kami
ff6a512630
Working tree (including .git) was lost to an rm. Rebuilt by replaying Write/Edit/ Read/attachment events from 25 Claude sessions and 22 successful codex apply_patch blocks into one timestamp-ordered timeline. Verified against ground truth recorded in the transcripts: wc -l on 10 files and ls -l on 5 files at 2026-07-18T13:13:44Z both match exactly; 18 files are byte-identical to their newest ~/.claude/file-history blob. See HANDOFF.md for sources, gaps, and how to rebuild .venv. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
# worker_ocr.py — stage 3 text extraction. FastAPI :8001. cpu (easyocr), no session.
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# panel in -> text blocks with bboxes + confidence out. orchestrator persists to sqlite.
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# easyocr (not tesseract): it reads stylized manga lettering far better -- recovers whole
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# lines tesseract garbles or drops. runs on GPU (~0.4s/page warm) by default; the OCR stage
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# runs before any LLM session opens so it doesn't contend with the resident model. set
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# OCR_GPU=0 to force CPU (~3s/page). GPU needs MIOPEN_FIND_MODE=FAST in the env or the first
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# ROCm run spends ~60s in MIOpen's exhaustive kernel search -- the launcher sets it.
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import os, uuid
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from fastapi import FastAPI
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from pydantic import BaseModel
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import transport
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app = FastAPI()
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SHM = "/dev/shm"
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MIN_CONF = 0.3 # easyocr line confidence floor
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OCR_GPU = os.environ.get("OCR_GPU", "1") == "1"
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_reader = None # easyocr.Reader, lazy-loaded on first request
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def _get_reader():
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global _reader
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if _reader is None:
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import easyocr
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_reader = easyocr.Reader(["en"], gpu=OCR_GPU, verbose=False)
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return _reader
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def _detections_to_texts(detections):
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"""easyocr readtext output [(box_pts, text, conf)] -> our text blocks with xywh bboxes.
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box_pts is 4 corner [x,y] points. drops low-confidence and art-noise (<2 letters).
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casing is left as-is (mixed) -- the vision stage re-cases from the image anyway."""
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texts = []
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for i, (box, txt, conf) in enumerate(detections):
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txt = txt.strip()
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if conf < MIN_CONF or sum(c.isalpha() for c in txt) < 2:
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continue
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xs = [p[0] for p in box]; ys = [p[1] for p in box]
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x, y = int(min(xs)), int(min(ys))
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texts.append({
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"id": f"t{i+1:03d}",
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"content": txt,
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"bbox": [x, y, int(max(xs)) - x, int(max(ys)) - y],
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"confidence": round(float(conf), 3),
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})
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return texts
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def ocr_image(path: str):
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return _detections_to_texts(_get_reader().readtext(path, detail=1, paragraph=False))
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class OCRInput(BaseModel):
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panel_uri: str
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job_id: str = ""
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panel_id: str = ""
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@app.post("/ocr")
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async def ocr(data: OCRInput):
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local = transport.get(data.panel_uri, f"{SHM}/ocr_{uuid.uuid4().hex[:8]}.png")
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texts = ocr_image(local)
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os.remove(local)
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return {"panel_id": data.panel_id, "texts": texts}
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@app.post("/unload")
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async def unload():
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"""free the resident easyocr reader (~1-2GB) once the OCR stage is done, before gemma4 loads.
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ocr isn't session-managed, so the orchestrator calls this at stage end."""
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global _reader
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was = _reader is not None
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_reader = None
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import gc; gc.collect()
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try:
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import torch; torch.cuda.empty_cache()
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except Exception:
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pass
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return {"ok": True, "unloaded": was}
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@app.get("/health")
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async def health():
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return {"status": "ok"}
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if __name__ == "__main__":
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# self-check: detection->text-block conversion (pure, no model needed).
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dets = [
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([[10, 10], [110, 10], [110, 40], [10, 40]], "HELLO", 0.9), # kept
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([[10, 200], [70, 200], [70, 230], [10, 230]], "WORLD", 0.8), # kept
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([[5, 5], [13, 5], [13, 13], [5, 13]], "=", 0.9), # art-noise: <2 letters
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([[0, 0], [50, 0], [50, 20], [0, 20]], "REAL", 0.1), # below MIN_CONF
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]
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texts = _detections_to_texts(dets)
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assert [t["content"] for t in texts] == ["HELLO", "WORLD"], texts
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assert texts[0]["bbox"] == [10, 10, 100, 30], texts[0]["bbox"]
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assert texts[1]["confidence"] == 0.8
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print("worker_ocr self-check ok")
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