dekun
214 lines
7.3 KiB
Python
214 lines
7.3 KiB
Python
import logging
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import os
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import tempfile
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import threading
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from io import BytesIO
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from pathlib import Path
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import httpx
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from PIL import Image
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from app.core.config import settings
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# 无图形界面服务器：避免 OpenCV/Paddle 依赖 X11
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os.environ.setdefault("OPENCV_IO_ENABLE_OPENEXR", "0")
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logger = logging.getLogger(__name__)
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_ocr_engine = None
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_ocr_warmup_started = False
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def get_ocr_engine():
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global _ocr_engine
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if _ocr_engine is None:
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from paddleocr import PaddleOCR
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use_gpu = settings.OCR_USE_GPU
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_ocr_engine = PaddleOCR(
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use_angle_cls=False,
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lang="ch",
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show_log=False,
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use_gpu=use_gpu,
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enable_mkldnn=not use_gpu,
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det_limit_side_len=min(settings.OCR_MAX_SIDE, 1280),
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rec_batch_num=8,
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)
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return _ocr_engine
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def resolve_ocr_service_url(service_url: str | None = None) -> str | None:
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url = (service_url or settings.OCR_SERVICE_URL or "").strip()
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return url or None
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def uses_remote_ocr(service_url: str | None = None) -> bool:
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return resolve_ocr_service_url(service_url) is not None
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def warmup_ocr_engine() -> None:
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"""后台预加载 OCR 模型，避免首张图片等待数分钟。"""
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global _ocr_warmup_started
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if _ocr_warmup_started or not settings.OCR_WARMUP or uses_remote_ocr():
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return
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_ocr_warmup_started = True
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def _run() -> None:
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try:
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engine = get_ocr_engine()
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with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
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Image.new("RGB", (120, 40), color=(255, 255, 255)).save(tmp.name, format="JPEG")
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tmp_path = tmp.name
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try:
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engine.ocr(tmp_path, cls=False)
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logger.info("OCR engine warmed up")
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finally:
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Path(tmp_path).unlink(missing_ok=True)
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except Exception as exc:
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logger.warning("OCR warmup failed: %s", exc)
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threading.Thread(target=_run, daemon=True, name="ocr-warmup").start()
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def _bbox_from_box(box: list) -> list[float]:
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xs = [float(p[0]) for p in box]
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ys = [float(p[1]) for p in box]
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return [min(xs), min(ys), max(xs), max(ys)]
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def _scale_bbox(bbox: list[float], scale_x: float, scale_y: float) -> list[float]:
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return [bbox[0] * scale_x, bbox[1] * scale_y, bbox[2] * scale_x, bbox[3] * scale_y]
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def _scale_box(box: list, scale_x: float, scale_y: float) -> list:
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return [[float(p[0]) * scale_x, float(p[1]) * scale_y] for p in box]
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def _normalize_image_bytes(content: bytes, max_side: int) -> bytes:
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with Image.open(BytesIO(content)) as img:
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img = img.convert("RGB")
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width, height = img.size
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longest = max(width, height)
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if longest > max_side:
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ratio = max_side / longest
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img = img.resize((int(width * ratio), int(height * ratio)), Image.Resampling.LANCZOS)
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buf = BytesIO()
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img.save(buf, format="JPEG", quality=88, optimize=True)
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return buf.getvalue()
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def _prepare_ocr_image(image_path: str) -> tuple[str, float, float, int, int, Path | None]:
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"""若图片过大则生成临时缩小图供 OCR，返回缩放比例与原始尺寸。"""
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with Image.open(image_path) as img:
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orig_w, orig_h = img.size
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max_side = settings.OCR_MAX_SIDE
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longest = max(orig_w, orig_h)
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if longest <= max_side:
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return image_path, 1.0, 1.0, orig_w, orig_h, None
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with Image.open(image_path) as img:
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img = img.convert("RGB")
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ratio = max_side / longest
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new_w = max(1, int(orig_w * ratio))
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new_h = max(1, int(orig_h * ratio))
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resized = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
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tmp = Path(tempfile.gettempdir()) / f"ocr_{Path(image_path).stem}_{os.getpid()}.jpg"
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resized.save(tmp, format="JPEG", quality=85, optimize=True)
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scale_x = orig_w / new_w
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scale_y = orig_h / new_h
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return str(tmp), scale_x, scale_y, orig_w, orig_h, tmp
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def _run_remote_ocr(service_url: str, image_path: str) -> dict:
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url = f"{service_url.rstrip('/')}/api/ocr/regions"
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headers: dict[str, str] = {}
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if settings.OCR_API_KEY:
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headers["X-OCR-Key"] = settings.OCR_API_KEY
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with open(image_path, "rb") as handle:
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files = {"file": (Path(image_path).name, handle, "image/jpeg")}
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with httpx.Client(timeout=settings.OCR_TIMEOUT_SECONDS) as client:
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resp = client.post(url, files=files, headers=headers)
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if resp.status_code >= 400:
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detail = resp.text
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try:
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body = resp.json()
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if isinstance(body.get("detail"), str):
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detail = body["detail"]
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elif isinstance(body.get("detail"), list):
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detail = str(body["detail"])
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except Exception:
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pass
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raise RuntimeError(f"OCR 服务 {resp.status_code}: {detail}")
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return resp.json()
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def _run_local_ocr(image_path: str) -> dict:
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engine = get_ocr_engine()
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ocr_path, scale_x, scale_y, orig_w, orig_h, tmp_path = _prepare_ocr_image(image_path)
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try:
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result = engine.ocr(ocr_path, cls=False)
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finally:
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if tmp_path is not None:
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tmp_path.unlink(missing_ok=True)
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lines: list[dict] = []
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if result and result[0]:
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for item in result[0]:
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if not item or len(item) < 2:
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continue
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box, rec = item[0], item[1]
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text = rec[0] if rec else ""
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conf = float(rec[1]) if rec and len(rec) > 1 else 0.0
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if not text:
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continue
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if scale_x != 1.0 or scale_y != 1.0:
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box = _scale_box(box, scale_x, scale_y)
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bbox = _bbox_from_box(box)
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lines.append(
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{
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"text": text,
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"confidence": conf,
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"box": box,
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"bbox": bbox,
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}
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)
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return {
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"text": "\n".join(line["text"] for line in lines),
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"lines": lines,
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"width": orig_w,
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"height": orig_h,
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}
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def run_ocr_with_regions(image_path: str, service_url: str | None = None) -> dict:
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"""Return OCR text plus line-level bounding boxes for annotation."""
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remote = resolve_ocr_service_url(service_url)
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if remote:
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return _run_remote_ocr(remote, image_path)
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return _run_local_ocr(image_path)
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def run_ocr(image_path: str) -> str:
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return run_ocr_with_regions(image_path)["text"]
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def save_upload_file(user_id: str, question_id: str, filename: str, content: bytes) -> str:
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ext = Path(filename).suffix.lower() or ".jpg"
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if ext not in {".jpg", ".jpeg", ".png", ".webp"}:
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ext = ".jpg"
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user_dir = Path(settings.UPLOAD_DIR) / user_id
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user_dir.mkdir(parents=True, exist_ok=True)
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rel_path = f"{user_id}/{question_id}{ext}"
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full_path = Path(settings.UPLOAD_DIR) / rel_path
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normalized = _normalize_image_bytes(content, settings.UPLOAD_MAX_SIDE)
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full_path.write_bytes(normalized)
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return rel_path
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def annotated_rel_path(original_rel: str) -> str:
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p = Path(original_rel)
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return str(p.parent / f"{p.stem}_marked.jpg")
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