ", '' + '' + tmp = "".join(master_token_list[pointer : pointer + 4]) + pointer += 4 + elif any(master_token_list[pointer + 2].startswith(attr) for attr in [" colspan=", " rowspan="]): + # pattern , '' + '' + tmp = "".join(master_token_list[pointer : pointer + 5]) + pointer += 5 + else: + tmp = master_token_list[pointer] + pointer += 1 + new_master_token_list.append(tmp) + else: + new_master_token_list.append(master_token_list[pointer]) + pointer += 1 + except IndexError: + print("Break in merge due to IndexError...") + break + new_master_token_list.append("") + return new_master_token_list + + +def deal_eb_token(master_token): + replacements = { + "": "", + "": " ", + "": " ", + "": "\u2028\u2028", + "": " ", + "": "", + "": " ", + "": "", + "": " ", + "": "", + "": " \u2028 \u2028 ", + } + for old, new in replacements.items(): + master_token = master_token.replace(old, new) + return master_token + + +def insert_text_to_token(master_token_list, match_text_dict): + """ + Insert OCR text result to structure token. + """ + master_token_list = merge_span_token(master_token_list) + merged_result_list = [] + text_count = 0 + for master_token in master_token_list: + if master_token.startswith("<", f">{match_text_dict[text_count]}<") + text_count += 1 + master_token = deal_eb_token(master_token) + merged_result_list.append(master_token) + return "".join(merged_result_list) + + +def deal_isolate_span(thead_part): + """ + Deal with isolate span cases caused by wrong predictions in the structure recognition model. + """ + # 1. Find out isolate span tokens. + isolate_pattern = re.compile( + r' (rowspan="\d+" colspan="\d+"|colspan="\d+" rowspan="\d+"|rowspan="\d+"|colspan="\d+")>
' + ) + isolate_list = isolate_pattern.findall(thead_part) + # 2. Correct the isolated span tokens. + corrected_list = [f"" for span in isolate_list] + # 3. Replace original isolated tokens with corrected tokens. + thead_part = isolate_pattern.sub(lambda _: corrected_list.pop(0), thead_part) + return thead_part + + +def deal_duplicate_bb(thead_part): + """ + Deal with duplicate tags within tags in the part of an HTML table. + """ + # 1. Find all tags in . + td_pattern = re.compile(r'.*?') + td_list = td_pattern.findall(thead_part) + + # 2. Check for multiple tags within and correct them. + new_td_list = [] + for td_item in td_list: + if td_item.count("") > 1 or td_item.count("") > 1: + # Remove all tags and reapply them correctly. + td_item = td_item.replace("", "").replace("", "") + td_item = td_item.replace("", "").replace("", "") + new_td_list.append(td_item) + + # 3. Replace original tags with corrected ones. + for td_item, new_td_item in zip(td_list, new_td_list): + thead_part = thead_part.replace(td_item, new_td_item) + return thead_part + + +def deal_bb(result_token): + """ + Find out all tokens in and insert manually. + """ + # Find out parts. + thead_pattern = re.compile(r"(.*?)") + match = thead_pattern.search(result_token) + if not match: + return result_token + thead_part = match.group() + origin_thead_part = copy.deepcopy(thead_part) + + # Check if "rowspan" or "colspan" occur in parts. + span_pattern = re.compile( + r'' + ) + span_list = span_pattern.findall(thead_part) + has_span_in_head = bool(span_list) + + if not has_span_in_head: + # not include "rowspan" or "colspan". + thead_part = ( + thead_part.replace("", "") + .replace("", "") + .replace("", "") + .replace("", "") + ) + else: + # include "rowspan" or "colspan". + # Replace ">" with ">" and "" with "" + for sp in span_list: + thead_part = thead_part.replace(sp, sp.replace(">", ">")) + thead_part = thead_part.replace("", "") + + # Remove duplicated and tags + thead_part = re.sub(r"()+", "", thead_part) + thead_part = re.sub(r"()+", "", thead_part) + + # Handle ordinary cases + thead_part = thead_part.replace("", "").replace("", "") + + thead_part = thead_part.replace("", "") + # deal with duplicated + thead_part = deal_duplicate_bb(thead_part) + thead_part = deal_isolate_span(thead_part) + # replace original result with new thead part. + result_token = result_token.replace(origin_thead_part, thead_part) + return result_token + + +class Matcher: + def __init__(self, end2end_file, structure_master_file): + """ + This class process the end2end results and structure recognition results. + """ + self.end2end_file = end2end_file + self.structure_master_file = structure_master_file + self.end2end_results = pickle_load(end2end_file, prefix="end2end") + self.structure_master_results = pickle_load(structure_master_file, prefix="structure") + + def match(self): + """ + Match process: + pre-process : convert end2end and structure master results to xyxy, xywh ndnarray format. + 1. Use pseBbox is inside masterBbox judge rule + 2. Use iou between pseBbox and masterBbox rule + 3. Use min distance of center point rule + """ + match_results = {} + for file_name, end2end_result in self.end2end_results.items(): + if file_name not in self.structure_master_results: + continue + structure_master_result = self.structure_master_results[file_name] + ( + end2end_xyxy_bboxes, + end2end_xywh_bboxes, + structure_master_xywh_bboxes, + structure_master_xyxy_bboxes, + ) = get_bboxes_list(end2end_result, structure_master_result) + + match_list = self._apply_match_rule( + end2end_xywh_bboxes, end2end_xyxy_bboxes, structure_master_xywh_bboxes, structure_master_xyxy_bboxes + ) + no_match_end2end_indexes = find_no_match(match_list, len(end2end_xywh_bboxes), "end2end") + + if no_match_end2end_indexes: + no_match_end2end_xywh = end2end_xywh_bboxes[no_match_end2end_indexes] + (end2end_sorted_indexes_list, _, sorted_groups, sorted_bboxes_groups) = sort_bbox( + no_match_end2end_xywh, no_match_end2end_indexes + ) + extra_match_list = extra_match(end2end_sorted_indexes_list, len(structure_master_xywh_bboxes)) + match_list_add_extra_match = copy.deepcopy(match_list) + match_list_add_extra_match.extend(extra_match_list) + else: + match_list_add_extra_match = copy.deepcopy(match_list) + sorted_groups, sorted_bboxes_groups = [], [] + + match_result_dict = { + "match_list": match_list, + "match_list_add_extra_match": match_list_add_extra_match, + "sorted_groups": sorted_groups, + "sorted_bboxes_groups": sorted_bboxes_groups, + } + match_result_dict = self._format(match_result_dict, file_name) + match_results[file_name] = match_result_dict + return match_results + + def _apply_match_rule( + self, end2end_xywh_bboxes, end2end_xyxy_bboxes, structure_master_xywh_bboxes, structure_master_xyxy_bboxes + ): + match_list = [] + # Rule 1: Center rule + match_list.extend(center_rule_match(end2end_xywh_bboxes, structure_master_xyxy_bboxes)) + # Rule 2: IoU rule + center_no_match_end2end_indexes = find_no_match(match_list, len(end2end_xywh_bboxes), "end2end") + if center_no_match_end2end_indexes: + center_no_match_end2end_xyxy = end2end_xyxy_bboxes[center_no_match_end2end_indexes] + match_list.extend( + iou_rule_match( + center_no_match_end2end_xyxy, center_no_match_end2end_indexes, structure_master_xyxy_bboxes + ) + ) + # Rule 3: Distance rule + centerIou_no_match_end2end_indexes = find_no_match(match_list, len(end2end_xywh_bboxes), "end2end") + centerIou_no_match_master_indexes = find_no_match(match_list, len(structure_master_xywh_bboxes), "master") + if centerIou_no_match_end2end_indexes and centerIou_no_match_master_indexes: + centerIou_no_match_end2end_xywh = end2end_xywh_bboxes[centerIou_no_match_end2end_indexes] + centerIou_no_match_master_xywh = structure_master_xywh_bboxes[centerIou_no_match_master_indexes] + match_list.extend( + distance_rule_match( + centerIou_no_match_end2end_indexes, + centerIou_no_match_end2end_xywh, + centerIou_no_match_master_indexes, + centerIou_no_match_master_xywh, + ) + ) + return match_list + + def _format(self, match_result, file_name): + """ + Extend the master token (insert virtual master token), and format matching result. + """ + master_info = self.structure_master_results[file_name] + master_token = master_info["text"] + sorted_groups = match_result["sorted_groups"] + + # creat virtual master token + virtual_master_token_list = ["" + "" * len(line_group) + "" for line_group in sorted_groups] + # insert virtual master token + master_token_list = master_token.split(",") + if master_token_list[-1] == "": + master_token_list[:-1].extend(virtual_master_token_list) + elif master_token_list[-1] == "": + master_token_list.append("") + master_token_list.extend(virtual_master_token_list) + master_token_list.append("") + else: + master_token_list.extend(virtual_master_token_list) + master_token_list.append("") + + match_result["matched_master_token_list"] = master_token_list + return match_result + + def get_merge_result(self, match_results): + """ + Merge the OCR result into structure token to get final results. + """ + merged_results = {} + break_token = " " + + for file_name, match_info in match_results.items(): + end2end_info = self.end2end_results[file_name] + master_token_list = match_info["matched_master_token_list"] + match_list = match_info["match_list_add_extra_match"] + + match_dict = get_match_dict(match_list) + match_text_dict = get_match_text_dict(match_dict, end2end_info, break_token) + merged_result = insert_text_to_token(master_token_list, match_text_dict) + merged_result = deal_bb(merged_result) + merged_results[file_name] = merged_result + + return merged_results + + +class TableMasterMatcher(Matcher): + def __init__(self): + pass + + def __call__(self, structure_res, dt_boxes, rec_res, img_name=1): + self.end2end_results = { + img_name: [{"bbox": np.array(dt_box), "text": res[0]} for dt_box, res in zip(dt_boxes, rec_res)] + } + pred_structures, pred_bboxes = structure_res + self.structure_master_results = {img_name: {"text": ",".join(pred_structures[3:-3]), "bbox": pred_bboxes}} + + match_results = self.match() + merged_results = self.get_merge_result(match_results) + pred_html = merged_results[img_name] + return "" + pred_html + "
" diff --git a/tools/infer/text/utils.py b/tools/infer/text/utils/utils.py similarity index 100% rename from tools/infer/text/utils.py rename to tools/infer/text/utils/utils.py