# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # -------------------------------------------------------- # References: # NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer # -------------------------------------------------------- # Inherited from dataset v4, for visualization import cv2 import numpy as np import torch import os from PIL import Image from typing import Tuple import yaml import pickle import tqdm from torch.utils.data import Dataset from misc import angle_difference, get_data_path, get_delta_np, normalize_data, to_local_coords from project_functions import reproject_depth_to_other_pose_2seq, project_to_2d_image_2seq, reproject_depth_to_other_pose_seq2seq, project_to_2d_image_seq2seq, resize_image_half # ======== depth saving helper ======== def save_depth_pair(depth_map, out_png16, out_vis, vmax=None): if depth_map is None: return dm = np.asarray(depth_map) # 16-bit 原始 if dm.dtype in (np.float32, np.float64): mm = dm.copy() mm[~np.isfinite(mm)] = 0.0 dmax = float(np.max(mm)) if np.isfinite(mm).any() else 0.0 mm = np.clip(mm, 0.0, dmax) * 1000.0 png16 = np.clip(np.round(mm), 0, 65535).astype(np.uint16) elif dm.dtype == np.uint16: png16 = dm else: dm_f = dm.astype(np.float32) dm_f[~np.isfinite(dm_f)] = 0.0 dmax = float(np.max(dm_f)) if np.isfinite(dm_f).any() else 1.0 scale = 65535.0 / max(dmax, 1e-6) png16 = np.clip(np.round(dm_f * scale), 0, 65535).astype(np.uint16) Image.fromarray(png16).save(out_png16) # 伪彩 vis = dm.astype(np.float32) vis[~np.isfinite(vis)] = 0.0 if vmax is None: flat = vis[np.isfinite(vis)] vmax = np.percentile(flat, 95) if flat.size > 0 else (float(vis.max()) if np.isfinite(vis).any() else 1.0) vmax = max(vmax, 1e-6) vis_u8 = np.clip((vis / vmax) * 255.0, 0, 255).astype(np.uint8) vis_color_bgr = cv2.applyColorMap(vis_u8, cv2.COLORMAP_JET) vis_color_rgb = vis_color_bgr[..., ::-1] Image.fromarray(vis_color_rgb).save(out_vis) # =================================================== class BaseDataset(Dataset): def __init__( self, data_folder: str, data_split_folder: str, dataset_name: str, image_size: Tuple[int, int], min_dist_cat: int, max_dist_cat: int, len_traj_pred: int, traj_stride: int, context_size: int, transform: object, traj_names: str, normalize: bool = True, predefined_index: list = None, goals_per_obs: int = 1, ): self.data_folder = data_folder self.data_split_folder = data_split_folder self.dataset_name = dataset_name self.goals_per_obs = goals_per_obs traj_names_file = os.path.join(data_split_folder, traj_names) with open(traj_names_file, "r") as f: file_lines = f.read() self.traj_names = file_lines.split("\n") if "" in self.traj_names: self.traj_names.remove("") self.image_size = image_size self.distance_categories = list(range(min_dist_cat, max_dist_cat + 1)) self.min_dist_cat = self.distance_categories[0] self.max_dist_cat = self.distance_categories[-1] self.len_traj_pred = len_traj_pred self.traj_stride = traj_stride self.context_size = context_size self.normalize = normalize # load data/data_config.yaml with open("config/data_config.yaml", "r") as f: all_data_config = yaml.safe_load(f) dataset_names = list(all_data_config.keys()) dataset_names.sort() # use this index to retrieve the dataset name from the data_config.yaml self.data_config = all_data_config[self.dataset_name] self.transform = transform self._load_index(predefined_index) self.ACTION_STATS = {} for key in all_data_config['action_stats']: self.ACTION_STATS[key] = np.expand_dims(all_data_config['action_stats'][key], axis=0) def _load_index(self, predefined_index) -> None: """ Generates a list of tuples of (obs_traj_name, goal_traj_name, obs_time, goal_time) for each observation in the dataset """ if predefined_index: print(f"****** Using a predefined evaluation index... {predefined_index}******") with open(predefined_index, "rb") as f: self.index_to_data = pickle.load(f) return else: print("****** Evaluating from NON PREDEFINED index... ******") index_to_data_path = os.path.join( self.data_split_folder, f"dataset_dist_{self.min_dist_cat}_to_{self.max_dist_cat}_n{self.context_size}_len_traj_pred_{self.len_traj_pred}.pkl", ) self.index_to_data, self.goals_index = self._build_index() with open(index_to_data_path, "wb") as f: pickle.dump((self.index_to_data, self.goals_index), f) def _build_index(self, use_tqdm: bool = False): """ Build an index consisting of tuples (trajectory name, time, max goal distance) """ samples_index = [] goals_index = [] for traj_name in tqdm.tqdm(self.traj_names, disable=not use_tqdm, dynamic_ncols=True): traj_data = self._get_trajectory(traj_name) traj_len = len(traj_data["position"]) for goal_time in range(0, traj_len): goals_index.append((traj_name, goal_time)) begin_time = self.context_size - 1 end_time = traj_len - self.len_traj_pred for curr_time in range(begin_time, end_time, self.traj_stride): max_goal_distance = min(self.max_dist_cat, traj_len - curr_time - 1) min_goal_distance = max(self.min_dist_cat, -curr_time) samples_index.append((traj_name, curr_time, min_goal_distance, max_goal_distance)) return samples_index, goals_index def _get_trajectory(self, trajectory_name): with open(os.path.join(self.data_folder, trajectory_name, "traj_data.pkl"), "rb") as f: traj_data = pickle.load(f) for k,v in traj_data.items(): traj_data[k] = v.astype('float') return traj_data def __len__(self) -> int: return len(self.index_to_data) def _compute_projected_image_o(self, traj_data, curr_time, goal_time, rgb_img): pose_src = traj_data["pose"][curr_time] pose_dst = traj_data["pose"][goal_time] depth_map = traj_data["depth"][curr_time] K = traj_data["K"] projected_images = self.generate_augmented_image_o(K=K, depth_map=depth_map, rgb_img=rgb_img, pose_src=pose_src, pose_dst=pose_dst) return projected_images def generate_augmented_image_o(self, K, depth_map, rgb_img, pose_src, pose_dst) -> np.ndarray: """ 基于深度图 + pose 生成从另一个相机视角观察到的图像。 """ image_size = depth_map.shape # (H, W) if rgb_img.shape[:2] != image_size: rgb_img = resize_image_half(rgb_img) points_3d, colors = reproject_depth_to_other_pose_2seq(K, depth_map, rgb_img, pose_src, pose_dst) images = project_to_2d_image_2seq(K, points_3d, colors, image_size) # (H, W, 3, goal_time) return images # ============ seq2seq ============ def _compute_projected_images(self, traj_data, context_times, rgb_seq, goal_times_np): """ 使用多帧历史 (context_times) 的 depth/rgb/pose;重投影到多个目标位姿 (goal_times_np)。 返回: np.ndarray, 形状 (B, H, W, 3) ;B = len(goal_times_np) """ K = traj_data["K"] # (3,3) depth_seq = traj_data["depth"][context_times] # (T, H, W) poses_src_seq = traj_data["pose"][context_times]# (T, 4, 4) H, W = depth_seq.shape[-2:] poses_dst_seq = traj_data["pose"][goal_times_np] # (B, 4, 4) # 先用 seq2seq 得到每个目标位姿的点云/颜色 points_3d_all, colors_all = reproject_depth_to_other_pose_seq2seq( K=K, depth_maps=depth_seq, # (T,H,W) rgb_imgs=rgb_seq, # (T,H,W,3) poses_src=poses_src_seq, # (T,4,4) poses_dst=poses_dst_seq # (B,4,4) ) # 再做 z-buffer 投成图像 images = project_to_2d_image_seq2seq( K=K, points_3d=points_3d_all, # List[(Ni,3)], 长度 B colors=colors_all, # List[(Ni,3)], 长度 B image_size=(H, W) ) # (B, H, W, 3) return images # ============================================================ def _compute_actions(self, traj_data, curr_time, goal_time): start_index = curr_time end_index = curr_time + self.len_traj_pred + 1 yaw = traj_data["yaw"][start_index:end_index] positions = traj_data["point"][start_index:end_index] goal_pos = traj_data["point"][goal_time] goal_yaw = traj_data["yaw"][goal_time] if len(yaw.shape) == 2: yaw = yaw.squeeze(1) if yaw.shape != (self.len_traj_pred + 1,): raise ValueError("is used?") waypoints_pos = to_local_coords(positions, positions[0], yaw[0]) waypoints_yaw = angle_difference(yaw[0], yaw) actions = np.concatenate([waypoints_pos, waypoints_yaw.reshape(-1, 1)], axis=-1) actions = actions[1:] goal_pos = to_local_coords(goal_pos, positions[0], yaw[0]) goal_yaw = angle_difference(yaw[0], goal_yaw) if self.normalize: actions[:, :3] /= self.data_config["metric_waypoint_spacing"] goal_pos[:, :3] /= self.data_config["metric_waypoint_spacing"] goal_pos = np.concatenate([goal_pos, goal_yaw.reshape(-1, 1)], axis=-1) return actions, goal_pos class TrainingDataset(BaseDataset): def __init__( self, data_folder: str, data_split_folder: str, dataset_name: str, image_size: Tuple[int, int], min_dist_cat: int, max_dist_cat: int, len_traj_pred: int, traj_stride: int, context_size: int, transform: object, traj_names: str = 'traj_names.txt', normalize: bool = True, predefined_index: list = None, goals_per_obs: int = 1, ): super().__init__(data_folder, data_split_folder, dataset_name, image_size, min_dist_cat, max_dist_cat, len_traj_pred, traj_stride, context_size, transform, traj_names, normalize, predefined_index, goals_per_obs) def __getitem__(self, i: int) -> Tuple[torch.Tensor]: try: f_curr, curr_time, min_goal_dist, max_goal_dist = self.index_to_data[i] goal_offset = np.random.randint(min_goal_dist, max_goal_dist + 1, size=(self.goals_per_obs)) goal_time = (curr_time + goal_offset).astype('int') # (B,) rel_time = (goal_offset).astype('float')/(128.) # TODO: tune this const # 历史帧时间序列 context_times = list(range(curr_time - self.context_size + 1, curr_time + 1)) true_context = [(f_curr, t) for t in context_times] goal_context = [(f_curr, t) for t in goal_time] context = true_context + goal_context obs_image = torch.stack([self.transform(Image.open(get_data_path(self.data_folder, f, t))) for f, t in context]) # aug rgb_imgs = [cv2.imread(get_data_path(self.data_folder, f_img, t_img)) for f_img, t_img in true_context] rgb_imgs = [cv2.cvtColor(rgb_img, cv2.COLOR_BGR2RGB) for rgb_img in rgb_imgs] rgb_imgs_np = np.stack(rgb_imgs, axis=0) # Load other trajectory data curr_traj_data = self._get_trajectory(f_curr) # 计算动作/目标 _, goal_pos = self._compute_actions(curr_traj_data, curr_time, goal_time) goal_pos[:, :3] = normalize_data(goal_pos[:, :3], self.ACTION_STATS) # 使用多历史帧 → 多目标帧重投影（不再传单帧 rgb） his_projected_list = [] for t_idx, rgb_img in enumerate(rgb_imgs): src_time = context_times[t_idx] projected_images_o = self._compute_projected_image_o(curr_traj_data, src_time, goal_time, rgb_img) his_projected_list.append(projected_images_o) projected_images = self._compute_projected_images(curr_traj_data, context_times, rgb_imgs_np, goal_time) # (B,H,W,3) # 转成张量 projected_tensor_list = [self.transform(Image.fromarray(img)) for img in projected_images] projected_tensor = torch.stack(projected_tensor_list, dim=0) # ===================== 保存图像（GT一行 / 联合投影一行 / 单独投影T行） ===================== vis_root = './visualizations-seq2seq' sample_dir = os.path.join(vis_root, f'{self.dataset_name}', f'sample_{i}') os.makedirs(sample_dir, exist_ok=True) # 1) 历史原图（仅单独保存，便于比对） T = rgb_imgs_np.shape[0] for t_idx in range(T): im = rgb_imgs_np[t_idx] im = np.clip(im * (255.0 ** (im.max() <= 1)), 0, 255).astype(np.uint8) Image.fromarray(im).save(os.path.join(sample_dir, f'hist_{t_idx:03d}.png')) # 2) 收集 GT（第一行，列数应为目标数 B） gt_imgs = [] for j, (_, t_goal) in enumerate(goal_context): gt = Image.open(get_data_path(self.data_folder, f_curr, int(t_goal))).convert("RGB") gt_np = np.array(gt) gt_np = np.clip(gt_np * (255.0 ** (gt_np.max() <= 1)), 0, 255).astype(np.uint8) gt_imgs.append(gt_np) Image.fromarray(gt_np).save(os.path.join(sample_dir, f'gt_{j:03d}_t{int(t_goal)}.png')) # 3) 联合投影（第二行，列数应为 B） B = projected_images.shape[0] joint_imgs = [] for j in range(B): pj = projected_images[j] pj = np.clip(pj * (255.0 ** (pj.max() <= 1)), 0, 255).astype(np.uint8) joint_imgs.append(pj) Image.fromarray(pj).save(os.path.join(sample_dir, f'proj_joint_{j:03d}.png')) # 4) 单独投影（后续 T 行）：将 batch 轴移到前面 → (B,H,W,3)，每行应产出 B 张 per_hist_rows = [] for t_idx, proj_o in enumerate(his_projected_list): axes_equal_B = np.where(np.array(proj_o.shape) == B)[0] axis = int(axes_equal_B[0]) arr = np.moveaxis(proj_o, axis, 0) row = [] for j in range(B): im = arr[j] im = np.clip(im * (255.0 ** (im.max() <= 1)), 0, 255).astype(np.uint8) row.append(im) Image.fromarray(im).save(os.path.join(sample_dir, f'proj_single_t{t_idx:03d}_{j:03d}.png')) per_hist_rows.append(row) # 5) 组装大图 grid：第0列放行名标签；第1..N列为图像（GT一行、Joint一行、T行历史单独投影） from PIL import ImageDraw, ImageFont # 计算列数：0列为历史帧，其余为目标列 B = projected_images.shape[0] max_cols = B + 1 pad = 4 # 统一像素到 uint8 T = rgb_imgs_np.shape[0] hist_uint8 = [] for t_idx in range(T): im = rgb_imgs_np[t_idx] im = np.clip(im * (255.0 ** (im.max() <= 1)), 0, 255).astype(np.uint8) hist_uint8.append(im) gt_uint8 = [] for x in gt_imgs: im = np.clip(x * (255.0 ** (x.max() <= 1)), 0, 255).astype(np.uint8) gt_uint8.append(im) joint_uint8 = [] for x in joint_imgs: im = np.clip(x * (255.0 ** (x.max() <= 1)), 0, 255).astype(np.uint8) joint_uint8.append(im) # 先构造小尺寸的文字贴片（稍后统一 resize） label_size = (64, 48) font = ImageFont.load_default() label_gt = Image.new("RGB", label_size, (255, 255, 255)) draw_gt = ImageDraw.Draw(label_gt) draw_gt.text((4, 4), "GT", fill=(0, 0, 0), font=font) label_gt_np = np.array(label_gt) label_joint = Image.new("RGB", label_size, (255, 255, 255)) draw_joint = ImageDraw.Draw(label_joint) draw_joint.text((4, 4), "Joint", fill=(0, 0, 0), font=font) label_joint_np = np.array(label_joint) # 在历史帧图上写行名 hist_named = [] for t_idx in range(T): pil = Image.fromarray(hist_uint8[t_idx]) draw = ImageDraw.Draw(pil) draw.text((6, 6), f"Hist t={context_times[t_idx]}", fill=(255, 255, 255), font=font) hist_named.append(np.array(pil)) # 组装行：第0列 + 其余B列 rows = [] row0 = [label_gt_np] + gt_uint8 rows.append(row0) row1 = [label_joint_np] + joint_uint8 rows.append(row1) for t_idx in range(T): row_hist = [hist_named[t_idx]] + per_hist_rows[t_idx] rows.append(row_hist) # 统一到相同尺寸（以第一张GT为基准） H, W = rows[0][1].shape[:2] norm_rows = [] for r in rows: resized = [] for x in r: img = Image.fromarray(x) img = img.resize((W, H), Image.BILINEAR) resized.append(np.array(img)) while len(resized) < max_cols: resized.append(np.full((H, W, 3), 255, np.uint8)) norm_rows.append(resized) # 分隔条 row_width = max_cols * W + (max_cols - 1) * pad spacer_w = np.full((H, pad, 3), 255, np.uint8) spacer_h = np.full((pad, row_width, 3), 255, np.uint8) # 横向拼每一行 row_arrays = [] for r in norm_rows: row_img = r[0] for x in r[1:]: row_img = np.concatenate((row_img, spacer_w, x), axis=1) row_arrays.append(row_img) # 纵向拼所有行 grid = row_arrays[0] for rr in row_arrays[1:]: grid = np.concatenate((grid, spacer_h, rr), axis=0) Image.fromarray(grid).save(os.path.join(sample_dir, 'grid_all.png')) # ===== 保存深度（历史 + 目标） ===== if "depth" in curr_traj_data and curr_traj_data["depth"] is not None: depth_seq = curr_traj_data["depth"] for t_idx, t_ctx in enumerate(context_times): if 0 <= t_ctx < len(depth_seq): dm = depth_seq[t_ctx] out16 = os.path.join(sample_dir, f"depth_hist_{t_idx:03d}_t{t_ctx}.png") outvis = os.path.join(sample_dir, f"depth_hist_{t_idx:03d}_t{t_ctx}_vis.png") save_depth_pair(dm, out16, outvis) for j, (_, t_goal) in enumerate(goal_context): tg = int(t_goal) if 0 <= tg < len(depth_seq): dm = depth_seq[tg] out16 = os.path.join(sample_dir, f"depth_gt_{j:03d}_t{tg}.png") outvis = os.path.join(sample_dir, f"depth_gt_{j:03d}_t{tg}_vis.png") save_depth_pair(dm, out16, outvis) # ============================================= return ( torch.as_tensor(obs_image, dtype=torch.float32), torch.as_tensor(goal_pos, dtype=torch.float32), torch.as_tensor(rel_time, dtype=torch.float32), torch.as_tensor(projected_tensor, dtype=torch.float32), ) except Exception as e: print(f"Exception in {self.dataset_name}", e) raise Exception(e) class EvalDataset(BaseDataset): def __init__( self, data_folder: str, data_split_folder: str, dataset_name: str, image_size: Tuple[int, int], min_dist_cat: int, max_dist_cat: int, len_traj_pred: int, traj_stride: int, context_size: int, transform: object, traj_names: str, normalize: bool = True, predefined_index: list = None, goals_per_obs: int = 1, ): super().__init__(data_folder, data_split_folder, dataset_name, image_size, min_dist_cat, max_dist_cat, len_traj_pred, traj_stride, context_size, transform, traj_names, normalize, predefined_index, goals_per_obs) def __getitem__(self, i: int) -> Tuple[torch.Tensor]: try: f_curr, curr_time, _, _ = self.index_to_data[i] context_times = list(range(curr_time - self.context_size + 1, curr_time + 1)) pred_times = list(range(curr_time + 1, curr_time + self.len_traj_pred + 1)) # 未来 B 帧 context = [(f_curr, t) for t in context_times] pred = [(f_curr, t) for t in pred_times] obs_image = torch.stack([self.transform(Image.open(get_data_path(self.data_folder, f, t))) for f, t in context]) pred_image = torch.stack([self.transform(Image.open(get_data_path(self.data_folder, f, t))) for f, t in pred]) curr_traj_data = self._get_trajectory(f_curr) # 动作/delta actions, _ = self._compute_actions(curr_traj_data, curr_time, np.array(pred_times)) actions[:, :3] = normalize_data(actions[:, :3], self.ACTION_STATS) delta = get_delta_np(actions) # 历史帧 RGB（list 与 np 版本） rgb_list = [cv2.imread(get_data_path(self.data_folder, f_img, t_img)) for f_img, t_img in context] rgb_list = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in rgb_list] rgb_np = np.stack(rgb_list, axis=0) # 单历史帧 -> 多目标（逐历史帧可视化） his_projected_list = [] for t_idx, rgb_img in enumerate(rgb_list): src_time = context_times[t_idx] projected_images_o = self._compute_projected_image_o(curr_traj_data, src_time, np.array(pred_times), rgb_img) his_projected_list.append(projected_images_o) # 多历史帧 -> 多目标（联合） projected_images = self._compute_projected_images(curr_traj_data, context_times, rgb_np, np.array(pred_times)) projected_tensor_list = [self.transform(Image.fromarray(img)) for img in projected_images] projected_tensor = torch.stack(projected_tensor_list, dim=0) # ===== 与训练保持一致的可视化 ===== vis_root = './visualizations-seq2seq' sample_dir = os.path.join(vis_root, f'{self.dataset_name}', f'sample_{i}') os.makedirs(sample_dir, exist_ok=True) # 1) 历史原图 T = rgb_np.shape[0] for t_idx in range(T): im = rgb_np[t_idx] im = np.clip(im * (255.0 ** (im.max() <= 1)), 0, 255).astype(np.uint8) Image.fromarray(im).save(os.path.join(sample_dir, f'hist_{t_idx:03d}.png')) # 2) 未来 GT（和 pred 对齐） gt_imgs = [] for j, (_, t_pred) in enumerate(pred): gt = Image.open(get_data_path(self.data_folder, f_curr, int(t_pred))).convert("RGB") gt_np = np.array(gt) gt_np = np.clip(gt_np * (255.0 ** (gt_np.max() <= 1)), 0, 255).astype(np.uint8) gt_imgs.append(gt_np) Image.fromarray(gt_np).save(os.path.join(sample_dir, f'gt_{j:03d}_t{int(t_pred)}.png')) # 3) 联合投影 B = projected_images.shape[0] joint_imgs = [] for j in range(B): pj = projected_images[j] pj = np.clip(pj * (255.0 ** (pj.max() <= 1)), 0, 255).astype(np.uint8) joint_imgs.append(pj) Image.fromarray(pj).save(os.path.join(sample_dir, f'proj_joint_{j:03d}.png')) # 4) 单独投影（逐历史帧） per_hist_rows = [] for t_idx, proj_o in enumerate(his_projected_list): axes_equal_B = np.where(np.array(proj_o.shape) == B)[0] axis = int(axes_equal_B[0]) arr = np.moveaxis(proj_o, axis, 0) # (B,H,W,3) row = [] for j in range(B): im = arr[j] im = np.clip(im * (255.0 ** (im.max() <= 1)), 0, 255).astype(np.uint8) row.append(im) Image.fromarray(im).save(os.path.join(sample_dir, f'proj_single_t{t_idx:03d}_{j:03d}.png')) per_hist_rows.append(row) # 5) 组装 grid from PIL import ImageDraw, ImageFont B = projected_images.shape[0] max_cols = B + 1 pad = 4 hist_uint8 = [] for t_idx in range(T): im = rgb_np[t_idx] im = np.clip(im * (255.0 ** (im.max() <= 1)), 0, 255).astype(np.uint8) hist_uint8.append(im) gt_uint8 = [] for x in gt_imgs: im = np.clip(x * (255.0 ** (x.max() <= 1)), 0, 255).astype(np.uint8) gt_uint8.append(im) joint_uint8 = [] for x in joint_imgs: im = np.clip(x * (255.0 ** (x.max() <= 1)), 0, 255).astype(np.uint8) joint_uint8.append(im) label_size = (64, 48) font = ImageFont.load_default() label_gt = Image.new("RGB", label_size, (255, 255, 255)) ImageDraw.Draw(label_gt).text((4, 4), "GT", fill=(0, 0, 0), font=font) label_gt_np = np.array(label_gt) label_joint = Image.new("RGB", label_size, (255, 255, 255)) ImageDraw.Draw(label_joint).text((4, 4), "Joint", fill=(0, 0, 0), font=font) label_joint_np = np.array(label_joint) hist_named = [] for t_idx in range(T): pil = Image.fromarray(hist_uint8[t_idx]) ImageDraw.Draw(pil).text((6, 6), f"Hist t={context_times[t_idx]}", fill=(255, 255, 255), font=font) hist_named.append(np.array(pil)) rows = [] rows.append([label_gt_np] + gt_uint8) rows.append([label_joint_np] + joint_uint8) for t_idx in range(T): row_hist = [hist_named[t_idx]] + per_hist_rows[t_idx] rows.append(row_hist) H, W = rows[0][1].shape[:2] norm_rows = [] for r in rows: resized = [] for x in r: img = Image.fromarray(x) img = img.resize((W, H), Image.BILINEAR) resized.append(np.array(img)) while len(resized) < max_cols: resized.append(np.full((H, W, 3), 255, np.uint8)) norm_rows.append(resized) row_width = max_cols * W + (max_cols - 1) * pad spacer_w = np.full((H, pad, 3), 255, np.uint8) spacer_h = np.full((pad, row_width, 3), 255, np.uint8) row_arrays = [] for r in norm_rows: row_img = r[0] for x in r[1:]: row_img = np.concatenate((row_img, spacer_w, x), axis=1) row_arrays.append(row_img) grid = row_arrays[0] for rr in row_arrays[1:]: grid = np.concatenate((grid, spacer_h, rr), axis=0) Image.fromarray(grid).save(os.path.join(sample_dir, 'grid_all.png')) # ===== 保存深度（历史 + 未来） ===== if "depth" in curr_traj_data and curr_traj_data["depth"] is not None: depth_seq = curr_traj_data["depth"] for t_idx, t_ctx in enumerate(context_times): if 0 <= t_ctx < len(depth_seq): dm = depth_seq[t_ctx] out16 = os.path.join(sample_dir, f"depth_hist_{t_idx:03d}_t{t_ctx}.png") outvis = os.path.join(sample_dir, f"depth_hist_{t_idx:03d}_t{t_ctx}_vis.png") save_depth_pair(dm, out16, outvis) for j, t_pred in enumerate(pred_times): if 0 <= t_pred < len(depth_seq): dm = depth_seq[t_pred] out16 = os.path.join(sample_dir, f"depth_gt_{j:03d}_t{t_pred}.png") outvis = os.path.join(sample_dir, f"depth_gt_{j:03d}_t{t_pred}_vis.png") save_depth_pair(dm, out16, outvis) # ============================================= return ( torch.tensor([i], dtype=torch.float32), # for logging purposes torch.as_tensor(obs_image, dtype=torch.float32), torch.as_tensor(pred_image, dtype=torch.float32), torch.as_tensor(delta, dtype=torch.float32), torch.as_tensor(projected_tensor, dtype=torch.float32), ) except Exception as e: print(f"Exception in {self.dataset_name}", e) raise Exception(e) class TrajectoryEvalDataset(BaseDataset): def __init__( self, data_folder: str, data_split_folder: str, dataset_name: str, image_size: Tuple[int, int], min_dist_cat: int, max_dist_cat: int, len_traj_pred: int, traj_stride: int, context_size: int, transform: object, traj_names: str, normalize: bool = True, predefined_index: list = None, goals_per_obs: int = 1, ): super().__init__(data_folder, data_split_folder, dataset_name, image_size, min_dist_cat, max_dist_cat, len_traj_pred, traj_stride, context_size, transform, traj_names, normalize, predefined_index, goals_per_obs) def _sample_goal(self, trajectory_name, curr_time, min_goal_dist, max_goal_dist): """ Sample a goal from the future in the same trajectory. Returns: (trajectory_name, goal_time, goal_is_negative) """ goal_offset = np.random.randint(min_goal_dist, max_goal_dist + 1) goal_time = curr_time + int(goal_offset) return trajectory_name, goal_time, False def __getitem__(self, i: int) -> Tuple[torch.Tensor]: try: f_curr, curr_time, min_goal_dist, max_goal_dist = self.index_to_data[i] f_goal, goal_time, _ = self._sample_goal(f_curr, curr_time, min_goal_dist, max_goal_dist) context_times = list(range(curr_time - self.context_size + 1, curr_time + 1)) context = [(f_curr, t) for t in context_times] obs_image = torch.stack([self.transform(Image.open(get_data_path(self.data_folder, f, t))) for f, t in context]) goal_image = self.transform(Image.open(get_data_path(self.data_folder, f_goal, goal_time))).unsqueeze(0) curr_traj_data = self._get_trajectory(f_curr) # Compute actions, goal_pos, projected images actions, goal_pos = self._compute_actions(curr_traj_data, curr_time, np.array([goal_time])) rgb_imgs = [cv2.imread(get_data_path(self.data_folder, f_img, t_img)) for f_img, t_img in context] rgb_imgs = [cv2.cvtColor(rgb_img, cv2.COLOR_BGR2RGB) for rgb_img in rgb_imgs] rgb_imgs = np.stack(rgb_imgs, axis=0) projected_images = self._compute_projected_images(curr_traj_data, context_times, rgb_imgs, np.array([goal_time])) projected_tensor_list = [self.transform(Image.fromarray(img)) for img in projected_images] projected_tensor = torch.stack(projected_tensor_list, dim=0) return ( torch.tensor([i], dtype=torch.float32), # for logging purposes torch.as_tensor(obs_image, dtype=torch.float32), torch.as_tensor(goal_image, dtype=torch.float32), torch.as_tensor(actions, dtype=torch.float32), torch.as_tensor(goal_pos, dtype=torch.float32), torch.as_tensor(projected_tensor, dtype=torch.float32), ) except Exception as e: print(f"Exception in {self.dataset_name}", e) raise Exception(e)