51eed139cf
Two operator-value features: evidence that shows HOW it happened, and the one-line answer to "what happened overnight". Evidence timeline (closes the P1-B "前中後 스냅샷 배선" follow-up): - The ring buffer now stores per-frame head boxes, and an event captures the frames nearest T-2.5s / T-1.0s / T-0 (server cap: 3 snapshots). A fall's evidentiary value is in the seconds BEFORE the person hit the floor - one end-state frame never showed how it happened. - Each past frame is blurred with the head positions of ITS OWN moment - blurring history with the current tick's boxes would leave a face exactly where it actually was (PIPA). - Right after startup the ring is short: fewer frames go out, never zero and never duplicated. Detail view captions the sequence 이전/직전/발생 시점 by position (offsets are edge config the server doesn't know). 24h catch-up brief: - The events console renders one line above the stream on load - "지난 24시간 - 감지 N건 (중대 n) · 니어미스 n · 평균 대응 t · 미확인 n건 남음" - reusing GET /api/report?days=1, with a link into /report. High-tinted when the window held critical/high events; hidden entirely until data arrives. Verified: pytest 235 pass (3 new: timeline selection, short-buffer dedup, 3-frame capture through run_once) / PHPUnit 138 pass + Chrome real-render (brief line with severe tint on the console; detail page showing the 3-frame timeline with captions; no console errors).
64 lines
2.6 KiB
Python
64 lines
2.6 KiB
Python
from collections import deque
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import cv2
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from edge.models import BBox
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class RingBuffer:
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def __init__(self, maxlen: int = 90):
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self.frames: deque = deque(maxlen=maxlen) # (t, frame, head_boxes)
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def push(self, t: float, frame, head_boxes: list | None = None) -> None:
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# head_boxes ride along per frame: a PAST frame must be blurred with the head
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# positions of ITS OWN moment — blurring history with the current tick's boxes
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# would leave yesterday's face exactly where it actually was (PIPA).
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self.frames.append((t, frame, head_boxes or []))
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def window(self, pre: int, post: int, index: int) -> list:
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lo = max(0, index - pre)
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hi = min(len(self.frames), index + post + 1)
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return [f for (_t, f, _b) in list(self.frames)[lo:hi]]
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def timeline(self, now: float, offsets_s: tuple) -> tuple[list, list]:
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"""각 목표 시각(now - offset)에 가장 가까운 프레임을 골라 (frames, head_boxes)를
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과거→현재 순으로 반환. 버퍼가 짧으면(기동 직후) 있는 만큼만 — 같은 프레임이 여러
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목표에 걸리면 한 번만 들어간다."""
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items = list(self.frames)
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if not items:
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return [], []
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idxs: list[int] = []
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for off in sorted(offsets_s, reverse=True):
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target = now - off
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i = min(range(len(items)), key=lambda k: abs(items[k][0] - target))
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if i not in idxs:
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idxs.append(i)
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idxs.sort()
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return [items[i][1] for i in idxs], [items[i][2] for i in idxs]
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class FaceBlurrer:
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def blur(self, frame, head_boxes: list[BBox]):
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for b in head_boxes:
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x1, y1 = int(max(0, b.x1)), int(max(0, b.y1))
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x2, y2 = int(min(frame.shape[1], b.x2)), int(min(frame.shape[0], b.y2))
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if x2 > x1 and y2 > y1:
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roi = frame[y1:y2, x1:x2]
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frame[y1:y2, x1:x2] = cv2.GaussianBlur(roi, (0, 0), sigmaX=12)
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return frame
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class Snapshotter:
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def __init__(self, blurrer: FaceBlurrer, quality: int = 80):
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self.blurrer = blurrer
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self.quality = quality
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def capture(self, frames: list, head_boxes_per_frame: list) -> list[bytes]:
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out: list[bytes] = []
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for frame, heads in list(zip(frames, head_boxes_per_frame))[:3]:
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blurred = self.blurrer.blur(frame.copy(), heads)
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ok, buf = cv2.imencode(".jpg", blurred,
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[cv2.IMWRITE_JPEG_QUALITY, self.quality])
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if ok:
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out.append(buf.tobytes())
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return out
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