Merge pull request #1261 from opendatalab/release-0.10.6

Release 0.10.6

.github/workflows/cli.yml

@@ -30,7 +30,7 @@ jobs:
         source activate mineru
         conda env list
         pip show coverage
-        # cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
+        cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
         cd $GITHUB_WORKSPACE && python tests/clean_coverage.py      
         cd $GITHUB_WORKSPACE && coverage run -m pytest tests/unittest/ --cov=magic_pdf/  --cov-report html --cov-report term-missing
         cd $GITHUB_WORKSPACE && python tests/get_coverage.py
@@ -41,22 +41,6 @@ jobs:
     needs: cli-test
     runs-on: pdf
     steps:
-    - name: get_actor
-      run: |
-          metion_list="dt-yy"
-          echo $GITHUB_ACTOR
-          if [[ $GITHUB_ACTOR == "drunkpig" ]]; then
-            metion_list="xuchao"
-          elif [[ $GITHUB_ACTOR == "myhloli" ]]; then
-            metion_list="zhaoxiaomeng"
-          elif [[ $GITHUB_ACTOR == "icecraft" ]]; then
-            metion_list="xurui1"
-          fi
-          echo $metion_list
-          echo "METIONS=$metion_list" >> "$GITHUB_ENV"
-          echo ${{ env.METIONS }}
-
     - name: notify
       run: |
-        echo ${{ secrets.USER_ID }}
-        curl -X POST -H "Content-Type: application/json" -d '{"msg_type":"post","content":{"post":{"zh_cn":{"title":"'${{ github.repository }}' GitHubAction Failed","content":[[{"tag":"text","text":""},{"tag":"a","text":"Please click here for details ","href":"https://github.com/'${{ github.repository }}'/actions/runs/'${GITHUB_RUN_ID}'"},{"tag":"at","user_id":"'${{ secrets.USER_ID }}'"}]]}}}}'  ${{ secrets.WEBHOOK_URL }}
+        curl -X POST -H "Content-Type: application/json" -d '{"msg_type":"post","content":{"post":{"zh_cn":{"title":"'${{ github.repository }}' GitHubAction Failed","content":[[{"tag":"text","text":""},{"tag":"a","text":"Please click here for details ","href":"https://github.com/'${{ github.repository }}'/actions/runs/'${GITHUB_RUN_ID}'"},{"tag":"at","user_id":"'$USER_ID'"}]]}}}}'  $WEBHOOK_URL

.github/workflows/huigui.yml

@@ -29,14 +29,14 @@ jobs:
         source activate mineru
         conda env list
         pip show coverage
-        # cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
+        cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
         cd $GITHUB_WORKSPACE && python tests/clean_coverage.py      
         cd $GITHUB_WORKSPACE && coverage run -m pytest tests/unittest/ --cov=magic_pdf/  --cov-report html --cov-report term-missing
         cd $GITHUB_WORKSPACE && python tests/get_coverage.py
         cd $GITHUB_WORKSPACE && pytest -s -v tests/test_cli/test_cli_sdk.py
 
   notify_to_feishu:
-    if: ${{ always() && !cancelled() && contains(needs.*.result, 'failure') && (github.ref_name == 'master') }}
+    if: ${{ always() && !cancelled() && contains(needs.*.result, 'failure')}}
     needs: cli-test
     runs-on: pdf
     steps:
@@ -57,5 +57,5 @@ jobs:
 
     - name: notify
       run: |
-        echo ${{ secrets.USER_ID }}
-        curl -X POST -H "Content-Type: application/json" -d '{"msg_type":"post","content":{"post":{"zh_cn":{"title":"'${{ github.repository }}' GitHubAction Failed","content":[[{"tag":"text","text":""},{"tag":"a","text":"Please click here for details ","href":"https://github.com/'${{ github.repository }}'/actions/runs/'${GITHUB_RUN_ID}'"},{"tag":"at","user_id":"'${{ secrets.USER_ID }}'"}]]}}}}'  ${{ secrets.WEBHOOK_URL }}
+        #echo ${{ secrets.USER_ID }}
+        curl -X POST -H "Content-Type: application/json" -d '{"msg_type":"post","content":{"post":{"zh_cn":{"title":"'${{ github.repository }}' GitHubAction Failed","content":[[{"tag":"text","text":""},{"tag":"a","text":"Please click here for details ","href":"https://github.com/'${{ github.repository }}'/actions/runs/'${GITHUB_RUN_ID}'"},{"tag":"at","user_id":"'$USER_ID'"}]]}}}}'  $WEBHOOK_URL

docs/README_Windows_CUDA_Acceleration_en_US.md

@@ -67,14 +67,6 @@ If your graphics card has at least 8GB of VRAM, follow these steps to test CUDA-
    ```
    pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
    ```
-   > [!IMPORTANT]
-   > Ensure the following versions are specified in the command:
-   >
-   > ```
-   > torch==2.3.1 torchvision==0.18.1
-   > ```
-   >
-   > These are the highest versions we support. Installing higher versions without specifying them will cause the program to fail.
 
 2. **Modify the value of `"device-mode"`** in the `magic-pdf.json` configuration file located in your user directory.
 

docs/README_Windows_CUDA_Acceleration_zh_CN.md

@@ -69,15 +69,6 @@ pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com -i h
 pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
 ```
 
-> [!IMPORTANT]
-> 务必在命令中指定以下版本
->
-> ```bash
-> torch==2.3.1 torchvision==0.18.1
-> ```
->
-> 这是我们支持的最高版本，如果不指定版本会自动安装更高版本导致程序无法运行
-
 **2.修改【用户目录】中配置文件magic-pdf.json中"device-mode"的值**
 
 ```json

magic_pdf/config/constants.py

@@ -51,3 +51,8 @@ class MODEL_NAME:
     UniMerNet_v2_Small = 'unimernet_small'
 
     RAPID_TABLE = 'rapid_table'
+
+
+PARSE_TYPE_TXT = 'txt'
+PARSE_TYPE_OCR = 'ocr'
+

magic_pdf/data/data_reader_writer/base.py

@@ -48,4 +48,16 @@ class DataWriter(ABC):
             path (str): the target file where to write
             data (str): the data want to write
         """
-        self.write(path, data.encode())
+
+        def safe_encode(data: str, method: str):
+            try:
+                bit_data = data.encode(encoding=method, errors='replace')
+                return bit_data, True
+            except:  # noqa
+                return None, False
+
+        for method in ['utf-8', 'ascii']:
+            bit_data, flag = safe_encode(data, method)
+            if flag:
+                self.write(path, bit_data)
+                break

magic_pdf/data/dataset.py

@@ -1,11 +1,13 @@
+import os
 from abc import ABC, abstractmethod
-from typing import Iterator
+from typing import Callable, Iterator
 
 import fitz
 
 from magic_pdf.config.enums import SupportedPdfParseMethod
 from magic_pdf.data.schemas import PageInfo
 from magic_pdf.data.utils import fitz_doc_to_image
+from magic_pdf.filter import classify
 
 
 class PageableData(ABC):
@@ -28,6 +30,32 @@ class PageableData(ABC):
         """
         pass
 
+    @abstractmethod
+    def draw_rect(self, rect_coords, color, fill, fill_opacity, width, overlay):
+        """draw rectangle.
+
+        Args:
+            rect_coords (list[float]): four elements array contain the top-left and bottom-right coordinates, [x0, y0, x1, y1]
+            color (list[float] | None): three element tuple which describe the RGB of the board line, None means no board line
+            fill (list[float] | None): fill the board with RGB, None means will not fill with color
+            fill_opacity (float): opacity of the fill, range from [0, 1]
+            width (float): the width of board
+            overlay (bool): fill the color in foreground or background. True means fill in background.
+        """
+        pass
+
+    @abstractmethod
+    def insert_text(self, coord, content, fontsize, color):
+        """insert text.
+
+        Args:
+            coord (list[float]): four elements array contain the top-left and bottom-right coordinates, [x0, y0, x1, y1]
+            content (str): the text content
+            fontsize (int): font size of the text
+            color (list[float] | None):  three element tuple which describe the RGB of the board line, None will use the default font color!
+        """
+        pass
+
 
 class Dataset(ABC):
     @abstractmethod
@@ -66,6 +94,43 @@ class Dataset(ABC):
         """
         pass
 
+    @abstractmethod
+    def dump_to_file(self, file_path: str):
+        """Dump the file
+
+        Args: 
+            file_path (str): the file path 
+        """
+        pass
+
+    @abstractmethod
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(self, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        pass
+
+    @abstractmethod
+    def classify(self) -> SupportedPdfParseMethod:
+        """classify the dataset 
+
+        Returns:
+            SupportedPdfParseMethod: _description_
+        """
+        pass
+
+    @abstractmethod
+    def clone(self):
+        """clone this dataset
+        """
+        pass
+
 
 class PymuDocDataset(Dataset):
     def __init__(self, bits: bytes):
@@ -74,7 +139,8 @@ class PymuDocDataset(Dataset):
         Args:
             bits (bytes): the bytes of the pdf
         """
-        self._records = [Doc(v) for v in fitz.open('pdf', bits)]
+        self._raw_fitz = fitz.open('pdf', bits)
+        self._records = [Doc(v) for v in self._raw_fitz]
         self._data_bits = bits
         self._raw_data = bits
 
@@ -109,6 +175,43 @@ class PymuDocDataset(Dataset):
         """
         return self._records[page_id]
 
+    def dump_to_file(self, file_path: str):
+        """Dump the file
+
+        Args: 
+            file_path (str): the file path 
+        """
+        
+        dir_name = os.path.dirname(file_path)
+        if dir_name not in ('', '.', '..'):
+            os.makedirs(dir_name, exist_ok=True)
+        self._raw_fitz.save(file_path)
+
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(dataset, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        return proc(self, *args, **kwargs)
+
+    def classify(self) -> SupportedPdfParseMethod:
+        """classify the dataset 
+
+        Returns:
+            SupportedPdfParseMethod: _description_
+        """
+        return classify(self._data_bits)
+
+    def clone(self):
+        """clone this dataset
+        """
+        return PymuDocDataset(self._raw_data)
+
 
 class ImageDataset(Dataset):
     def __init__(self, bits: bytes):
@@ -118,7 +221,8 @@ class ImageDataset(Dataset):
             bits (bytes): the bytes of the photo which will be converted to pdf first. then converted to pymudoc.
         """
         pdf_bytes = fitz.open(stream=bits).convert_to_pdf()
-        self._records = [Doc(v) for v in fitz.open('pdf', pdf_bytes)]
+        self._raw_fitz = fitz.open('pdf', pdf_bytes)
+        self._records = [Doc(v) for v in self._raw_fitz]
         self._raw_data = bits
         self._data_bits = pdf_bytes
 
@@ -153,14 +257,50 @@ class ImageDataset(Dataset):
         """
         return self._records[page_id]
 
+    def dump_to_file(self, file_path: str):
+        """Dump the file
+
+        Args: 
+            file_path (str): the file path 
+        """
+        dir_name = os.path.dirname(file_path)
+        if dir_name not in ('', '.', '..'):
+            os.makedirs(dir_name, exist_ok=True)
+        self._raw_fitz.save(file_path)
+
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(dataset, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        return proc(self, *args, **kwargs)
+
+    def classify(self) -> SupportedPdfParseMethod:
+        """classify the dataset 
+
+        Returns:
+            SupportedPdfParseMethod: _description_
+        """
+        return SupportedPdfParseMethod.OCR
+
+    def clone(self):
+        """clone this dataset
+        """
+        return ImageDataset(self._raw_data)
 
 class Doc(PageableData):
     """Initialized with pymudoc object."""
+
     def __init__(self, doc: fitz.Page):
         self._doc = doc
 
     def get_image(self):
-        """Return the imge info.
+        """Return the image info.
 
         Returns:
             dict: {
@@ -192,3 +332,34 @@ class Doc(PageableData):
     def __getattr__(self, name):
         if hasattr(self._doc, name):
             return getattr(self._doc, name)
+
+    def draw_rect(self, rect_coords, color, fill, fill_opacity, width, overlay):
+        """draw rectangle.
+
+        Args:
+            rect_coords (list[float]): four elements array contain the top-left and bottom-right coordinates, [x0, y0, x1, y1]
+            color (list[float] | None): three element tuple which describe the RGB of the board line, None means no board line
+            fill (list[float] | None): fill the board with RGB, None means will not fill with color
+            fill_opacity (float): opacity of the fill, range from [0, 1]
+            width (float): the width of board
+            overlay (bool): fill the color in foreground or background. True means fill in background.
+        """
+        self._doc.draw_rect(
+            rect_coords,
+            color=color,
+            fill=fill,
+            fill_opacity=fill_opacity,
+            width=width,
+            overlay=overlay,
+        )
+
+    def insert_text(self, coord, content, fontsize, color):
+        """insert text.
+
+        Args:
+            coord (list[float]): four elements array contain the top-left and bottom-right coordinates, [x0, y0, x1, y1]
+            content (str): the text content
+            fontsize (int): font size of the text
+            color (list[float] | None):  three element tuple which describe the RGB of the board line, None will use the default font color!
+        """
+        self._doc.insert_text(coord, content, fontsize=fontsize, color=color)

magic_pdf/dict2md/ocr_mkcontent.py

@@ -165,8 +165,8 @@ def merge_para_with_text(para_block):
             if content:
                 langs = ['zh', 'ja', 'ko']
                 # logger.info(f'block_lang: {block_lang}, content: {content}')
-                if block_lang in langs: # 中文/日语/韩文语境下，换行不需要空格分隔
-                    if j == len(line['spans']) - 1:
+                if block_lang in langs: # 中文/日语/韩文语境下，换行不需要空格分隔,但是如果是行内公式结尾，还是要加空格
+                    if j == len(line['spans']) - 1 and span_type not in [ContentType.InlineEquation]:
                         para_text += content
                     else:
                         para_text += f'{content} '

magic_pdf/filter/__init__.py

@@ -0,0 +1,32 @@
+
+from magic_pdf.config.drop_reason import DropReason
+from magic_pdf.config.enums import SupportedPdfParseMethod
+from magic_pdf.filter.pdf_classify_by_type import classify as do_classify
+from magic_pdf.filter.pdf_meta_scan import pdf_meta_scan
+
+
+def classify(pdf_bytes: bytes) -> SupportedPdfParseMethod:
+    """根据pdf的元数据，判断是文本pdf，还是ocr pdf."""
+    pdf_meta = pdf_meta_scan(pdf_bytes)
+    if pdf_meta.get('_need_drop', False):  # 如果返回了需要丢弃的标志，则抛出异常
+        raise Exception(f"pdf meta_scan need_drop,reason is {pdf_meta['_drop_reason']}")
+    else:
+        is_encrypted = pdf_meta['is_encrypted']
+        is_needs_password = pdf_meta['is_needs_password']
+        if is_encrypted or is_needs_password:  # 加密的，需要密码的，没有页面的，都不处理
+            raise Exception(f'pdf meta_scan need_drop,reason is {DropReason.ENCRYPTED}')
+        else:
+            is_text_pdf, results = do_classify(
+                pdf_meta['total_page'],
+                pdf_meta['page_width_pts'],
+                pdf_meta['page_height_pts'],
+                pdf_meta['image_info_per_page'],
+                pdf_meta['text_len_per_page'],
+                pdf_meta['imgs_per_page'],
+                pdf_meta['text_layout_per_page'],
+                pdf_meta['invalid_chars'],
+            )
+            if is_text_pdf:
+                return SupportedPdfParseMethod.TXT
+            else:
+                return SupportedPdfParseMethod.OCR

magic_pdf/filter/pdf_meta_scan.py

@@ -8,7 +8,7 @@ from loguru import logger
 from magic_pdf.config.drop_reason import DropReason
 from magic_pdf.libs.commons import get_top_percent_list, mymax
 from magic_pdf.libs.language import detect_lang
-from magic_pdf.libs.pdf_check import detect_invalid_chars_by_pymupdf
+from magic_pdf.libs.pdf_check import detect_invalid_chars_by_pymupdf, detect_invalid_chars
 
 scan_max_page = 50
 junk_limit_min = 10
@@ -323,7 +323,8 @@ def get_language(doc: fitz.Document):
 
 def check_invalid_chars(pdf_bytes):
     """乱码检测."""
-    return detect_invalid_chars_by_pymupdf(pdf_bytes)
+    # return detect_invalid_chars_by_pymupdf(pdf_bytes)
+    return detect_invalid_chars(pdf_bytes)
 
 
 def pdf_meta_scan(pdf_bytes: bytes):

magic_pdf/libs/draw_bbox.py

@@ -1,7 +1,8 @@
 import fitz
 from magic_pdf.config.constants import CROSS_PAGE
-from magic_pdf.config.ocr_content_type import BlockType, CategoryId, ContentType
-from magic_pdf.data.dataset import PymuDocDataset
+from magic_pdf.config.ocr_content_type import (BlockType, CategoryId,
+                                               ContentType)
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.model.magic_model import MagicModel
 
 
@@ -194,7 +195,7 @@ def draw_layout_bbox(pdf_info, pdf_bytes, out_path, filename):
         )
 
     # Save the PDF
-    pdf_docs.save(f'{out_path}/{filename}_layout.pdf')
+    pdf_docs.save(f'{out_path}/{filename}')
 
 
 def draw_span_bbox(pdf_info, pdf_bytes, out_path, filename):
@@ -282,18 +283,17 @@ def draw_span_bbox(pdf_info, pdf_bytes, out_path, filename):
         draw_bbox_without_number(i, dropped_list, page, [158, 158, 158], False)
 
     # Save the PDF
-    pdf_docs.save(f'{out_path}/{filename}_spans.pdf')
+    pdf_docs.save(f'{out_path}/{filename}')
 
 
-def draw_model_bbox(model_list: list, pdf_bytes, out_path, filename):
+def draw_model_bbox(model_list, dataset: Dataset, out_path, filename):
     dropped_bbox_list = []
     tables_body_list, tables_caption_list, tables_footnote_list = [], [], []
     imgs_body_list, imgs_caption_list, imgs_footnote_list = [], [], []
     titles_list = []
     texts_list = []
     interequations_list = []
-    pdf_docs = fitz.open('pdf', pdf_bytes)
-    magic_model = MagicModel(model_list, PymuDocDataset(pdf_bytes))
+    magic_model = MagicModel(model_list, dataset)
     for i in range(len(model_list)):
         page_dropped_list = []
         tables_body, tables_caption, tables_footnote = [], [], []
@@ -337,7 +337,8 @@ def draw_model_bbox(model_list: list, pdf_bytes, out_path, filename):
         dropped_bbox_list.append(page_dropped_list)
         imgs_footnote_list.append(imgs_footnote)
 
-    for i, page in enumerate(pdf_docs):
+    for i in range(len(dataset)):
+        page = dataset.get_page(i)
         draw_bbox_with_number(
             i, dropped_bbox_list, page, [158, 158, 158], True
         )  # color !
@@ -352,7 +353,7 @@ def draw_model_bbox(model_list: list, pdf_bytes, out_path, filename):
         draw_bbox_with_number(i, interequations_list, page, [0, 255, 0], True)
 
     # Save the PDF
-    pdf_docs.save(f'{out_path}/{filename}_model.pdf')
+    dataset.dump_to_file(f'{out_path}/{filename}')
 
 
 def draw_line_sort_bbox(pdf_info, pdf_bytes, out_path, filename):
@@ -390,7 +391,7 @@ def draw_line_sort_bbox(pdf_info, pdf_bytes, out_path, filename):
     for i, page in enumerate(pdf_docs):
         draw_bbox_with_number(i, layout_bbox_list, page, [255, 0, 0], False)
 
-    pdf_docs.save(f'{out_path}/{filename}_line_sort.pdf')
+    pdf_docs.save(f'{out_path}/{filename}')
 
 
 def draw_layout_sort_bbox(pdf_info, pdf_bytes, out_path, filename):

magic_pdf/libs/pdf_check.py

@@ -1,9 +1,9 @@
 import fitz
 import numpy as np
 from loguru import logger
-# import re
-# from io import BytesIO
-# from pdfminer.high_level import extract_text
+import re
+from io import BytesIO
+from pdfminer.high_level import extract_text
 
 
 def calculate_sample_count(total_page: int):
@@ -33,33 +33,33 @@ def extract_pages(src_pdf_bytes: bytes) -> fitz.Document:
     return sample_docs
 
 
-# def detect_invalid_chars(src_pdf_bytes: bytes) -> bool:
-#     """"
-#     检测PDF中是否包含非法字符
-#     """
-#     '''pdfminer比较慢,需要先随机抽取10页左右的sample'''
-#     sample_docs = extract_pages(src_pdf_bytes)
-#     sample_pdf_bytes = sample_docs.tobytes()
-#     sample_pdf_file_like_object = BytesIO(sample_pdf_bytes)
-#     text = extract_text(sample_pdf_file_like_object)
-#     text = text.replace("\n", "")
-#     # logger.info(text)
-#     '''乱码文本用pdfminer提取出来的文本特征是(cid:xxx)'''
-#     cid_pattern = re.compile(r'\(cid:\d+\)')
-#     matches = cid_pattern.findall(text)
-#     cid_count = len(matches)
-#     cid_len = sum(len(match) for match in matches)
-#     text_len = len(text)
-#     if text_len == 0:
-#         cid_chars_radio = 0
-#     else:
-#         cid_chars_radio = cid_count/(cid_count + text_len - cid_len)
-#     logger.info(f"cid_count: {cid_count}, text_len: {text_len}, cid_chars_radio: {cid_chars_radio}")
-#     '''当一篇文章存在5%以上的文本是乱码时,认为该文档为乱码文档'''
-#     if cid_chars_radio > 0.05:
-#         return False  # 乱码文档
-#     else:
-#         return True   # 正常文档
+def detect_invalid_chars(src_pdf_bytes: bytes) -> bool:
+    """"
+    检测PDF中是否包含非法字符
+    """
+    '''pdfminer比较慢,需要先随机抽取10页左右的sample'''
+    sample_docs = extract_pages(src_pdf_bytes)
+    sample_pdf_bytes = sample_docs.tobytes()
+    sample_pdf_file_like_object = BytesIO(sample_pdf_bytes)
+    text = extract_text(sample_pdf_file_like_object)
+    text = text.replace("\n", "")
+    # logger.info(text)
+    '''乱码文本用pdfminer提取出来的文本特征是(cid:xxx)'''
+    cid_pattern = re.compile(r'\(cid:\d+\)')
+    matches = cid_pattern.findall(text)
+    cid_count = len(matches)
+    cid_len = sum(len(match) for match in matches)
+    text_len = len(text)
+    if text_len == 0:
+        cid_chars_radio = 0
+    else:
+        cid_chars_radio = cid_count/(cid_count + text_len - cid_len)
+    logger.info(f"cid_count: {cid_count}, text_len: {text_len}, cid_chars_radio: {cid_chars_radio}")
+    '''当一篇文章存在5%以上的文本是乱码时,认为该文档为乱码文档'''
+    if cid_chars_radio > 0.05:
+        return False  # 乱码文档
+    else:
+        return True   # 正常文档
 
 
 def count_replacement_characters(text: str) -> int:

magic_pdf/model/__init__.py

@@ -1,2 +1,126 @@
+from typing import Callable
+
+from abc import ABC, abstractmethod
+
+from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
+from magic_pdf.pipe.operators import PipeResult
+
+
 __use_inside_model__ = True
 __model_mode__ = "full"
+
+
+class InferenceResultBase(ABC):
+
+    @abstractmethod
+    def __init__(self, inference_results: list, dataset: Dataset):
+        """Initialized method.
+
+        Args:
+            inference_results (list): the inference result generated by model
+            dataset (Dataset): the dataset related with model inference result
+        """
+        self._infer_res = inference_results
+        self._dataset = dataset
+
+    @abstractmethod
+    def draw_model(self, file_path: str) -> None:
+        """Draw model inference result.
+
+        Args:
+            file_path (str): the output file path
+        """
+        pass
+
+    @abstractmethod
+    def dump_model(self, writer: DataWriter, file_path: str):
+        """Dump model inference result to file.
+
+        Args:
+            writer (DataWriter): writer handle
+            file_path (str): the location of target file
+        """
+        pass
+
+    @abstractmethod
+    def get_infer_res(self):
+        """Get the inference result.
+
+        Returns:
+            list: the inference result generated by model
+        """
+        pass
+
+    @abstractmethod
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(inference_result, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        pass
+
+    @abstractmethod
+    def pipe_auto_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        """Post-proc the model inference result.
+            step1: classify the dataset type
+            step2: based the result of step1, using `pipe_txt_mode` or `pipe_ocr_mode`
+
+        Args:
+            imageWriter (DataWriter): the image writer handle
+            start_page_id (int, optional): Defaults to 0. Let user select some pages He/She want to process
+            end_page_id (int, optional):  Defaults to the last page index of dataset. Let user select some pages He/She want to process
+            debug_mode (bool, optional): Defaults to False. will dump more log if enabled
+            lang (str, optional): Defaults to None.
+
+        Returns:
+            PipeResult: the result
+        """
+        pass
+
+    @abstractmethod
+    def pipe_txt_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        """Post-proc the model inference result, Extract the text using the
+        third library, such as `pymupdf`
+
+        Args:
+            imageWriter (DataWriter): the image writer handle
+            start_page_id (int, optional): Defaults to 0. Let user select some pages He/She want to process
+            end_page_id (int, optional):  Defaults to the last page index of dataset. Let user select some pages He/She want to process
+            debug_mode (bool, optional): Defaults to False. will dump more log if enabled
+            lang (str, optional): Defaults to None.
+
+        Returns:
+            PipeResult: the result
+        """
+        pass
+
+    @abstractmethod
+    def pipe_ocr_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        pass

magic_pdf/model/doc_analyze_by_custom_model.py

@@ -1,14 +1,34 @@
+import os
 import time
 
 import fitz
 import numpy as np
 from loguru import logger
 
+# 关闭paddle的信号处理
+import paddle
+paddle.disable_signal_handler()
+
+os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1'  # 禁止albumentations检查更新
+os.environ['YOLO_VERBOSE'] = 'False'  # disable yolo logger
+
+try:
+    import torchtext
+
+    if torchtext.__version__ >= '0.18.0':
+        torchtext.disable_torchtext_deprecation_warning()
+except ImportError:
+    pass
+
+import magic_pdf.model as model_config
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.libs.clean_memory import clean_memory
-from magic_pdf.libs.config_reader import get_local_models_dir, get_device, get_table_recog_config, get_layout_config, \
-    get_formula_config
+from magic_pdf.libs.config_reader import (get_device, get_formula_config,
+                                          get_layout_config,
+                                          get_local_models_dir,
+                                          get_table_recog_config)
 from magic_pdf.model.model_list import MODEL
-import magic_pdf.model as model_config
+from magic_pdf.model.operators import InferenceResult
 
 
 def dict_compare(d1, d2):
@@ -19,25 +39,31 @@ def remove_duplicates_dicts(lst):
     unique_dicts = []
     for dict_item in lst:
         if not any(
-                dict_compare(dict_item, existing_dict) for existing_dict in unique_dicts
+            dict_compare(dict_item, existing_dict) for existing_dict in unique_dicts
         ):
             unique_dicts.append(dict_item)
     return unique_dicts
 
 
-def load_images_from_pdf(pdf_bytes: bytes, dpi=200, start_page_id=0, end_page_id=None) -> list:
+def load_images_from_pdf(
+    pdf_bytes: bytes, dpi=200, start_page_id=0, end_page_id=None
+) -> list:
     try:
         from PIL import Image
     except ImportError:
-        logger.error("Pillow not installed, please install by pip.")
+        logger.error('Pillow not installed, please install by pip.')
         exit(1)
 
     images = []
-    with fitz.open("pdf", pdf_bytes) as doc:
+    with fitz.open('pdf', pdf_bytes) as doc:
         pdf_page_num = doc.page_count
-        end_page_id = end_page_id if end_page_id is not None and end_page_id >= 0 else pdf_page_num - 1
+        end_page_id = (
+            end_page_id
+            if end_page_id is not None and end_page_id >= 0
+            else pdf_page_num - 1
+        )
         if end_page_id > pdf_page_num - 1:
-            logger.warning("end_page_id is out of range, use images length")
+            logger.warning('end_page_id is out of range, use images length')
             end_page_id = pdf_page_num - 1
 
         for index in range(0, doc.page_count):
@@ -50,11 +76,11 @@ def load_images_from_pdf(pdf_bytes: bytes, dpi=200, start_page_id=0, end_page_id
                 if pm.width > 4500 or pm.height > 4500:
                     pm = page.get_pixmap(matrix=fitz.Matrix(1, 1), alpha=False)
 
-                img = Image.frombytes("RGB", (pm.width, pm.height), pm.samples)
+                img = Image.frombytes('RGB', (pm.width, pm.height), pm.samples)
                 img = np.array(img)
-                img_dict = {"img": img, "width": pm.width, "height": pm.height}
+                img_dict = {'img': img, 'width': pm.width, 'height': pm.height}
             else:
-                img_dict = {"img": [], "width": 0, "height": 0}
+                img_dict = {'img': [], 'width': 0, 'height': 0}
 
             images.append(img_dict)
     return images
@@ -69,117 +95,150 @@ class ModelSingleton:
             cls._instance = super().__new__(cls)
         return cls._instance
 
-    def get_model(self, ocr: bool, show_log: bool, lang=None, layout_model=None, formula_enable=None, table_enable=None):
+    def get_model(
+        self,
+        ocr: bool,
+        show_log: bool,
+        lang=None,
+        layout_model=None,
+        formula_enable=None,
+        table_enable=None,
+    ):
         key = (ocr, show_log, lang, layout_model, formula_enable, table_enable)
         if key not in self._models:
-            self._models[key] = custom_model_init(ocr=ocr, show_log=show_log, lang=lang, layout_model=layout_model,
-                                                  formula_enable=formula_enable, table_enable=table_enable)
+            self._models[key] = custom_model_init(
+                ocr=ocr,
+                show_log=show_log,
+                lang=lang,
+                layout_model=layout_model,
+                formula_enable=formula_enable,
+                table_enable=table_enable,
+            )
         return self._models[key]
 
 
-def custom_model_init(ocr: bool = False, show_log: bool = False, lang=None,
-                      layout_model=None, formula_enable=None, table_enable=None):
+def custom_model_init(
+    ocr: bool = False,
+    show_log: bool = False,
+    lang=None,
+    layout_model=None,
+    formula_enable=None,
+    table_enable=None,
+):
 
     model = None
 
-    if model_config.__model_mode__ == "lite":
-        logger.warning("The Lite mode is provided for developers to conduct testing only, and the output quality is "
-                       "not guaranteed to be reliable.")
+    if model_config.__model_mode__ == 'lite':
+        logger.warning(
+            'The Lite mode is provided for developers to conduct testing only, and the output quality is '
+            'not guaranteed to be reliable.'
+        )
         model = MODEL.Paddle
-    elif model_config.__model_mode__ == "full":
+    elif model_config.__model_mode__ == 'full':
         model = MODEL.PEK
 
     if model_config.__use_inside_model__:
         model_init_start = time.time()
         if model == MODEL.Paddle:
             from magic_pdf.model.pp_structure_v2 import CustomPaddleModel
+
             custom_model = CustomPaddleModel(ocr=ocr, show_log=show_log, lang=lang)
         elif model == MODEL.PEK:
             from magic_pdf.model.pdf_extract_kit import CustomPEKModel
+
             # 从配置文件读取model-dir和device
             local_models_dir = get_local_models_dir()
             device = get_device()
 
             layout_config = get_layout_config()
             if layout_model is not None:
-                layout_config["model"] = layout_model
+                layout_config['model'] = layout_model
 
             formula_config = get_formula_config()
             if formula_enable is not None:
-                formula_config["enable"] = formula_enable
+                formula_config['enable'] = formula_enable
 
             table_config = get_table_recog_config()
             if table_enable is not None:
-                table_config["enable"] = table_enable
+                table_config['enable'] = table_enable
 
             model_input = {
-                            "ocr": ocr,
-                            "show_log": show_log,
-                            "models_dir": local_models_dir,
-                            "device": device,
-                            "table_config": table_config,
-                            "layout_config": layout_config,
-                            "formula_config": formula_config,
-                            "lang": lang,
+                'ocr': ocr,
+                'show_log': show_log,
+                'models_dir': local_models_dir,
+                'device': device,
+                'table_config': table_config,
+                'layout_config': layout_config,
+                'formula_config': formula_config,
+                'lang': lang,
             }
 
             custom_model = CustomPEKModel(**model_input)
         else:
-            logger.error("Not allow model_name!")
+            logger.error('Not allow model_name!')
             exit(1)
         model_init_cost = time.time() - model_init_start
-        logger.info(f"model init cost: {model_init_cost}")
+        logger.info(f'model init cost: {model_init_cost}')
     else:
-        logger.error("use_inside_model is False, not allow to use inside model")
+        logger.error('use_inside_model is False, not allow to use inside model')
         exit(1)
 
     return custom_model
 
 
-def doc_analyze(pdf_bytes: bytes, ocr: bool = False, show_log: bool = False,
-                start_page_id=0, end_page_id=None, lang=None,
-                layout_model=None, formula_enable=None, table_enable=None):
+def doc_analyze(
+    dataset: Dataset,
+    ocr: bool = False,
+    show_log: bool = False,
+    start_page_id=0,
+    end_page_id=None,
+    lang=None,
+    layout_model=None,
+    formula_enable=None,
+    table_enable=None,
+) -> InferenceResult:
 
-    if lang == "":
+    if lang == '':
         lang = None
 
     model_manager = ModelSingleton()
-    custom_model = model_manager.get_model(ocr, show_log, lang, layout_model, formula_enable, table_enable)
-
-    with fitz.open("pdf", pdf_bytes) as doc:
-        pdf_page_num = doc.page_count
-        end_page_id = end_page_id if end_page_id is not None and end_page_id >= 0 else pdf_page_num - 1
-        if end_page_id > pdf_page_num - 1:
-            logger.warning("end_page_id is out of range, use images length")
-            end_page_id = pdf_page_num - 1
-
-    images = load_images_from_pdf(pdf_bytes, start_page_id=start_page_id, end_page_id=end_page_id)
+    custom_model = model_manager.get_model(
+        ocr, show_log, lang, layout_model, formula_enable, table_enable
+    )
 
     model_json = []
     doc_analyze_start = time.time()
 
-    for index, img_dict in enumerate(images):
-        img = img_dict["img"]
-        page_width = img_dict["width"]
-        page_height = img_dict["height"]
+    if end_page_id is None:
+        end_page_id = len(dataset)
+
+    for index in range(len(dataset)):
+        page_data = dataset.get_page(index)
+        img_dict = page_data.get_image()
+        img = img_dict['img']
+        page_width = img_dict['width']
+        page_height = img_dict['height']
         if start_page_id <= index <= end_page_id:
             page_start = time.time()
             result = custom_model(img)
             logger.info(f'-----page_id : {index}, page total time: {round(time.time() - page_start, 2)}-----')
         else:
             result = []
-        page_info = {"page_no": index, "height": page_height, "width": page_width}
-        page_dict = {"layout_dets": result, "page_info": page_info}
+
+        page_info = {'page_no': index, 'height': page_height, 'width': page_width}
+        page_dict = {'layout_dets': result, 'page_info': page_info}
         model_json.append(page_dict)
 
     gc_start = time.time()
     clean_memory()
     gc_time = round(time.time() - gc_start, 2)
-    logger.info(f"gc time: {gc_time}")
+    logger.info(f'gc time: {gc_time}')
 
     doc_analyze_time = round(time.time() - doc_analyze_start, 2)
-    doc_analyze_speed = round( (end_page_id + 1 - start_page_id) / doc_analyze_time, 2)
-    logger.info(f"doc analyze time: {round(time.time() - doc_analyze_start, 2)},"
-                f" speed: {doc_analyze_speed} pages/second")
+    doc_analyze_speed = round((end_page_id + 1 - start_page_id) / doc_analyze_time, 2)
+    logger.info(
+        f'doc analyze time: {round(time.time() - doc_analyze_start, 2)},'
+        f' speed: {doc_analyze_speed} pages/second'
+    )
 
-    return model_json
+    return InferenceResult(model_json, dataset)

magic_pdf/model/operators.py

@@ -0,0 +1,190 @@
+import copy
+import json
+import os
+from typing import Callable
+
+from magic_pdf.config.constants import PARSE_TYPE_OCR, PARSE_TYPE_TXT
+from magic_pdf.config.enums import SupportedPdfParseMethod
+from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
+from magic_pdf.filter import classify
+from magic_pdf.libs.draw_bbox import draw_model_bbox
+from magic_pdf.libs.version import __version__
+from magic_pdf.model import InferenceResultBase
+from magic_pdf.pdf_parse_union_core_v2 import pdf_parse_union
+from magic_pdf.pipe.operators import PipeResult
+
+
+class InferenceResult(InferenceResultBase):
+    def __init__(self, inference_results: list, dataset: Dataset):
+        """Initialized method.
+
+        Args:
+            inference_results (list): the inference result generated by model
+            dataset (Dataset): the dataset related with model inference result
+        """
+        self._infer_res = inference_results
+        self._dataset = dataset
+
+    def draw_model(self, file_path: str) -> None:
+        """Draw model inference result.
+
+        Args:
+            file_path (str): the output file path
+        """
+        dir_name = os.path.dirname(file_path)
+        base_name = os.path.basename(file_path)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name, exist_ok=True)
+        draw_model_bbox(
+            copy.deepcopy(self._infer_res), self._dataset, dir_name, base_name
+        )
+
+    def dump_model(self, writer: DataWriter, file_path: str):
+        """Dump model inference result to file.
+
+        Args:
+            writer (DataWriter): writer handle
+            file_path (str): the location of target file
+        """
+        writer.write_string(
+            file_path, json.dumps(self._infer_res, ensure_ascii=False, indent=4)
+        )
+
+    def get_infer_res(self):
+        """Get the inference result.
+
+        Returns:
+            list: the inference result generated by model
+        """
+        return self._infer_res
+
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(inference_result, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        return proc(copy.deepcopy(self._infer_res), *args, **kwargs)
+
+    def pipe_auto_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        """Post-proc the model inference result.
+            step1: classify the dataset type
+            step2: based the result of step1, using `pipe_txt_mode` or `pipe_ocr_mode`
+
+        Args:
+            imageWriter (DataWriter): the image writer handle
+            start_page_id (int, optional): Defaults to 0. Let user select some pages He/She want to process
+            end_page_id (int, optional):  Defaults to the last page index of dataset. Let user select some pages He/She want to process
+            debug_mode (bool, optional): Defaults to False. will dump more log if enabled
+            lang (str, optional): Defaults to None.
+
+        Returns:
+            PipeResult: the result
+        """
+
+        pdf_proc_method = classify(self._dataset.data_bits())
+
+        if pdf_proc_method == SupportedPdfParseMethod.TXT:
+            return self.pipe_txt_mode(
+                imageWriter, start_page_id, end_page_id, debug_mode, lang
+            )
+        else:
+            return self.pipe_ocr_mode(
+                imageWriter, start_page_id, end_page_id, debug_mode, lang
+            )
+
+    def pipe_txt_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        """Post-proc the model inference result, Extract the text using the
+        third library, such as `pymupdf`
+
+        Args:
+            imageWriter (DataWriter): the image writer handle
+            start_page_id (int, optional): Defaults to 0. Let user select some pages He/She want to process
+            end_page_id (int, optional):  Defaults to the last page index of dataset. Let user select some pages He/She want to process
+            debug_mode (bool, optional): Defaults to False. will dump more log if enabled
+            lang (str, optional): Defaults to None.
+
+        Returns:
+            PipeResult: the result
+        """
+
+        def proc(*args, **kwargs) -> PipeResult:
+            res = pdf_parse_union(*args, **kwargs)
+            res['_parse_type'] = PARSE_TYPE_TXT
+            res['_version_name'] = __version__
+            if 'lang' in kwargs and kwargs['lang'] is not None:
+                res['lang'] = kwargs['lang']
+            return PipeResult(res, self._dataset)
+
+        res = self.apply(
+            proc,
+            self._dataset,
+            imageWriter,
+            SupportedPdfParseMethod.TXT,
+            start_page_id=start_page_id,
+            end_page_id=end_page_id,
+            debug_mode=debug_mode,
+            lang=lang,
+        )
+        return res
+
+    def pipe_ocr_mode(
+        self,
+        imageWriter: DataWriter,
+        start_page_id=0,
+        end_page_id=None,
+        debug_mode=False,
+        lang=None,
+    ) -> PipeResult:
+        """Post-proc the model inference result, Extract the text using `OCR`
+        technical.
+
+        Args:
+            imageWriter (DataWriter): the image writer handle
+            start_page_id (int, optional): Defaults to 0. Let user select some pages He/She want to process
+            end_page_id (int, optional):  Defaults to the last page index of dataset. Let user select some pages He/She want to process
+            debug_mode (bool, optional): Defaults to False. will dump more log if enabled
+            lang (str, optional): Defaults to None.
+
+        Returns:
+            PipeResult: the result
+        """
+
+        def proc(*args, **kwargs) -> PipeResult:
+            res = pdf_parse_union(*args, **kwargs)
+            res['_parse_type'] = PARSE_TYPE_OCR
+            res['_version_name'] = __version__
+            if 'lang' in kwargs and kwargs['lang'] is not None:
+                res['lang'] = kwargs['lang']
+            return PipeResult(res, self._dataset)
+
+        res = self.apply(
+            proc,
+            self._dataset,
+            imageWriter,
+            SupportedPdfParseMethod.OCR,
+            start_page_id=start_page_id,
+            end_page_id=end_page_id,
+            debug_mode=debug_mode,
+            lang=lang,
+        )
+        return res

magic_pdf/model/pdf_extract_kit.py

@@ -179,7 +179,25 @@ class CustomPEKModel:
             layout_res = self.layout_model(image, ignore_catids=[])
         elif self.layout_model_name == MODEL_NAME.DocLayout_YOLO:
             # doclayout_yolo
-            layout_res = self.layout_model.predict(image)
+            img_pil = Image.fromarray(image)
+            width, height = img_pil.size
+            # logger.info(f'width: {width}, height: {height}')
+            input_res = {"poly":[0,0,width,0,width,height,0,height]}
+            new_image, useful_list = crop_img(input_res, img_pil, crop_paste_x=width//2, crop_paste_y=0)
+            paste_x, paste_y, xmin, ymin, xmax, ymax, new_width, new_height = useful_list
+            layout_res = self.layout_model.predict(new_image)
+            for res in layout_res:
+                p1, p2, p3, p4, p5, p6, p7, p8 = res['poly']
+                p1 = p1 - paste_x + xmin
+                p2 = p2 - paste_y + ymin
+                p3 = p3 - paste_x + xmin
+                p4 = p4 - paste_y + ymin
+                p5 = p5 - paste_x + xmin
+                p6 = p6 - paste_y + ymin
+                p7 = p7 - paste_x + xmin
+                p8 = p8 - paste_y + ymin
+                res['poly'] = [p1, p2, p3, p4, p5, p6, p7, p8]
+
         layout_cost = round(time.time() - layout_start, 2)
         logger.info(f'layout detection time: {layout_cost}')
 
@@ -215,6 +233,7 @@ class CustomPEKModel:
 
             # OCR recognition
             new_image = cv2.cvtColor(np.asarray(new_image), cv2.COLOR_RGB2BGR)
+
             if self.apply_ocr:
                 ocr_res = self.ocr_model.ocr(new_image, mfd_res=adjusted_mfdetrec_res)[0]
             else:

magic_pdf/model/sub_modules/model_init.py

@@ -92,14 +92,24 @@ class AtomModelSingleton:
         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)
+        table_model_name = kwargs.get('table_model_name', None)
+
+        if atom_model_name in [AtomicModel.OCR]:
+            key = (atom_model_name, lang)
+        elif atom_model_name in [AtomicModel.Layout]:
+            key = (atom_model_name, layout_model_name)
+        elif atom_model_name in [AtomicModel.Table]:
+            key = (atom_model_name, table_model_name)
+        else:
+            key = atom_model_name
+
         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):
     atom_model = None
     if model_name == AtomicModel.Layout:
@@ -129,7 +139,7 @@ def atom_model_init(model_name: str, **kwargs):
         atom_model = ocr_model_init(
             kwargs.get('ocr_show_log'),
             kwargs.get('det_db_box_thresh'),
-            kwargs.get('lang')
+            kwargs.get('lang'),
         )
     elif model_name == AtomicModel.Table:
         atom_model = table_model_init(

magic_pdf/model/sub_modules/model_utils.py

@@ -42,10 +42,16 @@ def get_res_list_from_layout_res(layout_res):
 
 
 def clean_vram(device, vram_threshold=8):
+    total_memory = get_vram(device)
+    if total_memory and total_memory <= vram_threshold:
+        gc_start = time.time()
+        clean_memory()
+        gc_time = round(time.time() - gc_start, 2)
+        logger.info(f"gc time: {gc_time}")
+
+
+def get_vram(device):
     if torch.cuda.is_available() and device != 'cpu':
         total_memory = torch.cuda.get_device_properties(device).total_memory / (1024 ** 3)  # 将字节转换为 GB
-        if total_memory <= vram_threshold:
-            gc_start = time.time()
-            clean_memory()
-            gc_time = round(time.time() - gc_start, 2)
-            logger.info(f"gc time: {gc_time}")
+        return total_memory
+    return None

magic_pdf/pdf_parse_by_ocr.py

@@ -1,9 +1,9 @@
 from magic_pdf.config.enums import SupportedPdfParseMethod
-from magic_pdf.data.dataset import PymuDocDataset
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.pdf_parse_union_core_v2 import pdf_parse_union
 
 
-def parse_pdf_by_ocr(pdf_bytes,
+def parse_pdf_by_ocr(dataset: Dataset,
                      model_list,
                      imageWriter,
                      start_page_id=0,
@@ -11,9 +11,8 @@ def parse_pdf_by_ocr(pdf_bytes,
                      debug_mode=False,
                      lang=None,
                      ):
-    dataset = PymuDocDataset(pdf_bytes)
-    return pdf_parse_union(dataset,
-                           model_list,
+    return pdf_parse_union(model_list,
+                           dataset,
                            imageWriter,
                            SupportedPdfParseMethod.OCR,
                            start_page_id=start_page_id,

magic_pdf/pdf_parse_by_txt.py

@@ -1,10 +1,10 @@
 from magic_pdf.config.enums import SupportedPdfParseMethod
-from magic_pdf.data.dataset import PymuDocDataset
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.pdf_parse_union_core_v2 import pdf_parse_union
 
 
 def parse_pdf_by_txt(
-    pdf_bytes,
+    dataset: Dataset,
     model_list,
     imageWriter,
     start_page_id=0,
@@ -12,9 +12,8 @@ def parse_pdf_by_txt(
     debug_mode=False,
     lang=None,
 ):
-    dataset = PymuDocDataset(pdf_bytes)
-    return pdf_parse_union(dataset,
-                           model_list,
+    return pdf_parse_union(model_list,
+                           dataset,
                            imageWriter,
                            SupportedPdfParseMethod.TXT,
                            start_page_id=start_page_id,

magic_pdf/pdf_parse_union_core_v2.py

@@ -4,8 +4,8 @@ import statistics
 import time
 from typing import List
 
-import torch
 import fitz
+import torch
 from loguru import logger
 
 from magic_pdf.config.enums import SupportedPdfParseMethod
@@ -16,17 +16,13 @@ from magic_pdf.libs.clean_memory import clean_memory
 from magic_pdf.libs.config_reader import get_local_layoutreader_model_dir
 from magic_pdf.libs.convert_utils import dict_to_list
 from magic_pdf.libs.hash_utils import compute_md5
-
 from magic_pdf.libs.pdf_image_tools import cut_image_to_pil_image
 from magic_pdf.model.magic_model import MagicModel
 
-os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1'  # 禁止albumentations检查更新
-os.environ['YOLO_VERBOSE'] = 'False'  # disable yolo logger
-
 try:
     import torchtext
 
-    if torchtext.__version__ >= "0.18.0":
+    if torchtext.__version__ >= '0.18.0':
         torchtext.disable_torchtext_deprecation_warning()
 except ImportError:
     pass
@@ -39,6 +35,9 @@ from magic_pdf.pre_proc.ocr_detect_all_bboxes import ocr_prepare_bboxes_for_layo
 from magic_pdf.pre_proc.ocr_dict_merge import fill_spans_in_blocks, fix_block_spans_v2, fix_discarded_block
 from magic_pdf.pre_proc.ocr_span_list_modify import get_qa_need_list_v2, remove_overlaps_low_confidence_spans, remove_overlaps_min_spans
 
+os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1'  # 禁止albumentations检查更新
+os.environ['YOLO_VERBOSE'] = 'False'  # disable yolo logger
+
 
 def __replace_STX_ETX(text_str: str):
     """Replace \u0002 and \u0003, as these characters become garbled when extracted using pymupdf. In fact, they were originally quotation marks.
@@ -233,7 +232,7 @@ def txt_spans_extract_v2(pdf_page, spans, all_bboxes, all_discarded_blocks, lang
         # 初始化ocr模型
         atom_model_manager = AtomModelSingleton()
         ocr_model = atom_model_manager.get_atom_model(
-            atom_model_name="ocr",
+            atom_model_name='ocr',
             ocr_show_log=False,
             det_db_box_thresh=0.3,
             lang=lang
@@ -241,7 +240,7 @@ def txt_spans_extract_v2(pdf_page, spans, all_bboxes, all_discarded_blocks, lang
 
         for span in empty_spans:
             # 对span的bbox截图再ocr
-            span_img = cut_image_to_pil_image(span['bbox'], pdf_page, mode="cv2")
+            span_img = cut_image_to_pil_image(span['bbox'], pdf_page, mode='cv2')
             ocr_res = ocr_model.ocr(span_img, det=False)
             if ocr_res and len(ocr_res) > 0:
                 if len(ocr_res[0]) > 0:
@@ -681,7 +680,7 @@ def parse_page_core(
     """根据parse_mode，构造spans，主要是文本类的字符填充"""
     if parse_mode == SupportedPdfParseMethod.TXT:
 
-        """使用新版本的混合ocr方案"""
+        """使用新版本的混合ocr方案."""
         spans = txt_spans_extract_v2(page_doc, spans, all_bboxes, all_discarded_blocks, lang)
 
     elif parse_mode == SupportedPdfParseMethod.OCR:
@@ -689,7 +688,6 @@ def parse_page_core(
     else:
         raise Exception('parse_mode must be txt or ocr')
 
-
     """先处理不需要排版的discarded_blocks"""
     discarded_block_with_spans, spans = fill_spans_in_blocks(
         all_discarded_blocks, spans, 0.4
@@ -762,8 +760,8 @@ def parse_page_core(
 
 
 def pdf_parse_union(
-    dataset: Dataset,
     model_list,
+    dataset: Dataset,
     imageWriter,
     parse_mode,
     start_page_id=0,
@@ -771,6 +769,7 @@ def pdf_parse_union(
     debug_mode=False,
     lang=None,
 ):
+
     pdf_bytes_md5 = compute_md5(dataset.data_bits())
 
     """初始化空的pdf_info_dict"""

magic_pdf/pipe/AbsPipe.py

@@ -3,6 +3,7 @@ from abc import ABC, abstractmethod
 from magic_pdf.config.drop_reason import DropReason
 from magic_pdf.config.make_content_config import DropMode, MakeMode
 from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.dict2md.ocr_mkcontent import union_make
 from magic_pdf.filter.pdf_classify_by_type import classify
 from magic_pdf.filter.pdf_meta_scan import pdf_meta_scan
@@ -14,9 +15,9 @@ class AbsPipe(ABC):
     PIP_OCR = 'ocr'
     PIP_TXT = 'txt'
 
-    def __init__(self, pdf_bytes: bytes, model_list: list, image_writer: DataWriter, is_debug: bool = False,
+    def __init__(self, dataset: Dataset, model_list: list, image_writer: DataWriter, is_debug: bool = False,
                  start_page_id=0, end_page_id=None, lang=None, layout_model=None, formula_enable=None, table_enable=None):
-        self.pdf_bytes = pdf_bytes
+        self.dataset = Dataset
         self.model_list = model_list
         self.image_writer = image_writer
         self.pdf_mid_data = None  # 未压缩

magic_pdf/pipe/OCRPipe.py

@@ -2,40 +2,79 @@ from loguru import logger
 
 from magic_pdf.config.make_content_config import DropMode, MakeMode
 from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 from magic_pdf.pipe.AbsPipe import AbsPipe
 from magic_pdf.user_api import parse_ocr_pdf
 
 
 class OCRPipe(AbsPipe):
-
-    def __init__(self, pdf_bytes: bytes, model_list: list, image_writer: DataWriter, is_debug: bool = False,
-                 start_page_id=0, end_page_id=None, lang=None,
-                 layout_model=None, formula_enable=None, table_enable=None):
-        super().__init__(pdf_bytes, model_list, image_writer, is_debug, start_page_id, end_page_id, lang,
-                         layout_model, formula_enable, table_enable)
+    def __init__(
+        self,
+        dataset: Dataset,
+        model_list: list,
+        image_writer: DataWriter,
+        is_debug: bool = False,
+        start_page_id=0,
+        end_page_id=None,
+        lang=None,
+        layout_model=None,
+        formula_enable=None,
+        table_enable=None,
+    ):
+        super().__init__(
+            dataset,
+            model_list,
+            image_writer,
+            is_debug,
+            start_page_id,
+            end_page_id,
+            lang,
+            layout_model,
+            formula_enable,
+            table_enable,
+        )
 
     def pipe_classify(self):
         pass
 
     def pipe_analyze(self):
-        self.model_list = doc_analyze(self.pdf_bytes, ocr=True,
-                                      start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                      lang=self.lang, layout_model=self.layout_model,
-                                      formula_enable=self.formula_enable, table_enable=self.table_enable)
+        self.infer_res = doc_analyze(
+            self.dataset,
+            ocr=True,
+            start_page_id=self.start_page_id,
+            end_page_id=self.end_page_id,
+            lang=self.lang,
+            layout_model=self.layout_model,
+            formula_enable=self.formula_enable,
+            table_enable=self.table_enable,
+        )
 
     def pipe_parse(self):
-        self.pdf_mid_data = parse_ocr_pdf(self.pdf_bytes, self.model_list, self.image_writer, is_debug=self.is_debug,
-                                          start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                          lang=self.lang, layout_model=self.layout_model,
-                                          formula_enable=self.formula_enable, table_enable=self.table_enable)
+        self.pdf_mid_data = parse_ocr_pdf(
+            self.dataset,
+            self.infer_res,
+            self.image_writer,
+            is_debug=self.is_debug,
+            start_page_id=self.start_page_id,
+            end_page_id=self.end_page_id,
+            lang=self.lang,
+            layout_model=self.layout_model,
+            formula_enable=self.formula_enable,
+            table_enable=self.table_enable,
+        )
 
     def pipe_mk_uni_format(self, img_parent_path: str, drop_mode=DropMode.WHOLE_PDF):
         result = super().pipe_mk_uni_format(img_parent_path, drop_mode)
         logger.info('ocr_pipe mk content list finished')
         return result
 
-    def pipe_mk_markdown(self, img_parent_path: str, drop_mode=DropMode.WHOLE_PDF, md_make_mode=MakeMode.MM_MD):
+    def pipe_mk_markdown(
+        self,
+        img_parent_path: str,
+        drop_mode=DropMode.WHOLE_PDF,
+        md_make_mode=MakeMode.MM_MD,
+    ):
         result = super().pipe_mk_markdown(img_parent_path, drop_mode, md_make_mode)
         logger.info(f'ocr_pipe mk {md_make_mode} finished')
         return result

magic_pdf/pipe/TXTPipe.py

@@ -2,6 +2,7 @@ from loguru import logger
 
 from magic_pdf.config.make_content_config import DropMode, MakeMode
 from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 from magic_pdf.pipe.AbsPipe import AbsPipe
 from magic_pdf.user_api import parse_txt_pdf
@@ -9,23 +10,23 @@ from magic_pdf.user_api import parse_txt_pdf
 
 class TXTPipe(AbsPipe):
 
-    def __init__(self, pdf_bytes: bytes, model_list: list, image_writer: DataWriter, is_debug: bool = False,
+    def __init__(self, dataset: Dataset, model_list: list, image_writer: DataWriter, is_debug: bool = False,
                  start_page_id=0, end_page_id=None, lang=None,
                  layout_model=None, formula_enable=None, table_enable=None):
-        super().__init__(pdf_bytes, model_list, image_writer, is_debug, start_page_id, end_page_id, lang,
+        super().__init__(dataset, model_list, image_writer, is_debug, start_page_id, end_page_id, lang,
                          layout_model, formula_enable, table_enable)
 
     def pipe_classify(self):
         pass
 
     def pipe_analyze(self):
-        self.model_list = doc_analyze(self.pdf_bytes, ocr=False,
+        self.model_list = doc_analyze(self.dataset, ocr=False,
                                       start_page_id=self.start_page_id, end_page_id=self.end_page_id,
                                       lang=self.lang, layout_model=self.layout_model,
                                       formula_enable=self.formula_enable, table_enable=self.table_enable)
 
     def pipe_parse(self):
-        self.pdf_mid_data = parse_txt_pdf(self.pdf_bytes, self.model_list, self.image_writer, is_debug=self.is_debug,
+        self.pdf_mid_data = parse_txt_pdf(self.dataset, self.model_list, self.image_writer, is_debug=self.is_debug,
                                           start_page_id=self.start_page_id, end_page_id=self.end_page_id,
                                           lang=self.lang, layout_model=self.layout_model,
                                           formula_enable=self.formula_enable, table_enable=self.table_enable)

magic_pdf/pipe/UNIPipe.py

@@ -4,6 +4,7 @@ from loguru import logger
 
 from magic_pdf.config.make_content_config import DropMode, MakeMode
 from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.libs.commons import join_path
 from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 from magic_pdf.pipe.AbsPipe import AbsPipe
@@ -12,12 +13,32 @@ from magic_pdf.user_api import parse_ocr_pdf, parse_union_pdf
 
 class UNIPipe(AbsPipe):
 
-    def __init__(self, pdf_bytes: bytes, jso_useful_key: dict, image_writer: DataWriter, is_debug: bool = False,
-                 start_page_id=0, end_page_id=None, lang=None,
-                 layout_model=None, formula_enable=None, table_enable=None):
+    def __init__(
+        self,
+        dataset: Dataset,
+        jso_useful_key: dict,
+        image_writer: DataWriter,
+        is_debug: bool = False,
+        start_page_id=0,
+        end_page_id=None,
+        lang=None,
+        layout_model=None,
+        formula_enable=None,
+        table_enable=None,
+    ):
         self.pdf_type = jso_useful_key['_pdf_type']
-        super().__init__(pdf_bytes, jso_useful_key['model_list'], image_writer, is_debug, start_page_id, end_page_id,
-                         lang, layout_model, formula_enable, table_enable)
+        super().__init__(
+            dataset,
+            jso_useful_key['model_list'],
+            image_writer,
+            is_debug,
+            start_page_id,
+            end_page_id,
+            lang,
+            layout_model,
+            formula_enable,
+            table_enable,
+        )
         if len(self.model_list) == 0:
             self.input_model_is_empty = True
         else:
@@ -28,35 +49,66 @@ class UNIPipe(AbsPipe):
 
     def pipe_analyze(self):
         if self.pdf_type == self.PIP_TXT:
-            self.model_list = doc_analyze(self.pdf_bytes, ocr=False,
-                                          start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                          lang=self.lang, layout_model=self.layout_model,
-                                          formula_enable=self.formula_enable, table_enable=self.table_enable)
+            self.model_list = doc_analyze(
+                self.dataset,
+                ocr=False,
+                start_page_id=self.start_page_id,
+                end_page_id=self.end_page_id,
+                lang=self.lang,
+                layout_model=self.layout_model,
+                formula_enable=self.formula_enable,
+                table_enable=self.table_enable,
+            )
         elif self.pdf_type == self.PIP_OCR:
-            self.model_list = doc_analyze(self.pdf_bytes, ocr=True,
-                                          start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                          lang=self.lang, layout_model=self.layout_model,
-                                          formula_enable=self.formula_enable, table_enable=self.table_enable)
+            self.model_list = doc_analyze(
+                self.dataset,
+                ocr=True,
+                start_page_id=self.start_page_id,
+                end_page_id=self.end_page_id,
+                lang=self.lang,
+                layout_model=self.layout_model,
+                formula_enable=self.formula_enable,
+                table_enable=self.table_enable,
+            )
 
     def pipe_parse(self):
         if self.pdf_type == self.PIP_TXT:
-            self.pdf_mid_data = parse_union_pdf(self.pdf_bytes, self.model_list, self.image_writer,
-                                                is_debug=self.is_debug, input_model_is_empty=self.input_model_is_empty,
-                                                start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                                lang=self.lang, layout_model=self.layout_model,
-                                                formula_enable=self.formula_enable, table_enable=self.table_enable)
+            self.pdf_mid_data = parse_union_pdf(
+                self.dataset,
+                self.model_list,
+                self.image_writer,
+                is_debug=self.is_debug,
+                start_page_id=self.start_page_id,
+                end_page_id=self.end_page_id,
+                lang=self.lang,
+                layout_model=self.layout_model,
+                formula_enable=self.formula_enable,
+                table_enable=self.table_enable,
+            )
         elif self.pdf_type == self.PIP_OCR:
-            self.pdf_mid_data = parse_ocr_pdf(self.pdf_bytes, self.model_list, self.image_writer,
-                                              is_debug=self.is_debug,
-                                              start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                              lang=self.lang)
-
-    def pipe_mk_uni_format(self, img_parent_path: str, drop_mode=DropMode.NONE_WITH_REASON):
+            self.pdf_mid_data = parse_ocr_pdf(
+                self.dataset,
+                self.model_list,
+                self.image_writer,
+                is_debug=self.is_debug,
+                start_page_id=self.start_page_id,
+                end_page_id=self.end_page_id,
+                lang=self.lang,
+            )
+
+    def pipe_mk_uni_format(
+        self, img_parent_path: str, drop_mode=DropMode.NONE_WITH_REASON
+    ):
         result = super().pipe_mk_uni_format(img_parent_path, drop_mode)
         logger.info('uni_pipe mk content list finished')
         return result
 
-    def pipe_mk_markdown(self, img_parent_path: str, drop_mode=DropMode.WHOLE_PDF, md_make_mode=MakeMode.MM_MD):
+    def pipe_mk_markdown(
+        self,
+        img_parent_path: str,
+        drop_mode=DropMode.WHOLE_PDF,
+        md_make_mode=MakeMode.MM_MD,
+    ):
         result = super().pipe_mk_markdown(img_parent_path, drop_mode, md_make_mode)
         logger.info(f'uni_pipe mk {md_make_mode} finished')
         return result
@@ -65,6 +117,7 @@ class UNIPipe(AbsPipe):
 if __name__ == '__main__':
     # 测试
     from magic_pdf.data.data_reader_writer import DataReader
+
     drw = DataReader(r'D:/project/20231108code-clean')
 
     pdf_file_path = r'linshixuqiu\19983-00.pdf'
@@ -82,10 +135,7 @@ if __name__ == '__main__':
     #     "model_list": model_list
     # }
 
-    jso_useful_key = {
-        '_pdf_type': '',
-        'model_list': model_list
-    }
+    jso_useful_key = {'_pdf_type': '', 'model_list': model_list}
     pipe = UNIPipe(pdf_bytes, jso_useful_key, img_writer)
     pipe.pipe_classify()
     pipe.pipe_parse()
@@ -94,5 +144,7 @@ if __name__ == '__main__':
 
     md_writer = DataWriter(write_path)
     md_writer.write_string('19983-00.md', md_content)
-    md_writer.write_string('19983-00.json', json.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4))
+    md_writer.write_string(
+        '19983-00.json', json.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4)
+    )
     md_writer.write_string('19983-00.txt', str(content_list))

magic_pdf/pipe/operators.py

@@ -0,0 +1,138 @@
+import json
+import os
+from typing import Callable
+import copy
+
+from magic_pdf.config.make_content_config import DropMode, MakeMode
+from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
+from magic_pdf.dict2md.ocr_mkcontent import union_make
+from magic_pdf.libs.draw_bbox import (draw_layout_bbox, draw_line_sort_bbox,
+                                      draw_span_bbox)
+from magic_pdf.libs.json_compressor import JsonCompressor
+
+
+class PipeResult:
+    def __init__(self, pipe_res, dataset: Dataset):
+        """Initialized.
+
+        Args:
+            pipe_res (list[dict]): the pipeline processed result of model inference result
+            dataset (Dataset): the dataset associated with pipe_res
+        """
+        self._pipe_res = pipe_res
+        self._dataset = dataset
+
+    def dump_md(
+        self,
+        writer: DataWriter,
+        file_path: str,
+        img_dir_or_bucket_prefix: str,
+        drop_mode=DropMode.WHOLE_PDF,
+        md_make_mode=MakeMode.MM_MD,
+    ):
+        """Dump The Markdown.
+
+        Args:
+            writer (DataWriter): File writer handle
+            file_path (str): The file location of markdown
+            img_dir_or_bucket_prefix (str): The s3 bucket prefix or local file directory which used to store the figure
+            drop_mode (str, optional): Drop strategy when some page which is corrupted or inappropriate. Defaults to DropMode.WHOLE_PDF.
+            md_make_mode (str, optional): The content Type of Markdown be made. Defaults to MakeMode.MM_MD.
+        """
+        pdf_info_list = self._pipe_res['pdf_info']
+        md_content = union_make(
+            pdf_info_list, md_make_mode, drop_mode, img_dir_or_bucket_prefix
+        )
+        writer.write_string(file_path, md_content)
+
+    def dump_content_list(
+        self, writer: DataWriter, file_path: str, image_dir_or_bucket_prefix: str
+    ):
+        """Dump Content List.
+
+        Args:
+            writer (DataWriter): File writer handle
+            file_path (str): The file location of content list
+            image_dir_or_bucket_prefix (str): The s3 bucket prefix or local file directory which used to store the figure
+        """
+        pdf_info_list = self._pipe_res['pdf_info']
+        content_list = union_make(
+            pdf_info_list,
+            MakeMode.STANDARD_FORMAT,
+            DropMode.NONE,
+            image_dir_or_bucket_prefix,
+        )
+        writer.write_string(
+            file_path, json.dumps(content_list, ensure_ascii=False, indent=4)
+        )
+
+    def dump_middle_json(self, writer: DataWriter, file_path: str):
+        """Dump the result of pipeline.
+
+        Args:
+            writer (DataWriter): File writer handler
+            file_path (str): The file location of middle json
+        """
+        writer.write_string(
+            file_path, json.dumps(self._pipe_res, ensure_ascii=False, indent=4)
+        )
+
+    def draw_layout(self, file_path: str) -> None:
+        """Draw the layout.
+
+        Args:
+            file_path (str): The file location of layout result file
+        """
+        dir_name = os.path.dirname(file_path)
+        base_name = os.path.basename(file_path)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name, exist_ok=True)
+        pdf_info = self._pipe_res['pdf_info']
+        draw_layout_bbox(pdf_info, self._dataset.data_bits(), dir_name, base_name)
+
+    def draw_span(self, file_path: str):
+        """Draw the Span.
+
+        Args:
+            file_path (str): The file location of span result file
+        """
+        dir_name = os.path.dirname(file_path)
+        base_name = os.path.basename(file_path)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name, exist_ok=True)
+        pdf_info = self._pipe_res['pdf_info']
+        draw_span_bbox(pdf_info, self._dataset.data_bits(), dir_name, base_name)
+
+    def draw_line_sort(self, file_path: str):
+        """Draw line sort.
+
+        Args:
+            file_path (str): The file location of line sort result file
+        """
+        dir_name = os.path.dirname(file_path)
+        base_name = os.path.basename(file_path)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name, exist_ok=True)
+        pdf_info = self._pipe_res['pdf_info']
+        draw_line_sort_bbox(pdf_info, self._dataset.data_bits(), dir_name, base_name)
+
+    def get_compress_pdf_mid_data(self):
+        """Compress the pipeline result.
+
+        Returns:
+            str: compress the pipeline result and return
+        """
+        return JsonCompressor.compress_json(self.pdf_mid_data)
+
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(pipeline_result, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        return proc(copy.deepcopy(self._pipe_res), *args, **kwargs)

magic_pdf/tools/common.py

@@ -1,5 +1,3 @@
-import copy
-import json as json_parse
 import os
 
 import click
@@ -7,13 +5,12 @@ import fitz
 from loguru import logger
 
 import magic_pdf.model as model_config
+from magic_pdf.config.enums import SupportedPdfParseMethod
 from magic_pdf.config.make_content_config import DropMode, MakeMode
 from magic_pdf.data.data_reader_writer import FileBasedDataWriter
-from magic_pdf.libs.draw_bbox import (draw_layout_bbox, draw_line_sort_bbox,
-                                      draw_model_bbox, draw_span_bbox)
-from magic_pdf.pipe.OCRPipe import OCRPipe
-from magic_pdf.pipe.TXTPipe import TXTPipe
-from magic_pdf.pipe.UNIPipe import UNIPipe
+from magic_pdf.data.dataset import PymuDocDataset
+from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
+from magic_pdf.model.operators import InferenceResult
 
 # from io import BytesIO
 # from pypdf import PdfReader, PdfWriter
@@ -56,7 +53,11 @@ def prepare_env(output_dir, pdf_file_name, method):
 def convert_pdf_bytes_to_bytes_by_pymupdf(pdf_bytes, start_page_id=0, end_page_id=None):
     document = fitz.open('pdf', pdf_bytes)
     output_document = fitz.open()
-    end_page_id = end_page_id if end_page_id is not None and end_page_id >= 0 else len(document) - 1
+    end_page_id = (
+        end_page_id
+        if end_page_id is not None and end_page_id >= 0
+        else len(document) - 1
+    )
     if end_page_id > len(document) - 1:
         logger.warning('end_page_id is out of range, use pdf_docs length')
         end_page_id = len(document) - 1
@@ -94,78 +95,126 @@ def do_parse(
         f_draw_model_bbox = True
         f_draw_line_sort_bbox = True
 
-    if lang == "":
+    if lang == '':
         lang = None
 
-    pdf_bytes = convert_pdf_bytes_to_bytes_by_pymupdf(pdf_bytes, start_page_id, end_page_id)
+    pdf_bytes = convert_pdf_bytes_to_bytes_by_pymupdf(
+        pdf_bytes, start_page_id, end_page_id
+    )
 
-    orig_model_list = copy.deepcopy(model_list)
-    local_image_dir, local_md_dir = prepare_env(output_dir, pdf_file_name,
-                                                parse_method)
+    local_image_dir, local_md_dir = prepare_env(output_dir, pdf_file_name, parse_method)
 
-    image_writer, md_writer = FileBasedDataWriter(
-        local_image_dir), FileBasedDataWriter(local_md_dir)
+    image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
+        local_md_dir
+    )
     image_dir = str(os.path.basename(local_image_dir))
 
-    if parse_method == 'auto':
-        jso_useful_key = {'_pdf_type': '', 'model_list': model_list}
-        pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer, is_debug=True,
-                       # start_page_id=start_page_id, end_page_id=end_page_id,
-                       lang=lang,
-                       layout_model=layout_model, formula_enable=formula_enable, table_enable=table_enable)
-    elif parse_method == 'txt':
-        pipe = TXTPipe(pdf_bytes, model_list, image_writer, is_debug=True,
-                       # start_page_id=start_page_id, end_page_id=end_page_id,
-                       lang=lang,
-                       layout_model=layout_model, formula_enable=formula_enable, table_enable=table_enable)
-    elif parse_method == 'ocr':
-        pipe = OCRPipe(pdf_bytes, model_list, image_writer, is_debug=True,
-                       # start_page_id=start_page_id, end_page_id=end_page_id,
-                       lang=lang,
-                       layout_model=layout_model, formula_enable=formula_enable, table_enable=table_enable)
-    else:
-        logger.error('unknown parse method')
-        exit(1)
-
-    pipe.pipe_classify()
+    ds = PymuDocDataset(pdf_bytes)
 
     if len(model_list) == 0:
         if model_config.__use_inside_model__:
-            pipe.pipe_analyze()
-            orig_model_list = copy.deepcopy(pipe.model_list)
+            if parse_method == 'auto':
+                if ds.classify() == SupportedPdfParseMethod.TXT:
+                    infer_result = ds.apply(
+                        doc_analyze,
+                        ocr=False,
+                        lang=lang,
+                        layout_model=layout_model,
+                        formula_enable=formula_enable,
+                        table_enable=table_enable,
+                    )
+                    pipe_result = infer_result.pipe_txt_mode(
+                        image_writer, debug_mode=True, lang=lang
+                    )
+                else:
+                    infer_result = ds.apply(
+                        doc_analyze,
+                        ocr=True,
+                        lang=lang,
+                        layout_model=layout_model,
+                        formula_enable=formula_enable,
+                        table_enable=table_enable,
+                    )
+                    pipe_result = infer_result.pipe_ocr_mode(
+                        image_writer, debug_mode=True, lang=lang
+                    )
+
+            elif parse_method == 'txt':
+                infer_result = ds.apply(
+                    doc_analyze,
+                    ocr=False,
+                    lang=lang,
+                    layout_model=layout_model,
+                    formula_enable=formula_enable,
+                    table_enable=table_enable,
+                )
+                pipe_result = infer_result.pipe_txt_mode(
+                    image_writer, debug_mode=True, lang=lang
+                )
+            elif parse_method == 'ocr':
+                infer_result = ds.apply(
+                    doc_analyze,
+                    ocr=True,
+                    lang=lang,
+                    layout_model=layout_model,
+                    formula_enable=formula_enable,
+                    table_enable=table_enable,
+                )
+                pipe_result = infer_result.pipe_ocr_mode(
+                    image_writer, debug_mode=True, lang=lang
+                )
+            else:
+                logger.error('unknown parse method')
+                exit(1)
         else:
             logger.error('need model list input')
             exit(2)
+    else:
+        infer_result = InferenceResult(model_list, ds)
+        if parse_method == 'ocr':
+            pipe_result = infer_result.pipe_ocr_mode(
+                image_writer, debug_mode=True, lang=lang
+            )
+        elif parse_method == 'txt':
+            pipe_result = infer_result.pipe_txt_mode(
+                image_writer, debug_mode=True, lang=lang
+            )
+        else:
+            pipe_result = infer_result.pipe_auto_mode(
+                image_writer, debug_mode=True, lang=lang
+            )
+
+    if f_draw_model_bbox:
+        infer_result.draw_model(
+            os.path.join(local_md_dir, f'{pdf_file_name}_model.pdf')
+        )
 
-    pipe.pipe_parse()
-    pdf_info = pipe.pdf_mid_data['pdf_info']
     if f_draw_layout_bbox:
-        draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name)
+        pipe_result.draw_layout(
+            os.path.join(local_md_dir, f'{pdf_file_name}_layout.pdf')
+        )
     if f_draw_span_bbox:
-        draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name)
-    if f_draw_model_bbox:
-        draw_model_bbox(copy.deepcopy(orig_model_list), pdf_bytes, local_md_dir, pdf_file_name)
+        pipe_result.draw_span(os.path.join(local_md_dir, f'{pdf_file_name}_spans.pdf'))
+
     if f_draw_line_sort_bbox:
-        draw_line_sort_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name)
+        pipe_result.draw_line_sort(
+            os.path.join(local_md_dir, f'{pdf_file_name}_line_sort.pdf')
+        )
 
-    md_content = pipe.pipe_mk_markdown(image_dir, drop_mode=DropMode.NONE, md_make_mode=f_make_md_mode)
     if f_dump_md:
-        md_writer.write_string(
+        pipe_result.dump_md(
+            md_writer,
             f'{pdf_file_name}.md',
-            md_content
+            image_dir,
+            drop_mode=DropMode.NONE,
+            md_make_mode=f_make_md_mode,
         )
 
     if f_dump_middle_json:
-        md_writer.write_string(
-            f'{pdf_file_name}_middle.json',
-            json_parse.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4)
-        )
+        pipe_result.dump_middle_json(md_writer, f'{pdf_file_name}_middle.json')
 
     if f_dump_model_json:
-        md_writer.write_string(
-            f'{pdf_file_name}_model.json',
-            json_parse.dumps(orig_model_list, ensure_ascii=False, indent=4)
-        )
+        infer_result.dump_model(md_writer, f'{pdf_file_name}_model.json')
 
     if f_dump_orig_pdf:
         md_writer.write(
@@ -173,11 +222,11 @@ def do_parse(
             pdf_bytes,
         )
 
-    content_list = pipe.pipe_mk_uni_format(image_dir, drop_mode=DropMode.NONE)
     if f_dump_content_list:
-        md_writer.write_string(
+        pipe_result.dump_content_list(
+            md_writer,
             f'{pdf_file_name}_content_list.json',
-            json_parse.dumps(content_list, ensure_ascii=False, indent=4)
+            image_dir
         )
 
     logger.info(f'local output dir is {local_md_dir}')

magic_pdf/user_api.py

@@ -10,22 +10,29 @@
 from loguru import logger
 
 from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.libs.version import __version__
 from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 from magic_pdf.pdf_parse_by_ocr import parse_pdf_by_ocr
 from magic_pdf.pdf_parse_by_txt import parse_pdf_by_txt
-
-PARSE_TYPE_TXT = 'txt'
-PARSE_TYPE_OCR = 'ocr'
-
-
-def parse_txt_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter, is_debug=False,
-                  start_page_id=0, end_page_id=None, lang=None,
-                  *args, **kwargs):
+from magic_pdf.config.constants import PARSE_TYPE_TXT, PARSE_TYPE_OCR
+
+
+def parse_txt_pdf(
+    dataset: Dataset,
+    model_list: list,
+    imageWriter: DataWriter,
+    is_debug=False,
+    start_page_id=0,
+    end_page_id=None,
+    lang=None,
+    *args,
+    **kwargs
+):
     """解析文本类pdf."""
     pdf_info_dict = parse_pdf_by_txt(
-        pdf_bytes,
-        pdf_models,
+        dataset,
+        model_list,
         imageWriter,
         start_page_id=start_page_id,
         end_page_id=end_page_id,
@@ -43,13 +50,21 @@ def parse_txt_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter, i
     return pdf_info_dict
 
 
-def parse_ocr_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter, is_debug=False,
-                  start_page_id=0, end_page_id=None, lang=None,
-                  *args, **kwargs):
+def parse_ocr_pdf(
+    dataset: Dataset,
+    model_list: list,
+    imageWriter: DataWriter,
+    is_debug=False,
+    start_page_id=0,
+    end_page_id=None,
+    lang=None,
+    *args,
+    **kwargs
+):
     """解析ocr类pdf."""
     pdf_info_dict = parse_pdf_by_ocr(
-        pdf_bytes,
-        pdf_models,
+        dataset,
+        model_list,
         imageWriter,
         start_page_id=start_page_id,
         end_page_id=end_page_id,
@@ -67,17 +82,24 @@ def parse_ocr_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter, i
     return pdf_info_dict
 
 
-def parse_union_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter, is_debug=False,
-                    input_model_is_empty: bool = False,
-                    start_page_id=0, end_page_id=None, lang=None,
-                    *args, **kwargs):
+def parse_union_pdf(
+    dataset: Dataset,
+    model_list: list,
+    imageWriter: DataWriter,
+    is_debug=False,
+    start_page_id=0,
+    end_page_id=None,
+    lang=None,
+    *args,
+    **kwargs
+):
     """ocr和文本混合的pdf，全部解析出来."""
 
     def parse_pdf(method):
         try:
             return method(
-                pdf_bytes,
-                pdf_models,
+                dataset,
+                model_list,
                 imageWriter,
                 start_page_id=start_page_id,
                 end_page_id=end_page_id,
@@ -91,12 +113,12 @@ def parse_union_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter,
     pdf_info_dict = parse_pdf(parse_pdf_by_txt)
     if pdf_info_dict is None or pdf_info_dict.get('_need_drop', False):
         logger.warning('parse_pdf_by_txt drop or error, switch to parse_pdf_by_ocr')
-        if input_model_is_empty:
+        if len(model_list) == 0:
             layout_model = kwargs.get('layout_model', None)
             formula_enable = kwargs.get('formula_enable', None)
             table_enable = kwargs.get('table_enable', None)
-            pdf_models = doc_analyze(
-                pdf_bytes,
+            infer_res = doc_analyze(
+                dataset,
                 ocr=True,
                 start_page_id=start_page_id,
                 end_page_id=end_page_id,
@@ -105,6 +127,7 @@ def parse_union_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter,
                 formula_enable=formula_enable,
                 table_enable=table_enable,
             )
+            model_list = infer_res.get_infer_res()
         pdf_info_dict = parse_pdf(parse_pdf_by_ocr)
         if pdf_info_dict is None:
             raise Exception('Both parse_pdf_by_txt and parse_pdf_by_ocr failed.')

next_docs/en/api.rst

@@ -7,3 +7,5 @@
    api/read_api
    api/schemas
    api/io
+   api/pipe_operators
+   api/model_operators

next_docs/en/api/model_operators.rst

@@ -0,0 +1,8 @@
+
+Model Api
+==========
+
+.. autoclass:: magic_pdf.model.InferenceResultBase
+   :members:
+   :inherited-members:
+   :show-inheritance:

next_docs/en/api/pipe_operators.rst

@@ -0,0 +1,9 @@
+
+
+Pipeline Api
+=============
+
+.. autoclass:: magic_pdf.pipe.operators.PipeResult
+   :members:
+   :inherited-members:
+   :show-inheritance:

next_docs/en/conf.py

@@ -114,7 +114,7 @@ autodoc_mock_imports = [
     'sentencepiece',
     'vllm.cuda_utils',
     'vllm._C',
-    'numpy',
+    # 'numpy',
     'tqdm',
 ]
 

next_docs/en/user_guide/quick_start/to_markdown.rst

@@ -12,17 +12,17 @@ Local File Example
     import os
 
     from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
-    from magic_pdf.config.make_content_config import DropMode, MakeMode
-    from magic_pdf.pipe.OCRPipe import OCRPipe
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 
-
-    ## args
-    model_list = []
+    # args
     pdf_file_name = "abc.pdf"  # replace with the real pdf path
+    name_without_suff = pdf_file_name.split(".")[0]
 
-
-    ## prepare env
+    # prepare env
     local_image_dir, local_md_dir = "output/images", "output"
+    image_dir = str(os.path.basename(local_image_dir))
+
     os.makedirs(local_image_dir, exist_ok=True)
 
     image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
@@ -30,27 +30,31 @@ Local File Example
     )
     image_dir = str(os.path.basename(local_image_dir))
 
+    # read bytes
     reader1 = FileBasedDataReader("")
-    pdf_bytes = reader1.read(pdf_file_name)   # read the pdf content
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
 
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
 
-    pipe = OCRPipe(pdf_bytes, model_list, image_writer)
+    ## inference 
+    infer_result = ds.apply(doc_analyze, ocr=True)
 
-    pipe.pipe_classify()
-    pipe.pipe_analyze()
-    pipe.pipe_parse()
+    ### draw model result on each page
+    infer_result.draw_model(os.path.join(local_md_dir, f"{name_without_suff}_model.pdf"))
 
-    pdf_info = pipe.pdf_mid_data["pdf_info"]
+    ## pipeline
+    pipe_result = infer_result.pipe_ocr_mode(image_writer)
 
+    ### draw layout result on each page
+    pipe_result.draw_layout(os.path.join(local_md_dir, f"{name_without_suff}_layout.pdf"))
 
-    md_content = pipe.pipe_mk_markdown(
-        image_dir, drop_mode=DropMode.NONE, md_make_mode=MakeMode.MM_MD
-    )
+    ### draw spans result on each page
+    pipe_result.draw_span(os.path.join(local_md_dir, f"{name_without_suff}_spans.pdf"))
 
-    if isinstance(md_content, list):
-        md_writer.write_string(f"{pdf_file_name}.md", "\n".join(md_content))
-    else:
-        md_writer.write_string(f"{pdf_file_name}.md", md_content)
+    ### dump markdown
+    pipe_result.dump_md(md_writer, f"{name_without_suff}.md", image_dir)
 
 
 S3 File Example
@@ -61,8 +65,8 @@ S3 File Example
     import os
 
     from magic_pdf.data.data_reader_writer import S3DataReader, S3DataWriter
-    from magic_pdf.config.make_content_config import DropMode, MakeMode
-    from magic_pdf.pipe.OCRPipe import OCRPipe
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 
     bucket_name = "{Your S3 Bucket Name}"  # replace with real bucket name
     ak = "{Your S3 access key}"  # replace with real s3 access key
@@ -74,29 +78,39 @@ S3 File Example
     writer = S3DataWriter('unittest/tmp', bucket_name, ak, sk, endpoint_url)
     image_writer = S3DataWriter('unittest/tmp/images', bucket_name, ak, sk, endpoint_url)
 
-    ## args
-    model_list = []
-    pdf_file_name = f"s3://{bucket_name}/{fake pdf path}"  # replace with the real s3 path
+    # args
+    pdf_file_name = (
+        "s3://llm-pdf-text-1/unittest/tmp/bug5-11.pdf"  # replace with the real s3 path
+    )
+
+    # prepare env
+    local_dir = "output"
+    name_without_suff = os.path.basename(pdf_file_name).split(".")[0]
 
+    # read bytes
     pdf_bytes = reader.read(pdf_file_name)  # read the pdf content
 
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
 
-    pipe = OCRPipe(pdf_bytes, model_list, image_writer)
+    ## inference 
+    infer_result = ds.apply(doc_analyze, ocr=True)
 
-    pipe.pipe_classify()
-    pipe.pipe_analyze()
-    pipe.pipe_parse()
+    ### draw model result on each page
+    infer_result.draw_model(os.path.join(local_dir, f'{name_without_suff}_model.pdf'))  # dump to local
 
-    pdf_info = pipe.pdf_mid_data["pdf_info"]
+    ## pipeline
+    pipe_result = infer_result.pipe_ocr_mode(image_writer)
 
-    md_content = pipe.pipe_mk_markdown(
-        "unittest/tmp/images", drop_mode=DropMode.NONE, md_make_mode=MakeMode.MM_MD
-    )
+    ### draw layout result on each page
+    pipe_result.draw_layout(os.path.join(local_dir, f'{name_without_suff}_layout.pdf'))  # dump to local
+
+    ### draw spans result on each page
+    pipe_result.draw_span(os.path.join(local_dir, f'{name_without_suff}_spans.pdf'))   # dump to local 
 
-    if isinstance(md_content, list):
-        writer.write_string(f"{pdf_file_name}.md", "\n".join(md_content))
-    else:
-        writer.write_string(f"{pdf_file_name}.md", md_content)
+    ### dump markdown
+    pipe_result.dump_md(writer, f'{name_without_suff}.md', "unittest/tmp/images")    # dump to remote s3
 
 
-Check :doc:`../data/data_reader_writer` for more [reader | writer] examples
+Check :doc:`../data/data_reader_writer` for more [reader | writer] examples and check :doc:`../../api/pipe_operators` or :doc:`../../api/model_operators` for api details

next_docs/en/user_guide/tutorial.rst

@@ -7,4 +7,6 @@ From the beginning to the end, Show how to using mineru via a minimal project
 .. toctree::
     :maxdepth: 1
 
-    tutorial/output_file_description
+    tutorial/output_file_description
+    tutorial/pipeline
+

next_docs/en/user_guide/tutorial/pipeline.rst

@@ -0,0 +1,185 @@
+
+
+Pipeline
+==========
+
+
+Minimal Example 
+^^^^^^^^^^^^^^^^^
+
+.. code:: python
+
+    import os
+
+    from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
+
+    # args
+    pdf_file_name = "abc.pdf"  # replace with the real pdf path
+    name_without_suff = pdf_file_name.split(".")[0]
+
+    # prepare env
+    local_image_dir, local_md_dir = "output/images", "output"
+    image_dir = str(os.path.basename(local_image_dir))
+
+    os.makedirs(local_image_dir, exist_ok=True)
+
+    image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
+        local_md_dir
+    )
+    image_dir = str(os.path.basename(local_image_dir))
+
+    # read bytes
+    reader1 = FileBasedDataReader("")
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(md_writer, f"{name_without_suff}.md", image_dir)
+
+Running the above code will result in the following
+
+
+.. code:: bash 
+
+    output/
+    ├── abc.md
+    └── images
+
+
+Excluding the setup of the environment, such as creating directories and importing dependencies, the actual code snippet for converting pdf to markdown is as follows
+
+
+.. code:: python 
+
+    # read bytes
+    reader1 = FileBasedDataReader("")
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(md_writer, f"{name_without_suff}.md", image_dir)
+
+``ds.apply(doc_analyze, ocr=True)`` generates an ``InferenceResult`` object. The ``InferenceResult`` object, when executing the ``pipe_ocr_mode`` method, produces a ``PipeResult`` object.
+The ``PipeResult`` object, upon executing ``dump_md``, generates a ``markdown`` file at the specified location.
+
+
+The pipeline execution process is illustrated in the following diagram
+
+
+.. image:: ../../_static/image/pipeline.drawio.svg 
+
+.. raw:: html
+
+    <br> </br>
+
+Currently, the process is divided into three stages: data, inference, and processing, which correspond to the ``Dataset``, ``InferenceResult``, and ``PipeResult`` entities in the diagram.
+These stages are linked together through methods like ``apply``, ``doc_analyze``, or ``pipe_ocr_mode``
+
+
+.. admonition:: Tip
+    :class: tip
+
+    For more examples on how to use ``Dataset``, ``InferenceResult``, and ``PipeResult``, please refer to :doc:`../quick_start/to_markdown`
+
+    For more detailed information about ``Dataset``, ``InferenceResult``, and ``PipeResult``, please refer to :doc:`../../api/dataset`, :doc:`../../api/model_operators`, :doc:`../../api/pipe_operators`
+
+
+Pipeline Composition
+^^^^^^^^^^^^^^^^^^^^^
+
+.. code:: python 
+
+    class Dataset(ABC):
+        @abstractmethod
+        def apply(self, proc: Callable, *args, **kwargs):
+            """Apply callable method which.
+
+            Args:
+                proc (Callable): invoke proc as follows:
+                    proc(self, *args, **kwargs)
+
+            Returns:
+                Any: return the result generated by proc
+            """
+            pass
+
+    class InferenceResult(InferenceResultBase):
+
+        def apply(self, proc: Callable, *args, **kwargs):
+            """Apply callable method which.
+
+            Args:
+                proc (Callable): invoke proc as follows:
+                    proc(inference_result, *args, **kwargs)
+
+            Returns:
+                Any: return the result generated by proc
+            """
+            return proc(copy.deepcopy(self._infer_res), *args, **kwargs)
+
+        def pipe_ocr_mode(
+            self,
+            imageWriter: DataWriter,
+            start_page_id=0,
+            end_page_id=None,
+            debug_mode=False,
+            lang=None,
+            ) -> PipeResult:
+            pass
+
+    class PipeResult:
+        def apply(self, proc: Callable, *args, **kwargs):
+            """Apply callable method which.
+
+            Args:
+                proc (Callable): invoke proc as follows:
+                    proc(pipeline_result, *args, **kwargs)
+
+            Returns:
+                Any: return the result generated by proc
+            """
+            return proc(copy.deepcopy(self._pipe_res), *args, **kwargs)
+
+
+The ``Dataset``, ``InferenceResult``, and ``PipeResult`` classes all have an ``apply`` method, which can be used to chain different stages of the computation. 
+As shown below, ``MinerU`` provides a set of methods to compose these classes.
+
+
+.. code:: python 
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(md_writer, f"{name_without_suff}.md", image_dir)
+
+
+Users can implement their own functions for chaining as needed. For example, a user could use the ``apply`` method to create a function that counts the number of pages in a ``pdf`` file.
+
+
+.. code:: python
+
+    from magic_pdf.data.data_reader_writer import  FileBasedDataReader
+    from magic_pdf.data.dataset import PymuDocDataset
+
+    # args
+    pdf_file_name = "abc.pdf"  # replace with the real pdf path
+
+    # read bytes
+    reader1 = FileBasedDataReader("")
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    def count_page(ds)-> int:
+        return len(ds)
+
+    print("page number: ", ds.apply(count_page)) # will output the page count of `abc.pdf`

next_docs/requirements.txt

@@ -1,3 +1,7 @@
+numpy==1.26.4
+click==8.1.7
+fast-langdetect==0.2.2
+Brotli==1.1.0
 boto3>=1.28.43
 loguru>=0.6.0
 myst-parser
@@ -9,4 +13,4 @@ sphinx-argparse>=0.5.2
 sphinx-book-theme>=1.1.3
 sphinx-copybutton>=0.5.2
 sphinx_rtd_theme>=3.0.1
-autodoc_pydantic>=2.2.0
+autodoc_pydantic>=2.2.0

next_docs/zh_cn/user_guide/quick_start/to_markdown.rst

@@ -1,28 +1,26 @@
 
-
 转换为 Markdown 文件
 ========================
 
-
 本地文件示例
-^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^
 
 .. code:: python
 
     import os
 
     from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
-    from magic_pdf.config.make_content_config import DropMode, MakeMode
-    from magic_pdf.pipe.OCRPipe import OCRPipe
-
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 
-    ## args
-    model_list = []
+    # args
     pdf_file_name = "abc.pdf"  # replace with the real pdf path
+    name_without_suff = pdf_file_name.split(".")[0]
 
-
-    ## prepare env
+    # prepare env
     local_image_dir, local_md_dir = "output/images", "output"
+    image_dir = str(os.path.basename(local_image_dir))
+
     os.makedirs(local_image_dir, exist_ok=True)
 
     image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
@@ -30,39 +28,43 @@
     )
     image_dir = str(os.path.basename(local_image_dir))
 
+    # read bytes
     reader1 = FileBasedDataReader("")
-    pdf_bytes = reader1.read(pdf_file_name)   # read the pdf content
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
 
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
 
-    pipe = OCRPipe(pdf_bytes, model_list, image_writer)
+    ## inference 
+    infer_result = ds.apply(doc_analyze, ocr=True)
 
-    pipe.pipe_classify()
-    pipe.pipe_analyze()
-    pipe.pipe_parse()
+    ### draw model result on each page
+    infer_result.draw_model(os.path.join(local_md_dir, f"{name_without_suff}_model.pdf"))
 
-    pdf_info = pipe.pdf_mid_data["pdf_info"]
+    ## pipeline
+    pipe_result = infer_result.pipe_ocr_mode(image_writer)
 
+    ### draw layout result on each page
+    pipe_result.draw_layout(os.path.join(local_md_dir, f"{name_without_suff}_layout.pdf"))
 
-    md_content = pipe.pipe_mk_markdown(
-        image_dir, drop_mode=DropMode.NONE, md_make_mode=MakeMode.MM_MD
-    )
+    ### draw spans result on each page
+    pipe_result.draw_span(os.path.join(local_md_dir, f"{name_without_suff}_spans.pdf"))
 
-    if isinstance(md_content, list):
-        md_writer.write_string(f"{pdf_file_name}.md", "\n".join(md_content))
-    else:
-        md_writer.write_string(f"{pdf_file_name}.md", md_content)
+    ### dump markdown
+    pipe_result.dump_md(md_writer, f"{name_without_suff}.md", image_dir)
 
 
-对象存储使用示例
-^^^^^^^^^^^^^^^
+对象存储文件示例
+^^^^^^^^^^^^^^^^
 
 .. code:: python
 
     import os
 
     from magic_pdf.data.data_reader_writer import S3DataReader, S3DataWriter
-    from magic_pdf.config.make_content_config import DropMode, MakeMode
-    from magic_pdf.pipe.OCRPipe import OCRPipe
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 
     bucket_name = "{Your S3 Bucket Name}"  # replace with real bucket name
     ak = "{Your S3 access key}"  # replace with real s3 access key
@@ -74,30 +76,39 @@
     writer = S3DataWriter('unittest/tmp', bucket_name, ak, sk, endpoint_url)
     image_writer = S3DataWriter('unittest/tmp/images', bucket_name, ak, sk, endpoint_url)
 
-    ## args
-    model_list = []
-    pdf_file_name = f"s3://{bucket_name}/{fake pdf path}"  # replace with the real s3 path
+    # args
+    pdf_file_name = (
+        "s3://llm-pdf-text-1/unittest/tmp/bug5-11.pdf"  # replace with the real s3 path
+    )
 
+    # prepare env
+    local_dir = "output"
+    name_without_suff = os.path.basename(pdf_file_name).split(".")[0]
+
+    # read bytes
     pdf_bytes = reader.read(pdf_file_name)  # read the pdf content
 
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
 
-    pipe = OCRPipe(pdf_bytes, model_list, image_writer)
+    ## inference 
+    infer_result = ds.apply(doc_analyze, ocr=True)
 
-    pipe.pipe_classify()
-    pipe.pipe_analyze()
-    pipe.pipe_parse()
+    ### draw model result on each page
+    infer_result.draw_model(os.path.join(local_dir, f'{name_without_suff}_model.pdf'))  # dump to local
 
-    pdf_info = pipe.pdf_mid_data["pdf_info"]
+    ## pipeline
+    pipe_result = infer_result.pipe_ocr_mode(image_writer)
 
-    md_content = pipe.pipe_mk_markdown(
-        "unittest/tmp/images", drop_mode=DropMode.NONE, md_make_mode=MakeMode.MM_MD
-    )
+    ### draw layout result on each page
+    pipe_result.draw_layout(os.path.join(local_dir, f'{name_without_suff}_layout.pdf'))  # dump to local
 
-    if isinstance(md_content, list):
-        writer.write_string(f"{pdf_file_name}.md", "\n".join(md_content))
-    else:
-        writer.write_string(f"{pdf_file_name}.md", md_content)
+    ### draw spans result on each page
+    pipe_result.draw_span(os.path.join(local_dir, f'{name_without_suff}_spans.pdf'))  # dump to local 
 
+    ### dump markdown
+    pipe_result.dump_md(writer, f'{name_without_suff}.md', "unittest/tmp/images")  # dump to remote s3
 
 
 前去 :doc:`../data/data_reader_writer` 获取更多有关 **读写** 示例

next_docs/zh_cn/user_guide/tutorial.rst

@@ -9,3 +9,5 @@
     :caption: 教程
 
     tutorial/output_file_description
+    tutorial/pipeline
+

next_docs/zh_cn/user_guide/tutorial/output_file_description.rst

@@ -137,49 +137,45 @@ poly 坐标的格式 [x0, y0, x1, y1, x2, y2, x3, y3],
 some_pdf_middle.json
 ~~~~~~~~~~~~~~~~~~~~
 
-+-----------+----------------------------------------------------------+
-| 字段名    | 解释                                                     |
-+===========+==========================================================+
-| pdf_info  | list，每个                                               |
-|           | 元素都是一个dict,这个dict是每一页pdf的解析结果，详见下表 |
-+-----------+----------------------------------------------------------+
-|              | ocr \| txt，用来标识本次解析的中间态使用的模式           |
-| \_parse_type |                                                          |
-+-----------+----------------------------------------------------------+
-|                | string, 表示本次解析使用的 magic-pdf 的版本号            |
-| \_version_name |                                                          |
-+-----------+----------------------------------------------------------+
++--------------------+----------------------------------------------------------+
+| 字段名              | 解释                                                    |
++====================+==========================================================+
+| pdf_info           | list，每个元素都是一个                                   |
+|                    | dict，这个dict是每一页pdf的解析结果，详见下表            |
++--------------------+----------------------------------------------------------+
+| \_parse_type       | ocr \| txt，用来标识本次解析的中间态使用的模式           |
++--------------------+----------------------------------------------------------+
+| \_version_name     | string，表示本次解析使用的 magic-pdf 的版本号            |
++-------------------------------------------------------------------------------+
 
 **pdf_info** 字段结构说明
 
-+--------------+-------------------------------------------------------+
-| 字段名       | 解释                                                  |
-+==============+=======================================================+
-|                 | pdf预处理后，未分段的中间结果                         |
-| preeproc_blocks |                                                       |
-+--------------+-------------------------------------------------------+
-|               | 布局分割的结果，                                      |
-| layout_bboxes | 含有布局的方向（垂直、水平），和bbox，按阅读顺序排序  |
-+--------------+-------------------------------------------------------+
-| page_idx     | 页码，从0开始                                         |
-+--------------+-------------------------------------------------------+
-| page_size    | 页面的宽度和高度                                      |
-+--------------+-------------------------------------------------------+
-| \            | 布局树状结构                                          |
-| _layout_tree |                                                       |
-+--------------+-------------------------------------------------------+
-| images       | list，每个元素是一个dict，每个dict表示一个img_block   |
-+--------------+-------------------------------------------------------+
-| tables       | list，每个元素是一个dict，每个dict表示一个table_block |
-+--------------+-------------------------------------------------------+
-|                     | list，每个元素                                        |
-| interline_equations | 是一个dict，每个dict表示一个interline_equation_block  |
-+--------------+-------------------------------------------------------+
-|                  | List, 模型返回的需要drop的block信息                   |
-| discarded_blocks |                                                       |
-+--------------+-------------------------------------------------------+
-| para_blocks  | 将preproc_blocks进行分段之后的结果                    |
-+--------------+-------------------------------------------------------+
++---------------------+-------------------------------------------------------+
+| 字段名               | 解释                                                 |
++=====================+=======================================================+
+| preproc_blocks      | pdf预处理后，未分段的中间结果                         |
++---------------------+-------------------------------------------------------+
+|                     | 布局分割的结果，                                      |
+| layout_bboxes       | 含有布局的方向（垂直、水平），和bbox，按阅读顺序排序  |
++---------------------+-------------------------------------------------------+
+| page_idx            | 页码，从0开始                                         |
++---------------------+-------------------------------------------------------+
+| page_size           | 页面的宽度和高度                                      |
++---------------------+-------------------------------------------------------+
+| \_layout_tree       | 布局树状结构                                          |
++---------------------+-------------------------------------------------------+
+| images              | list，每个元素是一个dict，每个dict表示一个img_block   |
++---------------------+-------------------------------------------------------+
+| tables              | list，每个元素是一个dict，每个dict表示一个table_block |
++---------------------+-------------------------------------------------------+
+|                     | list，每个元素是一个                                  |
+| interline_equations | dict，每个dict表示一个interline_equation_block        |
++---------------------+-------------------------------------------------------+
+|                     | List, 模型返回的需要drop的block信息                   |
+| discarded_blocks    |                                                       |
++---------------------+-------------------------------------------------------+
+| para_blocks         | 将preproc_blocks进行分段之后的结果                    |
++---------------------+-------------------------------------------------------+
 
 上表中 ``para_blocks``
 是个dict的数组，每个dict是一个block结构，block最多支持一次嵌套
@@ -200,20 +196,18 @@ blocks list，里面的每个元素都是一个dict格式的二级block
 
 二级block中的字段包括
 
-+-----+----------------------------------------------------------------+
-| 字  | 解释                                                           |
-| 段  |                                                                |
-| 名  |                                                                |
-+=====+================================================================+
-|      | block类型                                                      |
-| type |                                                                |
-+-----+----------------------------------------------------------------+
-|      | block矩形框坐标                                                |
-| bbox |                                                                |
-+-----+----------------------------------------------------------------+
-|       | list，每个元素都是一个dict表示的line，用来描述一行信息的构成   |
-| lines |                                                                |
-+-----+----------------------------------------------------------------+
++----------+----------------------------------------------------------------+
+| 字       | 解释                                                           |
+| 段       |                                                                |
+| 名       |                                                                |
++==========+================================================================+
+|          | block类型                                                      |
+| type     |                                                                |
++----------+----------------------------------------------------------------+
+| bbox     | block矩形框坐标                                                |
++----------+----------------------------------------------------------------+
+| lines    | list，每个元素都是一个dict表示的line，用来描述一行信息的构成   |
++----------+----------------------------------------------------------------+
 
 二级block的类型详解
 
@@ -237,22 +231,21 @@ interline_equation 行间公式块
 
 line 的 字段格式如下
 
-+----+-----------------------------------------------------------------+
-| 字 | 解释                                                            |
-| 段 |                                                                 |
-| 名 |                                                                 |
-+====+=================================================================+
-| bbox  | line的矩形框坐标                                                |
-|       |                                                                 |
-+----+-----------------------------------------------------------------+
-| spans  | list，                                                       |
-|        | 每个元素都是一个dict表示的span，用来描述一个最小组成单元的构成  |
-+----+-----------------------------------------------------------------+
++-----------+-----------------------------------------------------------------+
+| 字        | 解释                                                            |
+| 段        |                                                                 |
+| 名        |                                                                 |
++===========+=================================================================+
+| bbox      | line的矩形框坐标                                                |
++-----------+-----------------------------------------------------------------+
+| spans     | list，                                                          |
+|           | 每个元素都是一个dict表示的span，用来描述一个最小组成单元的构成  |
++-----------+-----------------------------------------------------------------+
 
 **span**
 
 +------------+---------------------------------------------------------+
-| 字段名     | 解释                                                    |
+| 字段名      | 解释                                                   |
 +============+=========================================================+
 | bbox       | span的矩形框坐标                                        |
 +------------+---------------------------------------------------------+

next_docs/zh_cn/user_guide/tutorial/pipeline.rst

@@ -0,0 +1,179 @@
+
+流水线管道
+===========
+
+
+极简示例
+^^^^^^^^
+
+.. code:: python
+
+    import os
+
+    from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
+
+    # args
+    pdf_file_name = "abc.pdf"  # replace with the real pdf path
+    name_without_suff = pdf_file_name.split(".")[0]
+
+    # prepare env
+    local_image_dir, local_md_dir = "output/images", "output"
+    image_dir = str(os.path.basename(local_image_dir))
+
+    os.makedirs(local_image_dir, exist_ok=True)
+
+    image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
+        local_md_dir
+    )
+    image_dir = str(os.path.basename(local_image_dir))
+
+    # read bytes
+    reader1 = FileBasedDataReader("")
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(md_writer, f"{name_without_suff}.md", image_dir)
+
+
+运行以上的代码，会得到如下的结果
+
+.. code:: bash 
+
+    output/
+    ├── abc.md
+    └── images
+
+
+除去初始化环境，如建立目录、导入依赖库等逻辑。真正将 ``pdf`` 转换为 ``markdown`` 的代码片段如下
+
+.. code::
+
+    # read bytes
+    reader1 = FileBasedDataReader("")
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(md_writer, f"{name_without_suff}.md", image_dir)
+
+
+``ds.apply(doc_analyze, ocr=True)`` 会生成 ``InferenceResult`` 对象。 ``InferenceResult`` 对象执行 ``pipe_ocr_mode`` 方法会生成 ``PipeResult`` 对象。
+``PipeResult`` 对象执行 ``dump_md`` 会在指定位置生成 ``markdown`` 文件。
+
+
+pipeline 的执行过程如下图所示
+
+.. image:: ../../_static/image/pipeline.drawio.svg 
+
+.. raw:: html 
+
+    <br> </br>
+
+目前划分出数据、推理、程序处理三个阶段，分别对应着图上的 ``Dataset``， ``InferenceResult``， ``PipeResult`` 这三个实体。通过 ``apply`` ， ``doc_analyze`` 或 ``pipe_ocr_mode`` 等方法链接在一起。
+
+
+.. admonition:: Tip
+    :class: tip
+
+    要想获得更多有关 Dataset、InferenceResult、PipeResult 的使用示例子，请前往 :doc:`../quick_start/to_markdown`
+
+    要想获得更多有关 Dataset、InferenceResult、PipeResult 的细节信息请前往英文版 MinerU 文档进行查看!
+
+
+
+管道组合
+^^^^^^^^^
+
+.. code:: python
+
+    class Dataset(ABC):
+        @abstractmethod
+        def apply(self, proc: Callable, *args, **kwargs):
+            """Apply callable method which.
+
+            Args:
+                proc (Callable): invoke proc as follows:
+                    proc(self, *args, **kwargs)
+
+            Returns:
+                Any: return the result generated by proc
+            """
+            pass
+
+    class InferenceResult(InferenceResultBase):
+
+        def apply(self, proc: Callable, *args, **kwargs):
+            """Apply callable method which.
+
+            Args:
+                proc (Callable): invoke proc as follows:
+                    proc(inference_result, *args, **kwargs)
+
+            Returns:
+                Any: return the result generated by proc
+            """
+            return proc(copy.deepcopy(self._infer_res), *args, **kwargs)
+
+        def pipe_ocr_mode(
+            self,
+            imageWriter: DataWriter,
+            start_page_id=0,
+            end_page_id=None,
+            debug_mode=False,
+            lang=None,
+            ) -> PipeResult:
+            pass
+
+    class PipeResult:
+        def apply(self, proc: Callable, *args, **kwargs):
+            """Apply callable method which.
+
+            Args:
+                proc (Callable): invoke proc as follows:
+                    proc(pipeline_result, *args, **kwargs)
+
+            Returns:
+                Any: return the result generated by proc
+            """
+            return proc(copy.deepcopy(self._pipe_res), *args, **kwargs)
+
+``Dataset`` 、 ``InferenceResult`` 和 ``PipeResult`` 类均有 ``apply`` method。可用于组合不同阶段的运算过程。
+如下所示，``MinerU`` 提供一套组合这些类的计算过程。
+
+.. code:: python 
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(md_writer, f"{name_without_suff}.md", image_dir)
+
+用户可以根据的需求，自行实现一些组合用的函数。比如用户通过 ``apply`` 方法实现一个统计 ``pdf`` 文件页数的功能。
+
+.. code:: python 
+
+    from magic_pdf.data.data_reader_writer import  FileBasedDataReader
+    from magic_pdf.data.dataset import PymuDocDataset
+
+    # args
+    pdf_file_name = "abc.pdf"  # replace with the real pdf path
+
+    # read bytes
+    reader1 = FileBasedDataReader("")
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    def count_page(ds)-> int:
+        return len(ds)
+
+    print("page number: ", ds.apply(count_page)) # will output the page count of `abc.pdf`

requirements-docker.txt

@@ -7,9 +7,9 @@ numpy>=1.21.6,<2.0.0
 fast-langdetect==0.2.0
 scikit-learn>=1.0.2
 pdfminer.six==20231228
-unimernet==0.2.1
+unimernet==0.2.2
 matplotlib
-ultralytics
+ultralytics>=8.3.48
 paddleocr==2.7.3
 paddlepaddle==3.0.0b1
 struct-eqtable==0.3.2

requirements.txt

@@ -7,7 +7,7 @@ numpy>=1.21.6,<2.0.0
 pydantic>=2.7.2,<2.8.0
 PyMuPDF>=1.24.9
 scikit-learn>=1.0.2
-torch>=2.2.2,<=2.3.1
+torch>=2.2.2
 transformers
-# pdfminer.six==20231228
+pdfminer.six==20231228
 # The requirements.txt must ensure that only necessary external dependencies are introduced. If there are new dependencies to add, please contact the project administrator.

setup.py

@@ -36,10 +36,12 @@ if __name__ == '__main__':
                      "paddlepaddle==3.0.0b1;platform_system=='Linux'",
                      "paddlepaddle==2.6.1;platform_system=='Windows' or platform_system=='Darwin'",
                      ],
-            "full": ["unimernet==0.2.1",  # unimernet升级0.2.1
+            "full": ["unimernet==0.2.2",  # unimernet升级0.2.2,移除torchtext的依赖
+                     "torch>=2.2.2,<=2.3.1",  # torch2.4.0及之后版本未测试，先卡住版本上限
+                     "torchvision>=0.17.2,<=0.18.1",  # torchvision 受torch版本约束
                      "matplotlib<=3.9.0;platform_system=='Windows'",  # 3.9.1及之后不提供windows的预编译包，避免一些没有编译环境的windows设备安装失败
                      "matplotlib;platform_system=='Linux' or platform_system=='Darwin'",  # linux 和 macos 不应限制matplotlib的最高版本，以避免无法更新导致的一些bug
-                     "ultralytics",  # yolov8,公式检测
+                     "ultralytics>=8.3.48",  # yolov8,公式检测
                      "paddleocr==2.7.3",  # 2.8.0及2.8.1版本与detectron2有冲突，需锁定2.7.3
                      "paddlepaddle==3.0.0b1;platform_system=='Linux'",  # 解决linux的段异常问题
                      "paddlepaddle==2.6.1;platform_system=='Windows' or platform_system=='Darwin'",  # windows版本3.0.0b1效率下降，需锁定2.6.1

tests/test_cli/test_cli_sdk.py

@@ -7,8 +7,11 @@ from lib import common
 import time
 import magic_pdf.model as model_config
 from magic_pdf.pipe.UNIPipe import UNIPipe
-from magic_pdf.rw.DiskReaderWriter import DiskReaderWriter
-from magic_pdf.rw.S3ReaderWriter import S3ReaderWriter
+import os
+from magic_pdf.data.data_reader_writer import FileBasedDataWriter
+from magic_pdf.data.data_reader_writer import S3DataReader, S3DataWriter
+from magic_pdf.config.make_content_config import DropMode, MakeMode
+from magic_pdf.pipe.OCRPipe import OCRPipe
 model_config.__use_inside_model__ = True
 pdf_res_path = conf.conf['pdf_res_path']
 code_path = conf.conf['code_path']
@@ -41,7 +44,7 @@ class TestCli:
             pdf_bytes = open(pdf_path, 'rb').read()
             local_image_dir = os.path.join(pdf_dev_path, 'pdf', 'images')
             image_dir = str(os.path.basename(local_image_dir))
-            image_writer = DiskReaderWriter(local_image_dir)
+            image_writer = FileBasedDataWriter(local_image_dir)
             model_json = list()
             jso_useful_key = {'_pdf_type': '', 'model_list': model_json}
             pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
@@ -77,7 +80,7 @@ class TestCli:
             pdf_bytes = open(pdf_path, 'rb').read()
             local_image_dir = os.path.join(pdf_dev_path, 'pdf', 'images')
             image_dir = str(os.path.basename(local_image_dir))
-            image_writer = DiskReaderWriter(local_image_dir)
+            image_writer = FileBasedDataWriter(local_image_dir)
             model_json = list()
             jso_useful_key = {'_pdf_type': 'ocr', 'model_list': model_json}
             pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
@@ -112,7 +115,7 @@ class TestCli:
             pdf_bytes = open(pdf_path, 'rb').read()
             local_image_dir = os.path.join(pdf_dev_path, 'pdf', 'images')
             image_dir = str(os.path.basename(local_image_dir))
-            image_writer = DiskReaderWriter(local_image_dir)
+            image_writer = FileBasedDataWriter(local_image_dir)
             model_json = list()
             jso_useful_key = {'_pdf_type': 'txt', 'model_list': model_json}
             pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
@@ -284,12 +287,13 @@ class TestCli:
         pdf_endpoint = os.environ.get('pdf_endpoint', "")
         s3_pdf_path = conf.conf["s3_pdf_path"]
         image_dir = "s3://" + pdf_bucket + "/mineru/test/output"
-        print (image_dir)
-        s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
-        s3image_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint, parent_path=image_dir)
-        pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
-        jso_useful_key = {"_pdf_type": "", "model_list": []}
-        pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
+        prefix = "mineru/test/output"
+        reader = S3DataReader(prefix, pdf_bucket, pdf_ak, pdf_sk, pdf_endpoint)
+        # = S3DataWriter(prefix, pdf_bucket, pdf_ak, pdf_sk, pdf_endpoint)
+        image_writer = S3DataWriter(prefix, pdf_bucket, pdf_ak, pdf_sk, pdf_endpoint)
+        pdf_bytes = reader.read(s3_pdf_path)
+        model_list = []
+        pipe = OCRPipe(pdf_bytes, model_list, image_writer)
         pipe.pipe_classify()
         pipe.pipe_analyze()
         pipe.pipe_parse()
@@ -427,3 +431,4 @@ class TestCli:
  
 if __name__ == '__main__':
     pytest.main()
+