[{"_1":2,"_288":-5,"_289":-5},"loaderData",{"_3":4,"_29":30,"_31":32,"_235":236},"root",{"_5":6,"_14":15},"toasts",[7],{"_8":9,"_10":11,"_12":13},"uid","01972649-b16b-730b-8b17-d53c406ac928","title","This MCP server does not support inspection","type","error","visitor",{"_16":17,"_18":19},"viewportWidth",1280,"visitorSession",{"_20":21,"_23":24,"_25":-5,"_26":27,"_28":-5},"attributes",[22],"BOT","id",7058884,"membership","referenceId","01971406-43e3-74e8-b880-2f000258007a","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_33":34,"_69":-5,"_70":35,"_36":71,"_154":155,"_234":118},"availableLocales",[35,46,52,58,64],{"_36":37,"_40":43,"_44":45},"mcpServer",{"_38":39,"_40":39,"_41":42},"description","mcp_3d_relief","name","readme","# MCP STL 3D Relief Generator\n\n

\n\n[中文](README_CN.md)\n·\n[Introduction to MCP](https://modelcontextprotocol.io/introduction)\n·\n[Wiki](https://deepwiki.com/Bigchx/mcp_3d_relief)\n\n

\n\nThis project provides a MCP server that converts 2D images into 3D relief models in STL format, suitable for 3D printing or rendering.\n\n## Features\n\n- Convert any image to a 3D relief model\n- Control model dimensions (width, thickness)\n- Add optional base to the 3D model\n- Invert depth for different relief effects\n- Fast processing with immediate download links\n\n## Installation\n\n### Prerequisites\n\n- Python 3.10+\n- [uv](https://docs.astral.sh/uv/#installation)\n\n### Option 1: Local Installation\n\n1. Clone the repository:\n\n```bash\ngit clone https://github.com/bigchx/mcp_3d_relief.git\ncd mcp_3d_relief\n```\n\n2. Install dependencies:\n\n```bash\nuv pip sync requirements.txt\n```\n\n3. Run/Inspect the server:\n\n```bash\nmcp run server.py\nmcp dev server.py\n```\n\n## Usage\n\n### JSON Configuration\n\n```json\n{\n \"mcpServers\": {\n \"mcp_3d_relief\": {\n \"command\": \"uv\",\n \"args\": [\"--directory\", \"{fill_in_your_path_here}\", \"run\", \"server.py\"]\n }\n }\n}\n```\n\n### MCP Tool Parameters\n\n- `image_path`: Local path or web URL to the input image file\n- `model_width`: Width of the 3D model in mm (default: 50.0)\n- `model_thickness`: Maximum thickness/height of the 3D model in mm (default: 5.0)\n- `base_thickness`: Thickness of the base in mm (default: 2.0)\n- `skip_depth`: Whether to use the image directly or generate a depth map (default: true)\n- `invert_depth`: Invert the relief (bright areas become low instead of high) (default: false)\n- `detail_level`: Controls the resolution of the processed image (default: 1.0). At detail_level = 1.0, the image is processed at 320px resolution, producing an STL file typically under 100MB. Higher values improve detail quality but significantly increase both processing time and STL file size. For example, doubling the detail_level can increase file size by 4x or more. Use with caution.\n\n### Response\n\nThe MCP Tool returns a JSON response with:\n\n```json\n{\n \"status\": \"success\",\n \"depth_map_path\": \"path/to/yourimage_depth_map.png\",\n \"stl_path\": \"path/to/yourimage.stl\"\n}\n```\n\nWhere LLMs can access the generated files from this MCP server, using the provided URLs.\n\n### Command Line\n\nYou can also use the script directly from the command line to generate a relief model from an image:\n\n```bash\npython3 relief.py path/to/your/image.jpg\n```\n\n### External Depth Map Generation\n\nFor higher quality depth maps, you can use external depth map generation services like [Depth-Anything-V2](https://huggingface.co/spaces/depth-anything/Depth-Anything-V2). This service can generate more accurate depth maps that you can then use with this project:\n\n1. Visit [https://huggingface.co/spaces/depth-anything/Depth-Anything-V2](https://huggingface.co/spaces/depth-anything/Depth-Anything-V2)\n2. Upload your image to generate a depth map\n3. Download the generated depth map\n4. Use this depth map with our converter by setting `skip_depth=false`\n\nThis approach can provide better 3D relief models, especially for complex images.\n\n## How It Works\n\n1. The image is processed to create a depth map (darker pixels = lower, brighter pixels = higher)\n2. The depth map is converted to a 3D mesh with triangular facets\n3. A base is added to the bottom of the model\n4. The model is saved as an STL file\n\n## Our partners\n\n

\n \n $\"voxeldance\"$ \n \n

\n \n $\"3dzyk\"$ \n \n

\n","English","tag","en-US",{"_36":47,"_40":50,"_44":51},{"_38":48,"_40":48,"_41":49},"mcp\\_3d\\_relieve","# Generador de relieve 3D MCP STL\n\n[中文](README_CN.md)· [Introducción a MCP](https://modelcontextprotocol.io/introduction) · [Wiki](https://deepwiki.com/Bigchx/mcp_3d_relief)\n\nEste proyecto proporciona un servidor MCP que convierte imágenes 2D en modelos de relieve 3D en formato STL, adecuados para impresión o renderizado 3D.\n\n## Características\n\n* Convierte cualquier imagen en un modelo de relieve 3D\n* Dimensiones del modelo de control (ancho, grosor)\n* Añadir base opcional al modelo 3D\n* Invertir profundidad para obtener diferentes efectos de relieve\n* Procesamiento rápido con enlaces de descarga inmediata\n\n## Instalación\n\n### Prerrequisitos\n\n* Python 3.10+\n* [ultravioleta](https://docs.astral.sh/uv/#installation)\n\n### Opción 1: Instalación local\n\n1. Clonar el repositorio:\n\n```bash\ngit clone https://github.com/bigchx/mcp_3d_relief.git\ncd mcp_3d_relief\n```\n\n2. Instalar dependencias:\n\n```bash\nuv pip sync requirements.txt\n```\n\n3. Ejecutar/Inspeccionar el servidor:\n\n```bash\nmcp run server.py\nmcp dev server.py\n```\n\n## Uso\n\n### Configuración JSON\n\n```json\n{\n \"mcpServers\": {\n \"mcp_3d_relief\": {\n \"command\": \"uv\",\n \"args\": [\"--directory\", \"{fill_in_your_path_here}\", \"run\", \"server.py\"]\n }\n }\n}\n```\n\n### Parámetros de la herramienta MCP\n\n* `image_path` : Ruta local o URL web al archivo de imagen de entrada\n* `model_width` : Ancho del modelo 3D en mm (predeterminado: 50,0)\n* `model_thickness` : Grosor/altura máximos del modelo 3D en mm (valor predeterminado: 5,0)\n* `base_thickness` : Espesor de la base en mm (predeterminado: 2.0)\n* `skip_depth` : si se utilizará la imagen directamente o se generará un mapa de profundidad (valor predeterminado: verdadero)\n* `invert_depth` : Invierte el relieve (las áreas brillantes se vuelven bajas en lugar de altas) (predeterminado: falso)\n* `detail_level` : Controla la resolución de la imagen procesada (predeterminado: 1.0). Con detail\\_level = 1.0, la imagen se procesa a una resolución de 320 px, lo que genera un archivo STL que normalmente ocupa menos de 100 MB. Valores más altos mejoran la calidad del detalle, pero aumentan significativamente el tiempo de procesamiento y el tamaño del archivo STL. Por ejemplo, duplicar detail\\_level puede aumentar el tamaño del archivo 4 veces o más. Úselo con precaución.\n\n### Respuesta\n\nLa herramienta MCP devuelve una respuesta JSON con:\n\n```json\n{\n \"status\": \"success\",\n \"depth_map_path\": \"path/to/yourimage_depth_map.png\",\n \"stl_path\": \"path/to/yourimage.stl\"\n}\n```\n\nDónde los LLM pueden acceder a los archivos generados desde este servidor MCP, utilizando las URL proporcionadas.\n\n### Línea de comandos\n\nTambién puedes utilizar el script directamente desde la línea de comandos para generar un modelo de relieve a partir de una imagen:\n\n```bash\npython3 relief.py path/to/your/image.jpg\n```\n\n### Generación de mapas de profundidad externos\n\nPara obtener mapas de profundidad de mayor calidad, puede usar servicios externos de generación de mapas de profundidad como [Depth-Anything-V2](https://huggingface.co/spaces/depth-anything/Depth-Anything-V2) . Este servicio puede generar mapas de profundidad más precisos que puede usar en este proyecto:\n\n1. Visita \n2. Sube tu imagen para generar un mapa de profundidad\n3. Descargar el mapa de profundidad generado\n4. Utilice este mapa de profundidad con nuestro convertidor configurando `skip_depth=false`\n\nEste enfoque puede proporcionar mejores modelos de relieve 3D, especialmente para imágenes complejas.\n\n## Cómo funciona\n\n1. La imagen se procesa para crear un mapa de profundidad (píxeles más oscuros = más bajos, píxeles más brillantes = más altos)\n2. El mapa de profundidad se convierte en una malla 3D con facetas triangulares.\n3. Se añade una base a la parte inferior del modelo.\n4. El modelo se guarda como un archivo STL\n\n## Nuestros socios","Spanish","es-ES",{"_36":53,"_40":56,"_44":57},{"_38":54,"_40":54,"_41":55},"mcp\\_3d\\_relief","# MCP STL 3D レリーフジェネレータ\n\n[中文](README_CN.md)· [MCP の紹介](https://modelcontextprotocol.io/introduction)· [Wiki](https://deepwiki.com/Bigchx/mcp_3d_relief)\n\nこのプロジェクトは、2D 画像を 3D 印刷やレンダリングに適した STL 形式の 3D レリーフモデルに変換する MCP サーバーを提供します。\n\n## 特徴\n\n* あらゆる画像を3Dレリーフモデルに変換\n* 制御モデルの寸法（幅、厚さ）\n* 3Dモデルにオプションのベースを追加する\n* 異なるレリーフ効果のために深さを反転する\n* 即時ダウンロードリンクによる高速処理\n\n## インストール\n\n### 前提条件\n\n* Python 3.10以上\n* [紫外線](https://docs.astral.sh/uv/#installation)\n\n### オプション1: ローカルインストール\n\n1. リポジトリをクローンします。\n\n```bash\ngit clone https://github.com/bigchx/mcp_3d_relief.git\ncd mcp_3d_relief\n```\n\n2. 依存関係をインストールします:\n\n```bash\nuv pip sync requirements.txt\n```\n\n3. サーバーを実行/検査します。\n\n```bash\nmcp run server.py\nmcp dev server.py\n```\n\n## 使用法\n\n### JSON構成\n\n```json\n{\n \"mcpServers\": {\n \"mcp_3d_relief\": {\n \"command\": \"uv\",\n \"args\": [\"--directory\", \"{fill_in_your_path_here}\", \"run\", \"server.py\"]\n }\n }\n}\n```\n\n### MCPツールパラメータ\n\n* `image_path` : 入力画像ファイルへのローカルパスまたはWeb URL\n* `model_width` : 3Dモデルの幅（mm）（デフォルト: 50.0）\n* `model_thickness` : 3Dモデルの最大厚さ/高さ（mm）（デフォルト: 5.0）\n* `base_thickness` : ベースの厚さ（mm）（デフォルト: 2.0）\n* `skip_depth` : 画像を直接使用するか、深度マップを生成するか（デフォルト: true）\n* `invert_depth` : レリーフを反転します（明るい領域は高くなるのではなく低くなります）（デフォルト: false）\n* `detail_level` : 処理画像の解像度を制御します（デフォルト：1.0）。detail\\_level = 1.0 の場合、画像は320ピクセルの解像度で処理され、通常100MB未満のSTLファイルが生成されます。値を大きくするとディテールの品質は向上しますが、処理時間とSTLファイルサイズが大幅に増加します。例えば、detail\\_level を2倍にすると、ファイルサイズは4倍以上になる可能性があります。使用には注意が必要です。\n\n### 応答\n\nMCP ツールは次の JSON 応答を返します。\n\n```json\n{\n \"status\": \"success\",\n \"depth_map_path\": \"path/to/yourimage_depth_map.png\",\n \"stl_path\": \"path/to/yourimage.stl\"\n}\n```\n\nLLM は、提供された URL を使用して、この MCP サーバーから生成されたファイルにアクセスできます。\n\n### コマンドライン\n\nコマンドラインから直接スクリプトを使用して、画像からレリーフモデルを生成することもできます。\n\n```bash\npython3 relief.py path/to/your/image.jpg\n```\n\n### 外部深度マップ生成\n\nより高品質な深度マップが必要な場合は、 [Depth-Anything-V2](https://huggingface.co/spaces/depth-anything/Depth-Anything-V2)などの外部深度マップ生成サービスを利用できます。このサービスは、より正確な深度マップを生成し、このプロジェクトで使用できます。\n\n1. をご覧ください\n2. 画像をアップロードして深度マップを生成します\n3. 生成された深度マップをダウンロードする\n4. `skip_depth=false`を設定して、この深度マップをコンバーターで使用します。\n\nこのアプローチにより、特に複雑な画像の場合、より優れた 3D レリーフモデルを提供できます。\n\n## 仕組み\n\n1. 画像は深度マップを作成するために処理されます（暗いピクセル = 低く、明るいピクセル = 高くなります）\n2. 深度マップは三角形のファセットを持つ3Dメッシュに変換されます\n3. モデルの底にベースが追加されます\n4. モデルはSTLファイルとして保存されます\n\n## 私たちのパートナー","Japanese","ja-JP",{"_36":59,"_40":62,"_44":63},{"_38":60,"_40":60,"_41":61},"mcp\\_3d\\_릴리프","# MCP STL 3D 릴리프 생성기\n\n[中文](README_CN.md)· [MCP 소개](https://modelcontextprotocol.io/introduction) · [Wiki](https://deepwiki.com/Bigchx/mcp_3d_relief)\n\n이 프로젝트는 2D 이미지를 3D 인쇄나 렌더링에 적합한 STL 형식의 3D 릴리프 모델로 변환하는 MCP 서버를 제공합니다.\n\n## 특징\n\n* 모든 이미지를 3D 릴리프 모델로 변환\n* 제어 모델 치수(폭, 두께)\n* 3D 모델에 선택적 기반 추가\n* 다양한 릴리프 효과를 위해 깊이를 반전합니다.\n* 즉시 다운로드 링크로 빠른 처리\n\n## 설치\n\n### 필수 조건\n\n* 파이썬 3.10+\n* [자외선](https://docs.astral.sh/uv/#installation)\n\n### 옵션 1: 로컬 설치\n\n1. 저장소를 복제합니다.\n\n지엑스피1\n\n2. 종속성 설치:\n\n```bash\nuv pip sync requirements.txt\n```\n\n3. 서버 실행/검사:\n\n```bash\nmcp run server.py\nmcp dev server.py\n```\n\n## 용법\n\n### JSON 구성\n\n```json\n{\n \"mcpServers\": {\n \"mcp_3d_relief\": {\n \"command\": \"uv\",\n \"args\": [\"--directory\", \"{fill_in_your_path_here}\", \"run\", \"server.py\"]\n }\n }\n}\n```\n\n### MCP 도구 매개변수\n\n* `image_path` : 입력 이미지 파일에 대한 로컬 경로 또는 웹 URL\n* `model_width` : 3D 모델의 너비(mm) (기본값: 50.0)\n* `model_thickness` : 3D 모델의 최대 두께/높이(mm) (기본값: 5.0)\n* `base_thickness` : 베이스의 두께(mm) (기본값: 2.0)\n* `skip_depth` : 이미지를 직접 사용할지 아니면 깊이 맵을 생성할지 여부(기본값: true)\n* `invert_depth` : 릴리프를 반전합니다(밝은 영역이 높아지는 대신 낮아집니다)(기본값: false)\n* `detail_level` : 처리된 이미지의 해상도를 제어합니다(기본값: 1.0). detail\\_level이 1.0이면 이미지는 320px 해상도로 처리되어 일반적으로 100MB 미만의 STL 파일을 생성합니다. 값이 높을수록 디테일 품질은 향상되지만 처리 시간과 STL 파일 크기가 크게 증가합니다. 예를 들어, detail\\_level을 두 배로 높이면 파일 크기가 4배 이상 증가할 수 있습니다. 주의해서 사용하세요.\n\n### 응답\n\nMCP 도구는 다음을 포함하는 JSON 응답을 반환합니다.\n\n```json\n{\n \"status\": \"success\",\n \"depth_map_path\": \"path/to/yourimage_depth_map.png\",\n \"stl_path\": \"path/to/yourimage.stl\"\n}\n```\n\nLLM은 제공된 URL을 사용하여 이 MCP 서버에서 생성된 파일에 액세스할 수 있습니다.\n\n### 명령줄\n\n명령줄에서 직접 스크립트를 사용하여 이미지에서 릴리프 모델을 생성할 수도 있습니다.\n\n```bash\npython3 relief.py path/to/your/image.jpg\n```\n\n### 외부 깊이 맵 생성\n\n더 높은 품질의 깊이 맵을 원하시면 [Depth-Anything-V2](https://huggingface.co/spaces/depth-anything/Depth-Anything-V2) 와 같은 외부 깊이 맵 생성 서비스를 사용하세요. 이 서비스를 통해 더욱 정확한 깊이 맵을 생성하여 이 프로젝트에 활용할 수 있습니다.\n\n1. 를 방문하세요\n2. 이미지를 업로드하여 깊이 맵을 생성하세요\n3. 생성된 깊이 맵을 다운로드하세요\n4. `skip_depth=false` 설정하여 변환기와 함께 이 깊이 맵을 사용하세요.\n\n이 접근 방식은 특히 복잡한 이미지에 대해 더 나은 3D 릴리프 모델을 제공할 수 있습니다.\n\n## 작동 원리\n\n1. 이미지는 깊이 맵을 생성하기 위해 처리됩니다(어두운 픽셀 = 낮음, 밝은 픽셀 = 높음)\n2. 깊이 맵은 삼각형 패싯이 있는 3D 메시로 변환됩니다.\n3. 모델의 하단에 베이스가 추가됩니다.\n4. 모델은 STL 파일로 저장됩니다.\n\n## 우리의 파트너들","Korean","ko-KR",{"_36":65,"_40":67,"_44":68},{"_38":54,"_40":54,"_41":66},"# MCP STL 3D 浮雕生成器\n\n[中文](README_CN.md)· [MCP简介](https://modelcontextprotocol.io/introduction)· [Wiki](https://deepwiki.com/Bigchx/mcp_3d_relief)\n\n该项目提供了一个 MCP 服务器，可将 2D 图像转换为 STL 格式的 3D 浮雕模型，适用于 3D 打印或渲染。\n\n## 特征\n\n* 将任何图像转换为 3D 浮雕模型\n* 控制模型尺寸（宽度、厚度）\n* 向 3D 模型添加可选基础\n* 不同地形效果的反转深度\n* 快速处理并立即下载链接\n\n## 安装\n\n### 先决条件\n\n* Python 3.10+\n* [紫外线](https://docs.astral.sh/uv/#installation)\n\n### 选项 1：本地安装\n\n1. 克隆存储库：\n\n```bash\ngit clone https://github.com/bigchx/mcp_3d_relief.git\ncd mcp_3d_relief\n```\n\n2. 安装依赖项：\n\n```bash\nuv pip sync requirements.txt\n```\n\n3. 运行/检查服务器：\n\n```bash\nmcp run server.py\nmcp dev server.py\n```\n\n## 用法\n\n### JSON 配置\n\n```json\n{\n \"mcpServers\": {\n \"mcp_3d_relief\": {\n \"command\": \"uv\",\n \"args\": [\"--directory\", \"{fill_in_your_path_here}\", \"run\", \"server.py\"]\n }\n }\n}\n```\n\n### MCP 工具参数\n\n* `image_path` ：输入图像文件的本地路径或网址\n* `model_width` ：3D 模型的宽度（毫米）（默认值：50.0）\n* `model_thickness` ：3D 模型的最大厚度/高度（毫米）（默认值：5.0）\n* `base_thickness` ：底座厚度（毫米）（默认值：2.0）\n* `skip_depth` ：是否直接使用图像或生成深度图（默认值：true）\n* `invert_depth` ：反转浮雕（明亮区域变低而不是变高）（默认值：false）\n* `detail_level` ：控制处理后图像的分辨率（默认值：1.0）。当 detail\\_level = 1.0 时，图像将以 320px 的分辨率进行处理，生成的 STL 文件通常小于 100MB。更高的值可以提高细节质量，但会显著增加处理时间和 STL 文件大小。例如，将 detail\\_level 加倍会导致文件大小增加 4 倍或更多。请谨慎使用。\n\n### 回复\n\nMCP 工具返回 JSON 响应，内容如下：\n\n```json\n{\n \"status\": \"success\",\n \"depth_map_path\": \"path/to/yourimage_depth_map.png\",\n \"stl_path\": \"path/to/yourimage.stl\"\n}\n```\n\nLLM 可以使用提供的 URL 从此 MCP 服务器访问生成的文件。\n\n### 命令行\n\n您还可以直接从命令行使用脚本从图像生成浮雕模型：\n\n```bash\npython3 relief.py path/to/your/image.jpg\n```\n\n### 外部深度图生成\n\n为了获得更高质量的深度图，您可以使用外部深度图生成服务，例如[Depth-Anything-V2](https://huggingface.co/spaces/depth-anything/Depth-Anything-V2) 。此服务可以生成更精确的深度图，您可以将其用于以下项目：\n\n1. 访问\n2. 上传图像以生成深度图\n3. 下载生成的深度图\n4. 通过设置`skip_depth=false`将此深度图与我们的转换器一起使用\n\n这种方法可以提供更好的 3D 浮雕模型，特别是对于复杂的图像。\n\n## 工作原理\n\n1. 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