convert_labels.py

import json
import os

def get_relative_position(bbox, img_width, img_height):
    """

    根据边界框在图像中的相对位置生成描述。

    bbox: [x, y, width, height] (Label Studio 导出的百分比坐标)

    img_width, img_height: 图像的原始宽度和高度

    """
    if img_width == 0 or img_height == 0:
        return "未知位置"

    center_x_percent = bbox[0] + bbox[2] / 2
    center_y_percent = bbox[1] + bbox[3] / 2

    parts = []
    if center_y_percent < 33.3:
        parts.append("顶部")
    elif center_y_percent > 66.6:
        parts.append("底部")
    else:
        parts.append("中部")

    if center_x_percent < 33.3:
        parts.append("偏左")
    elif center_x_percent > 66.6:
        parts.append("偏右")
    else:
        parts.append("中心")

    return "".join(parts) + "区域"

def generate_qwen_vl_data(label_studio_json_path, output_jsonl_path, image_base_dir):
    """

    将 Label Studio 导出的 JSON 转换为 Qwen-VL 微调所需的 JSONL 格式。



    Args:

        label_studio_json_path (str): Label Studio 导出的 JSON 文件路径。

        output_jsonl_path (str): 输出的 JSONL 文件路径。

        image_base_dir (str): 图片文件所在的根目录，Label Studio JSON 中的路径是相对此目录的。

                                例如，如果JSON中是"/data/upload/1/image.jpg"，而实际图片在"your_project_data/images/image.jpg"，

                                那么image_base_dir就是"your_project_data/images"。

    """
    with open(label_studio_json_path, 'r', encoding='utf-8') as f:
        data = json.load(f)

    qwen_vl_samples = []

    for task in data:
        image_relative_path = task['data']['image']
        # --- 修改这里 ---
        # 使用 os.path.basename 来安全地提取文件名
        image_filename = os.path.basename(image_relative_path)
        image_absolute_path = os.path.join(image_base_dir, image_filename).replace('\\', '/')
        # --- 修改结束 ---

        annotations = task.get('annotations', [])
        if not annotations:
            print(f"Warning: Task {task['id']} has no annotations. Skipping.")
            continue

        # 取第一个 annotation 结果，通常只有一个
        annotation_results = annotations[0].get('result', [])
        if not annotation_results:
            print(f"Warning: Task {task['id']} annotation has no results. Skipping.")
            continue

        component_type = "未知构件"
        crack_type = "未知裂缝类型"
        text_description = ""
        bbox_descriptions = []
        original_width = None
        original_height = None

        # 遍历所有标注结果
        for res in annotation_results:
            if res['from_name'] == 'componentClassification' and res['type'] == 'choices':
                component_type = res['value']['choices'][0] if res['value']['choices'] else component_type
            elif res['from_name'] == 'cracksClassification' and res['type'] == 'choices':
                crack_type = res['value']['choices'][0] if res['value']['choices'] else crack_type
            elif res['from_name'] == 'textTool' and res['type'] == 'textarea':
                text_description = res['value']['text'][0] if res['value']['text'] else text_description
            elif res['type'] in ['rectanglelabels', 'polygonlabels']:
                # 获取图像原始尺寸，用于计算相对位置
                if original_width is None: # 只需要获取一次
                    original_width = res.get('original_width', 0)
                    original_height = res.get('original_height', 0)
                # 如果当前结果没有原始尺寸，尝试从task['annotations'][0]中查找
                if original_width == 0 and annotations[0].get('result'):
                    for r_sub in annotations[0]['result']:
                        if r_sub.get('original_width') and r_sub.get('original_height'):
                            original_width = r_sub['original_width']
                            original_height = r_sub['original_height']
                            break

                if res['type'] == 'rectanglelabels':
                    bbox = res['value']
                    # Label Studio 导出的 x, y, width, height 是百分比
                    x_percent = bbox['x']
                    y_percent = bbox['y']
                    width_percent = bbox['width']
                    height_percent = bbox['height']
                    
                    label = bbox['rectanglelabels'][0] if bbox['rectanglelabels'] else "区域"
                    
                    # 尝试生成更具描述性的位置
                    position_desc = get_relative_position([x_percent, y_percent, width_percent, height_percent], original_width, original_height)
                    bbox_descriptions.append(f"一个 '{label}' 位于图像的 {position_desc}。")

                elif res['type'] == 'polygonlabels':
                    # 多边形通常更复杂，这里简化为描述其标签和大致位置
                    # 可以通过计算多边形中心来获取大致位置
                    points = res['value']['points']
                    if points:
                        # 计算多边形中心的大致位置
                        avg_x = sum([p[0] for p in points]) / len(points)
                        avg_y = sum([p[1] for p in points]) / len(points)
                        
                        label = res['value']['polygonlabels'][0] if res['value']['polygonlabels'] else "区域"
                        position_desc = get_relative_position([avg_x, avg_y, 0, 0], original_width, original_height) # width/height设为0，只用中心点
                        bbox_descriptions.append(f"一个 '{label}' 区域位于图像的 {position_desc}。")


        # 构建 Instruction 和 Response
        instruction = "请描述这张图片中的所有裂缝信息，包括构件、裂缝类型、位置和详细描述。"
        
        response_parts = []
        if component_type != "未知构件":
            response_parts.append(f"图片显示的是一个 '{component_type}'。")
        
        if crack_type != "未知裂缝类型":
            response_parts.append(f"主要裂缝类型是 '{crack_type}'。")
        
        if text_description:
            response_parts.append(f"详细情况描述为：'{text_description}'。")
            
        if bbox_descriptions:
            response_parts.append("图中发现以下缺陷区域：")
            for desc in bbox_descriptions:
                response_parts.append(f"- {desc}")
        
        if not response_parts: # 如果没有任何有效信息，跳过此任务
            print(f"Warning: Task {task['id']} has no meaningful annotations to generate a response. Skipping.")
            continue

        response = " ".join(response_parts)

        # 构建 Qwen-VL 格式的样本
        qwen_vl_sample = {
            "image": image_absolute_path,
            "conversations": [
                {"from": "user", "value": instruction},
                {"from": "assistant", "value": response}
            ]
        }
        qwen_vl_samples.append(qwen_vl_sample)

    with open(output_jsonl_path, 'w', encoding='utf-8') as f:
        for sample in qwen_vl_samples:
            f.write(json.dumps(sample, ensure_ascii=False) + '\n')

    print(f"成功生成 {len(qwen_vl_samples)} 条 Qwen-VL 微调数据到 {output_jsonl_path}")

# --- 使用示例 ---
if __name__ == "__main__":
    # 你的 Label Studio 导出 JSON 文件路径
    label_studio_json_file = 'annotations.json'
    # 输出的 Qwen-VL 微调 JSONL 文件路径
    output_jsonl_file = 'annotations.jsonl'
    # 你的图片文件所在的根目录。
    # Label Studio 导出的 JSON 中的图片路径是相对的，例如 "/data/upload/1/f238f029-0319.jpg_wh300.jpg"
    # 你需要根据实际情况调整 `image_base_dir`，使其指向你存储 Label Studio 图片的实际文件夹。
    # 例如：如果你的图片是 `my_project/images/f238f029-0319.jpg_wh300.jpg`
    # 那么 `image_base_dir` 就应该是 `my_project/images`
    image_base_directory = 'images' # <-- 请务必修改为你的实际图片根目录！

    # 运行转换
    generate_qwen_vl_data(label_studio_json_file, output_jsonl_file, image_base_directory)

    # 打印生成的第一个样本，方便检查
    with open(output_jsonl_file, 'r', encoding='utf-8') as f:
        first_sample = json.loads(f.readline())
        print("\n--- 第一个生成的样本示例 ---")
        print(json.dumps(first_sample, indent=2, ensure_ascii=False))