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Merge pull request #2625 from opendatalab/release-2.0.0 

Release 2.0.0
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next_docs/en/user_guide/quick_start/convert_image.rst deleted 100644 → 0
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Convert Image
===============
Command Line
^^^^^^^^^^^^^
.. code:: python
# make sure the file have correct suffix
magic-pdf -p a.png -o output -m auto
API
^^^^^^
.. code:: python
import os
from magic_pdf.data.data_reader_writer import FileBasedDataWriter
from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
from magic_pdf.data.read_api import read_local_images
# 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
)
# proc
## Create Dataset Instance
input_file = "some_image.jpg" # replace with real image file
input_file_name = input_file.split(".")[0]
ds = read_local_images(input_file)[0]
# ocr mode
ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(
md_writer, f"{input_file_name}.md", image_dir
)
next_docs/en/user_guide/quick_start/convert_ms_office.rst deleted 100644 → 0
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Convert Doc
=============
.. admonition:: Warning
:class: tip
When processing MS-Office files, we first use third-party software to convert the MS-Office files to PDF.
For certain MS-Office files, the quality of the converted PDF files may not be very high, which can affect the quality of the final output.
Command Line
^^^^^^^^^^^^^
.. code:: python
# replace with real ms-office file, we support MS-DOC, MS-DOCX, MS-PPT, MS-PPTX now
magic-pdf -p a.doc -o output -m auto
API
^^^^^^^^
.. code:: python
import os
from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
from magic_pdf.data.read_api import read_local_office
from magic_pdf.config.enums import SupportedPdfParseMethod
# 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
)
# proc
## Create Dataset Instance
input_file = "some_doc.doc" # replace with real ms-office file, we support MS-DOC, MS-DOCX, MS-PPT, MS-PPTX now
input_file_name = input_file.split(".")[0]
ds = read_local_office(input_file)[0]
## inference
if ds.classify() == SupportedPdfParseMethod.OCR:
ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(
md_writer, f"{input_file_name}.md", image_dir)
else:
ds.apply(doc_analyze, ocr=False).pipe_txt_mode(image_writer).dump_md(
md_writer, f"{input_file_name}.md", image_dir)
next_docs/en/user_guide/quick_start/convert_pdf.rst deleted 100644 → 0
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Convert PDF
============
Command Line
^^^^^^^^^^^^^
.. code:: python
# make sure the file have correct suffix
magic-pdf -p a.pdf -o output -m auto
API
^^^^^^
.. 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
)
# read bytes
reader1 = FileBasedDataReader("")
pdf_bytes = reader1.read(pdf_file_name) # read the pdf content
# proc
## Create Dataset Instance
ds = PymuDocDataset(pdf_bytes)
## inference
if ds.classify() == SupportedPdfParseMethod.OCR:
ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(
md_writer, f"{name_without_suff}.md", image_dir
)
else:
ds.apply(doc_analyze, ocr=False).pipe_txt_mode(image_writer).dump_md(
md_writer, f"{name_without_suff}.md", image_dir
)
next_docs/en/user_guide/tutorial.rst deleted 100644 → 0
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Tutorial
===========
From the beginning to the end, Show how to using mineru via a minimal project
.. toctree::
:maxdepth: 1
tutorial/pipeline
next_docs/en/user_guide/tutorial/output_file_description.rst deleted 100644 → 0
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Output File Description
=========================
After executing the ``magic-pdf`` command, in addition to outputting
files related to markdown, several other files unrelated to markdown
will also be generated. These files will be introduced one by one.
some_pdf_layout.pdf
~~~~~~~~~~~~~~~~~~~
Each page layout consists of one or more boxes. The number at the top
left of each box indicates its sequence number. Additionally, in
``layout.pdf``, different content blocks are highlighted with different
background colors.
.. figure:: ../../_static/image/layout_example.png
:alt: layout example
layout example
some_pdf_spans.pdf
~~~~~~~~~~~~~~~~~~
All spans on the page are drawn with different colored line frames
according to the span type. This file can be used for quality control,
allowing for quick identification of issues such as missing text or
unrecognized inline formulas.
.. figure:: ../../_static/image/spans_example.png
:alt: spans example
spans example
some_pdf_model.json
~~~~~~~~~~~~~~~~~~~
Structure Definition
^^^^^^^^^^^^^^^^^^^^
.. code:: python
from pydantic import BaseModel, Field
from enum import IntEnum
class CategoryType(IntEnum):
title = 0 # Title
plain_text = 1 # Text
abandon = 2 # Includes headers, footers, page numbers, and page annotations
figure = 3 # Image
figure_caption = 4 # Image description
table = 5 # Table
table_caption = 6 # Table description
table_footnote = 7 # Table footnote
isolate_formula = 8 # Block formula
formula_caption = 9 # Formula label
embedding = 13 # Inline formula
isolated = 14 # Block formula
text = 15 # OCR recognition result
class PageInfo(BaseModel):
page_no: int = Field(description="Page number, the first page is 0", ge=0)
height: int = Field(description="Page height", gt=0)
width: int = Field(description="Page width", ge=0)
class ObjectInferenceResult(BaseModel):
category_id: CategoryType = Field(description="Category", ge=0)
poly: list[float] = Field(description="Quadrilateral coordinates, representing the coordinates of the top-left, top-right, bottom-right, and bottom-left points respectively")
score: float = Field(description="Confidence of the inference result")
latex: str | None = Field(description="LaTeX parsing result", default=None)
html: str | None = Field(description="HTML parsing result", default=None)
class PageInferenceResults(BaseModel):
layout_dets: list[ObjectInferenceResult] = Field(description="Page recognition results", ge=0)
page_info: PageInfo = Field(description="Page metadata")
# The inference results of all pages, ordered by page number, are stored in a list as the inference results of MinerU
inference_result: list[PageInferenceResults] = []
The format of the poly coordinates is [x0, y0, x1, y1, x2, y2, x3, y3],
representing the coordinates of the top-left, top-right, bottom-right,
and bottom-left points respectively. |Poly Coordinate Diagram|
example
^^^^^^^
.. code:: json
[
{
"layout_dets": [
{
"category_id": 2,
"poly": [
99.1906967163086,
100.3119125366211,
730.3707885742188,
100.3119125366211,
730.3707885742188,
245.81326293945312,
99.1906967163086,
245.81326293945312
],
"score": 0.9999997615814209
}
],
"page_info": {
"page_no": 0,
"height": 2339,
"width": 1654
}
},
{
"layout_dets": [
{
"category_id": 5,
"poly": [
99.13092803955078,
2210.680419921875,
497.3183898925781,
2210.680419921875,
497.3183898925781,
2264.78076171875,
99.13092803955078,
2264.78076171875
],
"score": 0.9999997019767761
}
],
"page_info": {
"page_no": 1,
"height": 2339,
"width": 1654
}
}
]
some_pdf_middle.json
~~~~~~~~~~~~~~~~~~~~
+----------------+--------------------------------------------------------------+
| Field Name | Description |
| | |
+================+==============================================================+
| pdf_info | list, each element is a dict representing the parsing result |
| | of each PDF page, see the table below for details |
+----------------+--------------------------------------------------------------+
| \_ | ocr \| txt, used to indicate the mode used in this |
| parse_type | intermediate parsing state |
| | |
+----------------+--------------------------------------------------------------+
| \_version_name | string, indicates the version of magic-pdf used in this |
| | parsing |
| | |
+----------------+--------------------------------------------------------------+
**pdf_info**
Field structure description
+-------------------------+------------------------------------------------------------+
| Field | Description |
| Name | |
+=========================+============================================================+
| preproc_blocks | Intermediate result after PDF preprocessing, not yet |
| | segmented |
+-------------------------+------------------------------------------------------------+
| layout_bboxes | Layout segmentation results, containing layout direction |
| | (vertical, horizontal), and bbox, sorted by reading order |
+-------------------------+------------------------------------------------------------+
| page_idx | Page number, starting from 0 |
| | |
+-------------------------+------------------------------------------------------------+
| page_size | Page width and height |
| | |
+-------------------------+------------------------------------------------------------+
| \_layout_tree | Layout tree structure |
| | |
+-------------------------+------------------------------------------------------------+
| images | list, each element is a dict representing an img_block |
+-------------------------+------------------------------------------------------------+
| tables | list, each element is a dict representing a table_block |
+-------------------------+------------------------------------------------------------+
| interline_equation | list, each element is a dict representing an |
| | interline_equation_block |
| | |
+-------------------------+------------------------------------------------------------+
| discarded_blocks | List, block information returned by the model that needs |
| | to be dropped |
| | |
+-------------------------+------------------------------------------------------------+
| para_blocks | Result after segmenting preproc_blocks |
| | |
+-------------------------+------------------------------------------------------------+
In the above table, ``para_blocks`` is an array of dicts, each dict
representing a block structure. A block can support up to one level of
nesting.
**block**
The outer block is referred to as a first-level block, and the fields in
the first-level block include:
+------------------------+-------------------------------------------------------------+
| Field | Description |
| Name | |
+========================+=============================================================+
| type | Block type (table|image) |
+------------------------+-------------------------------------------------------------+
| bbox | Block bounding box coordinates |
+------------------------+-------------------------------------------------------------+
| blocks | list, each element is a dict representing a second-level |
| | block |
+------------------------+-------------------------------------------------------------+
There are only two types of first-level blocks: “table” and “image”. All
other blocks are second-level blocks.
The fields in a second-level block include:
+----------------------+----------------------------------------------------------------+
| Field | Description |
| Name | |
+======================+================================================================+
| | Block type |
| type | |
+----------------------+----------------------------------------------------------------+
| | Block bounding box coordinates |
| bbox | |
+----------------------+----------------------------------------------------------------+
| | list, each element is a dict representing a line, used to |
| lines | describe the composition of a line of information |
+----------------------+----------------------------------------------------------------+
Detailed explanation of second-level block types
================== ======================
type Description
================== ======================
image_body Main body of the image
image_caption Image description text
table_body Main body of the table
table_caption Table description text
table_footnote Table footnote
text Text block
title Title block
interline_equation Block formula
================== ======================
**line**
The field format of a line is as follows:
+---------------------+----------------------------------------------------------------+
| Field | Description |
| Name | |
+=====================+================================================================+
| | Bounding box coordinates of the line |
| bbox | |
+---------------------+----------------------------------------------------------------+
| spans | list, each element is a dict representing a span, used to |
| | describe the composition of the smallest unit |
+---------------------+----------------------------------------------------------------+
**span**
+---------------------+-----------------------------------------------------------+
| Field | Description |
| Name | |
+=====================+===========================================================+
| bbox | Bounding box coordinates of the span |
+---------------------+-----------------------------------------------------------+
| type | Type of the span |
+---------------------+-----------------------------------------------------------+
| content | Text spans use content, chart spans use img_path to store |
| \| | the actual text or screenshot path information |
| img_path | |
+---------------------+-----------------------------------------------------------+
The types of spans are as follows:
================== ==============
type Description
================== ==============
image Image
table Table
text Text
inline_equation Inline formula
interline_equation Block formula
================== ==============
**Summary**
A span is the smallest storage unit for all elements.
The elements stored within para_blocks are block information.
The block structure is as follows:
First-level block (if any) -> Second-level block -> Line -> Span
.. _example-1:
example
^^^^^^^
.. code:: json
{
"pdf_info": [
{
"preproc_blocks": [
{
"type": "text",
"bbox": [
52,
61.956024169921875,
294,
82.99800872802734
],
"lines": [
{
"bbox": [
52,
61.956024169921875,
294,
72.0000228881836
],
"spans": [
{
"bbox": [
54.0,
61.956024169921875,
296.2261657714844,
72.0000228881836
],
"content": "dependent on the service headway and the reliability of the departure ",
"type": "text",
"score": 1.0
}
]
}
]
}
],
"layout_bboxes": [
{
"layout_bbox": [
52,
61,
294,
731
],
"layout_label": "V",
"sub_layout": []
}
],
"page_idx": 0,
"page_size": [
612.0,
792.0
],
"_layout_tree": [],
"images": [],
"tables": [],
"interline_equations": [],
"discarded_blocks": [],
"para_blocks": [
{
"type": "text",
"bbox": [
52,
61.956024169921875,
294,
82.99800872802734
],
"lines": [
{
"bbox": [
52,
61.956024169921875,
294,
72.0000228881836
],
"spans": [
{
"bbox": [
54.0,
61.956024169921875,
296.2261657714844,
72.0000228881836
],
"content": "dependent on the service headway and the reliability of the departure ",
"type": "text",
"score": 1.0
}
]
}
]
}
]
}
],
"_parse_type": "txt",
"_version_name": "0.6.1"
}
.. |Poly Coordinate Diagram| image:: ../../_static/image/poly.png
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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
)
# 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 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`
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Usage
========
.. toctree::
:maxdepth: 1
usage/command_line
usage/api
usage/docker
next_docs/en/user_guide/usage/api.rst deleted 100644 → 0
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Api Usage
===========
PDF
----
Local File 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
from magic_pdf.config.enums import SupportedPdfParseMethod
# 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
)
# read bytes
reader1 = FileBasedDataReader("")
pdf_bytes = reader1.read(pdf_file_name) # read the pdf content
# proc
## Create Dataset Instance
ds = PymuDocDataset(pdf_bytes)
## inference
if ds.classify() == SupportedPdfParseMethod.OCR:
infer_result = ds.apply(doc_analyze, ocr=True)
## pipeline
pipe_result = infer_result.pipe_ocr_mode(image_writer)
else:
infer_result = ds.apply(doc_analyze, ocr=False)
## pipeline
pipe_result = infer_result.pipe_txt_mode(image_writer)
### draw model result on each page
infer_result.draw_model(os.path.join(local_md_dir, f"{name_without_suff}_model.pdf"))
### get model inference result
model_inference_result = infer_result.get_infer_res()
### draw layout result on each page
pipe_result.draw_layout(os.path.join(local_md_dir, f"{name_without_suff}_layout.pdf"))
### draw spans result on each page
pipe_result.draw_span(os.path.join(local_md_dir, f"{name_without_suff}_spans.pdf"))
### get markdown content
md_content = pipe_result.get_markdown(image_dir)
### dump markdown
pipe_result.dump_md(md_writer, f"{name_without_suff}.md", image_dir)
### get content list content
content_list_content = pipe_result.get_content_list(image_dir)
### dump content list
pipe_result.dump_content_list(md_writer, f"{name_without_suff}_content_list.json", image_dir)
### get middle json
middle_json_content = pipe_result.get_middle_json()
### dump middle json
pipe_result.dump_middle_json(md_writer, f'{name_without_suff}_middle.json')
S3 File Example
^^^^^^^^^^^^^^^^
.. code:: python
import os
from magic_pdf.data.data_reader_writer import S3DataReader, S3DataWriter
from magic_pdf.data.dataset import PymuDocDataset
from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
from magic_pdf.config.enums import SupportedPdfParseMethod
bucket_name = "{Your S3 Bucket Name}" # replace with real bucket name
ak = "{Your S3 access key}" # replace with real s3 access key
sk = "{Your S3 secret key}" # replace with real s3 secret key
endpoint_url = "{Your S3 endpoint_url}" # replace with real s3 endpoint_url
reader = S3DataReader('unittest/tmp/', bucket_name, ak, sk, endpoint_url) # replace `unittest/tmp` with the real s3 prefix
writer = S3DataWriter('unittest/tmp', bucket_name, ak, sk, endpoint_url)
image_writer = S3DataWriter('unittest/tmp/images', bucket_name, ak, sk, endpoint_url)
md_writer = S3DataWriter('unittest/tmp', bucket_name, ak, sk, endpoint_url)
local_image_dir, local_md_dir = "output/images", "output"
image_dir = str(os.path.basename(local_image_dir))
# args
pdf_file_name = (
f"s3://{bucket_name}/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)
## inference
if ds.classify() == SupportedPdfParseMethod.OCR:
infer_result = ds.apply(doc_analyze, ocr=True)
## pipeline
pipe_result = infer_result.pipe_ocr_mode(image_writer)
else:
infer_result = ds.apply(doc_analyze, ocr=False)
## pipeline
pipe_result = infer_result.pipe_txt_mode(image_writer)
### draw model result on each page
infer_result.draw_model(os.path.join(local_md_dir, f"{name_without_suff}_model.pdf"))
### get model inference result
model_inference_result = infer_result.get_infer_res()
### draw layout result on each page
pipe_result.draw_layout(os.path.join(local_md_dir, f"{name_without_suff}_layout.pdf"))
### draw spans result on each page
pipe_result.draw_span(os.path.join(local_md_dir, f"{name_without_suff}_spans.pdf"))
### dump markdown
pipe_result.dump_md(md_writer, f"{name_without_suff}.md", image_dir)
### dump content list
pipe_result.dump_content_list(md_writer, f"{name_without_suff}_content_list.json", image_dir)
### get markdown content
md_content = pipe_result.get_markdown(image_dir)
### get content list content
content_list_content = pipe_result.get_content_list(image_dir)
### get middle json
middle_json_content = pipe_result.get_middle_json()
### dump middle json
pipe_result.dump_middle_json(md_writer, f'{name_without_suff}_middle.json')
MS-Office
----------
.. code:: python
import os
from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
from magic_pdf.data.read_api import read_local_office
# 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
)
# proc
## Create Dataset Instance
input_file = "some_ppt.ppt" # replace with real ms-office file
input_file_name = input_file.split(".")[0]
ds = read_local_office(input_file)[0]
ds.apply(doc_analyze, ocr=True).pipe_txt_mode(image_writer).dump_md(
md_writer, f"{input_file_name}.md", image_dir
)
This code snippet can be used to manipulate **ppt**, **pptx**, **doc**, **docx** file
Image
---------
Single Image File
^^^^^^^^^^^^^^^^^^^
.. code:: python
import os
from magic_pdf.data.data_reader_writer import FileBasedDataWriter
from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
from magic_pdf.data.read_api import read_local_images
# 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
)
# proc
## Create Dataset Instance
input_file = "some_image.jpg" # replace with real image file
input_file_name = input_file.split(".")[0]
ds = read_local_images(input_file)[0]
ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(
md_writer, f"{input_file_name}.md", image_dir
)
Directory That Contains Images
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code:: python
import os
from magic_pdf.data.data_reader_writer import FileBasedDataWriter
from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
from magic_pdf.data.read_api import read_local_images
# 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
)
# proc
## Create Dataset Instance
input_directory = "some_image_dir/" # replace with real directory that contains images
dss = read_local_images(input_directory, suffixes=['.png', '.jpg'])
count = 0
for ds in dss:
ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(
md_writer, f"{count}.md", image_dir
)
count += 1
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/usage/command_line.rst deleted 100644 → 0
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Command Line
===================
.. code:: bash
magic-pdf --help
Usage: magic-pdf [OPTIONS]
Options:
-v, --version display the version and exit
-p, --path PATH local filepath or directory. support PDF, PPT,
PPTX, DOC, DOCX, PNG, JPG files [required]
-o, --output-dir PATH output local directory [required]
-m, --method [ocr|txt|auto] the method for parsing pdf. ocr: using ocr
technique to extract information from pdf. txt:
suitable for the text-based pdf only and
outperform ocr. auto: automatically choose the
best method for parsing pdf from ocr and txt.
without method specified, auto will be used by
default.
-l, --lang TEXT Input the languages in the pdf (if known) to
improve OCR accuracy. Optional. You should
input "Abbreviation" with language form url: ht
tps://paddlepaddle.github.io/PaddleOCR/en/ppocr
/blog/multi_languages.html#5-support-languages-
and-abbreviations
-d, --debug BOOLEAN Enables detailed debugging information during
the execution of the CLI commands.
-s, --start INTEGER The starting page for PDF parsing, beginning
from 0.
-e, --end INTEGER The ending page for PDF parsing, beginning from
0.
--help Show this message and exit.
## show version
magic-pdf -v
## command line example
magic-pdf -p {some_pdf} -o {some_output_dir} -m auto
.. admonition:: Important
:class: tip
The file must endswith with the following suffix.
.pdf
.png
.jpg
.ppt
.pptx
.doc
.docx
``{some_pdf}`` can be a single PDF file or a directory containing
multiple PDFs. The results will be saved in the ``{some_output_dir}``
directory. The output file list is as follows:
.. code:: text
├── some_pdf.md # markdown file
├── images # directory for storing images
├── some_pdf_layout.pdf # layout diagram
├── some_pdf_middle.json # MinerU intermediate processing result
├── some_pdf_model.json # model inference result
├── some_pdf_origin.pdf # original PDF file
├── some_pdf_spans.pdf # smallest granularity bbox position information diagram
└── some_pdf_content_list.json # Rich text JSON arranged in reading order
.. admonition:: Tip
:class: tip
For more information about the output files, please refer to the :doc:`../inference_result` or :doc:`../pipe_result`
next_docs/en/user_guide/usage/docker.rst deleted 100644 → 0
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Docker
=======
.. admonition:: Important
:class: tip
Docker requires a GPU with at least 16GB of VRAM, and all acceleration features are enabled by default.
Before running this Docker, you can use the following command to check if your device supports CUDA acceleration on Docker.
.. code-block:: bash
bash docker run --rm --gpus=all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
.. code:: sh
wget https://github.com/opendatalab/MinerU/raw/master/Dockerfile
docker build -t mineru:latest .
docker run --rm -it --gpus=all mineru:latest /bin/bash
magic-pdf --help
next_docs/requirements.txt deleted 100644 → 0
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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
Pillow==8.4.0
pydantic>=2.7.2,<2.8.0
PyMuPDF>=1.24.9
pdfminer.six==20231228
sphinx
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
next_docs/zh_cn/.readthedocs.yaml deleted 100644 → 0
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version: 2
build:
os: ubuntu-22.04
tools:
python: "3.10"
formats:
- epub
python:
install:
- requirements: next_docs/requirements.txt
sphinx:
configuration: next_docs/zh_cn/conf.py
next_docs/zh_cn/Makefile deleted 100644 → 0
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# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
SOURCEDIR = .
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
next_docs/zh_cn/_static/image/ReadTheDocs.svg deleted 100644 → 0
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