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Commit cf5c8f47 authored by myhloli's avatar myhloli
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docs: remove outdated documentation files 

- Deleted .readthedocs.yaml files from multiple directories
- Removed outdated API and user guide documentation files
- Deleted command line usage examples
- Removed CUDA acceleration guide
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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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