Rain-Bus
90fe705e8f
- 核心变更： - app/services/layout_detector.py: 重写布局检测器，从 PicoDet-S_layout_3cls 迁移到 DocLayout-YOLO (DocStructBench, imgsz=1024) - 支持多设备推理 (CPU/CUDA/DirectML/OpenVINO 等)，自动探测最优设备 - 预处理改为 letterbox (保比例缩放+灰边 padding)，坐标还原使用 (model_coord - padding) / ratio 公式 - 后处理解析 YOLOv10 end-to-end 输出 [N,6]=[x1,y1,x2,y2,conf,cls] - 类别映射改为按 class name 动态匹配 (figure/figure_group→picture, table/table_group→table) - 新增文件： - scripts/export_doclayout_yolo_onnx.py: DocLayout-YOLO ONNX 导出脚本 (独立 venv 运行) - tests/test_layout_detector.py: 布局检测器完整测试 (35 个用例) - 配置更新： - .env.example: 更新布局检测配置 (新增 LAYOUT_IMGSZ, LAYOUT_DEVICE, LAYOUT_DEVICE_ID) - app/config.py: Settings 类对应字段 - pyproject.toml: 新增 export 依赖组 (torch, doclayout-yolo, onnx 等) - 删除旧文件： - scripts/export_picodet_onnx.py: 旧 PicoDet 导出脚本 - 文档更新： - README.md: 更新环境变量说明 - 相关服务注释更新 (pdf_image_extractor.py, summary_persister.py, reextract_images.py) 此重构遵循项目初期开发阶段规范，大胆调整数据模型，无需向后兼容。
344 lines
12 KiB
Python
344 lines
12 KiB
Python
"""DocLayout-YOLO 布局检测 — ONNX Runtime 推理，支持 CPU/GPU/NPU 多设备.
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用 onnxruntime 加载 DocLayout-YOLO（DocStructBench, imgsz=1024）ONNX 模型，
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检测 PDF 页面中的 figure / table 区域。
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预处理：letterbox（保比例缩放 + 灰边 padding 到 imgsz×imgsz），RGB，仅 /255 归一化
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（不做 ImageNet mean/std）。缩放由 pymupdf Matrix 完成，不依赖 OpenCV。
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后处理：YOLOv10 end-to-end 输出 [N,6]=[x1,y1,x2,y2,conf,cls]（已内置 NMS）。
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坐标还原：(model_coord - padding) / ratio —— 渲染缩放与 letterbox 缩放在 pymupdf
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渲染阶段合二为一，故只需一次除法。
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设备：resolve_providers() 按 LAYOUT_DEVICE 产出候选 ExecutionProvider 列表；
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_init_session() 逐个 try，首个不可用则降级，CPU 永远兜底。
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输入:
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images: (1, 3, imgsz, imgsz) float32 —— letterbox + /255 后的图
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输出:
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output0: (1, N, 6) float32 —— [x1, y1, x2, y2, conf, cls]，已 NMS
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"""
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from __future__ import annotations
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import json
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import logging
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from dataclasses import dataclass
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from pathlib import Path
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import numpy as np
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import onnxruntime as ort
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import pymupdf
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from app.config import settings
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logger = logging.getLogger(__name__)
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# DocLayout-YOLO DocStructBench 标准 10 类（ONNX metadata 读不到时的兜底，以实际为准）
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_FALLBACK_NAMES: dict[int, str] = {
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0: "title",
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1: "plain text",
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2: "abandon",
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3: "figure",
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4: "figure_caption",
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5: "table",
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6: "table_caption",
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7: "table_footnote",
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8: "isolate_formula",
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9: "formula_caption",
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}
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# 下游只需 picture/table —— 按 class name 字符串动态匹配（不依赖 class index，
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# 规避 DocStructBench 不同发布的类别顺序差异）
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_PICTURE_NAMES = {"figure", "figure_group"}
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_TABLE_NAMES = {"table", "table_group"}
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# letterbox 灰边值（ultralytics 训练标准，不可改为 0/128，否则精度下降）
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_PAD_VALUE = 114
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# 最小 bbox 尺寸（PDF 点）
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_MIN_BOX_SIZE = 20
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# device → ExecutionProvider 映射
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_PROVIDER_MAP: dict[str, str] = {
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"cpu": "CPUExecutionProvider",
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"cuda": "CUDAExecutionProvider",
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"directml": "DmlExecutionProvider",
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"openvino": "OpenVINOExecutionProvider",
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"cann": "CannExecutionProvider",
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"tensorrt": "TensorrtExecutionProvider",
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"qnn": "QNNExecutionProvider",
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}
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# auto 探测优先级（不含 cpu，cpu 永远兜底）
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_AUTO_PRIORITY = ["cuda", "directml", "openvino", "cann", "tensorrt", "qnn"]
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@dataclass
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class LayoutBox:
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"""检测到的布局区域，坐标为 PDF 点，boxclass ∈ {"picture", "table"}。"""
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x0: float
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y0: float
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x1: float
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y1: float
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boxclass: str
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# ── 设备选择 ────────────────────────────────────────────────────────────
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def resolve_providers(device: str, device_id: int) -> list[tuple[str, dict]]:
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"""根据 LAYOUT_DEVICE 产出候选 ExecutionProvider 列表（首选在前，均带 CPU 兜底）。
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返回 list[tuple[ep_name, provider_options]]，供 _init_session() 逐个 try。
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onnxruntime 创建 session 时若指定 EP 在本机变体里未注册会直接抛错，
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故降级逻辑由 _init_session() 完成，这里只产出候选。
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"""
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if device == "cpu":
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return [("CPUExecutionProvider", {})]
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opts = {"device_id": str(device_id)}
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if device == "auto":
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available = set(ort.get_available_providers())
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for dev in _AUTO_PRIORITY:
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ep = _PROVIDER_MAP[dev]
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if ep in available:
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logger.info("auto: selected provider %s", ep)
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return [(ep, opts), ("CPUExecutionProvider", {})]
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logger.info("auto: no GPU/NPU provider available, using CPU")
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return [("CPUExecutionProvider", {})]
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ep = _PROVIDER_MAP.get(device)
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if ep is None:
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logger.warning("Unknown LAYOUT_DEVICE=%r, falling back to CPU", device)
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return [("CPUExecutionProvider", {})]
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return [(ep, opts), ("CPUExecutionProvider", {})]
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# ── 预处理：渲染几何与 letterbox ────────────────────────────────────────
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def _compute_render_geometry(page_w: float, page_h: float, imgsz: int) -> float:
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"""letterbox 渲染缩放 ratio = min(imgsz/page_w, imgsz/page_h)。
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pymupdf 以 Matrix(ratio, ratio) 渲染，长边贴到 imgsz，短边留灰边。
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"""
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return min(imgsz / page_w, imgsz / page_h)
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def _letterbox_padding(
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content_w: float, content_h: float, imgsz: int
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) -> tuple[float, float]:
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"""居中 padding：(imgsz - content) / 2。content 为实际 pixmap 尺寸（已取整）。"""
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return (imgsz - content_w) / 2.0, (imgsz - content_h) / 2.0
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def _padded_nchw_from_pixmap(
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pix: pymupdf.Pixmap, imgsz: int, dw: float, dh: float
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) -> np.ndarray:
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"""pixmap → letterbox padded (1, 3, imgsz, imgsz) float32，灰边=114，/255 归一化。"""
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arr = np.frombuffer(pix.samples, dtype=np.uint8).reshape(
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pix.height, pix.width, pix.n
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)
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if arr.shape[2] == 4: # 去 alpha（csRGB alpha=False 一般不会，防御性）
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arr = arr[:, :, :3]
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canvas = np.full((imgsz, imgsz, 3), _PAD_VALUE, dtype=np.uint8)
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top = int(round(dh))
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left = int(round(dw))
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canvas[top : top + pix.height, left : left + pix.width] = arr
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out = canvas.astype(np.float32) / 255.0
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return out.transpose(2, 0, 1)[np.newaxis] # (1, 3, imgsz, imgsz)
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def _model_to_pdf(
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model_x: float, model_y: float, dw: float, dh: float, ratio: float
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) -> tuple[float, float]:
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"""模型 imgsz 空间坐标 → PDF 点：(model - padding) / ratio。"""
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return (model_x - dw) / ratio, (model_y - dh) / ratio
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# ── 后处理 ──────────────────────────────────────────────────────────────
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def _postprocess_output(
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output: np.ndarray, threshold: float, names: dict[int, str]
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) -> list[tuple[int, float, float, float, float]]:
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"""解析 YOLOv10 end-to-end 输出，过滤 conf < threshold。
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Args:
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output: session.run 返回的第一个输出，shape [1, N, 6]
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threshold: 置信度阈值
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names: class id → name（仅用于日志，过滤不依赖）
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Returns:
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[(cls_id, x1, y1, x2, y2), ...]，坐标为模型 imgsz padded 空间。
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"""
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out = output[0] # 去 batch 维
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if out.ndim != 2 or out.shape[1] != 6:
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logger.warning(
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"Unexpected DocLayout-YOLO output shape %s (expected [N,6]); skip page",
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tuple(out.shape),
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)
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return []
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results: list[tuple[int, float, float, float, float]] = []
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for row in out:
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x1, y1, x2, y2, conf, cls = row.tolist()
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if conf < threshold:
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continue
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results.append((int(cls), x1, y1, x2, y2))
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return results
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def _map_class_to_boxclass(cls_id: int, names: dict[int, str]) -> str | None:
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"""按 class name 匹配 figure→picture / table→table，其余返回 None。"""
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name = names.get(cls_id, "")
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n = name.strip().lower()
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if n in _PICTURE_NAMES:
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return "picture"
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if n in _TABLE_NAMES:
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return "table"
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return None
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def _parse_names_from_meta(session: ort.InferenceSession) -> dict[int, str]:
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"""从 ONNX metadata 读 names（ultralytics 导出写入的 JSON），读不到用兜底。"""
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raw = None
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try:
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raw = session.get_modelmeta().custom_metadata_map.get("names")
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except Exception:
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raw = None
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if raw:
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try:
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d = json.loads(raw)
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return {int(k): str(v) for k, v in d.items()}
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except Exception:
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logger.warning("Failed to parse ONNX names metadata; using fallback")
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return dict(_FALLBACK_NAMES)
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# ── 检测器单例 ──────────────────────────────────────────────────────────
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class _LayoutDetector:
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"""单例：管理 ONNX InferenceSession 生命周期。"""
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def __init__(self) -> None:
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self._session: ort.InferenceSession | None = None
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self._names: dict[int, str] = {}
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self._input_name: str = ""
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self._imgsz: int = settings.LAYOUT_IMGSZ
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def _init_session(self) -> ort.InferenceSession:
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if self._session is not None:
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return self._session
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model_path = Path(settings.LAYOUT_MODEL_PATH)
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if not model_path.exists():
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raise FileNotFoundError(
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f"Layout model not found: {model_path}. "
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"Run scripts/export_doclayout_yolo_onnx.py first."
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)
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eps = resolve_providers(settings.LAYOUT_DEVICE, settings.LAYOUT_DEVICE_ID)
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logger.info(
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"Loading layout model %s, candidate providers: %s",
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model_path,
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[ep[0] for ep in eps],
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)
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# 逐个 EP 尝试，首个不可用则降级
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last_err: Exception | None = None
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for idx, (ep_name, ep_opts) in enumerate(eps):
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try:
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self._session = ort.InferenceSession(
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str(model_path), providers=[(ep_name, ep_opts)]
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)
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break
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except Exception as e:
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last_err = e
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if idx < len(eps) - 1:
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logger.warning(
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"Provider %s unavailable (%s); falling back to %s",
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ep_name,
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e,
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eps[idx + 1][0],
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)
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else:
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raise RuntimeError(f"Failed to create layout session: {last_err}")
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logger.info(
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"Layout session active providers: %s", self._session.get_providers()
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)
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self._input_name = self._session.get_inputs()[0].name
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self._names = _parse_names_from_meta(self._session)
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self._imgsz = settings.LAYOUT_IMGSZ
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return self._session
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def detect_page(self, page: pymupdf.Page) -> list[LayoutBox]:
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"""检测单页 PDF 的 figure / table 区域。
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流程：
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1. letterbox 渲染：保比例缩放到长边=imgsz，短边留灰边
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2. /255 + NCHW → ONNX 推理
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3. YOLOv10 end-to-end 后处理（已 NMS）
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4. 模型坐标 → PDF 点
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5. 过滤非 figure/table 类、极小框、越界 clip
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Returns:
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LayoutBox 列表，坐标为 PDF 点。
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"""
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session = self._init_session()
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page_w = page.rect.width
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page_h = page.rect.height
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ratio = _compute_render_geometry(page_w, page_h, self._imgsz)
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# 1. 保比例渲染（长边贴 imgsz）
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pix = page.get_pixmap(
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matrix=pymupdf.Matrix(ratio, ratio),
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colorspace=pymupdf.csRGB,
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alpha=False,
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)
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# 用 pixmap 实际尺寸（已取整）算 padding，消除取整导致的坐标偏移
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dw, dh = _letterbox_padding(pix.width, pix.height, self._imgsz)
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tensor = _padded_nchw_from_pixmap(pix, self._imgsz, dw, dh)
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# 2. 推理
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outputs = session.run(None, {self._input_name: tensor})
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detections = _postprocess_output(
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outputs[0], settings.LAYOUT_THRESHOLD, self._names
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)
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# 3. 坐标还原 + 过滤
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result: list[LayoutBox] = []
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for cls_id, x1m, y1m, x2m, y2m in detections:
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boxclass = _map_class_to_boxclass(cls_id, self._names)
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if boxclass is None:
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continue
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x0, y0 = _model_to_pdf(x1m, y1m, dw, dh, ratio)
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x1, y1 = _model_to_pdf(x2m, y2m, dw, dh, ratio)
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# clip 到页面范围
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x0 = max(0.0, min(x0, page_w))
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y0 = max(0.0, min(y0, page_h))
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x1 = max(0.0, min(x1, page_w))
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y1 = max(0.0, min(y1, page_h))
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if (x1 - x0) < _MIN_BOX_SIZE or (y1 - y0) < _MIN_BOX_SIZE:
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continue
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result.append(LayoutBox(x0=x0, y0=y0, x1=x1, y1=y1, boxclass=boxclass))
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return result
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# 模块级单例
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_detector = _LayoutDetector()
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def detect_page_layout(page: pymupdf.Page) -> list[LayoutBox]:
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"""检测 PDF 页面中的 figure / table 区域。
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Returns:
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LayoutBox 列表，坐标为 PDF 点，仅含 picture/table。
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"""
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return _detector.detect_page(page)
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