grounding-dino/data_precess_train.py

import json
import os
import cv2
from pathlib import Path

# ================= 配置区域 =================
BASE_DIR = Path(__file__).resolve().parent
WORKSPACE_DIR = BASE_DIR.parent.parent
jsonl_file = str(WORKSPACE_DIR / 'rex_data' / 'data' / 'rex-omni-data' / 'train' / 'merged_shuffled_no_rect_from_wuhan_plus_replenish_mixed_vis_temp_empty_gt.jsonl')  # 你的原始数据路径
output_dir = str(BASE_DIR / 'dataset')            # 输出文件夹名
json_name = 'train_traffic_data_2_25.json'       # 输出的 json 文件名
# 统一图片根目录：新文件里有多个子目录（如 PanoImages_data_all / crops_scaled1p5 / crop_empty）
img_root = str(WORKSPACE_DIR / 'rex_data' / 'data')
# 当图片不在 img_root 下时，是否允许在 COCO 的 file_name 里写绝对路径
allow_absolute_file_name = True

# 类别映射 (MMDet 中 ID 建议从 1 开始，或者保持 0，CocoDataset默认兼容)
# 这里的顺序非常重要，必须和后面 Config 里的 class_name 顺序一致
categories = [
    {"id": 0, "name": "traffic sign"},
    {"id": 1, "name": "street light"},
    {"id": 2, "name": "traffic light"},
    {"id": 3, "name": "surveillance camera"},
    {"id": 4, "name": "ball bollard"},
    {"id": 5, "name": "fire hydrant"},
    {"id": 6, "name": "trash bin"},
    {"id": 7, "name": "manhole"},
    {"id": 8, "name": "traffic cone"},
    {"id": 9, "name": "bollard"}
]
# ===========================================

def resolve_image_path(raw_path):
    """支持绝对路径和相对路径（相对 jsonl 或相对 img_root）。"""
    if not raw_path:
        return None
    raw_path = os.path.expanduser(str(raw_path))
    if os.path.isabs(raw_path):
        return raw_path

    candidates = [
        os.path.join(os.path.dirname(jsonl_file), raw_path),
        os.path.join(img_root, raw_path)
    ]
    for p in candidates:
        if os.path.exists(p):
            return os.path.abspath(p)
    return os.path.abspath(candidates[0])


def build_file_name(img_path):
    """优先写相对 img_root 的路径；不在 img_root 下时可回退到绝对路径。"""
    rel_path = os.path.relpath(img_path, img_root)
    if not rel_path.startswith('..'):
        return rel_path.replace('\\', '/')
    if allow_absolute_file_name:
        return os.path.abspath(img_path).replace('\\', '/')
    return None

def main():
    os.makedirs(output_dir, exist_ok=True)
    dst_img_dir = os.path.join(output_dir, 'images')
    os.makedirs(dst_img_dir, exist_ok=True)
    
    images = []
    annotations = []
    
    # 建立名字到ID的映射
    cat_map = {cat['name']: cat['id'] for cat in categories}
    
    print(f"Reading {jsonl_file}...")
    
    data_lines = []
    with open(jsonl_file, 'r', encoding='utf-8') as f:
        for line in f:
            if line.strip():
                data_lines.append(json.loads(line))

    print(f"Converting {len(data_lines)} images to COCO format...")
    
    ann_id = 0
    img_id = 0
    
    for entry in data_lines:
        raw_img_path = entry.get('image_name') or entry.get('image_path')
        img_path = resolve_image_path(raw_img_path)
        if not img_path:
            continue
        
        # 1. 检查并读取图片 (需要宽高)
        if not os.path.exists(img_path):
            print(f"Skip: {img_path} not found")
            continue
            
        img = cv2.imread(img_path)
        if img is None: continue
        h, w = img.shape[:2]
        
        file_name = build_file_name(img_path)
        if file_name is None:
            print(f"Skip: {img_path} is outside img_root={img_root}")
            continue
        
        # 2. 这里的策略是：不复制图片，直接用软链接，或者在 config 里指定原图路径
        # 为了方便，这里我们假设你不想复制几千张图，所以只生成 JSON
        # Config 里的 data_prefix 需要指向原图所在的【父目录】
        
        images.append({
            "id": img_id,
            "file_name": file_name,  # 存相对 img_root 的路径，避免同名文件冲突
            "height": h,
            "width": w
        })
        
        # 3. 处理标注
        boxes = entry.get('annotation', {}).get('boxes', [])
        for box_item in boxes:
            phrase = box_item.get('phrase')
            bbox = box_item.get('bbox') # [x1, y1, x2, y2]
            
            if phrase not in cat_map:
                continue
                
            cat_id = cat_map[phrase]
            
            if not isinstance(bbox, (list, tuple)) or len(bbox) != 4:
                continue

            # 坐标转换: xyxy -> xywh
            x1, y1, x2, y2 = map(float, bbox)
            coco_w = x2 - x1
            coco_h = y2 - y1
            if coco_w <= 0 or coco_h <= 0:
                continue
            
            annotations.append({
                "id": ann_id,
                "image_id": img_id,
                "category_id": cat_id,
                "bbox": [x1, y1, coco_w, coco_h],
                "area": coco_w * coco_h,
                "iscrowd": 0
            })
            ann_id += 1
            
        img_id += 1
        
        # 简单打印进度
        if img_id % 500 == 0:
            print(f"Processed {img_id}...")

    # 构建最终字典
    coco_output = {
        "images": images,
        "annotations": annotations,
        "categories": categories
    }
    
    save_path = os.path.join(output_dir, json_name)
    with open(save_path, 'w') as f:
        json.dump(coco_output, f)
        
    print(f"Done! Saved to {save_path}")
    print(f"Total Images: {len(images)}, Total Annotations: {len(annotations)}")

if __name__ == "__main__":
    main()