#!/usr/bin/env python3 """ForeHOI Dataset WebDataset Loader with Random Sampling""" import argparse import tarfile import json import random import io from pathlib import Path from PIL import Image import torch from torch.utils.data import IterableDataset import pickle import numpy as np from typing import List, Dict, Tuple import cv2 class ForeHOIRandomDataset(IterableDataset): """ - Sample 级别随机打乱（流式） - 每个 Sample 内随机选 N 个不同的 view/frame 组合 - 索引缓存，避免重复扫描 tar 文件 """ def __init__(self, tar_path: str, n_frames_per_sample: int = 4, cache_dir: str = None, reshuffle: bool = True): self.tar_path = Path(tar_path) self.n_frames = n_frames_per_sample self.reshuffle = reshuffle if cache_dir is None: cache_dir = self.tar_path.parent self.cache_path = Path(cache_dir) / f"{self.tar_path.stem}.index.pkl" self.sample_index = self._load_or_build_index() self.sample_ids = list(self.sample_index.keys()) print(f"Dataset loaded: {len(self.sample_ids)} samples, " f"avg {np.mean([len(v) for v in self.sample_index.values()]):.1f} frames/sample") def _build_index(self) -> Dict[str, List[Dict]]: """ 构建索引: {sample_id: [{key, view_id, frame_id, files}, ...]} """ print(f"Building index for {self.tar_path}...") index = {} with tarfile.open(self.tar_path, "r") as tar: for member in tar: if not member.isfile(): continue # 文件名示例: "sample_01/view_0/000.rgb.webp" full_name = member.name # 1. 确定文件类型 (data_type) 和 基础键 (base_key) if full_name.endswith('.rgb.webp'): data_type = 'rgb' base_key = full_name[:-9] # 去掉 .rgb.webp elif full_name.endswith('.obj_mask.webp'): data_type = 'obj_mask' base_key = full_name[:-14] # 去掉 .obj_mask.webp elif full_name.endswith('.hand_mask.webp'): data_type = 'hand_mask' base_key = full_name[:-15] # 去掉 .hand_mask.webp elif full_name.endswith('.meta.json'): data_type = 'meta' base_key = full_name[:-10] # 去掉 .meta.json else: # 跳过不符合命名规范的文件 continue # 2. 解析 sample/view/frame parts = base_key.split('/') if len(parts) != 3: continue sample_id, view_id, frame_id = parts if sample_id not in index: index[sample_id] = [] # 3. 查找或创建 entry existing = next((e for e in index[sample_id] if e["view_id"] == view_id and e["frame_id"] == frame_id), None) if existing is None: existing = { "key": base_key, "view_id": view_id, "frame_id": frame_id, "files": {} # data_type -> {offset, size} } index[sample_id].append(existing) # 4. 记录文件偏移量，使用 data_type (rgb/obj_mask...) 作为 key if hasattr(member, 'offset_data') and member.offset_data is not None: data_offset = member.offset_data else: data_offset = member.offset + 512 existing["files"][data_type] = { "offset": data_offset, "size": member.size } # 排序 for sid in index: index[sid].sort(key=lambda x: (x["view_id"], x["frame_id"])) return index def _load_or_build_index(self): if self.cache_path.exists(): print(f"Loading cached index from {self.cache_path}") with open(self.cache_path, 'rb') as f: return pickle.load(f) index = self._build_index() with open(self.cache_path, 'wb') as f: pickle.dump(index, f) print(f"Index cached to {self.cache_path}") return index def _read_file_at_offset(self, tar, offset_info: Dict) -> bytes: tar.fileobj.seek(offset_info["offset"]) return tar.fileobj.read(offset_info["size"]) def _decode_entry(self, tar, entry_info: Dict) -> Dict: """读取并解码单个 entry""" result = { "key": entry_info["key"], "view_id": entry_info["view_id"], "frame_id": entry_info["frame_id"] } # 遍历该 entry 下的所有文件类型 (rgb, obj_mask, hand_mask, meta) for data_type, offset_info in entry_info["files"].items(): data = self._read_file_at_offset(tar, offset_info) if data_type == 'rgb': img = Image.open(io.BytesIO(data)).convert("RGB") result['rgb'] = np.array(img) elif data_type in ['obj_mask', 'hand_mask']: img = Image.open(io.BytesIO(data)).convert("L") result[data_type] = np.array(img) elif data_type == 'meta': result['meta'] = json.loads(data.decode('utf-8')) return result def _sample_to_tensor(self, entry: Dict) -> Dict: """转换为 Tensor 格式""" if 'rgb' not in entry: # 这种情况通常不应该发生，除非 tar 包损坏 print(f"Warning: Missing RGB for {entry['key']}") return None # RGB: [3, H, W], 0-1 # entry['rgb'] 已经在 _decode_entry 中转为 numpy (或 PIL) rgb_np = np.array(entry["rgb"]) rgb_tensor = torch.from_numpy(rgb_np).permute(2, 0, 1).float() / 255.0 h, w = rgb_np.shape[:2] # Masks: [H, W], 0-1 # 处理可能缺失 mask 的情况 if "obj_mask" in entry: obj_mask_np = entry["obj_mask"] else: print(f"Warning: Missing obj mask for {entry['key']}") return None if "hand_mask" in entry: hand_mask_np = entry["hand_mask"] else: print(f"Warning: Missing hand mask for {entry['key']}") return None obj_tensor = torch.from_numpy(obj_mask_np).float() / 255.0 hand_tensor = torch.from_numpy(hand_mask_np).float() / 255.0 meta = entry.get("meta", {}) return { "key": entry["key"], "sample_id": entry["key"].split('/')[0], "view_id": entry["view_id"], "frame_id": entry["frame_id"], "rgb": rgb_tensor, "obj_mask": obj_tensor, "hand_mask": hand_tensor, "camera_pose": torch.tensor(meta.get("camera_pose", [])), "obj_pose": torch.tensor(meta.get("obj_pose", [])), "width": meta.get("width", w), "height": meta.get("height", h), } def __iter__(self): worker_info = torch.utils.data.get_worker_info() if worker_info is None: sample_ids = self.sample_ids.copy() else: per_worker = len(self.sample_ids) // worker_info.num_workers start = worker_info.id * per_worker end = start + per_worker if worker_info.id < worker_info.num_workers - 1 else len(self.sample_ids) sample_ids = self.sample_ids[start:end] if self.reshuffle: random.shuffle(sample_ids) with tarfile.open(self.tar_path, "r") as tar: for sample_id in sample_ids: entries = self.sample_index[sample_id] if len(entries) <= self.n_frames: selected_entries = random.choices(entries, k=self.n_frames) else: selected_entries = random.sample(entries, self.n_frames) batch = [] for entry_info in selected_entries: data = self._decode_entry(tar, entry_info) tensor_data = self._sample_to_tensor(data) if tensor_data is not None: batch.append(tensor_data) if batch: yield { "sample_id": sample_id, "keys": [b["key"] for b in batch], "rgb": torch.stack([b["rgb"] for b in batch]), "obj_mask": torch.stack([b["obj_mask"] for b in batch]), "hand_mask": torch.stack([b["hand_mask"] for b in batch]), "camera_pose": torch.stack([b["camera_pose"] for b in batch]) if batch[0]["camera_pose"].numel() > 0 else None, "meta": [b.get("meta", {}) for b in batch] } def visualize_and_save(batch: Dict, output_dir: str, max_samples: int = 10): output_path = Path(output_dir) output_path.mkdir(parents=True, exist_ok=True) sample_id = batch["sample_id"] n_frames = len(batch["keys"]) for i in range(min(n_frames, max_samples)): rgb = batch["rgb"][i].permute(1, 2, 0).cpu().numpy() obj_mask = batch["obj_mask"][i].cpu().numpy() hand_mask = batch["hand_mask"][i].cpu().numpy() rgb_img = (rgb * 255).astype(np.uint8) obj_mask_img = (obj_mask * 255).astype(np.uint8) hand_mask_img = (hand_mask * 255).astype(np.uint8) obj_mask_color = np.stack([obj_mask_img] * 3, axis=-1) hand_mask_color = np.stack([hand_mask_img] * 3, axis=-1) combined = np.hstack([rgb_img, obj_mask_color, hand_mask_color]) combined_pil = Image.fromarray(combined) view_id = batch["keys"][i].split('/')[1] frame_id = batch["keys"][i].split('/')[2] filename = f"{sample_id}_{view_id}_{frame_id}.png" save_path = output_path / filename combined_pil.save(save_path) print(f"Saved: {save_path} ({rgb.shape[1]}x{rgb.shape[0]})") return n_frames def main(): parser = argparse.ArgumentParser(description="随机采样 ForeHOI WebDataset 并保存可视化结果") parser.add_argument("--tar_path", type=str, required=True, help="输入的 tar 文件路径") parser.add_argument("--output_dir", type=str, default="./visualization", help="可视化输出目录") parser.add_argument("--n_samples", type=int, default=10, help="随机选多少个 sample") parser.add_argument("--n_frames", type=int, default=4, help="每个 sample 随机采多少帧") parser.add_argument("--save_per_sample", type=int, default=10, help="每个 sample 保存多少张可视化图") args = parser.parse_args() dataset = ForeHOIRandomDataset( args.tar_path, n_frames_per_sample=args.n_frames, reshuffle=True ) dataloader = torch.utils.data.DataLoader( dataset, batch_size=None, num_workers=0, pin_memory=False ) saved_samples = 0 print(f"\n开始随机采样 {args.n_samples} 个 samples...") for batch_idx, batch in enumerate(dataloader): if batch_idx >= args.n_samples: break print(f"\n[{batch_idx+1}/{args.n_samples}] Sample: {batch['sample_id']}, Frames: {len(batch['keys'])}") visualize_and_save(batch, args.output_dir, max_samples=args.save_per_sample) saved_samples += 1 print(f"\n完成!") if __name__ == "__main__": main()