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maziggy
eec7793955
Adds a Failure Detection tab under Settings that wires Bambuddy to a
self-hosted Obico ml_api container — no cloud, no account, no WebSocket.
While a print is running, the detection service periodically hands the
printer's camera snapshot URL to the ML API and smooths scores over
time (30-frame warmup + EWM, alpha=2/13, short/long rolling means) so
one noisy frame can't trigger an action. When the smoothed score
crosses HIGH, the configured action fires exactly once per print:
notify, pause, or pause-and-cut-power (via linked smart plugs).
- Backend: new obico_detection + obico_smoothing + obico_actions
services, /obico/status and /obico/test-connection routes
(SETTINGS_READ / SETTINGS_UPDATE), six obico_* AppSettings fields
with validators for sensitivity/action/enabled_printers.
- Frontend: FailureDetectionSettings component (enable, ML URL + test,
sensitivity, action, poll interval, per-printer monitor list, live
status + detection history), new sidebar tab with service-active
bullet, toast on save.
- Tests: 17 detection unit tests + 15 smoothing unit tests + 4
frontend component tests.
- Docs: README bullet, CHANGELOG entry, wiki page under Analytics,
website features.html entry.
106 lines
3.3 KiB
Python
106 lines
3.3 KiB
Python
"""Temporal smoothing for Obico ML detection scores.
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Ports Obico's failure-detection math:
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- per-frame `current_p` = sum of detection confidences
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- `ewm_mean` = exponentially weighted mean (alpha = 2 / (span + 1), span = 12)
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- `rolling_mean_short` = ~310 frames of recent activity (≈52 min at 10s/frame)
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- `rolling_mean_long` = ~7200 frames of long-term baseline noise
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- First `WARMUP_FRAMES` frames always report "safe" while the state settles
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- Final score = max(ewm_mean, rolling_mean_short - rolling_mean_long)
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- Thresholds: LOW < score < HIGH is "warning", >= HIGH is "failure"
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"""
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import math
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from collections import deque
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from dataclasses import dataclass, field
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EWM_SPAN = 12
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EWM_ALPHA = 2.0 / (EWM_SPAN + 1)
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ROLLING_SHORT = 310
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ROLLING_LONG = 7200
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WARMUP_FRAMES = 30
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# Base thresholds; sensitivity multipliers adjust them
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BASE_LOW = 0.38
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BASE_HIGH = 0.78
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SENSITIVITY_MULT = {
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"low": 1.25, # harder to trigger — higher thresholds
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"medium": 1.0,
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"high": 0.75, # easier to trigger — lower thresholds
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}
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def thresholds(sensitivity: str) -> tuple[float, float]:
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mult = SENSITIVITY_MULT.get(sensitivity, 1.0)
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return BASE_LOW * mult, BASE_HIGH * mult
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@dataclass
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class PrintState:
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"""Per-print smoothing state. Reset when a new print starts."""
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frame_count: int = 0
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ewm_mean: float = 0.0
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short_sum: float = 0.0
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long_sum: float = 0.0
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short_buf: deque = field(default_factory=lambda: deque(maxlen=ROLLING_SHORT))
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long_buf: deque = field(default_factory=lambda: deque(maxlen=ROLLING_LONG))
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def update(self, current_p: float) -> float:
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"""Feed a new per-frame score and return the smoothed score.
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Returns 0.0 during warmup so early noise doesn't trigger actions.
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"""
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self.frame_count += 1
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if self.frame_count == 1:
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self.ewm_mean = current_p
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else:
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self.ewm_mean = EWM_ALPHA * current_p + (1 - EWM_ALPHA) * self.ewm_mean
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if len(self.short_buf) == self.short_buf.maxlen:
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self.short_sum -= self.short_buf[0]
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self.short_buf.append(current_p)
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self.short_sum += current_p
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if len(self.long_buf) == self.long_buf.maxlen:
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self.long_sum -= self.long_buf[0]
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self.long_buf.append(current_p)
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self.long_sum += current_p
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if self.frame_count <= WARMUP_FRAMES:
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return 0.0
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short_mean = self.short_sum / len(self.short_buf)
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long_mean = self.long_sum / len(self.long_buf)
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return max(self.ewm_mean, short_mean - long_mean)
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def classify(score: float, sensitivity: str) -> str:
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"""Return 'safe', 'warning', or 'failure' for a smoothed score."""
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low, high = thresholds(sensitivity)
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if score >= high:
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return "failure"
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if score >= low:
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return "warning"
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return "safe"
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def score_from_detections(detections: list) -> float:
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"""Sum confidences from the ML API `detections` array.
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Each detection is `[label, confidence, [x, y, w, h]]`. We only care about
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the confidence column — label is always "failure" for the single-class model.
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"""
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total = 0.0
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for det in detections or []:
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try:
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value = float(det[1])
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except (IndexError, TypeError, ValueError):
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continue
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if math.isnan(value) or math.isinf(value):
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continue
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total += value
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return total
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