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TianLin0509

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Img2ppt Lite

Turn a slide mockup image into an editable PPTX: native text + semantic draggable sprites + inpainted background. Agent-as-VLM skill for Claude Code / Codex / any CLI agent.

Revisar el código fuenteVer en GitHub
Precio sin confirmar★ 23 Estrellas de GitHubRegistro actualizado · 10 sept 2026agent-skillskillagent

Resumen

Turn a slide mockup image into an editable PPTX: native text + semantic draggable sprites + inpainted background. Agent-as-VLM skill for Claude Code / Codex / any CLI agent.

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

img2ppt-lite

把一张 PPT 效果图（PNG/JPG）还原成高保真、可编辑的 PPTX。

Turn a slide mockup image (e.g. generated by GPT-image / Midjourney / any design tool) into an editable PowerPoint file: native text boxes + semantically-split draggable sprites + inpainted clean background. Built as an agent skill — the executing AI (Claude Code / Codex / any CLI agent with vision) acts as the VLM, deterministic Python scripts do everything else.

selftest ↗

左：合成样张源图 · 右：管线重建后由 PowerPoint 渲染回的图（文字全部为原生可编辑文本框，图表/图标为独立贴图）

它解决什么问题

AI 出图工具能生成非常漂亮的 PPT 效果图，但那只是一张图片。想拿它当真正的工作底稿，你需要：

  • 文字可编辑——改标题、改数据是最高频操作；
  • 元素可拖拽——每个图标、图表、色块都是独立对象，能单独移动缩放；
  • 视觉尽量 1:1——还原完的页面和效果图肉眼几乎一致。

传统"图转 PPT"路线要么把整页塞成一张图（完全不可编辑），要么全原生重建（图标丑、收敛循环极慢）。img2ppt-lite 走中间路线，并把速度做到了单页 3-5 分钟（脚本部分约 40-60 秒）。

核心设计

可编辑 PPTX = 干净底图 (inpaint 去前景)
            + 语义元素贴图 (每个图标/图表一张独立 picture，可拖拽)
            + 原生文本框 (PIL 墨水高度反解字号，可编辑)
            + 纯色矩形原生化 (KPI 条/按钮/色块 → 可改色的原生 shape)

三个反直觉的关键决策：

  1. 视觉一致性的最大敌人恰恰是"原生重建"。 从原图裁出来的贴图天然 100% 一致；会漂的只有文本层——而文本字号可以用 PIL 按墨水高度离线反解，不需要"渲染→对比→调整"的收敛循环。这是比前代管线快一个量级的根本原因。
  2. 执行 agent 自身就是 VLM。 不调外部视觉 API：agent 看图产出一份 elements.json（文本/色块/图形的语义标注），脚本吃 JSON 确定性完成其余一切。LLM 参与固定 2 次（标注 + 终检），零额外 token 成本。
  3. 测量代替猜测。 字号、文字颜色、卡片填充色、渐变、行首 marker 全部从像素实测，agent 只负责语义；每个测量器都经过阳性+阴性用例双验。

环境要求

  • Windows（PowerPoint COM 用于渲染回读验证；没有 PowerPoint 也能出 PPTX，只是跳过渲染验证）
  • Python 3.10+
pip install -r requirements.txt

装好后先跑自检（生成合成样张走全链路，含 marker 正/负回归用例）：

python scripts/pipeline.py --selftest

使用方式

作为 agent skill（推荐）

把本仓库放进你的 agent skill 目录（如 Claude Code 的 ~/.claude/skills/img2ppt-lite/），agent 会按 SKILL.md 的 5 步工作流执行：

1. pipeline.py --run <run> --seed     # OCR 冷启动，产出待审阅的文本骨架
2. [agent 看图] 校对 seed、补图形/色块标注 → elements.json
3. pipeline.py --run <run>            # OCR校准 → 切图 → 装配 → COM 渲染验收
4. [agent 看图] 对比 compare.png，必要时改 JSON 秒级重跑
5. 交付 pptx + 验收报告（修补硬上限 2 轮，禁无限迭代）
手动使用

不用 agent 也可以：

Ver fuente ↗
Ver texto original
# img2ppt-lite

**把一张 PPT 效果图（PNG/JPG）还原成高保真、可编辑的 PPTX。**

Turn a slide mockup image (e.g. generated by GPT-image / Midjourney / any design tool) into an editable PowerPoint file: native text boxes + semantically-split draggable sprites + inpainted clean background. Built as an agent skill — the executing AI (Claude Code / Codex / any CLI agent with vision) acts as the VLM, deterministic Python scripts do everything else.

![selftest](docs/selftest-compare.png)

*左：合成样张源图 · 右：管线重建后由 PowerPoint 渲染回的图（文字全部为原生可编辑文本框，图表/图标为独立贴图）*

## 它解决什么问题

AI 出图工具能生成非常漂亮的 PPT 效果图，但那只是一张图片。想拿它当真正的工作底稿，你需要：

- **文字可编辑**——改标题、改数据是最高频操作；
- **元素可拖拽**——每个图标、图表、色块都是独立对象，能单独移动缩放；
- **视觉尽量 1:1**——还原完的页面和效果图肉眼几乎一致。

传统"图转 PPT"路线要么把整页塞成一张图（完全不可编辑），要么全原生重建（图标丑、收敛循环极慢）。img2ppt-lite 走中间路线，并把速度做到了**单页 3-5 分钟**（脚本部分约 40-60 秒）。

## 核心设计

```
可编辑 PPTX = 干净底图 (inpaint 去前景)
            + 语义元素贴图 (每个图标/图表一张独立 picture，可拖拽)
            + 原生文本框 (PIL 墨水高度反解字号，可编辑)
            + 纯色矩形原生化 (KPI 条/按钮/色块 → 可改色的原生 shape)
```

三个反直觉的关键决策：

1. **视觉一致性的最大敌人恰恰是"原生重建"。** 从原图裁出来的贴图天然 100% 一致；会漂的只有文本层——而文本字号可以用 PIL 按墨水高度离线反解，**不需要"渲染→对比→调整"的收敛循环**。这是比前代管线快一个量级的根本原因。
2. **执行 agent 自身就是 VLM。** 不调外部视觉 API：agent 看图产出一份 `elements.json`（文本/色块/图形的语义标注），脚本吃 JSON 确定性完成其余一切。LLM 参与固定 2 次（标注 + 终检），零额外 token 成本。
3. **测量代替猜测。** 字号、文字颜色、卡片填充色、渐变、行首 marker 全部从像素实测，agent 只负责语义；每个测量器都经过阳性+阴性用例双验。

## 环境要求

- Windows（PowerPoint COM 用于渲染回读验证；**没有 PowerPoint 也能出 PPTX**，只是跳过渲染验证）
- Python 3.10+

```bash
pip install -r requirements.txt
```

装好后先跑自检（生成合成样张走全链路，含 marker 正/负回归用例）：

```bash
python scripts/pipeline.py --selftest
```

## 使用方式

### 作为 agent skill（推荐）

把本仓库放进你的 agent skill 目录（如 Claude Code 的 `~/.claude/skills/img2ppt-lite/`），agent 会按 `SKILL.md` 的 5 步工作流执行：

```
1. pipeline.py --run <run> --seed     # OCR 冷启动，产出待审阅的文本骨架
2. [agent 看图] 校对 seed、补图形/色块标注 → elements.json
3. pipeline.py --run <run>            # OCR校准 → 切图 → 装配 → COM 渲染验收
4. [agent 看图] 对比 compare.png，必要时改 JSON 秒级重跑
5. 交付 pptx + 验收报告（修补硬上限 2 轮，禁无限迭代）
```

### 手动使用

不用 agent 也可以：

Revisar el código fuente

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Licencia: MIT

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Low GitHub adoption signal
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • GitHub adoption: 23 GitHub stars
  • Stars/forks activity: 23 stars, 4 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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Empieza con una tarea pequeña

  1. 1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
  2. 2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
  3. 3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.

Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

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Repositorio fuente
TianLin0509/img2ppt-lite
Licencia
MIT
Versión
1.0.0
Último push de GitHub
24 jul 2026
Registro actualizado
10 sept 2026
Ruta de instrucciones
Estructura sin verificar

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

71/100

Sólido

Confianza

59/100

Do not auto-install

Auditoría

75/100

Requiere revisión

Abrir auditoría completaVer informe de evaluación
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Low GitHub adoption signal
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • GitHub adoption: 23 GitHub stars
  • Stars/forks activity: 23 stars, 4 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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Más detalles
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La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.

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Esta ficha Indexado por la comunidad se atribuye a TianLin0509, pero aún no está marcada como oficial. Reclámala para añadir una señal de propietario verificado y hacer más fiables futuras actualizaciones de lanzamiento, instalación y auditoría.

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/tianlin0509-img2ppt-lite?metric=listed&label=Listed)](https://www.openagentskill.com/skills/tianlin0509-img2ppt-lite?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/tianlin0509-img2ppt-lite?metric=trust&label=Trust)](https://www.openagentskill.com/skills/tianlin0509-img2ppt-lite?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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[![Agent Proven](https://www.openagentskill.com/api/badge/tianlin0509-img2ppt-lite?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/tianlin0509-img2ppt-lite?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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