feat(ocr): 扫描件与图片识别——OCR 进文字层、资质库上传识别、大图浏览
计划见 docs/architecture/vision-plan.md（VS1–VS13，docs/decisions.md）。 - 验证：OCR（RapidOCR + PP-OCRv6 small，CPU）和 DeepSeek Flash 看图在国动投标文件 和 7 张证书上初测（tech-validation §7，代码 spikes/s5-ocr/） - OCR 服务 services/ocr（镜像、Compose 服务）：整页转正（含 180°）、红章附近按红色通道重读 - 扫描页和插图识别后并入文字层 v6，句子标 source=ocr；出处标「OCR」（迁移 0013）； 依据识别文字的问题降级，规则不查扫描附件；国动端到端见 check-eval §16 - 资质库上传识别：添加资质对话框最上面传证书照片，识别后填表，只预填和 OCR 文字 对得上的字段；识别值留痕、照片挂成附件（迁移 0014，提示词 vision.cert_fields v1） - 资质库改为查看详情（只读键值 + 附件缩略图），附件用大图浏览控件看，不开新标签页 （设计规范 §7.23）；PDF 附件按页出预览图 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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YANG JIANKUAN
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spikes/s5-ocr/ocr_pages.py
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spikes/s5-ocr/ocr_pages.py
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"""S5：对 PDF 里的扫描页整页 OCR、对文字页里的图片区域 OCR，结果换算回 PDF 坐标。
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用法：.venv/bin/python -I ocr_pages.py <pdf> <out.json> [页码范围，如 16-33,104-131]
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不给页码时按文字层规则自动挑：扫描页（字少图多）整页识别，图片占比 ≥ 0.15 的文字页只识别图片区域。
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"""
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from __future__ import annotations
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import json
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import sys
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import time
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from pathlib import Path
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import numpy as np
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import pdfplumber
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import pypdfium2 as pdfium
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from PIL import Image, ImageOps
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from rapidocr import RapidOCR
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DPI = 200
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RCH = False # main() 按 --rch 打开
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SCALE = DPI / 72
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def parse_ranges(s: str) -> set[int]:
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out: set[int] = set()
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for part in s.split(","):
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a, _, b = part.partition("-")
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out.update(range(int(a), int(b or a) + 1))
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return out
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def image_cover(page) -> float:
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area = float(page.width) * float(page.height)
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total = 0.0
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for im in page.images:
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w = min(float(page.width), float(im["x1"])) - max(0.0, float(im["x0"]))
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h = min(float(page.height), float(im["bottom"])) - max(0.0, float(im["top"]))
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if w > 0 and h > 0:
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total += w * h
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return min(total / area, 1.0) if area else 0.0
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def is_scanned(chars: int, cover: float) -> bool:
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return chars < 20 or (cover >= 0.2 and chars < 60) or (cover >= 0.45 and chars < 120)
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def run_ocr(eng: RapidOCR, img: Image.Image) -> list[tuple[list[list[float]], str, float]]:
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r = eng(np.asarray(img.convert("RGB")))
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if r.boxes is None:
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return []
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return [(b.tolist(), t, float(s)) for b, t, s in zip(r.boxes, r.txts, r.scores)]
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def red_ratio(img: Image.Image) -> float:
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a = np.asarray(img.convert("RGB").resize((img.width // 4, img.height // 4))).astype(np.int16)
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r, g, b = a[..., 0], a[..., 1], a[..., 2]
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return float(((r > 120) & (r - g > 40) & (r - b > 40)).mean())
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def red_channel(img: Image.Image) -> Image.Image:
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"""只取红色通道当灰度图：红章变浅、黑字不变（章压字的页用）。"""
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return ImageOps.autocontrast(img.convert("RGB").split()[0]).convert("RGB")
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def best_orientation(eng: RapidOCR, img: Image.Image):
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"""整页转正：0° 识别结果里竖长框多、置信度低时，再试 90° / 270°，取「字数 × 平均置信度」最高的。"""
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lines = run_ocr(eng, img)
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def quality(ls):
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n = sum(len(t) for _, t, _ in ls)
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return n * (sum(s for *_, s in ls) / len(ls) if ls else 0)
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tall = sum(1 for b, t, _ in lines if len(t) >= 3 and (b[2][1] - b[0][1]) > 1.5 * (b[1][0] - b[0][0]))
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if lines and tall < len(lines) * 0.3:
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return 0, img, lines
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best = (quality(lines), 0, img, lines)
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for rot in (90, 270):
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r_img = img.rotate(rot, expand=True)
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r_lines = run_ocr(eng, r_img)
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q = quality(r_lines)
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if q > best[0]:
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best = (q, rot, r_img, r_lines)
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return best[1], best[2], best[3]
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def to_pdf_box(box, rot: int, img_w: int, img_h: int, ox: float, oy: float) -> list[float]:
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"""OCR 框（转正后图片的像素）→ PDF 坐标（pt，左上原点），返回外接矩形 [x0, top, x1, bottom]。"""
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pts = []
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for x, y in box:
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# 逆转回原图像素：PIL rotate(rot) 是逆时针；原图宽高为 (img_w, img_h)
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if rot == 90:
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x, y = img_w - y, x
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elif rot == 270:
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x, y = y, img_h - x
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pts.append((ox + x / SCALE, oy + y / SCALE))
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xs, ys = [p[0] for p in pts], [p[1] for p in pts]
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return [round(min(xs), 1), round(min(ys), 1), round(max(xs), 1), round(max(ys), 1)]
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def overlaps_text(bbox, chars) -> bool:
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"""OCR 行和文字层已有的字重叠（扫描页上方的文字说明、图片上叠的文字），去掉，免得重复。"""
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x0, t, x1, b = bbox
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hit = sum(1 for c in chars if c["x0"] >= x0 - 2 and c["x1"] <= x1 + 2 and c["top"] >= t - 2 and c["bottom"] <= b + 2)
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return hit >= 2
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def main() -> None:
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global RCH
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args = [a for a in sys.argv[1:] if a != "--rch"]
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RCH = "--rch" in sys.argv
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pdf_path, out_path = args[0], Path(args[1])
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only = parse_ranges(args[2]) if len(args) > 2 else None
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eng = RapidOCR()
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doc = pdfium.PdfDocument(pdf_path)
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results = []
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t_all = time.time()
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with pdfplumber.open(pdf_path) as pl:
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for i, page in enumerate(pl.pages):
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no = i + 1
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if only and no not in only:
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continue
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chars = [c for c in page.chars if str(c["text"]).strip()]
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cover = image_cover(page)
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scanned = is_scanned(len(chars), cover)
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if not scanned and cover < 0.15:
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continue
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t0 = time.time()
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full = doc[i].render(scale=SCALE).to_pil()
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regions = []
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if scanned:
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regions.append((0.0, 0.0, full))
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else:
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for im in page.images:
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x0, top = max(0.0, float(im["x0"])), max(0.0, float(im["top"]))
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x1, bot = min(float(page.width), float(im["x1"])), min(float(page.height), float(im["bottom"]))
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if (x1 - x0) * (bot - top) < 0.05 * float(page.width) * float(page.height):
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continue # 小图：logo、印章、图标
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crop = full.crop((int(x0 * SCALE), int(top * SCALE), int(x1 * SCALE), int(bot * SCALE)))
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regions.append((x0, top, crop))
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lines, rots = [], []
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for ox, oy, img in regions:
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if RCH and scanned and red_ratio(img) > 0.002:
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img = red_channel(img)
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rot, r_img, ls = best_orientation(eng, img) if scanned else (0, img, run_ocr(eng, img))
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rots.append(rot)
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for box, text, score in ls:
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bbox = to_pdf_box(box, rot, img.width, img.height, ox, oy)
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if overlaps_text(bbox, chars):
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continue
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lines.append({"bbox": bbox, "text": text, "score": round(score, 3)})
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results.append(
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{
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"page": no,
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"kind": "scan" if scanned else "figure",
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"text_chars": len(chars),
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"image_cover": round(cover, 3),
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"rotation": rots,
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"seconds": round(time.time() - t0, 2),
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"lines": lines,
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}
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)
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print(f"p{no} {results[-1]['kind']} rot={rots} lines={len(lines)} {results[-1]['seconds']}s", flush=True)
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out_path.write_text(json.dumps(results, ensure_ascii=False, indent=1))
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print(f"total {len(results)} pages {round(time.time() - t_all, 1)}s")
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if __name__ == "__main__":
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main()
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