# # -------------------------------------------------------- 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 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_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["position"][start_index:end_index] goal_pos = traj_data["position"][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?") # const_len = self.len_traj_pred + 1 - yaw.shape[0] # yaw = np.concatenate([yaw, np.repeat(yaw[-1], const_len)]) # positions = np.concatenate([positions, np.repeat(positions[-1][None], const_len, axis=0)], axis=0) 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[:, :2] /= self.data_config["metric_waypoint_spacing"] goal_pos[:, :2] /= self.data_config["metric_waypoint_spacing"] goal_pos = np.concatenate([goal_pos, goal_yaw.reshape(-1, 1)], axis=-1) return actions, goal_pos def _compute_projected_image(self, traj_data, curr_time, goal_time, goal_imgs): curr_yaw = traj_data["yaw"][curr_time] goal_yaw = traj_data["yaw"][goal_time] goal_yaw = angle_difference(curr_yaw, goal_yaw) projected_images = self.generate_augmented_images(goal_imgs=goal_imgs, goal_yaw=goal_yaw) return projected_images def generate_augmented_images(self, goal_imgs: np.ndarray, goal_yaw: float) -> np.ndarray: """ Apply augmentation: - If goal_yaw > threshold: mask left 15% (right turn) - If goal_yaw < -threshold: mask right 15% (left turn) """ imgs = goal_imgs.clone() height, width = imgs[-1].shape[:2] threshold = 0 # radians mask_width = int(width * 0.4) for i, yaw in enumerate(goal_yaw): if yaw > threshold: # 遮挡左边15% imgs[i, :, :, :mask_width] = 0 elif yaw < -threshold: # 遮挡右边15% imgs[i, :, :, -mask_width:] = 0 return imgs 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') rel_time = (goal_offset).astype('float')/(128.) # TODO: refactor, currently a fixed const context_times = list(range(curr_time - self.context_size + 1, curr_time + 1)) context = [(f_curr, t) for t in context_times] + [(f_curr, t) for t in goal_time] obs_image = torch.stack([self.transform(Image.open(get_data_path(self.data_folder, f, t))) for f, t in context]) goal_imgs = obs_image[-self.goals_per_obs:] # Load other trajectory data curr_traj_data = self._get_trajectory(f_curr) # Compute actions _, goal_pos = self._compute_actions(curr_traj_data, curr_time, goal_time) goal_pos[:, :2] = normalize_data(goal_pos[:, :2], self.ACTION_STATS) # Compute projected images projected_images = self._compute_projected_image(curr_traj_data, curr_time, goal_time, goal_imgs) # print("Shape:", projected_images.shape) projected_tensor_list = [] for img in projected_images: # 转置通道顺序 CxHxW -> HxWxC np_img = img.permute(1, 2, 0).cpu().numpy() # 乘255转换为0-255整数，转uint8 np_img_uint8 = (np_img * 255).astype(np.uint8) # 转为PIL Image pil_img = Image.fromarray(np_img_uint8) # 应用transform tensor_img = self.transform(pil_img) projected_tensor_list.append(tensor_img) projected_tensor = torch.stack(projected_tensor_list, dim=0) 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)) 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) # Compute actions actions, _ = self._compute_actions(curr_traj_data, curr_time, np.array([curr_time+1])) # last argument is dummy goal actions[:, :2] = normalize_data(actions[:, :2], self.ACTION_STATS) delta = get_delta_np(actions) # ============ Compute projected image ============ # print(f"context_times{len(context_times)}") # print(f"pred_times{len(pred_times)}") projected_images = self._compute_projected_image(curr_traj_data, curr_time, pred_times, pred_image) projected_tensor_list = [] for img in projected_images: np_img = img.permute(1, 2, 0).cpu().numpy() np_img_uint8 = (np_img * 255).astype(np.uint8) pil_img = Image.fromarray(np_img_uint8) tensor_img = self.transform(pil_img) projected_tensor_list.append(tensor_img) 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(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) actions, goal_pos = self._compute_actions(curr_traj_data, curr_time, np.array([goal_time])) # ============ Compute projected image ============ projected_images = self._compute_projected_image(curr_traj_data, curr_time, goal_time, goal_image) projected_tensor_list = [] for img in projected_images: np_img = img.permute(1, 2, 0).cpu().numpy() np_img_uint8 = (np_img * 255).astype(np.uint8) pil_img = Image.fromarray(np_img_uint8) tensor_img = self.transform(pil_img) projected_tensor_list.append(tensor_img) 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)