| import pandas as pd |
| import os |
| from pandarallel import pandarallel |
| import zipfile |
| from concurrent.futures import ThreadPoolExecutor, as_completed |
|
|
| pandarallel.initialize(progress_bar=True) |
|
|
| WORK_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'opensource_dataset') |
| os.makedirs(WORK_DIR, exist_ok=True) |
| os.chdir(WORK_DIR) |
| print(f"📂 工作目录: {os.getcwd()}") |
|
|
| def unzip_one(zip_path, extract_dir): |
| zip_name = os.path.splitext(os.path.basename(zip_path))[0] |
| with zipfile.ZipFile(zip_path, 'r') as zf: |
| names = zf.namelist() |
| common_prefix = os.path.commonprefix(names).split('/')[0] if names else '' |
| if common_prefix.lower() == zip_name.lower(): |
| target_dir = extract_dir |
| else: |
| target_dir = os.path.join(extract_dir, zip_name) |
| os.makedirs(target_dir, exist_ok=True) |
|
|
| zf.extractall(target_dir) |
| return f"✅ {zip_name}.zip 已解压到 {target_dir}" |
|
|
| def unzip_all(base_dir=".", extract_dir=None, max_workers=8): |
| if extract_dir is None: |
| extract_dir = base_dir |
|
|
| zip_files = [ |
| os.path.join(base_dir, f) |
| for f in os.listdir(base_dir) |
| if f.endswith(".zip") |
| ] |
|
|
| if not zip_files: |
| print("⚠️ 未找到 zip 文件") |
| return |
|
|
| print(f"🔍 共发现 {len(zip_files)} 个 zip 文件，开始多线程解压…") |
|
|
| results = [] |
| with ThreadPoolExecutor(max_workers=max_workers) as executor: |
| futures = {executor.submit(unzip_one, z, extract_dir): z for z in zip_files} |
| for future in as_completed(futures): |
| try: |
| result = future.result() |
| print(result) |
| results.append(result) |
| except Exception as e: |
| print(f"❌ 解压失败: {futures[future]} - {e}") |
|
|
| print("🎉 所有文件已解压完成！") |
|
|
| |
| unzip_all(base_dir=".", max_workers=8) |
|
|
| VID_CSV = 'opensource_data_vid.csv' |
| IMG_CSV = 'opensource_data_fashion_img.csv' |
| OUTPUT_CSV = 'combined_imgvid_dataset.csv' |
|
|
| |
| GROUP_RATIO = 6 |
|
|
| |
| |
| |
| print(f"\n📊 读取数据...") |
| df_vid = pd.read_csv(VID_CSV) |
| df_img = pd.read_csv(IMG_CSV) |
|
|
| |
| |
| |
| print(f"🔄 转换为绝对路径...") |
| for col in ['path', 'pose_path']: |
| df_vid[col] = df_vid[col].apply(lambda x: os.path.abspath(x) if isinstance(x, str) else x) |
|
|
| for col in ['path', 'target_path', 'pose_path']: |
| df_img[col] = df_img[col].apply(lambda x: os.path.abspath(x) if isinstance(x, str) else x) |
|
|
| |
| print(f"\n✅ 检查视频文件是否存在...") |
| for col in ['path', 'pose_path']: |
| df_vid[col + '_exists'] = df_vid[col].parallel_apply(lambda x: os.path.exists(x) if isinstance(x, str) else False) |
| print(f" {col} 存在文件数量: {df_vid[col + '_exists'].sum()} / {len(df_vid)}") |
|
|
| print(f"\n✅ 检查图片文件是否存在...") |
| for col in ['path', 'target_path', 'pose_path']: |
| df_img[col + '_exists'] = df_img[col].parallel_apply(lambda x: os.path.exists(x) if isinstance(x, str) else False) |
| print(f" {col} 存在文件数量: {df_img[col + '_exists'].sum()} / {len(df_img)}") |
|
|
| df_vid.drop(columns=[c for c in df_vid.columns if c.endswith('_exists')], inplace=True) |
| df_img.drop(columns=[c for c in df_img.columns if c.endswith('_exists')], inplace=True) |
|
|
|
|
| n_vid = len(df_vid) |
| n_img = len(df_img) |
|
|
| GROUP_NUMBER = int(n_vid / GROUP_RATIO) |
| GROUP_SIZE = max(1, n_img // GROUP_NUMBER) |
|
|
| print(f"\n📈 视频数量: {n_vid}, 图片数量: {n_img}") |
| print(f"📈 计算得到 GROUP_SIZE: {GROUP_SIZE}") |
|
|
| |
| |
| |
| print(f"\n🔄 对图片数据进行分组...") |
| fields = ['height', 'width', 'aspect_ratio', 'resolution'] |
| df_img_sorted = df_img.sort_values(fields + ['path']).reset_index(drop=True) |
|
|
| group_rows = [] |
| bad_rows = [] |
| group_id_counter = 0 |
|
|
| for combo, subdf in df_img_sorted.groupby(fields, sort=False): |
| n = len(subdf) |
| n_groups = n // GROUP_SIZE |
| remainder = n % GROUP_SIZE |
| idxs = subdf.index.to_list() |
|
|
| for g in range(n_groups): |
| slice_idx = idxs[g*GROUP_SIZE:(g+1)*GROUP_SIZE] |
| group = subdf.loc[slice_idx] |
|
|
| first = group.iloc[0] |
|
|
| img_pairs = [ |
| { |
| 'src_path' : row['path'], |
| 'tgt_path' : row['target_path'], |
| 'tgt_pose_path': row['pose_path'], |
| 'qwen_caption' : row['qwen_caption'] if 'qwen_caption' in row and pd.notnull(row['qwen_caption']) else "", |
| 'text' : row['text'] if 'text' in row and pd.notnull(row['text']) else "" |
| } |
| for _, row in group.iterrows() |
| ] |
|
|
| group_rows.append( |
| { |
| 'path' : f'image_{group_id_counter+2800:05d}', |
| 'img_pairs' : img_pairs, |
| 'height' : int(first['height']), |
| 'width' : int(first['width']), |
| 'aspect_ratio' : first['aspect_ratio'], |
| 'resolution' : first['resolution'], |
| 'num_frames' : 1 |
| } |
| ) |
| group_id_counter += 1 |
|
|
| if remainder: |
| bad_rows.extend(subdf.loc[idxs[-remainder:]].to_dict('records')) |
|
|
| df_groups = pd.DataFrame(group_rows) |
| df_bad = pd.DataFrame(bad_rows) |
|
|
| print(f"📊 生成 df_groups: {len(df_groups)} 条, df_bad: {len(df_bad)} 条") |
|
|
| |
| |
| |
| df_final = pd.concat([df_groups, df_vid], ignore_index=True, sort=False) |
| print(f"📊 最终合并后的数据集: {len(df_final)} 条") |
|
|
| |
| |
| |
| df_final.to_csv(OUTPUT_CSV, index=False) |
| print(f"\n✅ 已保存为 {OUTPUT_CSV}") |
| print(f"📂 完整路径: {os.path.abspath(OUTPUT_CSV)}") |
