tarsius

Local, training-free state detection for a region of a camera image: is the car in the garage, is the mailbox flag up, is the gate open.

You confirm a few frames per state, tarsius compares new frames against them and answers with a state or "unsure". Nothing leaves the machine and nothing gets trained.

Why tarsius

Tarsiers are small nocturnal primates whose eyes are each about as large as their brain. Lots of eye, little brain, and very good at seeing in the dark. That is the idea here too: no training and no model to learn, just careful looking, including at IR night frames.

from tarsius import Roi, StateDetector

# The detector picks the backend for the task; detector.backend_id tells which.
detector = StateDetector(Roi.from_percent(10, 10, 60, 50), ("on", "off"))

# Teaching: check the frame, store prepared.original and prepared.cache, add it.
prepared = detector.prepare(car_jpeg)
evicted = detector.add("ref-1", prepared, state="on")  # keys dropped by the cap
detector.add("ref-2", detector.prepare(empty_jpeg), state="off")

result = detector.classify(new_jpeg)
result.status  # Status.CONFIDENT, Status.UNSURE or Status.NOT_READY
result.state  # "on" or "off" when confident
result.candidate  # best guess, also when unsure
result.probabilities  # {"on": 0.97, "off": 0.03}, estimated from the scores

probabilities is a softmax of the per-state scores with a temperature per backend, fitted by scripts/calibrate.py on the week before the test week of the history below. Only the harder camera had both states in that week, so the fit rests on its 142 frames. The test week has 284 frames, each classified with 15 sets of references (1, 2 and 5 segments per state, 5 seeds each). Of those answers ncnn gives 0.9 or more to 97 %, all of them right, with a calibration error of 0.009. edges gives 0.9 or more to 79 %. With one segment per state the estimates are slightly too sure, ncnn erred at up to 0.85. With more references they are rather too cautious, edges clearly. The status comes from the margin, not from the probabilities: with two states, unsure means below 0.67 for ncnn. A blank region gets the same probability for every state, a detector that is not ready gives none.

After a restart, hand the stored originals back with detector.load([(key, original, state), ...], caches={key: cache, ...}). A cache spares the extraction, about 14 ms per reference on an i5-8400T, one reference is still extracted to check that the cached ones fit the backend. A cache made for another region, frame_side, backend or release of tarsius, Pillow or ncnn is ignored and the reference is extracted again. A cache that is damaged or does not hold a unit vector is ignored too. LoadReport.caches holds only the new caches: store them, keep the others and drop those of skipped and evicted keys. Development builds all report version 0.0.0, so clear stored caches after changing tarsius code there. Unusable references (blank, undecodable, unknown state) are skipped and reported in the returned LoadReport instead of failing. prepare() and classify() raise ImageError for undecodable data and BlankFrameError when the frame shows nothing, for example a grey frame from an offline camera. prepare() also raises BlankRegionError (the base class) when the region has nothing to compare, classify() answers such a region as unsure, without a candidate. tarsius.outline(frame, roi) draws the region on a frame with the same box rule as the comparison.

All methods block, and so does creating the first detector, which loads the model; call them in an executor. They are safe to call from several threads: changes are serialized and classify() never waits for them, only inferences on the shared backend take turns.

Backends

• NcnnBackend: SqueezeNet 1.1 ImageNet features on ncnn. Tolerates clutter and lighting changes, a rough region is enough.
• EdgeBackend: numpy only, a baseline. Needs a tight region that still shows some static structure when the object is gone.

StateDetector uses NcnnBackend unless you pass backend=. It loads one instance per process (about 20 MB) and shares it, inferences take turns on it. ncnn is a regular dependency. Since ncnn 1.0.20261020, opencv-python is an optional extra of ncnn, so tarsius installs on Alpine (musl) systems such as Home Assistant OS. Checked in python:3.14-alpine on x86_64 and aarch64. Both backends support Python 3.14 and 3.15.

Results so far

One household, two garage cameras, 24 days of recorded history, day and IR night. References come from older frames, the test is the newest week. The table is the harder camera: a dark car and clutter that gets moved around in front of it (459 frames, 164 in the test week). The other camera scores 100 % with both backends.

About 30 ms per 4 MP frame with edges and 36 ms with ncnn, including JPEG decoding, on one thread of an Intel i5-8400T. A small sample, not a guarantee. scripts/history_split.py runs the same evaluation with StateDetector on your own recordings. It also reports how often the detector would ask when unsure answers are confirmed.

Development

scripts/build_model.sh   # once, builds the ncnn model into src/tarsius/models
uv sync
uv run pytest
uvx prek run --all-files

The ncnn model is not stored in git. scripts/build_model.sh converts the original SqueezeNet 1.1 release with the ncnn tools. Inputs and outputs are checked against pinned sha256 sums, so CI and a local run produce the same files. It needs x86_64 Linux with curl, unzip, libgomp1 and libprotobuf32t64 (Ubuntu 24.04).

Releases: every PR gets a label, Release Drafter collects them into a draft, publishing the GitHub release builds and uploads to PyPI.

License

Apache-2.0. The bundled SqueezeNet model is BSD-2-Clause, see NOTICE.