DeepCamera segmentation-sam2

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

install

source · Clone the upstream repo

git clone https://github.com/SharpAI/DeepCamera

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/SharpAI/DeepCamera "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/segmentation/sam2-segmentation" ~/.claude/skills/sharpai-deepcamera-segmentation-sam2 && rm -rf "$T"

manifest: skills/segmentation/sam2-segmentation/SKILL.md

source content

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