import numpy as np from PytorchBoot.dataset import BaseDataset import PytorchBoot.namespace as namespace import PytorchBoot.stereotype as stereotype from PytorchBoot.config import ConfigManager from PytorchBoot.utils.log_util import Log import torch import os import sys sys.path.append(r"/home/data/hofee/project/nbv_rec/nbv_reconstruction") from utils.data_load import DataLoadUtil from utils.pose import PoseUtil from utils.pts import PtsUtil @stereotype.dataset("nbv_reconstruction_dataset") class NBVReconstructionDataset(BaseDataset): def __init__(self, config): super(NBVReconstructionDataset, self).__init__(config) self.config = config self.root_dir = config["root_dir"] self.split_file_path = config["split_file"] self.scene_name_list = self.load_scene_name_list() self.datalist = self.get_datalist() self.pts_num = config["pts_num"] self.type = config["type"] self.cache = config.get("cache") self.load_from_preprocess = config.get("load_from_preprocess", False) if self.type == namespace.Mode.TEST: self.model_dir = config["model_dir"] self.filter_degree = config["filter_degree"] if self.type == namespace.Mode.TRAIN: scale_ratio = 1 self.datalist = self.datalist*scale_ratio if self.cache: expr_root = ConfigManager.get("runner", "experiment", "root_dir") expr_name = ConfigManager.get("runner", "experiment", "name") self.cache_dir = os.path.join(expr_root, expr_name, "cache") # self.preprocess_cache() def load_scene_name_list(self): scene_name_list = [] with open(self.split_file_path, "r") as f: for line in f: scene_name = line.strip() scene_name_list.append(scene_name) return scene_name_list def get_datalist(self): datalist = [] for scene_name in self.scene_name_list: seq_num = DataLoadUtil.get_label_num(self.root_dir, scene_name) scene_max_coverage_rate = 0 max_coverage_rate_list = [] for seq_idx in range(seq_num): label_path = DataLoadUtil.get_label_path( self.root_dir, scene_name, seq_idx ) label_data = DataLoadUtil.load_label(label_path) max_coverage_rate = label_data["max_coverage_rate"] if max_coverage_rate > scene_max_coverage_rate: scene_max_coverage_rate = max_coverage_rate max_coverage_rate_list.append(max_coverage_rate) mean_coverage_rate = np.mean(max_coverage_rate_list) for seq_idx in range(seq_num): label_path = DataLoadUtil.get_label_path( self.root_dir, scene_name, seq_idx ) label_data = DataLoadUtil.load_label(label_path) if max_coverage_rate_list[seq_idx] > mean_coverage_rate - 0.1: for data_pair in label_data["data_pairs"]: scanned_views = data_pair[0] next_best_view = data_pair[1] datalist.append( { "scanned_views": scanned_views, "next_best_view": next_best_view, "seq_max_coverage_rate": max_coverage_rate, "scene_name": scene_name, "label_idx": seq_idx, "scene_max_coverage_rate": scene_max_coverage_rate, } ) return datalist def preprocess_cache(self): Log.info("preprocessing cache...") for item_idx in range(len(self.datalist)): self.__getitem__(item_idx) Log.success("finish preprocessing cache.") def load_from_cache(self, scene_name, curr_frame_idx): cache_name = f"{scene_name}_{curr_frame_idx}.txt" cache_path = os.path.join(self.cache_dir, cache_name) if os.path.exists(cache_path): data = np.loadtxt(cache_path) return data else: return None def save_to_cache(self, scene_name, curr_frame_idx, data): cache_name = f"{scene_name}_{curr_frame_idx}.txt" cache_path = os.path.join(self.cache_dir, cache_name) try: np.savetxt(cache_path, data) except Exception as e: Log.error(f"Save cache failed: {e}") def __getitem__(self, index): data_item_info = self.datalist[index] scanned_views = data_item_info["scanned_views"] nbv = data_item_info["next_best_view"] max_coverage_rate = data_item_info["seq_max_coverage_rate"] scene_name = data_item_info["scene_name"] ( scanned_views_pts, scanned_coverages_rate, scanned_n_to_world_pose, scanned_target_pts_num, ) = ([], [], [], []) target_pts_num_dict = DataLoadUtil.load_target_pts_num_dict( self.root_dir, scene_name ) for view in scanned_views: frame_idx = view[0] coverage_rate = view[1] view_path = DataLoadUtil.get_path(self.root_dir, scene_name, frame_idx) cam_info = DataLoadUtil.load_cam_info(view_path, binocular=True) target_pts_num = target_pts_num_dict[frame_idx] n_to_world_pose = cam_info["cam_to_world"] nR_to_world_pose = cam_info["cam_to_world_R"] if self.load_from_preprocess: downsampled_target_point_cloud = ( DataLoadUtil.load_from_preprocessed_pts(view_path) ) else: cached_data = None if self.cache: cached_data = self.load_from_cache(scene_name, frame_idx) if cached_data is None: print("load depth") depth_L, depth_R = DataLoadUtil.load_depth( view_path, cam_info["near_plane"], cam_info["far_plane"], binocular=True, ) point_cloud_L = DataLoadUtil.get_point_cloud( depth_L, cam_info["cam_intrinsic"], n_to_world_pose )["points_world"] point_cloud_R = DataLoadUtil.get_point_cloud( depth_R, cam_info["cam_intrinsic"], nR_to_world_pose )["points_world"] point_cloud_L = PtsUtil.random_downsample_point_cloud( point_cloud_L, 65536 ) point_cloud_R = PtsUtil.random_downsample_point_cloud( point_cloud_R, 65536 ) overlap_points = PtsUtil.get_overlapping_points( point_cloud_L, point_cloud_R ) downsampled_target_point_cloud = ( PtsUtil.random_downsample_point_cloud( overlap_points, self.pts_num ) ) if self.cache: self.save_to_cache( scene_name, frame_idx, downsampled_target_point_cloud ) else: downsampled_target_point_cloud = cached_data scanned_views_pts.append(downsampled_target_point_cloud) scanned_coverages_rate.append(coverage_rate) n_to_world_6d = PoseUtil.matrix_to_rotation_6d_numpy( np.asarray(n_to_world_pose[:3, :3]) ) n_to_world_trans = n_to_world_pose[:3, 3] n_to_world_9d = np.concatenate([n_to_world_6d, n_to_world_trans], axis=0) scanned_n_to_world_pose.append(n_to_world_9d) scanned_target_pts_num.append(target_pts_num) nbv_idx, nbv_coverage_rate = nbv[0], nbv[1] nbv_path = DataLoadUtil.get_path(self.root_dir, scene_name, nbv_idx) cam_info = DataLoadUtil.load_cam_info(nbv_path) best_frame_to_world = cam_info["cam_to_world"] best_to_world_6d = PoseUtil.matrix_to_rotation_6d_numpy( np.asarray(best_frame_to_world[:3, :3]) ) best_to_world_trans = best_frame_to_world[:3, 3] best_to_world_9d = np.concatenate( [best_to_world_6d, best_to_world_trans], axis=0 ) combined_scanned_views_pts = np.concatenate(scanned_views_pts, axis=0) voxel_downsampled_combined_scanned_pts_np = ( PtsUtil.voxel_downsample_point_cloud(combined_scanned_views_pts, 0.002) ) random_downsampled_combined_scanned_pts_np = ( PtsUtil.random_downsample_point_cloud( voxel_downsampled_combined_scanned_pts_np, self.pts_num ) ) data_item = { "scanned_pts": np.asarray(scanned_views_pts, dtype=np.float32), "combined_scanned_pts": np.asarray( random_downsampled_combined_scanned_pts_np, dtype=np.float32 ), "scanned_coverage_rate": scanned_coverages_rate, "scanned_n_to_world_pose_9d": np.asarray( scanned_n_to_world_pose, dtype=np.float32 ), "best_coverage_rate": nbv_coverage_rate, "best_to_world_pose_9d": np.asarray(best_to_world_9d, dtype=np.float32), "seq_max_coverage_rate": max_coverage_rate, "scene_name": scene_name, "scanned_target_points_num": np.asarray( scanned_target_pts_num, dtype=np.int32 ), } return data_item def __len__(self): return len(self.datalist) def get_collate_fn(self): def collate_fn(batch): collate_data = {} collate_data["scanned_pts"] = [ torch.tensor(item["scanned_pts"]) for item in batch ] collate_data["scanned_n_to_world_pose_9d"] = [ torch.tensor(item["scanned_n_to_world_pose_9d"]) for item in batch ] collate_data["scanned_target_points_num"] = [ torch.tensor(item["scanned_target_points_num"]) for item in batch ] collate_data["best_to_world_pose_9d"] = torch.stack( [torch.tensor(item["best_to_world_pose_9d"]) for item in batch] ) collate_data["combined_scanned_pts"] = torch.stack( [torch.tensor(item["combined_scanned_pts"]) for item in batch] ) if "first_frame_to_world" in batch[0]: collate_data["first_frame_to_world"] = torch.stack( [torch.tensor(item["first_frame_to_world"]) for item in batch] ) for key in batch[0].keys(): if key not in [ "scanned_pts", "scanned_n_to_world_pose_9d", "best_to_world_pose_9d", "first_frame_to_world", "combined_scanned_pts", "scanned_target_points_num", ]: collate_data[key] = [item[key] for item in batch] return collate_data return collate_fn # -------------- Debug ---------------- # if __name__ == "__main__": import torch seed = 0 torch.manual_seed(seed) np.random.seed(seed) config = { "root_dir": "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy", "model_dir": "/home/data/hofee/project/nbv_rec/data/scaled_object_meshes", "source": "nbv_reconstruction_dataset", "split_file": "/home/data/hofee/project/nbv_rec/data/OmniObject3d_test.txt", "load_from_preprocess": True, "ratio": 0.5, "batch_size": 2, "filter_degree": 75, "num_workers": 0, "pts_num": 4096, "type": namespace.Mode.TRAIN, } ds = NBVReconstructionDataset(config) print(len(ds)) # ds.__getitem__(10) dl = ds.get_loader(shuffle=True) for idx, data in enumerate(dl): data = ds.process_batch(data, "cuda:0") print(data) # ------ Debug Start ------ import ipdb ipdb.set_trace() # ------ Debug End ------