DeepSeek Harness 插件

dsh-imagedit

Local image editing toolkit for DeepSeek Harness: one image_edit tool for deterministic edits — cutout (quick flood-fill or rembg AI), trim, flip, rotate, brightness/contrast/saturation, blur(英文原文)

跳到安装方式

来源信息

GitHub 仓库
bbbz123/dsh-imagedit
最近更新
2026年8月20日
分类
工具与能力
GitHub stars
0
载体类型
plugin
目录证据
上游声明已找到 dsh.bundle
证据路径
package.json#dsh.bundle
核对版本
0.1.0-rc.8
上游核对日期
2026-08-21

该证据由上游目录提供。本站没有安装、运行或安全审核这个插件。

安装

默认先复制一段 Prompt,让 Agent 读 GitHub 仓库和源码;需要自己装时再切到命令。

复制这段 Prompt,发给 DSH、Codex 或其他 Agent,让它先读 GitHub 仓库和源码。

请先不要安装或执行任何命令。阅读这个插件的 GitHub 仓库、README 和关键源码,然后用清楚、直接的方式回答以下问题,帮助我判断它是否适合我的需求:

1. 这个插件是什么,解决什么问题;
2. 适合哪些用户和典型使用场景;
3. 安装后如何使用,并给出一个最小使用示例;
4. 有哪些已知限制,以及隐私、安全、兼容性或维护风险;
5. 给出“推荐 / 有条件推荐 / 不推荐”的明确建议和理由。

请区分仓库明确说明、根据源码推断和未知信息。证据不足时请明确说明,不要猜测或照抄 README。

GitHub:https://github.com/bbbz123/dsh-imagedit
插件名:dsh-imagedit
作者:bbbz123

检查来源文件

安装前先看这个插件目录里的 README 和其他文件。

文件资源管理器3 个文件
README.md来源说明 · 只读预览

dsh-imagedit

本地图像编辑工具箱 — A DeepSeek Harness (DSH) plugin and skill for deterministic local image editing: background cutout (rembg AI or instant flood-fill), trim, flip, rotate, brightness/contrast/saturation, blur, sharpen, rounded corners, border, canvas normalization, sprite sheets, and PNG/JPEG/WebP export.

Dual form: a cordis plugin that registers an image_edit agent tool (agent calls it directly with structured parameters), and a skill with the same functionality via a Python CLI. Originally adapted from a Codex skill and upgraded for DSH (dependency-free quick cutout, sprite sheets, JPEG export, batch-directory processing).

Features

AreaOperations
抠图remove_bg: quick (flood-fill, no model) · remove_bg: rembg (AI)
基础trim · flip h/v · rotate · padding · canvas WxH (scale+center)
调色brightness · contrast · saturation
滤镜blur · sharpen
修饰rounded (corners) · border W[,HEX] · auto-orient (EXIF)
批量JSON manifest · --dir recursive folder · sprite sheet + sprites.json
导出PNG (oxipng/pngquant auto) · JPEG · WebP (quality)

Install

As a plugin (agent tool image_edit) — recommended

# local link install
dsh plugin --profile web add link:D:\path\to\dsh-imagedit

# once published on npm / GitHub
dsh plugin --profile web add dsh-imagedit
# or
dsh plugin --profile web add github:bbbz123/dsh-imagedit

Restart dsh web; the image_edit tool then appears in the tool catalog and the agent can call it directly. Override the Python interpreter with the DSH_IMAGEDIT_PYTHON environment variable if needed.

As a skill (CLI)

git clone https://github.com/bbbz123/dsh-imagedit "$HOME\.dsh\skills\dsh-imagedit"
# or copy the folder into <project>\.dsh\skills\dsh-imagedit for one project

DSH discovers it automatically. The skill instructs the agent to prefer the image_edit tool when available and fall back to the CLI.

Usage

Agent tool

Call image_edit with structured params, e.g.:

{
  "input": "gen/sword.png",
  "remove_bg": "quick",
  "bg_color": "auto",
  "canvas": "256x256",
  "padding": 16,
  "formats": ["png", "webp"]
}

CLI

# quick flood-fill cutout + canvas
python scripts/asset_pipeline.py run --input item.png `
  --remove-bg-quick auto --canvas 256x256 --padding 16 --out-dir output/images/edited

# combined edits
python scripts/asset_pipeline.py run --input item.png `
  --remove-bg-quick #FFFFFF --rotate 90 --flip h `
  --brightness 1.1 --saturation 1.2 --rounded 30 --border "4,#FF0000" `
  --exports png jpg --out-dir output/images/edited

# batch a whole folder (recursive)
python scripts/asset_pipeline.py batch --dir ./photos --rotate 90 --exports jpg

# batch manifest + sprite sheet
python scripts/asset_pipeline.py batch --manifest manifest.json

See [references/cli.md](references/cli.md) for the full CLI reference and manifest schema, and SKILL.md for the agent-facing instructions.

Recommended pipeline (with dsh-draw + vision-router)

dsh-draw / image_generate  (prompt: "flat solid white background, no shadows")
  → image_edit (remove_bg: quick, canvas, padding)  →  engine-ready PNG
  → batch manifest with "sheet" for a sprite atlas

For complex assets prefer remove_bg: "rembg"; for one-off visual checks of the cutout use vision_extract_foreground.

Requirements

  • Python 3.10+ and Pillow (required)
  • rembg (optional, only for the rembg cutout mode)
  • oxipng / pngquant (optional, auto-used when on PATH)

License

MIT — see [LICENSE](LICENSE).