Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Vision Tagger
Tag and annotate images using Apple Vision framework (macOS only). Detects faces, bodies, hands, text (OCR), barcodes, objects, scene labels, and saliency re...
skill openclawclawhub Free
Tag and annotate images using Apple Vision framework (macOS only). Detects faces, bodies, hands, text (OCR), barcodes, objects, scene labels, and saliency re...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
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Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/setup.sh, scripts/image_tagger.swift, scripts/annotate_image.py
Validation
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- sagarjhaa
- Source page
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Documentation
Vision Tagger
macOS-native image analysis using Apple's Vision framework. All processing is local β no cloud APIs, no API keys needed.
Requirements
macOS 12+ (Monterey or later) Xcode Command Line Tools Python 3 with Pillow
Setup (one-time)
# Install Xcode CLI tools if needed xcode-select --install # Install Pillow pip3 install Pillow # Compile the Swift binary cd scripts/ swiftc -O -o image_tagger image_tagger.swift
Analyze image β JSON
./scripts/image_tagger /path/to/photo.jpg Output includes: faces β bounding boxes, roll/yaw/pitch, landmarks (eyes, nose, mouth) bodies β 18 skeleton joints with confidence scores hands β 21 joints per hand (left/right) text β OCR results with bounding boxes labels β scene classification (desk, outdoor, clothing, etc.) barcodes β QR codes, UPC, etc. saliency β attention and objectness regions
Annotate image with boxes
python3 scripts/annotate_image.py photo.jpg output.jpg Draws colored boxes: π’ Green: faces π Orange: body skeleton π£ Magenta: hands π΅ Cyan: text regions π‘ Yellow: rectangles/objects Scene labels at bottom
Python integration
import subprocess, json def analyze(path): r = subprocess.run(['./scripts/image_tagger', path], capture_output=True, text=True) return json.loads(r.stdout[r.stdout.find('{'):]) tags = analyze('photo.jpg') print(tags['labels']) # [{'label': 'desk', 'confidence': 0.85}, ...] print(tags['faces']) # [{'bbox': {...}, 'confidence': 0.99, 'yaw': 5.2}]
Example JSON Output
{ "dimensions": {"width": 1920, "height": 1080}, "faces": [{"bbox": {"x": 0.3, "y": 0.4, "width": 0.15, "height": 0.2}, "confidence": 0.99, "roll": -2, "yaw": 5}], "bodies": [{"joints": {"head_joint": {"x": 0.5, "y": 0.7, "confidence": 0.9}, "left_shoulder": {...}}, "confidence": 1}], "hands": [{"chirality": "left", "joints": {"VNHLKWRI": {"x": 0.4, "y": 0.3, "confidence": 0.85}}}], "text": [{"text": "HELLO", "confidence": 0.95, "bbox": {...}}], "labels": [{"label": "outdoor", "confidence": 0.88}, {"label": "sky", "confidence": 0.75}], "saliency": {"attentionBased": [{"x": 0.2, "y": 0.1, "width": 0.6, "height": 0.8}]} }
Detection Capabilities
FeatureDetailsFacesBounding box, confidence, roll/yaw/pitch angles, 76-point landmarksBodies18 joints: head, neck, shoulders, elbows, wrists, hips, knees, anklesHands21 joints per hand, left/right chiralityText (OCR)Recognized text with confidence and bounding boxesLabels1000+ scene/object categories (clothing, furniture, outdoor, etc.)BarcodesQR, UPC, EAN, Code128, PDF417, Aztec, DataMatrixSaliencyAttention-based and objectness-based regions
Use Cases
Photo tagging β Auto-tag photos with detected objects/scenes Posture monitoring β Track face/body position for ergonomics Document scanning β Extract text from images Security β Detect people in camera feeds Accessibility β Describe image contents
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Scripts1 Docs1 Files
- SKILL.md
- scripts/annotate_image.py
- scripts/setup.sh
- scripts/image_tagger.swift