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.

Quelle prüfenAuf GitHub ansehen
Preis unbestätigt★ 23 GitHub-StarsVerzeichnis aktualisiert · 10. Sept. 2026agent-skillskillagent

Übersicht

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.

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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:

Originaltext anzeigen
# 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:

Quelle prüfen

Preis und Betriebskosten

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Quelle erneut prüfen

Die Quelle wurde geändert oder konnte nicht synchronisiert werden. Vor der Installation prüfen.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: 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
Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

  1. 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
  2. 2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
  3. 3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository
TianLin0509/img2ppt-lite
Lizenz
MIT
Version
1.0.0
Letzter GitHub-Push
24. Juli 2026
Verzeichnis aktualisiert
10. Sept. 2026
Anleitungspfad
Quellstruktur ungeprüft

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

71/100

Stark

Vertrauen

59/100

Do not auto-install

Audit

75/100

Prüfung nötig

  • 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
Verified installs
—
Ergebnisse
—

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Weitere Details
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TianLin0509
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Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

Diesen Skill beanspruchen

Eigentümeranspruch

Diesen Skill-Eintrag beanspruchen

Dieser Community-indexiert-Eintrag wird TianLin0509 zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

Share-Kit

Creator-Backlink-Kit

Evidenz-Badges in deine README einfügen

Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.

[![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)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/tianlin0509-img2ppt-lite?metric=audit&label=Audit)](https://www.openagentskill.com/skills/tianlin0509-img2ppt-lite/audit)
[![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)

Community-Signal

Teile mit, ob dieser Skill für deinen Agent-Workflow nützlich ist. Zusammengefasstes Feedback verbessert das Ranking im Laufe der Zeit.