dsh-maclens
将 Apple 的设备端 Vision framework（macOS）接入 DeepSeek Harness：提供 OCR、图像分类、人脸检测和文档布局分析作为本地 dsh 工具。无需网络、API 密钥或守护进程。
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:Harzva/dsh-maclens#3d8c56110be0d7851332ecee2cf76707414b4a02Maclens 以 dsh-maclens 发布，当前版本为 0.1.2。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
Apple's on-device Vision framework, bridged into DeepSeek Harness (
dsh) as local tools: OCR, image classification, face detection, document layout, and a combined read — 100% offline, no API key, no daemon.
| 🔒 Privacy | Every pixel stays on your Mac. No network, no upload, no telemetry. |
| ⚡ Speed | Sub-second OCR on typical screenshots (Neural Engine). |
| 🇨🇳 Languages | zh-Hans + 30+ recognition languages out of the box. |
| 🖼️ Tall images | slice splits long screenshots so small text survives Vision's downscale. |
| 🧩 No deps | Swift CLI ships in the npm package — no build step to install. |
# 1. Install into your dsh profile
dsh plugin --profile desktop add dsh-maclens
# 2. Restart DSH. Then just ask the model:
# "OCR this screenshot: /Users/me/Desktop/shot.png"
The five tools the model can call:
| Tool | One-liner |
|---|---|
maclens_ocr | "Read all the text in this image" — every line + confidence + box |
maclens_classify | "What kind of image is this?" — document, chart, photo, … |
maclens_faces | "Are there people in this image?" — face boxes + count |
maclens_document | "Parse this page" — OCR + left/right column layout |
maclens_describe | "Give me everything at once" — OCR + classify + faces + layout |
When to pick maclens vs a VLM: maclens is a CV toolkit — it transcribes, classifies, detects but does not narrate "what this image is about". Need open-ended understanding? Pair it with a VLM bridge (e.g. modlens + qwen-vl). Need fast, free, private OCR/detection? maclens.
Plugin: dsh-maclens (npm), installs with dsh plugin add
Runtime: macOS 14+ with Xcode Command Line Tools (Swift 6+)
Tools: maclens_ocr | maclens_classify | maclens_faces | maclens_document | maclens_describe
Input: absolute local image path (string) — required on every tool
Output: one JSON object on stdout; {"error": "..."} + exit 1 on failure
Binary: bin/maclens in the package (auto-chmod'd), else MACLENS_BIN, else PATH
No network: the CLI makes zero network requests
# From npm — includes the prebuilt binary, no build step:
dsh plugin --profile desktop add dsh-maclens
# From a git checkout — build the Swift bridge first:
cd dsh-maclens && bash scripts/build.sh # produces bin/maclens
dsh plugin --profile desktop add ./dsh-maclens
Binary resolution order: bin/maclens in the package → $MACLENS_BIN → swift/.build/release/MaclensBridge → maclens on PATH. The plugin chmods the found binary to 0755 at resolve time (npm tarballs drop the exec bit).
All five tools take path (required, string). OCR-family tools additionally accept:
| Field | Type | Default | Meaning |
|---|---|---|---|
languages | string | zh-Hans,en-US | Comma-separated recognition languages |
maxLines | number | — | Cap returned OCR lines (large screenshots) |
slice | boolean | false | Slice tall images into overlapping strips |
sliceHeight | number | 4096 | Strip height in px when slicing |
top | number | 5 | Classify only: how many categories to return |
maclens_ocr returns:
{
"task": "ocr",
"language": ["zh-Hans", "en-US"],
"full_text": "跨境增长研究室\n从一个问题，抵达一个决定。",
"lines": [
{
"text": "跨境增长研究室",
"confidence": 1.0,
"bbox": { "x": 0.055, "y": 0.519, "width": 0.517, "height": 0.144 }
}
],
"line_count": 2,
"truncated": false,
"sliced": false
}
bbox is normalized (0–1), origin top-left (converted from Vision's bottom-left so it is intuitive).slice: true, tall images are split into overlapping strips, each strip OCR'd, results stitched back to whole-image coordinates, and duplicate lines in the overlap band de-duplicated. Output adds "sliced": true and "slice_count": N.maclens_document = ocr + layout.columns (left/right) + layout.image_dimensions.maclens_describe = ocr + classification.observations + faces + layout.maclens_faces → faces[] + face_count; maclens_classify → observations[] (identifier, confidence).| Exit | Meaning |
|---|---|
| 0 | Success |
| 1 | Runtime error — stdout is {"error": "..."} (e.g. file does not exist: <path>) |
| 2 | Usage / unknown task — stdout is {"error": "usage: ..."} |
bin/maclens ocr --image /path/to/img.png
bin/maclens classify --image /path/to/img.png --top 3
bin/maclens faces --image /path/to/img.png
bin/maclens document --image /path/to/img.png --slice
bin/maclens describe --image /path/to/img.png --slice --top 2
dsh (text-only model)
└─ maclens_* tools (lib/index.js)
└─ bin/maclens (Swift CLI, spawned per call — no daemon, no ports)
└─ Apple Vision: VNRecognizeTextRequest / VNClassifyImageRequest /
VNDetectFaceRectanglesRequest ← on-device, offline
cd swift && swift build -c release
swift test --package-path swift # 6 behavioral tests
CI (GitHub Actions): Swift release build + smoke tests on macOS, plugin-load + pack-contents check on Ubuntu. All green on main.
AGENTS.md — the agent-facing quick reference (mirrors this section).CHANGELOG.md — version history.SECURITY.md — security model & reporting.MIT — see LICENSE.