116 lines
4.8 KiB
Python
116 lines
4.8 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_keypoints_batch(json_dir, output_dir, target_label="夹具1", class_id=0, img_shape=None, num_keypoints=None):
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"""
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批量将 LabelMe JSON 文件中的关键点标注转换为 YOLO Pose (Keypoints) 格式的 .txt 文件
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仅转换指定标签（如 "夹具1"），忽略其他标签，且不生成空文件。
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:param json_dir: 包含 LabelMe 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 target_label: 要转换的目标标签名称
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:param class_id: 对应的 YOLO 类别 ID
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:param img_shape: 图像尺寸 (height, width)，如 (1440, 2506)
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:param num_keypoints: 预期关键点数量（可选，用于校验）
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"""
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if img_shape is None:
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raise ValueError("必须提供 img_shape 参数，例如 (1440, 2506)")
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if num_keypoints is not None and num_keypoints <= 0:
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raise ValueError("num_keypoints 必须为正整数")
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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）
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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) and not f.endswith("_mask.json")]
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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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skipped_count = 0
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print(f"🔍 开始转换关键点，仅处理标签: '{target_label}' (class_id={class_id})")
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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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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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has_valid_shapes = False
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keypoints_found = []
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with open(output_path, 'w', encoding='utf-8') as out_f:
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for shape in data.get('shapes', []):
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label = shape['label']
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points = shape['points'] # 每个 shape 是一个关键点 [x, y]
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if label != target_label:
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continue
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# 收集所有关键点坐标
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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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keypoints_found.extend([f"{nx:.6f}", f"{ny:.6f}", "2"]) # v=2: visible
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has_valid_shapes = True
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# 写入 YOLO 行（支持多个 shape？通常一个标签一个实例）
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if has_valid_shapes:
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if num_keypoints is not None and len(keypoints_found) // 3 != num_keypoints:
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print(f"⚠️ {os.path.basename(json_file)}: 关键点数量不匹配，期望 {num_keypoints}，实际 {len(keypoints_found)//3}")
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line = f"{class_id} {' '.join(keypoints_found)}"
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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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else:
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# 删除空文件
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os.remove(output_path)
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if not has_valid_shapes:
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print(f"🟡 跳过: {os.path.basename(json_file)} -> 未包含 '{target_label}'，不生成 .txt")
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skipped_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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if os.path.exists(output_path):
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os.remove(output_path)
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print("\n" + "="*50)
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print(f"🎉 批量关键点转换完成！")
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print(f"📊 成功转换: {converted_count}")
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print(f"📊 跳过文件: {skipped_count}")
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print(f"📁 输出目录: {output_dir}")
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if num_keypoints:
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print(f"📍 关键点数量: {num_keypoints}")
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print("="*50)
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# ================== 用户配置区 ==================
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JSON_DIR = "/home/hx/yolo/yolo11_point/folder_end" # LabelMe JSON 文件夹路径
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OUTPUT_DIR = "labels_keypoints" # 输出 YOLO 关键点标签目录
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TARGET_LABEL = "point" # 要提取的关键点标签
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CLASS_ID = 0 # YOLO 中该类的 ID
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IMG_SHAPE = (1440, 2506) # (高度, 宽度)
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NUM_KEYPOINTS = 4 # 可选：指定关键点数量用于校验
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# ================== 执行转换 ==================
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if __name__ == "__main__":
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print(f"🚀 开始转换 LabelMe 关键点 → YOLO Pose 格式 (仅 '{TARGET_LABEL}')")
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labelme_to_yolo_keypoints_batch(
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json_dir=JSON_DIR,
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output_dir=OUTPUT_DIR,
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target_label=TARGET_LABEL,
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class_id=CLASS_ID,
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img_shape=IMG_SHAPE,
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num_keypoints=NUM_KEYPOINTS
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)
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