Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
383 lines
13 KiB
Python
383 lines
13 KiB
Python
"""审核单关联的送标/回传数据：解析范围、列举图像、渲染 GT 叠加。"""
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from __future__ import annotations
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import hashlib
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import io
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any, Sequence
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import yaml
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from PIL import Image, ImageDraw, ImageFont
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from as_platform.data.batch import IMG_EXTS
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from as_platform.data.core import load_wf, proj_root, resolve_pack_dir
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IMAGE_EXTS = tuple(ext.lower() for ext in IMG_EXTS) + tuple(ext.upper() for ext in IMG_EXTS if ext.islower())
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@dataclass(frozen=True)
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class ImageRef:
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image_path: Path
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label_path: Path | None
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batch: str
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location: str
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split: str
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@property
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def id(self) -> str:
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key = f"{self.image_path}|{self.label_path or ''}"
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return hashlib.sha256(key.encode()).hexdigest()[:16]
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def _find_image(images_dir: Path, stem: str) -> Path | None:
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for ext in IMAGE_EXTS:
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p = images_dir / f"{stem}{ext}"
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if p.is_file():
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return p
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return None
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def _parse_yolo_line(line: str) -> dict[str, Any] | None:
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parts = line.strip().split()
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if len(parts) < 5:
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return None
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try:
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class_id = int(float(parts[0]))
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cx, cy, w, h = map(float, parts[1:5])
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except Exception:
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return None
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keypoints: list[tuple[float, float, float]] = []
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rest = parts[5:]
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if len(rest) >= 3:
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n = len(rest) // 3
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for i in range(n):
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keypoints.append((float(rest[i * 3]), float(rest[i * 3 + 1]), float(rest[i * 3 + 2])))
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return {"class_id": class_id, "bbox": (cx, cy, w, h), "keypoints": keypoints}
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def parse_label_file(label_path: Path) -> list[dict[str, Any]]:
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if not label_path.is_file():
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return []
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out: list[dict[str, Any]] = []
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for raw in label_path.read_text(encoding="utf-8", errors="ignore").splitlines():
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parsed = _parse_yolo_line(raw)
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if parsed is not None:
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out.append(parsed)
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return out
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def _load_font(size: int) -> ImageFont.ImageFont:
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for p in (
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"/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc",
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"/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc",
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"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
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):
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if Path(p).exists():
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try:
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return ImageFont.truetype(p, size)
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except Exception:
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continue
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return ImageFont.load_default()
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def _yolo_bbox_to_xyxy(bbox: tuple[float, float, float, float], width: int, height: int) -> tuple[int, int, int, int]:
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cx, cy, bw, bh = bbox
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x1 = int((cx - bw / 2.0) * width)
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y1 = int((cy - bh / 2.0) * height)
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x2 = int((cx + bw / 2.0) * width)
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y2 = int((cy + bh / 2.0) * height)
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return (
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max(0, min(width - 1, x1)),
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max(0, min(height - 1, y1)),
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max(0, min(width - 1, x2)),
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max(0, min(height - 1, y2)),
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)
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def render_overlay(
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image_path: Path,
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label_path: Path | None,
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class_names: dict[int, str],
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*,
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max_size: int | None = None,
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) -> bytes:
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with Image.open(image_path) as im:
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base = im.convert("RGB")
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if max_size and max(base.size) > max_size:
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base.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
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over = base.copy()
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draw = ImageDraw.Draw(over)
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w, h = over.size
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font = _load_font(max(12, min(18, w // 40)))
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anns = parse_label_file(label_path) if label_path else []
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palette = [
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(220, 20, 60),
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(30, 144, 255),
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(50, 205, 50),
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(255, 165, 0),
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(186, 85, 211),
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(0, 206, 209),
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]
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for ann in anns:
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cid = ann["class_id"]
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color = palette[cid % len(palette)]
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x1, y1, x2, y2 = _yolo_bbox_to_xyxy(ann["bbox"], w, h)
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draw.rectangle((x1, y1, x2, y2), outline=color, width=max(2, w // 320))
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label = class_names.get(cid, f"class_{cid}")
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draw.text((x1 + 2, max(0, y1 - 16)), label, fill=color, font=font)
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for kx, ky, kv in ann.get("keypoints") or []:
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if kv <= 0:
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continue
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px, py = int(kx * w), int(ky * h)
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r = max(2, w // 400)
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draw.ellipse((px - r, py - r, px + r, py + r), outline=color, fill=color)
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buf = io.BytesIO()
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over.save(buf, format="JPEG", quality=88)
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return buf.getvalue()
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def _collect_from_split(batch_dir: Path, split: str, *, batch: str, location: str) -> list[ImageRef]:
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if split:
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images_dir = batch_dir / "images" / split
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labels_dir = batch_dir / "labels" / split
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else:
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images_dir = batch_dir / "images"
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labels_dir = batch_dir / "labels"
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out: list[ImageRef] = []
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if not images_dir.is_dir():
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return out
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label_stems: dict[str, Path] = {}
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if labels_dir.is_dir():
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for lp in labels_dir.glob("*.txt"):
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label_stems[lp.stem] = lp
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seen: set[str] = set()
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for p in sorted(images_dir.iterdir()):
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if not p.is_file() or p.suffix.lower() not in {e.lower() for e in IMG_EXTS}:
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continue
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stem = p.stem
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seen.add(stem)
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out.append(
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ImageRef(
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image_path=p.resolve(),
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label_path=label_stems.get(stem),
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batch=batch,
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location=location,
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split=split or "root",
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)
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)
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for stem, lp in sorted(label_stems.items()):
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if stem in seen:
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continue
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img = _find_image(images_dir, stem)
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if img:
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out.append(
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ImageRef(
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image_path=img.resolve(),
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label_path=lp.resolve(),
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batch=batch,
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location=location,
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split=split or "root",
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)
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)
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return out
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def collect_batch_images(batch_dir: Path, *, batch: str, location: str) -> list[ImageRef]:
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if not batch_dir.is_dir():
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return []
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refs: list[ImageRef] = []
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for split in ("train", "val", "test", ""):
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refs.extend(_collect_from_split(batch_dir, split, batch=batch, location=location))
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# 去重（flat 与 train 可能重叠）
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dedup: dict[str, ImageRef] = {}
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for ref in refs:
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dedup[str(ref.image_path)] = ref
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return sorted(dedup.values(), key=lambda r: (r.batch, r.split, r.image_path.name))
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def _dms_task_cfg(root: Path, wf: dict, task: str) -> tuple[dict, dict]:
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reg_path = root / wf["projects"]["dms"]["registry"]
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reg = yaml.safe_load(reg_path.read_text(encoding="utf-8"))
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if task not in reg.get("tasks", {}):
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raise ValueError(f"未知 task: {task}")
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return reg, reg["tasks"][task]
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def resolve_approval_scope(action: str, params: dict[str, Any]) -> dict[str, Any]:
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"""解析审核单对应的数据目录与类别名。"""
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p = params or {}
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wf = load_wf()
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if action in ("build_dms", "register_batch"):
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task = p.get("task")
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if not task:
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raise ValueError("缺少 task 参数")
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root = proj_root(wf, "dms")
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reg, tcfg = _dms_task_cfg(root, wf, task)
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src_sub = (reg.get("ingest") or {}).get("sources_subdir", "sources")
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pack = p.get("pack") or "dms_v2"
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batches: list[dict[str, Any]] = []
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location = p.get("location", "inbox")
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if action == "build_dms" and p.get("all_sources"):
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location = "sources"
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if action == "build_dms" and p.get("batch") and not p.get("all_sources"):
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location = "inbox"
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if location == "inbox":
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batch_name = p.get("batch")
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if batch_name:
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batches.append({"path": root / "inbox" / task / batch_name, "batch": batch_name, "location": "inbox"})
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else:
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ib = root / "inbox" / task
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if ib.is_dir():
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for d in sorted(ib.iterdir()):
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if d.is_dir() and not d.name.startswith("."):
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batches.append({"path": d, "batch": d.name, "location": "inbox"})
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else:
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pack_dir = resolve_pack_dir("dms", root, wf, pack)
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src_root = pack_dir / tcfg.get("task_dir", task) / src_sub
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batch_name = p.get("batch")
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if batch_name:
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batches.append({"path": src_root / batch_name, "batch": batch_name, "location": "sources"})
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elif src_root.is_dir():
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for d in sorted(src_root.iterdir()):
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if d.is_dir() and d.name not in ("_ingested", "_merged") and not d.name.startswith("."):
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batches.append({"path": d, "batch": d.name, "location": "sources"})
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names = tcfg.get("names") or {}
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class_names = {int(k): v for k, v in names.items()} if isinstance(names, dict) else {i: n for i, n in enumerate(names)}
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return {
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"project": "dms",
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"task": task,
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"pack": pack,
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"scope_label": f"DMS · {task} · {pack}" + (" · 全部 sources" if location == "sources" and not p.get("batch") else ""),
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"class_names": class_names,
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"batches": batches,
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}
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if action == "delivery_ingest":
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data_path = (p.get("data_path") or "").strip()
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if not data_path:
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raise ValueError("缺少 data_path 参数")
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src = Path(data_path)
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project = p.get("project") or "dms"
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task = p.get("task") or ""
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batch_name = p.get("batch_name") or src.name
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scope_label = f"数据送标入湖 · {project}"
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if task:
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scope_label += f" · {task}"
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scope_label += f" · {batch_name}"
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return {
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"project": project,
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"task": task or None,
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"pack": None,
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"scope_label": scope_label,
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"class_names": {},
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"batches": [
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{
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"path": src,
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"batch": batch_name,
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"location": "delivery",
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}
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],
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}
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if action in ("train_dms", "promote_dms", "eval_dms"):
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task = p.get("task")
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if not task:
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raise ValueError("缺少 task 参数")
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root = proj_root(wf, "dms")
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_, tcfg = _dms_task_cfg(root, wf, task)
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pack = p.get("pack") or "dms_v2"
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pack_dir = resolve_pack_dir("dms", root, wf, pack)
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task_dir = pack_dir / tcfg.get("task_dir", task)
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batches = [{"path": task_dir, "batch": f"{pack}/{task}", "location": "pack"}]
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names = tcfg.get("names") or {}
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class_names = {int(k): v for k, v in names.items()} if isinstance(names, dict) else {i: n for i, n in enumerate(names)}
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label = "模型晋级" if action == "promote_dms" else ("评估" if action == "eval_dms" else "训练")
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return {
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"project": "dms",
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"task": task,
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"pack": pack,
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"scope_label": f"DMS · {task} · {pack} · pack 数据（{label}）",
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"class_names": class_names,
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"batches": batches,
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}
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raise ValueError(f"暂不支持预览的动作: {action}")
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def list_scope_images(scope: dict[str, Any], *, offset: int = 0, limit: int = 60) -> dict[str, Any]:
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all_refs: list[ImageRef] = []
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for b in scope.get("batches") or []:
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batch_dir = Path(b["path"])
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all_refs.extend(
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collect_batch_images(batch_dir, batch=b.get("batch", batch_dir.name), location=b.get("location", ""))
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)
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dedup: dict[str, ImageRef] = {str(r.image_path): r for r in all_refs}
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ordered = sorted(dedup.values(), key=lambda r: (r.batch, r.split, r.image_path.name))
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total = len(ordered)
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page = ordered[offset : offset + limit]
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items = []
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for ref in page:
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anns = parse_label_file(ref.label_path) if ref.label_path else []
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items.append(
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{
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"id": ref.id,
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"batch": ref.batch,
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"location": ref.location,
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"split": ref.split,
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"filename": ref.image_path.name,
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"has_label": ref.label_path is not None and ref.label_path.is_file(),
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"box_count": len(anns),
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"missing_label": ref.label_path is None or not ref.label_path.is_file(),
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}
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)
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return {"total": total, "offset": offset, "limit": limit, "items": items}
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def find_image_ref(scope: dict[str, Any], image_id: str) -> ImageRef | None:
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"""线性查找；审核场景批次有限，可接受。"""
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batches = scope.get("batches") or []
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for b in batches:
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batch_dir = Path(b["path"])
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refs = collect_batch_images(batch_dir, batch=b.get("batch", batch_dir.name), location=b.get("location", ""))
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for ref in refs:
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if ref.id == image_id:
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return ref
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return None
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def image_to_item(ref: ImageRef) -> dict[str, Any]:
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anns = parse_label_file(ref.label_path) if ref.label_path else []
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return {
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"id": ref.id,
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"batch": ref.batch,
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"location": ref.location,
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"split": ref.split,
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"filename": ref.image_path.name,
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"has_label": ref.label_path is not None and ref.label_path.is_file(),
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"box_count": len(anns),
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"missing_label": ref.label_path is None or not ref.label_path.is_file(),
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"annotations": [
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{
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"class_id": a["class_id"],
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"class_name": None,
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"bbox": a["bbox"],
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"keypoints": a.get("keypoints") or [],
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}
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for a in anns
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],
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}
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