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Segmentation Sam2

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segmentation-sam2

Segmentation Sam2 is an Agent Skill for documents published in the SharpAI/DeepCamera repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Interactive click-to-segment using Segment Anything 2 — AI-assisted labeling for Annotation Studio

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QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/SharpAI/DeepCamera/blob/HEAD/skills/segmentation/sam2-segmentation/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/segmentation-sam2, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

SAM2 Interactive Segmentation

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.

What You Get

  • Click-to-segment — click on any object to get its mask
  • Point & box prompts — positive/negative points and bounding box selection
  • Video tracking — segment in one frame, propagate across the clip
  • Annotation Studio — full integration with sidebar Annotation Studio

Protocol

Communicates via JSON lines over stdin/stdout.

Aegis → Skill (stdin)

{"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"}

Skill → Aegis (stdout)

{"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}}

Installation

The deploy.sh bootstrapper handles everything — Python environment, GPU detection, dependency installation, and model download. No manual setup required.

./deploy.sh