Merge pull request #969 from opendatalab/release-0.9.3

Release 0.9.3

.gitignore

@@ -48,3 +48,6 @@ debug_utils/
 
 # sphinx docs
 _build/
+
+
+output/
\ No newline at end of file

README.md

@@ -42,6 +42,7 @@
 </div>
 
 # Changelog
+- 2024/11/15 0.9.3 released. Integrated [RapidTable](https://github.com/RapidAI/RapidTable) for table recognition, improving single-table parsing speed by more than 10 times, with higher accuracy and lower GPU memory usage.
 - 2024/11/06 0.9.2 released. Integrated the [StructTable-InternVL2-1B](https://huggingface.co/U4R/StructTable-InternVL2-1B) model for table recognition functionality.
 - 2024/10/31 0.9.0 released. This is a major new version with extensive code refactoring, addressing numerous issues, improving performance, reducing hardware requirements, and enhancing usability:
   - Refactored the sorting module code to use [layoutreader](https://github.com/ppaanngggg/layoutreader) for reading order sorting, ensuring high accuracy in various layouts.
@@ -246,7 +247,7 @@ You can modify certain configurations in this file to enable or disable features
         "enable": true  // The formula recognition feature is enabled by default. If you need to disable it, please change the value here to "false".
     },
     "table-config": {
-        "model": "tablemaster",  // When using structEqTable, please change to "struct_eqtable".
+        "model": "rapid_table",  // When using structEqTable, please change to "struct_eqtable".
         "enable": false, // The table recognition feature is disabled by default. If you need to enable it, please change the value here to "true".
         "max_time": 400
     }
@@ -261,7 +262,7 @@ If your device supports CUDA and meets the GPU requirements of the mainline envi
 - [Windows 10/11 + GPU](docs/README_Windows_CUDA_Acceleration_en_US.md)
 - Quick Deployment with Docker
 > [!IMPORTANT]
-> Docker requires a GPU with at least 16GB of VRAM, and all acceleration features are enabled by default.
+> Docker requires a GPU with at least 8GB of VRAM, and all acceleration features are enabled by default.
 >
 > Before running this Docker, you can use the following command to check if your device supports CUDA acceleration on Docker.
 > 
@@ -421,7 +422,9 @@ This project currently uses PyMuPDF to achieve advanced functionality. However,
 # Acknowledgments
 
 - [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit)
+- [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO)
 - [StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy)
+- [RapidTable](https://github.com/RapidAI/RapidTable)
 - [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
 - [PyMuPDF](https://github.com/pymupdf/PyMuPDF)
 - [layoutreader](https://github.com/ppaanngggg/layoutreader)

README_ja-JP.md

+> [!Warning]
+> このドキュメントはすでに古くなっています。最新版のドキュメントを参照してください：[ENGLISH](README.md)。
 <div id="top">
 
 <p align="center">
@@ -18,9 +20,7 @@
 <a href="https://trendshift.io/repositories/11174" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11174" alt="opendatalab%2FMinerU | Trendshift" style="width: 200px; height: 55px;"/></a>
 
 
-<div align="center" style="color: red; background-color: #ffdddd; padding: 10px; border: 1px solid red; border-radius: 5px;">
-  <strong>NOTE：</strong> このドキュメントはすでに古くなっています。最新版のドキュメントを参照してください。
-</div>
+
 
 
 [English](README.md) | [简体中文](README_zh-CN.md) | [日本語](README_ja-JP.md)

README_zh-CN.md

@@ -42,7 +42,7 @@
 </div>
 
 # 更新记录
-
+- 2024/11/15 0.9.3发布，为表格识别功能接入了[RapidTable](https://github.com/RapidAI/RapidTable),单表解析速度提升10倍以上，准确率更高，显存占用更低
 - 2024/11/06 0.9.2发布，为表格识别功能接入了[StructTable-InternVL2-1B](https://huggingface.co/U4R/StructTable-InternVL2-1B)模型
 - 2024/10/31 0.9.0发布，这是我们进行了大量代码重构的全新版本，解决了众多问题，提升了性能，降低了硬件需求，并提供了更丰富的易用性：
   - 重构排序模块代码，使用 [layoutreader](https://github.com/ppaanngggg/layoutreader) 进行阅读顺序排序，确保在各种排版下都能实现极高准确率
@@ -188,13 +188,13 @@ https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c
         <td rowspan="2">GPU硬件支持列表</td>
         <td colspan="2">最低要求 8G+显存</td>
         <td colspan="2">3060ti/3070/4060<br>
-        8G显存可开启layout、公式识别和ocr加速</td>
+        8G显存可开启全部加速功能(表格仅限rapid_table)</td>
         <td rowspan="2">None</td>
     </tr>
     <tr>
         <td colspan="2">推荐配置 10G+显存</td>
         <td colspan="2">3080/3080ti/3090/3090ti/4070/4070ti/4070tisuper/4080/4090<br>
-        10G显存及以上可以同时开启layout、公式识别和ocr加速和表格识别加速<br>
+        10G显存及以上可开启全部加速功能<br>
         </td>
     </tr>
 </table>
@@ -251,7 +251,7 @@ pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com -i h
         "enable": true  // 公式识别功能默认是开启的，如果需要关闭请修改此处的值为"false"
     },
     "table-config": {
-        "model": "tablemaster",  // 使用structEqTable请修改为"struct_eqtable"
+        "model": "rapid_table",  // 使用structEqTable请修改为"struct_eqtable"
         "enable": false, // 表格识别功能默认是关闭的，如果需要开启请修改此处的值为"true"
         "max_time": 400
     }
@@ -266,7 +266,7 @@ pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com -i h
 - [Windows10/11 + GPU](docs/README_Windows_CUDA_Acceleration_zh_CN.md)
 - 使用Docker快速部署
 > [!IMPORTANT]
-> Docker 需设备gpu显存大于等于16GB，默认开启所有加速功能
+> Docker 需设备gpu显存大于等于8GB，默认开启所有加速功能
 > 
 > 运行本docker前可以通过以下命令检测自己的设备是否支持在docker上使用CUDA加速
 > 
@@ -431,6 +431,7 @@ TODO
 - [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit)
 - [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO)
 - [StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy)
+- [RapidTable](https://github.com/RapidAI/RapidTable)
 - [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
 - [PyMuPDF](https://github.com/pymupdf/PyMuPDF)
 - [layoutreader](https://github.com/ppaanngggg/layoutreader)

demo/magic_pdf_parse_main.py

@@ -19,9 +19,10 @@ def json_md_dump(
         pdf_name,
         content_list,
         md_content,
+        orig_model_list,
 ):
     # 写入模型结果到 model.json
-    orig_model_list = copy.deepcopy(pipe.model_list)
+
     md_writer.write(
         content=json.dumps(orig_model_list, ensure_ascii=False, indent=4),
         path=f"{pdf_name}_model.json"
@@ -87,9 +88,12 @@ def pdf_parse_main(
 
         pdf_bytes = open(pdf_path, "rb").read()  # 读取 pdf 文件的二进制数据
 
+        orig_model_list = []
+
         if model_json_path:
             # 读取已经被模型解析后的pdf文件的 json 原始数据，list 类型
             model_json = json.loads(open(model_json_path, "r", encoding="utf-8").read())
+            orig_model_list = copy.deepcopy(model_json)
         else:
             model_json = []
 
@@ -115,8 +119,9 @@ def pdf_parse_main(
         pipe.pipe_classify()
 
         # 如果没有传入模型数据，则使用内置模型解析
-        if not model_json:
+        if len(model_json) == 0:
             pipe.pipe_analyze()  # 解析
+            orig_model_list = copy.deepcopy(pipe.model_list)
 
         # 执行解析
         pipe.pipe_parse()
@@ -126,7 +131,7 @@ def pdf_parse_main(
         md_content = pipe.pipe_mk_markdown(image_path_parent, drop_mode="none")
 
         if is_json_md_dump:
-            json_md_dump(pipe, md_writer, pdf_name, content_list, md_content)
+            json_md_dump(pipe, md_writer, pdf_name, content_list, md_content, orig_model_list)
 
         if is_draw_visualization_bbox:
             draw_visualization_bbox(pipe.pdf_mid_data['pdf_info'], pdf_bytes, output_path, pdf_name)

magic-pdf.template.json

@@ -15,7 +15,7 @@
         "enable": true
     },
     "table-config": {
-        "model": "tablemaster",
+        "model": "rapid_table",
         "enable": false,
         "max_time": 400
     },

magic_pdf/dict2md/ocr_mkcontent.py

@@ -168,7 +168,7 @@ def merge_para_with_text(para_block):
                         # 如果是前一行带有-连字符，那么末尾不应该加空格
                         if __is_hyphen_at_line_end(content):
                             para_text += content[:-1]
-                        elif len(content) == 1 and content not in ['A', 'I', 'a', 'i']:
+                        elif len(content) == 1 and content not in ['A', 'I', 'a', 'i'] and not content.isdigit():
                             para_text += content
                         else:  # 西方文本语境下 content间需要空格分隔
                             para_text += f"{content} "

magic_pdf/libs/Constants.py

@@ -50,4 +50,6 @@ class MODEL_NAME:
 
     YOLO_V8_MFD = "yolo_v8_mfd"
 
-    UniMerNet_v2_Small = "unimernet_small"
\ No newline at end of file
+    UniMerNet_v2_Small = "unimernet_small"
+
+    RAPID_TABLE = "rapid_table"
\ No newline at end of file

magic_pdf/libs/config_reader.py

@@ -92,7 +92,7 @@ def get_table_recog_config():
     table_config = config.get('table-config')
     if table_config is None:
         logger.warning(f"'table-config' not found in {CONFIG_FILE_NAME}, use 'False' as default")
-        return json.loads(f'{{"model": "{MODEL_NAME.TABLE_MASTER}","enable": false, "max_time": 400}}')
+        return json.loads(f'{{"model": "{MODEL_NAME.RAPID_TABLE}","enable": false, "max_time": 400}}')
     else:
         return table_config
 

magic_pdf/libs/draw_bbox.py

@@ -369,10 +369,16 @@ def draw_line_sort_bbox(pdf_info, pdf_bytes, out_path, filename):
             if block['type'] in [BlockType.Image, BlockType.Table]:
                 for sub_block in block['blocks']:
                     if sub_block['type'] in [BlockType.ImageBody, BlockType.TableBody]:
-                        for line in sub_block['virtual_lines']:
-                            bbox = line['bbox']
-                            index = line['index']
-                            page_line_list.append({'index': index, 'bbox': bbox})
+                        if len(sub_block['virtual_lines']) > 0 and sub_block['virtual_lines'][0].get('index', None) is not None:
+                            for line in sub_block['virtual_lines']:
+                                bbox = line['bbox']
+                                index = line['index']
+                                page_line_list.append({'index': index, 'bbox': bbox})
+                        else:
+                            for line in sub_block['lines']:
+                                bbox = line['bbox']
+                                index = line['index']
+                                page_line_list.append({'index': index, 'bbox': bbox})
                     elif sub_block['type'] in [BlockType.ImageCaption, BlockType.TableCaption, BlockType.ImageFootnote, BlockType.TableFootnote]:
                         for line in sub_block['lines']:
                             bbox = line['bbox']

magic_pdf/model/pdf_extract_kit.py

+import numpy as np
+import torch
 from loguru import logger
 import os
 import time
-from pathlib import Path
-import shutil
-from magic_pdf.libs.Constants import *
-from magic_pdf.libs.clean_memory import clean_memory
-from magic_pdf.model.model_list import AtomicModel
+import cv2
+import yaml
+from PIL import Image
 
 os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1'  # 禁止albumentations检查更新
 os.environ['YOLO_VERBOSE'] = 'False'  # disable yolo logger
+
 try:
-    import cv2
-    import yaml
-    import argparse
-    import numpy as np
-    import torch
     import torchtext
 
     if torchtext.__version__ >= "0.18.0":
         torchtext.disable_torchtext_deprecation_warning()
-    from PIL import Image
-    from torchvision import transforms
-    from torch.utils.data import Dataset, DataLoader
-    from ultralytics import YOLO
-    from unimernet.common.config import Config
-    import unimernet.tasks as tasks
-    from unimernet.processors import load_processor
-    from doclayout_yolo import YOLOv10
-
-except ImportError as e:
-    logger.exception(e)
-    logger.error(
-        'Required dependency not installed, please install by \n'
-        '"pip install magic-pdf[full] --extra-index-url https://myhloli.github.io/wheels/"')
-    exit(1)
-
-from magic_pdf.model.pek_sub_modules.layoutlmv3.model_init import Layoutlmv3_Predictor
-from magic_pdf.model.pek_sub_modules.post_process import latex_rm_whitespace
-from magic_pdf.model.pek_sub_modules.self_modify import ModifiedPaddleOCR
-from magic_pdf.model.pek_sub_modules.structeqtable.StructTableModel import StructTableModel
-from magic_pdf.model.ppTableModel import ppTableModel
-
-
-def table_model_init(table_model_type, model_path, max_time, _device_='cpu'):
-    if table_model_type == MODEL_NAME.STRUCT_EQTABLE:
-        table_model = StructTableModel(model_path, max_time=max_time)
-    elif table_model_type == MODEL_NAME.TABLE_MASTER:
-        config = {
-            "model_dir": model_path,
-            "device": _device_
-        }
-        table_model = ppTableModel(config)
-    else:
-        logger.error("table model type not allow")
-        exit(1)
-    return table_model
-
-
-def mfd_model_init(weight):
-    mfd_model = YOLO(weight)
-    return mfd_model
-
-
-def mfr_model_init(weight_dir, cfg_path, _device_='cpu'):
-    args = argparse.Namespace(cfg_path=cfg_path, options=None)
-    cfg = Config(args)
-    cfg.config.model.pretrained = os.path.join(weight_dir, "pytorch_model.pth")
-    cfg.config.model.model_config.model_name = weight_dir
-    cfg.config.model.tokenizer_config.path = weight_dir
-    task = tasks.setup_task(cfg)
-    model = task.build_model(cfg)
-    model.to(_device_)
-    model.eval()
-    vis_processor = load_processor('formula_image_eval', cfg.config.datasets.formula_rec_eval.vis_processor.eval)
-    mfr_transform = transforms.Compose([vis_processor, ])
-    return [model, mfr_transform]
-
-
-def layout_model_init(weight, config_file, device):
-    model = Layoutlmv3_Predictor(weight, config_file, device)
-    return model
-
-
-def doclayout_yolo_model_init(weight):
-    model = YOLOv10(weight)
-    return model
-
-
-def ocr_model_init(show_log: bool = False, det_db_box_thresh=0.3, lang=None, use_dilation=True, det_db_unclip_ratio=1.8):
-    if lang is not None:
-        model = ModifiedPaddleOCR(show_log=show_log, det_db_box_thresh=det_db_box_thresh, lang=lang, use_dilation=use_dilation, det_db_unclip_ratio=det_db_unclip_ratio)
-    else:
-        model = ModifiedPaddleOCR(show_log=show_log, det_db_box_thresh=det_db_box_thresh, use_dilation=use_dilation, det_db_unclip_ratio=det_db_unclip_ratio)
-    return model
-
-
-class MathDataset(Dataset):
-    def __init__(self, image_paths, transform=None):
-        self.image_paths = image_paths
-        self.transform = transform
-
-    def __len__(self):
-        return len(self.image_paths)
-
-    def __getitem__(self, idx):
-        # if not pil image, then convert to pil image
-        if isinstance(self.image_paths[idx], str):
-            raw_image = Image.open(self.image_paths[idx])
-        else:
-            raw_image = self.image_paths[idx]
-        if self.transform:
-            image = self.transform(raw_image)
-            return image
-
-
-class AtomModelSingleton:
-    _instance = None
-    _models = {}
-
-    def __new__(cls, *args, **kwargs):
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-        return cls._instance
-
-    def get_atom_model(self, atom_model_name: str, **kwargs):
-        lang = kwargs.get("lang", None)
-        layout_model_name = kwargs.get("layout_model_name", None)
-        key = (atom_model_name, layout_model_name, lang)
-        if key not in self._models:
-            self._models[key] = atom_model_init(model_name=atom_model_name, **kwargs)
-        return self._models[key]
-
-
-def atom_model_init(model_name: str, **kwargs):
-
-    if model_name == AtomicModel.Layout:
-        if kwargs.get("layout_model_name") == MODEL_NAME.LAYOUTLMv3:
-            atom_model = layout_model_init(
-                kwargs.get("layout_weights"),
-                kwargs.get("layout_config_file"),
-                kwargs.get("device")
-            )
-        elif kwargs.get("layout_model_name") == MODEL_NAME.DocLayout_YOLO:
-            atom_model = doclayout_yolo_model_init(
-                kwargs.get("doclayout_yolo_weights"),
-            )
-    elif model_name == AtomicModel.MFD:
-        atom_model = mfd_model_init(
-            kwargs.get("mfd_weights")
-        )
-    elif model_name == AtomicModel.MFR:
-        atom_model = mfr_model_init(
-            kwargs.get("mfr_weight_dir"),
-            kwargs.get("mfr_cfg_path"),
-            kwargs.get("device")
-        )
-    elif model_name == AtomicModel.OCR:
-        atom_model = ocr_model_init(
-            kwargs.get("ocr_show_log"),
-            kwargs.get("det_db_box_thresh"),
-            kwargs.get("lang")
-        )
-    elif model_name == AtomicModel.Table:
-        atom_model = table_model_init(
-            kwargs.get("table_model_name"),
-            kwargs.get("table_model_path"),
-            kwargs.get("table_max_time"),
-            kwargs.get("device")
-        )
-    else:
-        logger.error("model name not allow")
-        exit(1)
-
-    return atom_model
-
+except ImportError:
+    pass
 
-#  Unified crop img logic
-def crop_img(input_res, input_pil_img, crop_paste_x=0, crop_paste_y=0):
-    crop_xmin, crop_ymin = int(input_res['poly'][0]), int(input_res['poly'][1])
-    crop_xmax, crop_ymax = int(input_res['poly'][4]), int(input_res['poly'][5])
-    # Create a white background with an additional width and height of 50
-    crop_new_width = crop_xmax - crop_xmin + crop_paste_x * 2
-    crop_new_height = crop_ymax - crop_ymin + crop_paste_y * 2
-    return_image = Image.new('RGB', (crop_new_width, crop_new_height), 'white')
-
-    # Crop image
-    crop_box = (crop_xmin, crop_ymin, crop_xmax, crop_ymax)
-    cropped_img = input_pil_img.crop(crop_box)
-    return_image.paste(cropped_img, (crop_paste_x, crop_paste_y))
-    return_list = [crop_paste_x, crop_paste_y, crop_xmin, crop_ymin, crop_xmax, crop_ymax, crop_new_width, crop_new_height]
-    return return_image, return_list
+from magic_pdf.libs.Constants import *
+from magic_pdf.model.model_list import AtomicModel
+from magic_pdf.model.sub_modules.model_init import AtomModelSingleton
+from magic_pdf.model.sub_modules.model_utils import get_res_list_from_layout_res, crop_img, clean_vram
+from magic_pdf.model.sub_modules.ocr.paddleocr.ocr_utils import get_adjusted_mfdetrec_res, get_ocr_result_list
 
 
 class CustomPEKModel:
@@ -226,7 +59,7 @@ class CustomPEKModel:
         self.table_config = kwargs.get("table_config")
         self.apply_table = self.table_config.get("enable", False)
         self.table_max_time = self.table_config.get("max_time", TABLE_MAX_TIME_VALUE)
-        self.table_model_name = self.table_config.get("model", MODEL_NAME.TABLE_MASTER)
+        self.table_model_name = self.table_config.get("model", MODEL_NAME.RAPID_TABLE)
 
         # ocr config
         self.apply_ocr = ocr
@@ -235,7 +68,8 @@ class CustomPEKModel:
         logger.info(
             "DocAnalysis init, this may take some times, layout_model: {}, apply_formula: {}, apply_ocr: {}, "
             "apply_table: {}, table_model: {}, lang: {}".format(
-                self.layout_model_name, self.apply_formula, self.apply_ocr, self.apply_table, self.table_model_name, self.lang
+                self.layout_model_name, self.apply_formula, self.apply_ocr, self.apply_table, self.table_model_name,
+                self.lang
             )
         )
         # 初始化解析方案
@@ -248,17 +82,17 @@ class CustomPEKModel:
 
         # 初始化公式识别
         if self.apply_formula:
-
             # 初始化公式检测模型
             self.mfd_model = atom_model_manager.get_atom_model(
                 atom_model_name=AtomicModel.MFD,
-                mfd_weights=str(os.path.join(models_dir, self.configs["weights"][self.mfd_model_name]))
+                mfd_weights=str(os.path.join(models_dir, self.configs["weights"][self.mfd_model_name])),
+                device=self.device
             )
 
             # 初始化公式解析模型
             mfr_weight_dir = str(os.path.join(models_dir, self.configs["weights"][self.mfr_model_name]))
             mfr_cfg_path = str(os.path.join(model_config_dir, "UniMERNet", "demo.yaml"))
-            self.mfr_model, self.mfr_transform = atom_model_manager.get_atom_model(
+            self.mfr_model = atom_model_manager.get_atom_model(
                 atom_model_name=AtomicModel.MFR,
                 mfr_weight_dir=mfr_weight_dir,
                 mfr_cfg_path=mfr_cfg_path,
@@ -278,7 +112,8 @@ class CustomPEKModel:
             self.layout_model = atom_model_manager.get_atom_model(
                 atom_model_name=AtomicModel.Layout,
                 layout_model_name=MODEL_NAME.DocLayout_YOLO,
-                doclayout_yolo_weights=str(os.path.join(models_dir, self.configs['weights'][self.layout_model_name]))
+                doclayout_yolo_weights=str(os.path.join(models_dir, self.configs['weights'][self.layout_model_name])),
+                device=self.device
             )
         # 初始化ocr
         if self.apply_ocr:
@@ -305,26 +140,15 @@ class CustomPEKModel:
 
         page_start = time.time()
 
-        latex_filling_list = []
-        mf_image_list = []
-
         # layout检测
         layout_start = time.time()
+        layout_res = []
         if self.layout_model_name == MODEL_NAME.LAYOUTLMv3:
             # layoutlmv3
             layout_res = self.layout_model(image, ignore_catids=[])
         elif self.layout_model_name == MODEL_NAME.DocLayout_YOLO:
             # doclayout_yolo
-            layout_res = []
-            doclayout_yolo_res = self.layout_model.predict(image, imgsz=1024, conf=0.25, iou=0.45, verbose=True, device=self.device)[0]
-            for xyxy, conf, cla in zip(doclayout_yolo_res.boxes.xyxy.cpu(), doclayout_yolo_res.boxes.conf.cpu(), doclayout_yolo_res.boxes.cls.cpu()):
-                xmin, ymin, xmax, ymax = [int(p.item()) for p in xyxy]
-                new_item = {
-                    'category_id': int(cla.item()),
-                    'poly': [xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax],
-                    'score': round(float(conf.item()), 3),
-                }
-                layout_res.append(new_item)
+            layout_res = self.layout_model.predict(image)
         layout_cost = round(time.time() - layout_start, 2)
         logger.info(f"layout detection time: {layout_cost}")
 
@@ -333,59 +157,21 @@ class CustomPEKModel:
         if self.apply_formula:
             # 公式检测
             mfd_start = time.time()
-            mfd_res = self.mfd_model.predict(image, imgsz=1888, conf=0.25, iou=0.45, verbose=True, device=self.device)[0]
+            mfd_res = self.mfd_model.predict(image)
             logger.info(f"mfd time: {round(time.time() - mfd_start, 2)}")
-            for xyxy, conf, cla in zip(mfd_res.boxes.xyxy.cpu(), mfd_res.boxes.conf.cpu(), mfd_res.boxes.cls.cpu()):
-                xmin, ymin, xmax, ymax = [int(p.item()) for p in xyxy]
-                new_item = {
-                    'category_id': 13 + int(cla.item()),
-                    'poly': [xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax],
-                    'score': round(float(conf.item()), 2),
-                    'latex': '',
-                }
-                layout_res.append(new_item)
-                latex_filling_list.append(new_item)
-                bbox_img = pil_img.crop((xmin, ymin, xmax, ymax))
-                mf_image_list.append(bbox_img)
 
             # 公式识别
             mfr_start = time.time()
-            dataset = MathDataset(mf_image_list, transform=self.mfr_transform)
-            dataloader = DataLoader(dataset, batch_size=64, num_workers=0)
-            mfr_res = []
-            for mf_img in dataloader:
-                mf_img = mf_img.to(self.device)
-                with torch.no_grad():
-                    output = self.mfr_model.generate({'image': mf_img})
-                mfr_res.extend(output['pred_str'])
-            for res, latex in zip(latex_filling_list, mfr_res):
-                res['latex'] = latex_rm_whitespace(latex)
+            formula_list = self.mfr_model.predict(mfd_res, image)
+            layout_res.extend(formula_list)
             mfr_cost = round(time.time() - mfr_start, 2)
-            logger.info(f"formula nums: {len(mf_image_list)}, mfr time: {mfr_cost}")
-
-        # Select regions for OCR / formula regions / table regions
-        ocr_res_list = []
-        table_res_list = []
-        single_page_mfdetrec_res = []
-        for res in layout_res:
-            if int(res['category_id']) in [13, 14]:
-                single_page_mfdetrec_res.append({
-                    "bbox": [int(res['poly'][0]), int(res['poly'][1]),
-                             int(res['poly'][4]), int(res['poly'][5])],
-                })
-            elif int(res['category_id']) in [0, 1, 2, 4, 6, 7]:
-                ocr_res_list.append(res)
-            elif int(res['category_id']) in [5]:
-                table_res_list.append(res)
-
-        if torch.cuda.is_available() and self.device != 'cpu':
-            properties = torch.cuda.get_device_properties(self.device)
-            total_memory = properties.total_memory / (1024 ** 3)  # 将字节转换为 GB
-            if total_memory <= 10:
-                gc_start = time.time()
-                clean_memory()
-                gc_time = round(time.time() - gc_start, 2)
-                logger.info(f"gc time: {gc_time}")
+            logger.info(f"formula nums: {len(formula_list)}, mfr time: {mfr_cost}")
+
+        # 清理显存
+        clean_vram(self.device, vram_threshold=8)
+
+        # 从layout_res中获取ocr区域、表格区域、公式区域
+        ocr_res_list, table_res_list, single_page_mfdetrec_res = get_res_list_from_layout_res(layout_res)
 
         # ocr识别
         if self.apply_ocr:
@@ -393,23 +179,7 @@ class CustomPEKModel:
             # Process each area that requires OCR processing
             for res in ocr_res_list:
                 new_image, useful_list = crop_img(res, pil_img, crop_paste_x=50, crop_paste_y=50)
-                paste_x, paste_y, xmin, ymin, xmax, ymax, new_width, new_height = useful_list
-                # Adjust the coordinates of the formula area
-                adjusted_mfdetrec_res = []
-                for mf_res in single_page_mfdetrec_res:
-                    mf_xmin, mf_ymin, mf_xmax, mf_ymax = mf_res["bbox"]
-                    # Adjust the coordinates of the formula area to the coordinates relative to the cropping area
-                    x0 = mf_xmin - xmin + paste_x
-                    y0 = mf_ymin - ymin + paste_y
-                    x1 = mf_xmax - xmin + paste_x
-                    y1 = mf_ymax - ymin + paste_y
-                    # Filter formula blocks outside the graph
-                    if any([x1 < 0, y1 < 0]) or any([x0 > new_width, y0 > new_height]):
-                        continue
-                    else:
-                        adjusted_mfdetrec_res.append({
-                            "bbox": [x0, y0, x1, y1],
-                        })
+                adjusted_mfdetrec_res = get_adjusted_mfdetrec_res(single_page_mfdetrec_res, useful_list)
 
                 # OCR recognition
                 new_image = cv2.cvtColor(np.asarray(new_image), cv2.COLOR_RGB2BGR)
@@ -417,22 +187,8 @@ class CustomPEKModel:
 
                 # Integration results
                 if ocr_res:
-                    for box_ocr_res in ocr_res:
-                        p1, p2, p3, p4 = box_ocr_res[0]
-                        text, score = box_ocr_res[1]
-
-                        # Convert the coordinates back to the original coordinate system
-                        p1 = [p1[0] - paste_x + xmin, p1[1] - paste_y + ymin]
-                        p2 = [p2[0] - paste_x + xmin, p2[1] - paste_y + ymin]
-                        p3 = [p3[0] - paste_x + xmin, p3[1] - paste_y + ymin]
-                        p4 = [p4[0] - paste_x + xmin, p4[1] - paste_y + ymin]
-
-                        layout_res.append({
-                            'category_id': 15,
-                            'poly': p1 + p2 + p3 + p4,
-                            'score': round(score, 2),
-                            'text': text,
-                        })
+                    ocr_result_list = get_ocr_result_list(ocr_res, useful_list)
+                    layout_res.extend(ocr_result_list)
 
             ocr_cost = round(time.time() - ocr_start, 2)
             logger.info(f"ocr time: {ocr_cost}")
@@ -443,41 +199,30 @@ class CustomPEKModel:
             for res in table_res_list:
                 new_image, _ = crop_img(res, pil_img)
                 single_table_start_time = time.time()
-                # logger.info("------------------table recognition processing begins-----------------")
-                latex_code = None
                 html_code = None
                 if self.table_model_name == MODEL_NAME.STRUCT_EQTABLE:
                     with torch.no_grad():
                         table_result = self.table_model.predict(new_image, "html")
                         if len(table_result) > 0:
                             html_code = table_result[0]
-                else:
+                elif self.table_model_name == MODEL_NAME.TABLE_MASTER:
                     html_code = self.table_model.img2html(new_image)
-
+                elif self.table_model_name == MODEL_NAME.RAPID_TABLE:
+                    html_code, table_cell_bboxes, elapse = self.table_model.predict(new_image)
                 run_time = time.time() - single_table_start_time
-                # logger.info(f"------------table recognition processing ends within {run_time}s-----")
                 if run_time > self.table_max_time:
-                    logger.warning(f"------------table recognition processing exceeds max time {self.table_max_time}s----------")
+                    logger.warning(f"table recognition processing exceeds max time {self.table_max_time}s")
                 # 判断是否返回正常
-
-                if latex_code:
-                    expected_ending = latex_code.strip().endswith('end{tabular}') or latex_code.strip().endswith('end{table}')
-                    if expected_ending:
-                        res["latex"] = latex_code
-                    else:
-                        logger.warning(f"table recognition processing fails, not found expected LaTeX table end")
-                elif html_code:
+                if html_code:
                     expected_ending = html_code.strip().endswith('</html>') or html_code.strip().endswith('</table>')
                     if expected_ending:
                         res["html"] = html_code
                     else:
                         logger.warning(f"table recognition processing fails, not found expected HTML table end")
                 else:
-                    logger.warning(f"table recognition processing fails, not get latex or html return")
+                    logger.warning(f"table recognition processing fails, not get html return")
             logger.info(f"table time: {round(time.time() - table_start, 2)}")
 
         logger.info(f"-----page total time: {round(time.time() - page_start, 2)}-----")
 
         return layout_res
-
-

magic_pdf/model/pek_sub_modules/post_process.py

-import re
-
-def layout_rm_equation(layout_res):
-    rm_idxs = []
-    for idx, ele in enumerate(layout_res['layout_dets']):
-        if ele['category_id'] == 10:
-            rm_idxs.append(idx)
-    
-    for idx in rm_idxs[::-1]:
-        del layout_res['layout_dets'][idx]
-    return layout_res
-
-
-def get_croped_image(image_pil, bbox):
-    x_min, y_min, x_max, y_max = bbox
-    croped_img = image_pil.crop((x_min, y_min, x_max, y_max))
-    return croped_img
-
-
-def latex_rm_whitespace(s: str):
-    """Remove unnecessary whitespace from LaTeX code.
-    """
-    text_reg = r'(\\(operatorname|mathrm|text|mathbf)\s?\*? {.*?})'
-    letter = '[a-zA-Z]'
-    noletter = '[\W_^\d]'
-    names = [x[0].replace(' ', '') for x in re.findall(text_reg, s)]
-    s = re.sub(text_reg, lambda match: str(names.pop(0)), s)
-    news = s
-    while True:
-        s = news
-        news = re.sub(r'(?!\\ )(%s)\s+?(%s)' % (noletter, noletter), r'\1\2', s)
-        news = re.sub(r'(?!\\ )(%s)\s+?(%s)' % (noletter, letter), r'\1\2', news)
-        news = re.sub(r'(%s)\s+?(%s)' % (letter, noletter), r'\1\2', news)
-        if news == s:
-            break
-    return s
\ No newline at end of file

magic_pdf/model/sub_modules/layout/doclayout_yolo/DocLayoutYOLO.py

+from doclayout_yolo import YOLOv10
+
+
+class DocLayoutYOLOModel(object):
+    def __init__(self, weight, device):
+        self.model = YOLOv10(weight)
+        self.device = device
+
+    def predict(self, image):
+        layout_res = []
+        doclayout_yolo_res = self.model.predict(image, imgsz=1024, conf=0.25, iou=0.45, verbose=True, device=self.device)[0]
+        for xyxy, conf, cla in zip(doclayout_yolo_res.boxes.xyxy.cpu(), doclayout_yolo_res.boxes.conf.cpu(),
+                                   doclayout_yolo_res.boxes.cls.cpu()):
+            xmin, ymin, xmax, ymax = [int(p.item()) for p in xyxy]
+            new_item = {
+                'category_id': int(cla.item()),
+                'poly': [xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax],
+                'score': round(float(conf.item()), 3),
+            }
+            layout_res.append(new_item)
+        return layout_res
\ No newline at end of file