Merge branch 'opendatalab:dev' into dev

docs/en/quick_start/extension_modules.md

@@ -6,7 +6,7 @@ MinerU supports installing extension modules on demand based on different needs
 ### Core Functionality Installation
 The `core` module is the core dependency of MinerU, containing all functional modules except `vllm`. Installing this module ensures the basic functionality of MinerU works properly.
 ```bash
-uv pip install mineru[core]
+uv pip install "mineru[core]"
 ```
 
 ---
@@ -15,7 +15,7 @@ uv pip install mineru[core]
 The `vllm` module provides acceleration support for VLM model inference, suitable for graphics cards with Turing architecture and later (8GB+ VRAM). Installing this module can significantly improve model inference speed.
 In the configuration, `all` includes both `core` and `vllm` modules, so `mineru[all]` and `mineru[core,vllm]` are equivalent.
 ```bash
-uv pip install mineru[all]
+uv pip install "mineru[all]"
 ```
 > [!TIP]
 > If exceptions occur during installation of the complete package including vllm, please refer to the [vllm official documentation](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) to try to resolve the issue, or directly use the [Docker](./docker_deployment.md) deployment method.

docs/zh/quick_start/extension_modules.md

@@ -6,7 +6,7 @@ MinerU 支持根据不同需求，按需安装扩展模块，以增强功能或
 ### 核心功能安装
 `core` 模块是 MinerU 的核心依赖，包含了除`vllm`外的所有功能模块。安装此模块可以确保 MinerU 的基本功能正常运行。
 ```bash
-uv pip install mineru[core]
+uv pip install "mineru[core]"
 ```
 
 ---
@@ -15,7 +15,7 @@ uv pip install mineru[core]
 `vllm` 模块提供了对 VLM 模型推理的加速支持，适用于具有 Turing 及以后架构的显卡（8G 显存及以上）。安装此模块可以显著提升模型推理速度。
 在配置中，`all`包含了`core`和`vllm`模块，因此`mineru[all]`和`mineru[core,vllm]`是等价的。
 ```bash
-uv pip install mineru[all]
+uv pip install "mineru[all]"
 ```
 > [!TIP]
 > 如在安装包含vllm的完整包过程中发生异常，请参考 [vllm 官方文档](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) 尝试解决，或直接使用 [Docker](./docker_deployment.md) 方式部署镜像。

mineru/backend/pipeline/batch_analyze.py

@@ -289,8 +289,8 @@ class BatchAnalyze:
                     h, w = cropped_img.shape[:2]
                     # 使用更大的分组容差，减少分组数量
                     # 将尺寸标准化到32的倍数
-                    normalized_h = ((h + RESOLUTION_GROUP_STRIDE) // RESOLUTION_GROUP_STRIDE) * RESOLUTION_GROUP_STRIDE  # 向上取整到32的倍数
-                    normalized_w = ((w + RESOLUTION_GROUP_STRIDE) // RESOLUTION_GROUP_STRIDE) * RESOLUTION_GROUP_STRIDE
+                    normalized_h = ((h + RESOLUTION_GROUP_STRIDE - 1) // RESOLUTION_GROUP_STRIDE) * RESOLUTION_GROUP_STRIDE  # 向上取整到32的倍数
+                    normalized_w = ((w + RESOLUTION_GROUP_STRIDE - 1) // RESOLUTION_GROUP_STRIDE) * RESOLUTION_GROUP_STRIDE
                     group_key = (normalized_h, normalized_w)
                     resolution_groups[group_key].append(crop_info)