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Interactive click-to-segment using Segment Anything 2 — AI-assisted labeling for Annotation Studio
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch)
LLM & VLM evaluation suite for home security AI applications
YOLO 2026 — state-of-the-art real-time object detection
Google Coral Edge TPU — real-time object detection natively (macOS / Linux)
Google Coral Edge TPU — real-time object detection natively via Windows WSL
Connectivity, chat, JSON & streaming regression tests for all enabled cloud LLM providers
| name | segmentation-sam2 |
| description | Interactive click-to-segment using Segment Anything 2 — AI-assisted labeling for Annotation Studio |
| version | 1.0.0 |
| entry | scripts/segment.py |
| deploy | deploy.sh |
| parameters | [{"name":"model","label":"SAM2 Model","type":"select","options":["sam2-tiny","sam2-small","sam2-base","sam2-large"],"default":"sam2-small","group":"Model"},{"name":"device","label":"Device","type":"select","options":["auto","cpu","cuda","mps"],"default":"auto","group":"Performance"}] |
| capabilities | {"live_transform":{"script":"scripts/segment.py","description":"Interactive segmentation on frames"}} |
Click anywhere on a video frame to segment objects using Meta's Segment Anything 2. Generates pixel-perfect masks for annotation, tracking, and dataset creation.
Communicates via JSON lines over stdin/stdout.
{"event": "frame", "frame_path": "/tmp/frame.jpg", "frame_id": "frame_1", "request_id": "req_001"}
{"command": "segment", "points": [{"x": 450, "y": 320, "label": 1}], "request_id": "req_002"}
{"command": "track", "frame_path": "/tmp/frame2.jpg", "frame_id": "frame_2", "request_id": "req_003"}
{"command": "stop"}
{"event": "segmentation", "type": "ready", "request_id": "", "data": {"model": "sam2-small", "device": "mps"}}
{"event": "segmentation", "type": "encoded", "request_id": "req_001", "data": {"frame_id": "frame_1", "width": 1920, "height": 1080}}
{"event": "segmentation", "type": "segmented", "request_id": "req_002", "data": {"mask_path": "/tmp/mask.png", "mask_b64": "...", "score": 0.95, "bbox": [100, 50, 350, 420]}}
{"event": "segmentation", "type": "tracked", "request_id": "req_003", "data": {"frame_id": "frame_2", "mask_path": "/tmp/track.png", "score": 0.93}}
The deploy.sh bootstrapper handles everything — Python environment, GPU detection, dependency installation, and model download. No manual setup required.
./deploy.sh