Shaun ZhouandClaude Opus 5
af0897f8c7
prepare_yolo_dataset.py 的产出先导入 Label Studio 复核，划分留到复核之后，于是：
- 去掉按类别分层的 train/val 切分以及 --val-ratio / --seed，images/ 与 labels/
下直接是文件；name_map.csv 去掉 split 列，summary.json 的逐类数量改为单列。
- label_studio/ 子目录的内容上移一层：扁平的数据集本身就是 label-studio-converter
要求的 <input>/{images,labels,classes.txt}，tasks.json 与 labeling config 直接
生成在根目录，不再需要额外的硬链接视图，DOCUMENT_ROOT 即数据集根目录。
- data.yaml 的 train 与 val 只能指向同一个 images/。ultralytics 能读，但验证指标
算的是训练集本身，mAP 虚高、早停与 best.pt 的选择失真。脚本结束时告警，
detect.py 检测到 train == val 时也告警。
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
518 lines
22 KiB
Python
518 lines
22 KiB
Python
#!/usr/bin/env python
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"""把 generate_boxes.py 的产出整理成扁平的 YOLO 数据集，放到 data/ 下。
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uv run python scripts/prepare_yolo_dataset.py --source runs/boxes_gradcam
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generate_boxes.py 的产出是**按类别**分目录的（便于人工核对）：
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```
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runs/boxes_gradcam/
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├── images/<类别>/<图名>.png
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└── labels/<类别>/<图名>.txt
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```
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而 YOLO 的类别信息在标签文件的第一列里，不靠目录名。这一步做三件事：
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1. 把按类别分的目录**拍平**，不划分 train/val——这份数据集先导入 Label Studio 复核，
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划分留到复核之后；
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2. 图片**统一成 PNG**：本来就是 PNG 的硬链接过去，不占额外磁盘；BMP 等其他格式
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（pearlescent / blue / uv 原始数据都是 BMP）无损转成 PNG。不缩放像素，
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归一化的框坐标原样有效。
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3. 文件**按类别重新编号**：`<类别>_<序号>.png`，序号在类内按源路径排序，重跑结果一致。
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原文件名里的正反面、位置、时间戳等信息记在 `name_map.csv` 里，不会丢。
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想保留原名加 `--keep-names`。
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```
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data/<name>/
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├── images/<类别>_<序号>.png
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├── labels/<类别>_<序号>.txt
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├── classes.txt # 每行一个类别名，行号即 class_id
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├── name_map.csv # 新名 ↔ 源文件对照
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├── data.yaml # train 与 val 都指向 images/，不能直接用于评估
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├── summary.json
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├── tasks.json # 仅 --label-studio：框作为 predictions
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└── tasks.label_config.xml # 仅 --label-studio
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```
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这个结构正是 label-studio-converter 的 YOLO 导入要求的 `<input>/{images,labels,classes.txt}`，
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所以 `--label-studio` 直接在数据集根目录上转换，DOCUMENT_ROOT 就是数据集根目录。
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没有框的图（标签为空）默认保留：YOLO 训练里它们是负样本，有助于压低误检。但**空标签的
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语义是"这张图什么都没有"**，不是"有缺陷但没定位出来"。来自缺陷类别的空标签如果照搬，
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等于在教模型抑制真实缺陷，所以可以用 `--empty-policy` 按来源类别分别处置：
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--empty-policy drop normal=keep black=full
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# 缺陷类没定位出来的直接丢弃；normal 留作负样本；整图属性的类退化成整图框
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"""
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from __future__ import annotations
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import argparse
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import contextlib
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import csv
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import errno
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import io
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import json
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import logging
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import os
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import shutil
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import sys
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from collections import Counter, defaultdict
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from collections.abc import Sequence
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from pathlib import Path
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from typing import NamedTuple
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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import yaml # noqa: E402
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from datasets.yolo import IMAGE_SUFFIXES, write_classes_txt, write_data_yaml # noqa: E402
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from utils import setup_logging # noqa: E402
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logger = None
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EMPTY_POLICIES = ("keep", "drop", "full")
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EMPTY_POLICY_DESC = {
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"keep": "保留为负样本",
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"drop": "丢弃",
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"full": "退化成整图框",
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}
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PNG_SIGNATURE = b"\x89PNG\r\n\x1a\n"
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# PNG 能原样存下的像素模式；其余（CMYK、YCbCr 等）先转 RGB
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PNG_MODES = frozenset({"1", "L", "LA", "I", "I;16", "P", "RGB", "RGBA"})
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def is_png(path: Path) -> bool:
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"""看文件头而不是后缀：后缀写着 .png、内容却是 BMP/JPEG 的也要转。"""
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with path.open("rb") as handle:
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return handle.read(len(PNG_SIGNATURE)) == PNG_SIGNATURE
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def materialize_png(source: Path, destination: Path) -> str:
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"""把图片落成 PNG：本来就是 PNG 的硬链接，其余格式无损转换。
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返回 "hard" / "copy" / "convert"。转换不缩放像素，归一化的框坐标原样有效。
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"""
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if is_png(source):
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return link_or_copy(source, destination)
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from PIL import Image
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if destination.exists() or destination.is_symlink():
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destination.unlink()
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destination.parent.mkdir(parents=True, exist_ok=True)
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with Image.open(source) as image:
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if image.mode not in PNG_MODES:
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image = image.convert("RGB")
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image.save(destination, format="PNG")
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return "convert"
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def link_or_copy(source: Path, destination: Path) -> str:
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"""硬链接；跨文件系统时退回复制。"""
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if destination.exists() or destination.is_symlink():
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destination.unlink()
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destination.parent.mkdir(parents=True, exist_ok=True)
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try:
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os.link(source, destination)
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return "hard"
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except OSError as exc:
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if exc.errno != errno.EXDEV:
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raise
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shutil.copy2(source, destination)
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return "copy"
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class Sample(NamedTuple):
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"""一张图及其标签。
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Attributes:
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image: 图片路径。
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label: 标签路径。
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class_id: 标签里第一个框的类别下标；无框时为 None。
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group: 来源的类别目录名——也就是人工归类给这张图的类别。
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标签为空时，这是唯一还能知道"这张图本该是什么"的线索。
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full_box: 整图框的类别下标。非 None 时 _emit 会**重写**标签而不是链接原文件。
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"""
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image: Path
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label: Path
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class_id: int | None
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group: str
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full_box: int | None = None
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def first_class_id(label_path: Path) -> int | None:
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"""取标签里第一个框的类别下标；空标签（无框）返回 None。"""
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for line in label_path.read_text(encoding="utf-8").splitlines():
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line = line.strip()
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if line:
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return int(float(line.split()[0]))
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return None
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def collect(source: Path) -> list[Sample]:
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"""配对 images/ 与 labels/。"""
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images_root, labels_root = source / "images", source / "labels"
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if not images_root.is_dir() or not labels_root.is_dir():
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raise FileNotFoundError(f"{source} 下缺少 images/ 或 labels/，先跑 scripts/generate_boxes.py")
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samples = []
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missing = []
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for image in sorted(p for p in images_root.rglob("*") if p.suffix.lower() in IMAGE_SUFFIXES):
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relative = image.relative_to(images_root)
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label = labels_root / relative.with_suffix(".txt")
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if not label.exists():
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missing.append(image)
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continue
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group = relative.parent.name # 顶层就是图片时为 ""
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samples.append(Sample(image, label, first_class_id(label), group))
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if missing:
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raise FileNotFoundError(
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f"有 {len(missing)} 张图没有对应的标签，例如 {missing[0]}；"
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"产出目录可能不完整，重新跑一次 generate_boxes.py"
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)
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if not samples:
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raise ValueError(f"{source} 下没有找到任何图片")
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return samples
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def class_of(sample: Sample, names: Sequence[str]) -> str:
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"""样本在输出里归属的类别名：优先看标签内容，空标签才退回来源目录名。
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以标签为准，重命名后的文件名就永远不会与标签里的 class_id 矛盾。
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"""
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if sample.class_id is not None:
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return names[sample.class_id]
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if sample.full_box is not None:
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return names[sample.full_box]
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return sample.group or "background"
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def assign_names(samples: Sequence[Sample], names: Sequence[str]) -> dict[Path, str]:
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"""按类别重新编号，返回 {源图片: 新文件名主干}，如 `blackdot_0042`。
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序号在类内按源路径排序：同一份来源重跑，同一张图拿到的名字不变，
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已经做过的人工复核记录还能对得上。
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"""
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by_class: dict[str, list[Sample]] = defaultdict(list)
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for sample in samples:
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by_class[class_of(sample, names)].append(sample)
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width = max(4, len(str(max((len(v) for v in by_class.values()), default=0))))
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mapping = {}
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for name, items in by_class.items():
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for number, sample in enumerate(sorted(items, key=lambda s: str(s.image)), start=1):
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mapping[sample.image] = f"{name}_{number:0{width}d}"
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return mapping
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def build_label_studio_tasks(out_dir: Path, num_images: int) -> dict:
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"""在数据集根目录上调用 label-studio-converter，生成 tasks.json 与 labeling config。
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converter 的 YOLO 导入只认 `<input>/{images,labels,classes.txt}`，且 os.listdir 不递归——
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这正是本脚本产出的扁平结构。图片是硬链接而不是软链接也有讲究：Label Studio 本地存储
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会检查文件是否在 DOCUMENT_ROOT 之下，软链接解析后可能落到外面。
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框以 predictions 导入：它们是 SHAP / Grad-CAM 生成的，不是人工标注。审核员修改提交
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之后才成为 annotation，Label Studio 导出时也只导出 annotation，没审过的框不会混进去。
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"""
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try:
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from label_studio_sdk.converter.imports.yolo import convert_yolo_to_ls
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except ImportError as exc:
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raise RuntimeError("--label-studio 需要 label-studio（uv sync 后可用）") from exc
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tasks_file = out_dir / "tasks.json"
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# converter 会往 stdout 打一份缺了环境变量的导入说明，还会刷一串 root logger 的 INFO；
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# 静音它们，由 main() 打印带实际路径的完整步骤
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root_logger = logging.getLogger()
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previous_level = root_logger.level
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root_logger.setLevel(logging.WARNING)
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try:
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with contextlib.redirect_stdout(io.StringIO()):
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# 不传 image_dims：图片尺寸不统一，传了会把同一个宽高写进每一条标注
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convert_yolo_to_ls(
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input_dir=str(out_dir),
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out_file=str(tasks_file),
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out_type="predictions",
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image_ext=".png",
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)
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finally:
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root_logger.setLevel(previous_level)
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tasks = json.loads(tasks_file.read_text(encoding="utf-8"))
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if len(tasks) != num_images:
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raise RuntimeError(
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f"label-studio-converter 只生成了 {len(tasks)} 个任务，应为 {num_images}；"
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f"检查 {out_dir / 'images'}"
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)
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return {
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"tasks": len(tasks),
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"document_root": str(out_dir.resolve()),
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"tasks_file": str(tasks_file.resolve()),
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"label_config": str((out_dir / "tasks.label_config.xml").resolve()),
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}
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def parse_empty_policy(tokens: Sequence[str]) -> tuple[str, dict[str, str]]:
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"""解析 --empty-policy：一个可选的全局默认，加若干 `类别=策略`。
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--empty-policy drop normal=keep black=full
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-> ("drop", {"normal": "keep", "black": "full"})
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"""
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default, per_class = "keep", {}
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for token in tokens:
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if "=" in token:
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name, _, policy = token.partition("=")
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if policy not in EMPTY_POLICIES:
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raise ValueError(f"未知策略 {policy!r}（类别 {name}）；可选 {EMPTY_POLICIES}")
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per_class[name] = policy
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elif token in EMPTY_POLICIES:
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default = token
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else:
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raise ValueError(
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f"看不懂 --empty-policy 的 {token!r}；"
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f"要么是全局默认（{'/'.join(EMPTY_POLICIES)}），要么是 `类别=策略`"
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)
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return default, per_class
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def apply_empty_policy(
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samples: Sequence[Sample], default: str, per_class: dict[str, str], names: Sequence[str]
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) -> tuple[list[Sample], dict[str, tuple[str, int]]]:
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"""按来源类别处置空标签，返回 (保留下来的样本, {类别: (策略, 数量)})。
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空标签在 YOLO 里的语义是"这张图什么都没有"——一个明确的负样本断言，而不是
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"有缺陷但没定位出来"。所以来自缺陷类别的空标签如果照搬，等于在教模型抑制真实缺陷。
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"""
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index = {name: i for i, name in enumerate(names)}
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unknown = {
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name for name in per_class if name not in index
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}
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if unknown:
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raise ValueError(
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f"--empty-policy 里的类别 {sorted(unknown)} 不在 data.yaml 中；可选 {list(names)}"
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)
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kept: list[Sample] = []
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stats: dict[str, tuple[str, int]] = {}
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for sample in samples:
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if sample.class_id is not None:
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kept.append(sample)
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continue
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policy = per_class.get(sample.group, default)
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counted = stats.get(sample.group, (policy, 0))
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stats[sample.group] = (policy, counted[1] + 1)
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if policy == "drop":
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continue
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if policy == "full":
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if sample.group not in index:
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raise ValueError(
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f"{sample.group!r} 要用整图框，但它不在 data.yaml 的类别里；可选 {list(names)}"
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)
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kept.append(sample._replace(full_box=index[sample.group]))
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else:
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kept.append(sample)
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return kept, stats
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def main() -> int:
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global logger
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parser = argparse.ArgumentParser(
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description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
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)
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parser.add_argument("--source", required=True, help="generate_boxes.py 的产出目录")
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parser.add_argument("--out", default=None, help="输出目录；缺省 data/<source 目录名>")
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parser.add_argument("--data-root", default="data", help="自动推导时的根目录，默认 data")
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parser.add_argument(
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"--empty-policy", nargs="*", default=[], metavar="POLICY|类别=POLICY",
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help=(
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"怎么处置没有框的图（标签为空）。可写一个全局默认，再按类别覆盖，如 "
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"`--empty-policy drop normal=keep black=full`。"
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"keep=当负样本保留（默认）、drop=丢弃、full=退化成整图框。"
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"注意空标签在 YOLO 里的意思是『此图无缺陷』，缺陷类的空标签照搬会教模型抑制真实缺陷"
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),
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)
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parser.add_argument(
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"--keep-names", action="store_true",
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help="保留源文件名，不按 <类别>_<序号> 重新编号（源文件名需去掉后缀后全局唯一）",
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)
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parser.add_argument(
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"--label-studio", action="store_true",
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help="额外在数据集根目录生成 tasks.json 与 labeling config，可直接导入 Label Studio 复核",
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)
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parser.add_argument("--overwrite", action="store_true", help="输出目录已存在时先清空")
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parser.add_argument("--dry-run", action="store_true", help="只打印计划")
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args = parser.parse_args()
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source = Path(args.source)
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out_dir = Path(args.out) if args.out else Path(args.data_root) / source.name
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logger = setup_logging()
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try:
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samples = collect(source)
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except (FileNotFoundError, ValueError) as exc:
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logger.error("%s", exc)
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return 1
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meta_path = source / "data.yaml"
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if not meta_path.exists():
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logger.error("找不到 %s", meta_path)
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return 1
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meta = yaml.safe_load(meta_path.read_text(encoding="utf-8"))
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names = [meta["names"][i] for i in sorted(meta["names"])]
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logger.info("来源: %s（%d 张，%d 类）", source, len(samples), len(names))
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try:
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default_policy, per_class_policy = parse_empty_policy(args.empty_policy)
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samples, empty_stats = apply_empty_policy(samples, default_policy, per_class_policy, names)
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except ValueError as exc:
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logger.error("%s", exc)
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return 1
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total_empty = sum(count for _, count in empty_stats.values())
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if total_empty:
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logger.info("无框图片 %d 张，按来源类别处置：", total_empty)
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for group, (policy, count) in sorted(empty_stats.items(), key=lambda kv: -kv[1][1]):
|
||
logger.info(" %-14s %5d 张 -> %s", group or "(顶层)", count, EMPTY_POLICY_DESC[policy])
|
||
# 只对"默认落到 keep"的类别告警——显式写了 `类别=keep` 的是用户的判断，不再唠叨
|
||
as_negative = sorted(
|
||
g for g, (policy, _) in empty_stats.items()
|
||
if policy == "keep" and g not in per_class_policy
|
||
)
|
||
if as_negative:
|
||
logger.warning(
|
||
"以上 %s 的空标签会以『此图无缺陷』的身份进入训练。"
|
||
"如果其中某些类其实是有缺陷、只是没能定位出来，它们会教模型抑制真实缺陷——"
|
||
"用 --empty-policy 逐类改成 drop 或 full。",
|
||
"/".join(as_negative),
|
||
)
|
||
|
||
counts = Counter(
|
||
names[s.class_id] if s.class_id is not None
|
||
else names[s.full_box] if s.full_box is not None
|
||
else "(无框)"
|
||
for s in samples
|
||
)
|
||
logger.info("%-14s %8s", "类别", "数量")
|
||
for name in names + ["(无框)"]:
|
||
if counts[name]:
|
||
logger.info("%-14s %8d", name, counts[name])
|
||
|
||
to_convert = sum(not is_png(sample.image) for sample in samples)
|
||
if to_convert:
|
||
logger.info(
|
||
"其中 %d 张不是 PNG，会无损转成 PNG（无法硬链接，占额外磁盘）；其余 %d 张硬链接",
|
||
to_convert, len(samples) - to_convert,
|
||
)
|
||
|
||
if args.dry_run:
|
||
if args.label_studio:
|
||
logger.info("--label-studio：会在 %s 下生成 tasks.json", out_dir)
|
||
logger.info("--dry-run：未写入任何文件")
|
||
return 0
|
||
|
||
if out_dir.exists() and any(out_dir.iterdir()):
|
||
if not args.overwrite:
|
||
logger.error("%s 已存在且非空；确认要覆盖请加 --overwrite", out_dir)
|
||
return 1
|
||
shutil.rmtree(out_dir)
|
||
|
||
renamed = None if args.keep_names else assign_names(samples, names)
|
||
modes: Counter[str] = Counter()
|
||
seen: dict[str, Path] = {}
|
||
name_rows = []
|
||
for sample in samples:
|
||
image = sample.image
|
||
stem = image.stem if renamed is None else renamed[image]
|
||
output_name = f"{stem}.png"
|
||
# 保留原名时，拍平并统一后缀后不同目录下的 a.png 与 a.bmp 会撞成同一个 a.png；
|
||
# 重新编号时只有类别名仅大小写不同才会撞。按小写比较：macOS 默认文件系统
|
||
# 不区分大小写，A.png 与 a.png 也会互相覆盖。
|
||
key = output_name.lower()
|
||
if key in seen:
|
||
hint = (
|
||
"源数据集的文件名（去掉后缀后）需要全局唯一，"
|
||
"去掉 --keep-names 按类别重新编号，"
|
||
"或用 scripts/export_clusters.py --prefix-parent 重新导出"
|
||
if renamed is None
|
||
else "data.yaml 里有只差大小写的类别名，请先改成互不相同"
|
||
)
|
||
logger.error("输出文件名冲突：%s 与 %s 都会落成 %s。%s", seen[key], image, output_name, hint)
|
||
return 1
|
||
seen[key] = image
|
||
modes[materialize_png(image, out_dir / "images" / output_name)] += 1
|
||
name_rows.append((output_name, class_of(sample, names), str(image.resolve())))
|
||
destination = out_dir / "labels" / f"{stem}.txt"
|
||
if sample.full_box is None:
|
||
link_or_copy(sample.label, destination)
|
||
else:
|
||
# 整图框：内容与源标签不同，只能新写一份，不能链接
|
||
destination.parent.mkdir(parents=True, exist_ok=True)
|
||
destination.write_text(f"{sample.full_box} 0.5 0.5 1.0 1.0\n", encoding="utf-8")
|
||
|
||
# 不划分时 train 与 val 只能指向同一目录：ultralytics 能读，但验证指标算的是训练集本身
|
||
write_data_yaml(out_dir / "data.yaml", names, root=out_dir.resolve(), train="images", val="images")
|
||
write_classes_txt(out_dir / "classes.txt", names)
|
||
|
||
label_studio = None
|
||
if args.label_studio:
|
||
try:
|
||
label_studio = build_label_studio_tasks(out_dir, len(samples))
|
||
except RuntimeError as exc:
|
||
logger.error("%s", exc)
|
||
return 1
|
||
# utf-8-sig：带 BOM，Excel / Numbers 直接打开中文源路径不乱码
|
||
with (out_dir / "name_map.csv").open("w", encoding="utf-8-sig", newline="") as handle:
|
||
writer = csv.writer(handle)
|
||
writer.writerow(["name", "class", "source"])
|
||
writer.writerows(sorted(name_rows, key=lambda row: (row[1], row[0])))
|
||
(out_dir / "summary.json").write_text(
|
||
json.dumps(
|
||
{
|
||
"source": str(source.resolve()),
|
||
"num_images": len(samples),
|
||
"empty_labels": {
|
||
group: {"policy": policy, "count": count}
|
||
for group, (policy, count) in sorted(empty_stats.items())
|
||
},
|
||
"empty_policy": {"default": default_policy, **per_class_policy},
|
||
"renamed": renamed is not None,
|
||
"label_studio": label_studio,
|
||
"split": False,
|
||
"per_class": dict(counts),
|
||
"link_modes": dict(modes),
|
||
},
|
||
ensure_ascii=False,
|
||
indent=2,
|
||
),
|
||
encoding="utf-8",
|
||
)
|
||
logger.info("图片落盘方式: %s（hard=硬链接, copy=跨盘复制, convert=转成 PNG）", dict(modes))
|
||
logger.info("完成 -> %s", out_dir)
|
||
logger.warning(
|
||
"数据集未划分 train/val，data.yaml 的 train 与 val 都指向 images/。"
|
||
"直接拿去训练的话验证指标算的是训练集本身，会虚高；训练前需要另行划分。"
|
||
)
|
||
if label_studio:
|
||
# label-studio-converter 自带的提示漏了环境变量（它在循环里改写了 image_root_url，
|
||
# 与默认值比较时就不相等了），这里给出带实际路径的完整步骤
|
||
logger.info(
|
||
"Label Studio 任务 %d 个，导入步骤：\n"
|
||
" 1) export LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true\n"
|
||
" export LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=%s\n"
|
||
" uv run label-studio start\n"
|
||
" 2) 新建项目 -> Labeling Setup -> Code，粘贴 %s\n"
|
||
" 3) Settings -> Cloud Storage -> Add Source Storage -> Local files，\n"
|
||
" 绝对路径填 %s/images。只保存，不要点 Sync（会给每张图再建一个空任务）\n"
|
||
" 4) 导入 %s",
|
||
label_studio["tasks"], label_studio["document_root"], label_studio["label_config"],
|
||
label_studio["document_root"], label_studio["tasks_file"],
|
||
)
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
raise SystemExit(main())
|