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feat: 更新MinerU(v2.5) 配置，修改镜像名称和后端，优化OCR处理逻辑

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This commit is contained in:
Wenjie Zhang 2025-10-24 01:09:24 +08:00
parent a1fdcbc36c
commit c8fa8a0131
4 changed files with 177 additions and 184 deletions
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10
docker-compose.yml
View File

@ -207,23 +207,27 @@ services:
networks:
- app-network
restart: unless-stopped
# lastest version: wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
mineru:
build:
context: .
dockerfile: docker/mineru.Dockerfile
image: mineru-sglang:latest
image: mineru-vllm:latest
container_name: mineru
profiles:
- all
ports:
- 30000:30000
environment:
MINERU_MODEL_SOURCE: modelscope
entrypoint: mineru-sglang-server
MINERU_MODEL_SOURCE: local
entrypoint: mineru-vllm-server
command:
--host 0.0.0.0
--port 30000
# parameters for vllm-engine
# --data-parallel-size 2 # If using multiple GPUs, increase throughput using vllm's multi-GPU parallel mode
# --gpu-memory-utilization 0.5 # If running on a single GPU and encountering VRAM shortage, reduce the KV cache size by this parameter, if VRAM issues persist, try lowering it further to `0.4` or below.
ulimits:
memlock: -1
stack: 67108864

16
docker/mineru.Dockerfile
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@ -1,9 +1,15 @@
# Lastest version: wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/china/Dockerfile
# Use DaoCloud mirrored vllm image for China region for gpu with Ampere architecture and above (Compute Capability>=8.0)
# Compute Capability version query (https://developer.nvidia.com/cuda-gpus)
FROM docker.m.daocloud.io/vllm/vllm-openai:v0.10.1.1
# Use the official sglang image
FROM lmsysorg/sglang:v0.4.9.post3-cu126
# For blackwell GPU, use the following line instead:
# FROM lmsysorg/sglang:v0.4.9.post3-cu128-b200
# Use the official vllm image
# FROM vllm/vllm-openai:v0.10.1.1
# Use DaoCloud mirrored vllm image for China region for gpu with Turing architecture and below (Compute Capability<8.0)
# FROM docker.m.daocloud.io/vllm/vllm-openai:v0.10.2
# Use the official vllm image
# FROM vllm/vllm-openai:v0.10.2
# Install libgl for opencv support & Noto fonts for Chinese characters
RUN apt-get update && \

2
src/plugins/_ocr.py
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@ -270,7 +270,7 @@ class OCRPlugin:
file_path_list = [file_path]
output_dir = os.path.join(os.getcwd(), "tmp", "mineru_ocr")
text = parse_doc(file_path_list, output_dir, backend="vlm-sglang-client", server_url=mineru_ocr_uri)[0]
text = parse_doc(file_path_list, output_dir, backend="vlm-vllm-client", server_url=mineru_ocr_uri)[0]
processing_time = time.time() - start_time
log_ocr_request("mineru_ocr", file_path, True, processing_time)

333
src/plugins/mineru.py
View File

@ -4,18 +4,18 @@ import json
import os
from pathlib import Path
from mineru.backend.pipeline.model_json_to_middle_json import result_to_middle_json as pipeline_result_to_middle_json
from mineru.backend.pipeline.pipeline_analyze import doc_analyze as pipeline_doc_analyze
from mineru.backend.pipeline.pipeline_middle_json_mkcontent import union_make as pipeline_union_make
from mineru.backend.vlm.vlm_analyze import doc_analyze as vlm_doc_analyze
from mineru.backend.vlm.vlm_middle_json_mkcontent import union_make as vlm_union_make
from loguru import logger
from mineru.cli.common import convert_pdf_bytes_to_bytes_by_pypdfium2, prepare_env, read_fn
from mineru.data.data_reader_writer import FileBasedDataWriter
from mineru.utils.draw_bbox import draw_layout_bbox, draw_span_bbox
from mineru.utils.enum_class import MakeMode
from tqdm import tqdm
from src.utils.logging_config import logger
from mineru.backend.vlm.vlm_analyze import doc_analyze as vlm_doc_analyze
from mineru.backend.pipeline.pipeline_analyze import doc_analyze as pipeline_doc_analyze
from mineru.backend.pipeline.pipeline_middle_json_mkcontent import union_make as pipeline_union_make
from mineru.backend.pipeline.model_json_to_middle_json import result_to_middle_json as pipeline_result_to_middle_json
from mineru.backend.vlm.vlm_middle_json_mkcontent import union_make as vlm_union_make
from mineru.utils.guess_suffix_or_lang import guess_suffix_by_path
def do_parse(
@ -25,9 +25,9 @@ def do_parse(
p_lang_list: list[str], # List of languages for each PDF, default is 'ch' (Chinese)
backend="pipeline", # The backend for parsing PDF, default is 'pipeline'
parse_method="auto", # The method for parsing PDF, default is 'auto'
p_formula_enable=True, # Enable formula parsing
p_table_enable=True, # Enable table parsing
server_url=None, # Server URL for vlm-sglang-client backend
formula_enable=True, # Enable formula parsing
table_enable=True, # Enable table parsing
server_url=None, # Server URL for vlm-http-client backend
f_draw_layout_bbox=True, # Whether to draw layout bounding boxes
f_draw_span_bbox=True, # Whether to draw span bounding boxes
f_dump_md=True, # Whether to dump markdown files
@ -38,22 +38,15 @@ def do_parse(
f_make_md_mode=MakeMode.MM_MD, # The mode for making markdown content, default is MM_MD
start_page_id=0, # Start page ID for parsing, default is 0
end_page_id=None, # End page ID for parsing, default is None (parse all pages until the end of the document)
) -> list[str]:
):
if backend == "pipeline":
for idx, pdf_bytes in enumerate(pdf_bytes_list):
new_pdf_bytes = convert_pdf_bytes_to_bytes_by_pypdfium2(pdf_bytes, start_page_id, end_page_id)
pdf_bytes_list[idx] = new_pdf_bytes
result = pipeline_doc_analyze(
pdf_bytes_list,
p_lang_list,
parse_method=parse_method,
formula_enable=p_formula_enable,
table_enable=p_table_enable,
)
infer_results, all_image_lists, all_pdf_docs, lang_list, ocr_enabled_list = result
infer_results, all_image_lists, all_pdf_docs, lang_list, ocr_enabled_list = pipeline_doc_analyze(pdf_bytes_list, p_lang_list, parse_method=parse_method, formula_enable=formula_enable,table_enable=table_enable)
md_results = []
for idx, model_list in enumerate(infer_results):
model_json = copy.deepcopy(model_list)
pdf_file_name = pdf_file_names[idx]
@ -64,193 +57,183 @@ def do_parse(
pdf_doc = all_pdf_docs[idx]
_lang = lang_list[idx]
_ocr_enable = ocr_enabled_list[idx]
middle_json = pipeline_result_to_middle_json(
model_list, images_list, pdf_doc, image_writer, _lang, _ocr_enable, p_formula_enable
)
middle_json = pipeline_result_to_middle_json(model_list, images_list, pdf_doc, image_writer, _lang, _ocr_enable, formula_enable)
pdf_info = middle_json["pdf_info"]
pdf_bytes = pdf_bytes_list[idx]
if f_draw_layout_bbox:
draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_layout.pdf")
if f_draw_span_bbox:
draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_span.pdf")
if f_dump_orig_pdf:
md_writer.write(
f"{pdf_file_name}_origin.pdf",
pdf_bytes,
)
if f_dump_md:
image_dir = str(os.path.basename(local_image_dir))
md_content_str = pipeline_union_make(pdf_info, f_make_md_mode, image_dir)
md_writer.write_string(
f"{pdf_file_name}.md",
md_content_str,
)
md_results.append(md_content_str)
if f_dump_content_list:
image_dir = str(os.path.basename(local_image_dir))
content_list = pipeline_union_make(pdf_info, MakeMode.CONTENT_LIST, image_dir)
md_writer.write_string(
f"{pdf_file_name}_content_list.json",
json.dumps(content_list, ensure_ascii=False, indent=4),
)
if f_dump_middle_json:
md_writer.write_string(
f"{pdf_file_name}_middle.json",
json.dumps(middle_json, ensure_ascii=False, indent=4),
)
if f_dump_model_output:
md_writer.write_string(
f"{pdf_file_name}_model.json",
json.dumps(model_json, ensure_ascii=False, indent=4),
)
logger.info(f"local output dir is {local_md_dir}")
return md_results
_process_output(
pdf_info, pdf_bytes, pdf_file_name, local_md_dir, local_image_dir,
md_writer, f_draw_layout_bbox, f_draw_span_bbox, f_dump_orig_pdf,
f_dump_md, f_dump_content_list, f_dump_middle_json, f_dump_model_output,
f_make_md_mode, middle_json, model_json, is_pipeline=True
)
else:
if backend.startswith("vlm-"):
backend = backend[4:]
f_draw_span_bbox = False
parse_method = "vlm"
md_results = []
for idx, pdf_bytes in enumerate(tqdm(pdf_bytes_list, desc="Parsing documents bytes")):
for idx, pdf_bytes in enumerate(pdf_bytes_list):
pdf_file_name = pdf_file_names[idx]
pdf_bytes = convert_pdf_bytes_to_bytes_by_pypdfium2(pdf_bytes, start_page_id, end_page_id)
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)
middle_json, infer_result = vlm_doc_analyze(
pdf_bytes, image_writer=image_writer, backend=backend, server_url=server_url
)
middle_json, infer_result = vlm_doc_analyze(pdf_bytes, image_writer=image_writer, backend=backend, server_url=server_url)
pdf_info = middle_json["pdf_info"]
if f_draw_layout_bbox:
draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_layout.pdf")
_process_output(
pdf_info, pdf_bytes, pdf_file_name, local_md_dir, local_image_dir,
md_writer, f_draw_layout_bbox, f_draw_span_bbox, f_dump_orig_pdf,
f_dump_md, f_dump_content_list, f_dump_middle_json, f_dump_model_output,
f_make_md_mode, middle_json, infer_result, is_pipeline=False
)
if f_draw_span_bbox:
draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_span.pdf")
if f_dump_orig_pdf:
md_writer.write(
f"{pdf_file_name}_origin.pdf",
pdf_bytes,
)
def _process_output(
pdf_info,
pdf_bytes,
pdf_file_name,
local_md_dir,
local_image_dir,
md_writer,
f_draw_layout_bbox,
f_draw_span_bbox,
f_dump_orig_pdf,
f_dump_md,
f_dump_content_list,
f_dump_middle_json,
f_dump_model_output,
f_make_md_mode,
middle_json,
model_output=None,
is_pipeline=True
):
"""处理输出文件"""
if f_draw_layout_bbox:
draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_layout.pdf")
if f_dump_md:
image_dir = str(os.path.basename(local_image_dir))
md_content_str = vlm_union_make(pdf_info, f_make_md_mode, image_dir)
md_writer.write_string(
f"{pdf_file_name}.md",
md_content_str,
)
md_results.append(md_content_str)
if f_draw_span_bbox:
draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_span.pdf")
if f_dump_content_list:
image_dir = str(os.path.basename(local_image_dir))
content_list = vlm_union_make(pdf_info, MakeMode.CONTENT_LIST, image_dir)
md_writer.write_string(
f"{pdf_file_name}_content_list.json",
json.dumps(content_list, ensure_ascii=False, indent=4),
)
if f_dump_orig_pdf:
md_writer.write(
f"{pdf_file_name}_origin.pdf",
pdf_bytes,
)
if f_dump_middle_json:
md_writer.write_string(
f"{pdf_file_name}_middle.json",
json.dumps(middle_json, ensure_ascii=False, indent=4),
)
image_dir = str(os.path.basename(local_image_dir))
if f_dump_model_output:
model_output = ("\n" + "-" * 50 + "\n").join(infer_result)
md_writer.write_string(
f"{pdf_file_name}_model_output.txt",
model_output,
)
if f_dump_md:
make_func = pipeline_union_make if is_pipeline else vlm_union_make
md_content_str = make_func(pdf_info, f_make_md_mode, image_dir)
md_writer.write_string(
f"{pdf_file_name}.md",
md_content_str,
)
logger.info(f"local output dir is {local_md_dir}")
if f_dump_content_list:
make_func = pipeline_union_make if is_pipeline else vlm_union_make
content_list = make_func(pdf_info, MakeMode.CONTENT_LIST, image_dir)
md_writer.write_string(
f"{pdf_file_name}_content_list.json",
json.dumps(content_list, ensure_ascii=False, indent=4),
)
return md_results
if f_dump_middle_json:
md_writer.write_string(
f"{pdf_file_name}_middle.json",
json.dumps(middle_json, ensure_ascii=False, indent=4),
)
if f_dump_model_output:
md_writer.write_string(
f"{pdf_file_name}_model.json",
json.dumps(model_output, ensure_ascii=False, indent=4),
)
logger.info(f"local output dir is {local_md_dir}")
def parse_doc(
path_list: list[Path],
output_dir,
lang="ch",
backend="pipeline",
method="auto",
server_url=None,
start_page_id=0, # Start page ID for parsing, default is 0
end_page_id=None, # End page ID for parsing, default is None (parse all pages until the end of the document)
) -> list[str]:
path_list: list[Path],
output_dir,
lang="ch",
backend="pipeline",
method="auto",
server_url=None,
start_page_id=0,
end_page_id=None
):
"""
Parameter description:
path_list: List of document paths to be parsed, can be PDF or image files.
output_dir: Output directory for storing parsing results.
lang: Language option, default is 'ch',
optional values include[
'ch', 'ch_server', 'ch_lite', 'en', 'korean', 'japan', 'chinese_cht', 'ta', 'te', 'ka'
]。
Input the languages in the pdf (if known) to improve OCR accuracy. Optional.
Adapted only for the case where the backend is set to "pipeline"
backend: the backend for parsing pdf:
pipeline: More general.
vlm-transformers: More general.
vlm-sglang-engine: Faster(engine).
vlm-sglang-client: Faster(client).
without method specified, pipeline will be used by default.
method: the method for parsing pdf:
auto: Automatically determine the method based on the file type.
txt: Use text extraction method.
ocr: Use OCR method for image-based PDFs.
Without method specified, 'auto' will be used by default.
Adapted only for the case where the backend is set to "pipeline".
server_url: When the backend is `sglang-client`, you need to specify the server_url, for example:`http://127.0.0.1:30000`
Parameter description:
path_list: List of document paths to be parsed, can be PDF or image files.
output_dir: Output directory for storing parsing results.
lang: Language option, default is 'ch', optional values include['ch', 'ch_server', 'ch_lite', 'en', 'korean', 'japan', 'chinese_cht', 'ta', 'te', 'ka']。
Input the languages in the pdf (if known) to improve OCR accuracy. Optional.
Adapted only for the case where the backend is set to "pipeline"
backend: the backend for parsing pdf:
pipeline: More general.
vlm-transformers: More general.
vlm-vllm-engine: Faster(engine).
vlm-http-client: Faster(client).
without method specified, pipeline will be used by default.
method: the method for parsing pdf:
auto: Automatically determine the method based on the file type.
txt: Use text extraction method.
ocr: Use OCR method for image-based PDFs.
Without method specified, 'auto' will be used by default.
Adapted only for the case where the backend is set to "pipeline".
server_url: When the backend is `http-client`, you need to specify the server_url, for example:`http://127.0.0.1:30000`
start_page_id: Start page ID for parsing, default is 0
end_page_id: End page ID for parsing, default is None (parse all pages until the end of the document)
"""
file_name_list = []
pdf_bytes_list = []
lang_list = []
for path in tqdm(path_list, desc="Parsing documents"):
file_name = str(Path(path).stem)
pdf_bytes = read_fn(path)
file_name_list.append(file_name)
pdf_bytes_list.append(pdf_bytes)
lang_list.append(lang)
result = do_parse(
output_dir=output_dir,
pdf_file_names=file_name_list,
pdf_bytes_list=pdf_bytes_list,
p_lang_list=lang_list,
backend=backend,
parse_method=method,
server_url=server_url,
start_page_id=start_page_id,
end_page_id=end_page_id,
)
return result if result else [""]
try:
file_name_list = []
pdf_bytes_list = []
lang_list = []
for path in path_list:
file_name = str(Path(path).stem)
pdf_bytes = read_fn(path)
file_name_list.append(file_name)
pdf_bytes_list.append(pdf_bytes)
lang_list.append(lang)
do_parse(
output_dir=output_dir,
pdf_file_names=file_name_list,
pdf_bytes_list=pdf_bytes_list,
p_lang_list=lang_list,
backend=backend,
parse_method=method,
server_url=server_url,
start_page_id=start_page_id,
end_page_id=end_page_id
)
except Exception as e:
logger.exception(e)
if __name__ == "__main__":
pdf_files_dir = "/home/zwj/workspace/projects/Yuxi-Know/test/struct_pdf"
output_dir = "/home/zwj/workspace/projects/Yuxi-Know/test/struct_pdf_output"
pdf_suffixes = [".pdf"]
image_suffixes = [".png", ".jpeg", ".jpg"]
if __name__ == '__main__':
# args
__dir__ = os.path.dirname(os.path.abspath(__file__))
pdf_files_dir = os.path.join(__dir__, "pdfs")
output_dir = os.path.join(__dir__, "output")
pdf_suffixes = ["pdf"]
image_suffixes = ["png", "jpeg", "jp2", "webp", "gif", "bmp", "jpg"]
doc_path_list = []
for doc_path in Path(pdf_files_dir).glob("*"):
if doc_path.suffix in pdf_suffixes + image_suffixes:
for doc_path in Path(pdf_files_dir).glob('*'):
if guess_suffix_by_path(doc_path) in pdf_suffixes + image_suffixes:
doc_path_list.append(doc_path)
parse_doc(
doc_path_list, output_dir, backend="vlm-sglang-client", server_url="http://172.19.13.5:30000"
) # faster(client).
"""如果您由于网络问题无法下载模型，可以设置环境变量MINERU_MODEL_SOURCE为modelscope使用免代理仓库下载模型"""
# os.environ['MINERU_MODEL_SOURCE'] = "modelscope"
"""Use pipeline mode if your environment does not support VLM"""
parse_doc(doc_path_list, output_dir, backend="pipeline")
"""To enable VLM mode, change the backend to 'vlm-xxx'"""
# parse_doc(doc_path_list, output_dir, backend="vlm-transformers") # more general.
# parse_doc(doc_path_list, output_dir, backend="vlm-vllm-engine") # faster(engine).
# parse_doc(doc_path_list, output_dir, backend="vlm-http-client", server_url="http://127.0.0.1:30000") # faster(client).