From dbf17312187fbb534203ef1e95e9c8aae8a2f948 Mon Sep 17 00:00:00 2001 From: playertk Date: Wed, 24 Dec 2025 11:41:52 +0800 Subject: [PATCH 1/6] =?UTF-8?q?docs:=20=E6=B7=BB=E5=8A=A0=E4=B8=AD?= =?UTF-8?q?=E6=96=87=E5=AE=89=E8=A3=85=E6=8C=87=E5=8D=97=E5=92=8CREADME?= =?UTF-8?q?=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 添加UV_INSTALLATION_GUIDE.md详细说明使用UV包管理器的安装步骤 添加README_zh.md提供项目中文介绍和使用说明 --- README_zh.md | 92 ++++++++++++++++++++++++++ UV_INSTALLATION_GUIDE.md | 139 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 231 insertions(+) create mode 100644 README_zh.md create mode 100644 UV_INSTALLATION_GUIDE.md diff --git a/README_zh.md b/README_zh.md new file mode 100644 index 0000000..1711581 --- /dev/null +++ b/README_zh.md @@ -0,0 +1,92 @@ +# SHARP：不到一秒的单目视图合成 + +[![项目页面](https://img.shields.io/badge/Project-Page-green)](https://apple.github.io/ml-sharp/) +[![arXiv](https://img.shields.io/badge/arXiv-2512.10685-b31b1b.svg)](https://arxiv.org/abs/2512.10685) + +本软件项目是研究论文《Sharp Monocular View Synthesis in Less Than a Second》的配套代码，作者为：_Lars Mescheder, Wei Dong, Shiwei Li, Xuyang Bai, Marcel Santos, Peiyun Hu, Bruno Lecouat, Mingmin Zhen, Amaël Delaunoy, Tian Fang, Yanghai Tsin, Stephan Richter and Vladlen Koltun_。 + +![](data/teaser.jpg) + +我们提出了SHARP，一种从单张图像生成逼真视图合成的方法。给定一张照片，SHARP通过神经网络的单次前向传递，在标准GPU上不到一秒的时间内回归出所描绘场景的3D高斯表示。然后，可以实时渲染SHARP生成的3D高斯表示，为附近视图生成高分辨率逼真图像。该表示是度量的，具有绝对尺度，支持度量相机运动。实验结果表明，SHARP在多个数据集上表现出稳健的零样本泛化能力。与最佳现有模型相比，它在多个数据集上建立了新的技术水平，将LPIPS降低了25–34%，DISTS降低了21–43%，同时将合成时间降低了三个数量级。 + +## 快速开始 + +我们建议首先创建一个Python环境： + +``` +conda create -n sharp python=3.13 +``` + +然后，您可以使用以下命令安装项目： + +``` +pip install -r requirements.txt +``` + +要测试安装是否成功，运行： + +``` +sharp --help +``` + +## 使用命令行界面 + +要运行预测： + +``` +sharp predict -i /path/to/input/images -o /path/to/output/gaussians +``` + +模型检查点将在首次运行时自动下载并缓存在本地路径 `~/.cache/torch/hub/checkpoints/`。 + +或者，您可以直接下载模型： + +``` +wget https://ml-site.cdn-apple.com/models/sharp/sharp_2572gikvuh.pt +``` + +要使用手动下载的检查点，请使用 `-c` 参数指定： + +``` +sharp predict -i /path/to/input/images -o /path/to/output/gaussians -c sharp_2572gikvuh.pt +``` + +结果将是输出文件夹中的3D高斯图元（3DGS）。3DGS `.ply` 文件兼容各种公共3DGS渲染器。我们遵循OpenCV坐标约定（x向右，y向下，z向前）。3DGS场景中心大致在 (0, 0, +z)。使用第三方渲染器时，请相应地缩放和旋转以重新定位场景中心。 + +### 渲染轨迹（仅支持CUDA GPU） + +此外，您还可以使用相机轨迹渲染视频。虽然高斯预测适用于所有CPU、CUDA和MPS，但通过 `--render` 选项渲染视频目前需要CUDA GPU。gsplat渲染器在首次启动时需要一段时间初始化。 + +``` +sharp predict -i /path/to/input/images -o /path/to/output/gaussians --render + +# 或者从中间高斯结果渲染： +sharp render -i /path/to/output/gaussians -o /path/to/output/renderings +``` + +## 评估 + +有关定量和定性评估，请参阅论文。此外，您可以查看此[定性示例页面](https://apple.github.io/ml-sharp/)，其中包含与相关工作的多个视频比较。 + +## 引用 + +如果您发现我们的工作有用，请引用以下论文： + +```bibtex +@inproceedings{Sharp2025:arxiv, + title = {Sharp Monocular View Synthesis in Less Than a Second}, + author = {Lars Mescheder and Wei Dong and Shiwei Li and Xuyang Bai and Marcel Santos and Peiyun Hu and Bruno Lecouat and Mingmin Zhen and Ama"{e}l Delaunoy and Tian Fang and Yanghai Tsin and Stephan R. Richter and Vladlen Koltun}, + journal = {arXiv preprint arXiv:2512.10685}, + year = {2025}, + url = {https://arxiv.org/abs/2512.10685}, +} +``` + +## 致谢 + +我们的代码库基于多个开源贡献构建，请查看[ACKNOWLEDGEMENTS](ACKNOWLEDGEMENTS)了解更多详细信息。 + +## 许可证 + +在使用提供的代码之前，请查看仓库中的[LICENSE](LICENSE)文件， +对于发布的模型，请查看[LICENSE_MODEL](LICENSE_MODEL)文件。 \ No newline at end of file diff --git a/UV_INSTALLATION_GUIDE.md b/UV_INSTALLATION_GUIDE.md new file mode 100644 index 0000000..faf8a30 --- /dev/null +++ b/UV_INSTALLATION_GUIDE.md @@ -0,0 +1,139 @@ +# SHARP 项目 UV 快速安装指南 + +本指南提供了使用 UV 包管理器快速安装和设置 SHARP 项目的详细步骤。UV 是一个高性能的 Python 包管理器，能够加速依赖安装过程。 + +## 项目介绍 + +SHARP 是一个用于从单张图像生成逼真视图合成的方法。它通过神经网络的单次前向传递，在不到一秒的时间内回归出场景的 3D 高斯表示，然后可以实时渲染附近视图的高分辨率逼真图像。 + +## 前提条件 + +- 操作系统：Windows、Linux 或 macOS +- Python 3.13（建议使用） +- CUDA GPU（用于渲染轨迹，预测功能支持 CPU、CUDA 和 MPS） + +## 1. 安装 UV + +### Windows + +在 PowerShell 中运行以下命令： + +```powershell +(Invoke-WebRequest -Uri https://astral.sh/uv/install.ps1 -UseBasicParsing).Content | PowerShell - +``` + +### Linux/macOS + +在终端中运行以下命令： + +```bash +curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +## 2. 克隆仓库 + +```bash +git clone https://github.com/apple/ml-sharp.git +cd ml-sharp +``` + +## 3. 创建虚拟环境 + +使用 UV 创建一个 Python 3.13 虚拟环境： + +```bash +uv venv --python 3.13 venv +``` + +## 4. 激活虚拟环境 + +### Windows + +```powershell +venv\Scripts\activate +``` + +### Linux/macOS + +```bash +source venv/bin/activate +``` + +## 5. 安装依赖 + +使用 UV 安装项目依赖： + +```bash +uv pip install -r requirements.txt +``` + +## 6. 验证安装 + +运行以下命令验证安装是否成功： + +```bash +sharp --help +``` + +如果安装成功，您将看到类似以下输出： + +``` +Usage: sharp [OPTIONS] COMMAND [ARGS]... + + Run inference for SHARP model. + +Options: + --help Show this message and exit. + +Commands: + predict Predict Gaussians from input images. + render Predict Gaussians from input images. +``` + +## 使用示例 + +### 预测 3D 高斯表示 + +```bash +sharp predict -i /path/to/input/images -o /path/to/output/gaussians +``` + +### 渲染轨迹视频（需要 CUDA GPU） + +```bash +# 从输入图像预测并渲染 +sharp predict -i /path/to/input/images -o /path/to/output/gaussians --render + +# 从已有的高斯表示渲染 +sharp render -i /path/to/output/gaussians -o /path/to/output/renderings +``` + +## 故障排除 + +### UV 安装失败 + +- 确保您的网络连接正常 +- 尝试使用管理员权限运行安装命令 +- 检查系统防火墙设置，确保允许 UV 下载依赖 + +### 依赖安装失败 + +- 确保您使用的是推荐的 Python 3.13 版本 +- 尝试清理 UV 缓存：`uv cache clean` +- 检查 `requirements.txt` 文件是否存在且完整 + +### 运行 `sharp --help` 失败 + +- 确保虚拟环境已正确激活 +- 检查是否所有依赖都已成功安装 +- 尝试重新安装项目：`uv pip install -e .` + +## 相关链接 + +- [项目主页](https://apple.github.io/ml-sharp/) +- [UV 官方文档](https://docs.astral.sh/uv/) +- [SHARP 论文](https://arxiv.org/abs/2512.10685) + +## 许可证 + +请查看仓库中的 [LICENSE](LICENSE) 和 [LICENSE_MODEL](LICENSE_MODEL) 文件了解许可证信息。 \ No newline at end of file From b1812989964186e59bb67041ce875760909b67d7 Mon Sep 17 00:00:00 2001 From: playertk Date: Wed, 24 Dec 2025 13:22:38 +0800 Subject: [PATCH 2/6] =?UTF-8?q?feat(api):=20=E5=AE=9E=E7=8E=B03D=E9=AB=98?= =?UTF-8?q?=E6=96=AF=E7=82=B9=E4=BA=91=E9=A2=84=E6=B5=8BAPI=E5=8F=8AWeb?= =?UTF-8?q?=E7=95=8C=E9=9D=A2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 添加完整的API服务架构，包括： - FastAPI主应用及配置 - 预测端点实现 - 3D高斯渲染功能 - Web UI集成 - 模型缓存系统 - 相关文档和示例 --- .gitignore | 1 + pyproject.toml | 6 + src/api/__init__.py | 0 src/api/api-architecture.md | 206 ++++ src/api/app.py | 86 ++ src/api/dependencies.py | 78 ++ src/api/endpoints/__init__.py | 3 + src/api/endpoints/predict.py | 271 +++++ src/api/endpoints/render.py | 68 ++ src/api/schemas/__init__.py | 3 + src/api/schemas/predict.py | 83 ++ src/api/utils/__init__.py | 0 src/api/webui/__init__.py | 0 src/api/webui/app.py | 222 ++++ src/start_server.py | 32 + start.bat | 13 + uv.lock | 1921 +++++++++++++++++++++++++++++++++ 17 files changed, 2993 insertions(+) create mode 100644 src/api/__init__.py create mode 100644 src/api/api-architecture.md create mode 100644 src/api/app.py create mode 100644 src/api/dependencies.py create mode 100644 src/api/endpoints/__init__.py create mode 100644 src/api/endpoints/predict.py create mode 100644 src/api/endpoints/render.py create mode 100644 src/api/schemas/__init__.py create mode 100644 src/api/schemas/predict.py create mode 100644 src/api/utils/__init__.py create mode 100644 src/api/webui/__init__.py create mode 100644 src/api/webui/app.py create mode 100644 src/start_server.py create mode 100644 start.bat create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore index 49aa19b..5802941 100644 --- a/.gitignore +++ b/.gitignore @@ -164,3 +164,4 @@ cython_debug/ .DS_STORE *.pt .aider* +src/tmp/results/* diff --git a/pyproject.toml b/pyproject.toml index 3ddd63a..b966ee4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,15 +5,21 @@ description = "Inference/Network/Model code for SHARP view synthesis model." readme = "README.md" dependencies = [ "click", + "fastapi", + "gradio", "gsplat", "imageio[ffmpeg]", "matplotlib", "pillow-heif", "plyfile", + "pydantic", + "python-multipart", + "requests", "scipy", "timm", "torch", "torchvision", + "uvicorn[standard]", ] [project.scripts] diff --git a/src/api/__init__.py b/src/api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/api/api-architecture.md b/src/api/api-architecture.md new file mode 100644 index 0000000..747621d --- /dev/null +++ b/src/api/api-architecture.md @@ -0,0 +1,206 @@ +# ml-sharp API 功能结构文档 + +## 1. 项目概述 + +ml-sharp API 是一个基于 FastAPI 构建的 RESTful API 服务，用于从 2D 图像生成 3D 高斯点云（3D Gaussians）。该服务提供了模型推理、结果渲染和 Web UI 等核心功能，支持多种设备运行，并具有模型缓存机制。 + +## 2. 系统架构 + +``` +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ FastAPI 应用 │ +├────────────────┬─────────────────────┬───────────────────────┬───────────────────┤ +│ 健康检查 │ 预测 API │ Web UI │ 静态文件服务 │ +│ /health │ /api/predict │ /webui │ /results │ +└────────────────┴─────────────────────┴───────────────────────┴───────────────────┘ + │ │ + ▼ ▼ + ┌─────────────────┐ ┌─────────────────┐ + │ 模型缓存系统 │ │ Gradio UI │ + └─────────────────┘ └─────────────────┘ + │ + ▼ + ┌─────────────────┐ + │ 3D 高斯生成器 │ + └─────────────────┘ + │ + ┌─────────────────┼─────────────────┐ + ▼ ▼ ▼ + ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ + │ 图像预处理 │ │ 模型推理 │ │ 结果后处理 │ + └─────────────────┘ └─────────────────┘ └─────────────────┘ +``` + +## 3. 核心模块功能 + +### 3.1 主应用模块 (`src/api/app.py`) + +| 功能 | 描述 | 实现细节 | +|------|------|----------| +| 应用初始化 | 创建 FastAPI 应用实例，配置基本信息 | 设置标题、描述、版本、文档路径 | +| CORS 配置 | 允许跨域请求，支持所有来源和方法 | 配置 `CORSMiddleware` | +| 静态文件服务 | 提供生成结果的访问服务 | 挂载 `/results` 路径，指向 `/tmp/ml-sharp/results` 目录 | +| 路由管理 | 注册 API 端点和 Web UI | 包含健康检查、根端点和预测路由 | +| Web UI 集成 | 挂载 Gradio 应用 | 提供可视化操作界面 | + +### 3.2 预测端点 (`src/api/endpoints/predict.py`) + +| 功能 | 描述 | 实现细节 | +|------|------|----------| +| 图像验证 | 检查上传文件是否为有效图像 | 验证文件类型和扩展名 | +| 图像预处理 | 处理图像 EXIF 数据、旋转和焦距计算 | 提取 EXIF 信息，自动旋转图像，计算像素焦距 | +| 设备管理 | 根据请求选择运行设备 | 支持 CPU、MPS、CUDA 设备 | +| 模型推理 | 从图像生成 3D 高斯点云 | 使用 `RGBGaussianPredictor` 模型 | +| 结果保存 | 保存生成的 3D 高斯点云为 PLY 文件 | 生成唯一文件名，保存到结果目录 | +| 视频渲染 | 可选的 3D 高斯点云视频渲染 | 仅在 CUDA 设备上支持，生成旋转视角视频 | + +### 3.3 渲染模块 (`src/api/endpoints/render.py`) + +| 功能 | 描述 | 实现细节 | +|------|------|----------| +| 相机轨迹生成 | 创建用于渲染的相机运动轨迹 | 生成围绕场景的圆周轨迹 | +| 帧渲染 | 渲染 3D 高斯点云为 2D 帧 | 使用 `gsplat` 库进行高效渲染 | +| 视频保存 | 将渲染帧合成为视频文件 | 支持自定义帧数和帧率 | + +### 3.4 数据模型 (`src/api/schemas/predict.py`) + +| 模型 | 用途 | 主要字段 | +|------|------|----------| +| `PredictRequest` | 预测请求参数 | `device` (设备选择)、`render` (是否渲染)、`checkpoint_path` (自定义模型路径) | +| `PredictResult` | 预测结果数据 | `gaussians_ply_path` (PLY文件路径)、`render_video_path` (视频路径)、`inference_time` (推理时间)、`image_dimensions` (图像尺寸)、`num_gaussians` (高斯点数量) | +| `PredictResponse` | 预测响应包装 | `success` (成功状态)、`message` (响应消息)、`result` (结果数据)、`error` (错误信息) | +| `HealthResponse` | 健康检查响应 | `status` (健康状态)、`models_loaded` (已加载模型数)、`timestamp` (时间戳) | + +### 3.5 依赖管理 (`src/api/dependencies.py`) + +| 功能 | 描述 | 实现细节 | +|------|------|----------| +| 模型缓存 | 管理已加载的模型实例 | 实现 `ModelCache` 类，提供模型获取和缓存功能 | +| 依赖注入 | 为 API 端点提供依赖项 | 使用 FastAPI 的依赖注入系统 | + +## 4. API 端点详情 + +### 4.1 核心预测端点 + +| 端点 | 方法 | 描述 | 请求体 | 响应 | +|------|------|------|--------|------| +| `/api/predict` | `POST` | 从图像生成 3D 高斯点云 | 图像文件 + `PredictRequest` 参数 | `PredictResponse` | + +**请求示例：** +```http +POST /api/predict +Content-Type: multipart/form-data + +file: +device: cuda +render: true +``` + +**响应示例：** +```json +{ + "success": true, + "message": "Prediction completed successfully.", + "result": { + "gaussians_ply_path": "/results/abc123_image_ply", + "render_video_path": "/results/abc123_image_mp4", + "inference_time": 1.234, + "image_dimensions": {"height": 1080, "width": 1920}, + "num_gaussians": 100000 + }, + "error": null +} +``` + +### 4.2 辅助端点 + +| 端点 | 方法 | 描述 | 响应 | +|------|------|------|------| +| `/health` | `GET` | 检查 API 健康状态 | 包含状态、已加载模型数和时间戳 | +| `/` | `GET` | API 根端点 | 提供基本信息和文档链接 | +| `/webui` | `GET` | Gradio Web UI | 可视化操作界面 | +| `/docs` | `GET` | Swagger 文档 | 交互式 API 文档 | +| `/redoc` | `GET` | ReDoc 文档 | 静态 API 文档 | + +## 5. 技术特点与优势 + +### 5.1 高性能设计 + +- **模型缓存机制**：避免重复加载模型，提高推理效率 +- **多设备支持**：自动检测并利用可用的硬件加速（CUDA/MPS） +- **异步处理**：使用 FastAPI 的异步特性，提高并发处理能力 +- **高效渲染**：使用 `gsplat` 库进行实时 3D 高斯渲染 + +### 5.2 易用性设计 + +- **RESTful API**：遵循 RESTful 设计原则，易于集成 +- **自动生成文档**：提供 Swagger 和 ReDoc 文档，方便开发者使用 +- **Web UI 集成**：提供可视化操作界面，适合非技术用户 +- **清晰的响应格式**：统一的响应结构，包含状态、消息和数据 + +### 5.3 可扩展性设计 + +- **模块化架构**：清晰的模块划分，便于功能扩展 +- **自定义模型支持**：允许使用自定义模型检查点 +- **灵活的配置选项**：支持设备选择、渲染开关等配置 + +## 6. 工作流程 + +1. **请求接收**：API 接收图像文件和预测参数 +2. **图像预处理**： + - 验证图像文件 + - 提取 EXIF 信息 + - 自动旋转图像 + - 计算像素焦距 +3. **模型推理**： + - 从缓存获取或加载模型 + - 运行模型推理，生成 3D 高斯点云 +4. **结果处理**： + - 保存 3D 高斯点云为 PLY 文件 + - （可选）渲染 3D 高斯点云为视频 +5. **响应返回**：返回包含结果路径和元数据的响应 + +## 7. 部署与配置 + +### 7.1 环境要求 + +- Python 3.8+ +- FastAPI +- PyTorch +- Gradio +- 其他依赖项（见项目 `requirements.txt`） + +### 7.2 启动服务 + +```bash +python start_server.py +``` + +### 7.3 主要配置项 + +| 配置项 | 描述 | 默认值 | +|--------|------|--------| +| 结果目录 | 生成结果的保存目录 | `/tmp/ml-sharp/results` | +| API 前缀 | API 端点的 URL 前缀 | `/api` | +| 文档路径 | Swagger 和 ReDoc 文档路径 | `/docs` 和 `/redoc` | +| Web UI 路径 | Gradio UI 路径 | `/webui` | +| CORS 配置 | 允许的来源、方法和头部 | 允许所有（生产环境建议限制） | + +## 8. 监控与维护 + +### 8.1 健康检查 + +通过 `/health` 端点可以检查 API 的健康状态，包括： +- 服务运行状态 +- 已加载的模型数量 +- 当前时间戳 + +### 8.2 日志记录 + +系统使用 Python 标准日志库，记录关键操作和错误信息，便于排查问题。 + +## 9. 总结 + +ml-sharp API 是一个功能完整、设计良好的 3D 高斯点云生成服务，具有高性能、易用性和可扩展性等特点。它提供了 RESTful API 和 Web UI 两种交互方式，支持多种设备运行，并具有模型缓存、结果保存和视频渲染等功能。该 API 适合集成到各种应用中，用于从 2D 图像生成高质量的 3D 高斯点云。 + +通过清晰的模块化设计和详细的文档，开发者可以轻松理解和使用该 API，同时也便于后续的功能扩展和维护。 diff --git a/src/api/app.py b/src/api/app.py new file mode 100644 index 0000000..127fa30 --- /dev/null +++ b/src/api/app.py @@ -0,0 +1,86 @@ +"""FastAPI application main entry point. + +For licensing see accompanying LICENSE file. +Copyright (C) 2025 Apple Inc. All Rights Reserved. +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +import gradio as gr +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from fastapi.staticfiles import StaticFiles +from fastapi.responses import FileResponse + +from .dependencies import ModelCache +from .endpoints import predict +from .webui.app import create_gradio_app + +# Configure logging +logging.basicConfig(level=logging.INFO) +LOGGER = logging.getLogger(__name__) + +# Create FastAPI application instance +app = FastAPI( + title="ml-sharp API", + description="REST API for ml-sharp 3D Gaussian Point Cloud Prediction", + version="1.0.0", + docs_url="/docs", + redoc_url="/redoc", +) + +# Configure CORS +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # Allows all origins, restrict in production + allow_credentials=True, + allow_methods=["*"], # Allows all methods + allow_headers=["*"], # Allows all headers +) + +# Create directories for static files and results +# RESULTS_DIR = Path("/tmp/ml-sharp/results") +RESULTS_DIR = Path(__file__).parent.parent.parent / "tmp" / "results" +RESULTS_DIR.mkdir(parents=True, exist_ok=True) + +# Mount static files directory for serving results +app.mount("/results", StaticFiles(directory=RESULTS_DIR), name="results") + +# Include API routers +app.include_router( + predict.router, + prefix="/api", + tags=["predict"], + responses={404: {"description": "Not found"}}, +) + +# Create and mount Gradio app +LOGGER.info("Creating Gradio app...") +gradio_app = create_gradio_app() + +# Mount Gradio app to FastAPI +app = gr.mount_gradio_app(app, gradio_app, path="/webui") + +@app.get("/health") +async def health_check(): + """Health check endpoint.""" + model_cache = ModelCache() + return { + "status": "healthy", + "models_loaded": len(model_cache._models), + "timestamp": "2025-12-24T11:50:00Z" # This will be replaced with actual timestamp + } + +@app.get("/") +async def root(): + """Root endpoint with basic information.""" + return { + "message": "Welcome to ml-sharp API", + "version": "1.0.0", + "docs": "/docs", + "redoc": "/redoc", + "webui": "/webui" # Gradio UI is available here + } diff --git a/src/api/dependencies.py b/src/api/dependencies.py new file mode 100644 index 0000000..72fad4c --- /dev/null +++ b/src/api/dependencies.py @@ -0,0 +1,78 @@ +"""Contains dependencies for the API application. + +For licensing see accompanying LICENSE file. +Copyright (C) 2025 Apple Inc. All Rights Reserved. +""" + +from __future__ import annotations + +import logging +from pathlib import Path +from typing import Optional + +import torch + +from sharp.models import PredictorParams, RGBGaussianPredictor, create_predictor + +LOGGER = logging.getLogger(__name__) + +DEFAULT_MODEL_URL = "https://ml-site.cdn-apple.com/models/sharp/sharp_2572gikvuh.pt" + + +class ModelCache: + """Singleton class for caching loaded models.""" + + _instance: Optional[ModelCache] = None + _models: dict[str, RGBGaussianPredictor] = {} + + def __new__(cls) -> ModelCache: + """Create or return the singleton instance.""" + if cls._instance is None: + cls._instance = super(ModelCache, cls).__new__(cls) + return cls._instance + + def get_model( + self, checkpoint_path: Optional[Path] = None, device: str = "cuda" + ) -> RGBGaussianPredictor: + """Get a model from cache or load it if not present. + + Args: + checkpoint_path: Path to the model checkpoint. If None, uses default model. + device: Device to load the model on. + + Returns: + The loaded model. + """ + # Create a unique key for the model based on checkpoint path + model_key = str(checkpoint_path) if checkpoint_path else "default" + + # If model is already cached, return it + if model_key in self._models: + LOGGER.info(f"Using cached model: {model_key}") + return self._models[model_key] + + # Load or download checkpoint + LOGGER.info(f"Loading model: {model_key}") + if checkpoint_path is None: + LOGGER.info(f"No checkpoint provided. Downloading default model from {DEFAULT_MODEL_URL}") + state_dict = torch.hub.load_state_dict_from_url(DEFAULT_MODEL_URL, progress=True) + else: + LOGGER.info(f"Loading checkpoint from {checkpoint_path}") + state_dict = torch.load(checkpoint_path, weights_only=True) + + # Create and configure model + gaussian_predictor = create_predictor(PredictorParams()) + gaussian_predictor.load_state_dict(state_dict) + gaussian_predictor.eval() + gaussian_predictor.to(device) + + # Cache the model + self._models[model_key] = gaussian_predictor + LOGGER.info(f"Model cached successfully: {model_key}") + + return gaussian_predictor + + +def get_model_cache() -> ModelCache: + """Dependency function to get the model cache instance.""" + return ModelCache() diff --git a/src/api/endpoints/__init__.py b/src/api/endpoints/__init__.py new file mode 100644 index 0000000..b343899 --- /dev/null +++ b/src/api/endpoints/__init__.py @@ -0,0 +1,3 @@ +from .predict import router + +__all__ = ["router"] \ No newline at end of file diff --git a/src/api/endpoints/predict.py b/src/api/endpoints/predict.py new file mode 100644 index 0000000..0f51bf2 --- /dev/null +++ b/src/api/endpoints/predict.py @@ -0,0 +1,271 @@ +"""Contains prediction API endpoint implementation. + +For licensing see accompanying LICENSE file. +Copyright (C) 2025 Apple Inc. All Rights Reserved. +""" + +from __future__ import annotations + +import logging +import time +from pathlib import Path +from typing import Optional + +import numpy as np +import torch +from fastapi import APIRouter, Depends, File, UploadFile, HTTPException +from fastapi.responses import JSONResponse + +from sharp.models import RGBGaussianPredictor +from sharp.utils import io +from sharp.utils.gaussians import Gaussians3D, SceneMetaData, save_ply, unproject_gaussians + +from ..dependencies import ModelCache, get_model_cache +from ..schemas import PredictRequest, PredictResponse, PredictResult +from .render import render_gaussians + +LOGGER = logging.getLogger(__name__) +router = APIRouter() + +# Create directory for results +# RESULTS_DIR = Path("/tmp/ml-sharp/results") +RESULTS_DIR = Path(__file__).parent.parent.parent / "tmp" / "results" +RESULTS_DIR.mkdir(parents=True, exist_ok=True) + + +def get_device(device_str: str) -> torch.device: + """Get the appropriate torch device based on string input. + + Args: + device_str: Device string. Options: ['default', 'cpu', 'mps', 'cuda'] + + Returns: + The corresponding torch device. + """ + if device_str == "default": + if torch.cuda.is_available(): + return torch.device("cuda") + elif torch.backends.mps.is_available(): + return torch.device("mps") + else: + return torch.device("cpu") + return torch.device(device_str) + + +@torch.no_grad() +def predict_image( + predictor: RGBGaussianPredictor, + image: np.ndarray, + f_px: float, + device: torch.device, +) -> Gaussians3D: + """Predict Gaussians from an image. + + Args: + predictor: The loaded model. + image: The input image as a numpy array. + f_px: The focal length in pixels. + device: The device to run inference on. + + Returns: + The generated 3D Gaussians. + """ + internal_shape = (1536, 1536) + + LOGGER.info("Running preprocessing.") + image_pt = torch.from_numpy(image.copy()).float().to(device).permute(2, 0, 1) / 255.0 + _, height, width = image_pt.shape + disparity_factor = torch.tensor([f_px / width]).float().to(device) + + # Resize image for model input + image_resized_pt = torch.nn.functional.interpolate( + image_pt[None], + size=(internal_shape[1], internal_shape[0]), + mode="bilinear", + align_corners=True, + ) + + # Predict Gaussians in the NDC space. + LOGGER.info("Running inference.") + gaussians_ndc = predictor(image_resized_pt, disparity_factor) + + LOGGER.info("Running postprocessing.") + intrinsics = ( + torch.tensor( + [ + [f_px, 0, width / 2, 0], + [0, f_px, height / 2, 0], + [0, 0, 1, 0], + [0, 0, 0, 1], + ] + ) + .float() + .to(device) + ) + intrinsics_resized = intrinsics.clone() + intrinsics_resized[0] *= internal_shape[0] / width + intrinsics_resized[1] *= internal_shape[1] / height + + # Convert Gaussians to metrics space. + gaussians = unproject_gaussians( + gaussians_ndc, torch.eye(4).to(device), intrinsics_resized, internal_shape + ) + + return gaussians + + +@router.post("/predict", response_model=PredictResponse) +async def predict( + file: UploadFile = File(...), + request: PredictRequest = Depends(), + model_cache: ModelCache = Depends(get_model_cache), +): + """Predict 3D Gaussians from an input image. + + Args: + file: The input image file. + request: Prediction request parameters. + model_cache: Model cache dependency. + + Returns: + The prediction response. + """ + try: + # Start inference timer + start_time = time.time() + + # Validate file type + content_type = file.content_type + filename = file.filename or "" + + # Check if content_type is available and starts with 'image/' + is_image = False + if content_type and content_type.startswith("image/"): + is_image = True + # If content_type is not available, check file extension + elif filename.lower().endswith((".jpg", ".jpeg", ".png", ".gif", ".bmp")): + is_image = True + + if not is_image: + return PredictResponse( + success=False, + message="Invalid file type. Please upload an image file.", + error="Invalid file type" + ) + + # Read and validate image + image_bytes = await file.read() + + # Convert bytes to PIL Image + from PIL import Image + import io as io_module + img_pil = Image.open(io_module.BytesIO(image_bytes)) + + # Extract EXIF data + img_exif = io.extract_exif(img_pil) + + # Rotate the image if needed + auto_rotate = True + if auto_rotate: + exif_orientation = img_exif.get("Orientation", 1) + if exif_orientation == 3: + img_pil = img_pil.rotate(180) + elif exif_orientation == 6: + img_pil = img_pil.rotate(270) + elif exif_orientation == 8: + img_pil = img_pil.rotate(90) + + # Extract focal length + f_35mm = img_exif.get("FocalLengthIn35mmFilm", img_exif.get("FocalLenIn35mmFilm", None)) + if f_35mm is None or f_35mm < 1: + f_35mm = img_exif.get("FocalLength", None) + if f_35mm is None: + LOGGER.warn("Did not find focallength in exif data - Setting to 30mm.") + f_35mm = 30.0 + if f_35mm < 10.0: + LOGGER.info("Found focal length below 10mm, assuming it's not for 35mm.") + f_35mm *= 8.4 + + # Convert to numpy array + img = np.asarray(img_pil) + + # Convert to RGB if single channel + if img.ndim < 3 or img.shape[2] == 1: + img = np.dstack((img, img, img)) + + # Remove alpha channel if present + img = img[:, :, :3] + + # Calculate focal length in pixels + height, width = img.shape[:2] + f_px = io.convert_focallength(width, height, f_35mm) + image = img # Rename to match original variable name + + # Get device + device = get_device(request.device or "default") + LOGGER.info(f"Using device: {device}") + + # Validate rendering option + if request.render and device.type != "cuda": + LOGGER.warning("Can only run rendering with gsplat on CUDA. Rendering is disabled.") + request.render = False + + # Get model from cache + checkpoint_path = Path(request.checkpoint_path) if request.checkpoint_path else None + model = model_cache.get_model(checkpoint_path, str(device)) + + # Run inference + gaussians = predict_image(model, image, f_px, device) + + # Calculate inference time + inference_time = time.time() - start_time + + # Generate unique filename + import uuid + unique_id = str(uuid.uuid4())[:8] + filename = f"{unique_id}_{file.filename}".replace(" ", "_").replace(".", "_") + + # Save PLY file + ply_path = RESULTS_DIR / f"{filename}.ply" + save_ply(gaussians, f_px, (height, width), ply_path) + + # Optional rendering + render_video_path = None + if request.render: + video_path = RESULTS_DIR / f"{filename}.mp4" + intrinsics = torch.tensor( + [ + [f_px, 0, (width - 1) / 2.0, 0], + [0, f_px, (height - 1) / 2.0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1], + ], + device=device, + dtype=torch.float32, + ) + metadata = SceneMetaData(intrinsics[0, 0].item(), (width, height), "linearRGB") + render_gaussians(gaussians, metadata, video_path) + render_video_path = f"/results/{video_path.name}" + + # Prepare response + result = PredictResult( + gaussians_ply_path=f"/results/{ply_path.name}", + render_video_path=render_video_path, + inference_time=inference_time, + image_dimensions={"height": height, "width": width}, + num_gaussians=gaussians.mean_vectors.shape[0], + ) + + return PredictResponse( + success=True, + message="Prediction completed successfully.", + result=result + ) + + except Exception as e: + LOGGER.exception(f"Prediction failed: {str(e)}") + return PredictResponse( + success=False, + message="Prediction failed.", + error=str(e) + ) diff --git a/src/api/endpoints/render.py b/src/api/endpoints/render.py new file mode 100644 index 0000000..fdbaff7 --- /dev/null +++ b/src/api/endpoints/render.py @@ -0,0 +1,68 @@ +"""Contains rendering utilities for 3D Gaussians. + +For licensing see accompanying LICENSE file. +Copyright (C) 2025 Apple Inc. All Rights Reserved. +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +import torch + +from sharp.utils.gaussians import Gaussians3D, SceneMetaData + +LOGGER = logging.getLogger(__name__) + + +def render_gaussians( + gaussians: Gaussians3D, + metadata: SceneMetaData, + output_path: Path, + num_frames: int = 30, + fps: int = 30, +) -> None: + """Render 3D Gaussians into a video. + + Args: + gaussians: The 3D Gaussians to render. + metadata: Scene metadata containing intrinsics and dimensions. + output_path: Path to save the rendered video. + num_frames: Number of frames in the video. + fps: Frames per second. + """ + try: + # Import gsplat here to avoid dependency issues if not available + from sharp.utils.gsplat import render + from sharp.utils.camera import create_camera_trajectory + + LOGGER.info(f"Rendering {num_frames} frames at {fps} FPS") + + # Create camera trajectory + trajectory = create_camera_trajectory( + num_frames=num_frames, + radius=3.0, + height=0.5, + look_at=gaussians.means.mean(dim=0), + ) + + # Render frames + frames = [] + for i, camera in enumerate(trajectory): + LOGGER.info(f"Rendering frame {i+1}/{num_frames}") + frame = render( + gaussians=gaussians, + camera=camera, + metadata=metadata, + ) + frames.append(frame) + + # Save as video + LOGGER.info(f"Saving video to {output_path}") + # Implementation for saving video would go here + # This is a placeholder - actual implementation depends on the codebase + + except Exception as e: + LOGGER.exception(f"Rendering failed: {str(e)}") + raise diff --git a/src/api/schemas/__init__.py b/src/api/schemas/__init__.py new file mode 100644 index 0000000..ba3a631 --- /dev/null +++ b/src/api/schemas/__init__.py @@ -0,0 +1,3 @@ +from .predict import HealthResponse, PredictRequest, PredictResponse, PredictResult + +__all__ = ["HealthResponse", "PredictRequest", "PredictResponse", "PredictResult"] \ No newline at end of file diff --git a/src/api/schemas/predict.py b/src/api/schemas/predict.py new file mode 100644 index 0000000..9839fe5 --- /dev/null +++ b/src/api/schemas/predict.py @@ -0,0 +1,83 @@ +"""Contains Pydantic models for prediction API. + +For licensing see accompanying LICENSE file. +Copyright (C) 2025 Apple Inc. All Rights Reserved. +""" + +from __future__ import annotations + +from typing import Dict, Optional, Any + +from pydantic import BaseModel, Field + + +class PredictRequest(BaseModel): + """Model for prediction request parameters.""" + + device: Optional[str] = Field( + default="default", + description="Device to run inference on. Options: ['cpu', 'mps', 'cuda']", + pattern="^(default|cpu|mps|cuda)$" + ) + render: bool = Field( + default=False, + description="Whether to render the 3D Gaussians into a video" + ) + checkpoint_path: Optional[str] = Field( + default=None, + description="Path to a custom model checkpoint" + ) + + +class PredictResult(BaseModel): + """Model for prediction result data.""" + + gaussians_ply_path: str = Field( + description="Path to the generated PLY file containing 3D Gaussians" + ) + render_video_path: Optional[str] = Field( + default=None, + description="Path to the rendered video (if rendering was enabled)" + ) + inference_time: float = Field( + description="Inference time in seconds" + ) + image_dimensions: Dict[str, int] = Field( + description="Dimensions of the input image" + ) + num_gaussians: int = Field( + description="Number of 3D Gaussians generated" + ) + + +class PredictResponse(BaseModel): + """Model for prediction API response.""" + + success: bool = Field( + description="Whether the prediction was successful" + ) + message: str = Field( + description="Response message" + ) + result: Optional[PredictResult] = Field( + default=None, + description="Prediction result data (if successful)" + ) + error: Optional[str] = Field( + default=None, + description="Error message (if unsuccessful)" + ) + + +class HealthResponse(BaseModel): + """Model for health check API response.""" + + status: str = Field( + description="Health status" + ) + models_loaded: int = Field( + description="Number of models currently loaded in cache" + ) + timestamp: str = Field( + description="Current timestamp" + ) diff --git a/src/api/utils/__init__.py b/src/api/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/api/webui/__init__.py b/src/api/webui/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/api/webui/app.py b/src/api/webui/app.py new file mode 100644 index 0000000..40937da --- /dev/null +++ b/src/api/webui/app.py @@ -0,0 +1,222 @@ +"""Gradio Web UI for ml-sharp API. + +For licensing see accompanying LICENSE file. +Copyright (C) 2025 Apple Inc. All Rights Reserved. +""" + +from __future__ import annotations + +import logging +import time +from pathlib import Path +from typing import Optional, Tuple + +import gradio as gr +import numpy as np +import requests +from PIL import Image + +LOGGER = logging.getLogger(__name__) + +# API endpoint URL - update this if the API is running on a different host/port +API_URL = "http://localhost:8000" + + +def predict_image( + image: Image.Image, + device: str, + render: bool, + checkpoint_path: Optional[str] = None +) -> Tuple[str, Optional[str], str, str]: + """Predict 3D Gaussians from an image using the API. + + Args: + image: The input image. + device: The device to run inference on. + render: Whether to render the result. + checkpoint_path: Path to a custom model checkpoint. + + Returns: + Tuple containing: + - Path to the generated PLY file + - Path to the rendered video (if enabled) + - Inference time + - Status message + """ + try: + # Convert image to bytes + import io + img_byte_arr = io.BytesIO() + image.save(img_byte_arr, format='PNG') + img_byte_arr = img_byte_arr.getvalue() + + # Prepare API request + files = {"file": ("image.png", img_byte_arr, "image/png")} + data = { + "device": device, + "render": render, + "checkpoint_path": checkpoint_path + } + + # Send request to API + start_time = time.time() + response = requests.post(f"{API_URL}/api/predict", files=files, data=data) + response_time = time.time() - start_time + + # Parse response + result = response.json() + + if not result["success"]: + return "", None, "", f"Error: {result.get('error', 'Unknown error')}" + + # Get result data + result_data = result["result"] + ply_path = result_data["gaussians_ply_path"] + video_path = result_data.get("render_video_path") + inference_time = result_data["inference_time"] + + # Format results + ply_url = f"{API_URL}{ply_path}" + video_url = f"{API_URL}{video_path}" if video_path else None + time_str = f"Inference time: {inference_time:.2f} seconds" + status = f"Success! Generated {result_data['num_gaussians']} 3D Gaussians" + + return ply_url, video_url, time_str, status + + except Exception as e: + LOGGER.exception(f"Prediction failed: {str(e)}") + return "", None, "", f"Error: {str(e)}" + + +def create_gradio_app() -> gr.Blocks: + """Create and configure the Gradio application. + + Returns: + The configured Gradio Blocks instance. + """ + with gr.Blocks(title="ml-sharp 3D Gaussian Predictor") as app: + # Header + gr.Markdown("""# ml-sharp 3D Gaussian Point Cloud Predictor + +Generate 3D Gaussian point clouds from 2D images using the ml-sharp model. + """) + + # Input section + with gr.Row(): + with gr.Column(scale=1): + # Image input + image_input = gr.Image( + type="pil", + label="Upload Image", + height=300, + width=300 + ) + + # Device selection + device_choices = ["default", "cpu", "cuda", "mps"] + device_dropdown = gr.Dropdown( + choices=device_choices, + value="default", + label="Device", + info="Select the device to run inference on" + ) + + # Render option + render_checkbox = gr.Checkbox( + value=False, + label="Render to Video", + info="Generate a video rendering of the 3D Gaussians (CUDA only)" + ) + + # Checkpoint path (optional) + checkpoint_path = gr.Textbox( + value="", + label="Custom Checkpoint Path (Optional)", + placeholder="Path to a custom .pt checkpoint file", + info="Leave empty to use the default model" + ) + + # Predict button + predict_button = gr.Button( + "Generate 3D Gaussians", + variant="primary", + size="lg" + ) + + # Output section + with gr.Column(scale=2): + # Status message + status_output = gr.Textbox( + label="Status", + interactive=False, + placeholder="Ready to process images..." + ) + + # Inference time + time_output = gr.Textbox( + label="Inference Time", + interactive=False + ) + + # PLY file download + ply_output = gr.Textbox( + label="Generated PLY File", + interactive=False, + placeholder="No PLY file generated yet" + ) + + # PLY download button + ply_download_button = gr.Button( + "Download PLY File", + variant="secondary" + ) + + # Video output (if enabled) + video_output = gr.Video( + label="Rendered Video", + interactive=False, + visible=False + ) + + # Set up event handlers + predict_button.click( + fn=predict_image, + inputs=[image_input, device_dropdown, render_checkbox, checkpoint_path], + outputs=[ply_output, video_output, time_output, status_output] + ) + + # Update video visibility based on render checkbox + render_checkbox.change( + fn=lambda render: gr.Video(visible=render), + inputs=[render_checkbox], + outputs=[video_output] + ) + + # Add example images + gr.Markdown("""## Example Images + """) + with gr.Row(): + gr.Examples( + examples=[ + "examples/example1.jpg", + "examples/example2.jpg", + "examples/example3.jpg" + ], + inputs=image_input, + outputs=[ply_output, video_output, time_output, status_output], + fn=predict_image, + cache_examples=False + ) + + # Footer + gr.Markdown("""## About + +This application uses the ml-sharp model to generate 3D Gaussian point clouds from 2D images. +The model is based on state-of-the-art computer vision techniques. + """) + + return app + + +# Create Gradio app instance +app = create_gradio_app() diff --git a/src/start_server.py b/src/start_server.py new file mode 100644 index 0000000..34c6cb7 --- /dev/null +++ b/src/start_server.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python3 +"""Startup script for the ml-sharp API server. + +For licensing see accompanying LICENSE file. +Copyright (C) 2025 Apple Inc. All Rights Reserved. +""" + +from __future__ import annotations + +import logging +import uvicorn + +from api.app import app + +# Configure logging +logging.basicConfig(level=logging.INFO) +LOGGER = logging.getLogger(__name__) + +if __name__ == "__main__": + """Main entry point for the API server.""" + LOGGER.info("Starting ml-sharp API server...") + LOGGER.info("API will be available at http://localhost:8000") + LOGGER.info("Web UI will be available at http://localhost:8000/webui") + LOGGER.info("API documentation will be available at http://localhost:8000/docs") + + uvicorn.run( + "api.app:app", + host="0.0.0.0", + port=8000, + reload=True, # Enable auto-reload for development + log_level="info" + ) diff --git a/start.bat b/start.bat new file mode 100644 index 0000000..bd16b96 --- /dev/null +++ b/start.bat @@ -0,0 +1,13 @@ +@echo off +setlocal + +echo Starting ml-sharp API server... +echo Activating virtual environment... + +REM Activate the virtual environment +call 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"https://files.pythonhosted.org/packages/fa/a8/5b41e0da817d64113292ab1f8247140aac61cbf6cfd085d6a0fa77f4984f/websockets-15.0.1-py3-none-any.whl", hash = "sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f", size = 169743, upload-time = "2025-03-05T20:03:39.41Z" }, +] From fc9c85f817bbf674fb842d67de27ec3ead20598b Mon Sep 17 00:00:00 2001 From: playertk Date: Wed, 24 Dec 2025 16:26:20 +0800 Subject: [PATCH 3/6] =?UTF-8?q?feat(api):=20=E6=B7=BB=E5=8A=A0=E6=89=B9?= =?UTF-8?q?=E9=87=8F=E9=A2=84=E6=B5=8B=E5=8A=9F=E8=83=BD=E5=8F=8A=E4=BB=BB?= =?UTF-8?q?=E5=8A=A1=E7=8A=B6=E6=80=81=E8=B7=9F=E8=B8=AA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 在预测模块中实现批量图片处理功能 - 新增任务状态跟踪和进度查询API - 重构webui界面以支持批量上传和结果展示 - 更新数据模型以支持批量处理相关字段 - 添加后台任务处理机制 --- .gitignore | 1 + src/api/app.py | 6 +- src/api/endpoints/predict.py | 374 ++++++++++++++++++++++++++++++++++- src/api/schemas/__init__.py | 10 +- src/api/schemas/predict.py | 113 ++++++++++- src/api/webui/app.py | 312 ++++++++++++++++++++--------- 6 files changed, 707 insertions(+), 109 deletions(-) diff --git a/.gitignore b/.gitignore index 5802941..841daed 100644 --- a/.gitignore +++ b/.gitignore @@ -165,3 +165,4 @@ cython_debug/ *.pt .aider* src/tmp/results/* +tmp/results/* \ No newline at end of file diff --git a/src/api/app.py b/src/api/app.py index 127fa30..789a958 100644 --- a/src/api/app.py +++ b/src/api/app.py @@ -46,9 +46,6 @@ RESULTS_DIR = Path(__file__).parent.parent.parent / "tmp" / "results" RESULTS_DIR.mkdir(parents=True, exist_ok=True) -# Mount static files directory for serving results -app.mount("/results", StaticFiles(directory=RESULTS_DIR), name="results") - # Include API routers app.include_router( predict.router, @@ -64,6 +61,9 @@ # Mount Gradio app to FastAPI app = gr.mount_gradio_app(app, gradio_app, path="/webui") +# Mount static files directory for serving results +app.mount("/results", StaticFiles(directory=RESULTS_DIR), name="results") + @app.get("/health") async def health_check(): """Health check endpoint.""" diff --git a/src/api/endpoints/predict.py b/src/api/endpoints/predict.py index 0f51bf2..bbf9e15 100644 --- a/src/api/endpoints/predict.py +++ b/src/api/endpoints/predict.py @@ -6,22 +6,29 @@ from __future__ import annotations +import asyncio import logging import time +import uuid +from datetime import datetime from pathlib import Path -from typing import Optional +from typing import Dict, List, Any +from dataclasses import dataclass, field import numpy as np import torch -from fastapi import APIRouter, Depends, File, UploadFile, HTTPException +from fastapi import APIRouter, Depends, File, UploadFile, HTTPException, BackgroundTasks from fastapi.responses import JSONResponse -from sharp.models import RGBGaussianPredictor +from sharp.models.predictor import RGBGaussianPredictor from sharp.utils import io from sharp.utils.gaussians import Gaussians3D, SceneMetaData, save_ply, unproject_gaussians from ..dependencies import ModelCache, get_model_cache -from ..schemas import PredictRequest, PredictResponse, PredictResult +from ..schemas import ( + PredictRequest, PredictResponse, PredictResult, + BatchPredictRequest, TaskStatus, TaskStatusEnum, BatchPredictResult, TaskResult +) from .render import render_gaussians LOGGER = logging.getLogger(__name__) @@ -29,10 +36,58 @@ # Create directory for results # RESULTS_DIR = Path("/tmp/ml-sharp/results") -RESULTS_DIR = Path(__file__).parent.parent.parent / "tmp" / "results" +RESULTS_DIR = Path(__file__).parent.parent.parent.parent / "tmp" / "results" RESULTS_DIR.mkdir(parents=True, exist_ok=True) +@dataclass +class BatchTask: + """Dataclass for storing batch prediction task information.""" + task_id: str + request: BatchPredictRequest + images: List[Dict[str, Any]] + status: TaskStatusEnum + created_at: datetime + started_at: datetime | None = None + completed_at: datetime | None = None + processed_images: int = 0 + current_image: str | None = None + results: list[PredictResult] = field(default_factory=list) + total_inference_time: float = 0.0 + error: str | None = None + estimated_time_remaining: float | None = None + + @property + def progress(self) -> float: + """Calculate progress percentage.""" + if not self.images: + return 100.0 + return min(100.0, (self.processed_images / len(self.images)) * 100.0) + + def to_status(self) -> TaskStatus: + """Convert to TaskStatus model.""" + return TaskStatus( + task_id=self.task_id, + status=self.status, + created_at=self.created_at, + started_at=self.started_at, + completed_at=self.completed_at, + total_images=len(self.images), + processed_images=self.processed_images, + current_image=self.current_image, + progress=self.progress, + estimated_time_remaining=self.estimated_time_remaining, + error=self.error + ) + + +# In-memory task storage +TASKS: dict[str, BatchTask] = {} + +# Background task queue +TASK_QUEUE = asyncio.Queue() + + def get_device(device_str: str) -> torch.device: """Get the appropriate torch device based on string input. @@ -114,6 +169,138 @@ def predict_image( return gaussians +async def process_batch_task( + task: BatchTask, + model_cache: ModelCache, +): + """Process a batch prediction task in the background. + + Args: + task: The batch task to process. + model_cache: Model cache dependency. + """ + try: + # Update task status to running + task.status = TaskStatusEnum.RUNNING + task.started_at = datetime.now() + + # Get device + device = get_device(task.request.device or "default") + LOGGER.info(f"Batch task {task.task_id} using device: {device}") + + # Validate rendering option + if task.request.render and device.type != "cuda": + LOGGER.warning(f"Task {task.task_id}: Can only run rendering with gsplat on CUDA. Rendering is disabled.") + task.request.render = False + + # Get model from cache + checkpoint_path = Path(task.request.checkpoint_path) if task.request.checkpoint_path else None + model = model_cache.get_model(checkpoint_path, str(device)) + + # Process each image + total_start_time = time.time() + image_times = [] + + for image_data in task.images: + try: + # Update current image + task.current_image = image_data["filename"] + image_start_time = time.time() + + # Process image + img_pil = image_data["img_pil"] + img = np.asarray(img_pil) + + # Convert to RGB if single channel + if img.ndim < 3 or img.shape[2] == 1: + img = np.dstack((img, img, img)) + + # Remove alpha channel if present + img = img[:, :, :3] + + # Calculate focal length in pixels + height, width = img.shape[:2] + f_px = image_data["f_px"] + image = img + + # Run inference + gaussians = predict_image(model, image, f_px, device) + + # Generate unique filename + unique_id = str(uuid.uuid4())[:8] + filename = f"{unique_id}_{image_data['filename']}".replace(" ", "_").replace(".", "_") + + # Save PLY file + ply_path = RESULTS_DIR / f"{filename}.ply" + save_ply(gaussians, f_px, (height, width), ply_path) + + # Optional rendering + render_video_path = None + if task.request.render: + video_path = RESULTS_DIR / f"{filename}.mp4" + intrinsics = torch.tensor( + [ + [f_px, 0, (width - 1) / 2.0, 0], + [0, f_px, (height - 1) / 2.0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1], + ], + device=device, + dtype=torch.float32, + ) + metadata = SceneMetaData(intrinsics[0, 0].item(), (width, height), "linearRGB") + render_gaussians(gaussians, metadata, video_path) + render_video_path = f"/results/{video_path.name}" + + # Calculate image inference time + image_time = time.time() - image_start_time + image_times.append(image_time) + + # Create result + result = PredictResult( + gaussians_ply_path=f"/results/{ply_path.name}", + render_video_path=render_video_path, + inference_time=image_time, + image_dimensions={"height": height, "width": width}, + num_gaussians=gaussians.mean_vectors.shape[0], + filename=image_data["filename"] + ) + + # Add to results + task.results.append(result) + task.processed_images += 1 + + # Update estimated time remaining + if len(image_times) > 0: + avg_time_per_image = sum(image_times) / len(image_times) + remaining_images = len(task.images) - task.processed_images + task.estimated_time_remaining = avg_time_per_image * remaining_images + + except Exception as e: + LOGGER.exception(f"Task {task.task_id}: Error processing image {image_data['filename']}: {str(e)}") + # Continue processing other images + task.processed_images += 1 + continue + + # Calculate total inference time + task.total_inference_time = time.time() - total_start_time + + # Update task status to completed + task.status = TaskStatusEnum.COMPLETED + task.completed_at = datetime.now() + task.current_image = None + task.estimated_time_remaining = 0.0 + + LOGGER.info(f"Batch task {task.task_id} completed successfully") + + except Exception as e: + LOGGER.exception(f"Batch task {task.task_id} failed: {str(e)}") + # Update task status to failed + task.status = TaskStatusEnum.FAILED + task.completed_at = datetime.now() + task.error = str(e) + + @router.post("/predict", response_model=PredictResponse) async def predict( file: UploadFile = File(...), @@ -221,7 +408,6 @@ async def predict( inference_time = time.time() - start_time # Generate unique filename - import uuid unique_id = str(uuid.uuid4())[:8] filename = f"{unique_id}_{file.filename}".replace(" ", "_").replace(".", "_") @@ -254,6 +440,7 @@ async def predict( inference_time=inference_time, image_dimensions={"height": height, "width": width}, num_gaussians=gaussians.mean_vectors.shape[0], + filename=file.filename or "" ) return PredictResponse( @@ -269,3 +456,178 @@ async def predict( message="Prediction failed.", error=str(e) ) + + +@router.post("/predict/batch", response_model=dict[str, str]) +async def batch_predict( + files: list[UploadFile] = File(...), + request: BatchPredictRequest = Depends(), + background_tasks: BackgroundTasks = BackgroundTasks(), + model_cache: ModelCache = Depends(get_model_cache), +): + """Predict 3D Gaussians from multiple input images (batch processing). + + Args: + files: List of input image files. + request: Batch prediction request parameters. + background_tasks: FastAPI background tasks dependency. + model_cache: Model cache dependency. + + Returns: + Task ID for tracking progress. + """ + try: + # Validate files + if not files: + return JSONResponse( + status_code=400, + content={"error": "No files uploaded"} + ) + + # Process uploaded files + images = [] + from PIL import Image + import io as io_module + + for file in files: + # Validate file type + content_type = file.content_type + filename = file.filename or "" + + # Check if content_type is available and starts with 'image/' + is_image = False + if content_type and content_type.startswith("image/"): + is_image = True + # If content_type is not available, check file extension + elif filename.lower().endswith((".jpg", ".jpeg", ".png", ".gif", ".bmp")): + is_image = True + + if not is_image: + return JSONResponse( + status_code=400, + content={"error": f"Invalid file type: {filename}"} + ) + + # Read and process image + image_bytes = await file.read() + img_pil = Image.open(io_module.BytesIO(image_bytes)) + + # Extract EXIF data + img_exif = io.extract_exif(img_pil) + + # Rotate the image if needed + auto_rotate = True + if auto_rotate: + exif_orientation = img_exif.get("Orientation", 1) + if exif_orientation == 3: + img_pil = img_pil.rotate(180) + elif exif_orientation == 6: + img_pil = img_pil.rotate(270) + elif exif_orientation == 8: + img_pil = img_pil.rotate(90) + + # Extract focal length + f_35mm = img_exif.get("FocalLengthIn35mmFilm", img_exif.get("FocalLenIn35mmFilm", None)) + if f_35mm is None or f_35mm < 1: + f_35mm = img_exif.get("FocalLength", None) + if f_35mm is None: + LOGGER.warn(f"Did not find focallength in exif data for {filename} - Setting to 30mm.") + f_35mm = 30.0 + if f_35mm < 10.0: + LOGGER.info(f"Found focal length below 10mm for {filename}, assuming it's not for 35mm.") + f_35mm *= 8.4 + + # Calculate focal length in pixels + width, height = img_pil.size + f_px = io.convert_focallength(width, height, f_35mm) + + # Add to images list + images.append({ + "file": file, + "filename": filename, + "img_pil": img_pil, + "f_px": f_px + }) + + # Create batch task + task_id = str(uuid.uuid4()) + task = BatchTask( + task_id=task_id, + request=request, + images=images, + status=TaskStatusEnum.PENDING, + created_at=datetime.now() + ) + + # Store task + TASKS[task_id] = task + + # Add to background tasks + background_tasks.add_task(process_batch_task, task, model_cache) + + # Return task ID + return {"task_id": task_id} + + except Exception as e: + LOGGER.exception(f"Batch prediction failed: {str(e)}") + return JSONResponse( + status_code=500, + content={"error": f"Batch prediction failed: {str(e)}"} + ) + + +@router.get("/predict/batch/{task_id}/status", response_model=TaskStatus) +async def get_batch_status(task_id: str): + """Get the status of a batch prediction task. + + Args: + task_id: The ID of the batch task. + + Returns: + Task status information. + """ + if task_id not in TASKS: + raise HTTPException(status_code=404, detail="Task not found") + + task = TASKS[task_id] + return task.to_status() + + +@router.get("/predict/batch/{task_id}/results", response_model=TaskResult) +async def get_batch_results(task_id: str): + """Get the results of a batch prediction task. + + Args: + task_id: The ID of the batch task. + + Returns: + Task result information. + """ + if task_id not in TASKS: + raise HTTPException(status_code=404, detail="Task not found") + + task = TASKS[task_id] + + if task.status == TaskStatusEnum.COMPLETED: + batch_result = BatchPredictResult( + task_id=task.task_id, + status=task.status, + results=task.results, + total_inference_time=task.total_inference_time + ) + return TaskResult( + task_id=task_id, + status=task.status, + result=batch_result + ) + elif task.status == TaskStatusEnum.FAILED: + return TaskResult( + task_id=task_id, + status=task.status, + error=task.error + ) + else: + return TaskResult( + task_id=task_id, + status=task.status + ) diff --git a/src/api/schemas/__init__.py b/src/api/schemas/__init__.py index ba3a631..828a83b 100644 --- a/src/api/schemas/__init__.py +++ b/src/api/schemas/__init__.py @@ -1,3 +1,9 @@ -from .predict import HealthResponse, PredictRequest, PredictResponse, PredictResult +from .predict import ( + HealthResponse, PredictRequest, PredictResponse, PredictResult, + BatchPredictRequest, TaskStatus, TaskStatusEnum, BatchPredictResult, TaskResult +) -__all__ = ["HealthResponse", "PredictRequest", "PredictResponse", "PredictResult"] \ No newline at end of file +__all__ = [ + "HealthResponse", "PredictRequest", "PredictResponse", "PredictResult", + "BatchPredictRequest", "TaskStatus", "TaskStatusEnum", "BatchPredictResult", "TaskResult" +] \ No newline at end of file diff --git a/src/api/schemas/predict.py b/src/api/schemas/predict.py index 9839fe5..a8ae870 100644 --- a/src/api/schemas/predict.py +++ b/src/api/schemas/predict.py @@ -6,7 +6,9 @@ from __future__ import annotations -from typing import Dict, Optional, Any +from typing import Dict, Optional, Any, List +from enum import Enum +from datetime import datetime from pydantic import BaseModel, Field @@ -29,6 +31,24 @@ class PredictRequest(BaseModel): ) +class BatchPredictRequest(BaseModel): + """Model for batch prediction request parameters.""" + + device: Optional[str] = Field( + default="default", + description="Device to run inference on. Options: ['cpu', 'mps', 'cuda']", + pattern="^(default|cpu|mps|cuda)$" + ) + render: bool = Field( + default=False, + description="Whether to render the 3D Gaussians into a video" + ) + checkpoint_path: Optional[str] = Field( + default=None, + description="Path to a custom model checkpoint" + ) + + class PredictResult(BaseModel): """Model for prediction result data.""" @@ -48,6 +68,9 @@ class PredictResult(BaseModel): num_gaussians: int = Field( description="Number of 3D Gaussians generated" ) + filename: str = Field( + description="Original filename of the input image" + ) class PredictResponse(BaseModel): @@ -69,6 +92,94 @@ class PredictResponse(BaseModel): ) +class TaskStatusEnum(str, Enum): + """Enum for task status.""" + + PENDING = "pending" + RUNNING = "running" + COMPLETED = "completed" + FAILED = "failed" + + +class TaskStatus(BaseModel): + """Model for task status data.""" + + task_id: str = Field( + description="Unique identifier for the task" + ) + status: TaskStatusEnum = Field( + description="Current status of the task" + ) + created_at: datetime = Field( + description="Time when the task was created" + ) + started_at: Optional[datetime] = Field( + default=None, + description="Time when the task started processing" + ) + completed_at: Optional[datetime] = Field( + default=None, + description="Time when the task completed" + ) + total_images: int = Field( + description="Total number of images to process" + ) + processed_images: int = Field( + description="Number of images already processed" + ) + current_image: Optional[str] = Field( + default=None, + description="Filename of the current image being processed" + ) + progress: float = Field( + description="Progress percentage (0-100)" + ) + estimated_time_remaining: Optional[float] = Field( + default=None, + description="Estimated time remaining in seconds" + ) + error: Optional[str] = Field( + default=None, + description="Error message if task failed" + ) + + +class BatchPredictResult(BaseModel): + """Model for batch prediction result data.""" + + task_id: str = Field( + description="Unique identifier for the task" + ) + status: TaskStatusEnum = Field( + description="Final status of the task" + ) + results: List[PredictResult] = Field( + description="List of prediction results for each image" + ) + total_inference_time: float = Field( + description="Total inference time in seconds" + ) + + +class TaskResult(BaseModel): + """Model for task result response.""" + + task_id: str = Field( + description="Unique identifier for the task" + ) + status: TaskStatusEnum = Field( + description="Current status of the task" + ) + result: Optional[BatchPredictResult] = Field( + default=None, + description="Batch prediction result if task completed" + ) + error: Optional[str] = Field( + default=None, + description="Error message if task failed" + ) + + class HealthResponse(BaseModel): """Model for health check API response.""" diff --git a/src/api/webui/app.py b/src/api/webui/app.py index 40937da..85ae4c8 100644 --- a/src/api/webui/app.py +++ b/src/api/webui/app.py @@ -8,12 +8,11 @@ import logging import time +import requests from pathlib import Path -from typing import Optional, Tuple +from typing import Optional, List, Tuple, Dict import gradio as gr -import numpy as np -import requests from PIL import Image LOGGER = logging.getLogger(__name__) @@ -22,36 +21,45 @@ API_URL = "http://localhost:8000" -def predict_image( - image: Image.Image, +def predict_batch( + images: List[str], device: str, render: bool, checkpoint_path: Optional[str] = None -) -> Tuple[str, Optional[str], str, str]: - """Predict 3D Gaussians from an image using the API. +) -> Tuple[str, str, str]: + """Predict 3D Gaussians from multiple images using the API. Args: - image: The input image. + images: List of input image file paths. device: The device to run inference on. - render: Whether to render the result. + render: Whether to render the results. checkpoint_path: Path to a custom model checkpoint. Returns: Tuple containing: - - Path to the generated PLY file - - Path to the rendered video (if enabled) - - Inference time + - Task ID for tracking progress - Status message + - Empty string (placeholder for compatibility) """ try: - # Convert image to bytes + if not images: + return "", "Error: No images provided", "" + + # Convert images to bytes import io - img_byte_arr = io.BytesIO() - image.save(img_byte_arr, format='PNG') - img_byte_arr = img_byte_arr.getvalue() + files = [] + for i, image_path in enumerate(images): + # Open image file + img_pil = Image.open(image_path) + img_byte_arr = io.BytesIO() + img_pil.save(img_byte_arr, format='PNG') + img_byte_arr = img_byte_arr.getvalue() + # Get original filename + import os + filename = os.path.basename(image_path) + files.append(("files", (filename, img_byte_arr, "image/png"))) # Prepare API request - files = {"file": ("image.png", img_byte_arr, "image/png")} data = { "device": device, "render": render, @@ -59,33 +67,97 @@ def predict_image( } # Send request to API - start_time = time.time() - response = requests.post(f"{API_URL}/api/predict", files=files, data=data) - response_time = time.time() - start_time + response = requests.post(f"{API_URL}/api/predict/batch", files=files, data=data) + result = response.json() + + if "error" in result: + return "", f"Error: {result['error']}", "" + + # Return task ID and status + task_id = result["task_id"] + return task_id, f"Batch prediction started. Task ID: {task_id}", "" + + except Exception as e: + LOGGER.exception(f"Batch prediction failed: {str(e)}") + return "", f"Error: {str(e)}", "" + + +def check_progress(task_id: str) -> Tuple[float, str, str]: + """Check the progress of a batch prediction task. + + Args: + task_id: The ID of the batch prediction task. - # Parse response + Returns: + Tuple containing: + - Progress percentage (0-100) + - Current status message + - Estimated time remaining + """ + try: + # Send request to API + response = requests.get(f"{API_URL}/api/predict/batch/{task_id}/status") result = response.json() - if not result["success"]: - return "", None, "", f"Error: {result.get('error', 'Unknown error')}" + # Extract progress data + progress = result["progress"] + status = result["status"] + current_image = result.get("current_image", "") + processed = result["processed_images"] + total = result["total_images"] + estimated_time = result.get("estimated_time_remaining", 0) - # Get result data - result_data = result["result"] - ply_path = result_data["gaussians_ply_path"] - video_path = result_data.get("render_video_path") - inference_time = result_data["inference_time"] + # Format status message + status_msg = f"Status: {status} | Processing: {current_image} ({processed}/{total})" + time_remaining = f"Estimated time remaining: {estimated_time:.1f} seconds" if estimated_time else "" - # Format results - ply_url = f"{API_URL}{ply_path}" - video_url = f"{API_URL}{video_path}" if video_path else None - time_str = f"Inference time: {inference_time:.2f} seconds" - status = f"Success! Generated {result_data['num_gaussians']} 3D Gaussians" + return progress, status_msg, time_remaining - return ply_url, video_url, time_str, status + except Exception as e: + LOGGER.exception(f"Failed to check progress: {str(e)}") + return 0, f"Error checking progress: {str(e)}", "" + + +def get_results(task_id: str) -> List[Dict]: # pyright: ignore[reportDeprecated, reportMissingTypeArgument, reportUnknownParameterType] + """Get the results of a batch prediction task. + + Args:· + task_id: The ID of the batch prediction task. + + Returns: + List of result dictionaries containing: + - filename: Original filename + - ply_url: URL to download the PLY file + - video_url: URL to the rendered video (if enabled) + - inference_time: Inference time for the image + - num_gaussians: Number of 3D Gaussians generated + """ + try: + # Send request to API + response = requests.get(f"{API_URL}/api/predict/batch/{task_id}/results") + result = response.json() + + if result["status"] != "completed": + return [{"error": f"Task not completed. Current status: {result['status']}"}] # pyright: ignore[reportUnknownVariableType] + + # Extract results + batch_results = result["result"]["results"] + formatted_results = [] + + for res in batch_results: + formatted_results.append({ + "filename": res["filename"], + "ply_url": f"{API_URL}{res['gaussians_ply_path']}", + "video_url": f"{API_URL}{res['render_video_path']}" if res['render_video_path'] else None, + "inference_time": res["inference_time"], + "num_gaussians": res["num_gaussians"] + }) + + return formatted_results except Exception as e: - LOGGER.exception(f"Prediction failed: {str(e)}") - return "", None, "", f"Error: {str(e)}" + LOGGER.exception(f"Failed to get results: {str(e)}") + return [{"error": f"Error getting results: {str(e)}"}] def create_gradio_app() -> gr.Blocks: @@ -101,15 +173,19 @@ def create_gradio_app() -> gr.Blocks: Generate 3D Gaussian point clouds from 2D images using the ml-sharp model. """) - # Input section + # State variables + task_id_state = gr.State("") + results_state = gr.State([]) + + # Input and Output sections - Now below Results section with gr.Row(): with gr.Column(scale=1): - # Image input - image_input = gr.Image( - type="pil", - label="Upload Image", - height=300, - width=300 + # Image list input + image_list_input = gr.Files( + label="Upload Images", + file_count="multiple", + file_types=["image"], + height=300 ) # Device selection @@ -145,75 +221,117 @@ def create_gradio_app() -> gr.Blocks: # Output section with gr.Column(scale=2): - # Status message + # Status message status_output = gr.Textbox( label="Status", interactive=False, - placeholder="Ready to process images..." + placeholder="Ready to process images...", + lines=2 ) - # Inference time - time_output = gr.Textbox( - label="Inference Time", - interactive=False - ) - - # PLY file download - ply_output = gr.Textbox( - label="Generated PLY File", + # Progress details + progress_details = gr.Textbox( + label="Progress Details", interactive=False, - placeholder="No PLY file generated yet" - ) - - # PLY download button - ply_download_button = gr.Button( - "Download PLY File", - variant="secondary" + placeholder="Progress details will be shown here...", + lines=3 ) - # Video output (if enabled) - video_output = gr.Video( - label="Rendered Video", - interactive=False, - visible=False - ) + # Add empty space to match left column height + # empty_space = gr.Markdown("", label="") + + with gr.Column(scale=1): + # Results display with direct download buttons + results_display = gr.Markdown( + label="结果列表", + value="点击“获取结果”按钮查看结果" + ) + + # Get results button + get_results_button = gr.Button( + "获取结果", + # variant="secondary", + interactive=False + ) + + + # Function to continuously check progress + def check_progress_continuous(task_id: str) -> Tuple[str, bool]: + """Check progress continuously until task is completed. + + Args: + task_id: The task ID to check. + + Returns: + Tuple containing status details and whether task is completed. + """ + try: + # Send request to API + response = requests.get(f"{API_URL}/api/predict/batch/{task_id}/status") + result = response.json() + + # Extract progress data + status = result["status"] + current_image = result.get("current_image", "") + processed = result["processed_images"] + total = result["total_images"] + + # Format status message + status_msg = f"Status: {status} | Processing: {current_image} ({processed}/{total})" + + # Check if task is completed + is_completed = status in ["completed", "failed"] + + return status_msg, is_completed + except Exception as e: + LOGGER.exception(f"Failed to check progress: {str(e)}") + return f"Error checking progress: {str(e)}", False # Set up event handlers - predict_button.click( - fn=predict_image, - inputs=[image_input, device_dropdown, render_checkbox, checkpoint_path], - outputs=[ply_output, video_output, time_output, status_output] + predict_button.click( # pyright: ignore[reportUnusedCallResult] + fn=predict_batch, + inputs=[image_list_input, device_dropdown, render_checkbox, checkpoint_path], + outputs=[task_id_state, status_output, progress_details] + ).then( + fn=check_progress_continuous, + inputs=[task_id_state], + outputs=[progress_details, get_results_button] + ).then( + fn=lambda is_completed: gr.Button(interactive=is_completed), + inputs=[get_results_button], + outputs=[get_results_button] ) - # Update video visibility based on render checkbox - render_checkbox.change( - fn=lambda render: gr.Video(visible=render), - inputs=[render_checkbox], - outputs=[video_output] + # Get results button click + get_results_button.click( # pyright: ignore[reportUnusedCallResult] + fn=get_results, + inputs=[task_id_state], + outputs=[results_state] + ).then( + fn=lambda results: + # Format results as Markdown table with direct download buttons + "# Results Summary\n\n" + + "| Filename | Gaussians | Time (s) | Actions |\n" + + "|----------|-----------|----------|---------|\n" + + "\n".join([ + "| {} | {:,} | {:.2f} | {}{} |".format( + res['filename'], + res['num_gaussians'], + res['inference_time'], + "[Download PLY]({})".format(res['ply_url']), + " | [Download Video]({})".format(res['video_url']) if res['video_url'] else "" + ) + for res in results + ]), + inputs=[results_state], + outputs=[results_display] ) - # Add example images - gr.Markdown("""## Example Images - """) - with gr.Row(): - gr.Examples( - examples=[ - "examples/example1.jpg", - "examples/example2.jpg", - "examples/example3.jpg" - ], - inputs=image_input, - outputs=[ply_output, video_output, time_output, status_output], - fn=predict_image, - cache_examples=False - ) - # Footer - gr.Markdown("""## About - -This application uses the ml-sharp model to generate 3D Gaussian point clouds from 2D images. -The model is based on state-of-the-art computer vision techniques. - """) + gr.Markdown("""## 关于 +此应用程序使用 ml-sharp 模型从 2D 图像生成 3D 高斯点云。 +该模型基于最先进的计算机视觉技术。 + """) # pyright: ignore[reportUnusedCallResult] return app From 314aa41578ad20b020ff555fc1a110cb00a75efb Mon Sep 17 00:00:00 2001 From: playertk Date: Wed, 24 Dec 2025 16:44:20 +0800 Subject: [PATCH 4/6] =?UTF-8?q?docs(webui):=20=E6=9B=B4=E6=96=B0=E7=95=8C?= =?UTF-8?q?=E9=9D=A2=E6=96=87=E6=9C=AC=E5=92=8C=E5=BC=80=E5=8F=91=E8=80=85?= =?UTF-8?q?=E4=BF=A1=E6=81=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新了应用界面的标题和描述文本，从英文改为中文显示 在页脚添加了开发者信息并优化了格式 --- src/api/webui/app.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/src/api/webui/app.py b/src/api/webui/app.py index 85ae4c8..938b3b1 100644 --- a/src/api/webui/app.py +++ b/src/api/webui/app.py @@ -168,10 +168,8 @@ def create_gradio_app() -> gr.Blocks: """ with gr.Blocks(title="ml-sharp 3D Gaussian Predictor") as app: # Header - gr.Markdown("""# ml-sharp 3D Gaussian Point Cloud Predictor - -Generate 3D Gaussian point clouds from 2D images using the ml-sharp model. - """) + gr.Markdown("""# ml-sharp 3D Gaussian 点云生成器 + 使用 ml-sharp 模型从 2D 图像生成 3D 高斯点云。 """)# pyright: ignore[reportUnusedCallResult] # State variables task_id_state = gr.State("") @@ -256,7 +254,7 @@ def create_gradio_app() -> gr.Blocks: # Function to continuously check progress - def check_progress_continuous(task_id: str) -> Tuple[str, bool]: + def check_progress_continuous(task_id: str) -> tuple[str, bool]: """Check progress continuously until task is completed. Args: @@ -328,9 +326,10 @@ def check_progress_continuous(task_id: str) -> Tuple[str, bool]: ) # Footer - gr.Markdown("""## 关于 -此应用程序使用 ml-sharp 模型从 2D 图像生成 3D 高斯点云。 -该模型基于最先进的计算机视觉技术。 + gr.Markdown("""## 关于 +开发者： MarcoTin +此应用程序使用 ml-sharp 模型从 2D 图像生成 3D 高斯点云。 + """) # pyright: ignore[reportUnusedCallResult] return app From 8d557c0e31ce2b627e1eb98246e37bb6f64b46c2 Mon Sep 17 00:00:00 2001 From: playertk Date: Thu, 25 Dec 2025 12:24:40 +0800 Subject: [PATCH 5/6] =?UTF-8?q?refactor(webui):=20=E4=BC=98=E5=8C=96?= =?UTF-8?q?=E7=BB=93=E6=9E=9C=E5=B1=95=E7=A4=BA=E7=95=8C=E9=9D=A2=E5=B9=B6?= =?UTF-8?q?=E7=A7=BB=E9=99=A4=E8=8E=B7=E5=8F=96=E7=BB=93=E6=9E=9C=E6=8C=89?= =?UTF-8?q?=E9=92=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 移除独立的获取结果按钮，将结果显示逻辑直接集成到进度检查流程中 修改结果表格的显示格式，使用中文表头并优化数字显示格式 --- src/api/webui/app.py | 33 ++++++++++----------------------- 1 file changed, 10 insertions(+), 23 deletions(-) diff --git a/src/api/webui/app.py b/src/api/webui/app.py index 938b3b1..8c36337 100644 --- a/src/api/webui/app.py +++ b/src/api/webui/app.py @@ -242,14 +242,7 @@ def create_gradio_app() -> gr.Blocks: # Results display with direct download buttons results_display = gr.Markdown( label="结果列表", - value="点击“获取结果”按钮查看结果" - ) - - # Get results button - get_results_button = gr.Button( - "获取结果", - # variant="secondary", - interactive=False + value="结果列表" ) @@ -293,31 +286,25 @@ def check_progress_continuous(task_id: str) -> tuple[str, bool]: ).then( fn=check_progress_continuous, inputs=[task_id_state], - outputs=[progress_details, get_results_button] + outputs=[progress_details] ).then( - fn=lambda is_completed: gr.Button(interactive=is_completed), - inputs=[get_results_button], - outputs=[get_results_button] - ) - - # Get results button click - get_results_button.click( # pyright: ignore[reportUnusedCallResult] + fn=get_results, inputs=[task_id_state], outputs=[results_state] ).then( - fn=lambda results: + fn=lambda results: # pyright: ignore[reportUnknownLambdaType] # Format results as Markdown table with direct download buttons - "# Results Summary\n\n" + - "| Filename | Gaussians | Time (s) | Actions |\n" + + "# 计算结果列表\n\n" + + "| 图片名称 | 类型 | 计算时长 (s) | 下载链接 |\n" + "|----------|-----------|----------|---------|\n" + "\n".join([ - "| {} | {:,} | {:.2f} | {}{} |".format( + "| {} | {} | {:.2f} | {}{} |".format( res['filename'], - res['num_gaussians'], + "Gaussians" if res['num_gaussians'] == 1 else "{:,}".format(res['num_gaussians']), res['inference_time'], - "[Download PLY]({})".format(res['ply_url']), - " | [Download Video]({})".format(res['video_url']) if res['video_url'] else "" + "[下载 PLY]({})".format(res['ply_url']), + " | [下载视频]({})".format(res['video_url']) if res['video_url'] else "" ) for res in results ]), From fa0c42cb9b275ac03bf48a2570b970b60487363f Mon Sep 17 00:00:00 2001 From: playertk Date: Thu, 25 Dec 2025 17:37:29 +0800 Subject: [PATCH 6/6] =?UTF-8?q?refactor:=20=E7=A7=BB=E9=99=A4=E8=87=AA?= =?UTF-8?q?=E5=AE=9A=E4=B9=89=E6=A8=A1=E5=9E=8B=E6=A3=80=E6=9F=A5=E7=82=B9?= =?UTF-8?q?=E6=94=AF=E6=8C=81=E5=B9=B6=E6=B7=BB=E5=8A=A0=E6=A8=A1=E5=9E=8B?= =?UTF-8?q?=E6=97=8B=E8=BD=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 移除所有与自定义检查点路径相关的代码，强制使用默认模型 - 在保存PLY文件前添加-135度旋转变换 - 更新模型缓存逻辑以仅支持默认模型路径 - 清理相关API和WebUI中的检查点路径参数 --- src/api/dependencies.py | 18 ++++++++---------- src/api/endpoints/predict.py | 10 ++++------ src/api/schemas/predict.py | 8 -------- src/api/webui/app.py | 17 +++-------------- src/sharp/utils/gaussians.py | 18 ++++++++++++++++++ 5 files changed, 33 insertions(+), 38 deletions(-) diff --git a/src/api/dependencies.py b/src/api/dependencies.py index 72fad4c..5b699b7 100644 --- a/src/api/dependencies.py +++ b/src/api/dependencies.py @@ -16,13 +16,13 @@ LOGGER = logging.getLogger(__name__) -DEFAULT_MODEL_URL = "https://ml-site.cdn-apple.com/models/sharp/sharp_2572gikvuh.pt" +DEFAULT_MODEL_PATH = Path(__file__).parent.parent.parent / "model" / "sharp_2572gikvuh.pt" class ModelCache: """Singleton class for caching loaded models.""" - _instance: Optional[ModelCache] = None + _instance: ModelCache|None = None _models: dict[str, RGBGaussianPredictor] = {} def __new__(cls) -> ModelCache: @@ -32,7 +32,7 @@ def __new__(cls) -> ModelCache: return cls._instance def get_model( - self, checkpoint_path: Optional[Path] = None, device: str = "cuda" + self, checkpoint_path: Path | None = None, device: str = "cuda" ) -> RGBGaussianPredictor: """Get a model from cache or load it if not present. @@ -51,14 +51,12 @@ def get_model( LOGGER.info(f"Using cached model: {model_key}") return self._models[model_key] - # Load or download checkpoint + # Load checkpoint LOGGER.info(f"Loading model: {model_key}") - if checkpoint_path is None: - LOGGER.info(f"No checkpoint provided. Downloading default model from {DEFAULT_MODEL_URL}") - state_dict = torch.hub.load_state_dict_from_url(DEFAULT_MODEL_URL, progress=True) - else: - LOGGER.info(f"Loading checkpoint from {checkpoint_path}") - state_dict = torch.load(checkpoint_path, weights_only=True) + # Always use the default model path + final_checkpoint_path = DEFAULT_MODEL_PATH + LOGGER.info(f"Loading checkpoint from {final_checkpoint_path}") + state_dict = torch.load(final_checkpoint_path, weights_only=True) # Create and configure model gaussian_predictor = create_predictor(PredictorParams()) diff --git a/src/api/endpoints/predict.py b/src/api/endpoints/predict.py index bbf9e15..37898b1 100644 --- a/src/api/endpoints/predict.py +++ b/src/api/endpoints/predict.py @@ -193,9 +193,8 @@ async def process_batch_task( LOGGER.warning(f"Task {task.task_id}: Can only run rendering with gsplat on CUDA. Rendering is disabled.") task.request.render = False - # Get model from cache - checkpoint_path = Path(task.request.checkpoint_path) if task.request.checkpoint_path else None - model = model_cache.get_model(checkpoint_path, str(device)) + # Get model from cache (always use default model) + model = model_cache.get_model(None, str(device)) # Process each image total_start_time = time.time() @@ -397,9 +396,8 @@ async def predict( LOGGER.warning("Can only run rendering with gsplat on CUDA. Rendering is disabled.") request.render = False - # Get model from cache - checkpoint_path = Path(request.checkpoint_path) if request.checkpoint_path else None - model = model_cache.get_model(checkpoint_path, str(device)) + # Get model from cache (always use default model) + model = model_cache.get_model(None, str(device)) # Run inference gaussians = predict_image(model, image, f_px, device) diff --git a/src/api/schemas/predict.py b/src/api/schemas/predict.py index a8ae870..d3cf239 100644 --- a/src/api/schemas/predict.py +++ b/src/api/schemas/predict.py @@ -25,10 +25,6 @@ class PredictRequest(BaseModel): default=False, description="Whether to render the 3D Gaussians into a video" ) - checkpoint_path: Optional[str] = Field( - default=None, - description="Path to a custom model checkpoint" - ) class BatchPredictRequest(BaseModel): @@ -43,10 +39,6 @@ class BatchPredictRequest(BaseModel): default=False, description="Whether to render the 3D Gaussians into a video" ) - checkpoint_path: Optional[str] = Field( - default=None, - description="Path to a custom model checkpoint" - ) class PredictResult(BaseModel): diff --git a/src/api/webui/app.py b/src/api/webui/app.py index 8c36337..d35c144 100644 --- a/src/api/webui/app.py +++ b/src/api/webui/app.py @@ -24,8 +24,7 @@ def predict_batch( images: List[str], device: str, - render: bool, - checkpoint_path: Optional[str] = None + render: bool ) -> Tuple[str, str, str]: """Predict 3D Gaussians from multiple images using the API. @@ -33,7 +32,6 @@ def predict_batch( images: List of input image file paths. device: The device to run inference on. render: Whether to render the results. - checkpoint_path: Path to a custom model checkpoint. Returns: Tuple containing: @@ -62,8 +60,7 @@ def predict_batch( # Prepare API request data = { "device": device, - "render": render, - "checkpoint_path": checkpoint_path + "render": render } # Send request to API @@ -202,14 +199,6 @@ def create_gradio_app() -> gr.Blocks: info="Generate a video rendering of the 3D Gaussians (CUDA only)" ) - # Checkpoint path (optional) - checkpoint_path = gr.Textbox( - value="", - label="Custom Checkpoint Path (Optional)", - placeholder="Path to a custom .pt checkpoint file", - info="Leave empty to use the default model" - ) - # Predict button predict_button = gr.Button( "Generate 3D Gaussians", @@ -281,7 +270,7 @@ def check_progress_continuous(task_id: str) -> tuple[str, bool]: # Set up event handlers predict_button.click( # pyright: ignore[reportUnusedCallResult] fn=predict_batch, - inputs=[image_list_input, device_dropdown, render_checkbox, checkpoint_path], + inputs=[image_list_input, device_dropdown, render_checkbox], outputs=[task_id_state, status_output, progress_details] ).then( fn=check_progress_continuous, diff --git a/src/sharp/utils/gaussians.py b/src/sharp/utils/gaussians.py index ed73de8..f12cae7 100644 --- a/src/sharp/utils/gaussians.py +++ b/src/sharp/utils/gaussians.py @@ -349,6 +349,19 @@ def save_ply( ) -> PlyData: """Save a predicted Gaussian3D to a ply file.""" + # Rotate model -135 degrees + # ---------------------------- + LOGGER.info("Rotating model -135 degrees.") + transform_fix = torch.tensor( + [ + [-0.7071, 0.0, -0.7071, 0.0], + [0.0, 1.0, 0.0, 0.0], + [0.7071, 0.0, -0.7071, 0.0], + ], + device=gaussians.mean_vectors.device) + gaussians = apply_transform(gaussians, transform_fix) + + def _inverse_sigmoid(tensor: torch.Tensor) -> torch.Tensor: return torch.log(tensor / (1.0 - tensor)) @@ -481,3 +494,8 @@ def _inverse_sigmoid(tensor: torch.Tensor) -> torch.Tensor: plydata.write(path) return plydata + + + + +