简介：
最近做实例分割分割，使用Labelme生成json格式标签后，需要转换为txt标签，才能供YOLO进行训练。
在参看b站，github后，发现GitHub有相关项目：lableme2yolo
一个是ultralyics官方的JSON2YOLO项目
ultralytics/JSON2YOLO: Convert JSON annotations into YOLO format.
参考1：
将labelme数据标注格式转换为YoloV8语义分割数据集，并可自动划分训练集和验证集
python
import json
import random
import yaml
import argparse
import shutil
from pathlib import Path
from collections import defaultdict
from tqdm import tqdm
# 设定随机种子以确保可重复性
random.seed(114514)
# yoloV8支持的图像格式
# https://docs.ultralytics.com/modes/predict/?h=format+image#images
image_formats = ["jpg", "jpeg", "png", "bmp", "webp", "tif", ".dng", ".mpo", ".pfm"]
def copy_labled_img(json_path: Path, target_folder: Path, task: str):
# 遍历支持的图像格式，查找并复制图像文件
for format in image_formats:
image_path = json_path.with_suffix("." + format)
if image_path.exists():
# 构建目标文件夹中的目标路径
target_path = target_folder / "images" / task / image_path.name
shutil.copy(image_path, target_path)
def json_to_yolo(json_path: Path, sorted_keys: list):
with open(json_path, "r") as f:
labelme_data = json.load(f)
width = labelme_data["imageWidth"]
height = labelme_data["imageHeight"]
yolo_lines = []
for shape in labelme_data["shapes"]:
label = shape["label"]
points = shape["points"]
class_idx = sorted_keys.index(label)
txt_string = f"{class_idx} "
for x, y in points:
x /= width
y /= height
txt_string += f"{x} {y} "
yolo_lines.append(txt_string.strip() + "\n")
return yolo_lines
def create_directory_if_not_exists(directory_path):
# 使用 exist_ok=True 可以避免重复检查目录是否存在
directory_path.mkdir(parents=True, exist_ok=True)
# 创建训练使用的yaml文件
def create_yaml(output_folder: Path, sorted_keys: list):
train_img_path = Path("images") / "train"
val_img_path = Path("images") / "val"
train_label_path = Path("labels") / "train"
val_label_path = Path("labels") / "val"
# 创建所需目录
for path in [train_img_path, val_img_path, train_label_path, val_label_path]:
create_directory_if_not_exists(output_folder / path)
names_dict = {idx: name for idx, name in enumerate(sorted_keys)}
yaml_dict = {
"path": output_folder.as_posix(),
"train": train_img_path.as_posix(),
"val": val_img_path.as_posix(),
"names": names_dict,
}
yaml_file_path = output_folder / "yolo.yaml"
with open(yaml_file_path, "w") as yaml_file:
yaml.dump(yaml_dict, yaml_file, default_flow_style=False, sort_keys=False)
print(f"yaml created in {yaml_file_path.as_posix()}")
# Convert label to idx
def get_labels_and_json_path(input_folder: Path):
json_file_paths = list(input_folder.rglob("*.json"))
label_counts = defaultdict(int)
for json_file_path in json_file_paths:
with open(json_file_path, "r") as f:
labelme_data = json.load(f)
for shape in labelme_data["shapes"]:
label = shape["label"]
label_counts[label] += 1
# 根据标签出现次数排序标签
sorted_keys = sorted(label_counts, key=lambda k: label_counts[k], reverse=True)
return sorted_keys, json_file_paths
def labelme_to_yolo(
json_file_paths: list, output_folder: Path, sorted_keys: list, split_rate: float
):
# 随机打乱 JSON 文件路径列表
random.shuffle(json_file_paths)
# 计算训练集和验证集的分割点
split_point = int(split_rate * len(json_file_paths))
train_set = json_file_paths[:split_point]
val_set = json_file_paths[split_point:]
for json_file_path in tqdm(train_set):
txt_name = json_file_path.with_suffix(".txt").name
yolo_lines = json_to_yolo(json_file_path, sorted_keys)
output_json_path = Path(output_folder / "labels" / "train" / txt_name)
with open(output_json_path, "w") as f:
f.writelines(yolo_lines)
copy_labled_img(json_file_path, output_folder, task="train")
for json_file_path in tqdm(val_set):
txt_name = json_file_path.with_suffix(".txt").name
yolo_lines = json_to_yolo(json_file_path, sorted_keys)
output_json_path = Path(output_folder / "labels" / "val" / txt_name)
with open(output_json_path, "w") as f:
f.writelines(yolo_lines)
copy_labled_img(json_file_path, output_folder, task="val")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="labelme2yolo")
parser.add_argument("input_folder", help="输入LabelMe格式文件的文件夹")
parser.add_argument("output_folder", help="输出YOLO格式文件的文件夹")
parser.add_argument("split_rate", help="调整训练集和测试集的比重")
args = parser.parse_args()
input_folder = Path(args.input_folder)
output_folder = Path(args.output_folder)
split_rate = float(args.split_rate)
sorted_keys, json_file_paths = get_labels_and_json_path(input_folder)
create_yaml(output_folder, sorted_keys)
labelme_to_yolo(json_file_paths, output_folder, sorted_keys, split_rate)参考资料
1.参考1：KdaiP/labelme2YoloV8-segment: 将labelme数据标注格式转换为YoloV8语义分割数据集，并可自动划分训练集和验证集