82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
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import json
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import os
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import glob
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def labelme_to_yolo_segmentation_batch(json_dir, output_dir, class_mapping, img_shape):
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"""
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批量将 LabelMe JSON 文件转换为 YOLO 分割格式的 .txt 文件
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:param json_dir: 包含 JSON 文件的目录
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:param output_dir: 输出 .txt 文件的目录
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:param class_mapping: 类别映射字典，如 {"夹具": 0, "夹具1": 1}
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:param img_shape: 图像尺寸 (height, width)
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"""
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# 确保输出目录存在
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os.makedirs(output_dir, exist_ok=True)
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# 获取所有 .json 文件（排除 _mask.json 等非标注文件）
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json_files = glob.glob(os.path.join(json_dir, "*.json"))
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json_files = [f for f in json_files if os.path.isfile(f)]
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if not json_files:
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print(f"❌ 在 {json_dir} 中未找到任何 JSON 文件")
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return
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img_h, img_w = img_shape
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converted_count = 0
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for json_file in json_files:
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try:
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with open(json_file, 'r', encoding='utf-8') as f:
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data = json.load(f)
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# 获取文件名（不含扩展名）
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base_name = os.path.splitext(os.path.basename(json_file))[0]
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output_path = os.path.join(output_dir, f"{base_name}.txt")
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with open(output_path, 'w', encoding='utf-8') as out_f:
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for shape in data['shapes']:
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label = shape['label']
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points = shape['points']
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if label not in class_mapping:
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print(f"⚠️ 跳过未知标签 '{label}' in {json_file}")
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continue
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class_id = class_mapping[label]
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normalized = []
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for x, y in points:
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nx = max(0.0, min(1.0, x / img_w))
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ny = max(0.0, min(1.0, y / img_h))
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normalized.append(f"{nx:.6f}")
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normalized.append(f"{ny:.6f}")
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line = f"{class_id} {' '.join(normalized)}"
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out_f.write(line + '\n')
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print(f"✅ 已转换: {os.path.basename(json_file)} -> {os.path.basename(output_path)}")
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converted_count += 1
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except Exception as e:
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print(f"❌ 转换失败 {json_file}: {e}")
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print(f"\n🎉 批量转换完成！共处理 {converted_count} 个文件。")
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print(f"📁 输出目录: {output_dir}")
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# ================== 用户配置区 ==================
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JSON_DIR = "/home/hx/yolo/ultralytics_yolo11-main/dataset" # 输入：存放 LabelMe JSON 的文件夹
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OUTPUT_DIR = "labels" # 输出：存放 YOLO .txt 的文件夹
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CLASS_MAPPING = {
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"夹具": 0,
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"夹具1": 1
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# 可继续添加其他类别
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}
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IMG_SHAPE = (1440, 2506) # 替换为你的图像实际尺寸 (height, width)
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# ================== 执行转换 ==================
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if __name__ == "__main__":
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labelme_to_yolo_segmentation_batch(
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json_dir=JSON_DIR,
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output_dir=OUTPUT_DIR,
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class_mapping=CLASS_MAPPING,
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img_shape=IMG_SHAPE
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)
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