Shaun ZhouandClaude Opus 5
7adc5b6332
- 图片统一成 PNG。pearlescent / blue / uv 的原始数据是 BMP，generate_boxes.py 按原后缀链接，于是 runs/boxes_shap 里 3685 张 BMP 与 7526 张 PNG 混在一起， ripple 与 uneven 两类全是 BMP。按文件头（而非后缀）判断：真 PNG 照旧硬链接， 其余无损转换，不缩放像素，归一化框坐标原样有效；CMYK 等 PNG 存不下的模式转 RGB。 在真实数据上核对过 3685 张转换结果逐像素一致。 - 文件按 <类别>_<序号> 重新编号。类别取标签第一列，文件名不会与标签矛盾； 序号在类内按源路径排序、与划分无关，换种子重跑名字不变。新旧名对照写入 name_map.csv，源文件名里的正反面、位置、时间戳不丢。--keep-names 保留原名。 - 输出 classes.txt（行号即 class_id），供 LabelImg / X-AnyLabeling 复核框时使用。 - 撞名检查改为按输出文件名的小写比较：统一后缀后 a.png 与 a.bmp 会撞， macOS 默认文件系统也不区分大小写。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
456 lines
19 KiB
Python
456 lines
19 KiB
Python
#!/usr/bin/env python
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"""把 generate_boxes.py 的产出整理成 YOLO 训练所需的数据集，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 检测训练要的是 **train/val 划分**——类别信息在标签文件的第一列里，
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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，按 train/val 重新组织；
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2. **按类别分层**切分，小类不至于在验证集里挂零；
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3. 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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4. 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/{train,val}/<类别>_<序号>.png
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├── labels/{train,val}/<类别>_<序号>.txt
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├── classes.txt # 每行一个类别名，行号即 class_id，给标注工具复核用
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├── name_map.csv # 新名 ↔ 源文件对照
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├── data.yaml
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└── summary.json
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```
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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 csv
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import errno
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import json
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import os
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import random
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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、与 train/val 划分无关：换 --val-ratio 或 --seed 重跑，
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同一张图拿到的名字不变，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 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("--val-ratio", type=float, default=0.2)
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parser.add_argument("--seed", type=int, default=42)
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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("--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]):
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logger.info(" %-14s %5d 张 -> %s", group or "(顶层)", count, EMPTY_POLICY_DESC[policy])
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# 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
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as_negative = sorted(
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g for g, (policy, _) in empty_stats.items()
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if policy == "keep" and g not in per_class_policy
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)
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if as_negative:
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logger.warning(
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"以上 %s 的空标签会以『此图无缺陷』的身份进入训练。"
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"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——"
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"用 --empty-policy 逐类改成 drop 或 full。",
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"/".join(as_negative),
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)
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# 按类别分层切分；无框的图单独作为一组分配，避免它们全落进同一侧
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grouped: dict[int | None, list[Sample]] = defaultdict(list)
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for sample in samples:
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# 退化成整图框的那些已经有了确定类别，跟着本类一起分层
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grouped[sample.full_box if sample.class_id is None else sample.class_id].append(sample)
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rng = random.Random(args.seed)
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splits: dict[str, list] = {"train": [], "val": []}
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for _class_id, group in sorted(grouped.items(), key=lambda kv: (kv[0] is None, kv[0])):
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order = group[:]
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rng.shuffle(order)
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n_val = max(1, round(len(order) * args.val_ratio)) if len(order) > 1 else 0
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splits["val"].extend(order[:n_val])
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splits["train"].extend(order[n_val:])
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counts = {
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split: Counter(
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names[s.class_id] if s.class_id is not None
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else names[s.full_box] if s.full_box is not None
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else "(无框)"
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for s in items
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)
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for split, items in splits.items()
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}
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logger.info("划分: train %d / val %d", len(splits["train"]), len(splits["val"]))
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logger.info("%-14s %8s %8s", "类别", "train", "val")
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for name in names + ["(无框)"]:
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if counts["train"][name] or counts["val"][name]:
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logger.info("%-14s %8d %8d", name, counts["train"][name], counts["val"][name])
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to_convert = sum(not is_png(sample.image) for sample in samples)
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if to_convert:
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logger.info(
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"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:
|
||
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 split, items in splits.items():
|
||
for sample in items:
|
||
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" / split / output_name)] += 1
|
||
name_rows.append((split, output_name, class_of(sample, names), str(image.resolve())))
|
||
destination = out_dir / "labels" / split / 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")
|
||
|
||
write_data_yaml(out_dir / "data.yaml", names, root=out_dir.resolve(),
|
||
train="images/train", val="images/val")
|
||
write_classes_txt(out_dir / "classes.txt", names)
|
||
# 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(["split", "name", "class", "source"])
|
||
writer.writerows(sorted(name_rows, key=lambda row: (row[2], row[1])))
|
||
(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,
|
||
"val_ratio": args.val_ratio,
|
||
"seed": args.seed,
|
||
"split_sizes": {k: len(v) for k, v in splits.items()},
|
||
"per_class": {k: dict(v) for k, v in counts.items()},
|
||
"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)
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
raise SystemExit(main())
|