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camera.ui plugin that watches a fixed corner of the garden, classifies whether both wheelie bins are present using a locally-trained OpenVINO model, and exposes that as a sensor camera.ui automations (and Home Assistant, via camera.ui's HA bridge) can r","maintainers":[{"name":"deadeye920","email":"stevehatton@icloud.com"}],"readme":"# Bin Presence\n\nA camera.ui plugin that watches a fixed corner of the garden, classifies whether both wheelie bins are present using a locally-trained OpenVINO model, and exposes that as a sensor camera.ui automations (and Home Assistant, via camera.ui's HA bridge) can react to.\n\n## Why\n\nSteve has two bins that live side by side in the same garden spot. An existing Home Assistant automation already reminds him which bin to put out on collection day — this plugin's job is to confirm whether that actually happened, so a follow-up reminder can fire if both bins are still sitting there when they shouldn't be.\n\n## How it works\n\n1. The plugin attaches a Classifier-type sensor to each camera it's assigned to.\n2. It triggers whenever the camera's own object detector reports a person, animal, or vehicle — bins don't move on their own, so it piggybacks on other activity rather than watching continuously. A configurable cooldown (default 60s) avoids saving near-duplicate frames from the same burst of motion.\n3. On trigger, it pulls a fresh, full-resolution snapshot directly from the camera (not the auto-cropped trigger region the host would otherwise hand it) and crops it to a named Motion Zone, drawn visually via camera.ui's own Edit Zones UI rather than hand-typed pixel coordinates.\n4. The cropped image is classified by a MobileNetV2 model (trained via transfer learning on manually-sorted snapshots, then exported to OpenVINO IR format) running locally via openvino-node — no cloud calls.\n5. The result (both_present or one_present, plus confidence) is logged alongside the snapshot.\n6. If \"Save training images\" is on (see Setup), the cropped image is saved to disk when a bin is missing (both_present results are skipped once there's already plenty of that class), split into a per-camera subfolder, building up a labelled-by-hand training set over time for retraining.\n\nA separate, more reliable path also exists for driving automations directly: the plugin implements camera.ui's ClassifierDetectionInterface, so a \"Plugin Call\" automation action can classify Back Garden's bin corner on demand and get an immediate result — see Setup below for why this is preferred over watching the sensor's own properties.\n\n## Setup\n\n1. Install the plugin from the camera.ui plugin store: @deadeye920/camera-ui-bin-presence\n2. Assign it to each camera that can see the bin corner. Prefer a fixed-angle camera — a camera whose view can move (e.g. PTZ auto-repositioning at night) will crop the wrong area, since the zone's coordinates are percentage-based against whatever the camera is currently looking at.\n3. On each of those cameras, open Edit Zones -> Motion, draw a zone around the bin corner, and name it to match the plugin's Zone name setting (default: Bins).\n4. Adjust Cooldown (seconds) if needed (default: 60).\n5. Optionally turn off \"Save training images\" once you've collected enough data, or change \"Save directory\" if you want images saved somewhere other than the default.\n6. Build a camera.ui automation using the Plugin Call action — not Sensor Change/Sensor State (see note below):\n   - Trigger: Schedule (e.g. every 5-10 minutes)\n   - Action: Capture Snapshot (on the camera)\n   - Action: Plugin Call -> Bin Presence -> Classifier Detection -> Classify Image (Image Data can be left as whatever the automation offers; the plugin fetches its own fresh snapshot internally and ignores it)\n   - Condition: If/Else on {{plugin.detected}} == true\n   - Actions: Control Sensor on the \"both bins present\" switch -- true branch sets it on, false branch sets it off. That switch can then be exposed to Home Assistant.\n\nNote: watching the classifier sensor's own properties (Sensor Change trigger + Sensor State condition) was tried first and found unreliable -- camera.ui's sensor framework appears to auto-expire/reset detection-style sensor state on its own after a dwell timeout, generating spurious \"Sensor Change\" events unrelated to any real classification. Comparing the Labels property directly was also found to sometimes always evaluate true regardless of the sensor's actual value. Calling the plugin directly via \"Plugin Call\" avoids all of this, since it's a synchronous function call with no property-watching involved. The plugin also debounces low-confidence individual readings and requires two consecutive confident readings before accepting a real state change, to avoid flapping on borderline classifications (see src/index.ts).\n\n## Where snapshots land\n\nBy default: /mnt/recordings/bin-presence-snapshots/<Camera Name>/<timestamp>.png — configurable per-camera via the \"Save directory\" setting.\n\nOn the Proxmox host, the default path is also visible at /mnt/pve/cameraui-storage/bin-presence-snapshots/ (mergerfs-backed).\n\n## Training a new model\n\nTraining happens on a separate LXC (bin-training, CT 116) with the same storage mounted in, using a small Python/PyTorch pipeline:\n\n1. Sort newly-collected snapshots into training-data/both_present and training-data/one_present using the small local web tool (bin-sorter.py) run on the Proxmox host.\n2. Run train.py — MobileNetV2 transfer learning with class-weighted sampling, saves model.pth.\n3. Run export.py — converts to OpenVINO IR format (model.xml + model.bin), with explicit output tensor naming (required, or inference fails with \"Attempt to get a name for a Tensor without names\").\n4. Copy both files into this project's model/ folder, rebuild, and republish (see Development below).\n\n## Development\n\nEntry point is src/index.ts (moved here from the project root — required by camera.ui's CLI bundler). Type-check with:\n\nnpx tsc --noEmit\n\nBuild and publish now go through camera.ui's official CLI (@camera.ui/cli, installed as a dev dependency), not plain npm publish — camera.ui's plugin format changed to a CLI-generated zip bundle; see CHANGELOG 1.0.29-1.0.33 for why.\n\nnpm version patch\nnpx cui bundle\n\nThe CLI's own bundle only contains contract.cjs and a single bundled dist/index.js (plus model/, via additionalFiles in cameraui.config.ts) — it does not package native modules. openvino-node and sharp are marked external in cameraui.config.ts so their code isn't broken by inlining, but that means node_modules (with the real OpenVINO runtime — check for symlinks first, see below) has to be copied into dist/ and the whole thing re-zipped by hand before publishing:\n\nrm -rf /tmp/bundle-check\nmkdir -p /tmp/bundle-check\nunzip -o bundle/bundle.zip -d /tmp/bundle-check\ncp -r node_modules /tmp/bundle-check/dist/\ncd /tmp/bundle-check\nzip -r combined.zip contract.cjs dist\ncd -\ncp /tmp/bundle-check/combined.zip bundle/bundle.zip\n\nBefore publishing, open bundle/package.json and delete the \"dependencies\" block entirely. Its presence makes camera.ui's installer try to npm install openvino-node/sharp fresh on every install of this plugin, which fails with the same unprivileged-LXC chown EINVAL error described in CHANGELOG 1.0.10 — removing it stops the host from attempting any install at all, relying entirely on the pre-bundled node_modules already in the zip.\n\nThen publish the combined bundle:\n\nnpx cui publish --latest -y\n\nThen update the plugin version in camera.ui's plugin store.\n\nNote: if re-running npm install openvino-node locally (e.g. after a version bump), check node_modules/openvino-node/bin/ for symlinks before combining — they don't survive zipping intact and need converting to real file copies (see CHANGELOG 1.0.13-1.0.17 for the exact commands).\n\nSee CHANGELOG.md for full version history.\n","readmeFilename":"README.md"}