Merge branch 'cxz-dev' into dev

# Conflicts:
#	mineru/backend/pipeline/batch_analyze.py
#	tests/unittest/test_e2e.py

docker/china/Dockerfile

@@ -1,12 +1,12 @@
 # Use DaoCloud mirrored sglang image for China region
-FROM docker.m.daocloud.io/lmsysorg/sglang:v0.4.9.post6-cu126
+FROM docker.m.daocloud.io/lmsysorg/sglang:v0.4.10.post2-cu126
 # For blackwell GPU, use the following line instead:
-# FROM docker.m.daocloud.io/lmsysorg/sglang:v0.4.9.post6-cu128-b200
+# FROM docker.m.daocloud.io/lmsysorg/sglang:v0.4.10.post2-cu128-b200
 
 # Use the official sglang image
-# FROM lmsysorg/sglang:v0.4.9.post6-cu126
+# FROM lmsysorg/sglang:v0.4.10.post2-cu126
 # For blackwell GPU, use the following line instead:
-# FROM lmsysorg/sglang:v0.4.9.post6-cu128-b200
+# FROM lmsysorg/sglang:v0.4.10.post2-cu128-b200
 
 # Install libgl for opencv support & Noto fonts for Chinese characters
 RUN apt-get update && \

docker/global/Dockerfile

@@ -1,7 +1,7 @@
 # Use the official sglang image
-FROM lmsysorg/sglang:v0.4.9.post6-cu126
+FROM lmsysorg/sglang:v0.4.10.post2-cu126
 # For blackwell GPU, use the following line instead:
-# FROM lmsysorg/sglang:v0.4.9.post6-cu128-b200
+# FROM lmsysorg/sglang:v0.4.10.post2-cu128-b200
 
 # Install libgl for opencv support & Noto fonts for Chinese characters
 RUN apt-get update && \

docs/en/quick_start/docker_deployment.md

@@ -10,8 +10,8 @@ docker build -t mineru-sglang:latest -f Dockerfile .
 ```
 
 > [!TIP]
-> The [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/global/Dockerfile) uses `lmsysorg/sglang:v0.4.9.post6-cu126` as the base image by default, supporting Turing/Ampere/Ada Lovelace/Hopper platforms.
-> If you are using the newer `Blackwell` platform, please modify the base image to `lmsysorg/sglang:v0.4.9.post6-cu128-b200` before executing the build operation.
+> The [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/global/Dockerfile) uses `lmsysorg/sglang:v0.4.10.post2-cu126` as the base image by default, supporting Turing/Ampere/Ada Lovelace/Hopper platforms.
+> If you are using the newer `Blackwell` platform, please modify the base image to `lmsysorg/sglang:v0.4.10.post2-cu128-b200` before executing the build operation.
 
 ## Docker Description
 

docs/zh/quick_start/docker_deployment.md

@@ -10,8 +10,8 @@ docker build -t mineru-sglang:latest -f Dockerfile .
 ```
 
 > [!TIP]
-> [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/china/Dockerfile)默认使用`lmsysorg/sglang:v0.4.9.post6-cu126`作为基础镜像，支持Turing/Ampere/Ada Lovelace/Hopper平台，
-> 如您使用较新的`Blackwell`平台，请将基础镜像修改为`lmsysorg/sglang:v0.4.9.post6-cu128-b200` 再执行build操作。
+> [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/china/Dockerfile)默认使用`lmsysorg/sglang:v0.4.10.post2-cu126`作为基础镜像，支持Turing/Ampere/Ada Lovelace/Hopper平台，
+> 如您使用较新的`Blackwell`平台，请将基础镜像修改为`lmsysorg/sglang:v0.4.10.post2-cu128-b200` 再执行build操作。
 
 ## Docker说明
 

mineru/backend/pipeline/batch_analyze.py

@@ -10,14 +10,11 @@ from .model_init import AtomModelSingleton
 from .model_list import AtomicModel
 from ...utils.config_reader import get_formula_enable, get_table_enable
 from ...utils.model_utils import crop_img, get_res_list_from_layout_res
-from ...utils.ocr_utils import (
-    get_adjusted_mfdetrec_res,
-    get_ocr_result_list,
-    OcrConfidence,
-    get_rotate_crop_image,
-)
+from ...utils.ocr_utils import get_adjusted_mfdetrec_res, get_ocr_result_list, OcrConfidence, get_rotate_crop_image
 from ...utils.pdf_image_tools import get_crop_img
 from ...utils.ocr_utils import merge_det_boxes, update_det_boxes, sorted_boxes
+from ...utils.ocr_utils import get_adjusted_mfdetrec_res, get_ocr_result_list, OcrConfidence
+from ...utils.pdf_image_tools import get_crop_np_img
 
 YOLO_LAYOUT_BASE_BATCH_SIZE = 1
 MFD_BASE_BATCH_SIZE = 1
@@ -47,29 +44,28 @@ class BatchAnalyze:
         )
         atom_model_manager = AtomModelSingleton()
 
-        images = [image for image, _, _ in images_with_extra_info]
+        np_images = [np.asarray(image) for image, _, _ in images_with_extra_info]
 
         # doclayout_yolo
-        layout_images = images.copy()
 
         images_layout_res += self.model.layout_model.batch_predict(
-            layout_images, YOLO_LAYOUT_BASE_BATCH_SIZE
+            np_images, YOLO_LAYOUT_BASE_BATCH_SIZE
         )
 
         if self.formula_enable:
             # 公式检测
             images_mfd_res = self.model.mfd_model.batch_predict(
-                images, MFD_BASE_BATCH_SIZE
+                np_images, MFD_BASE_BATCH_SIZE
             )
 
             # 公式识别
             images_formula_list = self.model.mfr_model.batch_predict(
                 images_mfd_res,
-                images,
+                np_images,
                 batch_size=self.batch_ratio * MFR_BASE_BATCH_SIZE,
             )
             mfr_count = 0
-            for image_index in range(len(images)):
+            for image_index in range(len(np_images)):
                 images_layout_res[image_index] += images_formula_list[image_index]
                 mfr_count += len(images_formula_list[image_index])
 
@@ -78,10 +74,10 @@ class BatchAnalyze:
 
         ocr_res_list_all_page = []
         table_res_list_all_page = []
-        for index in range(len(images)):
+        for index in range(len(np_images)):
             _, ocr_enable, _lang = images_with_extra_info[index]
             layout_res = images_layout_res[index]
-            pil_img = images[index]
+            np_img = np_images[index]
 
             ocr_res_list, table_res_list, single_page_mfdetrec_res = (
                 get_res_list_from_layout_res(layout_res)
@@ -90,7 +86,7 @@ class BatchAnalyze:
             ocr_res_list_all_page.append({'ocr_res_list':ocr_res_list,
                                           'lang':_lang,
                                           'ocr_enable':ocr_enable,
-                                          'pil_img':pil_img,
+                                          'np_img':np_img,
                                           'single_page_mfdetrec_res':single_page_mfdetrec_res,
                                           'layout_res':layout_res,
                                           })
@@ -102,7 +98,7 @@ class BatchAnalyze:
                 crop_xmin, crop_ymin = int(table_res['poly'][0]), int(table_res['poly'][1])
                 crop_xmax, crop_ymax = int(table_res['poly'][4]), int(table_res['poly'][5])
                 bbox = (int(crop_xmin/scale), int(crop_ymin/scale), int(crop_xmax/scale), int(crop_ymax/scale))
-                table_img = get_crop_img(bbox, pil_img, scale=scale)
+                table_img = get_crop_np_img(bbox, np_img, scale=scale)
 
                 table_res_list_all_page.append({'table_res':table_res,
                                                 'lang':_lang,
@@ -120,17 +116,17 @@ class BatchAnalyze:
 
                 for res in ocr_res_list_dict['ocr_res_list']:
                     new_image, useful_list = crop_img(
-                        res, ocr_res_list_dict['pil_img'], crop_paste_x=50, crop_paste_y=50
+                        res, ocr_res_list_dict['np_img'], crop_paste_x=50, crop_paste_y=50
                     )
                     adjusted_mfdetrec_res = get_adjusted_mfdetrec_res(
                         ocr_res_list_dict['single_page_mfdetrec_res'], useful_list
                     )
 
                     # BGR转换
-                    new_image = cv2.cvtColor(np.asarray(new_image), cv2.COLOR_RGB2BGR)
+                    bgr_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR)
 
                     all_cropped_images_info.append((
-                        new_image, useful_list, ocr_res_list_dict, res, adjusted_mfdetrec_res, _lang
+                        bgr_image, useful_list, ocr_res_list_dict, res, adjusted_mfdetrec_res, _lang
                     ))
 
             # 按语言分组
@@ -195,10 +191,13 @@ class BatchAnalyze:
 
                     # 处理批处理结果
                     for i, (crop_info, (dt_boxes, elapse)) in enumerate(zip(group_crops, batch_results)):
-                        new_image, useful_list, ocr_res_list_dict, res, adjusted_mfdetrec_res, _lang = crop_info
+                        bgr_image, useful_list, ocr_res_list_dict, res, adjusted_mfdetrec_res, _lang = crop_info
 
                         if dt_boxes is not None and len(dt_boxes) > 0:
                             # 直接应用原始OCR流程中的关键处理步骤
+                            from mineru.utils.ocr_utils import (
+                                merge_det_boxes, update_det_boxes, sorted_boxes
+                            )
 
                             # 1. 排序检测框
                             if len(dt_boxes) > 0:
@@ -223,7 +222,7 @@ class BatchAnalyze:
 
                             if ocr_res:
                                 ocr_result_list = get_ocr_result_list(
-                                    ocr_res, useful_list, ocr_res_list_dict['ocr_enable'], new_image, _lang
+                                    ocr_res, useful_list, ocr_res_list_dict['ocr_enable'], bgr_image, _lang
                                 )
 
                                 ocr_res_list_dict['layout_res'].extend(ocr_result_list)
@@ -241,21 +240,21 @@ class BatchAnalyze:
                 )
                 for res in ocr_res_list_dict['ocr_res_list']:
                     new_image, useful_list = crop_img(
-                        res, ocr_res_list_dict['pil_img'], crop_paste_x=50, crop_paste_y=50
+                        res, ocr_res_list_dict['np_img'], crop_paste_x=50, crop_paste_y=50
                     )
                     adjusted_mfdetrec_res = get_adjusted_mfdetrec_res(
                         ocr_res_list_dict['single_page_mfdetrec_res'], useful_list
                     )
                     # OCR-det
-                    new_image = cv2.cvtColor(np.asarray(new_image), cv2.COLOR_RGB2BGR)
+                    bgr_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR)
                     ocr_res = ocr_model.ocr(
-                        new_image, mfd_res=adjusted_mfdetrec_res, rec=False
+                        bgr_image, mfd_res=adjusted_mfdetrec_res, rec=False
                     )[0]
 
                     # Integration results
                     if ocr_res:
                         ocr_result_list = get_ocr_result_list(
-                            ocr_res, useful_list, ocr_res_list_dict['ocr_enable'],new_image, _lang
+                            ocr_res, useful_list, ocr_res_list_dict['ocr_enable'],bgr_image, _lang
                         )
 
                         ocr_res_list_dict['layout_res'].extend(ocr_result_list)

mineru/backend/pipeline/model_init.py

@@ -4,7 +4,7 @@ import torch
 from loguru import logger
 
 from .model_list import AtomicModel
-from ...model.layout.doclayout_yolo import DocLayoutYOLOModel
+from ...model.layout.doclayoutyolo import DocLayoutYOLOModel
 from ...model.mfd.yolo_v8 import YOLOv8MFDModel
 from ...model.mfr.unimernet.Unimernet import UnimernetModel
 from ...model.ocr.paddleocr2pytorch.pytorch_paddle import PytorchPaddleOCR

mineru/cli/common.py

@@ -9,7 +9,7 @@ import pypdfium2 as pdfium
 from loguru import logger
 
 from mineru.data.data_reader_writer import FileBasedDataWriter
-from mineru.utils.draw_bbox import draw_layout_bbox, draw_span_bbox
+from mineru.utils.draw_bbox import draw_layout_bbox, draw_span_bbox, draw_line_sort_bbox
 from mineru.utils.enum_class import MakeMode
 from mineru.utils.pdf_image_tools import images_bytes_to_pdf_bytes
 from mineru.backend.vlm.vlm_middle_json_mkcontent import union_make as vlm_union_make
@@ -102,6 +102,7 @@ def _process_output(
         model_output=None,
         is_pipeline=True
 ):
+    f_draw_line_sort_bbox = False
     from mineru.backend.pipeline.pipeline_middle_json_mkcontent import union_make as pipeline_union_make
     """处理输出文件"""
     if f_draw_layout_bbox:
@@ -116,6 +117,9 @@ def _process_output(
             pdf_bytes,
         )
 
+    if f_draw_line_sort_bbox:
+        draw_line_sort_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_line_sort.pdf")
+
     image_dir = str(os.path.basename(local_image_dir))
 
     if f_dump_md:

mineru/cli/fast_api.py

@@ -1,12 +1,17 @@
 import uuid
 import os
+import re
+import tempfile
+import asyncio
 import uvicorn
 import click
+import zipfile
 from pathlib import Path
-from glob import glob
+import glob
 from fastapi import FastAPI, UploadFile, File, Form
 from fastapi.middleware.gzip import GZipMiddleware
-from fastapi.responses import JSONResponse
+from fastapi.responses import JSONResponse, FileResponse
+from starlette.background import BackgroundTask
 from typing import List, Optional
 from loguru import logger
 from base64 import b64encode
@@ -18,6 +23,27 @@ from mineru.version import __version__
 app = FastAPI()
 app.add_middleware(GZipMiddleware, minimum_size=1000)
 
+
+def sanitize_filename(filename: str) -> str:
+    """
+    格式化压缩文件的文件名
+    移除路径遍历字符, 保留 Unicode 字母、数字、._- 
+    禁止隐藏文件
+    """
+    sanitized = re.sub(r'[/\\\.]{2,}|[/\\]', '', filename)
+    sanitized = re.sub(r'[^\w.-]', '_', sanitized, flags=re.UNICODE)
+    if sanitized.startswith('.'):
+        sanitized = '_' + sanitized[1:]
+    return sanitized or 'unnamed'
+
+def cleanup_file(file_path: str) -> None:
+    """清理临时 zip 文件"""
+    try:
+        if os.path.exists(file_path):
+            os.remove(file_path)
+    except Exception as e:
+        logger.warning(f"fail clean file {file_path}: {e}")
+
 def encode_image(image_path: str) -> str:
     """Encode image using base64"""
     with open(image_path, "rb") as f:
@@ -48,6 +74,7 @@ async def parse_pdf(
         return_model_output: bool = Form(False),
         return_content_list: bool = Form(False),
         return_images: bool = Form(False),
+        response_format_zip: bool = Form(False),
         start_page_id: int = Form(0),
         end_page_id: int = Form(99999),
 ):
@@ -121,45 +148,101 @@ async def parse_pdf(
             **config
         )
 
-        # 构建结果路径
-        result_dict = {}
-        for pdf_name in pdf_file_names:
-            result_dict[pdf_name] = {}
-            data = result_dict[pdf_name]
-
-            if backend.startswith("pipeline"):
-                parse_dir = os.path.join(unique_dir, pdf_name, parse_method)
-            else:
-                parse_dir = os.path.join(unique_dir, pdf_name, "vlm")
-
-            if os.path.exists(parse_dir):
-                if return_md:
-                    data["md_content"] = get_infer_result(".md", pdf_name, parse_dir)
-                if return_middle_json:
-                    data["middle_json"] = get_infer_result("_middle.json", pdf_name, parse_dir)
-                if return_model_output:
+        # 根据 response_format_zip 决定返回类型
+        if response_format_zip:
+            zip_fd, zip_path = tempfile.mkstemp(suffix=".zip", prefix="mineru_results_")
+            os.close(zip_fd) 
+            with zipfile.ZipFile(zip_path, "w", compression=zipfile.ZIP_DEFLATED) as zf:
+                for pdf_name in pdf_file_names:
+                    safe_pdf_name = sanitize_filename(pdf_name)
                     if backend.startswith("pipeline"):
-                        data["model_output"] = get_infer_result("_model.json", pdf_name, parse_dir)
+                        parse_dir = os.path.join(unique_dir, pdf_name, parse_method)
                     else:
-                        data["model_output"] = get_infer_result("_model_output.txt", pdf_name, parse_dir)
-                if return_content_list:
-                    data["content_list"] = get_infer_result("_content_list.json", pdf_name, parse_dir)
-                if return_images:
-                    image_paths = glob(f"{parse_dir}/images/*.jpg")
-                    data["images"] = {
-                        os.path.basename(
-                            image_path
-                        ): f"data:image/jpeg;base64,{encode_image(image_path)}"
-                        for image_path in image_paths
-                    }
-        return JSONResponse(
-            status_code=200,
-            content={
-                "backend": backend,
-                "version": __version__,
-                "results": result_dict
-            }
-        )
+                        parse_dir = os.path.join(unique_dir, pdf_name, "vlm")
+
+                    if not os.path.exists(parse_dir):
+                        continue
+
+                    # 写入文本类结果
+                    if return_md:
+                        path = os.path.join(parse_dir, f"{pdf_name}.md")
+                        if os.path.exists(path):
+                            zf.write(path, arcname=os.path.join(safe_pdf_name, f"{safe_pdf_name}.md"))
+
+                    if return_middle_json:
+                        path = os.path.join(parse_dir, f"{pdf_name}_middle.json")
+                        if os.path.exists(path):
+                            zf.write(path, arcname=os.path.join(safe_pdf_name, f"{safe_pdf_name}_middle.json"))
+
+                    if return_model_output:
+                        if backend.startswith("pipeline"):
+                            path = os.path.join(parse_dir, f"{pdf_name}_model.json")
+                        else:
+                            path = os.path.join(parse_dir, f"{pdf_name}_model_output.txt")
+                        if os.path.exists(path): 
+                            zf.write(path, arcname=os.path.join(safe_pdf_name, os.path.basename(path)))
+
+                    if return_content_list:
+                        path = os.path.join(parse_dir, f"{pdf_name}_content_list.json")
+                        if os.path.exists(path):
+                            zf.write(path, arcname=os.path.join(safe_pdf_name, f"{safe_pdf_name}_content_list.json"))
+
+                    # 写入图片
+                    if return_images:
+                        images_dir = os.path.join(parse_dir, "images")
+                        image_paths = glob.glob(os.path.join(glob.escape(images_dir), "*.jpg"))
+                        for image_path in image_paths:
+                            zf.write(image_path, arcname=os.path.join(safe_pdf_name, "images", os.path.basename(image_path)))
+
+            return FileResponse(
+                path=zip_path,
+                media_type="application/zip",
+                filename="results.zip",
+                background=BackgroundTask(cleanup_file, zip_path)
+            )
+        else:
+            # 构建 JSON 结果
+            result_dict = {}
+            for pdf_name in pdf_file_names:
+                result_dict[pdf_name] = {}
+                data = result_dict[pdf_name]
+
+                if backend.startswith("pipeline"):
+                    parse_dir = os.path.join(unique_dir, pdf_name, parse_method)
+                else:
+                    parse_dir = os.path.join(unique_dir, pdf_name, "vlm")
+
+                if os.path.exists(parse_dir):
+                    if return_md:
+                        data["md_content"] = get_infer_result(".md", pdf_name, parse_dir)
+                    if return_middle_json:
+                        data["middle_json"] = get_infer_result("_middle.json", pdf_name, parse_dir)
+                    if return_model_output:
+                        if backend.startswith("pipeline"):
+                            data["model_output"] = get_infer_result("_model.json", pdf_name, parse_dir)
+                        else:
+                            data["model_output"] = get_infer_result("_model_output.txt", pdf_name, parse_dir)
+                    if return_content_list:
+                        data["content_list"] = get_infer_result("_content_list.json", pdf_name, parse_dir)
+                    if return_images:
+                        images_dir = os.path.join(parse_dir, "images")
+                        safe_pattern = os.path.join(glob.escape(images_dir), "*.jpg")
+                        image_paths = glob.glob(safe_pattern)
+                        data["images"] = {
+                            os.path.basename(
+                                image_path
+                            ): f"data:image/jpeg;base64,{encode_image(image_path)}"
+                            for image_path in image_paths
+                        }
+
+            return JSONResponse(
+                status_code=200,
+                content={
+                    "backend": backend,
+                    "version": __version__,
+                    "results": result_dict
+                }
+            )
     except Exception as e:
         logger.exception(e)
         return JSONResponse(

mineru/model/layout/doclayout_yolo.py → mineru/model/layout/doclayoutyolo.py

@@ -1,8 +1,13 @@
+import os
 from typing import List, Dict, Union
+
 from doclayout_yolo import YOLOv10
 from tqdm import tqdm
 import numpy as np
-from PIL import Image
+from PIL import Image, ImageDraw
+
+from mineru.utils.enum_class import ModelPath
+from mineru.utils.models_download_utils import auto_download_and_get_model_root_path
 
 
 class DocLayoutYOLOModel:
@@ -74,4 +79,41 @@ class DocLayoutYOLOModel:
                 for pred in predictions:
                     results.append(self._parse_prediction(pred))
                 pbar.update(len(batch))
-        return results
+        return results
+
+    def visualize(
+            self,
+            image: Union[np.ndarray, Image.Image],
+            results: List
+    ) -> Image.Image:
+
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+
+        draw = ImageDraw.Draw(image)
+        for res in results:
+            poly = res['poly']
+            xmin, ymin, xmax, ymax = poly[0], poly[1], poly[4], poly[5]
+            print(
+                f"Detected box: {xmin}, {ymin}, {xmax}, {ymax}, Category ID: {res['category_id']}, Score: {res['score']}")
+            # 使用PIL在图像上画框
+            draw.rectangle([xmin, ymin, xmax, ymax], outline="red", width=2)
+            # 在框旁边画置信度
+            draw.text((xmax + 10, ymin + 10), f"{res['score']:.2f}", fill="red", font_size=22)
+        return image
+
+
+if __name__ == '__main__':
+    image_path = r"C:\Users\zhaoxiaomeng\Downloads\下载1.jpg"
+    doclayout_yolo_weights = os.path.join(auto_download_and_get_model_root_path(ModelPath.doclayout_yolo), ModelPath.doclayout_yolo)
+    device = 'cuda'
+    model = DocLayoutYOLOModel(
+        weight=doclayout_yolo_weights,
+        device=device,
+    )
+    image = Image.open(image_path)
+    results = model.predict(image)
+
+    image = model.visualize(image, results)
+
+    image.show()  # 显示图像

mineru/model/mfd/yolo_v8.py

@@ -1,8 +1,12 @@
+import os
 from typing import List, Union
 from tqdm import tqdm
 from ultralytics import YOLO
 import numpy as np
-from PIL import Image
+from PIL import Image, ImageDraw
+
+from mineru.utils.enum_class import ModelPath
+from mineru.utils.models_download_utils import auto_download_and_get_model_root_path
 
 
 class YOLOv8MFDModel:
@@ -50,4 +54,53 @@ class YOLOv8MFDModel:
                 batch_preds = self._run_predict(batch, is_batch=True)
                 results.extend(batch_preds)
                 pbar.update(len(batch))
-        return results
+        return results
+
+    def visualize(
+        self,
+        image: Union[np.ndarray, Image.Image],
+        results: List
+    ) -> Image.Image:
+
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+
+        formula_list = []
+        for xyxy, conf, cla in zip(
+                results.boxes.xyxy.cpu(), results.boxes.conf.cpu(), results.boxes.cls.cpu()
+        ):
+            xmin, ymin, xmax, ymax = [int(p.item()) for p in xyxy]
+            new_item = {
+                "category_id": 13 + int(cla.item()),
+                "poly": [xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax],
+                "score": round(float(conf.item()), 2),
+            }
+            formula_list.append(new_item)
+
+        draw = ImageDraw.Draw(image)
+        for res in formula_list:
+            poly = res['poly']
+            xmin, ymin, xmax, ymax = poly[0], poly[1], poly[4], poly[5]
+            print(
+                f"Detected box: {xmin}, {ymin}, {xmax}, {ymax}, Category ID: {res['category_id']}, Score: {res['score']}")
+            # 使用PIL在图像上画框
+            draw.rectangle([xmin, ymin, xmax, ymax], outline="red", width=2)
+            # 在框旁边画置信度
+            draw.text((xmax + 10, ymin + 10), f"{res['score']:.2f}", fill="red", font_size=22)
+        return image
+
+if __name__ == '__main__':
+    image_path = r"C:\Users\zhaoxiaomeng\Downloads\screenshot-20250821-192948.png"
+    yolo_v8_mfd_weights = os.path.join(auto_download_and_get_model_root_path(ModelPath.yolo_v8_mfd),
+                                          ModelPath.yolo_v8_mfd)
+    device = 'cuda'
+    model = YOLOv8MFDModel(
+        weight=yolo_v8_mfd_weights,
+        device=device,
+    )
+    image = Image.open(image_path)
+    results = model.predict(image)
+
+    image = model.visualize(image, results)
+
+    image.show()  # 显示图像

mineru/model/mfr/unimernet/Unimernet.py

@@ -70,7 +70,7 @@ class UnimernetModel(object):
         # Collect images with their original indices
         for image_index in range(len(images_mfd_res)):
             mfd_res = images_mfd_res[image_index]
-            pil_img = images[image_index]
+            image = images[image_index]
             formula_list = []
 
             for idx, (xyxy, conf, cla) in enumerate(zip(
@@ -84,7 +84,7 @@ class UnimernetModel(object):
                     "latex": "",
                 }
                 formula_list.append(new_item)
-                bbox_img = pil_img.crop((xmin, ymin, xmax, ymax))
+                bbox_img = image[ymin:ymax, xmin:xmax]
                 area = (xmax - xmin) * (ymax - ymin)
 
                 curr_idx = len(mf_image_list)

mineru/utils/draw_bbox.py

@@ -381,6 +381,87 @@ def draw_span_bbox(pdf_info, pdf_bytes, out_path, filename):
         output_pdf.write(f)
 
 
+def draw_line_sort_bbox(pdf_info, pdf_bytes, out_path, filename):
+    layout_bbox_list = []
+
+    for page in pdf_info:
+        page_line_list = []
+        for block in page['preproc_blocks']:
+            if block['type'] in [BlockType.TEXT]:
+                for line in block['lines']:
+                    bbox = line['bbox']
+                    index = line['index']
+                    page_line_list.append({'index': index, 'bbox': bbox})
+            elif block['type'] in [BlockType.TITLE, BlockType.INTERLINE_EQUATION]:
+                if 'virtual_lines' in block:
+                    if len(block['virtual_lines']) > 0 and block['virtual_lines'][0].get('index', None) is not None:
+                        for line in block['virtual_lines']:
+                            bbox = line['bbox']
+                            index = line['index']
+                            page_line_list.append({'index': index, 'bbox': bbox})
+                else:
+                    for line in block['lines']:
+                        bbox = line['bbox']
+                        index = line['index']
+                        page_line_list.append({'index': index, 'bbox': bbox})
+            elif block['type'] in [BlockType.IMAGE, BlockType.TABLE]:
+                for sub_block in block['blocks']:
+                    if sub_block['type'] in [BlockType.IMAGE_BODY, BlockType.TABLE_BODY]:
+                        if len(sub_block['virtual_lines']) > 0 and sub_block['virtual_lines'][0].get('index', None) is not None:
+                            for line in sub_block['virtual_lines']:
+                                bbox = line['bbox']
+                                index = line['index']
+                                page_line_list.append({'index': index, 'bbox': bbox})
+                        else:
+                            for line in sub_block['lines']:
+                                bbox = line['bbox']
+                                index = line['index']
+                                page_line_list.append({'index': index, 'bbox': bbox})
+                    elif sub_block['type'] in [BlockType.IMAGE_CAPTION, BlockType.TABLE_CAPTION, BlockType.IMAGE_FOOTNOTE, BlockType.TABLE_FOOTNOTE]:
+                        for line in sub_block['lines']:
+                            bbox = line['bbox']
+                            index = line['index']
+                            page_line_list.append({'index': index, 'bbox': bbox})
+        sorted_bboxes = sorted(page_line_list, key=lambda x: x['index'])
+        layout_bbox_list.append(sorted_bbox['bbox'] for sorted_bbox in sorted_bboxes)
+    pdf_bytes_io = BytesIO(pdf_bytes)
+    pdf_docs = PdfReader(pdf_bytes_io)
+    output_pdf = PdfWriter()
+
+    for i, page in enumerate(pdf_docs.pages):
+        # 获取原始页面尺寸
+        page_width, page_height = float(page.cropbox[2]), float(page.cropbox[3])
+        custom_page_size = (page_width, page_height)
+
+        packet = BytesIO()
+        # 使用原始PDF的尺寸创建canvas
+        c = canvas.Canvas(packet, pagesize=custom_page_size)
+
+        # 获取当前页面的数据
+        draw_bbox_with_number(i, layout_bbox_list, page, c, [255, 0, 0], False)
+
+        c.save()
+        packet.seek(0)
+        overlay_pdf = PdfReader(packet)
+
+        # 添加检查确保overlay_pdf.pages不为空
+        if len(overlay_pdf.pages) > 0:
+            new_page = PageObject(pdf=None)
+            new_page.update(page)
+            page = new_page
+            page.merge_page(overlay_pdf.pages[0])
+        else:
+            # 记录日志并继续处理下一个页面
+            # logger.warning(f"span.pdf: 第{i + 1}页未能生成有效的overlay PDF")
+            pass
+
+        output_pdf.add_page(page)
+
+    # Save the PDF
+    with open(f"{out_path}/{filename}", "wb") as f:
+        output_pdf.write(f)
+
+
 if __name__ == "__main__":
     # 读取PDF文件
     pdf_path = "examples/demo1.pdf"

mineru/utils/model_utils.py

@@ -201,6 +201,10 @@ def filter_nested_tables(table_res_list, overlap_threshold=0.8, area_threshold=0
 
 
 def remove_overlaps_min_blocks(res_list):
+
+    for res in res_list:
+        res['bbox'] = [int(res['poly'][0]), int(res['poly'][1]), int(res['poly'][4]), int(res['poly'][5])]
+
     # 重叠block，小的不能直接删除，需要和大的那个合并成一个更大的。
     # 删除重叠blocks中较小的那些
     need_remove = []
@@ -248,6 +252,14 @@ def remove_overlaps_min_blocks(res_list):
     # 从列表中移除标记的元素
     for res in need_remove:
         res_list.remove(res)
+        del res['bbox']  # 删除bbox字段
+
+    for res in res_list:
+        # 将res的poly使用bbox重构
+        res['poly'] = [res['bbox'][0], res['bbox'][1], res['bbox'][2], res['bbox'][1],
+                       res['bbox'][2], res['bbox'][3], res['bbox'][0], res['bbox'][3]]
+        # 删除res的bbox
+        del res['bbox']
 
     return res_list, need_remove
 
@@ -328,7 +340,7 @@ def remove_overlaps_low_confidence_blocks(combined_res_list, overlap_threshold=0
                 marked_indices.add(i)  # 标记当前索引为已处理
     return blocks_to_remove
 
-# @todo 这个方法以后需要重构
+
 def get_res_list_from_layout_res(layout_res, iou_threshold=0.7, overlap_threshold=0.8, area_threshold=0.8):
     """Extract OCR, table and other regions from layout results."""
     ocr_res_list = []
@@ -352,7 +364,6 @@ def get_res_list_from_layout_res(layout_res, iou_threshold=0.7, overlap_threshol
             table_res_list.append(res)
             table_indices.append(i)
         elif category_id in [1]:  # Text regions
-            res['bbox'] = [int(res['poly'][0]), int(res['poly'][1]), int(res['poly'][4]), int(res['poly'][5])]
             text_res_list.append(res)
 
     # Process tables: merge high IoU tables first, then filter nested tables
@@ -362,23 +373,11 @@ def get_res_list_from_layout_res(layout_res, iou_threshold=0.7, overlap_threshol
     filtered_table_res_list = filter_nested_tables(
         table_res_list, overlap_threshold, area_threshold)
 
-    for table_res in filtered_table_res_list:
-        table_res['bbox'] = [int(table_res['poly'][0]), int(table_res['poly'][1]), int(table_res['poly'][4]), int(table_res['poly'][5])]
-
     filtered_table_res_list, table_need_remove = remove_overlaps_min_blocks(filtered_table_res_list)
 
-    for res in filtered_table_res_list:
-        # 将res的poly使用bbox重构
-        res['poly'] = [res['bbox'][0], res['bbox'][1], res['bbox'][2], res['bbox'][1],
-                       res['bbox'][2], res['bbox'][3], res['bbox'][0], res['bbox'][3]]
-        # 删除res的bbox
-        del res['bbox']
-
-    if len(table_need_remove) > 0:
-        for res in table_need_remove:
-            del res['bbox']
-            if res in layout_res:
-                layout_res.remove(res)
+    for res in table_need_remove:
+        if res in layout_res:
+            layout_res.remove(res)
 
     # Remove filtered out tables from layout_res
     if len(filtered_table_res_list) < len(table_res_list):
@@ -390,20 +389,12 @@ def get_res_list_from_layout_res(layout_res, iou_threshold=0.7, overlap_threshol
 
     # Remove overlaps in OCR and text regions
     text_res_list, need_remove = remove_overlaps_min_blocks(text_res_list)
-    for res in text_res_list:
-        # 将res的poly使用bbox重构
-        res['poly'] = [res['bbox'][0], res['bbox'][1], res['bbox'][2], res['bbox'][1],
-                       res['bbox'][2], res['bbox'][3], res['bbox'][0], res['bbox'][3]]
-        # 删除res的bbox
-        del res['bbox']
 
     ocr_res_list.extend(text_res_list)
 
-    if len(need_remove) > 0:
-        for res in need_remove:
-            del res['bbox']
-            if res in layout_res:
-                layout_res.remove(res)
+    for res in need_remove:
+        if res in layout_res:
+            layout_res.remove(res)
 
     # 检测大block内部是否包含多个小block, 合并ocr和table列表进行检测
     combined_res_list = ocr_res_list + filtered_table_res_list

mineru/utils/ocr_utils.py

@@ -330,10 +330,10 @@ def get_adjusted_mfdetrec_res(single_page_mfdetrec_res, useful_list):
     return adjusted_mfdetrec_res
 
 
-def get_ocr_result_list(ocr_res, useful_list, ocr_enable, new_image, lang):
+def get_ocr_result_list(ocr_res, useful_list, ocr_enable, bgr_image, lang):
     paste_x, paste_y, xmin, ymin, xmax, ymax, new_width, new_height = useful_list
     ocr_result_list = []
-    ori_im = new_image.copy()
+    ori_im = bgr_image.copy()
     for box_ocr_res in ocr_res:
 
         if len(box_ocr_res) == 2:

mineru/utils/pdf_image_tools.py

@@ -1,6 +1,7 @@
 # Copyright (c) Opendatalab. All rights reserved.
 from io import BytesIO
 
+import numpy as np
 import pypdfium2 as pdfium
 from loguru import logger
 from PIL import Image
@@ -91,6 +92,24 @@ def get_crop_img(bbox: tuple, pil_img, scale=2):
     return pil_img.crop(scale_bbox)
 
 
+def get_crop_np_img(bbox: tuple, input_img, scale=2):
+
+    if isinstance(input_img, Image.Image):
+        np_img = np.asarray(input_img)
+    elif isinstance(input_img, np.ndarray):
+        np_img = input_img
+    else:
+        raise ValueError("Input must be a pillow object or a numpy array.")
+
+    scale_bbox = (
+        int(bbox[0] * scale),
+        int(bbox[1] * scale),
+        int(bbox[2] * scale),
+        int(bbox[3] * scale),
+    )
+
+    return np_img[scale_bbox[1]:scale_bbox[3], scale_bbox[0]:scale_bbox[2]]
+
 def images_bytes_to_pdf_bytes(image_bytes):
     # 内存缓冲区
     pdf_buffer = BytesIO()

mkdocs.yml

@@ -50,6 +50,9 @@ theme:
     - toc.integrate
 
 extra:
+  analytics:
+    provider: google
+    property: G-44K480CC48
   social:
     - icon: fontawesome/brands/github
       link: https://github.com/opendatalab/MinerU

pyproject.toml

@@ -49,12 +49,12 @@ test = [
 ]
 vlm = [
     "transformers>=4.51.1",
-    "torch>=2.6.0",
+    "torch>=2.6.0,<2.8.0",
     "accelerate>=1.5.1",
     "pydantic",
 ]
 sglang = [
-    "sglang[all]>=0.4.7,<0.4.10",
+    "sglang[all]>=0.4.7,<0.4.11",
 ]
 pipeline = [
     "matplotlib>=3.10,<4",
@@ -67,7 +67,7 @@ pipeline = [
     "shapely>=2.0.7,<3",
     "pyclipper>=1.3.0,<2",
     "omegaconf>=2.3.0,<3",
-    "torch>=2.6.0,<3",
+    "torch>=2.6.0,<2.8.0",
     "torchvision",
     "transformers>=4.49.0,!=4.51.0,<5.0.0",
 ]

tests/unittest/test_e2e.py

@@ -90,7 +90,10 @@ def test_pipeline_with_two_config():
         output_dir,
         parse_method="ocr",
     )
-    assert_content("tests/unittest/output/test/ocr/test_content_list.json")
+    res_json_path = (
+        Path(__file__).parent / "output" / "test" / "ocr" / "test_content_list.json"
+    ).as_posix()
+    assert_content(res_json_path)
 
 
 def test_vlm_transformers_with_default_config():
@@ -159,7 +162,7 @@ def test_vlm_transformers_with_default_config():
 
         logger.info(f"local output dir is {local_md_dir}")
         res_json_path = (
-            Path(__file__).parent / "output" / "test" / "txt" / "test_content_list.json"
+            Path(__file__).parent / "output" / "test" / "vlm" / "test_content_list.json"
         ).as_posix()
         assert_content(res_json_path)
 
@@ -246,15 +249,21 @@ def assert_content(content_path):
             case "image":
                 type_set.add("image")
                 assert (
-                    content_dict["image_caption"][0].strip().lower()
-                    == "Figure 1: Figure Caption".lower()
+                    fuzz.ratio(
+                        content_dict["image_caption"][0],
+                        "Figure 1: Figure Caption",
+                    )
+                    > 90
                 )
             # 表格校验，校验 Caption，表格格式和表格内容
             case "table":
                 type_set.add("table")
                 assert (
-                    content_dict["table_caption"][0].strip().lower()
-                    == "Table 1: Table Caption".lower()
+                    fuzz.ratio(
+                        content_dict["table_caption"][0],
+                        "Table 1: Table Caption",
+                    )
+                    > 90
                 )
                 assert validate_html(content_dict["table_body"])
                 target_str_list = [