260 lines
9.2 KiB
Python
260 lines
9.2 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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将包含矩形两点(左上/右下)的 JSON 标注转换为 YOLO TXT 标注。
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- 适配示例结构：
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{
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"imagePath": "xxx.jpg", # 可选
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"imageWidth": 3840, # 必需其一(Width/Height或可从图像读)
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"imageHeight": 2160,
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"shapes": [
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{
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"label": "鱿鱼",
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"shape_type": "rectangle",
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"points": [[x1,y1],[x2,y2]]
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},
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...
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]
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}
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- 归一化为 YOLO: class x_center y_center w h
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- 递归遍历 --source 目录下所有 .json，生成与图片同名的 .txt
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- 自动收集类别，生成 dataset.yaml（可选指定类别顺序文件）
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用法：
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python train/convert_json_to_yolo.py \
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--source ./data/ \
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--images_subdir images \
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--labels_out ./datasets/yolo_dataset/labels \
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--images_out ./datasets/yolo_dataset/images \
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--copy_images \
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--names_out ./datasets/yolo_dataset/dataset.yaml
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"""
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import os
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import sys
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import json
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import argparse
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import shutil
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from pathlib import Path
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from typing import Dict, List, Tuple, Optional
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IMG_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff"}
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def parse_args() -> argparse.Namespace:
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ap = argparse.ArgumentParser(description="将两点矩形JSON转换为YOLO TXT")
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ap.add_argument("--source", required=True, help="源根目录(递归查找json)")
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ap.add_argument("--labels_out", required=True, help="输出labels根目录，内部会按train/val/test或保持平铺")
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ap.add_argument("--images_out", default="", help="可选：复制/硬链图片到该images根目录")
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ap.add_argument("--copy_images", action="store_true", help="复制图片到images_out，否则硬链接")
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ap.add_argument("--split", choices=["none","mirror"], default="mirror", help="输出子结构：none=不分层，mirror=镜像源目录层级")
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ap.add_argument("--names_out", default="", help="输出dataset.yaml(若指定且提供images_out)" )
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ap.add_argument("--class_order", default="", help="可选：提供一个txt，按行给定类别顺序")
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return ap.parse_args()
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def load_class_order(path: str) -> List[str]:
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if not path or not Path(path).exists():
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return []
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with open(path, "r", encoding="utf-8") as f:
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names = [ln.strip() for ln in f if ln.strip()]
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return names
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def ensure_parent(p: Path):
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p.parent.mkdir(parents=True, exist_ok=True)
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def norm_bbox_to_yolo(x1: float, y1: float, x2: float, y2: float, W: int, H: int) -> Tuple[float,float,float,float]:
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x_min, y_min = min(x1,x2), min(y1,y2)
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x_max, y_max = max(x1,x2), max(y1,y2)
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w = max(0.0, x_max - x_min)
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h = max(0.0, y_max - y_min)
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cx = x_min + w/2.0
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cy = y_min + h/2.0
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if W <= 0 or H <= 0:
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raise ValueError("Invalid image size")
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return cx/W, cy/H, w/W, h/H
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def find_image_for_json(jpath: Path, image_path_in_json: Optional[str], source_root: Path) -> Optional[Path]:
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# 优先使用 imagePath 相对 json 所在目录
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if image_path_in_json:
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p = (jpath.parent / image_path_in_json).resolve()
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if p.exists() and p.suffix.lower() in IMG_EXTS:
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return p
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# 尝试仅用文件名
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p2 = next((q for q in jpath.parent.glob(Path(image_path_in_json).name) if q.suffix.lower() in IMG_EXTS), None)
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if p2:
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return p2
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# 在整个source_root内按文件名搜索
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cands = list(source_root.rglob(Path(image_path_in_json).name))
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cands = [c for c in cands if c.suffix.lower() in IMG_EXTS]
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if cands:
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return cands[0]
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# 若无imagePath，尝试同名不同后缀
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stem = jpath.stem
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for ext in IMG_EXTS:
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p = (jpath.parent / f"{stem}{ext}")
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if p.exists():
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return p
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# 递归在同目录查找前缀匹配
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cands = list(jpath.parent.glob(f"{jpath.stem}.*"))
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for c in cands:
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if c.suffix.lower() in IMG_EXTS:
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return c
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return None
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def convert_one(json_path: Path, labels_root: Path, images_root: Optional[Path], copy_images: bool, split: str, class_order: List[str], seen_classes: Dict[str,int]) -> Optional[str]:
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# 尝试多种编码读取JSON文件
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encodings = ["utf-8", "gbk", "gb2312", "latin-1", "iso-8859-1"]
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data = None
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for enc in encodings:
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try:
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with open(json_path, "r", encoding=enc) as f:
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data = json.load(f)
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break
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except (UnicodeDecodeError, json.JSONDecodeError):
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continue
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except Exception:
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continue
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if data is None:
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return f"[跳过] 读取失败 {json_path}: 无法使用任何编码读取"
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imgW = int(data.get("imageWidth", 0) or 0)
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imgH = int(data.get("imageHeight", 0) or 0)
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imagePath_in = data.get("imagePath", "")
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src_img = find_image_for_json(json_path, imagePath_in, json_path.parents[2] if len(json_path.parents)>=2 else json_path.parent)
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# 若无宽高，尝试从图像读
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if (imgW <= 0 or imgH <= 0) and src_img and src_img.exists():
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try:
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from PIL import Image
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with Image.open(src_img) as im:
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imgW, imgH = im.size
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except Exception:
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pass
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if imgW <= 0 or imgH <= 0:
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return f"[跳过] 无法获取尺寸 {json_path}"
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shapes = data.get("shapes") or data.get("annotations") or []
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if not isinstance(shapes, list):
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return f"[跳过] 无有效shapes {json_path}"
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# 目标labels路径
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if split == "mirror":
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rel_dir = json_path.parent.relative_to(Path(args.source).resolve()) # type: ignore[name-defined]
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lbl_dir = labels_root / rel_dir
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else:
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lbl_dir = labels_root
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# 图片输出
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dst_img = None
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if images_root is not None and src_img is not None and src_img.exists():
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if split == "mirror":
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img_rel_dir = json_path.parent.relative_to(Path(args.source).resolve()) # type: ignore[name-defined]
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dst_img = images_root / img_rel_dir / src_img.name
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else:
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dst_img = images_root / src_img.name
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ensure_parent(dst_img)
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try:
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if copy_images:
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shutil.copy2(src_img, dst_img)
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else:
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if dst_img.exists():
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dst_img.unlink()
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os.link(src_img, dst_img)
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except Exception:
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shutil.copy2(src_img, dst_img)
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# 组装yolo行
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yolo_lines: List[str] = []
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for shp in shapes:
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if shp.get("shape_type") not in (None, "rectangle", "bbox", "box"):
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continue
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pts = shp.get("points")
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if not pts or len(pts) < 2:
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continue
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(x1,y1),(x2,y2) = pts[0], pts[1]
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try:
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xc,yc,w,h = norm_bbox_to_yolo(float(x1),float(y1),float(x2),float(y2), imgW, imgH)
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except Exception:
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continue
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label = shp.get("label", "object")
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if class_order:
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if label not in class_order:
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# 未知类别丢弃或追加？此处选择丢弃
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continue
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cid = class_order.index(label)
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else:
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if label not in seen_classes:
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seen_classes[label] = len(seen_classes)
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cid = seen_classes[label]
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yolo_lines.append(f"{cid} {xc:.6f} {yc:.6f} {w:.6f} {h:.6f}")
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# 写出txt
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lbl_path = (lbl_dir / f"{(src_img.stem if src_img else json_path.stem)}.txt")
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ensure_parent(lbl_path)
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with open(lbl_path, "w", encoding="utf-8") as f:
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f.write("\n".join(yolo_lines) + ("\n" if yolo_lines else ""))
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return None
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def write_dataset_yaml(images_root: Path, names_out: Path, class_names: List[str]):
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ensure_parent(names_out)
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yaml = (
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f"path: {images_root.parent.resolve()}\n"
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f"train: images\n"
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f"val: images\n"
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f"test: images\n"
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f"names: {class_names}\n"
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)
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with open(names_out, "w", encoding="utf-8") as f:
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f.write(yaml)
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def main():
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global args # 供 convert_one 使用 source 路径
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args = parse_args()
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source = Path(args.source).resolve()
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labels_root = Path(args.labels_out).resolve()
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images_root = Path(args.images_out).resolve() if args.images_out else None
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class_order = load_class_order(args.class_order)
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seen_classes: Dict[str,int] = {}
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json_files = list(source.rglob("*.json"))
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if not json_files:
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print(f"[错误] 未找到json: {source}")
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sys.exit(1)
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errors = 0
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for jp in json_files:
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err = convert_one(jp, labels_root, images_root, args.copy_images, args.split, class_order, seen_classes)
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if err:
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errors += 1
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if errors <= 20:
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print(err)
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print(f"完成。转换json: {len(json_files)}, 错误: {errors}, 类别数: {len(seen_classes) or len(class_order)}")
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# 生成dataset.yaml（仅当提供images_out时）
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if args.names_out and (args.images_out):
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names = class_order if class_order else [None]*len(seen_classes)
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if not class_order:
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# 根据seen_classes的索引顺序重建
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names = [None]*len(seen_classes)
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for name, idx in seen_classes.items():
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names[idx] = name
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write_dataset_yaml(Path(args.images_out), Path(args.names_out), names) # type: ignore[arg-type]
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print(f"[OK] 写出dataset.yaml: {args.names_out}")
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if __name__ == "__main__":
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main()
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