commit 7d3c36050bc1ec02fc18bfa26d449b09912b3777
Author: ravi <ravikumar@seekright.com>
Date:   Sat Apr 25 16:23:09 2026 +0530

    first version

diff --git a/CLAUDE.md b/CLAUDE.md
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+
+| Principle | Addresses |
+|-----------|-----------|
+| **Think Before Coding** | Wrong assumptions, hidden confusion, missing tradeoffs |
+| **Simplicity First** | Overcomplication, bloated abstractions |
+| **Surgical Changes** | Orthogonal edits, touching code you shouldn't |
+| **Goal-Driven Execution** | Leverage through tests-first, verifiable success criteria |
diff --git a/README.md b/README.md
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+# SAM-Tool
+
+Desktop video-frame extraction + image annotation tool, with optional
+SAM 2 / SAM 3 segmentation backend. Built to replace an earlier
+Python/Tkinter prototype (`sam_tool_v2.py`) for YOLO-style training
+dataset prep.
+
+```
+┌──────────────────┐        HTTP        ┌──────────────────┐
+│   sam-tool-tauri │ ◀───────────────▶ │   sam2-backend   │
+│ (Tauri/Rust + UI)│   /predict, etc   │ (FastAPI + GPU)  │
+└──────────────────┘                    └──────────────────┘
+        │                                        │
+        ▼                                        ▼
+   local images +                         SAM 2.x / SAM 3
+   sibling JSONs                          weights on disk
+```
+
+The two halves can run on the same machine or separate boxes — point the
+desktop app at any reachable backend URL from **Settings**.
+
+---
+
+## Layout
+
+```
+SAM-Tool/
+├── sam-tool-tauri/         # Desktop app (Tauri 2, React 18, TS, Vite)
+│   ├── src/                # Frontend
+│   ├── src-tauri/          # Rust commands (video, decoder, IPC)
+│   └── package.json        # `npm run tauri dev` / `tauri build`
+│
+├── sam2-backend/           # FastAPI HTTP server (SAM 2 + SAM 3)
+│   ├── server.py           # /, /models, /predict
+│   ├── registry.py         # Lazy-load + single-resident cache
+│   ├── adapters/
+│   │   ├── sam2_adapter.py
+│   │   ├── sam3_adapter.py
+│   │   └── utils.py        # mask → polygon helper
+│   ├── models.yaml         # Model registry (paths + family)
+│   ├── run.sh              # Convenience launcher (env vars)
+│   └── README.md           # Install + run on the server side
+│
+├── sam2/sam2/              # Meta SAM 2 source + checkpoints (vendored)
+├── files/sam3_weights/     # SAM 3 weights (3.4 GB sam3.pt)
+├── files/sam3_server.py    # Reference SAM3 server (used as a guide)
+│
+├── sam_tool_v2.py          # Original Python/Tkinter tool (legacy)
+├── sam_annotation_test/    # Sample data — dashcam MP4 + custom SAM JSON
+│
+├── docs/                   # ← you are here
+│   ├── POLYGON_SMOOTHING.md   How to tune mask→polygon output
+│   ├── HF_DEPLOY_OFFLINE.md   First-online-then-airgapped HF deploy
+│   └── BUILD_RELEASE.md       Building the Tauri app for release
+│
+└── CLAUDE.md               # Project rules (think → simplify → surgical)
+```
+
+---
+
+## Quick start (single-machine dev)
+
+### 1. SAM backend
+
+```bash
+cd sam2-backend
+python3 -m venv .venv && source .venv/bin/activate
+pip install --upgrade pip
+
+# Pick the right Torch wheel for your GPU (driver 570+ → cu128):
+pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128
+# CPU-only fallback (slow):
+# pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
+
+# SAM 2 source + deps
+pip install -e ../sam2/sam2
+pip install -r requirements.txt
+
+# Optional — SAM 3 support (needs HF account + accepted gating)
+git clone https://github.com/facebookresearch/sam3.git ..
+pip install -e ../sam3
+pip install "transformers>=4.57"
+hf auth login              # paste read token from https://huggingface.co/settings/tokens
+
+./run.sh                   # binds 0.0.0.0:9090, auto-picks cuda/cpu
+```
+
+Smoke-test:
+```bash
+curl http://localhost:9090/         # {"status":"ok",...}
+curl http://localhost:9090/models   # registered models
+```
+
+Full backend setup details: [`sam2-backend/README.md`](sam2-backend/README.md).
+
+### 2. Tauri desktop app
+
+```bash
+cd sam-tool-tauri
+npm install
+npm run tauri dev                 # hot-reload dev mode
+
+# Or for a real build / installer:
+npm run tauri build               # see docs/BUILD_RELEASE.md
+```
+
+In the running app:
+1. **Login** — pick or type a username (no password — used for annotation
+   author stamping).
+2. **Extract mode** — Upload a video → optionally `Import annotations…`
+   (COCO / SAM JSON) → click `choose output folder…` (or just open a
+   video; the default `<video>_extracted/` folder is auto-adopted) →
+   scrub timeline, press **E** to extract, **U** to undo, **[ … ]** for
+   bulk range.
+3. **Annotate mode** — Open the extracted folder. Draw bboxes (B) /
+   polygons (P), label them via the picker, switch to select (S) to
+   move/resize/reclass. Settings → enable SAM, pick a model, get
+   polygons back instead of bboxes.
+
+### 3. Deploying to another PC
+
+Backend → see the install steps in [`sam2-backend/README.md`](sam2-backend/README.md)
+("install on a different PC" section).
+
+Desktop app → [`docs/BUILD_RELEASE.md`](docs/BUILD_RELEASE.md) covers
+producing `.deb` / AppImage installers.
+
+---
+
+## Feature highlights
+
+### Extract mode
+- Persistent `ffmpeg -f image2pipe` subprocess + LRU cache + prefetch
+  worker → smooth playback up to ~2× on 1080p.
+- Per-video default output folder (`<video>_extracted/`) auto-restored on
+  reopen — extracted-frame state and "EXTRACTED" tint reflected
+  immediately.
+- Timeline ticks mark every previously-extracted frame at a glance.
+- COCO + custom SAM JSON import, smart overlay with per-class colours,
+  Review mode auto-pauses on annotated frames.
+- `Space` play/pause · `←→` ±1 frame · `Ctrl+←→` ±10 · `↑↓` speed 0.25–4×
+  · `H` overlay · `E` extract · `U` undo · `[ … ]` bulk range.
+
+### Annotate mode
+- Sibling JSON per image with `source_video` field — annotations know
+  where they came from.
+- Bbox + polygon tools, vertex drag (rAF-throttled), bbox handles, undo
+  (40 deep) + redo, Tab-reclass, inline class creation in the picker.
+- Multi-select image list (Shift-range, Ctrl-toggle) + bulk delete with
+  two-step confirmation.
+- A/D image navigation, +/- zoom, cursor-anchored Ctrl+wheel zoom.
+- SAM mode round-trip with live phase indicator showing model + inference
+  time (auto-fades after 3 s).
+- Auto-switch to **select** mode after committing each new shape.
+
+### SAM backend
+- Pluggable model registry — add SAM-3 / SAM-4 / etc. by dropping an
+  adapter into `adapters/` and one entry into `models.yaml`.
+- Lazy-load with single-resident eviction (set
+  `SAM_BACKEND_KEEP_ALL=1` to keep all loaded if you have VRAM).
+- Speaks the Label-Studio-ML `/predict` protocol the Tauri client
+  already used — drop-in compatible.
+- Local SAM 3 image-predictor + HuggingFace SAM 3 tracker both supported.
+- Privacy-first: telemetry off by default, fully air-gappable after the
+  first weight download — see
+  [`docs/HF_DEPLOY_OFFLINE.md`](docs/HF_DEPLOY_OFFLINE.md).
+
+---
+
+## Documentation index
+
+| Doc                                                | What it covers                                              |
+|----------------------------------------------------|-------------------------------------------------------------|
+| [docs/POLYGON_SMOOTHING.md](docs/POLYGON_SMOOTHING.md) | Tune mask→polygon: point count, kernel size, smoothing on/off |
+| [docs/HF_DEPLOY_OFFLINE.md](docs/HF_DEPLOY_OFFLINE.md) | Download HF SAM 3 weights, then run fully offline           |
+| [docs/BUILD_RELEASE.md](docs/BUILD_RELEASE.md)       | Build the Tauri app for production (deb / AppImage / binary) |
+| [sam2-backend/README.md](sam2-backend/README.md)     | Backend install, run, API reference, model-registry details |
+| [CLAUDE.md](CLAUDE.md)                               | Project rules — Think Before Coding · Simplicity First · Surgical Changes · Goal-Driven Execution |
+
+---
+
+## Stack
+
+- **Desktop:** Tauri 2 shell, Rust backend, React 18 + TypeScript, Vite,
+  WebKit2GTK 2.50.
+- **Backend:** Python 3.10+, FastAPI, uvicorn, Pillow, NumPy, OpenCV
+  (headless), PyTorch ≥ 2.4 with matching CUDA, optional `transformers ≥ 4.57`.
+- **Models:** Meta SAM 2 / SAM 2.1 (4 Hiera variants), Meta SAM 3.
+- **Target OS:** Linux (Ubuntu 22.04 / Pop!_OS), AMD APU + NVIDIA RTX
+  GPUs tested.
+
+---
+
+## Conventions
+
+From [CLAUDE.md](CLAUDE.md):
+
+- **Think Before Coding** — wrong assumptions are the most expensive bugs.
+- **Simplicity First** — fewer moving parts beats clever abstractions.
+- **Surgical Changes** — touch what the task needs, leave the rest.
+- **Goal-Driven Execution** — verify the user-facing behaviour, not just
+  the code path.
+
+A few specific rules learned the hard way:
+- `React.StrictMode` double-invokes setState updaters; don't put side
+  effects (like `queueMicrotask` calls or save requests) inside them.
+  Use refs + explicit calls instead.
+- No `crypto.randomUUID()` without a fallback — webview contexts
+  sometimes lack it; use the `uuid()` helper.
+- Output folders must live **outside** `sam-tool-tauri/` — the dev
+  watcher restarts the app on any file change inside the project tree.
+- Backend telemetry stays **off** by default; HF cache moves to a known
+  `HF_HOME` for explicit deployment.
diff --git a/docs/BUILD_RELEASE.md b/docs/BUILD_RELEASE.md
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+# Building the Tauri app for release (instead of `dev` mode)
+
+Day-to-day you've been running the desktop app via `npm run tauri dev` —
+that's the dev server: hot-reload, attached browser devtools, slower
+binary, and it stays bound to your terminal. For real use (or shipping to
+another machine), you build a standalone executable + installer.
+
+---
+
+## TL;DR
+
+```bash
+cd sam-tool-tauri
+npm install                  # only on first run / after pulling deps
+npm run tauri build          # produces a release binary + installer
+```
+
+Output lands under
+`sam-tool-tauri/src-tauri/target/release/bundle/`. On Linux you'll find:
+
+- `bundle/deb/sam-tool-tauri_<version>_amd64.deb` — installable on
+  Debian/Ubuntu hosts (`sudo dpkg -i …`).
+- `bundle/appimage/sam-tool-tauri_<version>_amd64.AppImage` — single
+  portable file, `chmod +x` and run.
+- `bundle/rpm/…` if rpm tooling is present.
+
+Plus the raw binary at
+`src-tauri/target/release/sam-tool-tauri` (no installer, just `./run` it).
+
+---
+
+## Prerequisites (Linux)
+
+If you've only ever run `tauri dev`, you might be missing the system
+libraries the bundler needs. On Ubuntu / Debian / Pop!_OS:
+
+```bash
+sudo apt update
+sudo apt install -y \
+    libwebkit2gtk-4.1-dev \
+    libgtk-3-dev \
+    libayatana-appindicator3-dev \
+    librsvg2-dev \
+    patchelf \
+    build-essential \
+    curl wget file \
+    libssl-dev pkg-config
+```
+
+You also need a recent Rust toolchain (Tauri 2 requires `rustc >= 1.77`):
+
+```bash
+rustup update stable
+rustc --version    # confirm >= 1.77
+```
+
+For AppImage bundling specifically, the Tauri CLI auto-fetches the
+AppImage tools on first build. The first run will be slower; subsequent
+builds reuse the cache.
+
+---
+
+## Build profiles
+
+Two ways to produce a release binary, with different tradeoffs:
+
+### A. `npm run tauri build` — full installer build
+
+```bash
+npm run tauri build
+```
+
+What it does:
+1. Runs `vite build` → produces optimized JS/CSS in `dist/`.
+2. Compiles `src-tauri/` Rust crate in `--release` mode.
+3. Bundles everything into platform-native installers under
+   `bundle/`.
+
+Pros: production-grade, optimised, signable, distributable.
+Cons: slow first time (Rust release builds + bundler downloads).
+
+### B. `cargo build --release` — binary only, skip installers
+
+If you only want the executable (e.g. for quick testing on a sibling
+machine on the same OS), drop the bundling step:
+
+```bash
+cd sam-tool-tauri
+npm run build                # bundles the frontend → dist/
+cd src-tauri
+cargo build --release        # binary at target/release/sam-tool-tauri
+```
+
+The resulting binary expects `dist/` from the same checkout to be
+present (Tauri 2 embeds frontend assets, so once compiled it's
+self-contained). Faster than the full bundle, but no `.deb`/`.AppImage`.
+
+### C. Debug build — fast compile, big binary
+
+For iterating on Rust changes without a full release re-compile:
+
+```bash
+cd src-tauri
+cargo build           # debug profile — fast compile, big slow binary
+./target/debug/sam-tool-tauri
+```
+
+This is essentially what `tauri dev` runs under the hood, minus the dev
+server.
+
+---
+
+## Where the artefacts go
+
+```
+sam-tool-tauri/
+├── dist/                                 ← bundled frontend (vite build)
+└── src-tauri/target/
+    ├── debug/sam-tool-tauri              ← debug binary
+    └── release/
+        ├── sam-tool-tauri                ← optimised binary
+        └── bundle/
+            ├── deb/sam-tool-tauri_<v>_amd64.deb
+            ├── appimage/sam-tool-tauri_<v>_amd64.AppImage
+            └── rpm/…    (if `rpm` tools are installed)
+```
+
+`bundle/deb/` and `bundle/appimage/` are the things you'd ship to other
+machines.
+
+---
+
+## Versioning
+
+The version string comes from
+[`src-tauri/tauri.conf.json`](../sam-tool-tauri/src-tauri/tauri.conf.json) →
+`"version": "..."`. Bump it before each release; the value is baked
+into both the binary and the installer filenames.
+
+---
+
+## Smoke-test the release build locally
+
+```bash
+# Run the binary directly:
+./src-tauri/target/release/sam-tool-tauri
+
+# Or install the .deb and launch from your app menu:
+sudo dpkg -i src-tauri/target/release/bundle/deb/sam-tool-tauri_*_amd64.deb
+sam-tool-tauri          # binary on $PATH after dpkg
+
+# Or run the AppImage portably:
+chmod +x src-tauri/target/release/bundle/appimage/sam-tool-tauri_*.AppImage
+./src-tauri/target/release/bundle/appimage/sam-tool-tauri_*.AppImage
+```
+
+The release build behaves exactly like `dev` (same UI, same SAM
+integration), with these differences:
+
+- No devtools by default. To re-enable in a release build, edit
+  `src-tauri/tauri.conf.json` → `"app.windows[0].devtools": true`,
+  rebuild.
+- Faster cold start, smaller memory footprint, optimised JS bundle.
+- React `StrictMode` still doubles state-updaters in dev — but that's a
+  React-side flag, not Tauri's. The stripped-down behaviour comes from
+  Vite's production mode (`process.env.NODE_ENV === 'production'`).
+
+---
+
+## Distributing to another PC
+
+The user only needs **one** of the bundles:
+
+**Debian/Ubuntu:**
+```bash
+scp src-tauri/target/release/bundle/deb/sam-tool-tauri_*_amd64.deb \
+    target-host:/tmp/
+ssh target-host "sudo dpkg -i /tmp/sam-tool-tauri_*_amd64.deb"
+```
+
+**Anywhere with glibc:**
+```bash
+scp src-tauri/target/release/bundle/appimage/sam-tool-tauri_*.AppImage \
+    target-host:/home/user/
+ssh target-host "chmod +x ~/sam-tool-tauri_*.AppImage && ~/sam-tool-tauri_*.AppImage"
+```
+
+The target host needs `webkit2gtk` runtime (most desktops already have
+it) and `ffmpeg` if they want the Extract mode to decode video. The
+sibling SAM2 backend and weights are *not* bundled into the desktop app —
+those live separately and are reached over HTTP, configurable from
+**Settings → SAM backend URL**.
+
+---
+
+## Common build errors
+
+| Error                                                        | Fix                                                                                       |
+|--------------------------------------------------------------|-------------------------------------------------------------------------------------------|
+| `error: linker 'cc' not found`                               | `sudo apt install build-essential`                                                        |
+| `failed to find webkit2gtk-4.1` / `gobject-introspection`    | `sudo apt install libwebkit2gtk-4.1-dev libgtk-3-dev` (see prerequisites)                 |
+| `failed to bundle .deb`                                      | Missing `dpkg-deb`. `sudo apt install dpkg`                                               |
+| AppImage step hangs on first build                           | Tauri downloading appimagetool; wait or pre-fetch under `~/.cache/tauri/`                  |
+| `Could not resolve "@tauri-apps/api"`                         | `npm install` again in `sam-tool-tauri/`                                                  |
+| Build OK on dev machine, fails on Ubuntu 22.04                | webkit2gtk-4.0 vs 4.1 mismatch — Tauri 2 needs 4.1; older Ubuntus may need a backport PPA |
+
+---
+
+## Cross-compilation (Windows / macOS targets)
+
+Out of scope for this repo (Linux-only per CLAUDE.md), but for
+completeness — Tauri supports it via extra rust targets and a Windows or
+macOS host machine. See the upstream Tauri docs:
+https://tauri.app/distribute/
+
+---
+
+## Recap — when to run what
+
+| You want to…                                | Command                                         |
+|---------------------------------------------|-------------------------------------------------|
+| Iterate on UI (hot reload)                  | `npm run tauri dev`                             |
+| Iterate on Rust quickly                     | `cd src-tauri && cargo build && ./target/debug/sam-tool-tauri` |
+| Make a release executable, no installer     | `npm run build && cd src-tauri && cargo build --release`        |
+| Make installers (.deb / AppImage)           | `npm run tauri build`                           |
+| Inspect production frontend without Tauri   | `npm run preview` (browser-only, no native APIs)|
diff --git a/docs/HF_DEPLOY_OFFLINE.md b/docs/HF_DEPLOY_OFFLINE.md
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+# Deploying SAM3 via HuggingFace, then going fully offline
+
+The SAM3 backend uses Meta's `facebook/sam3` weights via HuggingFace. The
+flow is **online once** (to download + cache the weights) → **then
+air-gapped** (server runs without ever talking to HuggingFace again).
+This doc walks through both phases and how to verify your box is actually
+offline at the end.
+
+---
+
+## Phase 0 — prerequisites
+
+On the target PC:
+
+- The `sam2-backend/` folder, copied or cloned, with its `.venv`
+  containing `torch`, `transformers>=4.57`, `huggingface_hub`, and the
+  rest of `requirements.txt`. See [`sam2-backend/README.md`](../sam2-backend/README.md)
+  for the full install.
+- A HuggingFace account that has been **granted access to
+  `facebook/sam3`** — gated. Request access at
+  https://huggingface.co/facebook/sam3 (usually approved within minutes).
+- A read token from https://huggingface.co/settings/tokens.
+
+---
+
+## Phase 1 — first-time online download
+
+The first call to `Sam3TrackerModel.from_pretrained("facebook/sam3")`
+streams the weights from HF and writes them to
+`~/.cache/huggingface/hub/`. Everything afterward can be served from that
+cache with no network.
+
+### 1.1 Authenticate
+
+```bash
+cd /home/<you>/sam2-backend
+source .venv/bin/activate
+hf auth login                 # paste your read token
+hf auth whoami                # confirm the right account
+```
+
+Token is stored at `~/.cache/huggingface/token`. It's a plain file with
+`chmod 600`.
+
+### 1.2 Set offline flag to OFF for the first start
+
+In `run.sh`, the relevant lines are:
+```bash
+export HF_HUB_DISABLE_TELEMETRY="${HF_HUB_DISABLE_TELEMETRY:-1}"
+export DO_NOT_TRACK="${DO_NOT_TRACK:-1}"
+export HF_HUB_OFFLINE="${HF_HUB_OFFLINE:-0}"        # ← MUST be 0 first time
+export TRANSFORMERS_OFFLINE="${TRANSFORMERS_OFFLINE:-0}"   # ← MUST be 0 first time
+```
+
+For a one-off override, just don't pass the env vars:
+```bash
+./run.sh
+```
+
+### 1.3 Make ONE successful predict call
+
+Either from the Tauri app (Settings → pick `SAM 3 · tracker (HuggingFace)` →
+draw a bbox), or directly:
+
+```bash
+curl -s -X POST "http://localhost:9090/predict?model=sam3_tracker" \
+  -H "Content-Type: application/json" \
+  --data '@some_test_payload.json'
+```
+
+A successful response means the weights are now fully cached. Confirm:
+
+```bash
+ls -la ~/.cache/huggingface/hub/models--facebook--sam3/
+# expect a snapshots/<hash>/ subdir with model.safetensors and configs
+du -sh ~/.cache/huggingface/hub/models--facebook--sam3/
+# ~3-4 GB for SAM3
+```
+
+### 1.4 Optional — pre-warm without a real predict
+
+If you want to download but not yet wire up Tauri, do it from a Python
+shell:
+
+```bash
+python3 - <<'EOF'
+from transformers import Sam3TrackerModel, Sam3TrackerProcessor
+Sam3TrackerProcessor.from_pretrained("facebook/sam3")
+Sam3TrackerModel.from_pretrained("facebook/sam3")
+print("cached.")
+EOF
+```
+
+Same effect — fills `~/.cache/huggingface/hub/`.
+
+---
+
+## Phase 2 — flip to fully offline
+
+Once the cache is populated, set the offline flags so the server never
+tries to revalidate metadata or check for updates.
+
+### 2.1 Edit `run.sh`
+
+Change the defaults to `1`:
+
+```bash
+export HF_HUB_DISABLE_TELEMETRY="${HF_HUB_DISABLE_TELEMETRY:-1}"
+export DO_NOT_TRACK="${DO_NOT_TRACK:-1}"
+export HF_HUB_OFFLINE="${HF_HUB_OFFLINE:-1}"           # ← 1
+export TRANSFORMERS_OFFLINE="${TRANSFORMERS_OFFLINE:-1}"   # ← 1
+```
+
+Or, if you're running via systemd, add to your unit file:
+
+```ini
+[Service]
+Environment=HF_HUB_DISABLE_TELEMETRY=1
+Environment=DO_NOT_TRACK=1
+Environment=HF_HUB_OFFLINE=1
+Environment=TRANSFORMERS_OFFLINE=1
+```
+
+Then `sudo systemctl daemon-reload && sudo systemctl restart sam-backend`.
+
+### 2.2 Restart and verify
+
+```bash
+./run.sh
+```
+
+You should see clean startup logs **without** any `httpx HTTP Request`
+lines hitting `huggingface.co`. The `Loading weights: 685/685 [00:00<00:00]`
+line confirms a cache hit (instant load, no download).
+
+### 2.3 Pin a custom cache location (optional)
+
+If you want the cache somewhere predictable (for backup, rsync to other
+boxes, or to keep `$HOME` clean), set `HF_HOME` in `run.sh`:
+
+```bash
+export HF_HOME=/var/cache/sam-backend/huggingface
+```
+
+Move the existing cache once:
+```bash
+mv ~/.cache/huggingface /var/cache/sam-backend/
+```
+
+Subsequent reads come from there.
+
+---
+
+## Verifying you're really offline
+
+Two-stage check:
+
+### Step 1 — disconnect & restart
+
+Easiest sanity test: pull the network cable (or `sudo ip link set eth0 down`),
+restart the server, hit `/predict` from the Tauri app on the same box.
+If it returns a polygon, you're truly offline.
+
+### Step 2 — packet-level proof
+
+If you can't disconnect (e.g., other services need the network), watch
+for any HF traffic during normal use:
+
+```bash
+sudo tcpdump -nn -i any \
+  'host huggingface.co or host cdn-lfs.huggingface.co' \
+  2>/dev/null
+```
+
+Restart the backend, hit `/predict` ten times from the Tauri app. With
+the OFFLINE flags set you should see **zero packets**.
+
+---
+
+## Moving the cache to a different machine
+
+Useful when you want to deploy to a new box without the first-run
+download (e.g., setting up a second annotation station behind the same
+firewall).
+
+```bash
+# On the source machine:
+tar czf hf-sam3-cache.tar.gz -C ~/.cache huggingface
+scp hf-sam3-cache.tar.gz target-box:/tmp/
+
+# On the target machine:
+mkdir -p ~/.cache
+tar xzf /tmp/hf-sam3-cache.tar.gz -C ~/.cache
+```
+
+Then start the new backend with `HF_HUB_OFFLINE=1` from day one — it'll
+load from the imported cache without ever calling out.
+
+---
+
+## Common gotchas
+
+| Symptom                                                                  | Cause / fix                                                                 |
+|--------------------------------------------------------------------------|-----------------------------------------------------------------------------|
+| `Cannot find the requested files in the cached path` while OFFLINE=1     | Cache is incomplete or in a different `HF_HOME`. Re-run online once.        |
+| `gated repo, access denied`                                               | Token doesn't have `facebook/sam3` access. Accept gate on the model page, then `hf auth login` again. |
+| Many `HEAD https://huggingface.co/...` lines on startup despite OFFLINE=1 | One of the two env flags is missing. Both `HF_HUB_OFFLINE=1` AND `TRANSFORMERS_OFFLINE=1` are required. |
+| `Loading weights: ...` takes minutes even with cache                     | The cache file is on a slow disk (NFS, HDD). Move it to local SSD via `HF_HOME`. |
+| Token leaked into logs                                                   | `hf auth login` only echoes your username, never the token. The token at `~/.cache/huggingface/token` is the only place it's stored. |
+
+---
+
+## What HF still sees in offline mode
+
+**Nothing.** With both `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1`:
+
+- `from_pretrained` reads only from `~/.cache/huggingface/hub/` — no API
+  calls, no HEAD revalidation pings.
+- Telemetry is independently off via `HF_HUB_DISABLE_TELEMETRY=1` /
+  `DO_NOT_TRACK=1`.
+- Inference itself never touches the network — your images, bboxes, and
+  resulting masks stay on the box regardless of online/offline mode.
+
+The only thing HF "knows" about you, ever, is "this account downloaded
+`facebook/sam3` once on date X" — recorded server-side at the moment
+`from_pretrained` first ran in Phase 1.
diff --git a/docs/POLYGON_SMOOTHING.md b/docs/POLYGON_SMOOTHING.md
new file mode 100644
index 0000000..49299dd
--- /dev/null
+++ b/docs/POLYGON_SMOOTHING.md
@@ -0,0 +1,164 @@
+# Tuning polygon output (point count + edge smoothness)
+
+When SAM returns a mask, the backend converts it to a polygon before sending
+it back to the Tauri annotation tool. Two things you can tune:
+
+1. **Point count** — how many vertices the polygon has. Each vertex is
+   draggable in the UI, so too many makes editing painful.
+2. **Edge smoothness** — how the polygon traces curved edges (smooth curves
+   vs. pixel staircases).
+
+Both are controlled in one file:
+[`sam2-backend/adapters/utils.py`](../sam2-backend/adapters/utils.py).
+
+---
+
+## Where it's called
+
+Both adapters do the same thing in their predict path:
+
+```python
+# sam2-backend/adapters/sam2_adapter.py
+return PredictResult(
+    polygon=_mask_to_polygon(masks_np[best]),
+    score=best_score,
+)
+
+# sam2-backend/adapters/sam3_adapter.py — same call site
+```
+
+The defaults are `min_points=4`, `max_points=10`, `smooth=True`. You can
+override per-call:
+
+```python
+polygon=_mask_to_polygon(masks_np[best], min_points=3, max_points=15)
+```
+
+…or change the defaults in `utils.py::mask_to_polygon` and they'll apply
+to every model.
+
+---
+
+## Knobs
+
+### 1. Point count band — `min_points` / `max_points`
+
+The simplifier uses Douglas–Peucker (`cv2.approxPolyDP`) with a binary
+search over `epsilon` to land in `[min_points, max_points]`. Within that
+band it prefers **more** detail (i.e. closer to `max_points`).
+
+| Band       | Use it when…                                                                |
+|------------|-----------------------------------------------------------------------------|
+| `4..10`    | Default. Comfortable to drag, captures common shapes.                       |
+| `4..15`    | Curvy / organic objects (animals, foliage). 15 vertices follow a curve well. |
+| `3..6`     | Very simple shapes (rectangles, traffic signs). Fewer drag handles.         |
+| `6..20`    | High-fidelity work, you don't mind dragging more.                           |
+
+**Avoid `max_points > 25`** — past that, the binary search needs many
+iterations and the marginal benefit per vertex drops sharply. If you need
+that many points, the bottleneck is mask quality, not simplification.
+
+### 2. Mask smoothing — `smooth=True | False`
+
+Before extracting the contour, the helper optionally runs:
+
+1. **Morphological close + open** (ellipse kernel) — fills single-pixel
+   notches and removes pepper noise.
+2. **Light Gaussian blur + re-threshold** — sub-pixel edge smoothing so
+   curves don't trace as staircases.
+
+Pass `smooth=False` to skip both steps and trace the raw mask exactly. Use
+this when:
+- The mask has fine features that smoothing erases (thin antennae, sharp
+  90° corners that get rounded).
+- You're A/B testing whether smoothing is helping or hurting.
+
+```python
+polygon=_mask_to_polygon(masks_np[best], smooth=False)
+```
+
+### 3. Smoothing kernel size — `_smooth_mask`
+
+Inside `_smooth_mask` (called by `mask_to_polygon` when `smooth=True`),
+kernel size scales with the **blob's bounding box diagonal**:
+
+```python
+k = int(round(blob_diag * 0.015))   # ← morphology kernel multiplier
+k = max(3, min(k | 1, 11))          # odd, clamped 3..11
+
+blur_k = max(3, min(k | 1, 9))      # ← Gaussian blur kernel
+```
+
+**The `0.015` multiplier is the main dial.**
+
+| Multiplier | Effect                                                          |
+|------------|-----------------------------------------------------------------|
+| `0.008`    | Very gentle. Preserves thin features. Use if smoothing dilates the polygon. |
+| `0.015`    | Default. Good balance for typical objects.                       |
+| `0.025`    | Aggressive. Use only on visibly noisy masks; will round corners. |
+
+The 11-pixel hard cap means giant masks don't get insanely smoothed.
+
+### 4. Drop the Gaussian, keep morphology
+
+If hard 90° corners get rounded but you still want morphology to clean up
+notches, delete just the Gaussian step in `_smooth_mask`:
+
+```python
+# remove these two lines
+m = cv2.GaussianBlur(m, (blur_k, blur_k), 0)
+_, m = cv2.threshold(m, 127, 255, cv2.THRESH_BINARY)
+```
+
+This keeps close+open (which is mostly noise removal) and skips the
+sub-pixel curve smoothing.
+
+---
+
+## Diagnostic recipe — find the right knob in 3 minutes
+
+1. **Draw a representative bbox in the Tauri app.** Screenshot the result.
+2. **Set `smooth=False`** in the adapter, restart, draw the same bbox.
+   - If polygon is *better* (less bloat, hugs edges) → smoothing is over-aggressive. Lower the kernel multiplier or delete the Gaussian step.
+   - If polygon is *worse* (jagged stairs) → smoothing was helping. Move on to step 3.
+3. **Bump `max_points` to 15 or 20**, restart.
+   - If polygon is *better* → simplification was too aggressive. Land on `max_points=12..15` as your new default.
+   - If polygon is the same → the mask itself is the bottleneck (see "If the raw mask is bad" below).
+4. **Restore `smooth=True`** and try a model swap (SAM 2.1 large ↔ SAM 3 tracker) — the underlying mask differs noticeably between models.
+
+---
+
+## If the raw mask is bad
+
+If after all the above the polygon still looks wrong, the input to the
+simplifier is bad — fix that, not the polygon code. Common moves:
+
+- **Pad the bbox before sending** to SAM (Meta's reference scripts use
+  `pad=10` pixels). SAM gives crisper masks with a small box margin.
+- **Try a larger SAM model** (e.g. switch the dropdown from `SAM 2.1
+  Hiera Small` to `SAM 2.1 Hiera Large`).
+- **Tighten the bbox** around the actual object — SAM tends to fill the
+  box, so a generous box gets a bloated mask.
+
+---
+
+## Quick reference — every dial in one block
+
+```python
+# sam2-backend/adapters/utils.py
+
+mask_to_polygon(
+    mask,
+    min_points=4,        # never below this
+    max_points=10,       # never above this; fewer = easier to drag
+    smooth=True,         # turn off to trace raw mask
+)
+
+# Inside _smooth_mask:
+k = int(round(blob_diag * 0.015))    # ← MAIN DIAL
+                                      #   smaller = preserve detail
+                                      #   bigger  = more rounded
+```
+
+If you change defaults, restart the backend (`./run.sh`). No frontend
+restart needed — the polygon arrives over the existing `/predict` call.
diff --git a/files/README.md b/files/README.md
new file mode 100644
index 0000000..b8b70d7
--- /dev/null
+++ b/files/README.md
@@ -0,0 +1,120 @@
+# SAM 3 local segmentation server
+
+A small FastAPI server that wraps Meta's **SAM 3** (released Nov 19, 2025) so your local annotation tool can send a bbox and get back a segmentation mask.
+
+## What it does
+
+Two endpoints, both take an image + bbox:
+
+| Endpoint | Behavior | Use for |
+|---|---|---|
+| `POST /segment/box` | Returns the mask of the **one** object inside your bbox. Uses the SAM 3 tracker (SAM2-style PVS). | Classic annotation — user draws a box around one object, you want that one mask. |
+| `POST /segment/exemplar` | Treats the bbox as an **exemplar** and returns masks for **all similar** objects in the image. | Auto-propagation — label one thing, get all instances. |
+
+Mask is returned as a base64 PNG, plus optional **COCO RLE** and **polygon contour** for easy integration with annotation-tool formats (CVAT, Label Studio, COCO JSON, etc).
+
+## Setup
+
+### 1. Prerequisites
+- Python 3.12+
+- PyTorch 2.7+ with CUDA 12.6 (CPU works too, just slow)
+- A CUDA-capable GPU is strongly recommended — SAM 3 is 848M params
+
+### 2. Request access to weights
+SAM 3 weights are gated on Hugging Face. Request access at https://huggingface.co/facebook/sam3, wait for approval, then:
+```bash
+hf auth login
+```
+
+### 3. Install
+```bash
+# SAM 3 itself
+git clone https://github.com/facebookresearch/sam3.git
+cd sam3
+pip install -e .
+
+# Server deps
+pip install fastapi uvicorn pillow numpy requests opencv-python-headless pycocotools
+# Optional but recommended — gives the clean tracker API used by /segment/box
+pip install "transformers>=4.57"
+```
+
+### 4. Run
+```bash
+python sam3_server.py --host 0.0.0.0 --port 8000
+# or on CPU:
+python sam3_server.py --device cpu
+```
+
+Check it's alive:
+```bash
+curl http://localhost:8000/health
+```
+
+## Calling it from your annotation tool
+
+### Python
+See `client_example.py`. Minimal version:
+```python
+import base64, requests
+img_b64 = base64.b64encode(open("img.jpg","rb").read()).decode()
+r = requests.post("http://localhost:8000/segment/box", json={
+    "image": img_b64,
+    "bbox":  [120, 80, 540, 420],    # [x1, y1, x2, y2] pixels
+})
+data = r.json()
+# data["mask"]    -> base64 PNG mask
+# data["polygon"] -> [[x,y], ...] contour
+# data["rle"]     -> COCO RLE dict
+```
+
+### JavaScript (browser annotation tools)
+```js
+const imgB64 = /* base64 of your image */;
+const res = await fetch("http://localhost:8000/segment/box", {
+  method: "POST",
+  headers: {"Content-Type": "application/json"},
+  body: JSON.stringify({ image: imgB64, bbox: [x1, y1, x2, y2] })
+});
+const { mask, polygon, rle, score } = await res.json();
+// Draw `mask` (PNG) or `polygon` onto your canvas
+```
+
+CORS is enabled for `*`, so browser clients work out of the box — lock this down for production.
+
+## Request/response reference
+
+**Request body** (JSON):
+```json
+{
+  "image":     "<base64 PNG/JPG>",   // OR image_path OR image_url
+  "bbox":      [x1, y1, x2, y2],     // absolute pixels, xyxy
+  "multimask_output": false,         // optional, /segment/box only — returns 3 candidates
+  "return_polygon": true,
+  "return_rle": true
+}
+```
+
+**Response**:
+```json
+{
+  "mask":     "<base64 PNG, 0/255 single channel>",
+  "score":    0.97,
+  "bbox_out": [x1, y1, x2, y2],
+  "rle":      {"size": [H, W], "counts": "..."},
+  "polygon":  [[x, y], ...],
+  "extra_masks": null
+}
+```
+
+## Notes on the two endpoints
+
+The SAM 3 reference repo's `Sam3Processor.set_box_prompt` is actually a **concept/exemplar** prompt — it finds *all* objects similar to what's in the box. For a bbox-to-single-mask annotation workflow you specifically want the **tracker** (`Sam3Tracker`), which is SAM 3's SAM2-compatible PVS head. That's what `/segment/box` uses when `transformers` is installed.
+
+If `transformers` isn't available, `/segment/box` falls back to the exemplar predictor and picks the returned instance whose predicted bbox has the highest IoU with your input — this usually gives you the right object but can pick a similar neighbor in cluttered scenes. Install `transformers` for best results.
+
+## Performance tips
+- Keep the process alive; model loading is the slow part (~10–20s).
+- For batch annotation, call `/segment/box` sequentially — `set_image` caches the image embedding in the session state.
+- On a single RTX 4090, per-request latency for a 1024px image is typically **~150–300ms** once warm.
+- For large images, consider resizing on the client to ≤2048px on the long side; SAM 3 resizes internally anyway.
diff --git a/files/client_example.py b/files/client_example.py
new file mode 100644
index 0000000..30856c2
--- /dev/null
+++ b/files/client_example.py
@@ -0,0 +1,50 @@
+"""
+Example client: how your annotation tool calls the SAM 3 server.
+
+Run the server first:
+    python sam3_server.py --host 0.0.0.0 --port 8000
+
+Then, from your annotation tool, POST an image + bbox and render the mask.
+"""
+
+import base64
+import io
+import requests
+from PIL import Image
+
+SERVER = "http://localhost:8000"
+
+
+def segment_with_bbox(image_path: str, bbox_xyxy: list[float]):
+    with open(image_path, "rb") as f:
+        img_b64 = base64.b64encode(f.read()).decode("ascii")
+
+    r = requests.post(
+        f"{SERVER}/segment/box",
+        json={
+            "image": img_b64,
+            "bbox": bbox_xyxy,                # [x1, y1, x2, y2] in pixels
+            "multimask_output": False,
+            "return_polygon": True,           # get contour for polygon-based annotation
+            "return_rle": True,               # get COCO RLE for COCO-format export
+        },
+        timeout=60,
+    )
+    r.raise_for_status()
+    data = r.json()
+
+    # Decode mask (single-channel PNG, 0 or 255)
+    mask_png = base64.b64decode(data["mask"])
+    mask = Image.open(io.BytesIO(mask_png))
+    mask.save("mask_out.png")
+
+    print(f"score:   {data['score']:.3f}")
+    print(f"bbox:    {data['bbox_out']}")
+    print(f"polygon: {len(data['polygon']) if data['polygon'] else 0} points")
+    print(f"rle:     size={data['rle']['size']} (counts length={len(str(data['rle']['counts']))})")
+    return data
+
+
+if __name__ == "__main__":
+    # Example: segment the object inside this bbox
+    segment_with_bbox("example.jpg", [120, 80, 540, 420])
diff --git a/files/sam3_server.py b/files/sam3_server.py
new file mode 100644
index 0000000..f10ea42
--- /dev/null
+++ b/files/sam3_server.py
@@ -0,0 +1,381 @@
+"""
+SAM 3 local inference server for annotation tools.
+
+Exposes a small FastAPI service that takes an image + bbox and returns a
+segmentation mask. Designed for the classic annotation workflow:
+    user draws a box around ONE object -> server returns the mask for that object.
+
+Two endpoints are provided:
+
+  POST /segment/box
+      Uses Sam3Tracker (the SAM2-style Promptable Visual Segmentation head).
+      Given ONE bbox, returns ONE mask for the object inside that box.
+      This is what you want for 99% of annotation-tool use cases.
+
+  POST /segment/exemplar
+      Uses the full SAM 3 image predictor with a box as a visual "exemplar".
+      Given one bbox, returns masks for ALL similar objects in the image.
+      Useful when you want to label one instance and auto-propagate.
+
+Run:
+    python sam3_server.py --host 0.0.0.0 --port 8000
+
+Request format (JSON):
+    {
+        "image":  "<base64-encoded image>"   # OR
+        "image_url": "http://..."            # OR
+        "image_path": "/absolute/path.jpg",
+        "bbox": [x1, y1, x2, y2],            # absolute pixel coords (xyxy)
+        "multimask_output": false             # optional, /segment/box only
+    }
+
+Response:
+    {
+        "mask":  "<base64 PNG, single-channel 0/255>",
+        "rle":   {"size":[H,W], "counts":"..."},   # COCO RLE, handy for COCO-format tools
+        "polygon": [[x,y], [x,y], ...],             # largest contour, optional
+        "score": 0.97,
+        "bbox_out": [x1,y1,x2,y2]
+    }
+"""
+
+from __future__ import annotations
+
+import argparse
+import base64
+import io
+import logging
+from typing import List, Optional
+
+import numpy as np
+import torch
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+from pydantic import BaseModel, Field
+
+# ----- SAM 3 imports ---------------------------------------------------------
+# The SAM 3 image predictor (concept / exemplar segmentation):
+from sam3.model_builder import build_sam3_image_model
+from sam3.model.sam3_image_processor import Sam3Processor
+
+# The SAM 3 tracker (SAM2-style single-instance PVS on images):
+# This is exposed in the HF `transformers` integration as Sam3TrackerModel,
+# and in the reference repo via the image predictor for single-box prompts.
+# We use HuggingFace transformers for the tracker because its API is clean
+# and documented for bbox -> single mask. Fall back to the repo's predictor
+# if transformers isn't available.
+try:
+    from transformers import Sam3TrackerModel, Sam3TrackerProcessor  # type: ignore
+    HAS_TRACKER = True
+except Exception:
+    HAS_TRACKER = False
+
+log = logging.getLogger("sam3-server")
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+
+
+# ============================================================================
+# Request / response schemas
+# ============================================================================
+
+class SegmentRequest(BaseModel):
+    # Exactly one of these three must be provided.
+    image: Optional[str] = Field(None, description="Base64-encoded image bytes.")
+    image_url: Optional[str] = None
+    image_path: Optional[str] = None
+
+    bbox: List[float] = Field(..., description="Box in absolute pixels, [x1,y1,x2,y2].")
+    multimask_output: bool = Field(
+        False,
+        description="If true (tracker only), return the 3 candidate masks instead of picking the highest-scoring one.",
+    )
+    return_polygon: bool = Field(True, description="Also return the largest contour as [[x,y], ...].")
+    return_rle: bool = Field(True, description="Also return COCO RLE encoding of the mask.")
+
+
+class SegmentResponse(BaseModel):
+    mask: str  # base64 PNG
+    score: float
+    bbox_out: List[float]
+    rle: Optional[dict] = None
+    polygon: Optional[List[List[float]]] = None
+    extra_masks: Optional[List[str]] = None  # only populated when multimask_output=True
+
+
+# ============================================================================
+# Model holder (loaded once at startup)
+# ============================================================================
+
+class SAM3Models:
+    def __init__(self, device: str, checkpoint_path: Optional[str] = None):
+        self.device = device
+
+        log.info("Loading SAM 3 image model (concept/exemplar predictor)...")
+        if checkpoint_path:
+            self.image_model = build_sam3_image_model(checkpoint_path=checkpoint_path)
+        else:
+            self.image_model = build_sam3_image_model()
+        self.image_model.to(device).eval()
+        self.image_processor = Sam3Processor(self.image_model)
+
+        if HAS_TRACKER:
+            log.info("Loading SAM 3 tracker (single-instance PVS) from HuggingFace...")
+            # facebook/sam3 exposes the tracker weights under this name
+            self.tracker_processor = Sam3TrackerProcessor.from_pretrained("facebook/sam3")
+            self.tracker_model = Sam3TrackerModel.from_pretrained("facebook/sam3").to(device).eval()
+        else:
+            log.warning(
+                "transformers not available or missing Sam3Tracker classes. "
+                "The /segment/box endpoint will fall back to the image predictor "
+                "(still works, but treats the box as an exemplar)."
+            )
+            self.tracker_processor = None
+            self.tracker_model = None
+
+
+MODELS: Optional[SAM3Models] = None
+
+
+# ============================================================================
+# Helpers
+# ============================================================================
+
+def _load_image(req: SegmentRequest) -> Image.Image:
+    if req.image:
+        raw = base64.b64decode(req.image)
+        return Image.open(io.BytesIO(raw)).convert("RGB")
+    if req.image_path:
+        return Image.open(req.image_path).convert("RGB")
+    if req.image_url:
+        import requests
+        r = requests.get(req.image_url, timeout=20)
+        r.raise_for_status()
+        return Image.open(io.BytesIO(r.content)).convert("RGB")
+    raise HTTPException(400, "Provide one of `image` (base64), `image_path`, or `image_url`.")
+
+
+def _mask_to_png_b64(mask: np.ndarray) -> str:
+    """mask: HxW bool/uint8 -> base64-encoded PNG string."""
+    if mask.dtype != np.uint8:
+        mask = (mask.astype(np.uint8) * 255)
+    img = Image.fromarray(mask, mode="L")
+    buf = io.BytesIO()
+    img.save(buf, format="PNG", optimize=True)
+    return base64.b64encode(buf.getvalue()).decode("ascii")
+
+
+def _mask_to_rle(mask: np.ndarray) -> dict:
+    """COCO-style RLE. Uses pycocotools if available, else a pure-python fallback."""
+    mask = (mask > 0).astype(np.uint8)
+    try:
+        from pycocotools import mask as mutil  # type: ignore
+        rle = mutil.encode(np.asfortranarray(mask))
+        rle["counts"] = rle["counts"].decode("ascii")
+        return rle
+    except Exception:
+        # Pure python fallback (uncompressed)
+        h, w = mask.shape
+        flat = mask.T.flatten()  # column-major, like COCO
+        counts: List[int] = []
+        last = 0
+        run = 0
+        for v in flat:
+            if v == last:
+                run += 1
+            else:
+                counts.append(run)
+                run = 1
+                last = v
+        counts.append(run)
+        return {"size": [h, w], "counts": counts}
+
+
+def _mask_to_polygon(mask: np.ndarray) -> Optional[List[List[float]]]:
+    """Largest-contour polygon, approximated. Returns None if mask is empty."""
+    try:
+        import cv2  # type: ignore
+    except Exception:
+        return None
+    m = (mask > 0).astype(np.uint8) * 255
+    contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1)
+    if not contours:
+        return None
+    c = max(contours, key=cv2.contourArea)
+    # Simplify a bit — saves bytes and is friendlier for annotation tools
+    epsilon = 0.0015 * cv2.arcLength(c, True)
+    c = cv2.approxPolyDP(c, epsilon, True)
+    return [[float(pt[0][0]), float(pt[0][1])] for pt in c]
+
+
+def _mask_bbox(mask: np.ndarray) -> List[float]:
+    ys, xs = np.where(mask > 0)
+    if len(xs) == 0:
+        return [0.0, 0.0, 0.0, 0.0]
+    return [float(xs.min()), float(ys.min()), float(xs.max()), float(ys.max())]
+
+
+def _build_response(mask: np.ndarray, score: float, req: SegmentRequest,
+                    extra_masks: Optional[List[np.ndarray]] = None) -> SegmentResponse:
+    return SegmentResponse(
+        mask=_mask_to_png_b64(mask),
+        score=float(score),
+        bbox_out=_mask_bbox(mask),
+        rle=_mask_to_rle(mask) if req.return_rle else None,
+        polygon=_mask_to_polygon(mask) if req.return_polygon else None,
+        extra_masks=[_mask_to_png_b64(m) for m in extra_masks] if extra_masks else None,
+    )
+
+
+# ============================================================================
+# FastAPI app
+# ============================================================================
+
+app = FastAPI(title="SAM 3 local server", version="1.0")
+
+# Allow local annotation tool UIs to call us from the browser
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/health")
+def health():
+    return {
+        "ok": True,
+        "device": MODELS.device if MODELS else "unloaded",
+        "tracker_available": HAS_TRACKER,
+    }
+
+
+@app.post("/segment/box", response_model=SegmentResponse)
+@torch.inference_mode()
+def segment_box(req: SegmentRequest):
+    """
+    Annotation-tool workflow:
+        user draws a bbox around ONE object -> return ONE mask for that object.
+
+    Uses the SAM3 tracker (SAM2-style PVS) for a clean single-instance result.
+    """
+    if MODELS is None:
+        raise HTTPException(500, "Models not loaded.")
+    image = _load_image(req)
+    x1, y1, x2, y2 = req.bbox
+
+    # ---- Preferred path: HuggingFace Sam3Tracker -------------------------
+    if HAS_TRACKER and MODELS.tracker_model is not None:
+        inputs = MODELS.tracker_processor(
+            images=image,
+            input_boxes=[[[x1, y1, x2, y2]]],  # batch[1] x obj[1] x 4
+            return_tensors="pt",
+        ).to(MODELS.device)
+
+        outputs = MODELS.tracker_model(**inputs, multimask_output=req.multimask_output)
+
+        # Post-process to image resolution, binary masks
+        masks = MODELS.tracker_processor.post_process_masks(
+            outputs.pred_masks.cpu(),
+            original_sizes=inputs["original_sizes"].cpu(),
+            reshaped_input_sizes=inputs["reshaped_input_sizes"].cpu(),
+        )[0]  # shape: (num_masks, H, W) tensor
+        scores = outputs.iou_scores[0].cpu().numpy().ravel()
+
+        masks_np = masks.numpy().astype(bool)
+        if req.multimask_output and masks_np.shape[0] > 1:
+            best = int(np.argmax(scores))
+            extras = [m for i, m in enumerate(masks_np) if i != best]
+            return _build_response(masks_np[best], scores[best], req, extra_masks=extras)
+
+        best = int(np.argmax(scores)) if masks_np.shape[0] > 1 else 0
+        return _build_response(masks_np[best], float(scores[best]), req)
+
+    # ---- Fallback: use image predictor with box as exemplar, pick the
+    #      detection whose predicted box has highest IoU with the user bbox.
+    state = MODELS.image_processor.set_image(image)
+    out = MODELS.image_processor.set_box_prompt(
+        state=state,
+        boxes=[[x1, y1, x2 - x1, y2 - y1]],  # xywh as documented
+        box_labels=[1],
+    )
+    masks = out["masks"]
+    boxes = out["boxes"]
+    scores = out["scores"]
+    if len(masks) == 0:
+        raise HTTPException(404, "No segmentation produced for the given bbox.")
+
+    # Pick mask whose predicted bbox most overlaps user bbox
+    def _iou(a, b):
+        ax1, ay1, ax2, ay2 = a
+        bx1, by1, bx2, by2 = b
+        ix1, iy1 = max(ax1, bx1), max(ay1, by1)
+        ix2, iy2 = min(ax2, bx2), min(ay2, by2)
+        iw, ih = max(0, ix2 - ix1), max(0, iy2 - iy1)
+        inter = iw * ih
+        ua = (ax2 - ax1) * (ay2 - ay1) + (bx2 - bx1) * (by2 - by1) - inter
+        return inter / ua if ua > 0 else 0.0
+
+    user_box = [x1, y1, x2, y2]
+    ious = [_iou(user_box, list(map(float, b))) for b in boxes]
+    idx = int(np.argmax(ious))
+    mask_np = np.asarray(masks[idx]).astype(bool)
+    return _build_response(mask_np, float(scores[idx]), req)
+
+
+@app.post("/segment/exemplar", response_model=SegmentResponse)
+@torch.inference_mode()
+def segment_exemplar(req: SegmentRequest):
+    """
+    Auto-propagation workflow:
+        user draws a box around ONE instance -> return a merged mask of ALL
+        similar instances found in the image. `extra_masks` contains each
+        individual instance separately.
+    """
+    if MODELS is None:
+        raise HTTPException(500, "Models not loaded.")
+    image = _load_image(req)
+    x1, y1, x2, y2 = req.bbox
+
+    state = MODELS.image_processor.set_image(image)
+    out = MODELS.image_processor.set_box_prompt(
+        state=state,
+        boxes=[[x1, y1, x2 - x1, y2 - y1]],
+        box_labels=[1],
+    )
+    masks = out["masks"]
+    scores = out["scores"]
+    if len(masks) == 0:
+        raise HTTPException(404, "No instances found.")
+
+    masks_np = [np.asarray(m).astype(bool) for m in masks]
+    merged = np.zeros_like(masks_np[0], dtype=bool)
+    for m in masks_np:
+        merged |= m
+    best_score = float(np.max(scores))
+    return _build_response(merged, best_score, req, extra_masks=masks_np)
+
+
+# ============================================================================
+# Entrypoint
+# ============================================================================
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host", default="0.0.0.0")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
+    parser.add_argument("--checkpoint", default=None,
+                        help="Optional path to sam3 checkpoint (otherwise uses HF default).")
+    args = parser.parse_args()
+
+    global MODELS
+    MODELS = SAM3Models(device=args.device, checkpoint_path=args.checkpoint)
+    log.info("SAM 3 server ready on %s:%d (device=%s)", args.host, args.port, args.device)
+    uvicorn.run(app, host=args.host, port=args.port, log_level="info")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..ade4f13
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+opencv-python
+Pillow
+numpy
+
diff --git a/sam-tool-tauri/.gitignore b/sam-tool-tauri/.gitignore
new file mode 100644
index 0000000..3a15d44
--- /dev/null
+++ b/sam-tool-tauri/.gitignore
@@ -0,0 +1,14 @@
+node_modules
+dist
+dist-ssr
+*.local
+
+.vite
+
+# Editor
+.vscode/*
+!.vscode/extensions.json
+.idea
+
+# System
+.DS_Store
diff --git a/sam-tool-tauri/index.html b/sam-tool-tauri/index.html
new file mode 100644
index 0000000..053a479
--- /dev/null
+++ b/sam-tool-tauri/index.html
@@ -0,0 +1,12 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>SAM Tool</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
+</html>
diff --git a/sam-tool-tauri/package-lock.json b/sam-tool-tauri/package-lock.json
new file mode 100644
index 0000000..f6c3ea6
--- /dev/null
+++ b/sam-tool-tauri/package-lock.json
@@ -0,0 +1,2149 @@
+{
+  "name": "sam-tool-tauri",
+  "version": "0.1.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "sam-tool-tauri",
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+        "react": "^18.3.1",
+        "react-dom": "^18.3.1"
+      },
+      "devDependencies": {
+        "@tauri-apps/cli": "^2.1.0",
+        "@types/react": "^18.3.12",
+        "@types/react-dom": "^18.3.1",
+        "@vitejs/plugin-react": "^4.3.4",
+        "typescript": "^5.7.2",
+        "vite": "^6.0.3"
+      }
+    },
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new file mode 100644
index 0000000..3ef3aff
--- /dev/null
+++ b/sam-tool-tauri/package.json
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new file mode 100644
index 0000000..e289517
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/.gitignore
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index 0000000..8144c67
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diff --git a/sam-tool-tauri/src-tauri/Cargo.toml b/sam-tool-tauri/src-tauri/Cargo.toml
new file mode 100644
index 0000000..d2791a0
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/Cargo.toml
@@ -0,0 +1,35 @@
+[package]
+name = "sam-tool-tauri"
+version = "0.1.0"
+description = "SAM Tool - video frame extraction and annotation"
+edition = "2021"
+rust-version = "1.77"
+
+[lib]
+name = "sam_tool_tauri_lib"
+crate-type = ["staticlib", "cdylib", "rlib"]
+
+[build-dependencies]
+tauri-build = { version = "2", features = [] }
+
+[dependencies]
+tauri = { version = "2", features = [] }
+tauri-plugin-dialog = "2"
+tauri-plugin-fs = "2"
+serde = { version = "1", features = ["derive"] }
+serde_json = "1"
+chrono = { version = "0.4", default-features = false, features = ["clock", "serde"] }
+uuid = { version = "1", features = ["v4"] }
+reqwest = { version = "0.12", default-features = false, features = ["json", "rustls-tls", "multipart"] }
+base64 = "0.22"
+tokio = { version = "1", features = ["rt-multi-thread", "macros"] }
+
+[profile.dev]
+incremental = true
+
+[profile.release]
+codegen-units = 1
+lto = true
+opt-level = "s"
+panic = "abort"
+strip = true
diff --git a/sam-tool-tauri/src-tauri/build.rs b/sam-tool-tauri/src-tauri/build.rs
new file mode 100644
index 0000000..d860e1e
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/build.rs
@@ -0,0 +1,3 @@
+fn main() {
+    tauri_build::build()
+}
diff --git a/sam-tool-tauri/src-tauri/capabilities/default.json b/sam-tool-tauri/src-tauri/capabilities/default.json
new file mode 100644
index 0000000..3a13474
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/capabilities/default.json
@@ -0,0 +1,11 @@
+{
+  "$schema": "../gen/schemas/desktop-schema.json",
+  "identifier": "default",
+  "description": "Default capabilities for the main window",
+  "windows": ["main"],
+  "permissions": [
+    "core:default",
+    "dialog:default",
+    "fs:default"
+  ]
+}
diff --git a/sam-tool-tauri/src-tauri/icons/128x128.png b/sam-tool-tauri/src-tauri/icons/128x128.png
new file mode 100644
index 0000000..2f40343
Binary files /dev/null and b/sam-tool-tauri/src-tauri/icons/128x128.png differ
diff --git a/sam-tool-tauri/src-tauri/icons/128x128@2x.png b/sam-tool-tauri/src-tauri/icons/128x128@2x.png
new file mode 100644
index 0000000..100bd12
Binary files /dev/null and b/sam-tool-tauri/src-tauri/icons/128x128@2x.png differ
diff --git a/sam-tool-tauri/src-tauri/icons/32x32.png b/sam-tool-tauri/src-tauri/icons/32x32.png
new file mode 100644
index 0000000..734d0dd
Binary files /dev/null and b/sam-tool-tauri/src-tauri/icons/32x32.png differ
diff --git a/sam-tool-tauri/src-tauri/icons/icon.png b/sam-tool-tauri/src-tauri/icons/icon.png
new file mode 100644
index 0000000..9a05c44
Binary files /dev/null and b/sam-tool-tauri/src-tauri/icons/icon.png differ
diff --git a/sam-tool-tauri/src-tauri/src/annotate.rs b/sam-tool-tauri/src-tauri/src/annotate.rs
new file mode 100644
index 0000000..e2a8e1d
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/annotate.rs
@@ -0,0 +1,169 @@
+use crate::annotation::{AnnotationRecord, ImageAnnotations};
+use serde::Serialize;
+use std::path::{Path, PathBuf};
+
+#[derive(Serialize, Clone)]
+pub struct ImageEntry {
+    pub filename: String,
+    pub has_annotations: bool,
+    pub annotation_count: u32,
+    pub last_updated: Option<String>,
+}
+
+fn updated_at_of(a: &AnnotationRecord) -> &str {
+    match a {
+        AnnotationRecord::Bbox { updated_at, .. } => updated_at,
+        AnnotationRecord::Polygon { updated_at, .. } => updated_at,
+    }
+}
+
+fn is_image(name: &str) -> bool {
+    let lower = name.to_lowercase();
+    lower.ends_with(".jpg") || lower.ends_with(".jpeg") || lower.ends_with(".png")
+}
+
+fn sibling_json(image_path: &Path) -> PathBuf {
+    image_path.with_extension("json")
+}
+
+pub fn list_images(folder: &Path) -> Result<Vec<ImageEntry>, String> {
+    if !folder.exists() {
+        return Err(format!("folder not found: {}", folder.display()));
+    }
+    if !folder.is_dir() {
+        return Err(format!("not a directory: {}", folder.display()));
+    }
+    let mut out = Vec::new();
+    let entries = std::fs::read_dir(folder)
+        .map_err(|e| format!("read_dir {}: {e}", folder.display()))?;
+    for entry in entries {
+        let entry = entry.map_err(|e| format!("dirent: {e}"))?;
+        let path = entry.path();
+        if !path.is_file() {
+            continue;
+        }
+        let Some(name) = path.file_name().and_then(|n| n.to_str()) else {
+            continue;
+        };
+        if !is_image(name) {
+            continue;
+        }
+        let json_path = sibling_json(&path);
+        let (annotation_count, last_updated) = if json_path.exists() {
+            match std::fs::read_to_string(&json_path)
+                .ok()
+                .and_then(|t| serde_json::from_str::<ImageAnnotations>(&t).ok())
+            {
+                Some(ia) => {
+                    let latest = ia
+                        .annotations
+                        .iter()
+                        .map(|a| updated_at_of(a).to_string())
+                        .max();
+                    (ia.annotations.len() as u32, latest)
+                }
+                None => (0, None),
+            }
+        } else {
+            (0, None)
+        };
+        out.push(ImageEntry {
+            filename: name.to_string(),
+            has_annotations: annotation_count > 0,
+            annotation_count,
+            last_updated,
+        });
+    }
+    out.sort_by(|a, b| a.filename.cmp(&b.filename));
+    Ok(out)
+}
+
+pub fn read_image_bytes(path: &Path) -> Result<Vec<u8>, String> {
+    if !path.exists() {
+        return Err(format!("file not found: {}", path.display()));
+    }
+    std::fs::read(path).map_err(|e| format!("read {}: {e}", path.display()))
+}
+
+pub fn load_image_annotations(
+    image_path: &Path,
+) -> Result<Option<ImageAnnotations>, String> {
+    let json_path = sibling_json(image_path);
+    if !json_path.exists() {
+        return Ok(None);
+    }
+    let text = std::fs::read_to_string(&json_path)
+        .map_err(|e| format!("read {}: {e}", json_path.display()))?;
+    let ia: ImageAnnotations =
+        serde_json::from_str(&text).map_err(|e| format!("parse sibling JSON: {e}"))?;
+    Ok(Some(ia))
+}
+
+pub fn load_classes(folder: &Path) -> Result<Vec<String>, String> {
+    let path = folder.join("classes.txt");
+    if !path.exists() {
+        return Ok(Vec::new());
+    }
+    let text = std::fs::read_to_string(&path)
+        .map_err(|e| format!("read {}: {e}", path.display()))?;
+    Ok(text
+        .lines()
+        .map(|l| l.trim().to_string())
+        .filter(|l| !l.is_empty())
+        .collect())
+}
+
+/// Delete a batch of image files along with their sibling annotation JSONs.
+/// Returns the count of images actually removed. Missing images are skipped;
+/// any hard I/O error aborts with what succeeded so far reflected in the log.
+pub fn delete_images(paths: &[String]) -> Result<u32, String> {
+    let mut removed: u32 = 0;
+    for p in paths {
+        let path = Path::new(p);
+        if !path.is_file() {
+            continue;
+        }
+        // Only allow image files — guard against a stray path that would
+        // wipe a JSON or classes.txt by name collision.
+        let name = path
+            .file_name()
+            .and_then(|n| n.to_str())
+            .unwrap_or_default();
+        if !is_image(name) {
+            return Err(format!("refusing to delete non-image: {}", path.display()));
+        }
+        std::fs::remove_file(path)
+            .map_err(|e| format!("remove {}: {e}", path.display()))?;
+        let json_path = sibling_json(path);
+        if json_path.exists() {
+            let _ = std::fs::remove_file(&json_path);
+        }
+        removed += 1;
+    }
+    Ok(removed)
+}
+
+pub fn append_class(folder: &Path, name: &str) -> Result<Vec<String>, String> {
+    let trimmed = name.trim();
+    if trimmed.is_empty() {
+        return Err("class name cannot be empty".into());
+    }
+    if trimmed.len() > 96 {
+        return Err("class name too long".into());
+    }
+    std::fs::create_dir_all(folder)
+        .map_err(|e| format!("mkdir {}: {e}", folder.display()))?;
+    let mut classes = load_classes(folder)?;
+    if classes.iter().any(|n| n == trimmed) {
+        return Err(format!("class '{trimmed}' already exists"));
+    }
+    classes.push(trimmed.to_string());
+    let mut text = String::new();
+    for c in &classes {
+        text.push_str(c);
+        text.push('\n');
+    }
+    let path = folder.join("classes.txt");
+    std::fs::write(&path, text).map_err(|e| format!("write {}: {e}", path.display()))?;
+    Ok(classes)
+}
diff --git a/sam-tool-tauri/src-tauri/src/annotation.rs b/sam-tool-tauri/src-tauri/src/annotation.rs
new file mode 100644
index 0000000..71f8b29
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/annotation.rs
@@ -0,0 +1,43 @@
+use serde::{Deserialize, Serialize};
+
+#[derive(Serialize, Deserialize, Clone)]
+pub struct ImageAnnotations {
+    pub version: u32,
+    pub image: String,
+    pub width: u32,
+    pub height: u32,
+    /// Absolute path to the source video the frame was extracted from.
+    /// Optional so older JSONs without this field still round-trip cleanly.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub source_video: Option<String>,
+    pub annotations: Vec<AnnotationRecord>,
+}
+
+/// Internally-tagged enum. Both variants share id/class/author/timestamps and
+/// differ only in the shape data (bbox | points). Using a single enum with
+/// `#[serde(tag = "type")]` gives clean round-trip serde (flatten + tagged
+/// enums together don't deserialize reliably).
+#[derive(Serialize, Deserialize, Clone)]
+#[serde(tag = "type")]
+pub enum AnnotationRecord {
+    #[serde(rename = "bbox")]
+    Bbox {
+        id: String,
+        class_id: i64,
+        class_name: String,
+        bbox: [f64; 4],
+        author: String,
+        created_at: String,
+        updated_at: String,
+    },
+    #[serde(rename = "polygon")]
+    Polygon {
+        id: String,
+        class_id: i64,
+        class_name: String,
+        points: Vec<[f64; 2]>,
+        author: String,
+        created_at: String,
+        updated_at: String,
+    },
+}
diff --git a/sam-tool-tauri/src-tauri/src/coco.rs b/sam-tool-tauri/src-tauri/src/coco.rs
new file mode 100644
index 0000000..c176ecb
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/coco.rs
@@ -0,0 +1,359 @@
+use serde::{Deserialize, Serialize};
+use std::collections::{BTreeSet, HashMap};
+use std::path::Path;
+
+#[derive(Deserialize)]
+struct CocoFile {
+    images: Vec<CocoImage>,
+    categories: Vec<CocoCategory>,
+    annotations: Vec<CocoAnnotation>,
+}
+
+#[derive(Deserialize)]
+struct CocoImage {
+    id: i64,
+    #[serde(default)]
+    file_name: String,
+}
+
+#[derive(Deserialize)]
+struct CocoCategory {
+    id: i64,
+    name: String,
+}
+
+#[derive(Deserialize)]
+struct CocoAnnotation {
+    image_id: i64,
+    category_id: i64,
+    #[serde(default)]
+    bbox: Option<Vec<f64>>,
+    #[serde(default)]
+    segmentation: Option<serde_json::Value>,
+}
+
+#[derive(Serialize, Deserialize, Clone)]
+#[serde(tag = "type")]
+pub enum Shape {
+    #[serde(rename = "bbox")]
+    Bbox {
+        class_id: i64,
+        class_name: String,
+        bbox: [f64; 4],
+    },
+    #[serde(rename = "polygon")]
+    Polygon {
+        class_id: i64,
+        class_name: String,
+        points: Vec<[f64; 2]>,
+    },
+}
+
+#[derive(Serialize, Deserialize, Clone)]
+pub struct Category {
+    pub id: i64,
+    pub name: String,
+}
+
+#[derive(Serialize, Clone, Default)]
+pub struct ImportStats {
+    pub total_annotations: usize,
+    pub frame_count: usize,
+    pub categories: usize,
+    pub skipped_rle: usize,
+    pub skipped_no_image: usize,
+    /// Filled in by the import — "coco" or "sam".
+    pub source_format: String,
+}
+
+#[derive(Serialize, Clone)]
+pub struct ImportedCoco {
+    pub categories: Vec<Category>,
+    /// Map of frame_idx (as string, per JSON) -> shapes on that frame.
+    pub frames: HashMap<String, Vec<Shape>>,
+    pub stats: ImportStats,
+}
+
+enum Format {
+    LabelStudioCoco,
+    SamAnno,
+    Unknown,
+}
+
+fn detect_format(v: &serde_json::Value) -> Format {
+    let Some(obj) = v.as_object() else {
+        return Format::Unknown;
+    };
+    if obj.contains_key("images") && obj.contains_key("annotations") {
+        return Format::LabelStudioCoco;
+    }
+    if !obj.is_empty()
+        && obj
+            .keys()
+            .all(|k| !k.is_empty() && k.chars().all(|c| c.is_ascii_digit()))
+        && obj.values().all(|v| v.is_object())
+    {
+        return Format::SamAnno;
+    }
+    Format::Unknown
+}
+
+pub fn import(path: &Path) -> Result<ImportedCoco, String> {
+    let text = std::fs::read_to_string(path)
+        .map_err(|e| format!("read {}: {e}", path.display()))?;
+    let root: serde_json::Value =
+        serde_json::from_str(&text).map_err(|e| format!("parse JSON: {e}"))?;
+
+    match detect_format(&root) {
+        Format::LabelStudioCoco => {
+            let coco: CocoFile = serde_json::from_value(root)
+                .map_err(|e| format!("parse COCO: {e}"))?;
+            Ok(build_from_coco(coco))
+        }
+        Format::SamAnno => build_from_sam_anno(&root),
+        Format::Unknown => Err(
+            "unrecognized annotation format — expected Label Studio COCO or SAM anno JSON"
+                .into(),
+        ),
+    }
+}
+
+// ---- Label Studio COCO -----------------------------------------------------
+
+fn build_from_coco(coco: CocoFile) -> ImportedCoco {
+    let mut image_frame: HashMap<i64, u64> = HashMap::with_capacity(coco.images.len());
+    for img in &coco.images {
+        let frame_idx =
+            trailing_int(&img.file_name).unwrap_or_else(|| img.id.max(0) as u64);
+        image_frame.insert(img.id, frame_idx);
+    }
+
+    let cat_name: HashMap<i64, String> = coco
+        .categories
+        .iter()
+        .map(|c| (c.id, c.name.clone()))
+        .collect();
+
+    let mut frames: HashMap<String, Vec<Shape>> = HashMap::new();
+    let mut stats = ImportStats {
+        categories: coco.categories.len(),
+        source_format: "coco".into(),
+        ..Default::default()
+    };
+
+    for ann in &coco.annotations {
+        stats.total_annotations += 1;
+        let Some(&frame_idx) = image_frame.get(&ann.image_id) else {
+            stats.skipped_no_image += 1;
+            continue;
+        };
+        let class_name = cat_name
+            .get(&ann.category_id)
+            .cloned()
+            .unwrap_or_else(|| format!("class_{}", ann.category_id));
+        let key = frame_idx.to_string();
+        let bucket = frames.entry(key).or_default();
+
+        if let Some(seg) = &ann.segmentation {
+            match seg {
+                serde_json::Value::Array(rings) => {
+                    for ring in rings {
+                        if let Some(flat) = ring.as_array() {
+                            let points = parse_flat_polygon(flat);
+                            if points.len() >= 3 {
+                                bucket.push(Shape::Polygon {
+                                    class_id: ann.category_id,
+                                    class_name: class_name.clone(),
+                                    points,
+                                });
+                            }
+                        }
+                    }
+                }
+                serde_json::Value::Object(_) => {
+                    stats.skipped_rle += 1;
+                }
+                _ => {}
+            }
+        }
+
+        if let Some(bbox) = &ann.bbox {
+            if bbox.len() == 4 {
+                bucket.push(Shape::Bbox {
+                    class_id: ann.category_id,
+                    class_name,
+                    bbox: [bbox[0], bbox[1], bbox[2], bbox[3]],
+                });
+            }
+        }
+    }
+
+    stats.frame_count = frames.len();
+    let categories = coco
+        .categories
+        .into_iter()
+        .map(|c| Category {
+            id: c.id,
+            name: c.name,
+        })
+        .collect();
+
+    ImportedCoco {
+        categories,
+        frames,
+        stats,
+    }
+}
+
+// ---- Custom SAM annotation format ------------------------------------------
+//
+// { "<frame_idx>": { "<class_name>": [[id, [tl_x,tl_y], [br_x,br_y],
+//                                       [[[x,y], ...], ...rings]]] } }
+// Bbox is redundant with the polygon's tight bounding box, so we emit only
+// polygons. Class IDs are assigned 0-indexed by sorted class name.
+fn build_from_sam_anno(root: &serde_json::Value) -> Result<ImportedCoco, String> {
+    let obj = root
+        .as_object()
+        .ok_or_else(|| "SAM anno root is not an object".to_string())?;
+
+    let mut class_names = BTreeSet::<String>::new();
+    let mut frames: HashMap<String, Vec<Shape>> = HashMap::new();
+    let mut stats = ImportStats {
+        source_format: "sam".into(),
+        ..Default::default()
+    };
+
+    for (frame_key, frame_val) in obj {
+        let Some(cls_obj) = frame_val.as_object() else {
+            continue;
+        };
+        let mut shapes: Vec<Shape> = Vec::new();
+        for (class_name, items) in cls_obj {
+            class_names.insert(class_name.clone());
+            let Some(items) = items.as_array() else {
+                continue;
+            };
+            for item in items {
+                let Some(arr) = item.as_array() else {
+                    continue;
+                };
+                if arr.len() < 4 {
+                    continue;
+                }
+                stats.total_annotations += 1;
+
+                // arr[3] is a list of polygon rings.
+                let Some(rings) = arr[3].as_array() else {
+                    continue;
+                };
+                for ring in rings {
+                    let Some(pts) = ring.as_array() else {
+                        continue;
+                    };
+                    let mut points = Vec::with_capacity(pts.len());
+                    for p in pts {
+                        let Some(pa) = p.as_array() else {
+                            continue;
+                        };
+                        let (Some(x), Some(y)) = (
+                            pa.first().and_then(|v| v.as_f64()),
+                            pa.get(1).and_then(|v| v.as_f64()),
+                        ) else {
+                            continue;
+                        };
+                        points.push([x, y]);
+                    }
+                    if points.len() >= 3 {
+                        shapes.push(Shape::Polygon {
+                            class_id: 0,
+                            class_name: class_name.clone(),
+                            points,
+                        });
+                    }
+                }
+            }
+        }
+        if !shapes.is_empty() {
+            frames.insert(frame_key.clone(), shapes);
+        }
+    }
+
+    // Assign class_ids from sorted class names.
+    let categories: Vec<Category> = class_names
+        .iter()
+        .enumerate()
+        .map(|(i, name)| Category {
+            id: i as i64,
+            name: name.clone(),
+        })
+        .collect();
+    let name_to_id: HashMap<String, i64> = categories
+        .iter()
+        .map(|c| (c.name.clone(), c.id))
+        .collect();
+
+    for shapes in frames.values_mut() {
+        for shape in shapes.iter_mut() {
+            let (cid, cname) = match shape {
+                Shape::Bbox {
+                    class_id,
+                    class_name,
+                    ..
+                }
+                | Shape::Polygon {
+                    class_id,
+                    class_name,
+                    ..
+                } => (class_id, class_name),
+            };
+            if let Some(&id) = name_to_id.get(cname) {
+                *cid = id;
+            }
+        }
+    }
+
+    stats.categories = categories.len();
+    stats.frame_count = frames.len();
+
+    Ok(ImportedCoco {
+        categories,
+        frames,
+        stats,
+    })
+}
+
+// ---- helpers ---------------------------------------------------------------
+
+fn trailing_int(file_name: &str) -> Option<u64> {
+    let stem = Path::new(file_name)
+        .file_stem()
+        .and_then(|s| s.to_str())
+        .unwrap_or(file_name);
+    let digits: String = stem
+        .chars()
+        .rev()
+        .take_while(|c| c.is_ascii_digit())
+        .collect::<Vec<_>>()
+        .into_iter()
+        .rev()
+        .collect();
+    if digits.is_empty() {
+        None
+    } else {
+        digits.parse().ok()
+    }
+}
+
+fn parse_flat_polygon(flat: &[serde_json::Value]) -> Vec<[f64; 2]> {
+    let mut out = Vec::with_capacity(flat.len() / 2);
+    let mut i = 0;
+    while i + 1 < flat.len() {
+        let x = flat[i].as_f64();
+        let y = flat[i + 1].as_f64();
+        if let (Some(x), Some(y)) = (x, y) {
+            out.push([x, y]);
+        }
+        i += 2;
+    }
+    out
+}
diff --git a/sam-tool-tauri/src-tauri/src/decoder.rs b/sam-tool-tauri/src-tauri/src/decoder.rs
new file mode 100644
index 0000000..a6f5ae5
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/decoder.rs
@@ -0,0 +1,351 @@
+use crate::video::VideoInfo;
+use std::collections::{HashMap, VecDeque};
+use std::io::{BufRead, BufReader};
+use std::path::PathBuf;
+use std::process::{Child, ChildStdout, Command, Stdio};
+use std::sync::{Arc, Condvar, Mutex};
+use std::thread::{self, JoinHandle};
+use std::time::Duration;
+
+pub const DEFAULT_CACHE_CAPACITY: usize = 500;
+pub const MIN_CACHE_CAPACITY: usize = 200;
+pub const MAX_CACHE_CAPACITY: usize = 2000;
+const READ_BUF_BYTES: usize = 256 * 1024;
+/// If the requested frame is this far ahead of the worker, kill + reseek.
+const SEEK_AHEAD_THRESHOLD: u64 = 60;
+
+struct FrameCache {
+    map: HashMap<u64, Vec<u8>>,
+    order: VecDeque<u64>,
+    capacity: usize,
+}
+
+impl FrameCache {
+    fn new(capacity: usize) -> Self {
+        Self {
+            map: HashMap::new(),
+            order: VecDeque::new(),
+            capacity,
+        }
+    }
+
+    fn contains(&self, idx: u64) -> bool {
+        self.map.contains_key(&idx)
+    }
+
+    fn get(&mut self, idx: u64) -> Option<Vec<u8>> {
+        let bytes = self.map.get(&idx)?.clone();
+        if let Some(pos) = self.order.iter().position(|&i| i == idx) {
+            self.order.remove(pos);
+        }
+        self.order.push_back(idx);
+        Some(bytes)
+    }
+
+    fn insert(&mut self, idx: u64, bytes: Vec<u8>) {
+        if self.map.contains_key(&idx) {
+            if let Some(pos) = self.order.iter().position(|&i| i == idx) {
+                self.order.remove(pos);
+            }
+        } else if self.map.len() >= self.capacity {
+            if let Some(oldest) = self.order.pop_front() {
+                self.map.remove(&oldest);
+            }
+        }
+        self.map.insert(idx, bytes);
+        self.order.push_back(idx);
+    }
+
+    fn set_capacity(&mut self, new_cap: usize) {
+        self.capacity = new_cap.clamp(MIN_CACHE_CAPACITY, MAX_CACHE_CAPACITY);
+        while self.map.len() > self.capacity {
+            if let Some(oldest) = self.order.pop_front() {
+                self.map.remove(&oldest);
+            } else {
+                break;
+            }
+        }
+    }
+}
+
+struct SharedState {
+    /// Owned by shared state (not the worker closure) so stop_worker can
+    /// kill ffmpeg directly and unblock a pipe read that's waiting for the
+    /// first post-seek frame.
+    child: Option<Child>,
+    cache: FrameCache,
+    /// Frame the worker's pipe will emit next.
+    next_idx: u64,
+    /// Most recent frame the frontend requested — used to bound lookahead.
+    reader_idx: u64,
+    /// Worker reads this many frames past reader_idx before parking.
+    lookahead: u64,
+    eof: bool,
+    error: Option<String>,
+    shutdown: bool,
+}
+
+struct Shared {
+    state: Mutex<SharedState>,
+    cond: Condvar,
+}
+
+pub struct DecoderSession {
+    path: PathBuf,
+    info: VideoInfo,
+    shared: Arc<Shared>,
+    worker: Option<JoinHandle<()>>,
+}
+
+impl DecoderSession {
+    pub fn open(path: PathBuf, info: VideoInfo) -> Self {
+        Self {
+            path,
+            info,
+            shared: Arc::new(Shared {
+                state: Mutex::new(SharedState {
+                    child: None,
+                    cache: FrameCache::new(DEFAULT_CACHE_CAPACITY),
+                    next_idx: 0,
+                    reader_idx: 0,
+                    lookahead: (DEFAULT_CACHE_CAPACITY as u64) / 2,
+                    eof: false,
+                    error: None,
+                    shutdown: false,
+                }),
+                cond: Condvar::new(),
+            }),
+            worker: None,
+        }
+    }
+
+    pub fn info(&self) -> &VideoInfo {
+        &self.info
+    }
+
+    pub fn set_cache_capacity(&self, capacity: usize) {
+        let clamped = capacity.clamp(MIN_CACHE_CAPACITY, MAX_CACHE_CAPACITY);
+        let mut st = self.shared.state.lock().unwrap();
+        st.cache.set_capacity(clamped);
+        st.lookahead = (clamped as u64) / 2;
+    }
+
+    pub fn decode_at(&mut self, idx: u64) -> Result<Vec<u8>, String> {
+        if idx >= self.info.total_frames {
+            return Err(format!(
+                "frame {} out of range (total {})",
+                idx, self.info.total_frames
+            ));
+        }
+
+        // Update reader cursor, check cache, note whether a seek is needed.
+        let need_seek = {
+            let mut st = self.shared.state.lock().unwrap();
+            st.reader_idx = idx;
+            if let Some(bytes) = st.cache.get(idx) {
+                self.shared.cond.notify_all();
+                return Ok(bytes);
+            }
+            self.worker.is_none()
+                || idx < st.next_idx
+                || idx > st.next_idx.saturating_add(SEEK_AHEAD_THRESHOLD)
+        };
+
+        if need_seek {
+            self.seek_and_start_worker(idx)?;
+        } else {
+            self.shared.cond.notify_all();
+        }
+
+        // Wait for the worker to produce idx.
+        let mut st = self.shared.state.lock().unwrap();
+        loop {
+            if st.cache.contains(idx) {
+                return st
+                    .cache
+                    .get(idx)
+                    .ok_or_else(|| "frame vanished from cache".to_string());
+            }
+            if let Some(e) = &st.error {
+                return Err(e.clone());
+            }
+            if st.eof {
+                return Err(format!("frame {} not produced (EOF)", idx));
+            }
+            if st.shutdown {
+                return Err("decoder shut down".into());
+            }
+            st = self.shared.cond.wait(st).unwrap();
+        }
+    }
+
+    fn seek_and_start_worker(&mut self, idx: u64) -> Result<(), String> {
+        self.stop_worker();
+
+        let (child, stdout) = spawn_ffmpeg(&self.path, &self.info, idx)?;
+
+        {
+            let mut st = self.shared.state.lock().unwrap();
+            st.child = Some(child);
+            st.next_idx = idx;
+            st.reader_idx = idx;
+            st.eof = false;
+            st.error = None;
+            st.shutdown = false;
+        }
+        self.shared.cond.notify_all();
+
+        let shared = self.shared.clone();
+        let handle = thread::spawn(move || {
+            worker_loop(stdout, shared);
+        });
+        self.worker = Some(handle);
+        Ok(())
+    }
+
+    fn stop_worker(&mut self) {
+        // Take ownership of the child out of shared state and kill it FIRST so
+        // a pipe read in the worker unblocks with EOF. Then join the worker.
+        let child_opt = {
+            let mut st = self.shared.state.lock().unwrap();
+            st.shutdown = true;
+            st.child.take()
+        };
+        if let Some(mut child) = child_opt {
+            let _ = child.kill();
+            let _ = child.wait();
+        }
+        self.shared.cond.notify_all();
+
+        if let Some(handle) = self.worker.take() {
+            let _ = handle.join();
+        }
+
+        let mut st = self.shared.state.lock().unwrap();
+        st.shutdown = false;
+        st.eof = false;
+        st.error = None;
+    }
+}
+
+impl Drop for DecoderSession {
+    fn drop(&mut self) {
+        self.stop_worker();
+    }
+}
+
+fn spawn_ffmpeg(
+    path: &PathBuf,
+    info: &VideoInfo,
+    idx: u64,
+) -> Result<(Child, BufReader<ChildStdout>), String> {
+    let path_str = path
+        .to_str()
+        .ok_or_else(|| "non-utf8 path".to_string())?;
+    let t = idx as f64 / info.fps;
+    let mut cmd = Command::new("ffmpeg");
+    cmd.args(["-v", "error", "-threads", "0"]);
+    if t > 1.0 {
+        cmd.args(["-ss", &format!("{:.3}", t - 1.0)]);
+        cmd.args(["-i", path_str]);
+        cmd.args(["-ss", "1.0"]);
+    } else {
+        cmd.args(["-i", path_str]);
+        cmd.args(["-ss", &format!("{:.3}", t)]);
+    }
+    cmd.args([
+        "-an",
+        "-q:v",
+        "3",
+        "-f",
+        "image2pipe",
+        "-vcodec",
+        "mjpeg",
+        "pipe:1",
+    ]);
+    cmd.stdout(Stdio::piped()).stderr(Stdio::piped());
+    let mut child = cmd.spawn().map_err(|e| format!("ffmpeg spawn: {e}"))?;
+    let stdout = child
+        .stdout
+        .take()
+        .ok_or_else(|| "ffmpeg stdout unavailable".to_string())?;
+    Ok((child, BufReader::with_capacity(READ_BUF_BYTES, stdout)))
+}
+
+fn worker_loop(mut stdout: BufReader<ChildStdout>, shared: Arc<Shared>) {
+    loop {
+        // Backpressure: wait if we're already far enough ahead.
+        {
+            let st = shared.state.lock().unwrap();
+            if st.shutdown {
+                return;
+            }
+            let ahead = st.next_idx.saturating_sub(st.reader_idx);
+            if ahead >= st.lookahead {
+                let (st2, _) = shared
+                    .cond
+                    .wait_timeout(st, Duration::from_millis(100))
+                    .unwrap();
+                if st2.shutdown {
+                    return;
+                }
+                continue;
+            }
+        }
+
+        match read_jpeg_bytes(&mut stdout) {
+            Ok(bytes) => {
+                let mut st = shared.state.lock().unwrap();
+                if st.shutdown {
+                    return;
+                }
+                let idx = st.next_idx;
+                st.cache.insert(idx, bytes);
+                st.next_idx = idx + 1;
+                shared.cond.notify_all();
+            }
+            Err(e) => {
+                let mut st = shared.state.lock().unwrap();
+                if e.contains("pipe closed") || e.contains("EOF") {
+                    st.eof = true;
+                } else {
+                    st.error = Some(e);
+                }
+                shared.cond.notify_all();
+                return;
+            }
+        }
+    }
+}
+
+fn read_jpeg_bytes(reader: &mut BufReader<ChildStdout>) -> Result<Vec<u8>, String> {
+    let mut out = Vec::with_capacity(64 * 1024);
+    let mut last = 0u8;
+    let mut in_jpeg = false;
+    loop {
+        let buf = reader.fill_buf().map_err(|e| format!("pipe read: {e}"))?;
+        if buf.is_empty() {
+            return Err("ffmpeg pipe closed before frame completed".into());
+        }
+        let mut consumed = 0usize;
+        for &b in buf {
+            consumed += 1;
+            if !in_jpeg {
+                if last == 0xFF && b == 0xD8 {
+                    in_jpeg = true;
+                    out.push(0xFF);
+                    out.push(0xD8);
+                }
+                last = b;
+            } else {
+                out.push(b);
+                if last == 0xFF && b == 0xD9 {
+                    reader.consume(consumed);
+                    return Ok(out);
+                }
+                last = b;
+            }
+        }
+        reader.consume(consumed);
+    }
+}
diff --git a/sam-tool-tauri/src-tauri/src/extract.rs b/sam-tool-tauri/src-tauri/src/extract.rs
new file mode 100644
index 0000000..d8718ae
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/extract.rs
@@ -0,0 +1,188 @@
+use crate::annotation::{AnnotationRecord, ImageAnnotations};
+use crate::coco::{Category, Shape};
+use crate::decoder::DecoderSession;
+use chrono::Utc;
+use std::path::Path;
+use uuid::Uuid;
+
+pub fn image_filename(idx: u64) -> String {
+    format!("f_{:04}.jpg", idx)
+}
+
+pub fn json_filename(idx: u64) -> String {
+    format!("f_{:04}.json", idx)
+}
+
+/// Parse a frame index from either the new `f_0042.jpg` pattern or the legacy
+/// `frame_000042.jpg` pattern. Returns None for unrelated files.
+fn parse_frame_idx(name: &str) -> Option<u64> {
+    let stem = name.strip_suffix(".jpg")?;
+    if let Some(rest) = stem.strip_prefix("f_") {
+        return rest.parse().ok();
+    }
+    if let Some(rest) = stem.strip_prefix("frame_") {
+        return rest.parse().ok();
+    }
+    None
+}
+
+pub fn extract_one(
+    decoder: &mut DecoderSession,
+    frame_idx: u64,
+    out_dir: &Path,
+    shapes: &[Shape],
+    author: &str,
+) -> Result<(), String> {
+    std::fs::create_dir_all(out_dir)
+        .map_err(|e| format!("mkdir {}: {e}", out_dir.display()))?;
+
+    // Snapshot info so we don't hold an immutable borrow across the mutable
+    // decode_at call below.
+    let info = decoder.info().clone();
+    write_folder_source(out_dir, &info.path);
+
+    let jpeg = decoder.decode_at(frame_idx)?;
+    let img_path = out_dir.join(image_filename(frame_idx));
+    std::fs::write(&img_path, &jpeg)
+        .map_err(|e| format!("write {}: {e}", img_path.display()))?;
+
+    let json_path = out_dir.join(json_filename(frame_idx));
+    if shapes.is_empty() {
+        // Clean up any stale sibling JSON from a prior extract with annotations.
+        let _ = std::fs::remove_file(&json_path);
+        return Ok(());
+    }
+
+    let now = Utc::now().to_rfc3339();
+    let annotations = shapes
+        .iter()
+        .map(|s| shape_to_record(s, author, &now))
+        .collect();
+
+    let ia = ImageAnnotations {
+        version: 1,
+        image: image_filename(frame_idx),
+        width: info.width,
+        height: info.height,
+        source_video: Some(info.path),
+        annotations,
+    };
+    let text = serde_json::to_string_pretty(&ia)
+        .map_err(|e| format!("serialize json: {e}"))?;
+    std::fs::write(&json_path, text)
+        .map_err(|e| format!("write {}: {e}", json_path.display()))?;
+    Ok(())
+}
+
+pub const FOLDER_SOURCE_FILENAME: &str = "source.json";
+
+#[derive(serde::Serialize, serde::Deserialize)]
+pub struct FolderSource {
+    pub source_video: String,
+    pub created_at: String,
+}
+
+/// Write (once, if missing) a folder-level metadata file recording which
+/// video this output folder was extracted from. Silent on failure — this is
+/// a best-effort breadcrumb, not a critical write.
+pub fn write_folder_source(out_dir: &Path, video_path: &str) {
+    let meta_path = out_dir.join(FOLDER_SOURCE_FILENAME);
+    if meta_path.exists() {
+        return;
+    }
+    let meta = FolderSource {
+        source_video: video_path.to_string(),
+        created_at: Utc::now().to_rfc3339(),
+    };
+    if let Ok(text) = serde_json::to_string_pretty(&meta) {
+        let _ = std::fs::write(&meta_path, text);
+    }
+}
+
+pub fn read_folder_source(out_dir: &Path) -> Option<FolderSource> {
+    let meta_path = out_dir.join(FOLDER_SOURCE_FILENAME);
+    let text = std::fs::read_to_string(&meta_path).ok()?;
+    serde_json::from_str(&text).ok()
+}
+
+pub fn undo_one(frame_idx: u64, out_dir: &Path) -> Result<(), String> {
+    // Try both naming schemes so legacy extracts are also cleanable.
+    for pair in [
+        (image_filename(frame_idx), json_filename(frame_idx)),
+        (format!("frame_{:06}.jpg", frame_idx), format!("frame_{:06}.json", frame_idx)),
+    ] {
+        let img = out_dir.join(&pair.0);
+        let json = out_dir.join(&pair.1);
+        if img.exists() {
+            std::fs::remove_file(&img).map_err(|e| format!("remove {}: {e}", img.display()))?;
+        }
+        if json.exists() {
+            std::fs::remove_file(&json).map_err(|e| format!("remove {}: {e}", json.display()))?;
+        }
+    }
+    Ok(())
+}
+
+pub fn list_extracted(out_dir: &Path) -> Result<Vec<u64>, String> {
+    if !out_dir.exists() {
+        return Ok(vec![]);
+    }
+    let mut out = Vec::new();
+    let entries = std::fs::read_dir(out_dir)
+        .map_err(|e| format!("read_dir {}: {e}", out_dir.display()))?;
+    for entry in entries {
+        let entry = entry.map_err(|e| format!("dirent: {e}"))?;
+        if let Some(name) = entry.file_name().to_str() {
+            if let Some(n) = parse_frame_idx(name) {
+                out.push(n);
+            }
+        }
+    }
+    out.sort();
+    Ok(out)
+}
+
+pub fn write_classes_txt(out_dir: &Path, categories: &[Category]) -> Result<(), String> {
+    std::fs::create_dir_all(out_dir)
+        .map_err(|e| format!("mkdir {}: {e}", out_dir.display()))?;
+    let mut sorted = categories.to_vec();
+    sorted.sort_by_key(|c| c.id);
+    let mut text = String::new();
+    for c in &sorted {
+        text.push_str(&c.name);
+        text.push('\n');
+    }
+    let path = out_dir.join("classes.txt");
+    std::fs::write(&path, text).map_err(|e| format!("write {}: {e}", path.display()))
+}
+
+fn shape_to_record(shape: &Shape, author: &str, ts: &str) -> AnnotationRecord {
+    match shape {
+        Shape::Bbox {
+            class_id,
+            class_name,
+            bbox,
+        } => AnnotationRecord::Bbox {
+            id: Uuid::new_v4().to_string(),
+            class_id: *class_id,
+            class_name: class_name.clone(),
+            bbox: *bbox,
+            author: author.to_string(),
+            created_at: ts.to_string(),
+            updated_at: ts.to_string(),
+        },
+        Shape::Polygon {
+            class_id,
+            class_name,
+            points,
+        } => AnnotationRecord::Polygon {
+            id: Uuid::new_v4().to_string(),
+            class_id: *class_id,
+            class_name: class_name.clone(),
+            points: points.clone(),
+            author: author.to_string(),
+            created_at: ts.to_string(),
+            updated_at: ts.to_string(),
+        },
+    }
+}
diff --git a/sam-tool-tauri/src-tauri/src/lib.rs b/sam-tool-tauri/src-tauri/src/lib.rs
new file mode 100644
index 0000000..d0af287
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/lib.rs
@@ -0,0 +1,307 @@
+mod annotate;
+mod annotation;
+mod coco;
+mod decoder;
+mod extract;
+mod sam;
+mod settings;
+mod state;
+mod user;
+mod video;
+
+use decoder::DecoderSession;
+use serde::Serialize;
+use state::AppState;
+use std::collections::HashMap;
+use std::path::{Path, PathBuf};
+use tauri::{ipc::Response, AppHandle, Emitter, State};
+use video::VideoInfo;
+
+#[tauri::command]
+fn ping() -> String {
+    "pong".into()
+}
+
+#[tauri::command]
+fn open_video(state: State<'_, AppState>, path: String) -> Result<VideoInfo, String> {
+    let p = PathBuf::from(&path);
+    if !p.exists() {
+        return Err(format!("file not found: {}", p.display()));
+    }
+    let info = video::probe(&p)?;
+    let mut guard = state.decoder.lock().map_err(|e| format!("lock: {e}"))?;
+    *guard = Some(DecoderSession::open(p, info.clone()));
+    Ok(info)
+}
+
+#[tauri::command]
+fn close_video(state: State<'_, AppState>) -> Result<(), String> {
+    let mut guard = state.decoder.lock().map_err(|e| format!("lock: {e}"))?;
+    *guard = None;
+    Ok(())
+}
+
+#[tauri::command]
+fn current_video(state: State<'_, AppState>) -> Result<Option<VideoInfo>, String> {
+    let guard = state.decoder.lock().map_err(|e| format!("lock: {e}"))?;
+    Ok(guard.as_ref().map(|d| d.info().clone()))
+}
+
+#[tauri::command]
+fn decode_frame(state: State<'_, AppState>, frame_idx: u64) -> Result<Response, String> {
+    let bytes = {
+        let mut guard = state.decoder.lock().map_err(|e| format!("lock: {e}"))?;
+        let decoder = guard.as_mut().ok_or("no video loaded")?;
+        decoder.decode_at(frame_idx)?
+    };
+    Ok(Response::new(bytes))
+}
+
+#[tauri::command]
+fn import_coco(path: String) -> Result<coco::ImportedCoco, String> {
+    let p = Path::new(&path);
+    if !p.exists() {
+        return Err(format!("file not found: {}", p.display()));
+    }
+    coco::import(p)
+}
+
+#[tauri::command]
+fn set_cache_capacity(state: State<'_, AppState>, capacity: usize) -> Result<usize, String> {
+    let clamped = capacity.clamp(decoder::MIN_CACHE_CAPACITY, decoder::MAX_CACHE_CAPACITY);
+    let guard = state.decoder.lock().map_err(|e| format!("lock: {e}"))?;
+    if let Some(d) = guard.as_ref() {
+        d.set_cache_capacity(clamped);
+    }
+    Ok(clamped)
+}
+
+#[tauri::command]
+fn extract_frame(
+    state: State<'_, AppState>,
+    frame_idx: u64,
+    out_dir: String,
+    shapes: Vec<coco::Shape>,
+    author: String,
+) -> Result<(), String> {
+    let mut guard = state.decoder.lock().map_err(|e| format!("lock: {e}"))?;
+    let decoder = guard.as_mut().ok_or("no video loaded")?;
+    extract::extract_one(decoder, frame_idx, Path::new(&out_dir), &shapes, &author)
+}
+
+#[tauri::command]
+fn undo_extract(frame_idx: u64, out_dir: String) -> Result<(), String> {
+    extract::undo_one(frame_idx, Path::new(&out_dir))
+}
+
+#[derive(Serialize, Clone)]
+struct BulkProgress {
+    done: u64,
+    total: u64,
+}
+
+#[tauri::command]
+fn bulk_extract(
+    state: State<'_, AppState>,
+    app: AppHandle,
+    start: u64,
+    end: u64,
+    out_dir: String,
+    shapes_by_frame: HashMap<String, Vec<coco::Shape>>,
+    author: String,
+) -> Result<u64, String> {
+    if end < start {
+        return Err("end must be >= start".into());
+    }
+    let mut guard = state.decoder.lock().map_err(|e| format!("lock: {e}"))?;
+    let decoder = guard.as_mut().ok_or("no video loaded")?;
+    let dir = PathBuf::from(&out_dir);
+    let total = end - start + 1;
+    let empty: Vec<coco::Shape> = Vec::new();
+    let mut done: u64 = 0;
+    for idx in start..=end {
+        let shapes = shapes_by_frame
+            .get(&idx.to_string())
+            .unwrap_or(&empty);
+        extract::extract_one(decoder, idx, &dir, shapes, &author)?;
+        done += 1;
+        if done == 1 || done % 5 == 0 || done == total {
+            let _ = app.emit("bulk-extract-progress", BulkProgress { done, total });
+        }
+    }
+    Ok(done)
+}
+
+#[tauri::command]
+fn bulk_undo(
+    app: AppHandle,
+    start: u64,
+    end: u64,
+    out_dir: String,
+) -> Result<u64, String> {
+    if end < start {
+        return Err("end must be >= start".into());
+    }
+    let dir = PathBuf::from(&out_dir);
+    let total = end - start + 1;
+    let mut done: u64 = 0;
+    for idx in start..=end {
+        extract::undo_one(idx, &dir)?;
+        done += 1;
+        if done == 1 || done % 20 == 0 || done == total {
+            let _ = app.emit("bulk-undo-progress", BulkProgress { done, total });
+        }
+    }
+    Ok(done)
+}
+
+#[tauri::command]
+fn list_extracted_frames(out_dir: String) -> Result<Vec<u64>, String> {
+    extract::list_extracted(Path::new(&out_dir))
+}
+
+#[tauri::command]
+fn write_classes_txt(out_dir: String, categories: Vec<coco::Category>) -> Result<(), String> {
+    extract::write_classes_txt(Path::new(&out_dir), &categories)
+}
+
+#[tauri::command]
+fn list_folder_images(folder: String) -> Result<Vec<annotate::ImageEntry>, String> {
+    annotate::list_images(Path::new(&folder))
+}
+
+#[tauri::command]
+fn read_image_bytes(path: String) -> Result<Response, String> {
+    let bytes = annotate::read_image_bytes(Path::new(&path))?;
+    Ok(Response::new(bytes))
+}
+
+#[tauri::command]
+fn load_image_annotations(
+    image_path: String,
+) -> Result<Option<annotation::ImageAnnotations>, String> {
+    annotate::load_image_annotations(Path::new(&image_path))
+}
+
+#[tauri::command]
+fn load_classes(folder: String) -> Result<Vec<String>, String> {
+    annotate::load_classes(Path::new(&folder))
+}
+
+#[tauri::command]
+fn append_class(folder: String, name: String) -> Result<Vec<String>, String> {
+    annotate::append_class(Path::new(&folder), &name)
+}
+
+#[tauri::command]
+fn delete_images(paths: Vec<String>) -> Result<u32, String> {
+    annotate::delete_images(&paths)
+}
+
+#[tauri::command]
+async fn sam_health_check(url: String) -> Result<bool, String> {
+    sam::health_check(&url).await
+}
+
+#[tauri::command]
+async fn sam_list_models(url: String) -> Result<sam::ModelList, String> {
+    sam::list_models(&url).await
+}
+
+#[tauri::command]
+async fn sam_segment_bbox(
+    url: String,
+    image_path: String,
+    bbox: [f64; 4],
+    image_width: u32,
+    image_height: u32,
+    model: Option<String>,
+) -> Result<Vec<[f64; 2]>, String> {
+    sam::segment_bbox(
+        &url,
+        Path::new(&image_path),
+        bbox,
+        image_width,
+        image_height,
+        model,
+    )
+    .await
+}
+
+#[tauri::command]
+fn save_image_annotations(
+    image_path: String,
+    payload: annotation::ImageAnnotations,
+) -> Result<(), String> {
+    let p = Path::new(&image_path);
+    let json_path = p.with_extension("json");
+    if payload.annotations.is_empty() {
+        if json_path.exists() {
+            std::fs::remove_file(&json_path)
+                .map_err(|e| format!("remove {}: {e}", json_path.display()))?;
+        }
+        return Ok(());
+    }
+    // Backfill source_video from folder-level source.json when the payload
+    // doesn't carry it — handles annotations authored via AnnotateMode that
+    // never went through extract_one.
+    let mut payload = payload;
+    if payload.source_video.is_none() {
+        if let Some(parent) = p.parent() {
+            if let Some(meta) = extract::read_folder_source(parent) {
+                payload.source_video = Some(meta.source_video);
+            }
+        }
+    }
+    let text = serde_json::to_string_pretty(&payload)
+        .map_err(|e| format!("serialize: {e}"))?;
+    std::fs::write(&json_path, text)
+        .map_err(|e| format!("write {}: {e}", json_path.display()))
+}
+
+#[tauri::command]
+fn read_folder_source(folder: String) -> Option<String> {
+    extract::read_folder_source(Path::new(&folder)).map(|s| s.source_video)
+}
+
+#[cfg_attr(mobile, tauri::mobile_entry_point)]
+pub fn run() {
+    tauri::Builder::default()
+        .plugin(tauri_plugin_dialog::init())
+        .plugin(tauri_plugin_fs::init())
+        .manage(AppState::new())
+        .invoke_handler(tauri::generate_handler![
+            ping,
+            user::current_user,
+            user::list_known_users,
+            user::set_current_user,
+            user::clear_current_user,
+            open_video,
+            close_video,
+            current_video,
+            decode_frame,
+            set_cache_capacity,
+            import_coco,
+            extract_frame,
+            undo_extract,
+            bulk_extract,
+            bulk_undo,
+            list_extracted_frames,
+            write_classes_txt,
+            list_folder_images,
+            read_image_bytes,
+            load_image_annotations,
+            load_classes,
+            save_image_annotations,
+            append_class,
+            delete_images,
+            read_folder_source,
+            settings::get_settings,
+            settings::save_settings,
+            sam_health_check,
+            sam_list_models,
+            sam_segment_bbox,
+        ])
+        .run(tauri::generate_context!())
+        .expect("error while running tauri application");
+}
diff --git a/sam-tool-tauri/src-tauri/src/main.rs b/sam-tool-tauri/src-tauri/src/main.rs
new file mode 100644
index 0000000..512243a
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/main.rs
@@ -0,0 +1,6 @@
+// Prevents a console window on Windows release builds (harmless on Linux).
+#![cfg_attr(not(debug_assertions), windows_subsystem = "windows")]
+
+fn main() {
+    sam_tool_tauri_lib::run()
+}
diff --git a/sam-tool-tauri/src-tauri/src/sam.rs b/sam-tool-tauri/src-tauri/src/sam.rs
new file mode 100644
index 0000000..525e832
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/sam.rs
@@ -0,0 +1,222 @@
+use base64::Engine as _;
+use serde::{Deserialize, Serialize};
+use serde_json::{json, Value};
+use std::path::Path;
+use std::time::Duration;
+
+#[derive(Serialize, Deserialize, Clone, Debug)]
+pub struct ModelInfo {
+    pub id: String,
+    pub name: String,
+    pub family: String,
+    pub available: bool,
+    #[serde(default)]
+    pub checkpoint: String,
+}
+
+#[derive(Serialize, Deserialize, Clone, Debug)]
+pub struct ModelList {
+    #[serde(default)]
+    pub default: Option<String>,
+    pub models: Vec<ModelInfo>,
+}
+
+/// Fetch the list of models the backend advertises. The Tauri Settings
+/// modal uses this to populate a dropdown so users can pick which variant
+/// to hit.
+pub async fn list_models(url: &str) -> Result<ModelList, String> {
+    let client = reqwest::Client::builder()
+        .timeout(Duration::from_secs(6))
+        .build()
+        .map_err(|e| format!("http client: {e}"))?;
+    let endpoint = format!("{}/models", url.trim_end_matches('/'));
+    let res = client
+        .get(&endpoint)
+        .send()
+        .await
+        .map_err(|e| format!("connect to {endpoint}: {e}"))?;
+    if !res.status().is_success() {
+        return Err(format!("GET /models → {}", res.status()));
+    }
+    res.json::<ModelList>()
+        .await
+        .map_err(|e| format!("parse /models: {e}"))
+}
+
+/// Minimal XML that makes a Label-Studio-ML SAM backend happy. The names
+/// (`bbox`, `polygon`, `tag`) are referenced from the request / response by
+/// their `from_name` / `to_name` fields — if your backend expects different
+/// names we can wire them up.
+const LABEL_CONFIG: &str = r##"<View>
+  <Image name="image" value="$image" zoom="true"/>
+  <BrushLabels name="tag" toName="image">
+    <Label value="Object" background="#ff0000"/>
+  </BrushLabels>
+  <RectangleLabels name="bbox" toName="image" smart="true">
+    <Label value="Object" background="#ff0000"/>
+  </RectangleLabels>
+  <PolygonLabels name="polygon" toName="image" smart="true">
+    <Label value="Object" background="#ff0000"/>
+  </PolygonLabels>
+</View>"##;
+
+/// Ping the SAM backend's root.
+pub async fn health_check(url: &str) -> Result<bool, String> {
+    let client = reqwest::Client::builder()
+        .timeout(Duration::from_secs(4))
+        .build()
+        .map_err(|e| format!("http client: {e}"))?;
+    let res = client
+        .get(url)
+        .send()
+        .await
+        .map_err(|e| format!("connect: {e}"))?;
+    Ok(res.status().is_success() || res.status().is_client_error())
+}
+
+/// POST the image + bbox prompt to a Label-Studio-ML SAM backend, return the
+/// polygon in **image-pixel** coords.
+///
+/// Wire protocol (LS-ML):
+/// ```json
+/// {
+///   "tasks": [{ "id": 1, "data": { "image": "data:image/jpeg;base64,..." } }],
+///   "label_config": "<View>...</View>",
+///   "params": {
+///     "context": {
+///       "result": [{
+///         "original_width": W, "original_height": H, "image_rotation": 0,
+///         "value": { "x": %, "y": %, "width": %, "height": %,
+///                    "rotation": 0, "rectanglelabels": ["Object"] },
+///         "id": "box_0",
+///         "from_name": "bbox", "to_name": "image",
+///         "type": "rectanglelabels"
+///       }]
+///     }
+///   }
+/// }
+/// ```
+pub async fn segment_bbox(
+    url: &str,
+    image_path: &Path,
+    bbox: [f64; 4],
+    image_width: u32,
+    image_height: u32,
+    model: Option<String>,
+) -> Result<Vec<[f64; 2]>, String> {
+    let image_bytes = std::fs::read(image_path)
+        .map_err(|e| format!("read image {}: {e}", image_path.display()))?;
+    let image_b64 = base64::engine::general_purpose::STANDARD.encode(&image_bytes);
+    let data_url = format!("data:image/jpeg;base64,{}", image_b64);
+
+    let [x, y, w, h] = bbox;
+    let width_f = image_width.max(1) as f64;
+    let height_f = image_height.max(1) as f64;
+    let x_pct = (x / width_f) * 100.0;
+    let y_pct = (y / height_f) * 100.0;
+    let w_pct = (w / width_f) * 100.0;
+    let h_pct = (h / height_f) * 100.0;
+
+    let body = json!({
+        "tasks": [{
+            "id": 1,
+            "data": { "image": data_url }
+        }],
+        "label_config": LABEL_CONFIG,
+        "params": {
+            "context": {
+                "result": [{
+                    "original_width": image_width,
+                    "original_height": image_height,
+                    "image_rotation": 0,
+                    "value": {
+                        "x": x_pct,
+                        "y": y_pct,
+                        "width": w_pct,
+                        "height": h_pct,
+                        "rotation": 0,
+                        "rectanglelabels": ["Object"]
+                    },
+                    "id": "box_0",
+                    "from_name": "bbox",
+                    "to_name": "image",
+                    "type": "rectanglelabels"
+                }]
+            }
+        }
+    });
+
+    let client = reqwest::Client::builder()
+        .timeout(Duration::from_secs(60))
+        .build()
+        .map_err(|e| format!("http client: {e}"))?;
+
+    let endpoint = format!("{}/predict", url.trim_end_matches('/'));
+    let mut req = client.post(&endpoint);
+    if let Some(ref m) = model {
+        if !m.is_empty() {
+            req = req.query(&[("model", m.as_str())]);
+        }
+    }
+    let res = req
+        .json(&body)
+        .send()
+        .await
+        .map_err(|e| format!("request to {endpoint}: {e}"))?;
+
+    if !res.status().is_success() {
+        let status = res.status();
+        let body_text = res.text().await.unwrap_or_default();
+        return Err(format!("SAM {} — {}", status, body_text));
+    }
+    let raw: Value = res
+        .json()
+        .await
+        .map_err(|e| format!("parse SAM response: {e}"))?;
+
+    extract_polygon(&raw, width_f, height_f)
+        .ok_or_else(|| format!("unrecognized SAM response shape: {}", raw))
+}
+
+/// Walk the JSON tree looking for a polygon — LS-ML usually returns
+/// `{ results: [{ result: [{ value: { points: [[x%, y%], ...] } }] }] }`,
+/// but we also accept plain `{ polygon: [[x, y], ...] }` in pixels.
+fn extract_polygon(v: &Value, w: f64, h: f64) -> Option<Vec<[f64; 2]>> {
+    // Plain shape: pixel polygon at the top level.
+    if let Some(poly) = v.get("polygon").and_then(|p| p.as_array()) {
+        let out = parse_points(poly, 1.0, 1.0);
+        if out.len() >= 3 {
+            return Some(out);
+        }
+    }
+
+    // LS-ML shape.
+    let results = v.get("results").and_then(|r| r.as_array())?;
+    let first = results.first()?;
+    let inner = first.get("result").and_then(|r| r.as_array())?;
+    for entry in inner {
+        let value = entry.get("value")?;
+        if let Some(points) = value.get("points").and_then(|p| p.as_array()) {
+            // LS polygons are in percentages of the original image size.
+            let out = parse_points(points, w / 100.0, h / 100.0);
+            if out.len() >= 3 {
+                return Some(out);
+            }
+        }
+    }
+    None
+}
+
+fn parse_points(arr: &[Value], sx: f64, sy: f64) -> Vec<[f64; 2]> {
+    let mut out = Vec::with_capacity(arr.len());
+    for pt in arr {
+        if let Some(p) = pt.as_array() {
+            if p.len() >= 2 {
+                if let (Some(x), Some(y)) = (p[0].as_f64(), p[1].as_f64()) {
+                    out.push([x * sx, y * sy]);
+                }
+            }
+        }
+    }
+    out
+}
diff --git a/sam-tool-tauri/src-tauri/src/settings.rs b/sam-tool-tauri/src-tauri/src/settings.rs
new file mode 100644
index 0000000..7f0858a
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/settings.rs
@@ -0,0 +1,71 @@
+use serde::{Deserialize, Serialize};
+use std::fs;
+use std::path::PathBuf;
+use tauri::{AppHandle, Manager};
+
+#[derive(Serialize, Deserialize, Clone)]
+pub struct Settings {
+    #[serde(default = "default_sam_url")]
+    pub sam_url: String,
+    #[serde(default)]
+    pub sam_enabled: bool,
+    /// Model id chosen from the backend's /models list (e.g.
+    /// "sam2.1_hiera_small"). `None` lets the backend pick its default.
+    #[serde(default)]
+    pub sam_model: Option<String>,
+    #[serde(default = "default_cache_capacity")]
+    pub cache_capacity: usize,
+}
+
+fn default_sam_url() -> String {
+    "http://localhost:9090".into()
+}
+
+fn default_cache_capacity() -> usize {
+    500
+}
+
+impl Default for Settings {
+    fn default() -> Self {
+        Self {
+            sam_url: default_sam_url(),
+            sam_enabled: false,
+            sam_model: None,
+            cache_capacity: default_cache_capacity(),
+        }
+    }
+}
+
+fn settings_path(app: &AppHandle) -> Result<PathBuf, String> {
+    let dir = app
+        .path()
+        .app_local_data_dir()
+        .map_err(|e| format!("app data dir: {e}"))?;
+    fs::create_dir_all(&dir).map_err(|e| format!("mkdir {}: {e}", dir.display()))?;
+    Ok(dir.join("settings.json"))
+}
+
+pub fn load(app: &AppHandle) -> Result<Settings, String> {
+    let path = settings_path(app)?;
+    if !path.exists() {
+        return Ok(Settings::default());
+    }
+    let text = fs::read_to_string(&path).map_err(|e| format!("read {}: {e}", path.display()))?;
+    serde_json::from_str(&text).map_err(|e| format!("parse settings: {e}"))
+}
+
+pub fn save(app: &AppHandle, s: &Settings) -> Result<(), String> {
+    let path = settings_path(app)?;
+    let text = serde_json::to_string_pretty(s).map_err(|e| format!("serialize: {e}"))?;
+    fs::write(&path, text).map_err(|e| format!("write {}: {e}", path.display()))
+}
+
+#[tauri::command]
+pub fn get_settings(app: AppHandle) -> Result<Settings, String> {
+    load(&app)
+}
+
+#[tauri::command]
+pub fn save_settings(app: AppHandle, settings: Settings) -> Result<(), String> {
+    save(&app, &settings)
+}
diff --git a/sam-tool-tauri/src-tauri/src/state.rs b/sam-tool-tauri/src-tauri/src/state.rs
new file mode 100644
index 0000000..01c57dc
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/state.rs
@@ -0,0 +1,14 @@
+use crate::decoder::DecoderSession;
+use std::sync::Mutex;
+
+pub struct AppState {
+    pub decoder: Mutex<Option<DecoderSession>>,
+}
+
+impl AppState {
+    pub fn new() -> Self {
+        Self {
+            decoder: Mutex::new(None),
+        }
+    }
+}
diff --git a/sam-tool-tauri/src-tauri/src/user.rs b/sam-tool-tauri/src-tauri/src/user.rs
new file mode 100644
index 0000000..e09a5cd
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/user.rs
@@ -0,0 +1,76 @@
+use serde::{Deserialize, Serialize};
+use std::fs;
+use std::path::PathBuf;
+use tauri::{AppHandle, Manager};
+
+#[derive(Default, Serialize, Deserialize)]
+struct UserStore {
+    current_user: Option<String>,
+    #[serde(default)]
+    known_users: Vec<String>,
+}
+
+fn store_path(app: &AppHandle) -> Result<PathBuf, String> {
+    let dir = app
+        .path()
+        .app_local_data_dir()
+        .map_err(|e| format!("could not resolve app data dir: {e}"))?;
+    fs::create_dir_all(&dir).map_err(|e| format!("mkdir {}: {e}", dir.display()))?;
+    Ok(dir.join("users.json"))
+}
+
+fn load(app: &AppHandle) -> Result<UserStore, String> {
+    let path = store_path(app)?;
+    if !path.exists() {
+        return Ok(UserStore::default());
+    }
+    let text = fs::read_to_string(&path).map_err(|e| format!("read {}: {e}", path.display()))?;
+    serde_json::from_str(&text).map_err(|e| format!("parse users.json: {e}"))
+}
+
+fn save(app: &AppHandle, store: &UserStore) -> Result<(), String> {
+    let path = store_path(app)?;
+    let text = serde_json::to_string_pretty(store).map_err(|e| format!("serialize: {e}"))?;
+    fs::write(&path, text).map_err(|e| format!("write {}: {e}", path.display()))
+}
+
+fn normalize(name: &str) -> Result<String, String> {
+    let trimmed = name.trim();
+    if trimmed.is_empty() {
+        return Err("username cannot be empty".into());
+    }
+    if trimmed.len() > 64 {
+        return Err("username too long (max 64 chars)".into());
+    }
+    Ok(trimmed.to_string())
+}
+
+#[tauri::command]
+pub fn current_user(app: AppHandle) -> Result<Option<String>, String> {
+    Ok(load(&app)?.current_user)
+}
+
+#[tauri::command]
+pub fn list_known_users(app: AppHandle) -> Result<Vec<String>, String> {
+    Ok(load(&app)?.known_users)
+}
+
+#[tauri::command]
+pub fn set_current_user(app: AppHandle, name: String) -> Result<String, String> {
+    let name = normalize(&name)?;
+    let mut store = load(&app)?;
+    if !store.known_users.iter().any(|u| u == &name) {
+        store.known_users.push(name.clone());
+        store.known_users.sort();
+    }
+    store.current_user = Some(name.clone());
+    save(&app, &store)?;
+    Ok(name)
+}
+
+#[tauri::command]
+pub fn clear_current_user(app: AppHandle) -> Result<(), String> {
+    let mut store = load(&app)?;
+    store.current_user = None;
+    save(&app, &store)
+}
diff --git a/sam-tool-tauri/src-tauri/src/video.rs b/sam-tool-tauri/src-tauri/src/video.rs
new file mode 100644
index 0000000..31146c1
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/src/video.rs
@@ -0,0 +1,106 @@
+use serde::Serialize;
+use std::path::Path;
+use std::process::Command;
+
+#[derive(Serialize, Clone, Debug)]
+pub struct VideoInfo {
+    pub path: String,
+    pub width: u32,
+    pub height: u32,
+    pub fps: f64,
+    pub total_frames: u64,
+    pub duration_s: f64,
+    /// Suggested output folder for this video, derived from the video path so
+    /// the same folder is produced on each re-open. Frontend uses this to
+    /// auto-restore the extracted-frame set without the user re-picking.
+    pub default_output_folder: String,
+}
+
+pub fn default_output_folder(path: &Path) -> String {
+    let stem = path
+        .file_stem()
+        .and_then(|s| s.to_str())
+        .unwrap_or("video");
+    let parent = path.parent().unwrap_or_else(|| Path::new("."));
+    parent
+        .join(format!("{stem}_extracted"))
+        .display()
+        .to_string()
+}
+
+fn parse_rational(s: &str) -> Option<f64> {
+    let (n, d) = s.split_once('/')?;
+    let n: f64 = n.parse().ok()?;
+    let d: f64 = d.parse().ok()?;
+    if d == 0.0 {
+        None
+    } else {
+        Some(n / d)
+    }
+}
+
+pub fn probe(path: &Path) -> Result<VideoInfo, String> {
+    let path_str = path.to_str().ok_or_else(|| "invalid path".to_string())?;
+    let out = Command::new("ffprobe")
+        .args([
+            "-v",
+            "error",
+            "-select_streams",
+            "v:0",
+            "-show_entries",
+            "stream=width,height,r_frame_rate,avg_frame_rate,nb_frames,duration",
+            "-show_entries",
+            "format=duration",
+            "-of",
+            "json",
+            path_str,
+        ])
+        .output()
+        .map_err(|e| format!("ffprobe spawn failed: {e}"))?;
+    if !out.status.success() {
+        return Err(format!(
+            "ffprobe exited {}: {}",
+            out.status,
+            String::from_utf8_lossy(&out.stderr)
+        ));
+    }
+    let json: serde_json::Value =
+        serde_json::from_slice(&out.stdout).map_err(|e| format!("ffprobe json: {e}"))?;
+    let stream = &json["streams"][0];
+    if stream.is_null() {
+        return Err("no video stream found".into());
+    }
+    let width = stream["width"]
+        .as_u64()
+        .ok_or("stream.width missing")? as u32;
+    let height = stream["height"]
+        .as_u64()
+        .ok_or("stream.height missing")? as u32;
+    let fps = stream["r_frame_rate"]
+        .as_str()
+        .and_then(parse_rational)
+        .or_else(|| stream["avg_frame_rate"].as_str().and_then(parse_rational))
+        .ok_or("cannot parse fps")?;
+    let duration_s = stream["duration"]
+        .as_str()
+        .and_then(|s| s.parse::<f64>().ok())
+        .or_else(|| {
+            json["format"]["duration"]
+                .as_str()
+                .and_then(|s| s.parse::<f64>().ok())
+        })
+        .ok_or("cannot parse duration")?;
+    let total_frames = stream["nb_frames"]
+        .as_str()
+        .and_then(|s| s.parse::<u64>().ok())
+        .unwrap_or_else(|| (fps * duration_s).round() as u64);
+    Ok(VideoInfo {
+        path: path.display().to_string(),
+        width,
+        height,
+        fps,
+        total_frames,
+        duration_s,
+        default_output_folder: default_output_folder(path),
+    })
+}
diff --git a/sam-tool-tauri/src-tauri/tauri.conf.json b/sam-tool-tauri/src-tauri/tauri.conf.json
new file mode 100644
index 0000000..10e93ae
--- /dev/null
+++ b/sam-tool-tauri/src-tauri/tauri.conf.json
@@ -0,0 +1,33 @@
+{
+  "$schema": "https://schema.tauri.app/config/2",
+  "productName": "SAM Tool",
+  "version": "0.1.0",
+  "identifier": "com.seekright.samtool",
+  "build": {
+    "beforeDevCommand": "npm run dev",
+    "devUrl": "http://localhost:1420",
+    "beforeBuildCommand": "npm run build",
+    "frontendDist": "../dist"
+  },
+  "app": {
+    "windows": [
+      {
+        "title": "SAM Tool",
+        "width": 1280,
+        "height": 860,
+        "resizable": true,
+        "fullscreen": false
+      }
+    ],
+    "security": {
+      "csp": null
+    }
+  },
+  "bundle": {
+    "active": false,
+    "targets": "all",
+    "icon": [
+      "icons/icon.png"
+    ]
+  }
+}
diff --git a/sam-tool-tauri/src/App.tsx b/sam-tool-tauri/src/App.tsx
new file mode 100644
index 0000000..d61df39
--- /dev/null
+++ b/sam-tool-tauri/src/App.tsx
@@ -0,0 +1,104 @@
+import { useEffect, useState } from "react";
+import type { Mode, Settings } from "./types";
+import { api } from "./ipc";
+import { Login } from "./components/auth/Login";
+import { TopNav } from "./components/shell/TopNav";
+import { ExtractMode } from "./modes/ExtractMode";
+import { AnnotateMode } from "./modes/AnnotateMode";
+import { ErrorBoundary } from "./components/shell/ErrorBoundary";
+import { SettingsModal } from "./components/shell/SettingsModal";
+
+export default function App() {
+  const [username, setUsername] = useState<string | null>(null);
+  const [checking, setChecking] = useState(true);
+  const [mode, setMode] = useState<Mode>("extract");
+  const [settingsOpen, setSettingsOpen] = useState(false);
+  const [settings, setSettings] = useState<Settings | null>(null);
+  const [fullview, setFullview] = useState(false);
+
+  useEffect(() => {
+    api.currentUser().then((u) => {
+      setUsername(u);
+      setChecking(false);
+    });
+    api.getSettings().then(setSettings).catch(() => {});
+  }, []);
+
+  useEffect(() => {
+    function onError(e: ErrorEvent) {
+      // eslint-disable-next-line no-console
+      console.error("[uncaught error]", e.message, e.error);
+    }
+    function onRejection(e: PromiseRejectionEvent) {
+      // eslint-disable-next-line no-console
+      console.error("[unhandled rejection]", e.reason);
+    }
+    window.addEventListener("error", onError);
+    window.addEventListener("unhandledrejection", onRejection);
+    return () => {
+      window.removeEventListener("error", onError);
+      window.removeEventListener("unhandledrejection", onRejection);
+    };
+  }, []);
+
+  useEffect(() => {
+    function onKey(e: KeyboardEvent) {
+      if (e.key === "F11") {
+        e.preventDefault();
+        setFullview((v) => !v);
+      }
+    }
+    window.addEventListener("keydown", onKey);
+    return () => window.removeEventListener("keydown", onKey);
+  }, []);
+
+  if (checking) {
+    return <div className="boot">…</div>;
+  }
+
+  if (!username) {
+    return <Login onLogin={setUsername} />;
+  }
+
+  return (
+    <div className={`app${fullview ? " fullview" : ""}`}>
+      {!fullview && (
+        <TopNav
+          mode={mode}
+          onModeChange={setMode}
+          username={username}
+          onLogout={() => setUsername(null)}
+          onOpenSettings={() => setSettingsOpen(true)}
+          onToggleFullview={() => setFullview(true)}
+        />
+      )}
+      <main>
+        {mode === "extract" && (
+          <ErrorBoundary label="Extract mode">
+            <ExtractMode username={username} />
+          </ErrorBoundary>
+        )}
+        {mode === "annotate" && (
+          <ErrorBoundary label="Annotate mode">
+            <AnnotateMode username={username} settings={settings} />
+          </ErrorBoundary>
+        )}
+      </main>
+      {fullview && (
+        <button
+          className="exit-fullview"
+          onClick={() => setFullview(false)}
+          title="Exit fullview (F11)"
+          aria-label="Exit fullview"
+        >
+          ⛶
+        </button>
+      )}
+      <SettingsModal
+        open={settingsOpen}
+        onClose={() => setSettingsOpen(false)}
+        onSaved={setSettings}
+      />
+    </div>
+  );
+}
diff --git a/sam-tool-tauri/src/assets/seek-logo.svg b/sam-tool-tauri/src/assets/seek-logo.svg
new file mode 100644
index 0000000..cffe8f1
--- /dev/null
+++ b/sam-tool-tauri/src/assets/seek-logo.svg
@@ -0,0 +1,20 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 128 128" fill="none" aria-label="SeekRight">
+  <defs>
+    <linearGradient id="srBg" x1="0%" y1="0%" x2="100%" y2="100%">
+      <stop offset="0%" stop-color="#FFD657"/>
+      <stop offset="55%" stop-color="#F5C230"/>
+      <stop offset="100%" stop-color="#E5A800"/>
+    </linearGradient>
+    <linearGradient id="srGloss" x1="0%" y1="0%" x2="0%" y2="100%">
+      <stop offset="0%" stop-color="#FFFFFF" stop-opacity="0.35"/>
+      <stop offset="45%" stop-color="#FFFFFF" stop-opacity="0"/>
+    </linearGradient>
+  </defs>
+  <rect x="6" y="6" width="116" height="116" rx="28" fill="url(#srBg)"/>
+  <rect x="6" y="6" width="116" height="62" rx="28" fill="url(#srGloss)"/>
+  <rect x="6.5" y="6.5" width="115" height="115" rx="27.5" fill="none"
+        stroke="#C08A00" stroke-opacity="0.35" stroke-width="1"/>
+  <text x="22" y="92"
+        font-family="Inter, system-ui, -apple-system, 'Segoe UI', sans-serif"
+        font-weight="900" font-size="64" fill="#1F3488" letter-spacing="-3">SR</text>
+</svg>
diff --git a/sam-tool-tauri/src/assets/seek-mark.svg b/sam-tool-tauri/src/assets/seek-mark.svg
new file mode 100644
index 0000000..120a7ab
--- /dev/null
+++ b/sam-tool-tauri/src/assets/seek-mark.svg
@@ -0,0 +1,12 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" fill="none" aria-label="Seek">
+  <defs>
+    <linearGradient id="mark" x1="0" y1="0" x2="1" y2="1">
+      <stop offset="0%" stop-color="#F5C838"/>
+      <stop offset="100%" stop-color="#ECBA20"/>
+    </linearGradient>
+  </defs>
+  <path d="M 6 6 L 46 6 Q 54 6 56 14 L 60 54 Q 61 60 54 60 L 6 60 Q 2 60 2 54 L 2 12 Q 2 6 6 6 Z"
+        fill="url(#mark)"/>
+  <text x="10" y="48" font-family="Inter, system-ui, -apple-system, 'Segoe UI', sans-serif"
+        font-weight="900" font-size="44" fill="#1F3488" letter-spacing="-2">S</text>
+</svg>
diff --git a/sam-tool-tauri/src/components/annotate/ClassList.tsx b/sam-tool-tauri/src/components/annotate/ClassList.tsx
new file mode 100644
index 0000000..c7d1d1a
--- /dev/null
+++ b/sam-tool-tauri/src/components/annotate/ClassList.tsx
@@ -0,0 +1,98 @@
+import { useState } from "react";
+import { colorForClass } from "../extract/overlay";
+
+interface Props {
+  classes: string[];
+  activeClassId: number | null;
+  onSelect: (id: number) => void;
+  onAdd?: (name: string) => Promise<void> | void;
+}
+
+export function ClassList({ classes, activeClassId, onSelect, onAdd }: Props) {
+  const [adding, setAdding] = useState(false);
+  const [name, setName] = useState("");
+  const [busy, setBusy] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+
+  async function submit() {
+    if (!onAdd) return;
+    const trimmed = name.trim();
+    if (!trimmed) return;
+    setBusy(true);
+    setError(null);
+    try {
+      await onAdd(trimmed);
+      setName("");
+      setAdding(false);
+    } catch (e) {
+      setError(String(e));
+    } finally {
+      setBusy(false);
+    }
+  }
+
+  return (
+    <div className="class-list-wrap">
+      {classes.length === 0 ? (
+        <div className="class-list empty">
+          no <code>classes.txt</code> in folder
+        </div>
+      ) : (
+        <ul className="class-list">
+          {classes.map((name, id) => (
+            <li
+              key={`${id}-${name}`}
+              className={id === activeClassId ? "active" : ""}
+              onClick={() => onSelect(id)}
+              title={`Activate "${name}"`}
+            >
+              <span
+                className="swatch"
+                style={{ background: colorForClass(id) }}
+              />
+              <span className="name">{name}</span>
+              <span className="id">{id}</span>
+            </li>
+          ))}
+        </ul>
+      )}
+
+      {onAdd && (
+        <div className="class-add">
+          {adding ? (
+            <div className="class-add-row">
+              <input
+                autoFocus
+                type="text"
+                value={name}
+                maxLength={96}
+                placeholder="class name"
+                onChange={(e) => setName(e.target.value)}
+                onKeyDown={(e) => {
+                  if (e.key === "Enter") { e.preventDefault(); submit(); }
+                  if (e.key === "Escape") {
+                    e.preventDefault();
+                    setAdding(false); setName(""); setError(null);
+                  }
+                }}
+              />
+              <button disabled={busy || !name.trim()} onClick={submit}>add</button>
+              <button
+                className="link"
+                disabled={busy}
+                onClick={() => { setAdding(false); setName(""); setError(null); }}
+              >
+                cancel
+              </button>
+            </div>
+          ) : (
+            <button className="link add-class-trigger" onClick={() => setAdding(true)}>
+              + add class
+            </button>
+          )}
+          {error && <div className="class-add-error">{error}</div>}
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/sam-tool-tauri/src/components/annotate/DeleteConfirmModal.tsx b/sam-tool-tauri/src/components/annotate/DeleteConfirmModal.tsx
new file mode 100644
index 0000000..c59853f
--- /dev/null
+++ b/sam-tool-tauri/src/components/annotate/DeleteConfirmModal.tsx
@@ -0,0 +1,99 @@
+import { useEffect, useState } from "react";
+
+interface Props {
+  count: number;
+  sampleNames: string[];
+  busy: boolean;
+  error: string | null;
+  onConfirm: () => void;
+  onCancel: () => void;
+}
+
+/**
+ * Two-step delete confirmation. Step 1 asks plainly, step 2 warns that the
+ * action is irreversible. The user has to click twice, so accidental Enter
+ * presses can't nuke a folder.
+ */
+export function DeleteConfirmModal({
+  count,
+  sampleNames,
+  busy,
+  error,
+  onConfirm,
+  onCancel,
+}: Props) {
+  const [step, setStep] = useState<1 | 2>(1);
+
+  useEffect(() => {
+    function onKey(e: KeyboardEvent) {
+      if (e.key === "Escape") {
+        e.preventDefault();
+        onCancel();
+      }
+    }
+    window.addEventListener("keydown", onKey, true);
+    return () => window.removeEventListener("keydown", onKey, true);
+  }, [onCancel]);
+
+  const preview = sampleNames.slice(0, 6).join(", ");
+  const more = sampleNames.length > 6 ? ` +${sampleNames.length - 6} more` : "";
+
+  return (
+    <div className="label-picker-backdrop" onMouseDown={(e) => {
+      if (e.target === e.currentTarget && !busy) onCancel();
+    }}>
+      <div className="delete-modal" onMouseDown={(e) => e.stopPropagation()}>
+        <div className="delete-modal-header">
+          {step === 1 ? "Delete images?" : "This cannot be undone"}
+        </div>
+        <div className="delete-modal-body">
+          {step === 1 ? (
+            <>
+              <p>
+                You are about to delete <strong>{count}</strong>{" "}
+                image{count === 1 ? "" : "s"}
+                {" "}and any sibling <code>.json</code> annotations.
+              </p>
+              {preview && (
+                <p className="dim delete-preview">{preview}{more}</p>
+              )}
+            </>
+          ) : (
+            <>
+              <p>
+                <strong>{count}</strong> image{count === 1 ? "" : "s"} will be
+                permanently removed from disk. There is no undo.
+              </p>
+              <p className="dim">
+                Click <strong>Delete permanently</strong> to proceed, or{" "}
+                <strong>Cancel</strong> to back out.
+              </p>
+            </>
+          )}
+          {error && <p className="error">{error}</p>}
+        </div>
+        <div className="delete-modal-actions">
+          <button onClick={onCancel} disabled={busy}>Cancel</button>
+          {step === 1 ? (
+            <button
+              className="danger-ghost"
+              onClick={() => setStep(2)}
+              disabled={busy}
+              autoFocus
+            >
+              Continue
+            </button>
+          ) : (
+            <button
+              className="danger"
+              onClick={onConfirm}
+              disabled={busy}
+            >
+              {busy ? "Deleting…" : "Delete permanently"}
+            </button>
+          )}
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/sam-tool-tauri/src/components/annotate/ImageList.tsx b/sam-tool-tauri/src/components/annotate/ImageList.tsx
new file mode 100644
index 0000000..7741dcd
--- /dev/null
+++ b/sam-tool-tauri/src/components/annotate/ImageList.tsx
@@ -0,0 +1,68 @@
+import type { ImageEntry } from "../../types";
+
+interface Props {
+  images: ImageEntry[];
+  selectedIdx: number | null;
+  selectedSet: Set<number>;
+  onSelect: (idx: number, e: React.MouseEvent) => void;
+}
+
+function timeAgo(iso: string | null | undefined): string {
+  if (!iso) return "";
+  const t = new Date(iso).getTime();
+  if (!Number.isFinite(t)) return "";
+  const diff = Math.max(0, Date.now() - t);
+  const s = Math.floor(diff / 1000);
+  if (s < 5) return "now";
+  if (s < 60) return `${s}s`;
+  const m = Math.floor(s / 60);
+  if (m < 60) return `${m}m`;
+  const h = Math.floor(m / 60);
+  if (h < 24) return `${h}h`;
+  const d = Math.floor(h / 24);
+  if (d < 30) return `${d}d`;
+  const mo = Math.floor(d / 30);
+  return `${mo}mo`;
+}
+
+export function ImageList({ images, selectedIdx, selectedSet, onSelect }: Props) {
+  if (images.length === 0) {
+    return <div className="image-list empty">no images in folder</div>;
+  }
+  return (
+    <ul className="image-list">
+      {images.map((img, i) => {
+        const isPrimary = i === selectedIdx;
+        const isInSet = selectedSet.has(i);
+        const cls = [
+          isPrimary ? "selected" : "",
+          isInSet && !isPrimary ? "multi-selected" : "",
+          img.has_annotations ? "annotated" : "",
+        ]
+          .filter(Boolean)
+          .join(" ");
+        return (
+          <li
+            key={img.filename}
+            className={cls}
+            onClick={(e) => onSelect(i, e)}
+            title={img.filename}
+          >
+            <span className="marker">{img.has_annotations ? "✓" : "·"}</span>
+            <span className="name">{img.filename}</span>
+            {img.annotation_count > 0 && (
+              <span className="count" title={`${img.annotation_count} annotation${img.annotation_count === 1 ? "" : "s"}`}>
+                {img.annotation_count}
+              </span>
+            )}
+            {img.last_updated && (
+              <span className="updated dim" title={img.last_updated}>
+                {timeAgo(img.last_updated)}
+              </span>
+            )}
+          </li>
+        );
+      })}
+    </ul>
+  );
+}
diff --git a/sam-tool-tauri/src/components/annotate/LabelPicker.tsx b/sam-tool-tauri/src/components/annotate/LabelPicker.tsx
new file mode 100644
index 0000000..5c224ec
--- /dev/null
+++ b/sam-tool-tauri/src/components/annotate/LabelPicker.tsx
@@ -0,0 +1,277 @@
+import { useEffect, useMemo, useRef, useState } from "react";
+import { colorForClass } from "../extract/overlay";
+
+interface Props {
+  classes: string[];
+  /** Pre-selected class id (last-picked) to highlight when the picker opens. */
+  defaultClassId: number | null;
+  /** Tag shown at the top of the panel — "new bbox", "new polygon", "reclass". */
+  mode: "new-bbox" | "new-polygon" | "reclass";
+  onConfirm: (classId: number) => void;
+  onCancel: () => void;
+  /** When present, shows "+ add '<query>'" below the list when the query has
+   *  no matches. Must resolve with the id of the newly created class. */
+  onAdd?: (name: string) => Promise<number>;
+}
+
+function initials(name: string): string {
+  return name
+    .split(/[_\s-]+/)
+    .filter(Boolean)
+    .map((p) => p[0]!.toUpperCase())
+    .join("");
+}
+
+function scoreMatch(query: string, name: string): number {
+  if (!query) return 1;
+  const qu = query.toUpperCase();
+  const ql = query.toLowerCase();
+  const init = initials(name);
+  const lo = name.toLowerCase();
+  if (lo === ql) return 105;
+  if (init === qu) return 100;
+  if (init.startsWith(qu)) return 90;
+  if (init.includes(qu)) return 75;
+  if (lo.startsWith(ql)) return 70;
+  if (lo.includes(ql)) return 50;
+  return 0;
+}
+
+export function LabelPicker({
+  classes,
+  defaultClassId,
+  mode,
+  onConfirm,
+  onCancel,
+  onAdd,
+}: Props) {
+  const [query, setQuery] = useState("");
+  const [busyAdding, setBusyAdding] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [highlightIdx, setHighlightIdx] = useState(0);
+  const inputRef = useRef<HTMLInputElement>(null);
+  const listRef = useRef<HTMLUListElement>(null);
+
+  const filtered = useMemo(() => {
+    const out = classes
+      .map((name, id) => ({ name, id, score: scoreMatch(query, name) }))
+      .filter((x) => x.score > 0)
+      .sort((a, b) => b.score - a.score || a.id - b.id);
+    return out;
+  }, [classes, query]);
+
+  // Reset highlight when list shape changes; prefer the default class if
+  // present in the current filtered set.
+  useEffect(() => {
+    if (query.trim() === "") {
+      const pos =
+        defaultClassId != null
+          ? filtered.findIndex((f) => f.id === defaultClassId)
+          : 0;
+      setHighlightIdx(pos >= 0 ? pos : 0);
+    } else {
+      setHighlightIdx(0);
+    }
+  }, [query, filtered, defaultClassId]);
+
+  useEffect(() => {
+    const el = listRef.current?.querySelector<HTMLElement>("li.highlight");
+    el?.scrollIntoView({ block: "nearest" });
+  }, [highlightIdx]);
+
+  // Force focus on mount — `autoFocus` alone has been unreliable here because
+  // the picker mounts on a mouseup event whose focus target (document.body)
+  // sometimes wins the race. Keeping focus on the input is what makes the
+  // "start typing immediately" UX work.
+  useEffect(() => {
+    const el = inputRef.current;
+    if (!el) return;
+    el.focus();
+    el.select();
+  }, []);
+
+  // Capture all keydowns at the window level while the picker is open. This
+  // routes key events to the picker even if focus escapes (e.g. the user
+  // clicked the backdrop). Esc closes, printable keys feed the input,
+  // navigation/confirm keys run through `onKey`.
+  useEffect(() => {
+    function onWin(e: KeyboardEvent) {
+      if (e.key === "Escape") {
+        e.preventDefault();
+        e.stopPropagation();
+        onCancel();
+        return;
+      }
+      const el = inputRef.current;
+      if (!el) return;
+      // Prevent the parent (AnnotateMode) window handler from hijacking A/D,
+      // space, etc. while the picker is open.
+      if (document.activeElement === el) {
+        // Still guard keys the parent would act on — A/D navigation, etc.
+        if (
+          e.key.length === 1 &&
+          !e.ctrlKey && !e.metaKey && !e.altKey
+        ) {
+          e.stopPropagation();
+        }
+        return;
+      }
+      // Input not focused: pull focus back. For printable chars, also append
+      // to the query so the first keystroke isn't lost.
+      if (e.key.length === 1 && !e.ctrlKey && !e.metaKey && !e.altKey) {
+        e.preventDefault();
+        e.stopPropagation();
+        setQuery((q) => q + e.key);
+        el.focus();
+      } else if (
+        e.key === "Enter" ||
+        e.key === "ArrowUp" ||
+        e.key === "ArrowDown" ||
+        e.key === "Tab" ||
+        e.key === "Backspace"
+      ) {
+        e.stopPropagation();
+        el.focus();
+      }
+    }
+    window.addEventListener("keydown", onWin, true);
+    return () => window.removeEventListener("keydown", onWin, true);
+  }, [onCancel]);
+
+  async function confirmCurrent() {
+    if (filtered.length > 0) {
+      onConfirm(filtered[highlightIdx]?.id ?? filtered[0].id);
+      return;
+    }
+    // No matches — offer to add a new class from the query.
+    if (onAdd && query.trim()) {
+      await addFromQuery();
+    }
+  }
+
+  async function addFromQuery() {
+    if (!onAdd) return;
+    const q = query.trim();
+    if (!q) return;
+    // If query exactly matches an existing class, select it instead.
+    const exact = classes.findIndex(
+      (c) => c.toLowerCase() === q.toLowerCase()
+    );
+    if (exact >= 0) {
+      onConfirm(exact);
+      return;
+    }
+    setBusyAdding(true);
+    setError(null);
+    try {
+      const newId = await onAdd(q);
+      onConfirm(newId);
+    } catch (e) {
+      setError(String(e));
+      setBusyAdding(false);
+    }
+  }
+
+  function onKey(e: React.KeyboardEvent<HTMLInputElement>) {
+    switch (e.key) {
+      case "Escape":
+        e.preventDefault();
+        onCancel();
+        break;
+      case "Enter":
+        e.preventDefault();
+        confirmCurrent();
+        break;
+      case "ArrowDown":
+        e.preventDefault();
+        setHighlightIdx((i) => Math.min(filtered.length - 1, i + 1));
+        break;
+      case "ArrowUp":
+        e.preventDefault();
+        setHighlightIdx((i) => Math.max(0, i - 1));
+        break;
+      case "Tab":
+        // Tab moves highlight forward (like autocomplete)
+        e.preventDefault();
+        setHighlightIdx((i) => (filtered.length > 0 ? (i + 1) % filtered.length : 0));
+        break;
+    }
+  }
+
+  const title =
+    mode === "new-bbox"
+      ? "label bbox"
+      : mode === "new-polygon"
+      ? "label polygon"
+      : "change class";
+
+  return (
+    <div className="label-picker-backdrop" onMouseDown={(e) => {
+      // Click on backdrop (not modal) cancels.
+      if (e.target === e.currentTarget) onCancel();
+    }}>
+      <div className="label-picker" onMouseDown={(e) => e.stopPropagation()}>
+        <div className="label-picker-header">
+          <span className="mode-tag">{title}</span>
+          <span className="dim">{classes.length} classes</span>
+        </div>
+        <input
+          ref={inputRef}
+          autoFocus
+          type="text"
+          placeholder="type letters — e.g. SL for Street_Light or 'st' for substring"
+          value={query}
+          onChange={(e) => setQuery(e.target.value)}
+          onKeyDown={onKey}
+        />
+        <ul ref={listRef} className="label-picker-list">
+          {filtered.map((x, i) => (
+            <li
+              key={`${x.id}-${x.name}`}
+              className={i === highlightIdx ? "highlight" : ""}
+              onMouseEnter={() => setHighlightIdx(i)}
+              onClick={() => onConfirm(x.id)}
+            >
+              <span className="swatch" style={{ background: colorForClass(x.id) }} />
+              <span className="name">{x.name}</span>
+              <span className="init">{initials(x.name)}</span>
+              <span className="id">{x.id}</span>
+            </li>
+          ))}
+          {filtered.length === 0 && !query.trim() && (
+            <li className="empty">
+              <span className="dim">type to filter, or add new below</span>
+            </li>
+          )}
+          {filtered.length === 0 && query.trim() && !onAdd && (
+            <li className="empty"><span className="dim">no matches</span></li>
+          )}
+        </ul>
+
+        {onAdd && (
+          <div className="label-picker-add-row">
+            <button
+              className="add-inline"
+              onClick={addFromQuery}
+              disabled={busyAdding || !query.trim()}
+              title={
+                query.trim()
+                  ? `Create "${query.trim()}" as a new class`
+                  : "Type a name first, then click to add"
+              }
+            >
+              {query.trim()
+                ? `+ add "${query.trim()}" as new class`
+                : "+ add new class (type name above)"}
+            </button>
+          </div>
+        )}
+        {error && <p className="picker-error">{error}</p>}
+        <div className="label-picker-hint">
+          <kbd>↑↓</kbd> nav · <kbd>Enter</kbd> pick · <kbd>Tab</kbd> next match ·{" "}
+          <kbd>Esc</kbd> cancel
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/sam-tool-tauri/src/components/auth/Login.tsx b/sam-tool-tauri/src/components/auth/Login.tsx
new file mode 100644
index 0000000..60836fe
--- /dev/null
+++ b/sam-tool-tauri/src/components/auth/Login.tsx
@@ -0,0 +1,106 @@
+import { useEffect, useState } from "react";
+import { api } from "../../ipc";
+import seekLogo from "../../assets/seek-logo.svg";
+
+interface Props {
+  onLogin: (username: string) => void;
+}
+
+export function Login({ onLogin }: Props) {
+  const [known, setKnown] = useState<string[]>([]);
+  const [name, setName] = useState("");
+  const [mode, setMode] = useState<"pick" | "new">("new");
+  const [error, setError] = useState<string | null>(null);
+  const [busy, setBusy] = useState(false);
+
+  useEffect(() => {
+    api.listKnownUsers().then((users) => {
+      setKnown(users);
+      if (users.length > 0) setMode("pick");
+    });
+  }, []);
+
+  async function submit(username: string) {
+    const trimmed = username.trim();
+    if (!trimmed) {
+      setError("Please enter a name");
+      return;
+    }
+    setBusy(true);
+    setError(null);
+    try {
+      const confirmed = await api.setCurrentUser(trimmed);
+      onLogin(confirmed);
+    } catch (e) {
+      setError(String(e));
+      setBusy(false);
+    }
+  }
+
+  return (
+    <div className="login-page">
+      <div className="login-card">
+        <img className="login-logo" src={seekLogo} alt="Seek" />
+        <p className="subtitle">Who's annotating?</p>
+
+        {mode === "pick" && known.length > 0 && (
+          <div className="known-users">
+            {known.map((u) => (
+              <button
+                key={u}
+                className="known-user"
+                onClick={() => submit(u)}
+                disabled={busy}
+              >
+                {u}
+              </button>
+            ))}
+            <button
+              className="link"
+              onClick={() => {
+                setMode("new");
+                setName("");
+              }}
+            >
+              + new user
+            </button>
+          </div>
+        )}
+
+        {mode === "new" && (
+          <form
+            onSubmit={(e) => {
+              e.preventDefault();
+              submit(name);
+            }}
+          >
+            <input
+              autoFocus
+              type="text"
+              placeholder="your name"
+              value={name}
+              onChange={(e) => setName(e.target.value)}
+              maxLength={64}
+            />
+            <div className="actions">
+              {known.length > 0 && (
+                <button
+                  type="button"
+                  className="link"
+                  onClick={() => setMode("pick")}
+                >
+                  ← back
+                </button>
+              )}
+              <button type="submit" disabled={busy || !name.trim()}>
+                continue
+              </button>
+            </div>
+          </form>
+        )}
+
+        {error && <p className="error">{error}</p>}
+      </div>
+    </div>
+  );
+}
diff --git a/sam-tool-tauri/src/components/extract/FrameCanvas.tsx b/sam-tool-tauri/src/components/extract/FrameCanvas.tsx
new file mode 100644
index 0000000..23de3fb
--- /dev/null
+++ b/sam-tool-tauri/src/components/extract/FrameCanvas.tsx
@@ -0,0 +1,26 @@
+import { forwardRef } from "react";
+import type { VideoInfo } from "../../types";
+
+interface Props {
+  video: VideoInfo;
+  extracted?: boolean;
+}
+
+export const FrameCanvas = forwardRef<HTMLCanvasElement, Props>(function FrameCanvas(
+  { video, extracted },
+  ref
+) {
+  return (
+    <div className="canvas-stage">
+      <div
+        className="canvas-frame"
+        style={{ aspectRatio: `${video.width} / ${video.height}` }}
+      >
+        <canvas ref={ref} width={video.width} height={video.height} />
+        {extracted && (
+          <div className="frame-extracted-banner">EXTRACTED</div>
+        )}
+      </div>
+    </div>
+  );
+});
diff --git a/sam-tool-tauri/src/components/extract/overlay.ts b/sam-tool-tauri/src/components/extract/overlay.ts
new file mode 100644
index 0000000..5b2475a
--- /dev/null
+++ b/sam-tool-tauri/src/components/extract/overlay.ts
@@ -0,0 +1,149 @@
+import type { CocoShape } from "../../types";
+
+const CLASS_COLORS = [
+  "#ff6b6b",
+  "#4ecdc4",
+  "#ffd166",
+  "#a78bfa",
+  "#67e8f9",
+  "#fbbf24",
+  "#86efac",
+  "#f9a8d4",
+  "#a3e635",
+  "#fca5a5",
+];
+
+export function colorForClass(classId: number): string {
+  const idx =
+    ((classId % CLASS_COLORS.length) + CLASS_COLORS.length) % CLASS_COLORS.length;
+  return CLASS_COLORS[idx];
+}
+
+export function drawOverlay(
+  ctx: CanvasRenderingContext2D,
+  shapes: CocoShape[],
+  canvasW: number,
+  canvasH: number
+) {
+  const baseLineWidth = Math.max(2, Math.round(Math.min(canvasW, canvasH) / 400));
+  const fontSize = Math.max(12, Math.round(Math.min(canvasW, canvasH) / 60));
+  ctx.font = `${fontSize}px sans-serif`;
+  ctx.textBaseline = "top";
+
+  for (const shape of shapes) {
+    const color = colorForClass(shape.class_id);
+    ctx.strokeStyle = color;
+    ctx.lineWidth = baseLineWidth;
+
+    if (shape.type === "bbox") {
+      const [x, y, w, h] = shape.bbox;
+      // Soft class-tinted fill for quick visual ID.
+      ctx.fillStyle = hexWithAlpha(color, 0.1);
+      ctx.fillRect(x, y, w, h);
+      // Dark outer stroke for contrast against any background.
+      ctx.strokeStyle = "rgba(0, 0, 0, 0.55)";
+      ctx.lineWidth = baseLineWidth + Math.max(1, Math.round(baseLineWidth * 0.6));
+      ctx.strokeRect(x, y, w, h);
+      // Bright inner stroke in class color.
+      ctx.strokeStyle = color;
+      ctx.lineWidth = baseLineWidth;
+      ctx.strokeRect(x, y, w, h);
+      drawLabel(ctx, shape.class_name, x, y, h, color, fontSize, canvasW, canvasH);
+    } else {
+      ctx.beginPath();
+      shape.points.forEach(([x, y], i) => {
+        if (i === 0) ctx.moveTo(x, y);
+        else ctx.lineTo(x, y);
+      });
+      ctx.closePath();
+      ctx.stroke();
+      ctx.fillStyle = hexWithAlpha(color, 0.18);
+      ctx.fill();
+
+      if (shape.points.length > 0) {
+        // Anchor label at the bounding box of the polygon so placement is
+        // stable regardless of which vertex happens to be first.
+        const [bx, by, , bh] = boundingBox(shape.points);
+        drawLabel(
+          ctx,
+          shape.class_name,
+          bx,
+          by,
+          bh,
+          color,
+          fontSize,
+          canvasW,
+          canvasH
+        );
+      }
+    }
+  }
+}
+
+/**
+ * Place a label near a shape without letting it fall off the canvas.
+ *
+ * Vertical: prefer *above* the shape. If that clips the top, flip to *below*.
+ * If below also clips, clamp inside the canvas.
+ *
+ * Horizontal: left-align with the shape. If the label would exceed the right
+ * edge, shift left so it ends at the edge. Finally clamp to the left edge.
+ */
+function drawLabel(
+  ctx: CanvasRenderingContext2D,
+  text: string,
+  shapeX: number,
+  shapeY: number,
+  shapeH: number,
+  color: string,
+  fontSize: number,
+  canvasW: number,
+  canvasH: number
+) {
+  const pad = Math.round(fontSize * 0.3);
+  const metrics = ctx.measureText(text);
+  const w = Math.min(metrics.width + pad * 2, canvasW);
+  const h = fontSize + pad * 2;
+
+  // Vertical
+  let ly: number;
+  if (shapeY - h >= 0) {
+    ly = shapeY - h;
+  } else if (shapeY + shapeH + h <= canvasH) {
+    ly = shapeY + shapeH;
+  } else {
+    ly = Math.max(0, Math.min(canvasH - h, shapeY));
+  }
+
+  // Horizontal
+  let lx = shapeX;
+  if (lx + w > canvasW) lx = canvasW - w;
+  if (lx < 0) lx = 0;
+
+  ctx.fillStyle = color;
+  ctx.fillRect(lx, ly, w, h);
+  ctx.fillStyle = "#0e0e14";
+  ctx.fillText(text, lx + pad, ly + pad);
+}
+
+function boundingBox(points: [number, number][]): [number, number, number, number] {
+  let minX = Infinity;
+  let minY = Infinity;
+  let maxX = -Infinity;
+  let maxY = -Infinity;
+  for (const [x, y] of points) {
+    if (x < minX) minX = x;
+    if (y < minY) minY = y;
+    if (x > maxX) maxX = x;
+    if (y > maxY) maxY = y;
+  }
+  return [minX, minY, maxX - minX, maxY - minY];
+}
+
+function hexWithAlpha(hex: string, alpha: number): string {
+  const c = hex.replace("#", "");
+  const r = parseInt(c.slice(0, 2), 16);
+  const g = parseInt(c.slice(2, 4), 16);
+  const b = parseInt(c.slice(4, 6), 16);
+  return `rgba(${r},${g},${b},${alpha})`;
+}
diff --git a/sam-tool-tauri/src/components/shell/ErrorBoundary.tsx b/sam-tool-tauri/src/components/shell/ErrorBoundary.tsx
new file mode 100644
index 0000000..33a64cf
--- /dev/null
+++ b/sam-tool-tauri/src/components/shell/ErrorBoundary.tsx
@@ -0,0 +1,42 @@
+import React from "react";
+
+interface State {
+  error: Error | null;
+}
+
+interface Props {
+  label: string;
+  children: React.ReactNode;
+}
+
+export class ErrorBoundary extends React.Component<Props, State> {
+  state: State = { error: null };
+
+  static getDerivedStateFromError(error: Error): State {
+    return { error };
+  }
+
+  componentDidCatch(error: Error, info: React.ErrorInfo) {
+    // Surface in DevTools console for diagnosis.
+    // eslint-disable-next-line no-console
+    console.error(`[${this.props.label}] error:`, error, info);
+  }
+
+  reset = () => this.setState({ error: null });
+
+  render() {
+    if (this.state.error) {
+      return (
+        <div className="error-boundary">
+          <h2>{this.props.label} crashed</h2>
+          <pre>{this.state.error.message}</pre>
+          <pre className="dim">{this.state.error.stack}</pre>
+          <button className="primary" onClick={this.reset}>
+            Try again
+          </button>
+        </div>
+      );
+    }
+    return this.props.children;
+  }
+}
diff --git a/sam-tool-tauri/src/components/shell/SettingsModal.tsx b/sam-tool-tauri/src/components/shell/SettingsModal.tsx
new file mode 100644
index 0000000..40a7f6a
--- /dev/null
+++ b/sam-tool-tauri/src/components/shell/SettingsModal.tsx
@@ -0,0 +1,274 @@
+import { useEffect, useState } from "react";
+import { api } from "../../ipc";
+import type { SamModelInfo, Settings } from "../../types";
+
+interface Props {
+  open: boolean;
+  onClose: () => void;
+  onSaved?: (s: Settings) => void;
+}
+
+type TestState =
+  | { kind: "idle" }
+  | { kind: "testing" }
+  | { kind: "ok" }
+  | { kind: "fail"; message: string };
+
+type ModelsState =
+  | { kind: "idle" }
+  | { kind: "loading" }
+  | { kind: "ok"; models: SamModelInfo[]; defaultId: string | null }
+  | { kind: "fail"; message: string };
+
+const URL_PRESETS: { label: string; url: string }[] = [
+  { label: "localhost", url: "http://localhost:9090" },
+  { label: "127.0.0.1", url: "http://127.0.0.1:9090" },
+];
+
+export function SettingsModal({ open, onClose, onSaved }: Props) {
+  const [settings, setSettings] = useState<Settings | null>(null);
+  const [saving, setSaving] = useState(false);
+  const [test, setTest] = useState<TestState>({ kind: "idle" });
+  const [models, setModels] = useState<ModelsState>({ kind: "idle" });
+  const [error, setError] = useState<string | null>(null);
+
+  useEffect(() => {
+    if (!open) return;
+    setError(null);
+    setTest({ kind: "idle" });
+    setModels({ kind: "idle" });
+    api
+      .getSettings()
+      .then((s) => {
+        setSettings(s);
+        // Kick off a model list fetch in the background so the dropdown
+        // populates without the user having to click "test".
+        void loadModels(s.sam_url);
+      })
+      .catch((e) => setError(String(e)));
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [open]);
+
+  async function loadModels(url: string) {
+    if (!url) return;
+    setModels({ kind: "loading" });
+    try {
+      const list = await api.samListModels(url);
+      setModels({ kind: "ok", models: list.models, defaultId: list.default });
+    } catch (e) {
+      setModels({ kind: "fail", message: String(e) });
+    }
+  }
+
+  if (!open || !settings) return open ? (
+    <div className="modal-backdrop">
+      <div className="modal settings-modal">
+        <div className="dim">loading…</div>
+      </div>
+    </div>
+  ) : null;
+
+  async function save() {
+    if (!settings) return;
+    setSaving(true);
+    setError(null);
+    try {
+      await api.saveSettings(settings);
+      onSaved?.(settings);
+      onClose();
+    } catch (e) {
+      setError(String(e));
+    } finally {
+      setSaving(false);
+    }
+  }
+
+  async function testConnection() {
+    setTest({ kind: "testing" });
+    try {
+      const ok = await api.samHealthCheck(settings!.sam_url);
+      setTest(ok ? { kind: "ok" } : { kind: "fail", message: "server replied but not 2xx" });
+      if (ok) await loadModels(settings!.sam_url);
+    } catch (e) {
+      setTest({ kind: "fail", message: String(e) });
+    }
+  }
+
+  function setUrl(url: string) {
+    setSettings({ ...settings!, sam_url: url });
+    setTest({ kind: "idle" });
+    setModels({ kind: "idle" });
+  }
+
+  const currentModelExistsInList =
+    models.kind === "ok" &&
+    settings.sam_model != null &&
+    models.models.some((m) => m.id === settings.sam_model);
+
+  return (
+    <div
+      className="modal-backdrop"
+      onMouseDown={(e) => {
+        if (e.target === e.currentTarget) onClose();
+      }}
+    >
+      <div className="modal settings-modal" onMouseDown={(e) => e.stopPropagation()}>
+        <div className="modal-header">
+          <h2>Settings</h2>
+          <button className="modal-close" onClick={onClose} aria-label="close">×</button>
+        </div>
+
+        <section className="settings-section">
+          <div className="settings-row">
+            <label>
+              <div className="settings-label">SAM backend URL</div>
+              <input
+                type="text"
+                value={settings.sam_url}
+                onChange={(e) => setUrl(e.target.value)}
+                placeholder="http://localhost:9090"
+              />
+            </label>
+            <div className="url-presets">
+              {URL_PRESETS.map((p) => (
+                <button
+                  key={p.url}
+                  type="button"
+                  className="link preset"
+                  onClick={() => setUrl(p.url)}
+                  title={`use ${p.url}`}
+                >
+                  {p.label}
+                </button>
+              ))}
+            </div>
+          </div>
+
+          <div className="settings-row test-row">
+            <button
+              className="ghost"
+              disabled={test.kind === "testing"}
+              onClick={testConnection}
+            >
+              {test.kind === "testing" ? "testing…" : "test connection"}
+            </button>
+            {test.kind === "ok" && <span className="chip ok">✓ reachable</span>}
+            {test.kind === "fail" && (
+              <span className="chip fail" title={test.message}>
+                ✗ {test.message.slice(0, 60)}
+              </span>
+            )}
+          </div>
+
+          <div className="settings-row">
+            <label>
+              <div className="settings-label">
+                Model
+                {models.kind === "loading" && <span className="dim"> · loading…</span>}
+                {models.kind === "fail" && (
+                  <span className="dim" title={models.message}> · couldn't load list</span>
+                )}
+              </div>
+              <select
+                value={settings.sam_model ?? ""}
+                onChange={(e) =>
+                  setSettings({
+                    ...settings,
+                    sam_model: e.target.value || null,
+                  })
+                }
+                disabled={models.kind !== "ok"}
+              >
+                <option value="">
+                  {models.kind === "ok" && models.defaultId
+                    ? `backend default (${models.defaultId})`
+                    : "backend default"}
+                </option>
+                {models.kind === "ok" &&
+                  models.models.map((m) => (
+                    <option
+                      key={m.id}
+                      value={m.id}
+                      disabled={!m.available}
+                    >
+                      {m.name}
+                      {!m.available ? " · checkpoint missing" : ""}
+                    </option>
+                  ))}
+                {!currentModelExistsInList && settings.sam_model && (
+                  <option value={settings.sam_model}>
+                    {settings.sam_model} (not in list)
+                  </option>
+                )}
+              </select>
+            </label>
+            {models.kind === "fail" && (
+              <button
+                className="link"
+                onClick={() => loadModels(settings.sam_url)}
+              >
+                retry
+              </button>
+            )}
+          </div>
+
+          <div className="settings-row">
+            <label className="switch-row">
+              <input
+                type="checkbox"
+                checked={settings.sam_enabled}
+                onChange={(e) =>
+                  setSettings({ ...settings, sam_enabled: e.target.checked })
+                }
+              />
+              <span>
+                <strong>SAM mode</strong>
+                <span className="dim"> — auto-segment bboxes via the backend</span>
+              </span>
+            </label>
+          </div>
+        </section>
+
+        <section className="settings-section">
+          <div className="settings-row">
+            <label>
+              <div className="settings-label">
+                Frame cache capacity
+                <span className="dim"> · {settings.cache_capacity} frames</span>
+              </div>
+              <input
+                type="range"
+                min={200}
+                max={2000}
+                step={50}
+                value={settings.cache_capacity}
+                onChange={(e) =>
+                  setSettings({
+                    ...settings,
+                    cache_capacity: Number(e.target.value),
+                  })
+                }
+              />
+              <div className="range-ticks">
+                <span>200</span>
+                <span>1000</span>
+                <span>2000</span>
+              </div>
+            </label>
+          </div>
+        </section>
+
+        {error && <div className="picker-error">{error}</div>}
+
+        <div className="modal-footer">
+          <button className="ghost" onClick={onClose} disabled={saving}>
+            cancel
+          </button>
+          <button className="primary" onClick={save} disabled={saving}>
+            {saving ? "saving…" : "save"}
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/sam-tool-tauri/src/components/shell/TopNav.tsx b/sam-tool-tauri/src/components/shell/TopNav.tsx
new file mode 100644
index 0000000..75e77fe
--- /dev/null
+++ b/sam-tool-tauri/src/components/shell/TopNav.tsx
@@ -0,0 +1,80 @@
+import { useState } from "react";
+import type { Mode } from "../../types";
+import { api } from "../../ipc";
+import seekLogo from "../../assets/seek-logo.svg";
+
+interface Props {
+  mode: Mode;
+  onModeChange: (m: Mode) => void;
+  username: string;
+  onLogout: () => void;
+  onOpenSettings: () => void;
+  onToggleFullview: () => void;
+}
+
+export function TopNav({
+  mode,
+  onModeChange,
+  username,
+  onLogout,
+  onOpenSettings,
+  onToggleFullview,
+}: Props) {
+  const [menuOpen, setMenuOpen] = useState(false);
+
+  async function logout() {
+    await api.clearCurrentUser();
+    setMenuOpen(false);
+    onLogout();
+  }
+
+  return (
+    <header className="topnav">
+      <div className="brand-mark">
+        <img className="brand-icon" src={seekLogo} alt="SR" />
+        <span className="brand-text">SAM Tool</span>
+      </div>
+      <nav>
+        <button
+          className={mode === "extract" ? "active" : ""}
+          onClick={() => onModeChange("extract")}
+        >
+          Extract
+        </button>
+        <button
+          className={mode === "annotate" ? "active" : ""}
+          onClick={() => onModeChange("annotate")}
+        >
+          Annotate
+        </button>
+      </nav>
+      <div className="spacer" />
+      <button
+        className="icon-btn"
+        onClick={onToggleFullview}
+        title="Full view (F11)"
+        aria-label="Full view"
+      >
+        ⛶
+      </button>
+      <button
+        className="icon-btn"
+        onClick={onOpenSettings}
+        title="Settings"
+        aria-label="Settings"
+      >
+        ⚙
+      </button>
+      <div className="user-chip">
+        <button className="chip" onClick={() => setMenuOpen((v) => !v)}>
+          {username} ▾
+        </button>
+        {menuOpen && (
+          <div className="menu" onMouseLeave={() => setMenuOpen(false)}>
+            <button onClick={logout}>Switch user</button>
+          </div>
+        )}
+      </div>
+    </header>
+  );
+}
diff --git a/sam-tool-tauri/src/index.css b/sam-tool-tauri/src/index.css
new file mode 100644
index 0000000..760920a
--- /dev/null
+++ b/sam-tool-tauri/src/index.css
@@ -0,0 +1,2063 @@
+/* -------- Seek brand tokens -------- */
+:root {
+  --seek-yellow: #F5C838;
+  --seek-yellow-deep: #E8B018;
+  --seek-navy: #1F3488;
+  --seek-navy-deep: #14235C;
+  --seek-navy-ink: #0D173F;
+
+  --bg-0: #0E1222;
+  --bg-1: #151A30;
+  --bg-2: #1C223D;
+  --bg-3: #262C49;
+  --border-subtle: #232B47;
+  --border-strong: #323B5D;
+  --text-primary: #E8E9F1;
+  --text-dim: #8F95AE;
+  --text-faint: #5D6485;
+  --accent-link: #9BB3FF;
+  --success: #5CCF75;
+  --danger: #FF7A7A;
+  --shadow-card: 0 8px 28px rgba(0, 0, 0, 0.45);
+}
+
+* { box-sizing: border-box; }
+
+body, html, #root {
+  margin: 0;
+  padding: 0;
+  height: 100%;
+  font-family: Inter, -apple-system, BlinkMacSystemFont, "Segoe UI", system-ui, sans-serif;
+  background:
+    radial-gradient(1200px 600px at 15% -10%, rgba(245, 200, 56, 0.06), transparent 60%),
+    radial-gradient(1000px 500px at 110% 120%, rgba(31, 52, 136, 0.22), transparent 60%),
+    var(--bg-0);
+  color: var(--text-primary);
+  -webkit-font-smoothing: antialiased;
+  text-rendering: optimizeLegibility;
+}
+
+button { font-family: inherit; }
+
+.app {
+  display: flex;
+  flex-direction: column;
+  height: 100vh;
+}
+
+.boot {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  height: 100vh;
+  color: #666;
+  font-size: 2rem;
+}
+
+/* Fullview — topnav hidden, main takes the whole window. A floating
+   exit button stays accessible in the top-right. */
+.app.fullview .topnav { display: none; }
+
+.exit-fullview {
+  position: fixed;
+  top: 6px;
+  right: 6px;
+  z-index: 200;
+  width: 28px;
+  height: 28px;
+  border-radius: 999px;
+  border: 1px solid var(--border-strong);
+  background: rgba(14, 18, 34, 0.85);
+  color: var(--text-dim);
+  cursor: pointer;
+  font-size: 0.85rem;
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  backdrop-filter: blur(6px);
+  transition: all 120ms;
+}
+
+.exit-fullview:hover {
+  background: var(--bg-3);
+  color: var(--seek-yellow);
+  border-color: var(--seek-yellow);
+}
+
+/* Top nav */
+.topnav {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+  padding: 0.3rem 0.85rem;
+  background: linear-gradient(180deg, #0F1430 0%, #0A0F24 100%);
+  border-bottom: 1px solid var(--border-subtle);
+  box-shadow: 0 1px 0 rgba(255, 255, 255, 0.02) inset;
+  min-height: 40px;
+}
+
+.topnav .brand-mark {
+  display: flex;
+  align-items: center;
+  gap: 0.45rem;
+}
+
+.topnav .brand-icon {
+  height: 22px;
+  width: 22px;
+  display: block;
+  filter: drop-shadow(0 1px 2px rgba(0, 0, 0, 0.4));
+}
+
+.topnav .brand-text {
+  font-size: 0.88rem;
+  font-weight: 700;
+  color: var(--text-primary);
+  letter-spacing: -0.01em;
+}
+
+.topnav nav {
+  display: flex;
+  gap: 0.2rem;
+}
+
+.topnav nav button {
+  background: transparent;
+  color: var(--text-dim);
+  border: 1px solid transparent;
+  padding: 0.3rem 0.75rem;
+  border-radius: 6px;
+  cursor: pointer;
+  font-size: 0.82rem;
+  font-weight: 500;
+  transition: background 120ms, color 120ms, border-color 120ms;
+}
+
+.topnav nav button:hover {
+  color: var(--text-primary);
+  background: rgba(255, 255, 255, 0.03);
+}
+
+.topnav nav button.active {
+  background: var(--seek-yellow);
+  border-color: var(--seek-yellow-deep);
+  color: var(--seek-navy-ink);
+  font-weight: 700;
+  box-shadow: 0 1px 0 rgba(255, 255, 255, 0.25) inset, 0 1px 4px rgba(245, 200, 56, 0.25);
+}
+
+.spacer { flex: 1; }
+
+/* User chip */
+.user-chip { position: relative; }
+
+.user-chip .chip {
+  background: var(--bg-2);
+  color: var(--text-primary);
+  border: 1px solid var(--border-strong);
+  padding: 0.3rem 0.75rem;
+  border-radius: 999px;
+  cursor: pointer;
+  font-size: 0.8rem;
+  font-weight: 500;
+  transition: background 120ms;
+}
+
+.user-chip .chip:hover {
+  background: var(--bg-3);
+}
+
+.user-chip .menu {
+  position: absolute;
+  top: calc(100% + 6px);
+  right: 0;
+  background: var(--bg-2);
+  border: 1px solid var(--border-strong);
+  border-radius: 8px;
+  min-width: 160px;
+  z-index: 10;
+  padding: 4px;
+  box-shadow: var(--shadow-card);
+}
+
+.user-chip .menu button {
+  display: block;
+  width: 100%;
+  background: transparent;
+  color: var(--text-primary);
+  border: 0;
+  padding: 0.5rem 0.7rem;
+  text-align: left;
+  cursor: pointer;
+  border-radius: 5px;
+  font-size: 0.85rem;
+}
+
+.user-chip .menu button:hover {
+  background: var(--bg-3);
+}
+
+/* Main */
+main {
+  flex: 1;
+  overflow: auto;
+  padding: 0.5rem 0.75rem;
+}
+
+.placeholder {
+  display: flex;
+  flex-direction: column;
+  gap: 0.6rem;
+  align-items: flex-start;
+}
+
+.dim { color: var(--text-dim); }
+
+/* Login page */
+.login-page {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  height: 100vh;
+  background:
+    radial-gradient(800px 400px at 20% 10%, rgba(245, 200, 56, 0.08), transparent 60%),
+    radial-gradient(900px 500px at 85% 90%, rgba(31, 52, 136, 0.35), transparent 65%),
+    var(--bg-0);
+}
+
+.login-card {
+  background: linear-gradient(180deg, #1A2146 0%, #13193A 100%);
+  border: 1px solid var(--border-strong);
+  border-radius: 14px;
+  padding: 2.25rem 2.25rem;
+  width: 380px;
+  box-shadow: 0 24px 64px rgba(0, 0, 0, 0.55), 0 1px 0 rgba(255, 255, 255, 0.04) inset;
+}
+
+.login-logo {
+  height: 44px;
+  width: auto;
+  display: block;
+  margin: 0 0 0.75rem 0;
+  filter: drop-shadow(0 2px 4px rgba(0, 0, 0, 0.4));
+}
+
+.login-card .subtitle {
+  margin: 0 0 1.4rem 0;
+  color: var(--text-dim);
+  font-size: 0.9rem;
+}
+
+.login-card input[type="text"] {
+  width: 100%;
+  background: var(--bg-0);
+  border: 1px solid var(--border-strong);
+  color: var(--text-primary);
+  padding: 0.65rem 0.85rem;
+  border-radius: 8px;
+  font-size: 0.95rem;
+  margin-bottom: 1rem;
+  transition: border-color 120ms, box-shadow 120ms;
+}
+
+.login-card input[type="text"]:focus {
+  outline: none;
+  border-color: var(--seek-yellow);
+  box-shadow: 0 0 0 3px rgba(245, 200, 56, 0.15);
+}
+
+.login-card .actions {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+}
+
+.login-card button[type="submit"] {
+  background: var(--seek-yellow);
+  color: var(--seek-navy-ink);
+  border: 0;
+  padding: 0.6rem 1.1rem;
+  border-radius: 8px;
+  cursor: pointer;
+  font-size: 0.9rem;
+  font-weight: 700;
+  box-shadow: 0 1px 0 rgba(255, 255, 255, 0.25) inset, 0 2px 6px rgba(245, 200, 56, 0.25);
+  transition: transform 80ms, box-shadow 120ms;
+}
+
+.login-card button[type="submit"]:hover:not(:disabled) {
+  transform: translateY(-1px);
+  box-shadow: 0 1px 0 rgba(255, 255, 255, 0.3) inset, 0 4px 12px rgba(245, 200, 56, 0.35);
+}
+
+.login-card button[type="submit"]:disabled {
+  background: var(--bg-3);
+  color: var(--text-faint);
+  cursor: not-allowed;
+  box-shadow: none;
+}
+
+.login-card .known-users {
+  display: flex;
+  flex-direction: column;
+  gap: 0.45rem;
+  margin-bottom: 0.5rem;
+}
+
+.login-card .known-user {
+  background: var(--bg-2);
+  color: var(--text-primary);
+  border: 1px solid var(--border-strong);
+  padding: 0.55rem 0.9rem;
+  border-radius: 8px;
+  text-align: left;
+  cursor: pointer;
+  font-size: 0.9rem;
+  transition: border-color 120ms, background 120ms;
+}
+
+.login-card .known-user:hover {
+  background: var(--bg-3);
+  border-color: var(--seek-yellow);
+}
+
+.login-card .link {
+  background: transparent;
+  color: var(--accent-link);
+  border: 0;
+  cursor: pointer;
+  padding: 0.4rem 0;
+  font-size: 0.85rem;
+  text-align: left;
+}
+
+.login-card .error {
+  color: #ff6b6b;
+  font-size: 0.85rem;
+  margin-top: 0.5rem;
+}
+
+/* Extract mode */
+.upload-pane {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 1rem;
+  padding: 5rem 2rem;
+}
+
+.upload-pane .primary {
+  background: var(--seek-yellow);
+  color: var(--seek-navy-ink);
+  border: 0;
+  padding: 0.8rem 1.6rem;
+  border-radius: 10px;
+  font-size: 1rem;
+  font-weight: 700;
+  cursor: pointer;
+  box-shadow: 0 1px 0 rgba(255, 255, 255, 0.3) inset, 0 4px 16px rgba(245, 200, 56, 0.25);
+  transition: transform 80ms, box-shadow 120ms;
+}
+
+.upload-pane .primary:hover {
+  transform: translateY(-1px);
+  box-shadow: 0 1px 0 rgba(255, 255, 255, 0.35) inset, 0 6px 20px rgba(245, 200, 56, 0.35);
+}
+
+.upload-pane .error {
+  color: #ff6b6b;
+  font-size: 0.85rem;
+}
+
+.extract-mode {
+  display: flex;
+  flex-direction: column;
+  gap: 0.35rem;
+  height: 100%;
+  max-height: 100%;
+}
+
+.video-info {
+  display: flex;
+  align-items: baseline;
+  gap: 1rem;
+  padding: 0 0.25rem;
+  font-size: 0.82rem;
+  min-height: 20px;
+  white-space: nowrap;
+  overflow: hidden;
+}
+
+.video-info .path {
+  font-weight: 600;
+  color: #ddd;
+}
+
+.video-info .link {
+  background: transparent;
+  border: 0;
+  color: #7aa0ff;
+  cursor: pointer;
+  font-size: 0.85rem;
+  padding: 0;
+  margin-left: auto;
+}
+
+.coco-info {
+  display: flex;
+  align-items: baseline;
+  gap: 0.75rem;
+  padding: 0 0.25rem;
+  font-size: 0.82rem;
+  min-height: 20px;
+  white-space: nowrap;
+  overflow: hidden;
+}
+
+.coco-info .link {
+  background: transparent;
+  border: 0;
+  color: #7aa0ff;
+  cursor: pointer;
+  font-size: 0.85rem;
+  padding: 0;
+}
+
+.badge {
+  display: inline-block;
+  margin-left: 0.75rem;
+  padding: 0.15rem 0.65rem;
+  border-radius: 999px;
+  background: rgba(245, 200, 56, 0.14);
+  color: #FFDA75;
+  border: 1px solid rgba(245, 200, 56, 0.35);
+  font-size: 0.72rem;
+  font-weight: 600;
+  letter-spacing: 0.02em;
+}
+
+.overlay-toggle {
+  background: var(--bg-2);
+  color: var(--text-dim);
+  border: 1px solid var(--border-strong);
+  padding: 0.45rem 0.9rem;
+  border-radius: 10px;
+  cursor: pointer;
+  font-size: 0.85rem;
+  min-width: 134px;
+  text-align: center;
+  font-weight: 500;
+  transition: all 120ms;
+}
+
+.overlay-toggle:hover {
+  background: var(--bg-3);
+  color: var(--text-primary);
+}
+
+.overlay-toggle.on {
+  background: linear-gradient(180deg, rgba(31,52,136,0.45), rgba(31,52,136,0.22));
+  border-color: var(--seek-navy);
+  color: #CEE0FF;
+  box-shadow: 0 0 0 3px rgba(31, 52, 136, 0.15);
+}
+
+/* Extraction row — fixed two-row height so progress bar / errors don't shift
+   the canvas when they appear. */
+.extract-info {
+  display: flex;
+  flex-wrap: nowrap;
+  align-items: center;
+  gap: 0.5rem 0.75rem;
+  padding: 0.3rem 0.25rem 0.3rem;
+  font-size: 0.82rem;
+  border-top: 1px solid var(--border-subtle);
+  position: relative;
+  min-height: 52px;
+  white-space: nowrap;
+}
+
+.extract-info.collapsed {
+  min-height: 44px;
+  padding-top: 0.25rem;
+  padding-bottom: 0.25rem;
+}
+
+.collapse-toggle {
+  background: var(--bg-2);
+  color: var(--text-dim);
+  border: 1px solid var(--border-strong);
+  padding: 0.35rem 0.7rem;
+  border-radius: 999px;
+  cursor: pointer;
+  font-size: 0.85rem;
+  font-weight: 700;
+  margin-left: 0.4rem;
+  min-width: 36px;
+  text-align: center;
+  transition: all 120ms;
+}
+
+.collapse-toggle:hover {
+  background: var(--bg-3);
+  color: var(--text-primary);
+  border-color: var(--seek-yellow);
+}
+
+.extract-info .link {
+  background: transparent;
+  border: 0;
+  color: #7aa0ff;
+  cursor: pointer;
+  font-size: 0.85rem;
+  padding: 0;
+}
+
+.extract-actions {
+  display: flex;
+  flex-wrap: nowrap;
+  align-items: center;
+  gap: 0.4rem;
+  margin-left: auto;
+}
+
+.extract-actions button {
+  background: linear-gradient(180deg, rgba(255,255,255,0.04), rgba(255,255,255,0.01)), var(--bg-2);
+  color: var(--text-primary);
+  border: 1px solid var(--border-strong);
+  padding: 0.45rem 0.9rem;
+  border-radius: 10px;
+  cursor: pointer;
+  font-size: 0.85rem;
+  font-weight: 500;
+  transition: all 120ms;
+  box-shadow: 0 1px 0 rgba(255,255,255,0.04) inset, 0 1px 2px rgba(0,0,0,0.25);
+}
+
+.extract-actions button:hover:not(:disabled) {
+  background: linear-gradient(180deg, rgba(255,255,255,0.06), rgba(255,255,255,0.02)), var(--bg-3);
+  border-color: #3E4876;
+  transform: translateY(-1px);
+  box-shadow: 0 1px 0 rgba(255,255,255,0.06) inset, 0 4px 10px rgba(0,0,0,0.35);
+}
+
+.extract-actions button:active:not(:disabled) { transform: translateY(0); }
+
+.extract-actions button:disabled {
+  opacity: 0.45;
+  cursor: not-allowed;
+  box-shadow: none;
+}
+
+.extract-actions button.primary-ghost,
+.extract-actions button.extracted {
+  min-width: 155px;
+  text-align: center;
+  font-weight: 600;
+}
+
+.extract-actions button.primary-ghost {
+  background: var(--seek-yellow);
+  border-color: var(--seek-yellow-deep);
+  color: var(--seek-navy-ink);
+  box-shadow: 0 1px 0 rgba(255, 255, 255, 0.25) inset;
+}
+
+.extract-actions button.primary-ghost:hover:not(:disabled) {
+  background: #FCD34D;
+}
+
+.extract-actions button.extracted {
+  background: rgba(92, 207, 117, 0.12);
+  border-color: rgba(92, 207, 117, 0.55);
+  color: var(--success);
+}
+
+.range-picker {
+  display: inline-flex;
+  align-items: center;
+  gap: 0.3rem;
+  padding: 0 0.6rem;
+  border-left: 1px solid var(--border-subtle);
+  border-right: 1px solid var(--border-subtle);
+}
+
+.range-picker button {
+  font-variant-numeric: tabular-nums;
+  min-width: 88px;
+  text-align: center;
+}
+
+.range-picker .link {
+  font-size: 0.75rem;
+}
+
+.progress {
+  position: absolute;
+  left: 0.25rem;
+  right: 0.25rem;
+  bottom: 0.25rem;
+  height: 20px;
+  background: #111;
+  border: 1px solid #333;
+  border-radius: 4px;
+  overflow: hidden;
+  font-size: 0.75rem;
+}
+
+.progress .progress-bar {
+  position: absolute;
+  top: 0; left: 0; bottom: 0;
+  background: linear-gradient(90deg, #2b5edc, #4a84ff);
+  transition: width 0.15s ease;
+}
+
+.progress span {
+  position: absolute;
+  top: 0; left: 0; right: 0; bottom: 0;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  color: #fff;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  font-variant-numeric: tabular-nums;
+}
+
+.badge.extracted {
+  background: rgba(92, 207, 117, 0.14);
+  color: var(--success);
+  border-color: rgba(92, 207, 117, 0.45);
+}
+
+/* -------- Annotate mode -------- */
+.annotate-mode {
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+  height: 100%;
+  max-height: 100%;
+}
+
+.annotate-topbar {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  padding: 0 0.25rem;
+  font-size: 0.82rem;
+  min-height: 34px;
+  white-space: nowrap;
+}
+
+.tool-cluster {
+  display: inline-flex;
+  align-items: center;
+  background: var(--bg-1);
+  border: 1px solid var(--border-strong);
+  border-radius: 999px;
+  overflow: hidden;
+  box-shadow: 0 1px 2px rgba(0,0,0,0.25);
+}
+
+.tool-cluster button {
+  background: transparent;
+  border: 0;
+  padding: 0.4rem 0.85rem;
+  color: var(--text-dim);
+  cursor: pointer;
+  font-size: 0.8rem;
+  font-weight: 600;
+  transition: background 120ms, color 120ms;
+  border-right: 1px solid var(--border-subtle);
+}
+
+.tool-cluster button:last-child { border-right: 0; }
+
+.tool-cluster button:hover {
+  background: rgba(255,255,255,0.04);
+  color: var(--text-primary);
+}
+
+.tool-cluster button.on {
+  background: linear-gradient(180deg, #FFD14A, #E8B018);
+  color: var(--seek-navy-ink);
+  font-weight: 700;
+  box-shadow: 0 1px 0 rgba(255,255,255,0.3) inset;
+}
+
+.annotate-folder-chip {
+  display: inline-flex;
+  align-items: center;
+  gap: 0.25rem;
+  font-size: 0.82rem;
+  padding-left: 0.35rem;
+}
+
+.status-msg {
+  color: var(--success);
+  font-size: 0.78rem;
+  font-weight: 600;
+  background: rgba(92, 207, 117, 0.14);
+  border: 1px solid rgba(92, 207, 117, 0.4);
+  padding: 0.2rem 0.6rem;
+  border-radius: 999px;
+  animation: fade-in 120ms ease-out;
+}
+
+@keyframes fade-in {
+  from { opacity: 0; transform: translateY(-2px); }
+  to { opacity: 1; transform: translateY(0); }
+}
+
+.active-class-hint {
+  color: var(--text-primary);
+  font-weight: 700;
+  text-transform: none;
+  letter-spacing: 0;
+}
+
+.zoom-cluster {
+  display: inline-flex;
+  align-items: center;
+  background: var(--bg-1);
+  border: 1px solid var(--border-strong);
+  border-radius: 999px;
+  overflow: hidden;
+  box-shadow: 0 1px 2px rgba(0,0,0,0.25);
+}
+
+.zoom-cluster button {
+  background: transparent;
+  border: 0;
+  padding: 0.3rem 0.65rem;
+  color: var(--text-dim);
+  cursor: pointer;
+  font-size: 0.9rem;
+  font-weight: 700;
+  transition: background 120ms, color 120ms;
+}
+
+.zoom-cluster button:hover {
+  background: rgba(255, 255, 255, 0.04);
+  color: var(--text-primary);
+}
+
+.zoom-cluster .zoom-readout {
+  min-width: 56px;
+  text-align: center;
+  color: var(--seek-yellow);
+  font-size: 0.78rem;
+  font-variant-numeric: tabular-nums;
+  border-left: 1px solid var(--border-subtle);
+  border-right: 1px solid var(--border-subtle);
+}
+
+.canvas-stage.zoomable {
+  user-select: none;
+}
+
+.canvas-stage.zoomable .canvas-frame {
+  transition: transform 60ms ease-out;
+  will-change: transform;
+}
+
+.annotate-topbar .spacer { flex: 1; }
+
+.annotate-topbar .link {
+  background: transparent;
+  border: 0;
+  color: #7aa0ff;
+  cursor: pointer;
+  font-size: 0.85rem;
+  padding: 0;
+}
+
+.annotate-layout {
+  display: grid;
+  grid-template-columns: 220px 1fr 240px;
+  gap: 0.75rem;
+  flex: 1;
+  min-height: 0;
+}
+
+.annotate-mode.frame-info-hidden .annotate-layout {
+  grid-template-columns: 220px 1fr;
+}
+
+.annotate-sidebar {
+  background: linear-gradient(180deg, var(--bg-1) 0%, #11152B 100%);
+  border: 1px solid var(--border-subtle);
+  border-radius: 10px;
+  display: flex;
+  flex-direction: column;
+  overflow: hidden;
+  min-height: 0;
+  box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2);
+}
+
+.sidebar-header {
+  padding: 0.6rem 0.85rem;
+  font-size: 0.72rem;
+  font-weight: 700;
+  color: var(--seek-yellow);
+  border-bottom: 1px solid var(--border-subtle);
+  background: rgba(31, 52, 136, 0.12);
+  text-transform: uppercase;
+  letter-spacing: 0.08em;
+}
+
+.image-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+  overflow-y: auto;
+  flex: 1;
+}
+
+.image-list.empty {
+  padding: 1rem;
+  color: #666;
+  font-style: italic;
+  font-size: 0.85rem;
+}
+
+.image-list li {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  padding: 0.35rem 0.75rem;
+  font-size: 0.8rem;
+  cursor: pointer;
+  border-bottom: 1px solid #1d1d24;
+  font-variant-numeric: tabular-nums;
+  color: #bbb;
+}
+
+.image-list li:hover { background: #1c1c24; }
+
+.image-list li.selected {
+  background: linear-gradient(90deg, rgba(245, 200, 56, 0.2) 0%, rgba(31, 52, 136, 0.4) 100%);
+  color: var(--text-primary);
+  border-left: 2px solid var(--seek-yellow);
+  padding-left: calc(0.75rem - 2px);
+}
+
+.image-list li .marker {
+  width: 1em;
+  color: #555;
+  text-align: center;
+}
+
+.image-list li.annotated .marker { color: #5ccf75; }
+
+.image-list li .name {
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+  flex: 1;
+}
+
+.image-list li .count {
+  min-width: 20px;
+  text-align: center;
+  font-size: 0.7rem;
+  font-weight: 700;
+  color: var(--seek-navy-ink);
+  background: var(--seek-yellow);
+  border-radius: 999px;
+  padding: 0 0.35rem;
+  line-height: 1.5;
+  font-variant-numeric: tabular-nums;
+}
+
+.image-list li .updated {
+  font-size: 0.7rem;
+  min-width: 28px;
+  text-align: right;
+  font-variant-numeric: tabular-nums;
+}
+
+.class-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+  overflow-y: auto;
+}
+
+.class-list.empty {
+  padding: 1rem;
+  color: #666;
+  font-style: italic;
+  font-size: 0.85rem;
+}
+
+.class-list li {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  padding: 0.3rem 0.75rem;
+  font-size: 0.82rem;
+  color: #ccc;
+  cursor: pointer;
+  border-left: 3px solid transparent;
+  transition: background 120ms, border-color 120ms;
+}
+
+.class-list li:hover {
+  background: rgba(255,255,255,0.03);
+}
+
+.class-list li.active {
+  background: linear-gradient(90deg, rgba(245,200,56,0.18), rgba(245,200,56,0.02));
+  border-left-color: var(--seek-yellow);
+  color: var(--text-primary);
+}
+
+.class-list-wrap {
+  display: flex;
+  flex-direction: column;
+  min-height: 0;
+}
+
+.class-add {
+  border-top: 1px solid var(--border-subtle);
+  padding: 0.45rem 0.75rem;
+  background: rgba(0,0,0,0.15);
+}
+
+.class-add .add-class-trigger {
+  background: transparent;
+  border: 0;
+  color: var(--accent-link);
+  cursor: pointer;
+  font-size: 0.78rem;
+  padding: 0.1rem 0;
+}
+
+.class-add-row {
+  display: flex;
+  align-items: center;
+  gap: 0.35rem;
+}
+
+.class-add-row input {
+  flex: 1;
+  background: var(--bg-0);
+  border: 1px solid var(--border-strong);
+  color: var(--text-primary);
+  padding: 0.35rem 0.5rem;
+  border-radius: 6px;
+  font-size: 0.8rem;
+}
+
+.class-add-row input:focus {
+  outline: none;
+  border-color: var(--seek-yellow);
+  box-shadow: 0 0 0 2px rgba(245, 200, 56, 0.18);
+}
+
+.class-add-row button {
+  background: var(--seek-yellow);
+  border: 0;
+  color: var(--seek-navy-ink);
+  padding: 0.35rem 0.7rem;
+  border-radius: 6px;
+  cursor: pointer;
+  font-size: 0.78rem;
+  font-weight: 700;
+}
+
+.class-add-row button:disabled { opacity: 0.45; cursor: not-allowed; }
+
+.class-add-row .link {
+  background: transparent;
+  border: 0;
+  color: var(--text-dim);
+  cursor: pointer;
+  font-size: 0.78rem;
+  padding: 0.35rem 0.25rem;
+}
+
+.class-add-error {
+  color: var(--danger);
+  font-size: 0.72rem;
+  padding-top: 0.25rem;
+}
+
+.class-list .swatch {
+  width: 14px;
+  height: 14px;
+  border-radius: 3px;
+  flex-shrink: 0;
+}
+
+.class-list .name { flex: 1; }
+
+.class-list .id {
+  color: #666;
+  font-size: 0.75rem;
+  font-variant-numeric: tabular-nums;
+}
+
+.current-anns {
+  border-top: 1px solid #262630;
+  display: flex;
+  flex-direction: column;
+  min-height: 0;
+}
+
+.ann-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+  overflow-y: auto;
+}
+
+.ann-list li {
+  padding: 0.3rem 0.75rem;
+  font-size: 0.78rem;
+  color: #bbb;
+  border-bottom: 1px solid #1d1d24;
+  cursor: pointer;
+  border-left: 3px solid transparent;
+}
+
+.ann-list li:hover { background: rgba(255,255,255,0.03); }
+
+.ann-list li.selected {
+  background: linear-gradient(90deg, rgba(245,200,56,0.22), rgba(245,200,56,0.05));
+  border-left-color: var(--seek-yellow);
+  color: var(--text-primary);
+}
+
+.ann-list .type {
+  display: inline-block;
+  background: #262630;
+  padding: 0 0.4rem;
+  border-radius: 3px;
+  font-size: 0.7rem;
+  color: #9aa;
+  margin-right: 0.3rem;
+}
+
+.annotate-canvas-wrap {
+  display: flex;
+  flex-direction: column;
+  min-height: 0;
+  min-width: 0;
+  overflow: hidden;
+}
+
+.placeholder-centered {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  flex: 1;
+}
+
+.error-boundary {
+  padding: 2rem;
+  max-width: 920px;
+  margin: 0 auto;
+}
+
+.error-boundary h2 {
+  color: var(--danger);
+  margin: 0 0 0.75rem;
+  font-size: 1.2rem;
+}
+
+.error-boundary pre {
+  background: var(--bg-1);
+  border: 1px solid var(--border-subtle);
+  border-radius: 8px;
+  padding: 0.85rem 1rem;
+  overflow: auto;
+  font-size: 0.78rem;
+  color: var(--text-primary);
+  white-space: pre-wrap;
+  word-break: break-word;
+}
+
+.error-boundary pre.dim {
+  color: var(--text-faint);
+  font-size: 0.72rem;
+  max-height: 260px;
+}
+
+.error-boundary .primary {
+  margin-top: 0.75rem;
+  background: var(--seek-yellow);
+  color: var(--seek-navy-ink);
+  border: 0;
+  padding: 0.55rem 1.1rem;
+  border-radius: 8px;
+  font-weight: 700;
+  cursor: pointer;
+}
+
+/* -------- Cog button (TopNav) -------- */
+.icon-btn {
+  background: var(--bg-2);
+  color: var(--text-dim);
+  border: 1px solid var(--border-strong);
+  width: 28px;
+  height: 28px;
+  border-radius: 999px;
+  cursor: pointer;
+  font-size: 0.9rem;
+  margin-right: 0.3rem;
+  transition: all 120ms;
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.icon-btn:hover {
+  background: var(--bg-3);
+  color: var(--seek-yellow);
+  border-color: var(--seek-yellow);
+}
+
+/* -------- Modal shared -------- */
+.modal-backdrop {
+  position: fixed;
+  inset: 0;
+  background: rgba(5, 7, 20, 0.7);
+  backdrop-filter: blur(2px);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  z-index: 100;
+}
+
+.modal {
+  background: linear-gradient(180deg, #1A2146 0%, #13193A 100%);
+  border: 1px solid var(--border-strong);
+  border-radius: 14px;
+  width: 520px;
+  max-width: calc(100vw - 2rem);
+  max-height: calc(100vh - 4rem);
+  overflow: auto;
+  box-shadow: 0 32px 80px rgba(0, 0, 0, 0.55);
+}
+
+.modal-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 1rem 1.25rem 0.5rem;
+}
+
+.modal-header h2 {
+  margin: 0;
+  font-size: 1.05rem;
+  color: var(--text-primary);
+}
+
+.modal-close {
+  background: transparent;
+  border: 0;
+  color: var(--text-dim);
+  cursor: pointer;
+  font-size: 1.5rem;
+  line-height: 1;
+  padding: 0.15rem 0.55rem;
+  border-radius: 999px;
+}
+
+.modal-close:hover {
+  background: var(--bg-3);
+  color: var(--text-primary);
+}
+
+.modal-footer {
+  display: flex;
+  justify-content: flex-end;
+  gap: 0.5rem;
+  padding: 0.75rem 1.25rem 1rem;
+}
+
+.modal-footer .primary {
+  background: var(--seek-yellow);
+  color: var(--seek-navy-ink);
+  border: 0;
+  padding: 0.5rem 1.1rem;
+  border-radius: 8px;
+  cursor: pointer;
+  font-weight: 700;
+}
+
+.modal-footer .primary:disabled { opacity: 0.5; cursor: not-allowed; }
+
+.modal-footer .ghost,
+.settings-modal .ghost {
+  background: transparent;
+  color: var(--text-primary);
+  border: 1px solid var(--border-strong);
+  padding: 0.5rem 1rem;
+  border-radius: 8px;
+  cursor: pointer;
+}
+
+.modal-footer .ghost:hover,
+.settings-modal .ghost:hover { background: var(--bg-3); }
+
+/* -------- Settings -------- */
+.settings-section {
+  padding: 0.75rem 1.25rem;
+  border-top: 1px solid var(--border-subtle);
+}
+
+.settings-section:first-of-type { border-top: 0; }
+
+.settings-row {
+  margin-bottom: 0.75rem;
+}
+
+.settings-row:last-child { margin-bottom: 0; }
+
+.settings-row label { display: block; }
+
+.settings-label {
+  font-size: 0.82rem;
+  color: var(--text-dim);
+  margin-bottom: 0.35rem;
+  font-weight: 600;
+}
+
+.settings-row input[type="text"] {
+  width: 100%;
+  background: var(--bg-0);
+  border: 1px solid var(--border-strong);
+  color: var(--text-primary);
+  padding: 0.55rem 0.75rem;
+  border-radius: 8px;
+  font-size: 0.88rem;
+}
+
+.settings-row input[type="text"]:focus {
+  outline: none;
+  border-color: var(--seek-yellow);
+  box-shadow: 0 0 0 3px rgba(245, 200, 56, 0.15);
+}
+
+.settings-row input[type="range"] {
+  width: 100%;
+  accent-color: var(--seek-yellow);
+}
+
+.range-ticks {
+  display: flex;
+  justify-content: space-between;
+  font-size: 0.7rem;
+  color: var(--text-faint);
+  margin-top: 0.25rem;
+}
+
+.test-row {
+  display: flex;
+  gap: 0.6rem;
+  align-items: center;
+}
+
+.chip.ok {
+  background: rgba(92, 207, 117, 0.14);
+  color: var(--success);
+  border: 1px solid rgba(92, 207, 117, 0.4);
+  padding: 0.2rem 0.55rem;
+  border-radius: 999px;
+  font-size: 0.75rem;
+}
+
+.chip.fail {
+  background: rgba(255, 122, 122, 0.12);
+  color: var(--danger);
+  border: 1px solid rgba(255, 122, 122, 0.4);
+  padding: 0.2rem 0.55rem;
+  border-radius: 999px;
+  font-size: 0.75rem;
+  max-width: 280px;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.switch-row {
+  display: flex !important;
+  align-items: center;
+  gap: 0.6rem;
+  cursor: pointer;
+}
+
+.switch-row input[type="checkbox"] {
+  accent-color: var(--seek-yellow);
+  width: 18px;
+  height: 18px;
+}
+
+.picker-error {
+  color: var(--danger);
+  font-size: 0.78rem;
+  padding: 0.35rem 1.25rem;
+}
+
+/* -------- Label picker -------- */
+.label-picker-backdrop {
+  position: fixed;
+  inset: 0;
+  background: rgba(5, 7, 20, 0.55);
+  backdrop-filter: blur(2px);
+  display: flex;
+  align-items: flex-start;
+  justify-content: center;
+  padding-top: 12vh;
+  z-index: 110;
+}
+
+.label-picker {
+  width: 460px;
+  max-width: calc(100vw - 2rem);
+  background: linear-gradient(180deg, #1A2146 0%, #13193A 100%);
+  border: 1px solid var(--border-strong);
+  border-radius: 12px;
+  box-shadow: 0 24px 64px rgba(0, 0, 0, 0.5);
+  display: flex;
+  flex-direction: column;
+  overflow: hidden;
+}
+
+.label-picker-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 0.6rem 0.9rem;
+  border-bottom: 1px solid var(--border-subtle);
+  font-size: 0.78rem;
+}
+
+.label-picker-header .mode-tag {
+  background: var(--seek-yellow);
+  color: var(--seek-navy-ink);
+  padding: 0.15rem 0.65rem;
+  border-radius: 999px;
+  font-weight: 700;
+  font-size: 0.72rem;
+  letter-spacing: 0.04em;
+  text-transform: uppercase;
+}
+
+.label-picker input[type="text"] {
+  border: 0;
+  border-bottom: 1px solid var(--border-subtle);
+  background: transparent;
+  color: var(--text-primary);
+  padding: 0.75rem 0.9rem;
+  font-size: 0.95rem;
+  outline: none;
+}
+
+.label-picker input[type="text"]:focus {
+  border-bottom-color: var(--seek-yellow);
+}
+
+.label-picker-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+  max-height: 40vh;
+  overflow-y: auto;
+}
+
+.label-picker-list li {
+  display: flex;
+  align-items: center;
+  gap: 0.6rem;
+  padding: 0.45rem 0.9rem;
+  font-size: 0.85rem;
+  cursor: pointer;
+  color: var(--text-primary);
+  border-left: 3px solid transparent;
+}
+
+.label-picker-list li.highlight {
+  background: linear-gradient(90deg, rgba(245,200,56,0.22), rgba(245,200,56,0.05));
+  border-left-color: var(--seek-yellow);
+}
+
+.label-picker-list .swatch {
+  width: 14px;
+  height: 14px;
+  border-radius: 3px;
+  flex-shrink: 0;
+}
+
+.label-picker-list .name {
+  flex: 1;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+.label-picker-list .init {
+  font-family: monospace;
+  color: var(--text-faint);
+  background: rgba(255,255,255,0.04);
+  padding: 0.05rem 0.35rem;
+  border-radius: 4px;
+  font-size: 0.7rem;
+}
+
+.label-picker-list .id {
+  color: var(--text-faint);
+  font-size: 0.72rem;
+  font-variant-numeric: tabular-nums;
+  min-width: 28px;
+  text-align: right;
+}
+
+.label-picker-list li.empty {
+  padding: 0.75rem 0.9rem;
+  color: var(--text-dim);
+}
+
+.label-picker-list .add-inline {
+  background: transparent;
+  border: 1px dashed var(--seek-yellow);
+  color: var(--seek-yellow);
+  padding: 0.45rem 0.75rem;
+  border-radius: 8px;
+  cursor: pointer;
+  font-size: 0.82rem;
+  width: 100%;
+  text-align: left;
+}
+
+.label-picker-add-row {
+  padding: 0.5rem 0.9rem;
+  border-top: 1px solid var(--border-subtle);
+  background: rgba(0, 0, 0, 0.15);
+}
+
+.label-picker-add-row .add-inline {
+  background: transparent;
+  border: 1px dashed var(--seek-yellow);
+  color: var(--seek-yellow);
+  padding: 0.4rem 0.7rem;
+  border-radius: 8px;
+  cursor: pointer;
+  font-size: 0.8rem;
+  width: 100%;
+  text-align: left;
+  transition: background 120ms;
+}
+
+.label-picker-add-row .add-inline:hover:not(:disabled) {
+  background: rgba(245, 200, 56, 0.08);
+}
+
+.label-picker-add-row .add-inline:disabled {
+  opacity: 0.45;
+  cursor: not-allowed;
+  border-style: solid;
+  border-color: var(--border-strong);
+  color: var(--text-faint);
+}
+
+.label-picker-hint {
+  padding: 0.5rem 0.9rem;
+  font-size: 0.72rem;
+  color: var(--text-faint);
+  border-top: 1px solid var(--border-subtle);
+}
+
+.label-picker-hint kbd {
+  background: var(--bg-0);
+  border: 1px solid var(--border-strong);
+  border-radius: 4px;
+  padding: 0 0.35rem;
+  font-family: monospace;
+  font-size: 0.7rem;
+  color: var(--text-primary);
+}
+
+/* SAM phase indicator — toast that shows running/done/error, auto-clears
+   3 s after completion. Lives top-right so it's visible without covering
+   the canvas. */
+.sam-indicator {
+  position: fixed;
+  top: 64px;
+  right: 1rem;
+  display: flex;
+  align-items: center;
+  gap: 0.55rem;
+  padding: 0.45rem 0.85rem;
+  border-radius: 999px;
+  font-size: 0.82rem;
+  background: var(--bg-1);
+  box-shadow: 0 8px 24px rgba(0,0,0,0.45);
+  z-index: 90;
+  border: 1px solid var(--border-strong);
+  color: var(--text-primary);
+  transition: opacity 200ms, transform 200ms, border-color 200ms;
+  font-variant-numeric: tabular-nums;
+}
+
+.sam-indicator.phase-running {
+  border-color: var(--seek-yellow);
+  color: var(--seek-yellow);
+}
+
+.sam-indicator.phase-done {
+  border-color: #5ccf75;
+  color: #b8e9c5;
+  animation: sam-toast-in 220ms ease-out;
+}
+
+.sam-indicator.phase-error {
+  border-color: #d96565;
+  color: #ffb0b0;
+  animation: sam-toast-in 220ms ease-out;
+}
+
+.sam-indicator .sam-model {
+  font-weight: 600;
+  letter-spacing: 0.01em;
+}
+
+.sam-indicator .sam-time {
+  font-family: "SF Mono", Menlo, Consolas, monospace;
+  font-size: 0.78rem;
+  padding: 0.05rem 0.45rem;
+  border-radius: 6px;
+  background: rgba(92, 207, 117, 0.18);
+  color: #5ccf75;
+}
+
+.sam-indicator .sam-time.err {
+  background: rgba(217, 101, 101, 0.18);
+  color: #ff8b8b;
+}
+
+.sam-indicator .sam-tick {
+  font-weight: 700;
+  font-size: 0.95rem;
+  color: #5ccf75;
+}
+
+.sam-indicator .sam-tick.err { color: #ff8b8b; }
+
+@keyframes sam-toast-in {
+  from { transform: translateY(-6px); opacity: 0; }
+  to   { transform: translateY(0);    opacity: 1; }
+}
+
+.sam-indicator .spinner {
+  width: 14px;
+  height: 14px;
+  border: 2px solid rgba(245,200,56,0.25);
+  border-top-color: var(--seek-yellow);
+  border-radius: 50%;
+  animation: spin 800ms linear infinite;
+}
+
+@keyframes spin { to { transform: rotate(360deg); } }
+
+.swatch-tiny {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  border-radius: 2px;
+  margin-right: 0.3rem;
+  vertical-align: middle;
+}
+
+.sidebar-footer-hint {
+  padding: 0.5rem 0.75rem;
+  font-size: 0.72rem;
+  border-top: 1px solid var(--border-subtle);
+  text-align: center;
+}
+
+/* Canvas area: .canvas-stage fills available vertical space (flex: 1) and
+   centers its .canvas-frame child; .canvas-frame uses the video/image
+   aspect-ratio + max-* 100% so the browser picks the largest fit — no
+   forced letterboxing on the sides. */
+.canvas-stage {
+  flex: 1;
+  min-height: 0;
+  min-width: 0;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  padding: 0;
+  overflow: hidden;
+}
+
+.canvas-frame {
+  position: relative;
+  max-width: 100%;
+  max-height: 100%;
+  background: #000;
+  border: 1px solid #1A2042;
+  border-radius: 12px;
+  overflow: hidden;
+  box-shadow:
+    0 6px 24px rgba(0, 0, 0, 0.45),
+    0 1px 0 rgba(255, 255, 255, 0.04) inset;
+}
+
+.canvas-frame canvas {
+  width: 100%;
+  height: 100%;
+  display: block;
+}
+
+.timeline {
+  display: flex;
+  flex-direction: column;
+  gap: 0.15rem;
+}
+
+.timeline input[type="range"] {
+  width: 100%;
+  -webkit-appearance: none;
+  appearance: none;
+  background: transparent;
+  cursor: pointer;
+  height: 20px;
+}
+
+.timeline input[type="range"]::-webkit-slider-runnable-track {
+  height: 6px;
+  border-radius: 999px;
+  background: linear-gradient(
+    to right,
+    var(--seek-yellow) 0%,
+    var(--seek-yellow) var(--val, 0%),
+    var(--bg-3) var(--val, 0%),
+    var(--bg-3) 100%
+  );
+}
+
+.timeline input[type="range"]::-moz-range-track {
+  height: 6px;
+  border-radius: 999px;
+  background: var(--bg-3);
+}
+
+.timeline input[type="range"]::-moz-range-progress {
+  height: 6px;
+  border-radius: 999px;
+  background: var(--seek-yellow);
+}
+
+.timeline input[type="range"]::-webkit-slider-thumb {
+  -webkit-appearance: none;
+  appearance: none;
+  width: 16px;
+  height: 16px;
+  border-radius: 50%;
+  background: var(--seek-yellow);
+  border: 2px solid var(--seek-navy-ink);
+  margin-top: -5px;
+  box-shadow: 0 2px 6px rgba(245, 200, 56, 0.5);
+  cursor: pointer;
+  transition: transform 120ms;
+}
+
+.timeline input[type="range"]:hover::-webkit-slider-thumb {
+  transform: scale(1.15);
+}
+
+.timeline input[type="range"]::-moz-range-thumb {
+  width: 16px;
+  height: 16px;
+  border-radius: 50%;
+  background: var(--seek-yellow);
+  border: 2px solid var(--seek-navy-ink);
+  box-shadow: 0 2px 6px rgba(245, 200, 56, 0.5);
+  cursor: pointer;
+}
+
+.timeline-track-wrap {
+  position: relative;
+  height: 20px;
+}
+
+.timeline-track-wrap input[type="range"] {
+  position: relative;
+  z-index: 2;
+}
+
+.timeline-ticks {
+  position: absolute;
+  left: 0;
+  right: 0;
+  top: 3px;
+  height: 14px;
+  pointer-events: none;
+  z-index: 1;
+  opacity: 0.85;
+}
+
+.timeline-readout {
+  font-size: 0.78rem;
+  color: var(--text-dim);
+  min-height: 18px;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+.controls {
+  display: flex;
+  align-items: center;
+  gap: 0.45rem;
+  flex-wrap: nowrap;
+  overflow-x: auto;
+  min-height: 42px;
+  padding: 0.15rem 0;
+}
+
+.controls button {
+  background: linear-gradient(180deg, rgba(255,255,255,0.04), rgba(255,255,255,0.01)), var(--bg-2);
+  color: var(--text-primary);
+  border: 1px solid var(--border-strong);
+  padding: 0.4rem 0.85rem;
+  border-radius: 9px;
+  cursor: pointer;
+  font-size: 0.82rem;
+  font-weight: 500;
+  transition: transform 80ms ease, background 120ms, border-color 120ms, box-shadow 120ms;
+  box-shadow: 0 1px 0 rgba(255,255,255,0.04) inset, 0 1px 2px rgba(0,0,0,0.25);
+  font-variant-numeric: tabular-nums;
+}
+
+.controls button:hover:not(:disabled) {
+  background: linear-gradient(180deg, rgba(255,255,255,0.06), rgba(255,255,255,0.02)), var(--bg-3);
+  border-color: #3E4876;
+  transform: translateY(-1px);
+  box-shadow: 0 1px 0 rgba(255,255,255,0.06) inset, 0 4px 10px rgba(0,0,0,0.35);
+}
+
+.controls button:active { transform: translateY(0); }
+
+.controls button.active {
+  background: linear-gradient(180deg, #FFD14A, #E8B018);
+  border-color: var(--seek-yellow-deep);
+  color: var(--seek-navy-ink);
+  font-weight: 700;
+  box-shadow: 0 1px 0 rgba(255,255,255,0.35) inset, 0 4px 14px rgba(245, 200, 56, 0.35);
+}
+
+.controls > button:first-of-type {
+  /* Play/pause toggle — fixed width so text swap doesn't reflow the row. */
+  min-width: 96px;
+  text-align: center;
+}
+
+.controls .speed {
+  display: inline-flex;
+  align-items: center;
+  background: var(--bg-1);
+  border: 1px solid var(--border-strong);
+  border-radius: 10px;
+  overflow: hidden;
+  box-shadow: 0 1px 2px rgba(0,0,0,0.25);
+}
+
+.controls .speed button {
+  border: 0;
+  border-radius: 0;
+  background: transparent;
+  padding: 0.4rem 0.7rem;
+  box-shadow: none;
+  transform: none;
+  color: var(--text-dim);
+  font-weight: 700;
+}
+
+.controls .speed button:hover:not(:disabled) {
+  background: rgba(255,255,255,0.04);
+  color: var(--text-primary);
+  transform: none;
+  box-shadow: none;
+}
+
+.controls .speed-readout {
+  padding: 0 0.75rem;
+  font-variant-numeric: tabular-nums;
+  min-width: 2.75rem;
+  text-align: center;
+  color: var(--seek-yellow);
+  font-size: 0.85rem;
+  font-weight: 700;
+  border-left: 1px solid var(--border-subtle);
+  border-right: 1px solid var(--border-subtle);
+}
+
+.review-toggle {
+  display: inline-flex;
+  align-items: center;
+  gap: 0.45rem;
+  padding: 0.45rem 0.85rem;
+  border: 1px solid var(--border-strong);
+  border-radius: 10px;
+  cursor: pointer;
+  font-size: 0.85rem;
+  color: var(--text-dim);
+  background: var(--bg-2);
+  font-weight: 500;
+  transition: background 120ms, border-color 120ms, color 120ms;
+  user-select: none;
+}
+
+.review-toggle:hover {
+  background: var(--bg-3);
+  color: var(--text-primary);
+}
+
+.review-toggle.on {
+  background: linear-gradient(180deg, rgba(245,200,56,0.22), rgba(245,200,56,0.08));
+  border-color: var(--seek-yellow);
+  color: #FFDA75;
+  box-shadow: 0 0 0 3px rgba(245, 200, 56, 0.1);
+}
+
+.review-toggle input {
+  accent-color: var(--seek-yellow);
+  margin: 0;
+}
+
+.controls .controls .shortcuts {
+  margin-left: auto;
+  font-size: 0.7rem;
+  font-family: "SF Mono", Menlo, Consolas, monospace;
+  letter-spacing: 0.04em;
+  color: var(--text-faint);
+  padding: 0.35rem 0.6rem;
+  background: rgba(255, 255, 255, 0.02);
+  border-radius: 8px;
+  border: 1px solid var(--border-subtle);
+}
+
+main {
+  display: flex;
+  flex-direction: column;
+  min-height: 0;
+}
+
+/* -------- Image list toolbar + multi-select -------- */
+.image-list-toolbar {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 0.4rem;
+  padding: 0.45rem 0.7rem;
+  border-bottom: 1px solid var(--border-subtle);
+  font-size: 0.72rem;
+  background: rgba(31, 52, 136, 0.12);
+}
+
+.image-list-hint {
+  padding: 0.35rem 0.7rem;
+  font-size: 0.68rem;
+  border-bottom: 1px solid var(--border-subtle);
+  line-height: 1.3;
+}
+
+.image-list li.multi-selected {
+  background: rgba(245, 200, 56, 0.08);
+  border-left: 2px solid rgba(245, 200, 56, 0.6);
+  padding-left: calc(0.75rem - 2px);
+  color: var(--text-primary);
+}
+
+/* -------- Danger buttons -------- */
+button.danger-ghost {
+  background: transparent;
+  border: 1px solid rgba(220, 70, 70, 0.55);
+  color: #ff7b7b;
+  padding: 0.3rem 0.7rem;
+  border-radius: 6px;
+  cursor: pointer;
+  font-size: 0.76rem;
+  transition: background 120ms, color 120ms;
+}
+
+button.danger-ghost:hover:not(:disabled) {
+  background: rgba(220, 70, 70, 0.12);
+  color: #ff9a9a;
+}
+
+button.danger-ghost:disabled {
+  opacity: 0.35;
+  cursor: not-allowed;
+}
+
+button.danger {
+  background: linear-gradient(180deg, #c24141 0%, #962828 100%);
+  border: 1px solid #d15656;
+  color: #fff;
+  padding: 0.4rem 0.9rem;
+  border-radius: 6px;
+  cursor: pointer;
+  font-weight: 600;
+  font-size: 0.82rem;
+}
+
+button.danger:hover:not(:disabled) {
+  background: linear-gradient(180deg, #d04c4c 0%, #a52e2e 100%);
+}
+
+button.danger:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+}
+
+/* -------- Delete confirm modal -------- */
+.delete-modal {
+  width: 440px;
+  max-width: calc(100vw - 2rem);
+  background: linear-gradient(180deg, #1A2146 0%, #13193A 100%);
+  border: 1px solid var(--border-strong);
+  border-radius: 12px;
+  box-shadow: 0 24px 64px rgba(0, 0, 0, 0.5);
+}
+
+.delete-modal-header {
+  padding: 0.8rem 1.1rem;
+  font-size: 0.95rem;
+  font-weight: 700;
+  color: #ff9a9a;
+  border-bottom: 1px solid var(--border-subtle);
+}
+
+.delete-modal-body {
+  padding: 0.9rem 1.1rem;
+  font-size: 0.88rem;
+  line-height: 1.45;
+  color: var(--text-primary);
+}
+
+.delete-modal-body p { margin: 0 0 0.6rem 0; }
+.delete-modal-body p:last-child { margin-bottom: 0; }
+
+.delete-modal-body .delete-preview {
+  font-size: 0.78rem;
+  font-family: "SF Mono", Menlo, Consolas, monospace;
+  background: rgba(0, 0, 0, 0.25);
+  padding: 0.4rem 0.6rem;
+  border-radius: 6px;
+  white-space: normal;
+  word-break: break-all;
+}
+
+.delete-modal-body .error {
+  color: #ff8080;
+  font-size: 0.82rem;
+  margin-top: 0.5rem;
+}
+
+.delete-modal-actions {
+  display: flex;
+  gap: 0.5rem;
+  justify-content: flex-end;
+  padding: 0.7rem 1.1rem;
+  border-top: 1px solid var(--border-subtle);
+  background: rgba(0, 0, 0, 0.15);
+}
+
+.delete-modal-actions button {
+  padding: 0.4rem 0.9rem;
+  border-radius: 6px;
+  border: 1px solid var(--border-strong);
+  background: transparent;
+  color: var(--text-primary);
+  cursor: pointer;
+  font-size: 0.82rem;
+}
+
+.delete-modal-actions button:hover:not(:disabled) {
+  background: rgba(255, 255, 255, 0.04);
+}
+
+.delete-modal-actions button:disabled {
+  opacity: 0.45;
+  cursor: not-allowed;
+}
+
+.delete-modal-actions button.danger-ghost,
+.delete-modal-actions button.danger {
+  border-width: 1px;
+}
+
+/* -------- Extracted-frame full-canvas tint (Extract mode) -------- */
+.settings-modal select {
+  width: 100%;
+  padding: 0.5rem 0.7rem;
+  background: var(--bg-0);
+  color: var(--text-primary);
+  border: 1px solid var(--border-strong);
+  border-radius: 6px;
+  font-size: 0.85rem;
+}
+
+.settings-modal select:disabled {
+  opacity: 0.5;
+}
+
+.url-presets {
+  display: flex;
+  gap: 0.5rem;
+  margin-top: 0.35rem;
+  font-size: 0.72rem;
+}
+
+.url-presets .preset {
+  background: transparent;
+  border: 1px dashed var(--border-strong);
+  color: var(--text-dim);
+  padding: 0.15rem 0.55rem;
+  border-radius: 999px;
+  cursor: pointer;
+}
+
+.url-presets .preset:hover {
+  border-color: var(--seek-yellow);
+  color: var(--seek-yellow);
+}
+
+.source-video-chip {
+  padding: 0.4rem 0.85rem;
+  font-size: 0.72rem;
+  color: var(--text-dim);
+  border-bottom: 1px solid var(--border-subtle);
+  background: rgba(31, 52, 136, 0.08);
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+.source-video-chip strong {
+  color: var(--text-primary);
+  font-weight: 600;
+  margin-left: 0.2rem;
+}
+
+.frame-extracted-banner {
+  position: absolute;
+  inset: 0;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  pointer-events: none;
+  background: rgba(48, 180, 96, 0.18);
+  border: 3px solid rgba(92, 207, 117, 0.85);
+  color: #fff;
+  font-size: clamp(1.5rem, 6vw, 4rem);
+  font-weight: 800;
+  letter-spacing: 0.15em;
+  text-shadow: 0 2px 12px rgba(0, 0, 0, 0.65), 0 0 24px rgba(20, 60, 30, 0.8);
+  mix-blend-mode: normal;
+}
diff --git a/sam-tool-tauri/src/ipc.ts b/sam-tool-tauri/src/ipc.ts
new file mode 100644
index 0000000..d465ecf
--- /dev/null
+++ b/sam-tool-tauri/src/ipc.ts
@@ -0,0 +1,94 @@
+import { invoke } from "@tauri-apps/api/core";
+import type {
+  CocoCategory,
+  CocoShape,
+  ImageAnnotations,
+  ImageEntry,
+  ImportedCoco,
+  SamModelList,
+  Settings,
+  VideoInfo,
+} from "./types";
+
+export const api = {
+  ping: () => invoke<string>("ping"),
+
+  currentUser: () => invoke<string | null>("current_user"),
+  listKnownUsers: () => invoke<string[]>("list_known_users"),
+  setCurrentUser: (name: string) => invoke<string>("set_current_user", { name }),
+  clearCurrentUser: () => invoke<void>("clear_current_user"),
+
+  openVideo: (path: string) => invoke<VideoInfo>("open_video", { path }),
+  closeVideo: () => invoke<void>("close_video"),
+  currentVideo: () => invoke<VideoInfo | null>("current_video"),
+  decodeFrame: (frameIdx: number) =>
+    invoke<ArrayBuffer>("decode_frame", { frameIdx }),
+  setCacheCapacity: (capacity: number) =>
+    invoke<number>("set_cache_capacity", { capacity }),
+
+  importCoco: (path: string) => invoke<ImportedCoco>("import_coco", { path }),
+
+  extractFrame: (frameIdx: number, outDir: string, shapes: CocoShape[], author: string) =>
+    invoke<void>("extract_frame", { frameIdx, outDir, shapes, author }),
+  undoExtract: (frameIdx: number, outDir: string) =>
+    invoke<void>("undo_extract", { frameIdx, outDir }),
+  bulkExtract: (
+    start: number,
+    end: number,
+    outDir: string,
+    shapesByFrame: Record<string, CocoShape[]>,
+    author: string
+  ) =>
+    invoke<number>("bulk_extract", {
+      start,
+      end,
+      outDir,
+      shapesByFrame,
+      author,
+    }),
+  bulkUndo: (start: number, end: number, outDir: string) =>
+    invoke<number>("bulk_undo", { start, end, outDir }),
+  listExtractedFrames: (outDir: string) =>
+    invoke<number[]>("list_extracted_frames", { outDir }),
+  writeClassesTxt: (outDir: string, categories: CocoCategory[]) =>
+    invoke<void>("write_classes_txt", { outDir, categories }),
+
+  listFolderImages: (folder: string) =>
+    invoke<ImageEntry[]>("list_folder_images", { folder }),
+  readImageBytes: (path: string) =>
+    invoke<ArrayBuffer>("read_image_bytes", { path }),
+  loadImageAnnotations: (imagePath: string) =>
+    invoke<ImageAnnotations | null>("load_image_annotations", { imagePath }),
+  loadClasses: (folder: string) => invoke<string[]>("load_classes", { folder }),
+  saveImageAnnotations: (imagePath: string, payload: ImageAnnotations) =>
+    invoke<void>("save_image_annotations", { imagePath, payload }),
+  appendClass: (folder: string, name: string) =>
+    invoke<string[]>("append_class", { folder, name }),
+  deleteImages: (paths: string[]) => invoke<number>("delete_images", { paths }),
+  readFolderSource: (folder: string) =>
+    invoke<string | null>("read_folder_source", { folder }),
+
+  getSettings: () => invoke<Settings>("get_settings"),
+  saveSettings: (settings: Settings) =>
+    invoke<void>("save_settings", { settings }),
+  samHealthCheck: (url: string) =>
+    invoke<boolean>("sam_health_check", { url }),
+  samListModels: (url: string) =>
+    invoke<SamModelList>("sam_list_models", { url }),
+  samSegmentBbox: (
+    url: string,
+    imagePath: string,
+    bbox: [number, number, number, number],
+    imageWidth: number,
+    imageHeight: number,
+    model?: string | null
+  ) =>
+    invoke<[number, number][]>("sam_segment_bbox", {
+      url,
+      imagePath,
+      bbox,
+      imageWidth,
+      imageHeight,
+      model: model ?? null,
+    }),
+};
diff --git a/sam-tool-tauri/src/main.tsx b/sam-tool-tauri/src/main.tsx
new file mode 100644
index 0000000..9b67590
--- /dev/null
+++ b/sam-tool-tauri/src/main.tsx
@@ -0,0 +1,10 @@
+import React from "react";
+import ReactDOM from "react-dom/client";
+import App from "./App";
+import "./index.css";
+
+ReactDOM.createRoot(document.getElementById("root")!).render(
+  <React.StrictMode>
+    <App />
+  </React.StrictMode>
+);
diff --git a/sam-tool-tauri/src/modes/AnnotateMode.tsx b/sam-tool-tauri/src/modes/AnnotateMode.tsx
new file mode 100644
index 0000000..58311bd
--- /dev/null
+++ b/sam-tool-tauri/src/modes/AnnotateMode.tsx
@@ -0,0 +1,1825 @@
+import { useCallback, useEffect, useMemo, useRef, useState } from "react";
+import { open as openDialog } from "@tauri-apps/plugin-dialog";
+import { api } from "../ipc";
+import type {
+  Annotation,
+  BboxAnnotation,
+  CocoShape,
+  ImageAnnotations,
+  ImageEntry,
+  PolygonAnnotation,
+} from "../types";
+import { ImageList } from "../components/annotate/ImageList";
+import { LabelPicker } from "../components/annotate/LabelPicker";
+import { DeleteConfirmModal } from "../components/annotate/DeleteConfirmModal";
+import { drawOverlay, colorForClass } from "../components/extract/overlay";
+import type { Settings } from "../types";
+
+interface Props {
+  username: string;
+  settings: Settings | null;
+}
+
+type PendingShape =
+  | { kind: "new-bbox"; bbox: [number, number, number, number] }
+  | { kind: "new-polygon"; points: [number, number][] }
+  | { kind: "reclass"; id: string };
+
+type Tool = "select" | "bbox" | "polygon";
+
+type InProgress =
+  | { type: "bbox"; start: [number, number]; end: [number, number] }
+  | {
+      type: "polygon";
+      points: [number, number][];
+      cursor: [number, number] | null;
+    };
+
+type BboxHandle = "body" | "tl" | "tr" | "bl" | "br" | "t" | "b" | "l" | "r";
+
+interface BboxDrag {
+  id: string;
+  kind: BboxHandle;
+  origBbox: [number, number, number, number];
+  start: [number, number];
+  annBefore: ImageAnnotations;
+}
+
+interface VertexDrag {
+  id: string;
+  vertexIdx: number;
+  origPoints: [number, number][];
+  start: [number, number];
+  annBefore: ImageAnnotations;
+}
+
+const MAX_UNDO = 40;
+
+const MIN_ZOOM = 1;
+const MAX_ZOOM = 8;
+const ZOOM_STEP = 1.25;
+
+function uuid(): string {
+  try {
+    const c = (globalThis as unknown as { crypto?: { randomUUID?: () => string } }).crypto;
+    if (c && typeof c.randomUUID === "function") return c.randomUUID();
+  } catch {}
+  return "xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx".replace(/[xy]/g, (ch) => {
+    const r = (Math.random() * 16) | 0;
+    const v = ch === "x" ? r : (r & 0x3) | 0x8;
+    return v.toString(16);
+  });
+}
+
+export function AnnotateMode({ username, settings }: Props) {
+  const [folder, setFolder] = useState<string | null>(null);
+  const [images, setImages] = useState<ImageEntry[]>([]);
+  const [selectedIdx, setSelectedIdx] = useState<number | null>(null);
+  // Multi-select for bulk actions (delete). selectedSet always contains
+  // selectedIdx when a single image is in focus; shift/ctrl click extends it.
+  const [selectedSet, setSelectedSet] = useState<Set<number>>(new Set());
+  const [anchorIdx, setAnchorIdx] = useState<number | null>(null);
+  const [deleteOpen, setDeleteOpen] = useState(false);
+  const [deleteBusy, setDeleteBusy] = useState(false);
+  const [deleteError, setDeleteError] = useState<string | null>(null);
+  const [ann, setAnn] = useState<ImageAnnotations | null>(null);
+  const [classes, setClasses] = useState<string[]>([]);
+  const [activeClassId, setActiveClassId] = useState<number | null>(null);
+  const [tool, setTool] = useState<Tool>("select");
+  const [drawing, setDrawing] = useState<InProgress | null>(null);
+  const [selectedAnnId, setSelectedAnnId] = useState<string | null>(null);
+  const [dims, setDims] = useState<{ w: number; h: number } | null>(null);
+  const [overlayVisible, setOverlayVisible] = useState(true);
+  const [frameInfoHidden, setFrameInfoHidden] = useState(false);
+  const [loadError, setLoadError] = useState<string | null>(null);
+  const [statusMsg, setStatusMsg] = useState<string | null>(null);
+
+  // Zoom + pan. Refs mirror state so `zoomBy` can compute the new pan
+  // atomically *outside* any setState updater — React.StrictMode
+  // double-invokes functional updaters and setPan-inside-setZoom makes the
+  // cursor-anchor math compound (pan drifts with every wheel tick).
+  const [zoom, setZoom] = useState(1);
+  const [pan, setPan] = useState({ x: 0, y: 0 });
+  const zoomRef = useRef(1);
+  const panRef = useRef({ x: 0, y: 0 });
+  useEffect(() => { zoomRef.current = zoom; }, [zoom]);
+  useEffect(() => { panRef.current = pan; }, [pan]);
+
+  const canvasRef = useRef<HTMLCanvasElement>(null);
+  const bitmapRef = useRef<ImageBitmap | null>(null);
+  const loadedForImageRef = useRef<string | null>(null);
+  const stageRef = useRef<HTMLDivElement>(null);
+  // Mirror of `classes` that can be updated synchronously — needed because
+  // `pickerAddClass` calls setClasses and then immediately calls onConfirm,
+  // which reads `classes` from a stale closure. Without this ref the freshly-
+  // added class's name is "class_N" instead of the user-typed name.
+  const classesRef = useRef<string[]>([]);
+  // Mirror of `drawing` that can be read inside event listeners without
+  // subscribing — also lets us commit outside of a state updater (avoids
+  // React.StrictMode double-invocation side effects).
+  const drawingRef = useRef<InProgress | null>(null);
+  useEffect(() => {
+    drawingRef.current = drawing;
+  }, [drawing]);
+  useEffect(() => {
+    classesRef.current = classes;
+  }, [classes]);
+
+  // Bbox drag (resize/move) — live state so the canvas re-renders while
+  // dragging. pre-drag state is stashed so we can push it to undo on commit.
+  const [bboxDrag, setBboxDrag] = useState<BboxDrag | null>(null);
+  const [vertexDrag, setVertexDrag] = useState<VertexDrag | null>(null);
+  const [pending, setPending] = useState<PendingShape | null>(null);
+  // SAM phase drives the status chip near the canvas: spinner while a
+  // request is in flight, then a "model · 1.2s" toast that auto-clears so
+  // users can see how long inference takes.
+  const [samPhase, setSamPhase] = useState<
+    | { state: "running"; model: string }
+    | { state: "done"; model: string; elapsedMs: number }
+    | { state: "error"; model: string; message: string }
+    | null
+  >(null);
+  const samToastTimerRef = useRef<number | null>(null);
+
+  // Undo / redo stacks of ImageAnnotations snapshots. Refs so pushing doesn't
+  // re-render. Both cleared on image change in the image-load effect below.
+  const undoStackRef = useRef<ImageAnnotations[]>([]);
+  const redoStackRef = useRef<ImageAnnotations[]>([]);
+
+  const selectedImagePath = useMemo(() => {
+    if (folder == null || selectedIdx == null || !images[selectedIdx]) return null;
+    return `${folder}/${images[selectedIdx].filename}`;
+  }, [folder, selectedIdx, images]);
+
+  // ---- folder / image loaders ----------------------------------------
+
+  async function pickFolder() {
+    const selected = await openDialog({ directory: true, multiple: false });
+    if (!selected || typeof selected !== "string") return;
+    await openFolder(selected);
+  }
+
+  async function openFolder(path: string) {
+    setLoadError(null);
+    try {
+      const [list, cls] = await Promise.all([
+        api.listFolderImages(path),
+        api.loadClasses(path),
+      ]);
+      setFolder(path);
+      setImages(list);
+      setClasses(cls);
+      setActiveClassId(cls.length > 0 ? 0 : null);
+      const initial = list.length > 0 ? 0 : null;
+      setSelectedIdx(initial);
+      setSelectedSet(initial != null ? new Set([initial]) : new Set());
+      setAnchorIdx(initial);
+      setAnn(null);
+      setDims(null);
+      bitmapRef.current?.close();
+      bitmapRef.current = null;
+      loadedForImageRef.current = null;
+      resetZoom();
+      setDrawing(null);
+    } catch (e) {
+      setLoadError(String(e));
+    }
+  }
+
+  // Click handler for ImageList items. Plain click = single select.
+  // Shift-click = range from anchor. Ctrl/Cmd-click = toggle membership.
+  function handleImageSelect(idx: number, e: React.MouseEvent) {
+    if (e.shiftKey && anchorIdx != null) {
+      const lo = Math.min(anchorIdx, idx);
+      const hi = Math.max(anchorIdx, idx);
+      const range = new Set<number>();
+      for (let i = lo; i <= hi; i++) range.add(i);
+      setSelectedSet(range);
+      setSelectedIdx(idx);
+      return;
+    }
+    if (e.ctrlKey || e.metaKey) {
+      setSelectedSet((prev) => {
+        const next = new Set(prev);
+        if (next.has(idx)) next.delete(idx);
+        else next.add(idx);
+        return next;
+      });
+      setSelectedIdx(idx);
+      setAnchorIdx(idx);
+      return;
+    }
+    setSelectedSet(new Set([idx]));
+    setSelectedIdx(idx);
+    setAnchorIdx(idx);
+  }
+
+  // Keyboard navigation between images (A/D, arrows). Collapses multi-select
+  // to a single focused image, since the user is clearly single-stepping.
+  function stepSelection(delta: 1 | -1) {
+    if (images.length === 0) return;
+    setSelectedIdx((i) => {
+      const base = i ?? (delta > 0 ? -1 : images.length);
+      const next = Math.max(0, Math.min(images.length - 1, base + delta));
+      setSelectedSet(new Set([next]));
+      setAnchorIdx(next);
+      return next;
+    });
+  }
+
+  async function confirmDelete() {
+    if (!folder || selectedSet.size === 0) return;
+    const indices = [...selectedSet].sort((a, b) => a - b);
+    const paths = indices
+      .filter((i) => images[i])
+      .map((i) => `${folder}/${images[i].filename}`);
+    setDeleteBusy(true);
+    setDeleteError(null);
+    try {
+      await api.deleteImages(paths);
+      // Reload the folder so image list, annotation counts, etc. stay honest.
+      const fresh = await api.listFolderImages(folder);
+      setImages(fresh);
+      const nextIdx = fresh.length === 0
+        ? null
+        : Math.min(indices[0], fresh.length - 1);
+      setSelectedIdx(nextIdx);
+      setSelectedSet(nextIdx != null ? new Set([nextIdx]) : new Set());
+      setAnchorIdx(nextIdx);
+      setDeleteOpen(false);
+      setStatusMsg(`deleted ${paths.length} image${paths.length === 1 ? "" : "s"}`);
+      window.setTimeout(() => setStatusMsg(null), 1600);
+    } catch (e) {
+      setDeleteError(String(e));
+    } finally {
+      setDeleteBusy(false);
+    }
+  }
+
+  const drawInProgress = useCallback(
+    (
+      ctx: CanvasRenderingContext2D,
+      d: InProgress,
+      w: number,
+      h: number
+    ) => {
+      const color =
+        activeClassId != null ? colorForClass(activeClassId) : "#FFD657";
+      const line = Math.max(2, Math.round(Math.min(w, h) / 400));
+      ctx.save();
+      ctx.setLineDash([10, 6]);
+      ctx.strokeStyle = color;
+      ctx.fillStyle = `${color}33`;
+      ctx.lineWidth = line;
+
+      if (d.type === "bbox") {
+        const [x0, y0] = d.start;
+        const [x1, y1] = d.end;
+        const rx = Math.min(x0, x1);
+        const ry = Math.min(y0, y1);
+        const rw = Math.abs(x1 - x0);
+        const rh = Math.abs(y1 - y0);
+        ctx.fillRect(rx, ry, rw, rh);
+        ctx.strokeRect(rx, ry, rw, rh);
+      } else {
+        // Is the cursor near the start vertex? If so we'll snap-close.
+        const snap =
+          d.points.length >= 3 &&
+          d.cursor != null &&
+          pointDistance(d.cursor, d.points[0]) < getCloseThreshold();
+
+        ctx.beginPath();
+        d.points.forEach(([x, y], i) => {
+          if (i === 0) ctx.moveTo(x, y);
+          else ctx.lineTo(x, y);
+        });
+        if (d.cursor) {
+          // If snapping, connect the preview line to the start vertex directly
+          // so the shape reads as a closed loop.
+          if (snap) ctx.lineTo(d.points[0][0], d.points[0][1]);
+          else ctx.lineTo(d.cursor[0], d.cursor[1]);
+        }
+        ctx.stroke();
+
+        // Vertices
+        ctx.setLineDash([]);
+        ctx.fillStyle = color;
+        const r = Math.max(4, Math.round(Math.min(w, h) / 250));
+        for (const [px, py] of d.points) {
+          ctx.beginPath();
+          ctx.arc(px, py, r, 0, Math.PI * 2);
+          ctx.fill();
+        }
+
+        // Emphasize start vertex when close enough to snap.
+        if (snap) {
+          const ringR = getCloseThreshold() * 0.9;
+          ctx.strokeStyle = color;
+          ctx.fillStyle = `${color}55`;
+          ctx.lineWidth = Math.max(line, 3);
+          ctx.beginPath();
+          ctx.arc(d.points[0][0], d.points[0][1], ringR, 0, Math.PI * 2);
+          ctx.fill();
+          ctx.stroke();
+        }
+      }
+      ctx.restore();
+    },
+    [activeClassId]
+  );
+
+  const drawCanvas = useCallback(() => {
+    const canvas = canvasRef.current;
+    const bitmap = bitmapRef.current;
+    if (!canvas || !bitmap) return;
+    if (canvas.width !== bitmap.width) canvas.width = bitmap.width;
+    if (canvas.height !== bitmap.height) canvas.height = bitmap.height;
+    const ctx = canvas.getContext("2d");
+    if (!ctx) return;
+    ctx.clearRect(0, 0, canvas.width, canvas.height);
+    ctx.drawImage(bitmap, 0, 0);
+    if (overlayVisible && ann && ann.annotations.length > 0) {
+      drawOverlay(
+        ctx,
+        annotationsToShapes(ann.annotations),
+        canvas.width,
+        canvas.height
+      );
+    }
+    if (drawing) {
+      drawInProgress(ctx, drawing, canvas.width, canvas.height);
+    }
+    if (selectedAnnId && ann) {
+      const sel = ann.annotations.find((a) => a.id === selectedAnnId);
+      if (sel) {
+        drawSelectionHighlight(ctx, sel, canvas.width, canvas.height);
+        const rect = canvas.getBoundingClientRect();
+        const s2i = rect.width > 0 ? canvas.width / rect.width : 1;
+        if (sel.type === "bbox") {
+          drawBboxHandles(ctx, sel.bbox, s2i);
+        } else {
+          drawPolygonHandles(ctx, sel.points, s2i);
+        }
+      }
+    }
+  }, [ann, overlayVisible, drawing, drawInProgress, selectedAnnId, bboxDrag, vertexDrag]);
+
+  const loadImage = useCallback(async (imgPath: string) => {
+    try {
+      const bytes = await api.readImageBytes(imgPath);
+      const blob = new Blob([bytes], { type: "image/jpeg" });
+      const bitmap = await createImageBitmap(blob);
+      bitmapRef.current?.close();
+      bitmapRef.current = bitmap;
+      loadedForImageRef.current = imgPath;
+      setDims((prev) =>
+        prev && prev.w === bitmap.width && prev.h === bitmap.height
+          ? prev
+          : { w: bitmap.width, h: bitmap.height }
+      );
+    } catch (e) {
+      setLoadError(`image load: ${e}`);
+    }
+  }, []);
+
+  useEffect(() => {
+    if (!selectedImagePath) return;
+    let cancelled = false;
+    setLoadError(null);
+    api
+      .loadImageAnnotations(selectedImagePath)
+      .then((loaded) => {
+        if (cancelled) return;
+        setAnn(
+          loaded ??
+            (dims
+              ? {
+                  version: 1,
+                  image: selectedImagePath.split("/").pop() ?? "",
+                  width: dims.w,
+                  height: dims.h,
+                  annotations: [],
+                }
+              : null)
+        );
+      })
+      .catch((e) => {
+        if (cancelled) return;
+        setAnn(null);
+        setLoadError(String(e));
+      });
+    return () => {
+      cancelled = true;
+    };
+    // Intentionally not depending on `dims` — first selection may still have
+    // null dims; subsequent effect from loadImage will fill them and this
+    // effect reruns on path change only.
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [selectedImagePath]);
+
+  // Hold a ref to the latest drawCanvas so the image-change effect can call
+  // it without subscribing to its identity (which flips on every `drawing` /
+  // `ann` / `overlayVisible` change and would reset state mid-interaction).
+  const drawCanvasRef = useRef<() => void>(() => {});
+  useEffect(() => {
+    drawCanvasRef.current = drawCanvas;
+  }, [drawCanvas]);
+
+  // On image selection change: reset per-image state, load bytes, draw once.
+  useEffect(() => {
+    if (!selectedImagePath) return;
+    resetZoom();
+    setDrawing(null);
+    setSelectedAnnId(null);
+    setBboxDrag(null);
+    setVertexDrag(null);
+    undoStackRef.current = [];
+    redoStackRef.current = [];
+    if (loadedForImageRef.current === selectedImagePath) {
+      drawCanvasRef.current();
+    } else {
+      loadImage(selectedImagePath).then(() => drawCanvasRef.current());
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [selectedImagePath]);
+
+  // Redraw whenever render-relevant state changes (new annotations, overlay
+  // toggle, in-progress drawing updates).
+  useEffect(() => {
+    drawCanvas();
+  }, [drawCanvas]);
+
+  useEffect(() => {
+    return () => {
+      bitmapRef.current?.close();
+      bitmapRef.current = null;
+      if (samToastTimerRef.current != null) {
+        window.clearTimeout(samToastTimerRef.current);
+        samToastTimerRef.current = null;
+      }
+    };
+  }, []);
+
+  // ---- zoom + pan -----------------------------------------------------
+
+  const resetZoom = useCallback(() => {
+    zoomRef.current = 1;
+    panRef.current = { x: 0, y: 0 };
+    setZoom(1);
+    setPan({ x: 0, y: 0 });
+  }, []);
+
+  const zoomBy = useCallback(
+    (factor: number, anchor?: { x: number; y: number }) => {
+      const z = zoomRef.current;
+      const next = Math.min(MAX_ZOOM, Math.max(MIN_ZOOM, z * factor));
+      if (next === z) return;
+      let newPan = panRef.current;
+      if (next === 1) {
+        newPan = { x: 0, y: 0 };
+      } else if (anchor && stageRef.current) {
+        // Keep the image point under the cursor fixed on screen:
+        //   cursor_screen = (image_point - center) * zoom + pan
+        // Solve for newPan given newZoom so cursor_screen stays the same.
+        const rect = stageRef.current.getBoundingClientRect();
+        const cx = anchor.x - rect.left - rect.width / 2;
+        const cy = anchor.y - rect.top - rect.height / 2;
+        const ratio = next / z;
+        const p = panRef.current;
+        newPan = {
+          x: cx - (cx - p.x) * ratio,
+          y: cy - (cy - p.y) * ratio,
+        };
+      }
+      // Commit both atomically — keeping pan out of any setState updater
+      // avoids StrictMode's double-invoke drift.
+      zoomRef.current = next;
+      panRef.current = newPan;
+      setZoom(next);
+      setPan(newPan);
+    },
+    []
+  );
+
+  useEffect(() => {
+    const el = stageRef.current;
+    if (!el) return;
+    function onWheel(e: WheelEvent) {
+      if (!e.ctrlKey && !e.metaKey) return;
+      e.preventDefault();
+      const factor = e.deltaY < 0 ? ZOOM_STEP : 1 / ZOOM_STEP;
+      zoomBy(factor, { x: e.clientX, y: e.clientY });
+    }
+    el.addEventListener("wheel", onWheel, { passive: false });
+    return () => el.removeEventListener("wheel", onWheel);
+  }, [zoomBy, selectedImagePath]);
+
+  const panDragRef = useRef<{
+    startX: number;
+    startY: number;
+    origX: number;
+    origY: number;
+  } | null>(null);
+
+  function onStageMouseDown(e: React.MouseEvent) {
+    if (tool !== "select") return;
+    if (zoom <= 1) return;
+    if (e.button !== 0 && e.button !== 1) return;
+    panDragRef.current = {
+      startX: e.clientX,
+      startY: e.clientY,
+      origX: pan.x,
+      origY: pan.y,
+    };
+    (e.currentTarget as HTMLElement).style.cursor = "grabbing";
+  }
+
+  function onStageContextMenu(e: React.MouseEvent) {
+    // Right-click cycles through tools: select → bbox → polygon → select.
+    e.preventDefault();
+    setDrawing(null);
+    setTool((t) =>
+      t === "select" ? "bbox" : t === "bbox" ? "polygon" : "select"
+    );
+  }
+
+  useEffect(() => {
+    function onMove(e: MouseEvent) {
+      const d = panDragRef.current;
+      if (!d) return;
+      const next = {
+        x: d.origX + (e.clientX - d.startX),
+        y: d.origY + (e.clientY - d.startY),
+      };
+      panRef.current = next;
+      setPan(next);
+    }
+    function onUp() {
+      panDragRef.current = null;
+      if (stageRef.current) {
+        stageRef.current.style.cursor = zoom > 1 && tool === "select" ? "grab" : "";
+      }
+    }
+    window.addEventListener("mousemove", onMove);
+    window.addEventListener("mouseup", onUp);
+    return () => {
+      window.removeEventListener("mousemove", onMove);
+      window.removeEventListener("mouseup", onUp);
+    };
+  }, [zoom, tool]);
+
+  // ---- drawing --------------------------------------------------------
+
+  const canvasToImageCoords = useCallback(
+    (clientX: number, clientY: number): [number, number] | null => {
+      const canvas = canvasRef.current;
+      if (!canvas) return null;
+      const rect = canvas.getBoundingClientRect();
+      if (rect.width === 0 || rect.height === 0) return null;
+      const x = ((clientX - rect.left) / rect.width) * canvas.width;
+      const y = ((clientY - rect.top) / rect.height) * canvas.height;
+      return [Math.max(0, Math.min(canvas.width, x)), Math.max(0, Math.min(canvas.height, y))];
+    },
+    []
+  );
+
+  // Snap threshold expressed in image-space pixels, derived from a constant
+  // screen-pixel target so it stays consistent when zoomed.
+  const getCloseThreshold = useCallback((): number => {
+    const canvas = canvasRef.current;
+    if (!canvas) return 12;
+    const rect = canvas.getBoundingClientRect();
+    if (rect.width === 0) return 12;
+    return (12 * canvas.width) / rect.width;
+  }, []);
+
+
+  function commitAnnotation(record: Annotation) {
+    // eslint-disable-next-line no-console
+    console.log("[commit] begin", { tool: record.type, record, selectedImagePath, dims });
+    if (!selectedImagePath || !dims) {
+      // eslint-disable-next-line no-console
+      console.warn("[commit] abort — missing selectedImagePath or dims");
+      return;
+    }
+    let current: ImageAnnotations;
+    let updated: ImageAnnotations;
+    try {
+      current =
+        ann ??
+        ({
+          version: 1,
+          image: selectedImagePath.split("/").pop() ?? "",
+          width: dims.w,
+          height: dims.h,
+          annotations: [],
+        } as ImageAnnotations);
+      updated = {
+        ...current,
+        version: 1,
+        width: dims.w,
+        height: dims.h,
+        annotations: [...current.annotations, record],
+      };
+      setAnn(updated);
+    } catch (err) {
+      // eslint-disable-next-line no-console
+      console.error("[commit] sync error:", err);
+      setLoadError(`commit: ${err}`);
+      return;
+    }
+    const rollback = current;
+    api
+      .saveImageAnnotations(selectedImagePath, updated)
+      .then(() => {
+        // eslint-disable-next-line no-console
+        console.log("[commit] saved ok");
+        setImages((imgs) =>
+          imgs.map((img, i) =>
+            i === selectedIdx ? { ...img, has_annotations: true } : img
+          )
+        );
+        setStatusMsg(`saved · ${record.type} · ${record.class_name}`);
+        window.setTimeout(() => setStatusMsg(null), 1400);
+      })
+      .catch((e) => {
+        // eslint-disable-next-line no-console
+        console.error("[commit] save failed:", e);
+        setLoadError(`save: ${e}`);
+        setAnn(rollback);
+      });
+  }
+
+  function pushTo(stack: ImageAnnotations[], state: ImageAnnotations) {
+    stack.push(state);
+    if (stack.length > MAX_UNDO) stack.shift();
+  }
+
+  /**
+   * options.skipHistory — don't push to either stack (used by undo/redo).
+   * options.preUndoState — push this (pre-change) snapshot to undo instead of
+   *   the current `ann` (used when we've already mutated in-memory during drag).
+   * Any user-initiated change clears the redo stack; undo/redo fill it.
+   */
+  function persist(
+    next: ImageAnnotations,
+    options?: {
+      skipHistory?: boolean;
+      preUndoState?: ImageAnnotations;
+      keepRedo?: boolean;
+    }
+  ) {
+    if (!selectedImagePath) return;
+    const rollback = ann;
+    if (!options?.skipHistory) {
+      const snap = options?.preUndoState ?? ann;
+      if (snap) pushTo(undoStackRef.current, snap);
+      if (!options?.keepRedo) redoStackRef.current = [];
+    }
+    setAnn(next);
+    api
+      .saveImageAnnotations(selectedImagePath, next)
+      .then(() => {
+        setImages((imgs) =>
+          imgs.map((img, i) =>
+            i === selectedIdx
+              ? { ...img, has_annotations: next.annotations.length > 0 }
+              : img
+          )
+        );
+      })
+      .catch((e) => {
+        setLoadError(`save: ${e}`);
+        setAnn(rollback);
+      });
+  }
+
+  function undo() {
+    const prev = undoStackRef.current.pop();
+    if (!prev) {
+      setStatusMsg("nothing to undo");
+      window.setTimeout(() => setStatusMsg(null), 1000);
+      return;
+    }
+    if (ann) pushTo(redoStackRef.current, ann);
+    setSelectedAnnId(null);
+    setBboxDrag(null);
+    persist(prev, { skipHistory: true });
+    setStatusMsg("undone");
+    window.setTimeout(() => setStatusMsg(null), 1000);
+  }
+
+  function redo() {
+    const next = redoStackRef.current.pop();
+    if (!next) {
+      setStatusMsg("nothing to redo");
+      window.setTimeout(() => setStatusMsg(null), 1000);
+      return;
+    }
+    if (ann) pushTo(undoStackRef.current, ann);
+    setSelectedAnnId(null);
+    setBboxDrag(null);
+    persist(next, { skipHistory: true });
+    setStatusMsg("redone");
+    window.setTimeout(() => setStatusMsg(null), 1000);
+  }
+
+  function deleteSelected() {
+    if (!selectedAnnId || !ann) return;
+    const next: ImageAnnotations = {
+      ...ann,
+      annotations: ann.annotations.filter((a) => a.id !== selectedAnnId),
+    };
+    setSelectedAnnId(null);
+    persist(next);
+    setStatusMsg("deleted");
+    window.setTimeout(() => setStatusMsg(null), 1200);
+  }
+
+  function reclassSelected(cid: number) {
+    if (!selectedAnnId || !ann) return;
+    const now = new Date().toISOString();
+    const className = classNameFor(cid);
+    const next: ImageAnnotations = {
+      ...ann,
+      annotations: ann.annotations.map((a) =>
+        a.id === selectedAnnId
+          ? {
+              ...a,
+              class_id: cid,
+              class_name: className,
+              author: username,
+              updated_at: now,
+            }
+          : a
+      ),
+    };
+    persist(next);
+  }
+
+  function classNameFor(classId: number): string {
+    return (
+      classesRef.current[classId] ??
+      classes[classId] ??
+      `class_${classId}`
+    );
+  }
+
+  function mkBboxFor(
+    x: number, y: number, w: number, h: number, classId: number
+  ): BboxAnnotation {
+    const now = new Date().toISOString();
+    return {
+      id: uuid(),
+      class_id: classId,
+      class_name: classNameFor(classId),
+      type: "bbox",
+      bbox: [x, y, w, h],
+      author: username,
+      created_at: now,
+      updated_at: now,
+    };
+  }
+
+  function mkPolygonFor(
+    points: [number, number][], classId: number
+  ): PolygonAnnotation {
+    const now = new Date().toISOString();
+    return {
+      id: uuid(),
+      class_id: classId,
+      class_name: classNameFor(classId),
+      type: "polygon",
+      points,
+      author: username,
+      created_at: now,
+      updated_at: now,
+    };
+  }
+
+  function flashSamToast(
+    next:
+      | { state: "done"; model: string; elapsedMs: number }
+      | { state: "error"; model: string; message: string }
+  ) {
+    setSamPhase(next);
+    if (samToastTimerRef.current != null) {
+      window.clearTimeout(samToastTimerRef.current);
+    }
+    samToastTimerRef.current = window.setTimeout(() => {
+      setSamPhase((cur) => (cur && cur.state !== "running" ? null : cur));
+      samToastTimerRef.current = null;
+    }, 3000);
+  }
+
+  async function commitPending(classId: number) {
+    if (!pending) return;
+    const captured = pending;
+    setPending(null);
+    setActiveClassId(classId);
+
+    if (captured.kind === "reclass") {
+      reclassSelected(classId);
+      return;
+    }
+
+    // New shape. If it's a bbox and SAM mode is on, try to get a polygon back
+    // from the backend and commit THAT instead. On any failure, fall back to
+    // saving the bbox so the user never loses their work.
+    let samCommitted = false;
+    if (
+      captured.kind === "new-bbox" &&
+      settings?.sam_enabled &&
+      settings.sam_url &&
+      selectedImagePath &&
+      dims
+    ) {
+      const modelLabel = settings.sam_model ?? "default";
+      setSamPhase({ state: "running", model: modelLabel });
+      const t0 = performance.now();
+      try {
+        const poly = await api.samSegmentBbox(
+          settings.sam_url,
+          selectedImagePath,
+          captured.bbox,
+          dims.w,
+          dims.h,
+          settings.sam_model ?? null
+        );
+        const elapsedMs = performance.now() - t0;
+        commitAnnotation(mkPolygonFor(poly, classId));
+        flashSamToast({ state: "done", model: modelLabel, elapsedMs });
+        samCommitted = true;
+      } catch (e) {
+        flashSamToast({ state: "error", model: modelLabel, message: String(e) });
+        setLoadError(`SAM failed, saved as bbox: ${e}`);
+      }
+    }
+
+    if (!samCommitted) {
+      if (captured.kind === "new-bbox") {
+        const [x, y, w, h] = captured.bbox;
+        commitAnnotation(mkBboxFor(x, y, w, h, classId));
+      } else {
+        commitAnnotation(mkPolygonFor(captured.points, classId));
+      }
+    }
+
+    // After committing a new shape, drop into select mode so the user can
+    // immediately fine-tune (move handles, reclass, delete) without
+    // accidentally starting another bbox/polygon on the next click.
+    setTool("select");
+    setDrawing(null);
+  }
+
+  async function pickerAddClass(name: string): Promise<number> {
+    if (!folder) throw new Error("no folder");
+    const updated = await api.appendClass(folder, name);
+    // Sync ref before setClasses so the very next `onConfirm` (which reads
+    // from closure `classes`) can fall back to classesRef.current.
+    classesRef.current = updated;
+    setClasses(updated);
+    return updated.length - 1;
+  }
+
+  function onCanvasMouseDown(e: React.MouseEvent<HTMLCanvasElement>) {
+    // In select mode, a click may (in order of priority):
+    //   1. Land on a handle/body of the currently-selected bbox -> start drag.
+    //   2. Land on any annotation -> select it.
+    //   3. Land on empty canvas -> clear selection.
+    if (tool === "select") {
+      const p = canvasToImageCoords(e.clientX, e.clientY);
+      if (!p) {
+        setSelectedAnnId(null);
+        return;
+      }
+      const canvas = canvasRef.current;
+      if (canvas && ann && selectedAnnId) {
+        const sel = ann.annotations.find((a) => a.id === selectedAnnId);
+        const rect = canvas.getBoundingClientRect();
+        const s2i = rect.width > 0 ? canvas.width / rect.width : 1;
+        if (sel && sel.type === "bbox") {
+          const kind = bboxHandleAt(p[0], p[1], sel, s2i);
+          if (kind) {
+            setBboxDrag({
+              id: sel.id,
+              kind,
+              origBbox: sel.bbox,
+              start: p,
+              annBefore: ann,
+            });
+            e.stopPropagation();
+            return;
+          }
+        } else if (sel && sel.type === "polygon") {
+          const v = polygonVertexAt(p[0], p[1], sel.points, s2i);
+          if (v != null) {
+            setVertexDrag({
+              id: sel.id,
+              vertexIdx: v,
+              origPoints: sel.points,
+              start: p,
+              annBefore: ann,
+            });
+            e.stopPropagation();
+            return;
+          }
+        }
+      }
+      if (!ann) {
+        setSelectedAnnId(null);
+        return;
+      }
+      for (let i = ann.annotations.length - 1; i >= 0; i--) {
+        if (hitTest(p[0], p[1], ann.annotations[i])) {
+          setSelectedAnnId(ann.annotations[i].id);
+          e.stopPropagation();
+          return;
+        }
+      }
+      setSelectedAnnId(null);
+      return;
+    }
+    if (activeClassId == null) {
+      setStatusMsg("Pick a class on the right first");
+      return;
+    }
+    e.stopPropagation(); // don't let the pan handler see this
+    const p = canvasToImageCoords(e.clientX, e.clientY);
+    if (!p) return;
+    if (tool === "bbox") {
+      setDrawing({ type: "bbox", start: p, end: p });
+    } else {
+      // Close polygon if clicking near the start vertex (with ≥ 3 points).
+      if (
+        drawing &&
+        drawing.type === "polygon" &&
+        drawing.points.length >= 3 &&
+        pointDistance(p, drawing.points[0]) < getCloseThreshold()
+      ) {
+        setPending({ kind: "new-polygon", points: drawing.points });
+        setDrawing(null);
+        return;
+      }
+      setDrawing((prev) => {
+        if (prev && prev.type === "polygon") {
+          return { ...prev, points: [...prev.points, p] };
+        }
+        return { type: "polygon", points: [p], cursor: p };
+      });
+    }
+  }
+
+  function onCanvasMouseMove(e: React.MouseEvent<HTMLCanvasElement>) {
+    // Handle-cursor hint when hovering the selected shape in select mode.
+    if (
+      tool === "select" &&
+      !bboxDrag &&
+      !vertexDrag &&
+      selectedAnnId &&
+      ann
+    ) {
+      const sel = ann.annotations.find((a) => a.id === selectedAnnId);
+      const canvas = canvasRef.current;
+      if (sel && canvas) {
+        const p = canvasToImageCoords(e.clientX, e.clientY);
+        if (p) {
+          const rect = canvas.getBoundingClientRect();
+          const s2i = rect.width > 0 ? canvas.width / rect.width : 1;
+          if (sel.type === "bbox") {
+            const kind = bboxHandleAt(p[0], p[1], sel, s2i);
+            (e.currentTarget as HTMLCanvasElement).style.cursor = kind
+              ? cursorForHandle(kind)
+              : "default";
+          } else {
+            const v = polygonVertexAt(p[0], p[1], sel.points, s2i);
+            (e.currentTarget as HTMLCanvasElement).style.cursor = v != null
+              ? "grab"
+              : "default";
+          }
+        }
+      }
+    }
+
+    if (!drawing) return;
+    const p = canvasToImageCoords(e.clientX, e.clientY);
+    if (!p) return;
+    setDrawing((prev) => {
+      if (!prev) return prev;
+      if (prev.type === "bbox") return { ...prev, end: p };
+      return { ...prev, cursor: p };
+    });
+  }
+
+  // Bbox drag (resize / move) — rAF-throttled.
+  useEffect(() => {
+    if (!bboxDrag || !dims) return;
+    const W = dims.w;
+    const H = dims.h;
+    let raf: number | null = null;
+    let pending: [number, number, number, number] | null = null;
+    // Last bbox we actually committed via setAnn — needed at mouseup so we can
+    // build the final state from `annBefore` (instead of reading `ann` inside
+    // a setAnn updater, which StrictMode would double-invoke and double-save).
+    let lastApplied: [number, number, number, number] | null = null;
+
+    function applyPending() {
+      raf = null;
+      const next = pending;
+      pending = null;
+      if (!next || !bboxDrag) return;
+      lastApplied = next;
+      setAnn((curr) => {
+        if (!curr) return curr;
+        return {
+          ...curr,
+          annotations: curr.annotations.map((a) =>
+            a.id === bboxDrag.id && a.type === "bbox"
+              ? { ...a, bbox: next }
+              : a
+          ),
+        };
+      });
+    }
+
+    function onMove(e: MouseEvent) {
+      const p = canvasToImageCoords(e.clientX, e.clientY);
+      if (!p || !bboxDrag) return;
+      const dx = p[0] - bboxDrag.start[0];
+      const dy = p[1] - bboxDrag.start[1];
+      pending = clampBbox(
+        applyHandleDrag(bboxDrag.kind, bboxDrag.origBbox, dx, dy),
+        W,
+        H
+      );
+      if (raf == null) raf = requestAnimationFrame(applyPending);
+    }
+
+    function onUp() {
+      if (raf != null) cancelAnimationFrame(raf);
+      if (pending) applyPending();
+      const dragInfo = bboxDrag;
+      setBboxDrag(null);
+      if (!dragInfo || !selectedImagePath) return;
+      const finalBbox = lastApplied ?? dragInfo.origBbox;
+      const now = new Date().toISOString();
+      const next: ImageAnnotations = {
+        ...dragInfo.annBefore,
+        annotations: dragInfo.annBefore.annotations.map((a) =>
+          a.id === dragInfo.id && a.type === "bbox"
+            ? { ...a, bbox: finalBbox, author: username, updated_at: now }
+            : a
+        ),
+      };
+      persist(next, { preUndoState: dragInfo.annBefore });
+    }
+
+    window.addEventListener("mousemove", onMove);
+    window.addEventListener("mouseup", onUp);
+    return () => {
+      if (raf != null) cancelAnimationFrame(raf);
+      window.removeEventListener("mousemove", onMove);
+      window.removeEventListener("mouseup", onUp);
+    };
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [bboxDrag, dims]);
+
+  // Polygon vertex drag. setAnn is rAF-throttled so we render at the frame
+  // rate instead of the mousemove rate (120 Hz+ on some trackpads) — without
+  // this, big images freeze mid-drag.
+  useEffect(() => {
+    if (!vertexDrag || !dims) return;
+    const W = dims.w;
+    const H = dims.h;
+    let raf: number | null = null;
+    let pending: [number, number] | null = null;
+    let lastApplied: [number, number] | null = null;
+
+    function applyPending() {
+      raf = null;
+      const pos = pending;
+      pending = null;
+      if (!pos || !vertexDrag) return;
+      lastApplied = pos;
+      setAnn((curr) => {
+        if (!curr) return curr;
+        return {
+          ...curr,
+          annotations: curr.annotations.map((a) => {
+            if (a.id !== vertexDrag.id || a.type !== "polygon") return a;
+            const nextPts = a.points.map((pt, idx) =>
+              idx === vertexDrag.vertexIdx ? pos : pt
+            );
+            return { ...a, points: nextPts };
+          }),
+        };
+      });
+    }
+
+    function onMove(e: MouseEvent) {
+      const p = canvasToImageCoords(e.clientX, e.clientY);
+      if (!p) return;
+      pending = [
+        Math.max(0, Math.min(W, p[0])),
+        Math.max(0, Math.min(H, p[1])),
+      ];
+      if (raf == null) raf = requestAnimationFrame(applyPending);
+    }
+
+    function onUp() {
+      if (raf != null) cancelAnimationFrame(raf);
+      if (pending) applyPending();
+      const dragInfo = vertexDrag;
+      setVertexDrag(null);
+      if (!dragInfo || !selectedImagePath) return;
+      const finalPos = lastApplied ?? dragInfo.origPoints[dragInfo.vertexIdx];
+      const now = new Date().toISOString();
+      const next: ImageAnnotations = {
+        ...dragInfo.annBefore,
+        annotations: dragInfo.annBefore.annotations.map((a) => {
+          if (a.id !== dragInfo.id || a.type !== "polygon") return a;
+          const nextPts = a.points.map((pt, idx) =>
+            idx === dragInfo.vertexIdx ? finalPos : pt
+          );
+          return {
+            ...a,
+            points: nextPts,
+            author: username,
+            updated_at: now,
+          };
+        }),
+      };
+      persist(next, { preUndoState: dragInfo.annBefore });
+    }
+
+    window.addEventListener("mousemove", onMove);
+    window.addEventListener("mouseup", onUp);
+    return () => {
+      if (raf != null) cancelAnimationFrame(raf);
+      window.removeEventListener("mousemove", onMove);
+      window.removeEventListener("mouseup", onUp);
+    };
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [vertexDrag, dims]);
+
+  function onCanvasDoubleClick(e: React.MouseEvent<HTMLCanvasElement>) {
+    if (!ann) return;
+    const p = canvasToImageCoords(e.clientX, e.clientY);
+    if (!p) return;
+    for (let i = ann.annotations.length - 1; i >= 0; i--) {
+      if (hitTest(p[0], p[1], ann.annotations[i])) {
+        setSelectedAnnId(ann.annotations[i].id);
+        setTool("select");
+        setDrawing(null);
+        e.stopPropagation();
+        return;
+      }
+    }
+  }
+
+  // While drawing a bbox, track mousemove/up on the window so dragging
+  // outside the canvas still works.
+  useEffect(() => {
+    if (!drawing || drawing.type !== "bbox") return;
+    function onMove(e: MouseEvent) {
+      const p = canvasToImageCoords(e.clientX, e.clientY);
+      if (!p) return;
+      setDrawing((prev) =>
+        prev && prev.type === "bbox" ? { ...prev, end: p } : prev
+      );
+    }
+    function onUp() {
+      const current = drawingRef.current;
+      setDrawing(null);
+      if (!current || current.type !== "bbox") return;
+      const [x0, y0] = current.start;
+      const [x1, y1] = current.end;
+      const w = Math.abs(x1 - x0);
+      const h = Math.abs(y1 - y0);
+      if (w < 2 || h < 2) return;
+      setPending({
+        kind: "new-bbox",
+        bbox: [Math.min(x0, x1), Math.min(y0, y1), w, h],
+      });
+    }
+    window.addEventListener("mousemove", onMove);
+    window.addEventListener("mouseup", onUp);
+    return () => {
+      window.removeEventListener("mousemove", onMove);
+      window.removeEventListener("mouseup", onUp);
+    };
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [drawing]);
+
+  // ---- keyboard -------------------------------------------------------
+
+  useEffect(() => {
+    function onKey(e: KeyboardEvent) {
+      const tag = (e.target as HTMLElement | null)?.tagName;
+      if (tag === "INPUT" || tag === "TEXTAREA") return;
+      switch (e.key) {
+        case "ArrowDown":
+        case "d":
+        case "D":
+          if (images.length === 0) return;
+          // Don't hijack 'd' while drawing — user might be mid-shape.
+          if ((e.key === "d" || e.key === "D") && drawing) return;
+          e.preventDefault();
+          stepSelection(1);
+          break;
+        case "ArrowUp":
+        case "a":
+        case "A":
+          if (images.length === 0) return;
+          if ((e.key === "a" || e.key === "A") && drawing) return;
+          e.preventDefault();
+          stepSelection(-1);
+          break;
+        case "h":
+        case "H":
+          e.preventDefault();
+          setOverlayVisible((v) => !v);
+          break;
+        case "+":
+        case "=":
+          e.preventDefault();
+          zoomBy(ZOOM_STEP);
+          break;
+        case "-":
+        case "_":
+          e.preventDefault();
+          zoomBy(1 / ZOOM_STEP);
+          break;
+        case "0":
+          e.preventDefault();
+          resetZoom();
+          break;
+        case "Escape":
+          e.preventDefault();
+          if (pending) setPending(null);
+          else if (drawing) setDrawing(null);
+          else resetZoom();
+          break;
+        case "s":
+        case "S":
+          e.preventDefault();
+          setTool("select");
+          setDrawing(null);
+          break;
+        case "b":
+        case "B":
+          e.preventDefault();
+          setTool("bbox");
+          setDrawing(null);
+          break;
+        case "p":
+        case "P":
+          e.preventDefault();
+          setTool("polygon");
+          setDrawing(null);
+          break;
+        case "Enter":
+          if (drawing && drawing.type === "polygon" && drawing.points.length >= 3) {
+            e.preventDefault();
+            setPending({ kind: "new-polygon", points: drawing.points });
+            setDrawing(null);
+          }
+          break;
+        case "Tab":
+          if (selectedAnnId && !pending && !drawing) {
+            e.preventDefault();
+            setPending({ kind: "reclass", id: selectedAnnId });
+          }
+          break;
+        case "Delete":
+        case "Backspace":
+          if (selectedAnnId) {
+            e.preventDefault();
+            deleteSelected();
+          }
+          break;
+        case "z":
+        case "Z":
+          if (e.ctrlKey || e.metaKey) {
+            e.preventDefault();
+            if (e.shiftKey) redo();
+            else undo();
+          }
+          break;
+        case "y":
+        case "Y":
+          if (e.ctrlKey || e.metaKey) {
+            e.preventDefault();
+            redo();
+          }
+          break;
+      }
+    }
+    window.addEventListener("keydown", onKey);
+    return () => window.removeEventListener("keydown", onKey);
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [images.length, zoomBy, resetZoom, drawing, activeClassId, classes, selectedImagePath, dims, ann, selectedAnnId, pending]);
+
+  // ---- render ---------------------------------------------------------
+
+  if (!folder) {
+    return (
+      <div className="upload-pane">
+        <button className="primary" onClick={pickFolder}>
+          Open Folder
+        </button>
+        {loadError && <p className="error">{loadError}</p>}
+        <p className="dim">
+          Select a folder of extracted frames. Sibling JSON and{" "}
+          <code>classes.txt</code> will be loaded automatically if present.
+        </p>
+      </div>
+    );
+  }
+
+  const zoomPct = Math.round(zoom * 100);
+  const transform = `translate(${pan.x}px, ${pan.y}px) scale(${zoom})`;
+  const canvasCursor =
+    tool === "bbox" || tool === "polygon"
+      ? "crosshair"
+      : zoom > 1
+      ? "grab"
+      : "default";
+
+  return (
+    <div className={`annotate-mode${frameInfoHidden ? " frame-info-hidden" : ""}`}>
+      <div className="annotate-topbar">
+        <div className="tool-cluster" role="group" aria-label="Tool">
+          <button
+            className={tool === "select" ? "on" : ""}
+            onClick={() => { setTool("select"); setDrawing(null); }}
+            title="Select / pan (S)"
+          >
+            ↖ select
+          </button>
+          <button
+            className={tool === "bbox" ? "on" : ""}
+            onClick={() => { setTool("bbox"); setDrawing(null); }}
+            title="Bounding box (B)"
+          >
+            ▭ bbox
+          </button>
+          <button
+            className={tool === "polygon" ? "on" : ""}
+            onClick={() => { setTool("polygon"); setDrawing(null); }}
+            title="Polygon (P)"
+          >
+            ⬡ polygon
+          </button>
+        </div>
+        <span className="annotate-folder-chip">
+          <strong title={folder}>{folder.split("/").pop()}</strong>
+          <span className="dim">
+            {" · "}
+            {images.length} images ·{" "}
+            {images.filter((i) => i.has_annotations).length} annotated
+          </span>
+        </span>
+        <button className="link" onClick={pickFolder}>
+          change…
+        </button>
+        <div className="spacer" />
+        {statusMsg && <span className="status-msg">{statusMsg}</span>}
+        <div className="zoom-cluster">
+          <button onClick={() => zoomBy(1 / ZOOM_STEP)} title="−">−</button>
+          <button onClick={resetZoom} title="0" className="zoom-readout">
+            {zoomPct}%
+          </button>
+          <button onClick={() => zoomBy(ZOOM_STEP)} title="+">+</button>
+        </div>
+        <button
+          className={`overlay-toggle ${overlayVisible ? "on" : ""}`}
+          onClick={() => setOverlayVisible((v) => !v)}
+          title="H"
+        >
+          {overlayVisible ? "👁 overlay on" : "◌ overlay off"}
+        </button>
+        <button
+          className="collapse-toggle"
+          onClick={() => setFrameInfoHidden((v) => !v)}
+          title={frameInfoHidden ? "show frame info" : "hide frame info"}
+        >
+          {frameInfoHidden ? "<" : ">"}
+        </button>
+      </div>
+
+      <div className="annotate-layout">
+        <aside className="annotate-sidebar left">
+          <div className="image-list-toolbar">
+            <span className="dim">
+              {selectedSet.size > 0
+                ? `${selectedSet.size} selected`
+                : `${images.length} image${images.length === 1 ? "" : "s"}`}
+            </span>
+            <button
+              className="danger-ghost"
+              disabled={selectedSet.size === 0}
+              onClick={() => {
+                setDeleteError(null);
+                setDeleteOpen(true);
+              }}
+              title="Delete selected images and their annotations"
+            >
+              ✕ Delete{selectedSet.size > 0 ? ` (${selectedSet.size})` : ""}
+            </button>
+          </div>
+          <div className="image-list-hint dim">
+            Click to pick · Shift-click range · Ctrl-click toggle · A/D to navigate
+          </div>
+          <ImageList
+            images={images}
+            selectedIdx={selectedIdx}
+            selectedSet={selectedSet}
+            onSelect={handleImageSelect}
+          />
+        </aside>
+        <section className="annotate-canvas-wrap">
+          {selectedImagePath ? (
+            <div
+              ref={stageRef}
+              className="canvas-stage zoomable"
+              style={{ cursor: canvasCursor }}
+              onMouseDown={onStageMouseDown}
+              onContextMenu={onStageContextMenu}
+            >
+              <div
+                className="canvas-frame"
+                style={{
+                  aspectRatio: dims ? `${dims.w} / ${dims.h}` : undefined,
+                  transform,
+                  transformOrigin: "center center",
+                }}
+              >
+                <canvas
+                  ref={canvasRef}
+                  onMouseDown={onCanvasMouseDown}
+                  onMouseMove={onCanvasMouseMove}
+                  onDoubleClick={onCanvasDoubleClick}
+                  style={{ cursor: canvasCursor }}
+                />
+              </div>
+            </div>
+          ) : (
+            <div className="dim placeholder-centered">
+              Select an image from the list.
+            </div>
+          )}
+          {loadError && <p className="error">{loadError}</p>}
+        </section>
+        {!frameInfoHidden && ann && (
+          <aside className="annotate-sidebar right">
+            <div className="sidebar-header">
+              On this image ({ann.annotations.length})
+              {activeClassId != null && classes[activeClassId] && (
+                <span className="active-class-hint"> · last: {classes[activeClassId]}</span>
+              )}
+            </div>
+            {ann.source_video && (
+              <div className="source-video-chip" title={ann.source_video}>
+                from <strong>{ann.source_video.split("/").pop()}</strong>
+              </div>
+            )}
+            <ul className="ann-list">
+              {ann.annotations.map((a) => (
+                <li
+                  key={a.id}
+                  className={a.id === selectedAnnId ? "selected" : ""}
+                  onClick={() => setSelectedAnnId(a.id)}
+                >
+                  <span
+                    className="swatch-tiny"
+                    style={{ background: colorForClass(a.class_id) }}
+                  />
+                  <span className="type" title={a.type} aria-label={a.type}>
+                    {a.type === "bbox" ? "▭" : "⬡"}
+                  </span>
+                  <span className="name">{a.class_name}</span>
+                </li>
+              ))}
+              {ann.annotations.length === 0 && (
+                <li className="empty dim">no annotations yet</li>
+              )}
+            </ul>
+            <div className="sidebar-footer-hint dim">
+              Tab · reclass selected &nbsp;·&nbsp; Del · remove
+            </div>
+          </aside>
+        )}
+      </div>
+
+      {pending && (
+        <LabelPicker
+          classes={classes}
+          defaultClassId={
+            pending.kind === "reclass" && ann
+              ? ann.annotations.find((a) => a.id === pending.id)?.class_id ?? activeClassId
+              : activeClassId
+          }
+          mode={
+            pending.kind === "new-bbox"
+              ? "new-bbox"
+              : pending.kind === "new-polygon"
+              ? "new-polygon"
+              : "reclass"
+          }
+          onConfirm={(cid) => {
+            commitPending(cid);
+          }}
+          onCancel={() => setPending(null)}
+          onAdd={folder ? pickerAddClass : undefined}
+        />
+      )}
+
+      {samPhase && (
+        <div className={`sam-indicator phase-${samPhase.state}`}>
+          {samPhase.state === "running" && (
+            <>
+              <div className="spinner" />
+              <span className="sam-model">{samPhase.model}</span>
+              <span className="dim">running…</span>
+            </>
+          )}
+          {samPhase.state === "done" && (
+            <>
+              <span className="sam-tick">✓</span>
+              <span className="sam-model">{samPhase.model}</span>
+              <span className="sam-time">{formatMs(samPhase.elapsedMs)}</span>
+            </>
+          )}
+          {samPhase.state === "error" && (
+            <>
+              <span className="sam-tick err">✗</span>
+              <span className="sam-model">{samPhase.model}</span>
+              <span
+                className="sam-time err"
+                title={samPhase.message}
+              >
+                failed
+              </span>
+            </>
+          )}
+        </div>
+      )}
+
+      {deleteOpen && (
+        <DeleteConfirmModal
+          count={selectedSet.size}
+          sampleNames={[...selectedSet]
+            .sort((a, b) => a - b)
+            .map((i) => images[i]?.filename ?? "")
+            .filter(Boolean)}
+          busy={deleteBusy}
+          error={deleteError}
+          onConfirm={confirmDelete}
+          onCancel={() => {
+            if (deleteBusy) return;
+            setDeleteOpen(false);
+            setDeleteError(null);
+          }}
+        />
+      )}
+    </div>
+  );
+}
+
+function formatMs(ms: number): string {
+  if (ms < 1000) return `${Math.round(ms)} ms`;
+  return `${(ms / 1000).toFixed(2)} s`;
+}
+
+function pointDistance(a: [number, number], b: [number, number]) {
+  const dx = a[0] - b[0];
+  const dy = a[1] - b[1];
+  return Math.sqrt(dx * dx + dy * dy);
+}
+
+function pointInPolygon(
+  x: number,
+  y: number,
+  pts: [number, number][]
+): boolean {
+  let inside = false;
+  for (let i = 0, j = pts.length - 1; i < pts.length; j = i++) {
+    const [xi, yi] = pts[i];
+    const [xj, yj] = pts[j];
+    const denom = yj - yi || 1e-12;
+    const intersect =
+      yi > y !== yj > y && x < ((xj - xi) * (y - yi)) / denom + xi;
+    if (intersect) inside = !inside;
+  }
+  return inside;
+}
+
+function hitTest(x: number, y: number, a: Annotation): boolean {
+  if (a.type === "bbox") {
+    const [bx, by, bw, bh] = a.bbox;
+    return x >= bx && x <= bx + bw && y >= by && y <= by + bh;
+  }
+  return pointInPolygon(x, y, a.points);
+}
+
+function drawSelectionHighlight(
+  ctx: CanvasRenderingContext2D,
+  a: Annotation,
+  canvasW: number,
+  canvasH: number
+) {
+  const line = Math.max(3, Math.round(Math.min(canvasW, canvasH) / 220));
+  ctx.save();
+  ctx.strokeStyle = "#FFFFFF";
+  ctx.lineWidth = line;
+  ctx.setLineDash([14, 8]);
+  if (a.type === "bbox") {
+    const [x, y, w, h] = a.bbox;
+    const pad = line;
+    ctx.strokeRect(x - pad, y - pad, w + pad * 2, h + pad * 2);
+  } else {
+    ctx.beginPath();
+    a.points.forEach(([x, y], i) =>
+      i === 0 ? ctx.moveTo(x, y) : ctx.lineTo(x, y)
+    );
+    ctx.closePath();
+    ctx.stroke();
+  }
+  ctx.restore();
+}
+
+function bboxHandleAt(
+  x: number,
+  y: number,
+  a: BboxAnnotation,
+  screenToImage: number
+): BboxHandle | null {
+  const r = Math.max(6, 9 * screenToImage);
+  const [bx, by, bw, bh] = a.bbox;
+  const cx = bx + bw / 2;
+  const cy = by + bh / 2;
+  const xr = bx + bw;
+  const yb = by + bh;
+  const near = (px: number, py: number) =>
+    Math.hypot(x - px, y - py) < r;
+  if (near(bx, by)) return "tl";
+  if (near(xr, by)) return "tr";
+  if (near(bx, yb)) return "bl";
+  if (near(xr, yb)) return "br";
+  if (near(cx, by)) return "t";
+  if (near(cx, yb)) return "b";
+  if (near(bx, cy)) return "l";
+  if (near(xr, cy)) return "r";
+  if (x >= bx && x <= xr && y >= by && y <= yb) return "body";
+  return null;
+}
+
+function cursorForHandle(k: BboxHandle): string {
+  switch (k) {
+    case "tl":
+    case "br":
+      return "nwse-resize";
+    case "tr":
+    case "bl":
+      return "nesw-resize";
+    case "t":
+    case "b":
+      return "ns-resize";
+    case "l":
+    case "r":
+      return "ew-resize";
+    case "body":
+      return "move";
+  }
+}
+
+function applyHandleDrag(
+  kind: BboxHandle,
+  orig: [number, number, number, number],
+  dx: number,
+  dy: number
+): [number, number, number, number] {
+  let [x, y, w, h] = orig;
+  let x2 = x + w;
+  let y2 = y + h;
+  switch (kind) {
+    case "body":
+      x += dx; y += dy; x2 += dx; y2 += dy; break;
+    case "tl":
+      x += dx; y += dy; break;
+    case "tr":
+      x2 += dx; y += dy; break;
+    case "bl":
+      x += dx; y2 += dy; break;
+    case "br":
+      x2 += dx; y2 += dy; break;
+    case "t":
+      y += dy; break;
+    case "b":
+      y2 += dy; break;
+    case "l":
+      x += dx; break;
+    case "r":
+      x2 += dx; break;
+  }
+  // Normalize so w/h stay positive (handles can flip through the opposite edge).
+  const nx = Math.min(x, x2);
+  const nx2 = Math.max(x, x2);
+  const ny = Math.min(y, y2);
+  const ny2 = Math.max(y, y2);
+  return [nx, ny, Math.max(1, nx2 - nx), Math.max(1, ny2 - ny)];
+}
+
+function polygonVertexAt(
+  x: number,
+  y: number,
+  points: [number, number][],
+  screenToImage: number
+): number | null {
+  const r = Math.max(8, 10 * screenToImage);
+  for (let i = 0; i < points.length; i++) {
+    const [px, py] = points[i];
+    if (Math.hypot(x - px, y - py) < r) return i;
+  }
+  return null;
+}
+
+function drawPolygonHandles(
+  ctx: CanvasRenderingContext2D,
+  points: [number, number][],
+  screenToImage: number
+) {
+  const r = Math.max(5, 5 * screenToImage);
+  ctx.save();
+  ctx.setLineDash([]);
+  ctx.lineWidth = Math.max(1.5, 1.5 * screenToImage);
+  for (const [px, py] of points) {
+    ctx.fillStyle = "#FFFFFF";
+    ctx.strokeStyle = "#1F3488";
+    ctx.beginPath();
+    ctx.arc(px, py, r, 0, Math.PI * 2);
+    ctx.fill();
+    ctx.stroke();
+  }
+  ctx.restore();
+}
+
+function drawBboxHandles(
+  ctx: CanvasRenderingContext2D,
+  bbox: [number, number, number, number],
+  screenToImage: number
+) {
+  const [x, y, w, h] = bbox;
+  const r = Math.max(5, 5 * screenToImage);
+  const pts: [number, number][] = [
+    [x, y],           [x + w / 2, y],       [x + w, y],
+    [x, y + h / 2],                         [x + w, y + h / 2],
+    [x, y + h],       [x + w / 2, y + h],   [x + w, y + h],
+  ];
+  ctx.save();
+  ctx.setLineDash([]);
+  ctx.lineWidth = Math.max(1.5, 1.5 * screenToImage);
+  for (const [px, py] of pts) {
+    ctx.fillStyle = "#FFFFFF";
+    ctx.strokeStyle = "#1F3488";
+    ctx.beginPath();
+    ctx.rect(px - r, py - r, r * 2, r * 2);
+    ctx.fill();
+    ctx.stroke();
+  }
+  ctx.restore();
+}
+
+function clampBbox(
+  bbox: [number, number, number, number],
+  W: number,
+  H: number
+): [number, number, number, number] {
+  let [x, y, w, h] = bbox;
+  if (x < 0) { w += x; x = 0; }
+  if (y < 0) { h += y; y = 0; }
+  if (x + w > W) w = W - x;
+  if (y + h > H) h = H - y;
+  w = Math.max(1, w);
+  h = Math.max(1, h);
+  return [x, y, w, h];
+}
+
+function annotationsToShapes(annotations: Annotation[]): CocoShape[] {
+  return annotations.map((a) =>
+    a.type === "bbox"
+      ? {
+          type: "bbox",
+          class_id: a.class_id,
+          class_name: a.class_name,
+          bbox: a.bbox,
+        }
+      : {
+          type: "polygon",
+          class_id: a.class_id,
+          class_name: a.class_name,
+          points: a.points,
+        }
+  );
+}
diff --git a/sam-tool-tauri/src/modes/ExtractMode.tsx b/sam-tool-tauri/src/modes/ExtractMode.tsx
new file mode 100644
index 0000000..9821284
--- /dev/null
+++ b/sam-tool-tauri/src/modes/ExtractMode.tsx
@@ -0,0 +1,648 @@
+import { useCallback, useEffect, useMemo, useRef, useState } from "react";
+import { open as openDialog } from "@tauri-apps/plugin-dialog";
+import { listen } from "@tauri-apps/api/event";
+import { api } from "../ipc";
+import type { CocoShape, ImportedCoco, VideoInfo } from "../types";
+import { FrameCanvas } from "../components/extract/FrameCanvas";
+import { drawOverlay } from "../components/extract/overlay";
+
+const SPEEDS = [0.25, 0.5, 1, 1.5, 2, 3, 4];
+
+interface Props {
+  username: string;
+}
+
+interface BulkProgress {
+  done: number;
+  total: number;
+  phase: "extract" | "undo";
+}
+
+export function ExtractMode({ username }: Props) {
+  const [video, setVideo] = useState<VideoInfo | null>(null);
+  const [frameIdx, setFrameIdx] = useState(0);
+  const [playing, setPlaying] = useState(false);
+  const [speed, setSpeed] = useState(1);
+  const [reviewMode, setReviewMode] = useState(false);
+  const [overlayVisible, setOverlayVisible] = useState(true);
+  const [loadError, setLoadError] = useState<string | null>(null);
+
+  const [coco, setCoco] = useState<ImportedCoco | null>(null);
+
+  // Extraction state
+  const [outputFolder, setOutputFolder] = useState<string | null>(null);
+  const [extractedFrames, setExtractedFrames] = useState<Set<number>>(new Set());
+  const [bulkStart, setBulkStart] = useState<number | null>(null);
+  const [bulkEnd, setBulkEnd] = useState<number | null>(null);
+  const [bulkProgress, setBulkProgress] = useState<BulkProgress | null>(null);
+  const [bulkError, setBulkError] = useState<string | null>(null);
+  const [infoCollapsed, setInfoCollapsed] = useState(false);
+
+  const canvasRef = useRef<HTMLCanvasElement>(null);
+
+  const frameMap = useMemo(() => {
+    const m = new Map<number, CocoShape[]>();
+    if (!coco) return m;
+    for (const [k, v] of Object.entries(coco.frames)) m.set(Number(k), v);
+    return m;
+  }, [coco]);
+
+  // Refs mirroring state for effects that shouldn't depend on fast-changing values.
+  const frameIdxRef = useRef(0);
+  const frameMapRef = useRef<Map<number, CocoShape[]>>(new Map());
+  const overlayVisibleRef = useRef(overlayVisible);
+  const annotatedFrames = useRef<Set<number>>(new Set());
+  // Playback clock anchor — set when playback starts, rebased on seeks so a
+  // slider drag during play doesn't get overwritten by the tick loop.
+  const playAnchorRef = useRef<{ time: number; idx: number } | null>(null);
+
+  useEffect(() => { frameIdxRef.current = frameIdx; }, [frameIdx]);
+  useEffect(() => {
+    frameMapRef.current = frameMap;
+    annotatedFrames.current = new Set(frameMap.keys());
+  }, [frameMap]);
+  useEffect(() => { overlayVisibleRef.current = overlayVisible; }, [overlayVisible]);
+
+  // --- decode pump (latest-wins coalescing) ----------------------------
+  const requestedIdx = useRef(0);
+  const renderedIdx = useRef(-1);
+  const decodingInFlight = useRef(false);
+
+  const renderFrame = useCallback(
+    async (bytes: ArrayBuffer, atFrameIdx: number) => {
+      const canvas = canvasRef.current;
+      if (!canvas) return;
+      const blob = new Blob([bytes], { type: "image/jpeg" });
+      let bitmap: ImageBitmap;
+      try { bitmap = await createImageBitmap(blob); }
+      catch (e) { console.error("createImageBitmap failed", e); return; }
+      const ctx = canvas.getContext("2d");
+      if (!ctx) { bitmap.close(); return; }
+      ctx.clearRect(0, 0, canvas.width, canvas.height);
+      ctx.drawImage(bitmap, 0, 0, canvas.width, canvas.height);
+      bitmap.close();
+
+      if (overlayVisibleRef.current) {
+        const shapes = frameMapRef.current.get(atFrameIdx);
+        if (shapes && shapes.length > 0) {
+          drawOverlay(ctx, shapes, canvas.width, canvas.height);
+        }
+      }
+    },
+    []
+  );
+
+  const pump = useCallback(async () => {
+    if (decodingInFlight.current) return;
+    decodingInFlight.current = true;
+    try {
+      while (requestedIdx.current !== renderedIdx.current) {
+        const want = requestedIdx.current;
+        try {
+          const bytes = await api.decodeFrame(want);
+          if (requestedIdx.current !== want) continue;
+          await renderFrame(bytes, want);
+          renderedIdx.current = want;
+        } catch (e) {
+          if (requestedIdx.current !== want) continue;
+          console.error("decode_frame failed", e);
+          break;
+        }
+      }
+    } finally {
+      decodingInFlight.current = false;
+    }
+  }, [renderFrame]);
+
+  useEffect(() => {
+    if (!video) return;
+    requestedIdx.current = frameIdx;
+    pump();
+  }, [frameIdx, pump, video]);
+
+  useEffect(() => {
+    renderedIdx.current = -1;
+    pump();
+  }, [overlayVisible, frameMap, pump]);
+
+  // --- playback loop ---------------------------------------------------
+  useEffect(() => {
+    if (!playing || !video) return;
+    playAnchorRef.current = {
+      time: performance.now(),
+      idx: frameIdxRef.current,
+    };
+
+    let raf = 0;
+    const tick = () => {
+      const anchor = playAnchorRef.current;
+      if (!anchor) return;
+      const elapsed = (performance.now() - anchor.time) / 1000;
+      const want = Math.min(
+        video.total_frames - 1,
+        anchor.idx + Math.floor(elapsed * video.fps * speed)
+      );
+      if (reviewMode && annotatedFrames.current.has(want)) {
+        setFrameIdx(want);
+        setPlaying(false);
+        return;
+      }
+      setFrameIdx(want);
+      if (want >= video.total_frames - 1) { setPlaying(false); return; }
+      raf = requestAnimationFrame(tick);
+    };
+    raf = requestAnimationFrame(tick);
+    return () => {
+      cancelAnimationFrame(raf);
+      playAnchorRef.current = null;
+    };
+  }, [playing, video, speed, reviewMode]);
+
+  // --- navigation helpers ----------------------------------------------
+  const seekToFrame = useCallback((idx: number) => {
+    if (!video) return;
+    const clamped = Math.max(0, Math.min(video.total_frames - 1, idx));
+    setFrameIdx(clamped);
+    // If a playback tick is active, rebase its anchor so the loop continues
+    // from the new position instead of snapping back.
+    if (playAnchorRef.current) {
+      playAnchorRef.current = { time: performance.now(), idx: clamped };
+    }
+  }, [video]);
+
+  const stepFrames = useCallback((delta: number) => {
+    if (!video) return;
+    setPlaying(false);
+    setFrameIdx((i) => Math.max(0, Math.min(video.total_frames - 1, i + delta)));
+  }, [video]);
+
+  const togglePlay = useCallback(() => {
+    if (!video) return;
+    setPlaying((p) => !p);
+  }, [video]);
+
+  const bumpSpeed = useCallback((dir: 1 | -1) => {
+    setSpeed((s) => {
+      const i = SPEEDS.indexOf(s);
+      const next = Math.max(0, Math.min(SPEEDS.length - 1, (i < 0 ? 2 : i) + dir));
+      return SPEEDS[next];
+    });
+  }, []);
+
+  // --- extraction helpers ----------------------------------------------
+  const applyOutputFolder = useCallback(async (path: string) => {
+    setOutputFolder(path);
+    try {
+      const existing = await api.listExtractedFrames(path);
+      setExtractedFrames(new Set(existing));
+    } catch (e) {
+      // Folder may not exist yet — that's fine, we'll create it on extract.
+      setExtractedFrames(new Set());
+      console.warn("listExtractedFrames failed (non-fatal):", e);
+    }
+    if (coco && coco.categories.length > 0) {
+      try { await api.writeClassesTxt(path, coco.categories); } catch (e) { console.error(e); }
+    }
+  }, [coco]);
+
+  async function pickOutputFolder() {
+    const selected = await openDialog({ directory: true, multiple: false });
+    if (!selected || typeof selected !== "string") return;
+    await applyOutputFolder(selected);
+  }
+
+  const extractCurrentFrame = useCallback(async () => {
+    if (!outputFolder || !video) return;
+    setBulkError(null);
+    try {
+      const shapes = frameMap.get(frameIdx) ?? [];
+      await api.extractFrame(frameIdx, outputFolder, shapes, username);
+      setExtractedFrames((prev) => new Set(prev).add(frameIdx));
+    } catch (e) {
+      setBulkError(String(e));
+    }
+  }, [outputFolder, video, frameIdx, frameMap, username]);
+
+  const undoCurrentFrame = useCallback(async () => {
+    if (!outputFolder) return;
+    setBulkError(null);
+    try {
+      await api.undoExtract(frameIdx, outputFolder);
+      setExtractedFrames((prev) => {
+        const n = new Set(prev);
+        n.delete(frameIdx);
+        return n;
+      });
+    } catch (e) {
+      setBulkError(String(e));
+    }
+  }, [outputFolder, frameIdx]);
+
+  const setRangeStart = useCallback(() => setBulkStart(frameIdx), [frameIdx]);
+  const setRangeEnd = useCallback(() => setBulkEnd(frameIdx), [frameIdx]);
+  const clearRange = useCallback(() => { setBulkStart(null); setBulkEnd(null); }, []);
+
+  const bulkRange = useMemo(() => {
+    if (bulkStart == null || bulkEnd == null) return null;
+    return {
+      start: Math.min(bulkStart, bulkEnd),
+      end: Math.max(bulkStart, bulkEnd),
+    };
+  }, [bulkStart, bulkEnd]);
+
+  async function runBulkExtract() {
+    if (!outputFolder || !bulkRange) return;
+    setBulkError(null);
+    setBulkProgress({ done: 0, total: bulkRange.end - bulkRange.start + 1, phase: "extract" });
+    const unlisten = await listen<{ done: number; total: number }>(
+      "bulk-extract-progress",
+      (e) => setBulkProgress({ ...e.payload, phase: "extract" })
+    );
+    try {
+      await api.bulkExtract(
+        bulkRange.start,
+        bulkRange.end,
+        outputFolder,
+        coco?.frames ?? {},
+        username
+      );
+      const existing = await api.listExtractedFrames(outputFolder);
+      setExtractedFrames(new Set(existing));
+    } catch (e) {
+      setBulkError(String(e));
+    } finally {
+      unlisten();
+      setBulkProgress(null);
+    }
+  }
+
+  async function runBulkUndo() {
+    if (!outputFolder || !bulkRange) return;
+    setBulkError(null);
+    setBulkProgress({ done: 0, total: bulkRange.end - bulkRange.start + 1, phase: "undo" });
+    const unlisten = await listen<{ done: number; total: number }>(
+      "bulk-undo-progress",
+      (e) => setBulkProgress({ ...e.payload, phase: "undo" })
+    );
+    try {
+      await api.bulkUndo(bulkRange.start, bulkRange.end, outputFolder);
+      const existing = await api.listExtractedFrames(outputFolder);
+      setExtractedFrames(new Set(existing));
+    } catch (e) {
+      setBulkError(String(e));
+    } finally {
+      unlisten();
+      setBulkProgress(null);
+    }
+  }
+
+  // --- keyboard --------------------------------------------------------
+  useEffect(() => {
+    function onKey(e: KeyboardEvent) {
+      const tag = (e.target as HTMLElement | null)?.tagName;
+      if (tag === "INPUT" || tag === "TEXTAREA") return;
+      const step = e.ctrlKey || e.metaKey ? 10 : 1;
+      switch (e.key) {
+        case " ":
+          e.preventDefault(); togglePlay(); break;
+        case "ArrowRight":
+          e.preventDefault(); stepFrames(step); break;
+        case "ArrowLeft":
+          e.preventDefault(); stepFrames(-step); break;
+        case "ArrowUp":
+          e.preventDefault(); bumpSpeed(1); break;
+        case "ArrowDown":
+          e.preventDefault(); bumpSpeed(-1); break;
+        case "h": case "H":
+          e.preventDefault(); setOverlayVisible((v) => !v); break;
+        case "e": case "E":
+          e.preventDefault(); extractCurrentFrame(); break;
+        case "u": case "U":
+          e.preventDefault(); undoCurrentFrame(); break;
+        case "[":
+          e.preventDefault(); setRangeStart(); break;
+        case "]":
+          e.preventDefault(); setRangeEnd(); break;
+      }
+    }
+    window.addEventListener("keydown", onKey);
+    return () => window.removeEventListener("keydown", onKey);
+  }, [togglePlay, stepFrames, bumpSpeed, extractCurrentFrame, undoCurrentFrame, setRangeStart, setRangeEnd]);
+
+  // --- video/coco open -------------------------------------------------
+  useEffect(() => {
+    api.currentVideo().then((v) => {
+      if (!v) return;
+      setVideo(v);
+      // Re-adopt the default folder on mount too, so switching modes and
+      // coming back doesn't drop the extracted-frames overlay.
+      applyOutputFolder(v.default_output_folder);
+    });
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
+  async function chooseVideo() {
+    setLoadError(null);
+    const selected = await openDialog({
+      multiple: false,
+      filters: [
+        {
+          name: "Video",
+          extensions: [
+            "mp4","MP4","mov","MOV","avi","AVI","mkv","MKV","webm","WEBM","flv","FLV","wmv","WMV",
+          ],
+        },
+        { name: "All files", extensions: ["*"] },
+      ],
+    });
+    if (!selected || typeof selected !== "string") return;
+    try {
+      const info = await api.openVideo(selected);
+      setVideo(info);
+      setFrameIdx(0);
+      setPlaying(false);
+      setSpeed(1);
+      renderedIdx.current = -1;
+      // Reset per-video extraction context, then auto-adopt the per-video
+      // default folder. If the user already extracted frames into it in a
+      // prior session, listExtractedFrames rehydrates them immediately.
+      setBulkStart(null);
+      setBulkEnd(null);
+      await applyOutputFolder(info.default_output_folder);
+    } catch (e) {
+      setLoadError(String(e));
+    }
+  }
+
+  async function chooseCoco() {
+    const selected = await openDialog({
+      multiple: false,
+      filters: [{ name: "Annotations JSON", extensions: ["json"] }, { name: "All files", extensions: ["*"] }],
+    });
+    if (!selected || typeof selected !== "string") return;
+    try {
+      const imported = await api.importCoco(selected);
+      setCoco(imported);
+      // If a folder is already picked, write (or refresh) classes.txt.
+      if (outputFolder && imported.categories.length > 0) {
+        try { await api.writeClassesTxt(outputFolder, imported.categories); } catch (e) { console.error(e); }
+      }
+    } catch (e) {
+      setLoadError(String(e));
+    }
+  }
+
+  function clearCoco() { setCoco(null); }
+
+  // --- render ----------------------------------------------------------
+
+  if (!video) {
+    return (
+      <div className="upload-pane">
+        <button className="primary" onClick={chooseVideo}>Upload Video</button>
+        {loadError && <p className="error">{loadError}</p>}
+        <p className="dim">
+          Supported: mp4, mov, avi, mkv, webm, flv, wmv. Decoded via <code>ffmpeg</code>.
+        </p>
+      </div>
+    );
+  }
+
+  const t = frameIdx / video.fps;
+  const currentShapes = frameMap.get(frameIdx);
+  const isCurrentExtracted = extractedFrames.has(frameIdx);
+  const pct = bulkProgress ? Math.round((bulkProgress.done / bulkProgress.total) * 100) : 0;
+
+  return (
+    <div className="extract-mode">
+      {!infoCollapsed && (
+        <>
+          <div className="video-info">
+            <span className="path" title={video.path}>{video.path.split("/").pop()}</span>
+            <span className="dim">
+              {video.width}×{video.height} · {video.fps.toFixed(2)} fps · {video.total_frames} frames · {video.duration_s.toFixed(1)}s
+            </span>
+            <button className="link" onClick={chooseVideo}>change…</button>
+          </div>
+
+          <div className="coco-info">
+            {coco ? (
+              <>
+                <span>
+                  <span className="dim">{coco.stats.source_format === "sam" ? "SAM" : "COCO"}:</span>{" "}
+                  <strong>{coco.stats.total_annotations}</strong> annotations ·{" "}
+                  <strong>{coco.stats.frame_count}</strong> frames ·{" "}
+                  <strong>{coco.stats.categories}</strong> categories
+                  {coco.stats.skipped_rle > 0 && <span className="dim"> · {coco.stats.skipped_rle} RLE skipped</span>}
+                  {coco.stats.skipped_no_image > 0 && <span className="dim"> · {coco.stats.skipped_no_image} unmappable</span>}
+                </span>
+                <button className="link" onClick={chooseCoco}>replace…</button>
+                <button className="link" onClick={clearCoco}>clear</button>
+              </>
+            ) : (
+              <>
+                <span className="dim">no annotations loaded</span>
+                <button className="link" onClick={chooseCoco}>import annotations…</button>
+              </>
+            )}
+          </div>
+        </>
+      )}
+
+      <div className={`extract-info${infoCollapsed ? " collapsed" : ""}`}>
+        {outputFolder ? (
+          <>
+            <span>Output: <strong title={outputFolder}>{outputFolder.split("/").pop()}</strong></span>
+            <span className="dim"> · {extractedFrames.size} extracted</span>
+            <button className="link" onClick={pickOutputFolder}>change…</button>
+          </>
+        ) : (
+          <button className="link" onClick={pickOutputFolder}>choose output folder…</button>
+        )}
+        <div className="extract-actions">
+          <button
+            className={isCurrentExtracted ? "extracted" : "primary-ghost"}
+            onClick={extractCurrentFrame}
+            disabled={!outputFolder}
+            title="E"
+          >
+            {isCurrentExtracted ? "✓ re-extract (E)" : "Extract (E)"}
+          </button>
+          <button
+            onClick={undoCurrentFrame}
+            disabled={!outputFolder || !isCurrentExtracted}
+            title="U"
+          >
+            Undo (U)
+          </button>
+          <div className="range-picker">
+            <button onClick={setRangeStart} title="[">
+              Start [ {bulkStart ?? "–"}
+            </button>
+            <button onClick={setRangeEnd} title="]">
+              End ] {bulkEnd ?? "–"}
+            </button>
+            {(bulkStart != null || bulkEnd != null) && (
+              <button className="link" onClick={clearRange}>clear</button>
+            )}
+          </div>
+          <button
+            onClick={runBulkExtract}
+            disabled={!outputFolder || !bulkRange || !!bulkProgress}
+          >
+            Bulk Extract{bulkRange && ` (${bulkRange.end - bulkRange.start + 1})`}
+          </button>
+          <button
+            onClick={runBulkUndo}
+            disabled={!outputFolder || !bulkRange || !!bulkProgress}
+          >
+            Bulk Undo
+          </button>
+          <button
+            className="collapse-toggle"
+            onClick={() => setInfoCollapsed((v) => !v)}
+            title={infoCollapsed ? "show info" : "hide info"}
+          >
+            {infoCollapsed ? "v" : "^"}
+          </button>
+        </div>
+        {bulkProgress && (
+          <div className="progress">
+            <div className="progress-bar" style={{ width: `${pct}%` }} />
+            <span>
+              {bulkProgress.phase === "extract" ? "extracting" : "undoing"} ·{" "}
+              {bulkProgress.done} / {bulkProgress.total}
+            </span>
+          </div>
+        )}
+        {bulkError && <p className="error">{bulkError}</p>}
+      </div>
+
+      <FrameCanvas ref={canvasRef} video={video} extracted={isCurrentExtracted} />
+
+      <div className="timeline">
+        <div className="timeline-track-wrap">
+          <input
+            type="range"
+            min={0}
+            max={video.total_frames - 1}
+            value={frameIdx}
+            onChange={(e) => seekToFrame(Number(e.target.value))}
+            style={
+              {
+                "--val": `${(frameIdx / Math.max(1, video.total_frames - 1)) * 100}%`,
+              } as React.CSSProperties
+            }
+          />
+          {extractedFrames.size > 0 && (
+            <ExtractedTicks
+              extracted={extractedFrames}
+              totalFrames={video.total_frames}
+            />
+          )}
+        </div>
+        <div className="timeline-readout">
+          frame <strong>{frameIdx}</strong> / {video.total_frames - 1}
+          <span className="dim"> · {formatTime(t)}</span>
+          {currentShapes && currentShapes.length > 0 && (
+            <span className="badge">
+              {currentShapes.length} annotation{currentShapes.length === 1 ? "" : "s"}
+            </span>
+          )}
+          {isCurrentExtracted && <span className="badge extracted">✓ extracted</span>}
+        </div>
+      </div>
+
+      <div className="controls">
+        <button className={playing ? "active" : ""} onClick={togglePlay}>
+          {playing ? "❚❚ pause" : "▶ play"}
+        </button>
+        <button onClick={() => stepFrames(-1)} title="←">◀ 1</button>
+        <button onClick={() => stepFrames(1)} title="→">1 ▶</button>
+        <button onClick={() => stepFrames(-10)} title="Ctrl+←">◀◀ 10</button>
+        <button onClick={() => stepFrames(10)} title="Ctrl+→">10 ▶▶</button>
+        <div className="speed">
+          <button onClick={() => bumpSpeed(-1)} title="↓">–</button>
+          <span className="speed-readout">{speed}×</span>
+          <button onClick={() => bumpSpeed(1)} title="↑">+</button>
+        </div>
+        <label className={`review-toggle ${reviewMode ? "on" : ""}`}>
+          <input
+            type="checkbox"
+            checked={reviewMode}
+            onChange={(e) => setReviewMode(e.target.checked)}
+          />
+          Review mode
+        </label>
+        <button
+          className={`overlay-toggle ${overlayVisible ? "on" : ""}`}
+          onClick={() => setOverlayVisible((v) => !v)}
+          title="H"
+        >
+          {overlayVisible ? "👁 overlay on" : "◌ overlay off"}
+        </button>
+        <span className="dim shortcuts">space · ← → · ctrl+← → · ↑ ↓ · H · E · U · [ ]</span>
+      </div>
+    </div>
+  );
+}
+
+function formatTime(s: number) {
+  const m = Math.floor(s / 60);
+  const r = s - m * 60;
+  return `${m}:${r.toFixed(2).padStart(5, "0")}`;
+}
+
+/**
+ * Canvas overlay that draws one green tick per pixel-column of the timeline
+ * track where at least one extracted frame falls. Canvas avoids the DOM
+ * blow-up of 1-div-per-frame on long videos, and collapses dense neighboring
+ * frames into a single visible stripe at narrow timeline widths.
+ */
+function ExtractedTicks({
+  extracted,
+  totalFrames,
+}: {
+  extracted: Set<number>;
+  totalFrames: number;
+}) {
+  const ref = useRef<HTMLCanvasElement>(null);
+  useEffect(() => {
+    const canvas = ref.current;
+    if (!canvas) return;
+    let raf = 0;
+
+    const draw = () => {
+      raf = 0;
+      const rect = canvas.getBoundingClientRect();
+      const dpr = window.devicePixelRatio || 1;
+      const w = Math.max(1, Math.floor(rect.width * dpr));
+      const h = Math.max(1, Math.floor(rect.height * dpr));
+      if (canvas.width !== w) canvas.width = w;
+      if (canvas.height !== h) canvas.height = h;
+      const ctx = canvas.getContext("2d");
+      if (!ctx) return;
+      ctx.clearRect(0, 0, w, h);
+      if (extracted.size === 0 || totalFrames <= 0) return;
+      ctx.fillStyle = "rgba(92, 207, 117, 0.85)";
+      const max = Math.max(1, totalFrames - 1);
+      for (const f of extracted) {
+        const x = Math.round((f / max) * (w - 1));
+        ctx.fillRect(x, 0, Math.max(1, Math.floor(dpr)), h);
+      }
+    };
+
+    const schedule = () => {
+      if (raf) return;
+      raf = requestAnimationFrame(draw);
+    };
+    schedule();
+    const ro = new ResizeObserver(schedule);
+    ro.observe(canvas);
+    return () => {
+      if (raf) cancelAnimationFrame(raf);
+      ro.disconnect();
+    };
+  }, [extracted, totalFrames]);
+  return <canvas ref={ref} className="timeline-ticks" aria-hidden="true" />;
+}
diff --git a/sam-tool-tauri/src/types.ts b/sam-tool-tauri/src/types.ts
new file mode 100644
index 0000000..adc1eb7
--- /dev/null
+++ b/sam-tool-tauri/src/types.ts
@@ -0,0 +1,109 @@
+export type Mode = "extract" | "annotate";
+
+export interface VideoInfo {
+  path: string;
+  width: number;
+  height: number;
+  fps: number;
+  total_frames: number;
+  duration_s: number;
+  default_output_folder: string;
+}
+
+export interface CocoCategory {
+  id: number;
+  name: string;
+}
+
+export interface CocoBboxShape {
+  type: "bbox";
+  class_id: number;
+  class_name: string;
+  bbox: [number, number, number, number];
+}
+
+export interface CocoPolygonShape {
+  type: "polygon";
+  class_id: number;
+  class_name: string;
+  points: [number, number][];
+}
+
+export type CocoShape = CocoBboxShape | CocoPolygonShape;
+
+export interface CocoImportStats {
+  total_annotations: number;
+  frame_count: number;
+  categories: number;
+  skipped_rle: number;
+  skipped_no_image: number;
+  source_format: "coco" | "sam" | string;
+}
+
+export interface ImportedCoco {
+  categories: CocoCategory[];
+  frames: Record<string, CocoShape[]>;
+  stats: CocoImportStats;
+}
+
+export type AnnotationType = "bbox" | "polygon";
+
+export interface BboxAnnotation {
+  id: string;
+  class_id: number;
+  class_name: string;
+  type: "bbox";
+  bbox: [number, number, number, number];
+  author: string;
+  created_at: string;
+  updated_at: string;
+}
+
+export interface PolygonAnnotation {
+  id: string;
+  class_id: number;
+  class_name: string;
+  type: "polygon";
+  points: [number, number][];
+  author: string;
+  created_at: string;
+  updated_at: string;
+}
+
+export type Annotation = BboxAnnotation | PolygonAnnotation;
+
+export interface ImageAnnotations {
+  version: 1;
+  image: string;
+  width: number;
+  height: number;
+  source_video?: string | null;
+  annotations: Annotation[];
+}
+
+export interface ImageEntry {
+  filename: string;
+  has_annotations: boolean;
+  annotation_count: number;
+  last_updated: string | null;
+}
+
+export interface Settings {
+  sam_url: string;
+  sam_enabled: boolean;
+  sam_model?: string | null;
+  cache_capacity: number;
+}
+
+export interface SamModelInfo {
+  id: string;
+  name: string;
+  family: string;
+  available: boolean;
+  checkpoint: string;
+}
+
+export interface SamModelList {
+  default: string | null;
+  models: SamModelInfo[];
+}
diff --git a/sam-tool-tauri/src/vite-env.d.ts b/sam-tool-tauri/src/vite-env.d.ts
new file mode 100644
index 0000000..11f02fe
--- /dev/null
+++ b/sam-tool-tauri/src/vite-env.d.ts
@@ -0,0 +1 @@
+/// <reference types="vite/client" />
diff --git a/sam-tool-tauri/tsconfig.json b/sam-tool-tauri/tsconfig.json
new file mode 100644
index 0000000..3934b8f
--- /dev/null
+++ b/sam-tool-tauri/tsconfig.json
@@ -0,0 +1,21 @@
+{
+  "compilerOptions": {
+    "target": "ES2020",
+    "useDefineForClassFields": true,
+    "lib": ["ES2020", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "skipLibCheck": true,
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "noEmit": true,
+    "jsx": "react-jsx",
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "noFallthroughCasesInSwitch": true
+  },
+  "include": ["src"],
+  "references": [{ "path": "./tsconfig.node.json" }]
+}
diff --git a/sam-tool-tauri/tsconfig.node.json b/sam-tool-tauri/tsconfig.node.json
new file mode 100644
index 0000000..97ede7e
--- /dev/null
+++ b/sam-tool-tauri/tsconfig.node.json
@@ -0,0 +1,11 @@
+{
+  "compilerOptions": {
+    "composite": true,
+    "skipLibCheck": true,
+    "module": "ESNext",
+    "moduleResolution": "bundler",
+    "allowSyntheticDefaultImports": true,
+    "strict": true
+  },
+  "include": ["vite.config.ts"]
+}
diff --git a/sam-tool-tauri/vite.config.ts b/sam-tool-tauri/vite.config.ts
new file mode 100644
index 0000000..e22e536
--- /dev/null
+++ b/sam-tool-tauri/vite.config.ts
@@ -0,0 +1,18 @@
+import { defineConfig } from "vite";
+import react from "@vitejs/plugin-react";
+
+const host = process.env.TAURI_DEV_HOST;
+
+export default defineConfig(async () => ({
+  plugins: [react()],
+  clearScreen: false,
+  server: {
+    port: 1420,
+    strictPort: true,
+    host: host || false,
+    hmr: host
+      ? { protocol: "ws", host, port: 1421 }
+      : undefined,
+    watch: { ignored: ["**/src-tauri/**"] },
+  },
+}));
diff --git a/sam2-backend/.gitignore b/sam2-backend/.gitignore
new file mode 100644
index 0000000..8c91fa7
--- /dev/null
+++ b/sam2-backend/.gitignore
@@ -0,0 +1,7 @@
+.venv/
+__pycache__/
+*.pyc
+*.pt
+*.pth
+.vscode/
+.idea/
diff --git a/sam2-backend/README.md b/sam2-backend/README.md
new file mode 100644
index 0000000..fcabd4b
--- /dev/null
+++ b/sam2-backend/README.md
@@ -0,0 +1,191 @@
+# SAM2 Backend
+
+Standalone HTTP server that runs Meta's SAM-family image predictors and
+speaks the Label-Studio-ML `/predict` protocol the SAM-Tool Tauri app
+already uses. Can host locally or on any remote PC on your network — point
+the Tauri app at `http://<host>:9090` from Settings.
+
+Ships with adapters for both **SAM 2 / 2.1** and **SAM 3**. The registry
+(`models.yaml`) is pluggable — new families just drop an adapter file into
+`adapters/` and register in `adapters/__init__.py`.
+
+---
+
+## Layout
+
+```
+sam2-backend/
+├── server.py            FastAPI app — /, /models, /predict
+├── registry.py          Model spec loader + lazy-load cache
+├── adapters/
+│   ├── base.py          Adapter contract
+│   ├── sam2_adapter.py  SAM2 implementation
+│   └── __init__.py      Family → adapter class map
+├── models.yaml          Which models exist + where their checkpoints live
+├── requirements.txt     HTTP server deps (Torch + SAM2 installed separately)
+├── run.sh               Convenience launcher
+└── README.md
+```
+
+---
+
+## Install (one-time, on the host PC)
+
+### 1. Python environment
+
+```bash
+cd sam2-backend
+python -m venv .venv
+source .venv/bin/activate
+pip install --upgrade pip
+```
+
+### 2. PyTorch (choose one)
+
+Pick the build that matches your GPU/CPU — do NOT rely on the generic
+`torch` pulled in transitively by SAM2.
+
+```bash
+# CUDA 12.1 (typical modern NVIDIA)
+pip install torch==2.4.* torchvision==0.19.* --index-url https://download.pytorch.org/whl/cu121
+
+# CPU-only (works but inference is ~50× slower)
+pip install torch==2.4.* torchvision==0.19.* --index-url https://download.pytorch.org/whl/cpu
+```
+
+### 3. SAM 2 package
+
+The SAM2 source is already in this repo at `../sam2/sam2/`. Install it as
+an editable package:
+
+```bash
+pip install -e ../sam2/sam2
+```
+
+> If you see `RuntimeError: You're likely running Python from the parent
+> directory of the sam2 repository`, you started Python from the *wrong*
+> directory — always run `server.py` from `sam2-backend/` (not from the
+> repo root).
+
+### 4. SAM 3 package (optional — only if you want SAM3 models)
+
+```bash
+# Upstream repo (adjust if you're using a fork):
+pip install "sam3 @ git+https://github.com/facebookresearch/sam3"
+# For the HuggingFace tracker path (optional, used by `config: tracker`):
+pip install transformers
+```
+
+The SAM3 image-predictor path uses the `.pt` file in `../sam3_weights/sam3.pt`
+— already referenced by `models.yaml`. No additional downloads needed for
+the default SAM3 entry.
+
+### 5. The rest
+
+```bash
+pip install -r requirements.txt
+```
+
+### 6. Checkpoints
+
+**SAM 2 / 2.1** — present at `../sam2/sam2/checkpoints/sam2.1_hiera_{tiny,small,base_plus,large}.pt`.
+If any are missing, download them with:
+
+```bash
+cd ../sam2/sam2/checkpoints
+./download_ckpts.sh
+```
+
+**SAM 3** — present at `../files/sam3_weights/sam3.pt`.
+
+`models.yaml` points at all of these by default. Edit the file to add,
+remove, or relocate entries.
+
+---
+
+## Run
+
+```bash
+# From sam2-backend/
+./run.sh                          # defaults: 0.0.0.0:9090, auto device
+HOST=127.0.0.1 PORT=9090 ./run.sh # localhost only
+DEVICE=cuda:1 ./run.sh            # pin a specific GPU
+
+# or directly
+python server.py --host 0.0.0.0 --port 9090 --device auto
+```
+
+First request for a given model triggers a one-time load (few seconds). By
+default the previously-loaded model is evicted on switch to keep GPU memory
+bounded. Set `SAM_BACKEND_KEEP_ALL=1` to keep them all resident.
+
+---
+
+## Smoke-test
+
+```bash
+curl -s http://localhost:9090/          # {"status":"ok",...}
+curl -s http://localhost:9090/models    # {"default":"...","models":[...]}
+```
+
+From the Tauri app: open **Settings**, set the SAM URL to
+`http://<host>:9090`, enable SAM, pick a model, save. Drawing a bbox should
+now round-trip through SAM2 and come back as a polygon.
+
+---
+
+## API
+
+### `GET /`
+```json
+{"status":"ok","device":"cuda","models":4,"default":"sam2.1_hiera_small"}
+```
+
+### `GET /models`
+```json
+{
+  "default": "sam2.1_hiera_small",
+  "models": [
+    {"id":"sam2.1_hiera_tiny","name":"SAM 2.1 · Hiera Tiny",
+     "family":"sam2","available":true,
+     "checkpoint":"/abs/path/sam2.1_hiera_tiny.pt"},
+    ...
+  ]
+}
+```
+
+### `POST /predict?model=<id>`
+Body: Label-Studio-ML task + bbox prompt (see `server.py` docstring).
+Optional `?model=<id>` query selects which model. Omit to use the default.
+
+Response: LS-ML `results[].result[].value.points` as percentages.
+
+---
+
+## SAM 3 notes
+
+Two `config` modes are supported per entry in `models.yaml`:
+
+| `config`          | Loader                                                          | Checkpoint                                 | Notes                                                                 |
+|-------------------|-----------------------------------------------------------------|--------------------------------------------|-----------------------------------------------------------------------|
+| `image_predictor` | `sam3.model_builder.build_sam3_image_model`                     | Local `.pt` file                           | Default. Box = visual exemplar; picks mask with highest IoU to user box. |
+| `tracker`         | `transformers.Sam3TrackerModel.from_pretrained("facebook/sam3")` | HF model id (set `remote: true` in yaml) | Cleaner single-box → single-mask. Requires `transformers` + first-run network access. |
+
+The shipped default SAM3 entry uses `image_predictor` + the local weights.
+To enable the tracker path, uncomment the `sam3_tracker` block in
+`models.yaml`.
+
+---
+
+## Adding another model family later
+
+1. Drop a new file into `adapters/`, e.g. `adapters/sam4_adapter.py`, that
+   implements `BaseAdapter`.
+2. Register it in `adapters/__init__.py`:
+   ```python
+   ADAPTERS = {"sam2": Sam2Adapter, "sam3": Sam3Adapter, "sam4": Sam4Adapter}
+   ```
+3. Add entries to `models.yaml` with `family: sam4`.
+4. Restart the server.
+
+No changes to `server.py` or `registry.py` should be needed.
diff --git a/sam2-backend/adapters/__init__.py b/sam2-backend/adapters/__init__.py
new file mode 100644
index 0000000..bcf4cf9
--- /dev/null
+++ b/sam2-backend/adapters/__init__.py
@@ -0,0 +1,10 @@
+from .base import BaseAdapter, PredictResult
+from .sam2_adapter import Sam2Adapter
+from .sam3_adapter import Sam3Adapter
+
+ADAPTERS: dict[str, type[BaseAdapter]] = {
+    "sam2": Sam2Adapter,
+    "sam3": Sam3Adapter,
+}
+
+__all__ = ["BaseAdapter", "PredictResult", "Sam2Adapter", "Sam3Adapter", "ADAPTERS"]
diff --git a/sam2-backend/adapters/base.py b/sam2-backend/adapters/base.py
new file mode 100644
index 0000000..6312827
--- /dev/null
+++ b/sam2-backend/adapters/base.py
@@ -0,0 +1,34 @@
+from dataclasses import dataclass
+from typing import Protocol
+
+import numpy as np
+
+
+@dataclass
+class PredictResult:
+    """Result of one bbox-prompted segmentation.
+
+    `polygon` is a list of (x, y) pixel coordinates describing the outer
+    contour of the largest mask blob. `score` is the model's confidence for
+    the selected mask (in [0, 1]).
+    """
+    polygon: list[tuple[float, float]]
+    score: float
+
+
+class BaseAdapter(Protocol):
+    """Minimal adapter contract. Each model family implements this."""
+
+    def load(self, config: str, checkpoint: str, device: str) -> None:
+        """Load weights into memory. Called once, lazily on first predict."""
+        ...
+
+    def predict_bbox(
+        self, image_rgb: np.ndarray, bbox_xyxy: tuple[int, int, int, int]
+    ) -> PredictResult:
+        """Run segmentation with a bbox prompt, return the best polygon."""
+        ...
+
+    def unload(self) -> None:
+        """Free GPU memory. Called when the registry evicts this model."""
+        ...
diff --git a/sam2-backend/adapters/sam2_adapter.py b/sam2-backend/adapters/sam2_adapter.py
new file mode 100644
index 0000000..8cdaabc
--- /dev/null
+++ b/sam2-backend/adapters/sam2_adapter.py
@@ -0,0 +1,82 @@
+"""SAM2 adapter — wraps `sam2.sam2_image_predictor.SAM2ImagePredictor`.
+
+The SAM2 repo uses Hydra to resolve config files relative to the `sam2`
+package. We rely on that resolution: pass `config="configs/sam2.1/..."` and
+it works as long as the `sam2` package is importable (pip-installed or on
+PYTHONPATH).
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Optional
+
+import numpy as np
+
+from .base import BaseAdapter, PredictResult
+from .utils import mask_to_polygon as _mask_to_polygon
+
+log = logging.getLogger("sam2-backend.sam2")
+
+
+class Sam2Adapter(BaseAdapter):
+    def __init__(self) -> None:
+        self._predictor: Optional[object] = None
+        self._device: str = "cpu"
+
+    def load(self, config: str, checkpoint: str, device: str) -> None:
+        # Imports are deferred so the server can start even without the SAM2
+        # deps installed (the model stays in `available: false` until loaded).
+        from sam2.build_sam import build_sam2
+        from sam2.sam2_image_predictor import SAM2ImagePredictor
+
+        log.info("loading SAM2: config=%s ckpt=%s device=%s",
+                 config, checkpoint, device)
+        model = build_sam2(config, checkpoint, device=device)
+        self._predictor = SAM2ImagePredictor(model)
+        self._device = device
+        log.info("SAM2 loaded")
+
+    def unload(self) -> None:
+        if self._predictor is None:
+            return
+        self._predictor = None
+        try:
+            import torch
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+        except Exception:
+            pass
+        log.info("SAM2 unloaded")
+
+    def predict_bbox(
+        self, image_rgb: np.ndarray, bbox_xyxy: tuple[int, int, int, int]
+    ) -> PredictResult:
+        if self._predictor is None:
+            raise RuntimeError("SAM2 adapter not loaded")
+
+        import torch
+
+        # set_image re-embeds on every call; that's fine for an interactive
+        # one-box-per-request workflow. Batch prompts would want caching.
+        inference_ctx = (
+            torch.inference_mode()
+            if self._device.startswith("cuda") or self._device == "cpu"
+            else torch.no_grad()
+        )
+        with inference_ctx:
+            self._predictor.set_image(image_rgb)  # type: ignore[attr-defined]
+            box = np.array(list(bbox_xyxy), dtype=np.float32)
+            masks, scores, _ = self._predictor.predict(  # type: ignore[attr-defined]
+                box=box,
+                multimask_output=True,
+            )
+
+        # multimask_output returns 3 candidates — take the highest-scoring one.
+        best_idx = int(np.argmax(scores))
+        best_mask = masks[best_idx]
+        best_score = float(scores[best_idx])
+        polygon = _mask_to_polygon(best_mask)
+        return PredictResult(polygon=polygon, score=best_score)
+
+
diff --git a/sam2-backend/adapters/sam3_adapter.py b/sam2-backend/adapters/sam3_adapter.py
new file mode 100644
index 0000000..7627c1d
--- /dev/null
+++ b/sam2-backend/adapters/sam3_adapter.py
@@ -0,0 +1,206 @@
+"""SAM3 adapter.
+
+Mirrors the loading pattern from `files/sam3_server.py` in this repo. Two
+modes are supported, selected via the model spec's `config` field in
+models.yaml:
+
+  config: image_predictor   — uses `sam3.model_builder.build_sam3_image_model`
+                              with a local `.pt` checkpoint. Treats the user's
+                              bbox as a visual exemplar and returns the mask
+                              whose predicted box has highest IoU with the
+                              user box (single-object annotation workflow).
+
+  config: tracker           — uses HuggingFace `transformers.Sam3TrackerModel`
+                              (SAM2-style PVS on images). `checkpoint` here is
+                              a HF model ID like "facebook/sam3" rather than
+                              a local .pt. Requires `transformers` installed
+                              and network access for first download.
+
+Both paths return a single polygon + confidence for the best mask, matching
+the adapter contract the server's `/predict` route expects.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Optional
+
+import numpy as np
+
+from .base import BaseAdapter, PredictResult
+from .utils import mask_to_polygon as _mask_to_polygon
+
+log = logging.getLogger("sam2-backend.sam3")
+
+
+class Sam3Adapter(BaseAdapter):
+    def __init__(self) -> None:
+        self._mode: str = "image_predictor"
+        self._device: str = "cpu"
+        # image_predictor path
+        self._image_model: Optional[object] = None
+        self._image_processor: Optional[object] = None
+        # tracker path
+        self._tracker_model: Optional[object] = None
+        self._tracker_processor: Optional[object] = None
+
+    def load(self, config: str, checkpoint: str, device: str) -> None:
+        self._device = device
+        mode = (config or "image_predictor").strip().lower()
+        self._mode = mode
+
+        if mode == "tracker":
+            self._load_tracker(checkpoint, device)
+        elif mode in ("image_predictor", "image", "exemplar"):
+            self._load_image_predictor(checkpoint, device)
+        else:
+            raise ValueError(
+                f"unknown SAM3 config mode '{mode}'. "
+                f"expected 'image_predictor' or 'tracker'."
+            )
+
+    def _load_image_predictor(self, checkpoint: str, device: str) -> None:
+        # Deferred imports so the server can start even without sam3 installed
+        # (the model stays unavailable until first use).
+        from sam3.model_builder import build_sam3_image_model
+        from sam3.model.sam3_image_processor import Sam3Processor
+
+        log.info("loading SAM3 image predictor: ckpt=%s device=%s",
+                 checkpoint, device)
+        model = (
+            build_sam3_image_model(checkpoint_path=checkpoint)
+            if checkpoint
+            else build_sam3_image_model()
+        )
+        self._image_model = model.to(device).eval()
+        self._image_processor = Sam3Processor(self._image_model)
+        log.info("SAM3 image predictor loaded")
+
+    def _load_tracker(self, hf_id: str, device: str) -> None:
+        from transformers import Sam3TrackerModel, Sam3TrackerProcessor  # type: ignore
+
+        model_id = hf_id or "facebook/sam3"
+        log.info("loading SAM3 tracker: hf_id=%s device=%s", model_id, device)
+        self._tracker_processor = Sam3TrackerProcessor.from_pretrained(model_id)
+        self._tracker_model = (
+            Sam3TrackerModel.from_pretrained(model_id).to(device).eval()
+        )
+        log.info("SAM3 tracker loaded")
+
+    def unload(self) -> None:
+        self._image_model = None
+        self._image_processor = None
+        self._tracker_model = None
+        self._tracker_processor = None
+        try:
+            import torch
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+        except Exception:
+            pass
+        log.info("SAM3 unloaded")
+
+    def predict_bbox(
+        self, image_rgb: np.ndarray, bbox_xyxy: tuple[int, int, int, int]
+    ) -> PredictResult:
+        import torch
+        from PIL import Image
+
+        pil_image = Image.fromarray(image_rgb)
+        # SAM3 weights are loaded in BFloat16 by `build_sam3_image_model`, but
+        # the processor hands the model Float32 image tensors → matmul dtype
+        # mismatch. Autocast does the input cast for us. Tracker path goes
+        # through HF transformers which already handles dtype internally, but
+        # autocasting is harmless there too.
+        device_type = "cuda" if self._device.startswith("cuda") else "cpu"
+        with torch.inference_mode():
+            with torch.autocast(device_type=device_type, dtype=torch.bfloat16):
+                if self._mode == "tracker":
+                    return self._predict_tracker(pil_image, bbox_xyxy)
+                return self._predict_image_predictor(pil_image, bbox_xyxy)
+
+    # --------------------------------------------------------------------
+    # Tracker path — HF transformers `Sam3Tracker`
+    # --------------------------------------------------------------------
+    def _predict_tracker(self, image, bbox_xyxy) -> PredictResult:
+        if self._tracker_model is None or self._tracker_processor is None:
+            raise RuntimeError("SAM3 tracker not loaded")
+        x1, y1, x2, y2 = [float(v) for v in bbox_xyxy]
+        inputs = self._tracker_processor(
+            images=image,
+            input_boxes=[[[x1, y1, x2, y2]]],   # batch[1] × obj[1] × 4
+            return_tensors="pt",
+        ).to(self._device)
+        outputs = self._tracker_model(**inputs, multimask_output=True)
+        # `post_process_masks` lost `reshaped_input_sizes` in newer
+        # transformers — only `masks` and `original_sizes` are accepted now.
+        # Cast to float32 before .numpy() — autocast leaves outputs in
+        # bfloat16, which NumPy can't ingest.
+        masks = self._tracker_processor.post_process_masks(
+            outputs.pred_masks.float().cpu(),
+            original_sizes=inputs["original_sizes"].cpu(),
+        )[0]                                     # (num_masks, H, W)
+        scores = outputs.iou_scores[0].float().cpu().numpy().ravel()
+        masks_np = masks.float().numpy().astype(bool) if masks.dtype != bool else masks.numpy()
+        n_masks = masks_np.shape[0]
+        if n_masks == 0:
+            return PredictResult(polygon=[], score=0.0)
+        # Mask and score counts can disagree (`multimask_output=True` returns 3
+        # IoU scores even when the head only emits 1 mask, e.g. when a
+        # `sam3_video` checkpoint is loaded into `Sam3TrackerModel`). Pick the
+        # best within whichever count is smaller.
+        valid = scores[:n_masks] if scores.size >= n_masks else scores
+        best = int(np.argmax(valid)) if valid.size > 0 else 0
+        best_score = float(valid[best]) if valid.size > 0 else 0.0
+        return PredictResult(
+            polygon=_mask_to_polygon(masks_np[best]),
+            score=best_score,
+        )
+
+    # --------------------------------------------------------------------
+    # Image-predictor path — box-as-exemplar, pick highest-IoU result
+    # --------------------------------------------------------------------
+    def _predict_image_predictor(self, image, bbox_xyxy) -> PredictResult:
+        if self._image_processor is None:
+            raise RuntimeError("SAM3 image predictor not loaded")
+        x1, y1, x2, y2 = [float(v) for v in bbox_xyxy]
+        state = self._image_processor.set_image(image)           # type: ignore[attr-defined]
+        out = self._image_processor.set_box_prompt(              # type: ignore[attr-defined]
+            state=state,
+            boxes=[[x1, y1, x2 - x1, y2 - y1]],   # xywh per SAM3 API
+            box_labels=[1],
+        )
+        masks = out.get("masks", [])
+        boxes = out.get("boxes", [])
+        scores = out.get("scores", [])
+        if len(masks) == 0:
+            return PredictResult(polygon=[], score=0.0)
+        # Pick the proposal whose predicted bbox overlaps the user bbox most.
+        user = (x1, y1, x2, y2)
+        ious = [_iou(user, tuple(float(v) for v in b)) for b in boxes]
+        idx = int(np.argmax(ious))
+        mask_np = np.asarray(masks[idx]).astype(bool)
+        return PredictResult(
+            polygon=_mask_to_polygon(mask_np),
+            score=float(scores[idx]),
+        )
+
+
+# --------------------------------------------------------------------------
+# Helpers
+# --------------------------------------------------------------------------
+def _iou(
+    a: tuple[float, float, float, float],
+    b: tuple[float, float, float, float],
+) -> float:
+    ax1, ay1, ax2, ay2 = a
+    bx1, by1, bx2, by2 = b
+    ix1, iy1 = max(ax1, bx1), max(ay1, by1)
+    ix2, iy2 = min(ax2, bx2), min(ay2, by2)
+    iw = max(0.0, ix2 - ix1)
+    ih = max(0.0, iy2 - iy1)
+    inter = iw * ih
+    ua = (ax2 - ax1) * (ay2 - ay1) + (bx2 - bx1) * (by2 - by1) - inter
+    return inter / ua if ua > 0 else 0.0
+
+
diff --git a/sam2-backend/adapters/utils.py b/sam2-backend/adapters/utils.py
new file mode 100644
index 0000000..f71fb8c
--- /dev/null
+++ b/sam2-backend/adapters/utils.py
@@ -0,0 +1,141 @@
+"""Shared helpers for the SAM-family adapters."""
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+
+def mask_to_polygon(
+    mask: np.ndarray,
+    *,
+    min_points: int = 4,
+    max_points: int = 10,
+    smooth: bool = True,
+) -> list[tuple[float, float]]:
+    """Largest external contour, simplified to a vertex count in
+    [min_points, max_points].
+
+    Pipeline:
+      1. Squeeze + binarise the mask.
+      2. Optional morphological close+open + Gaussian smoothing — this is
+         what turns staircase-jagged SAM mask edges into curves the polygon
+         can actually trace cleanly.
+      3. Pull the full contour (`CHAIN_APPROX_NONE`) so the simplifier has
+         every pixel-edge sample to choose from.
+      4. Binary-search Douglas-Peucker epsilon to hit a vertex count in
+         [min_points, max_points], biased toward more points (more detail).
+
+    Why this matters: the user has to drag every vertex by hand in the
+    annotation UI. Raw `findContours` output is often hundreds of points
+    along a single edge — unusable. Without step 2, even a 15-point output
+    looks choppy because the chosen vertices sit on stair-step pixels.
+    """
+    m = np.asarray(mask)
+    # Tolerate stray leading singleton dims (some processors return
+    # [1, H, W] or [1, 1, H, W]).
+    m = np.squeeze(m)
+    while m.ndim > 2:
+        m = m[0]
+    if m.ndim != 2 or m.size == 0:
+        return []
+
+    m = m.astype(np.uint8)
+    if m.max() == 1:
+        m = m * 255
+
+    if smooth:
+        m = _smooth_mask(m)
+
+    contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+    if not contours:
+        return []
+    cnt = max(contours, key=cv2.contourArea)
+    if len(cnt) < 3:
+        return []
+
+    simplified = _simplify_to_band(cnt, min_points=min_points, max_points=max_points)
+    if simplified is None or len(simplified) < 3:
+        return []
+    pts = simplified.reshape(-1, 2)
+    return [(float(x), float(y)) for x, y in pts]
+
+
+def _smooth_mask(mask_u8: np.ndarray) -> np.ndarray:
+    """Clean up a binary mask before contour extraction.
+
+    Kernel size scales mildly with the mask's bounding box so a tiny object
+    doesn't get erased and a huge object gets enough smoothing to matter.
+    """
+    h, w = mask_u8.shape[:2]
+    # Find the actual blob size (not the canvas size) for kernel sizing.
+    ys, xs = np.where(mask_u8 > 0)
+    if xs.size == 0:
+        return mask_u8
+    bw = xs.max() - xs.min() + 1
+    bh = ys.max() - ys.min() + 1
+    blob_diag = max(1.0, float(np.hypot(bw, bh)))
+    # Kernel ~1.5% of blob diagonal, clamped to a usable range.
+    k = int(round(blob_diag * 0.015))
+    k = max(3, min(k | 1, 11))   # odd, between 3 and 11
+
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
+    # Close fills tiny holes / single-pixel notches; open removes specks.
+    m = cv2.morphologyEx(mask_u8, cv2.MORPH_CLOSE, kernel)
+    m = cv2.morphologyEx(m, cv2.MORPH_OPEN, kernel)
+
+    # A light Gaussian + re-threshold smooths the edge by a sub-pixel amount,
+    # which makes the contour follow curves cleanly instead of staircasing.
+    blur_k = max(3, min(k | 1, 9))
+    m = cv2.GaussianBlur(m, (blur_k, blur_k), 0)
+    _, m = cv2.threshold(m, 127, 255, cv2.THRESH_BINARY)
+    return m
+
+
+def _simplify_to_band(
+    contour: np.ndarray,
+    *,
+    min_points: int,
+    max_points: int,
+    iterations: int = 24,
+) -> np.ndarray | None:
+    """Binary-search epsilon for `cv2.approxPolyDP` so vertex count lands in
+    [min_points, max_points], biased toward the larger end of the band.
+
+    Strategy:
+    - epsilon=0   → original (many) points
+    - epsilon=arc → degenerates to a single point
+    - smaller epsilon ⇒ more points; larger ⇒ fewer.
+
+    We track the in-band result with the highest vertex count seen
+    (most-detailed pick within the cap) and return it.
+    """
+    arc = cv2.arcLength(contour, True)
+    if arc == 0:
+        return contour
+    if len(contour) <= max_points:
+        return contour if len(contour) >= min_points else None
+
+    lo, hi = 0.0, arc
+    best: np.ndarray | None = None
+    best_n = -1
+
+    for _ in range(iterations):
+        mid = 0.5 * (lo + hi)
+        approx = cv2.approxPolyDP(contour, mid, True)
+        n = len(approx)
+        if n > max_points:
+            lo = mid
+        elif n < min_points:
+            hi = mid
+        else:
+            if n > best_n:
+                best = approx
+                best_n = n
+            hi = mid
+        if hi - lo < 1e-6 * arc:
+            break
+
+    if best is not None:
+        return best
+    return cv2.approxPolyDP(contour, lo, True)
diff --git a/sam2-backend/models.yaml b/sam2-backend/models.yaml
new file mode 100644
index 0000000..e7e1ca9
--- /dev/null
+++ b/sam2-backend/models.yaml
@@ -0,0 +1,69 @@
+# Model registry for the SAM backend.
+#
+# Add/remove entries freely. Paths may be absolute or relative to this file.
+# Checkpoints marked as missing on disk are still listed by /models but flagged
+# `available: false` so the frontend can grey them out.
+#
+# `family` selects the adapter code-path. Currently supported:
+#   sam2   — Meta SAM 2 / SAM 2.1 (image predictor, bbox prompts)
+#   sam3   — Meta SAM 3 (image predictor with box-as-exemplar, or HF tracker)
+#
+# Per-entry fields:
+#   id           — stable key used by /predict?model=<id>
+#   name         — human-readable, shown in the Tauri model dropdown
+#   family       — adapter to use
+#   config       — family-specific (SAM2: hydra yaml path; SAM3: "image_predictor"
+#                  or "tracker")
+#   checkpoint   — file path (relative paths resolved against this yaml's
+#                  directory) OR an opaque identifier when `remote: true`
+#   remote       — optional; true = treat checkpoint as a HuggingFace model id
+#                  (no on-disk existence check)
+
+default: sam2.1_hiera_small
+
+models:
+  - id: sam2.1_hiera_tiny
+    name: "SAM 2.1 · Hiera Tiny"
+    family: sam2
+    config: configs/sam2.1/sam2.1_hiera_t.yaml
+    checkpoint: ../sam2/sam2/checkpoints/sam2.1_hiera_tiny.pt
+
+  - id: sam2.1_hiera_small
+    name: "SAM 2.1 · Hiera Small"
+    family: sam2
+    config: configs/sam2.1/sam2.1_hiera_s.yaml
+    checkpoint: ../sam2/sam2/checkpoints/sam2.1_hiera_small.pt
+
+  - id: sam2.1_hiera_base_plus
+    name: "SAM 2.1 · Hiera Base+"
+    family: sam2
+    config: configs/sam2.1/sam2.1_hiera_b+.yaml
+    checkpoint: ../sam2/sam2/checkpoints/sam2.1_hiera_base_plus.pt
+
+  - id: sam2.1_hiera_large
+    name: "SAM 2.1 · Hiera Large"
+    family: sam2
+    config: configs/sam2.1/sam2.1_hiera_l.yaml
+    checkpoint: ../sam2/sam2/checkpoints/sam2.1_hiera_large.pt
+
+  # ---- SAM 3 -----------------------------------------------------------
+  # Local image-predictor path — uses the .pt weights in this repo. Box is
+  # treated as a visual exemplar; the adapter picks the mask whose predicted
+  # bbox best overlaps the user bbox (classic single-instance annotation).
+  - id: sam3
+    name: "SAM 3 · image predictor"
+    family: sam3
+    config: image_predictor
+    checkpoint: ../files/sam3_weights/sam3.pt
+
+  # Optional HuggingFace tracker path. Requires `pip install transformers`
+  # and pulls weights from HF hub on first use. Gives cleaner single-box
+  # → single-mask results than the exemplar path but needs a network round
+  # trip the first time. Disabled by leaving `remote: false` — flip to
+  # `remote: true` once your machine has transformers installed.
+  # - id: sam3_tracker
+  #   name: "SAM 3 · tracker (HuggingFace)"
+  #   family: sam3
+  #   config: tracker
+  #   checkpoint: facebook/sam3
+  #   remote: true
diff --git a/sam2-backend/registry.py b/sam2-backend/registry.py
new file mode 100644
index 0000000..5938604
--- /dev/null
+++ b/sam2-backend/registry.py
@@ -0,0 +1,141 @@
+"""Model registry. Reads models.yaml, lazy-loads adapters, caches instances.
+
+Only one adapter is resident at a time by default — switching models unloads
+the previous one to keep GPU memory bounded. Set SAM_BACKEND_KEEP_ALL=1 to
+disable eviction (useful if you have VRAM headroom and switch often).
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import threading
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional
+
+import yaml
+
+from adapters import ADAPTERS, BaseAdapter, PredictResult
+
+log = logging.getLogger("sam2-backend.registry")
+
+
+@dataclass
+class ModelSpec:
+    id: str
+    name: str
+    family: str
+    config: str
+    checkpoint: str
+    # If true, `checkpoint` is treated as an opaque identifier (e.g. a
+    # HuggingFace model id like "facebook/sam3") rather than a file on disk.
+    # Availability check is skipped for remote entries.
+    remote: bool = False
+    available: bool = False
+    # Resolved absolute paths for logging / responses.
+    checkpoint_abs: str = ""
+
+
+@dataclass
+class Registry:
+    specs: dict[str, ModelSpec] = field(default_factory=dict)
+    default_id: Optional[str] = None
+    device: str = "cpu"
+    keep_all: bool = False
+    _loaded: dict[str, BaseAdapter] = field(default_factory=dict)
+    _lock: threading.Lock = field(default_factory=threading.Lock)
+
+    def list_specs(self) -> list[ModelSpec]:
+        return list(self.specs.values())
+
+    def resolve(self, model_id: Optional[str]) -> ModelSpec:
+        if not model_id:
+            if self.default_id is None:
+                raise KeyError("no default model configured")
+            model_id = self.default_id
+        if model_id not in self.specs:
+            raise KeyError(f"unknown model: {model_id}")
+        return self.specs[model_id]
+
+    def get_adapter(self, model_id: str) -> BaseAdapter:
+        """Returns a loaded adapter for `model_id`, loading on first use."""
+        with self._lock:
+            if model_id in self._loaded:
+                return self._loaded[model_id]
+            spec = self.resolve(model_id)
+            if not spec.available:
+                raise FileNotFoundError(
+                    f"checkpoint missing: {spec.checkpoint_abs}"
+                )
+            cls = ADAPTERS.get(spec.family)
+            if cls is None:
+                raise ValueError(
+                    f"no adapter registered for family '{spec.family}'"
+                )
+            if not self.keep_all:
+                # Evict whichever model is currently resident.
+                for old_id, adapter in list(self._loaded.items()):
+                    log.info("evicting %s to make room for %s", old_id, model_id)
+                    adapter.unload()
+                    del self._loaded[old_id]
+            adapter = cls()
+            adapter.load(spec.config, spec.checkpoint_abs, self.device)
+            self._loaded[model_id] = adapter
+            return adapter
+
+    def predict_bbox(self, model_id: Optional[str], image_rgb, bbox_xyxy) -> tuple[ModelSpec, PredictResult]:
+        spec = self.resolve(model_id)
+        adapter = self.get_adapter(spec.id)
+        # Serialize inference: most of these adapters aren't reentrant.
+        with self._lock:
+            result = adapter.predict_bbox(image_rgb, bbox_xyxy)
+        return spec, result
+
+
+def load_registry(models_yaml: Path, device: str) -> Registry:
+    with open(models_yaml, "r") as f:
+        data = yaml.safe_load(f)
+
+    base_dir = models_yaml.parent
+    keep_all = os.environ.get("SAM_BACKEND_KEEP_ALL", "0") == "1"
+    reg = Registry(device=device, keep_all=keep_all)
+    reg.default_id = data.get("default")
+
+    for entry in data.get("models", []):
+        remote = bool(entry.get("remote", False))
+        ckpt_str = entry["checkpoint"]
+        if remote:
+            # Opaque identifier — leave as-is, mark available so the UI
+            # shows it. The adapter resolves/downloads on first use.
+            ckpt_abs = ckpt_str
+            available = True
+        else:
+            ckpt = Path(ckpt_str)
+            ckpt_path = (ckpt if ckpt.is_absolute() else (base_dir / ckpt)).resolve()
+            ckpt_abs = str(ckpt_path)
+            available = ckpt_path.is_file()
+        spec = ModelSpec(
+            id=entry["id"],
+            name=entry["name"],
+            family=entry["family"],
+            config=entry["config"],
+            checkpoint=ckpt_str,
+            remote=remote,
+            available=available,
+            checkpoint_abs=ckpt_abs,
+        )
+        reg.specs[spec.id] = spec
+        log.info(
+            "registered model %s (available=%s, ckpt=%s)",
+            spec.id, spec.available, spec.checkpoint_abs,
+        )
+
+    # Fall back the default to the first *available* model if the configured
+    # default's checkpoint is missing.
+    if reg.default_id and not reg.specs.get(reg.default_id, ModelSpec("", "", "", "", "")).available:
+        for s in reg.specs.values():
+            if s.available:
+                reg.default_id = s.id
+                break
+    return reg
diff --git a/sam2-backend/requirements.txt b/sam2-backend/requirements.txt
new file mode 100644
index 0000000..19ac154
--- /dev/null
+++ b/sam2-backend/requirements.txt
@@ -0,0 +1,13 @@
+# Runtime deps for the SAM2 backend HTTP server.
+# Torch is intentionally NOT pinned here — install the right build for your
+# CUDA/CPU first (see README.md), then `pip install -r requirements.txt`.
+
+fastapi>=0.110
+uvicorn[standard]>=0.27
+pydantic>=2.6
+pyyaml>=6.0
+pillow>=10.0
+numpy>=1.24,<2.0
+opencv-python-headless>=4.8
+hydra-core>=1.3
+iopath>=0.1.10
diff --git a/sam2-backend/run.sh b/sam2-backend/run.sh
new file mode 100755
index 0000000..5f8b0c9
--- /dev/null
+++ b/sam2-backend/run.sh
@@ -0,0 +1,32 @@
+#!/usr/bin/env bash
+# Convenience launcher for the SAM2 backend.
+# Assumes a venv at ./.venv with requirements.txt + torch + sam2 installed.
+# See README.md for install steps.
+
+set -euo pipefail
+cd "$(dirname "$0")"
+
+if [ -d ".venv" ]; then
+  # shellcheck disable=SC1091
+  source .venv/bin/activate
+fi
+
+HOST="${HOST:-0.0.0.0}"
+PORT="${PORT:-9090}"
+DEVICE="${DEVICE:-auto}"
+
+# Privacy: disable HF/transformers telemetry, and (after weights are cached)
+# refuse any outbound calls to huggingface.co. Inference runs fully local
+# regardless; these just stop the cosmetic pings on startup.
+export HF_HUB_DISABLE_TELEMETRY="${HF_HUB_DISABLE_TELEMETRY:-1}"
+export DO_NOT_TRACK="${DO_NOT_TRACK:-1}"
+# Flip both OFFLINE flags to 1 once you've done the first `from_pretrained`
+# and the weights are cached under ~/.cache/huggingface/. Leaving them 0
+# the first time lets the model download. After that, set them 1 to fully
+# air-gap and silence the HEAD-request revalidation pings on every startup.
+# HF_HUB_OFFLINE        — used by huggingface_hub
+# TRANSFORMERS_OFFLINE  — used by transformers itself (some legacy paths)
+export HF_HUB_OFFLINE="${HF_HUB_OFFLINE:-0}"
+export TRANSFORMERS_OFFLINE="${TRANSFORMERS_OFFLINE:-0}"
+
+exec python server.py --host "$HOST" --port "$PORT" --device "$DEVICE" "$@"
diff --git a/sam2-backend/server.py b/sam2-backend/server.py
new file mode 100644
index 0000000..8fecf63
--- /dev/null
+++ b/sam2-backend/server.py
@@ -0,0 +1,345 @@
+"""FastAPI server exposing SAM-family predictors over HTTP.
+
+Implements the Label-Studio-ML `/predict` contract the SAM-Tool Tauri app
+already speaks, and an LS-orthogonal `/models` endpoint so the frontend can
+offer a model picker.
+
+Run:
+    python server.py --host 0.0.0.0 --port 9090 --device cuda
+
+Endpoints:
+    GET  /            → liveness + backend info
+    GET  /models      → registered models + availability
+    POST /predict     → LS-ML protocol, accepts optional `?model=<id>` query
+"""
+
+from __future__ import annotations
+
+import argparse
+import base64
+import io
+import logging
+import os
+import sys
+from contextlib import asynccontextmanager
+from pathlib import Path
+
+import numpy as np
+import uvicorn
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+from pydantic import BaseModel
+
+from registry import Registry, load_registry
+
+# --------------------------------------------------------------------------
+# Logging
+# --------------------------------------------------------------------------
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s %(name)s: %(message)s",
+    stream=sys.stdout,
+)
+log = logging.getLogger("sam2-backend.server")
+
+# --------------------------------------------------------------------------
+# App + registry. Registry is loaded inside a FastAPI lifespan so it works
+# regardless of how uvicorn ends up importing this module — `python
+# server.py` runs us as `__main__` and uvicorn then re-imports as `server`,
+# so initialising at module top-level (or only in main()) leaves the live
+# copy uninitialised.
+# --------------------------------------------------------------------------
+REGISTRY: Registry | None = None
+
+
+@asynccontextmanager
+async def lifespan(_app: FastAPI):
+    global REGISTRY
+    if REGISTRY is None:
+        models_path = os.environ.get(
+            "SAM_BACKEND_MODELS",
+            str(Path(__file__).parent / "models.yaml"),
+        )
+        device = _pick_device(os.environ.get("SAM_BACKEND_DEVICE", "auto"))
+        log.info("lifespan: loading registry (models=%s device=%s)",
+                 models_path, device)
+        REGISTRY = load_registry(Path(models_path), device=device)
+        log.info(
+            "lifespan: %d model specs (%d available), default=%s",
+            len(REGISTRY.specs),
+            sum(1 for s in REGISTRY.specs.values() if s.available),
+            REGISTRY.default_id,
+        )
+    yield
+
+
+app = FastAPI(title="SAM Backend", version="0.1.0", lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+def _registry() -> Registry:
+    if REGISTRY is None:
+        raise RuntimeError("registry not initialised")
+    return REGISTRY
+
+
+# --------------------------------------------------------------------------
+# Schemas
+# --------------------------------------------------------------------------
+class ModelInfo(BaseModel):
+    id: str
+    name: str
+    family: str
+    available: bool
+    checkpoint: str
+
+
+class ModelListResponse(BaseModel):
+    default: str | None
+    models: list[ModelInfo]
+
+
+class HealthResponse(BaseModel):
+    status: str
+    device: str
+    models: int
+    default: str | None
+
+
+# --------------------------------------------------------------------------
+# Routes
+# --------------------------------------------------------------------------
+@app.get("/", response_model=HealthResponse)
+def health() -> HealthResponse:
+    reg = _registry()
+    return HealthResponse(
+        status="ok",
+        device=reg.device,
+        models=len(reg.specs),
+        default=reg.default_id,
+    )
+
+
+@app.get("/models", response_model=ModelListResponse)
+def list_models() -> ModelListResponse:
+    reg = _registry()
+    return ModelListResponse(
+        default=reg.default_id,
+        models=[
+            ModelInfo(
+                id=s.id,
+                name=s.name,
+                family=s.family,
+                available=s.available,
+                checkpoint=s.checkpoint_abs,
+            )
+            for s in reg.list_specs()
+        ],
+    )
+
+
+@app.post("/predict")
+def predict(payload: dict, model: str | None = Query(default=None)) -> dict:
+    """Label-Studio-ML protocol.
+
+    Body shape (simplified — we ignore fields we don't need):
+        {
+          "tasks": [{ "data": { "image": "data:image/jpeg;base64,..." } }],
+          "params": {
+            "context": {
+              "result": [{
+                "original_width": W, "original_height": H,
+                "value": { "x": %, "y": %, "width": %, "height": %, ... }
+              }]
+            }
+          }
+        }
+
+    Response shape (what sam-tool-tauri / LS Studio expects):
+        {
+          "results": [{
+            "model_version": "<id>",
+            "result": [{
+              "original_width": W, "original_height": H,
+              "value": {
+                "points": [[x%, y%], ...],
+                "closed": true,
+                "polygonlabels": ["Object"]
+              },
+              "from_name": "polygon", "to_name": "image",
+              "type": "polygonlabels"
+            }]
+          }]
+        }
+    """
+    reg = _registry()
+
+    try:
+        tasks = payload.get("tasks") or []
+        if not tasks:
+            raise HTTPException(400, "tasks[] missing or empty")
+        data = tasks[0].get("data") or {}
+        image_field = data.get("image")
+        if not isinstance(image_field, str):
+            raise HTTPException(400, "tasks[0].data.image missing")
+        image_rgb = _decode_image(image_field)
+
+        ctx_result = (
+            payload.get("params", {})
+                   .get("context", {})
+                   .get("result", [])
+        )
+        if not ctx_result:
+            raise HTTPException(400, "params.context.result[] missing (bbox prompt)")
+        prompt = ctx_result[0]
+        value = prompt.get("value", {})
+        orig_w = int(prompt.get("original_width") or image_rgb.shape[1])
+        orig_h = int(prompt.get("original_height") or image_rgb.shape[0])
+        x_pct = float(value.get("x", 0.0))
+        y_pct = float(value.get("y", 0.0))
+        w_pct = float(value.get("width", 0.0))
+        h_pct = float(value.get("height", 0.0))
+
+        # Percentages → absolute pixel xyxy, clamped to image bounds.
+        x1 = max(0, int(round(x_pct / 100.0 * orig_w)))
+        y1 = max(0, int(round(y_pct / 100.0 * orig_h)))
+        x2 = min(orig_w, int(round((x_pct + w_pct) / 100.0 * orig_w)))
+        y2 = min(orig_h, int(round((y_pct + h_pct) / 100.0 * orig_h)))
+        if x2 <= x1 or y2 <= y1:
+            raise HTTPException(400, "degenerate bbox after clamping")
+
+        # Model selection: query param wins, then payload.params.model, then default.
+        model_id = (
+            model
+            or payload.get("params", {}).get("model")
+            or payload.get("params", {}).get("model_id")
+        )
+        # Resolve up-front so an unknown id is a clean 400; any error from
+        # the actual predict path falls into the generic 500 with traceback
+        # below (and is no longer mis-reported as "unknown model").
+        try:
+            reg.resolve(model_id)
+        except KeyError as e:
+            raise HTTPException(400, f"unknown model: {e}")
+
+        spec, result = reg.predict_bbox(model_id, image_rgb, (x1, y1, x2, y2))
+
+    except HTTPException:
+        raise
+    except FileNotFoundError as e:
+        raise HTTPException(503, str(e))
+    except Exception as e:
+        log.exception("predict failed")
+        raise HTTPException(500, f"{type(e).__name__}: {e}")
+
+    if not result.polygon:
+        raise HTTPException(500, "no polygon returned from model")
+
+    # Convert pixel polygon back to percentages for LS-ML round-trip.
+    points_pct = [
+        [px / orig_w * 100.0, py / orig_h * 100.0]
+        for (px, py) in result.polygon
+    ]
+
+    return {
+        "results": [{
+            "model_version": spec.id,
+            "score": result.score,
+            "result": [{
+                "original_width": orig_w,
+                "original_height": orig_h,
+                "image_rotation": 0,
+                "value": {
+                    "points": points_pct,
+                    "closed": True,
+                    "polygonlabels": ["Object"],
+                },
+                "id": "sam_poly_0",
+                "from_name": "polygon",
+                "to_name": "image",
+                "type": "polygonlabels",
+            }],
+        }]
+    }
+
+
+# --------------------------------------------------------------------------
+# Helpers
+# --------------------------------------------------------------------------
+def _decode_image(field: str) -> np.ndarray:
+    """Accept `data:<mime>;base64,...` or a bare base64 string."""
+    b64 = field
+    if field.startswith("data:"):
+        comma = field.find(",")
+        if comma == -1:
+            raise HTTPException(400, "malformed data URL")
+        b64 = field[comma + 1:]
+    try:
+        raw = base64.b64decode(b64)
+    except Exception as e:
+        raise HTTPException(400, f"base64 decode failed: {e}")
+    try:
+        img = Image.open(io.BytesIO(raw)).convert("RGB")
+    except Exception as e:
+        raise HTTPException(400, f"image decode failed: {e}")
+    return np.array(img)
+
+
+# --------------------------------------------------------------------------
+# Entrypoint
+# --------------------------------------------------------------------------
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host", default="0.0.0.0")
+    parser.add_argument("--port", type=int, default=9090)
+    parser.add_argument(
+        "--models",
+        default=str(Path(__file__).parent / "models.yaml"),
+        help="Path to models.yaml registry file",
+    )
+    parser.add_argument(
+        "--device",
+        default="auto",
+        help="'auto' (prefer cuda), 'cuda', 'cpu', or specific cuda:N",
+    )
+    parser.add_argument("--reload", action="store_true",
+                        help="Enable uvicorn auto-reload (dev only)")
+    args = parser.parse_args()
+
+    # Hand config to the worker module via env — the lifespan reads these
+    # whether uvicorn imports this file as `__main__` or as `server`.
+    os.environ["SAM_BACKEND_MODELS"] = args.models
+    os.environ["SAM_BACKEND_DEVICE"] = args.device
+
+    log.info("starting uvicorn on %s:%d (device=%s, models=%s)",
+             args.host, args.port, args.device, args.models)
+    uvicorn.run(
+        "server:app",
+        host=args.host,
+        port=args.port,
+        reload=args.reload,
+        log_level="info",
+    )
+
+
+def _pick_device(preference: str) -> str:
+    if preference != "auto":
+        return preference
+    try:
+        import torch
+        if torch.cuda.is_available():
+            return "cuda"
+    except ImportError:
+        pass
+    return "cpu"
+
+
+if __name__ == "__main__":
+    main()
diff --git a/sam2/sam2 b/sam2/sam2
new file mode 160000
index 0000000..2b90b9f
--- /dev/null
+++ b/sam2/sam2
@@ -0,0 +1 @@
+Subproject commit 2b90b9f5ceec907a1c18123530e92e794ad901a4
diff --git a/sam2/sam_extract.py b/sam2/sam_extract.py
new file mode 100644
index 0000000..c01d126
--- /dev/null
+++ b/sam2/sam_extract.py
@@ -0,0 +1,500 @@
+"""
+Runs on video and tracks objects across time, generating masks and polygons for multiple frames.
+Takes one annotation of each asset and fetches the frames before and after it.
+"""
+import os
+import cv2
+import ast
+import json
+import torch
+import numpy as np
+
+from collections import deque
+from scipy.optimize import linear_sum_assignment
+from segment_anything import sam_model_registry, SamPredictor
+
+# =========================================================
+#  CONFIG
+# =========================================================
+VIDEO_PATH = "/media/bs5/New Volume1/Brinda/Dataset/GPS_location/videos/2026_0330_094759_F.MP4"
+JSON_PATH = "/media/bs5/New Volume1/Brinda/Dataset/GPS_location/output/2026_0330_094759_F.json"
+OUTPUT_DIR = "/media/bs5/New Volume1/Brinda/Dataset/GPS_location/sam/"
+
+FRAME_GAP = 5
+NUM_FRAMES = 5
+
+SAM_CHECKPOINT = "sam_vit_h_4b8939.pth"
+MODEL_TYPE = "vit_h"
+
+
+# =========================================================
+#  OUTPUT DIRS
+# =========================================================
+IMG_DIR = os.path.join(OUTPUT_DIR, "images")
+MASK_DIR = os.path.join(OUTPUT_DIR, "masks")
+EXTRACT_DIR = os.path.join(OUTPUT_DIR, "masked")
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+os.makedirs(IMG_DIR, exist_ok=True)
+os.makedirs(MASK_DIR, exist_ok=True)
+os.makedirs(EXTRACT_DIR, exist_ok=True)
+
+5
+# ================== KALMAN FILTER ==================
+
+class KalmanFilter:
+    """Kalman filter for smoother track predictions"""
+    def __init__(self):
+        self.kf = cv2.KalmanFilter(8, 4)
+        
+        # State transition matrix
+        self.kf.transitionMatrix = np.eye(8, dtype=np.float32)
+        self.kf.transitionMatrix[0, 4] = 1
+        self.kf.transitionMatrix[1, 5] = 1
+        self.kf.transitionMatrix[2, 6] = 1
+        self.kf.transitionMatrix[3, 7] = 1
+        
+        # Measurement matrix
+        self.kf.measurementMatrix = np.zeros((4, 8), dtype=np.float32)
+        self.kf.measurementMatrix[0, 0] = 1
+        self.kf.measurementMatrix[1, 1] = 1
+        self.kf.measurementMatrix[2, 2] = 1
+        self.kf.measurementMatrix[3, 3] = 1
+        
+        self.kf.processNoiseCov = np.eye(8, dtype=np.float32) * 0.01
+        self.kf.measurementNoiseCov = np.eye(4, dtype=np.float32) * 0.5
+        self.kf.errorCovPost = np.eye(8, dtype=np.float32)
+        
+    def predict(self):
+        prediction = self.kf.predict()
+        return prediction[:4].flatten()
+    
+    def update(self, measurement):
+        self.kf.correct(np.array(measurement, dtype=np.float32).reshape(4, 1))
+
+
+# ================== TRACK CLASS ==================
+
+class Track:
+    def __init__(self, tlwh, score, track_id, class_id=0, class_name='unknown'):
+        self.tlwh = np.asarray(tlwh, dtype=np.float32)
+        self.score = score
+        self.track_id = track_id
+        self.class_id = class_id
+        self.class_name = class_name
+        self.time_since_update = 0
+        self.hits = 1
+        self.age = 1
+        
+        # Kalman filter for motion prediction
+        self.kf = KalmanFilter()
+        self.kf.kf.statePost = np.array([*tlwh, 0, 0, 0, 0], dtype=np.float32).reshape(8, 1)
+        
+        # Track confidence history for stability
+        self.confidence_history = deque([score], maxlen=10)
+        self.avg_confidence = score
+
+    def predict(self):
+        """Predict next position using Kalman filter"""
+        self.age += 1
+        self.time_since_update += 1
+        predicted = self.kf.predict()
+        
+        if self.time_since_update < 5:
+            self.tlwh = predicted
+
+    def update(self, tlwh, score, class_id, class_name):
+        """Update track with new detection"""
+        self.tlwh = np.asarray(tlwh, dtype=np.float32)
+        self.score = score
+        self.class_id = class_id
+        self.class_name = class_name
+        self.time_since_update = 0
+        self.hits += 1
+        
+        self.confidence_history.append(score)
+        self.avg_confidence = np.mean(list(self.confidence_history))
+        
+        self.kf.update(tlwh)
+    
+    def is_stable(self):
+        """Check if track is stable enough to display"""
+        return (self.hits >= 3 and 
+                self.avg_confidence > 0.3 and 
+                self.time_since_update == 0)
+
+
+# ================== BYTETRACK TRACKER ==================
+
+class ByteTrackEnhanced:
+    """
+    Enhanced ByteTrack with per-class track ID sequencing
+    """
+    def __init__(self, track_thresh=0.4, match_thresh=0.5, buffer_size=60, 
+                 high_thresh=0.5, low_thresh=0.1, class_names=None):
+        self.track_thresh = track_thresh
+        self.high_thresh = high_thresh
+        self.low_thresh = low_thresh
+        self.match_thresh = match_thresh
+        self.buffer_size = buffer_size
+        
+        self.tracks = []
+        self.class_names = class_names if class_names else []
+        
+        # Per-class track ID counters
+        self.next_id_per_class = {}
+
+    def get_next_id(self, class_id):
+        """Get next track ID for specific class"""
+        if class_id not in self.next_id_per_class:
+            self.next_id_per_class[class_id] = 1
+        
+        track_id = self.next_id_per_class[class_id]
+        self.next_id_per_class[class_id] += 1
+        return track_id
+    
+    def get_class_name(self, class_id):
+        """Get class name from class ID"""
+        if class_id < len(self.class_names):
+            return self.class_names[class_id]
+        return f'class_{class_id}'
+    
+    def iou(self, a, b):
+        """Calculate IoU between two boxes"""
+        ax, ay, aw, ah = a
+        bx, by, bw, bh = b
+        ax2, ay2 = ax + aw, ay + ah
+        bx2, by2 = bx + bw, by + bh
+        
+        inter_x1 = max(ax, bx)
+        inter_y1 = max(ay, by)
+        inter_x2 = min(ax2, bx2)
+        inter_y2 = min(ay2, by2)
+        
+        inter = max(0, inter_x2 - inter_x1) * max(0, inter_y2 - inter_y1)
+        union = aw * ah + bw * bh - inter
+        
+        return inter / union if union > 0 else 0
+    
+    def iou_distance(self, tracks, detections):
+        """Compute IoU distance matrix"""
+        iou_mat = np.zeros((len(tracks), len(detections)), dtype=np.float32)
+        
+        for i, t in enumerate(tracks):
+            for j, d in enumerate(detections):
+                iou = self.iou(t.tlwh, d[:4])
+                
+                # Penalize class mismatch
+                if len(d) > 5 and t.class_id != int(d[5]):
+                    iou *= 0.3
+                
+                # Boost for active tracks
+                if t.time_since_update == 0:
+                    iou *= 1.1
+                    
+                iou_mat[i, j] = iou
+                
+        return iou_mat
+
+    def linear_assignment(self, cost_matrix, thresh):
+        """Perform linear assignment with threshold"""
+        if cost_matrix.size == 0:
+            return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
+        
+        matches, unmatched_a, unmatched_b = [], [], []
+        row_ind, col_ind = linear_sum_assignment(-cost_matrix)
+        
+        for r, c in zip(row_ind, col_ind):
+            if cost_matrix[r, c] < thresh:
+                unmatched_a.append(r)
+                unmatched_b.append(c)
+            else:
+                matches.append([r, c])
+        
+        unmatched_a += list(set(range(cost_matrix.shape[0])) - set(row_ind))
+        unmatched_b += list(set(range(cost_matrix.shape[1])) - set(col_ind))
+        
+        return np.array(matches), tuple(unmatched_a), tuple(unmatched_b)
+
+    def update(self, detections):
+        """Two-stage association with stability fixes"""
+        # Predict all tracks
+        for t in self.tracks:
+            t.predict()
+
+        confirmed_tracks = [t for t in self.tracks if t.hits >= 2]
+        unconfirmed_tracks = [t for t in self.tracks if t.hits < 2]
+
+        # Separate high and low confidence detections
+        high_dets = [d for d in detections if d[4] >= self.high_thresh]
+        low_dets = [d for d in detections if self.low_thresh <= d[4] < self.high_thresh]
+
+        # First association
+        unmatched_tracks = []
+        unmatched_dets = []
+        
+        if len(confirmed_tracks) > 0 and len(high_dets) > 0:
+            iou_mat = self.iou_distance(confirmed_tracks, high_dets)
+            matches, u_tracks, u_dets = self.linear_assignment(iou_mat, self.match_thresh)
+            
+            for m in matches:
+                track_idx, det_idx = m[0], m[1]
+                class_id = int(high_dets[det_idx][5]) if len(high_dets[det_idx]) > 5 else 0
+                class_name = self.get_class_name(class_id)
+                confirmed_tracks[track_idx].update(high_dets[det_idx][:4], 
+                                                   high_dets[det_idx][4],
+                                                   class_id,
+                                                   class_name)
+            
+            unmatched_tracks = [confirmed_tracks[i] for i in u_tracks]
+            unmatched_dets = [high_dets[i] for i in u_dets]
+        else:
+            unmatched_tracks = confirmed_tracks
+            unmatched_dets = high_dets
+
+        # Second association with low confidence
+        if len(unmatched_tracks) > 0 and len(low_dets) > 0:
+            iou_mat = self.iou_distance(unmatched_tracks, low_dets)
+            matches, u_tracks, _ = self.linear_assignment(iou_mat, self.match_thresh * 0.6)
+            
+            for m in matches:
+                track_idx, det_idx = m[0], m[1]
+                class_id = int(low_dets[det_idx][5]) if len(low_dets[det_idx]) > 5 else 0
+                class_name = self.get_class_name(class_id)
+                unmatched_tracks[track_idx].update(low_dets[det_idx][:4],
+                                                   low_dets[det_idx][4],
+                                                   class_id,
+                                                   class_name)
+            
+            unmatched_tracks = [unmatched_tracks[i] for i in u_tracks]
+
+        # Deal with unconfirmed tracks
+        if len(unmatched_dets) > 0:
+            for t in unconfirmed_tracks:
+                iou_mat = self.iou_distance([t], unmatched_dets)
+                if iou_mat.size > 0:
+                    best_match = np.argmax(iou_mat[0])
+                    if iou_mat[0, best_match] > self.match_thresh * 0.5:
+                        class_id = int(unmatched_dets[best_match][5]) if len(unmatched_dets[best_match]) > 5 else 0
+                        class_name = self.get_class_name(class_id)
+                        t.update(unmatched_dets[best_match][:4],
+                                unmatched_dets[best_match][4],
+                                class_id,
+                                class_name)
+                        unmatched_dets.pop(best_match)
+
+        # Create new tracks
+        for d in unmatched_dets:
+            if d[4] > self.track_thresh:
+                overlaps = False
+                for t in self.tracks:
+                    if self.iou(t.tlwh, d[:4]) > 0.3:
+                        overlaps = True
+                        break
+                
+                if not overlaps:
+                    class_id = int(d[5]) if len(d) > 5 else 0
+                    class_name = self.get_class_name(class_id)
+                    track_id = self.get_next_id(class_id)
+                    self.tracks.append(Track(d[:4], d[4], track_id, class_id, class_name))
+
+        # Remove old tracks
+        self.tracks = [t for t in self.tracks if t.time_since_update < self.buffer_size]
+        
+        return self.tracks
+
+
+
+# =========================================================
+#  CLEAN CLASS NAME
+# =========================================================
+def clean_name(name):
+    return name.replace("LEFT_", "").replace("RIGHT_", "").replace("_Start", "").replace("_Stop", "")
+
+# =========================================================
+#  PARSE JSON
+# =========================================================
+def parse_json(path):
+    with open(path, 'r') as f:
+        content = f.read()
+
+    try:
+        data = json.loads(content)   # proper JSON
+    except json.JSONDecodeError:
+        data = ast.literal_eval(content)  # handles single quotes etc.
+
+    parsed = {}
+
+    for asset in data["Assets"]:
+        cls = clean_name(asset[0])
+        frame = int(asset[2])
+
+        x1, y1 = asset[3]
+        x2, y2 = asset[4]
+
+        if frame not in parsed:
+            parsed[frame] = []
+
+        parsed[frame].append({
+            "class": cls,
+            "bbox": [x1, y1, x2, y2],
+            "id": asset[1]
+        })
+
+    return parsed
+
+# =========================================================
+#  SAM
+# =========================================================
+def load_sam():
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    sam = sam_model_registry[MODEL_TYPE](checkpoint=SAM_CHECKPOINT)
+    sam.to(device)
+    return SamPredictor(sam)
+
+def get_mask(image, bbox, predictor):
+    masks, scores, _ = predictor.predict(
+        box=np.array(bbox),
+        multimask_output=True
+    )
+    return masks[np.argmax(scores)]
+
+# =========================================================
+#  TEMPORAL FRAMES"""
+Reads YOLO label--> convert to pixel format --> Feed the box to Sam ---> Get mask
+"""
+# =========================================================
+def get_frames(center, total):
+    frames = []
+    for i in range(-NUM_FRAMES, NUM_FRAMES + 1):
+        f = center + i * FRAME_GAP
+        if 0 <= f < total:
+            frames.append(f)
+    return frames
+
+# =========================================================
+# MASK → POLYGON
+# =========================================================
+def mask_to_polygon(mask):
+    mask_uint8 = (mask * 255).astype(np.uint8)
+    contours, _ = cv2.findContours(mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    polygons = []
+    for cnt in contours:
+        if len(cnt) >= 3:
+            poly = cnt.squeeze().tolist()
+            polygons.append(poly)
+
+    return polygons
+
+# =========================================================
+#  OVERLAY MASK
+# =========================================================
+def overlay_mask(image, mask):
+    overlay = image.copy()
+    color = np.array([255, 0, 0])  # RED
+
+    overlay[mask == 1] = overlay[mask == 1] * 0.5 + color * 0.5
+    return overlay.astype(np.uint8)
+
+# =========================================================
+#  MAIN PIPELINE
+# =========================================================
+def process_video():
+    predictor = load_sam()
+    parsed = parse_json(JSON_PATH)
+
+    cap = cv2.VideoCapture(VIDEO_PATH)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    bbox_json = {}
+    poly_json = {}
+
+    for center_frame in parsed:
+
+        temporal_frames = get_frames(center_frame, total_frames)
+
+        for obj_idx, obj in enumerate(parsed[center_frame]):
+            cls = obj["class"]
+            init_bbox = obj["bbox"]
+            obj_id = obj["id"]
+
+            tracker = ByteTrackEnhanced(class_names=[cls])
+
+            x1, y1, x2, y2 = init_bbox
+            tlwh = [x1, y1, x2-x1, y2-y1]
+
+            init_det = np.array([[*tlwh, 0.9, 0]])
+            tracker.update(init_det)
+
+            for f in temporal_frames:
+                cap.set(cv2.CAP_PROP_POS_FRAMES, f)
+                ret, frame = cap.read()
+                if not ret:
+                    continue
+
+                detections = init_det if f == center_frame else np.empty((0, 6))
+                tracks = tracker.update(detections)
+
+                if len(tracks) == 0:
+                    continue
+
+                t = tracks[0]
+                x, y, w, h = t.tlwh
+                bbox = [int(x), int(y), int(x+w), int(y+h)]
+
+                image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                predictor.set_image(image_rgb)
+
+                mask = get_mask(image_rgb, bbox, predictor)
+                mask_uint8 = (mask * 255).astype(np.uint8)
+
+                #  SAVE MASK
+                cv2.imwrite(os.path.join(MASK_DIR, f"{f}.png"), mask_uint8)
+
+                #  SAVE MASKED IMAGE
+                extracted = cv2.bitwise_and(image_rgb, image_rgb, mask=mask_uint8)
+                cv2.imwrite(os.path.join(EXTRACT_DIR, f"{f}.png"),
+                            cv2.cvtColor(extracted, cv2.COLOR_RGB2BGR))
+
+                #  OVERLAY IMAGE
+                overlay = overlay_mask(image_rgb, mask)
+                cv2.imwrite(os.path.join(IMG_DIR, f"{f}.png"),
+                            cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))
+
+                #  STORE BBOX JSON
+                if str(f) not in bbox_json:
+                    bbox_json[str(f)] = {}
+
+                if cls not in bbox_json[str(f)]:
+                    bbox_json[str(f)][cls] = []
+
+                bbox_json[str(f)][cls].append([str(obj_id), [bbox[0], bbox[1]], [bbox[2], bbox[3]]])
+
+                #  STORE POLYGON JSON
+                polygons = mask_to_polygon(mask)
+
+                if str(f) not in poly_json:
+                    poly_json[str(f)] = {}
+
+                if cls not in poly_json[str(f)]:
+                    poly_json[str(f)][cls] = []
+
+                poly_json[str(f)][cls].append([str(obj_id), polygons])
+
+                print(f"[DONE] Frame {f}")
+
+    cap.release()
+
+    #  SAVE JSON FILES
+    with open(os.path.join(OUTPUT_DIR, "bboxes.json"), "w") as f:
+        json.dump(bbox_json, f, indent=2)
+
+    with open(os.path.join(OUTPUT_DIR, "polygons.json"), "w") as f:
+        json.dump(poly_json, f, indent=2)
+
+
+# =========================================================
+#  ENTRY
+# =========================================================
+if __name__ == "__main__":
+    process_video()
\ No newline at end of file
diff --git a/sam2/sam_image_mask_extract.py b/sam2/sam_image_mask_extract.py
new file mode 100644
index 0000000..faae942
--- /dev/null
+++ b/sam2/sam_image_mask_extract.py
@@ -0,0 +1,152 @@
+import os
+import cv2
+import json
+import torch
+import numpy as np
+
+from segment_anything import sam_model_registry, SamPredictor
+
+# ---------------- CONFIG ----------------
+SAM_CHECKPOINT = "/media/bs5/New Volume/Brinda/Codes/Florida/SAM/sam_vit_h_4b8939.pth"
+MODEL_TYPE = "vit_h"
+
+IMAGE_DIR = "/media/bs5/New Volume/Brinda/Dataset/Florida/New_Dataset/set2/images_seg"
+LABEL_DIR = "/media/bs5/New Volume/Brinda/Dataset/Florida/New_Dataset/set2/labels_seg/"
+OUTPUT_DIR = "/media/bs5/New Volume/Brinda/Dataset/Florida/classification_data/"
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+os.makedirs(os.path.join(OUTPUT_DIR, "masks"), exist_ok=True)
+os.makedirs(os.path.join(OUTPUT_DIR, "overlay"), exist_ok=True)
+os.makedirs(os.path.join(OUTPUT_DIR, "polygons"), exist_ok=True)
+
+# ---------------- LOAD SAM ----------------
+sam = sam_model_registry[MODEL_TYPE](checkpoint=SAM_CHECKPOINT)
+sam.to(DEVICE)
+predictor = SamPredictor(sam)
+
+# ---------------- HELPERS ----------------
+def yolo_to_xyxy(label, img_w, img_h):
+    """
+    YOLO format: class cx cy w h (normalized)
+    Convert → pixel [x1, y1, x2, y2]
+    """
+    cls, cx, cy, w, h = label
+
+    x1 = int((cx - w / 2) * img_w)
+    y1 = int((cy - h / 2) * img_h)
+    x2 = int((cx + w / 2) * img_w)
+    y2 = int((cy + h / 2) * img_h)
+
+    return [x1, y1, x2, y2]
+
+
+def expand_box(x1, y1, x2, y2, pad, w, h):
+    return [
+        max(0, x1 - pad),
+        max(0, y1 - pad),
+        min(w, x2 + pad),
+        min(h, y2 + pad),
+    ]
+
+
+def mask_to_polygon(mask):
+    contours, _ = cv2.findContours(
+        mask.astype(np.uint8),
+        cv2.RETR_EXTERNAL,
+        cv2.CHAIN_APPROX_SIMPLE
+    )
+
+    polygons = []
+    for cnt in contours:
+        cnt = cnt.squeeze()
+        if len(cnt.shape) == 2 and len(cnt) > 2:
+            polygons.append(cnt.tolist())
+
+    return polygons
+
+
+# ---------------- PROCESS ----------------
+for img_file in os.listdir(IMAGE_DIR):
+
+    if not img_file.lower().endswith((".jpg", ".png", ".jpeg")):
+        continue
+
+    image_path = os.path.join(IMAGE_DIR, img_file)
+    label_path = os.path.join(LABEL_DIR, img_file.replace(".jpg", ".txt").replace(".png", ".txt"))
+
+    if not os.path.exists(label_path):
+        continue
+
+    image = cv2.imread(image_path)
+    if image is None:
+        continue
+
+    h, w = image.shape[:2]
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    predictor.set_image(image_rgb)
+
+    overlay = image.copy()
+    output_data = []
+
+    # -------- Read YOLO labels --------
+    with open(label_path, "r") as f:
+        lines = f.readlines()
+
+    for idx, line in enumerate(lines):
+        parts = list(map(float, line.strip().split()))
+        cls_id = int(parts[0])
+        cx, cy, bw, bh = parts[1:]
+
+        # YOLO → XYXY
+        x1, y1, x2, y2 = yolo_to_xyxy([cls_id, cx, cy, bw, bh], w, h)
+
+        # Expand bbox (important for SAM)
+        x1, y1, x2, y2 = expand_box(x1, y1, x2, y2, pad=10, w=w, h=h)
+
+        box = np.array([x1, y1, x2, y2])
+
+        # -------- SAM Prediction --------
+        masks, scores, _ = predictor.predict(
+            box=box,
+            multimask_output=False
+        )
+
+        mask = masks[0]
+
+        # -------- Save mask --------
+        mask_name = f"{img_file.split('.')[0]}_{idx}.png"
+        mask_path = os.path.join(OUTPUT_DIR, "masks", mask_name)
+        cv2.imwrite(mask_path, mask.astype(np.uint8) * 255)
+
+        # -------- Overlay --------
+        color = np.random.randint(0, 255, (3,))
+        overlay[mask] = (overlay[mask] * 0.5 + color * 0.5).astype(np.uint8)
+
+        # -------- Polygon --------
+        polygons = mask_to_polygon(mask)
+
+        output_data.append({
+            "id": idx,
+            "class": cls_id,
+            "bbox": [x1, y1, x2, y2],
+            "polygon": polygons
+        })
+
+    # -------- Save overlay --------
+    cv2.imwrite(
+        os.path.join(OUTPUT_DIR, "overlay", img_file),
+        overlay
+    )
+
+    # -------- Save JSON --------
+    json_path = os.path.join(
+        OUTPUT_DIR, "polygons", img_file.replace(".jpg", ".json").replace(".png", ".json")
+    )
+
+    with open(json_path, "w") as f:
+        json.dump(output_data, f, indent=4)
+
+print(" Processing Complete")
\ No newline at end of file