import numpy as np from PytorchBoot.dataset import BaseDataset import PytorchBoot.stereotype as stereotype import torch import sys sys.path.append(r"/media/hofee/data/project/python/nbv_reconstruction/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"] 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: label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name) label_data = DataLoadUtil.load_label(label_path) for data_pair in label_data["data_pairs"]: scanned_views = data_pair[0] next_best_view = data_pair[1] max_coverage_rate = label_data["max_coverage_rate"] datalist.append( { "scanned_views": scanned_views, "next_best_view": next_best_view, "max_coverage_rate": max_coverage_rate, "scene_name": scene_name, } ) return datalist 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["max_coverage_rate"] scene_name = data_item_info["scene_name"] scanned_views_pts, scanned_coverages_rate, scanned_n_to_1_pose = [], [], [] first_frame_idx = scanned_views[0][0] first_cam_info = DataLoadUtil.load_cam_info(DataLoadUtil.get_path(self.root_dir, scene_name, first_frame_idx), binocular=True) first_frame_to_world = first_cam_info["cam_to_world"] 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) n_to_world_pose = cam_info["cam_to_world"] nR_to_world_pose = cam_info["cam_to_world_R"] n_to_1_pose = np.dot(np.linalg.inv(first_frame_to_world), n_to_world_pose) nR_to_1_pose = np.dot(np.linalg.inv(first_frame_to_world), nR_to_world_pose) 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_1_pose)['points_world'] point_cloud_R = DataLoadUtil.get_point_cloud(depth_R, cam_info['cam_intrinsic'], nR_to_1_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 = DataLoadUtil.get_overlapping_points(point_cloud_L, point_cloud_R) downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(overlap_points, self.pts_num) scanned_views_pts.append(downsampled_target_point_cloud) scanned_coverages_rate.append(coverage_rate) n_to_1_6d = PoseUtil.matrix_to_rotation_6d_numpy(np.asarray(n_to_1_pose[:3,:3])) n_to_1_trans = n_to_1_pose[:3,3] n_to_1_9d = np.concatenate([n_to_1_6d, n_to_1_trans], axis=0) scanned_n_to_1_pose.append(n_to_1_9d) 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_1_pose = np.dot(np.linalg.inv(first_frame_to_world), best_frame_to_world) best_to_1_6d = PoseUtil.matrix_to_rotation_6d_numpy(np.asarray(best_to_1_pose[:3,:3])) best_to_1_trans = best_to_1_pose[:3,3] best_to_1_9d = np.concatenate([best_to_1_6d, best_to_1_trans], axis=0) data_item = { "scanned_pts": np.asarray(scanned_views_pts,dtype=np.float32), "scanned_coverage_rate": scanned_coverages_rate, "scanned_n_to_1_pose_9d": np.asarray(scanned_n_to_1_pose,dtype=np.float32), "best_coverage_rate": nbv_coverage_rate, "best_to_1_pose_9d": np.asarray(best_to_1_9d,dtype=np.float32), "max_coverage_rate": max_coverage_rate, "scene_name": scene_name } 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_1_pose_9d"] = [torch.tensor(item['scanned_n_to_1_pose_9d']) for item in batch] collate_data["best_to_1_pose_9d"] = torch.stack([torch.tensor(item['best_to_1_pose_9d']) for item in batch]) for key in batch[0].keys(): if key not in ["scanned_pts", "scanned_n_to_1_pose_9d", "best_to_1_pose_9d"]: collate_data[key] = [item[key] for item in batch] return collate_data return collate_fn if __name__ == "__main__": import torch seed = 0 torch.manual_seed(seed) np.random.seed(seed) config = { "root_dir": "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/scenes", "split_file": "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/OmniObject3d_train.txt", "ratio": 0.5, "batch_size": 2, "num_workers": 0, "pts_num": 32684 } 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) break # # for idx, data in enumerate(dl): # cnt=0 # print(data["scene_name"]) # print(data["scanned_coverage_rate"]) # print(data["best_coverage_rate"]) # for pts in data["scanned_pts"][0]: # #np.savetxt(f"pts_{cnt}.txt", pts) # cnt+=1 # #np.savetxt("best_pts.txt", best_pts) # for key, value in data.items(): # if isinstance(value, torch.Tensor): # print(key, ":" ,value.shape) # else: # print(key, ":" ,len(value)) # if key == "scanned_n_to_1_pose_9d": # for val in value: # print(val.shape) # if key == "scanned_pts": # print("scanned_pts") # for val in value: # print(val.shape) # cnt = 0 # for v in val: # import ipdb;ipdb.set_trace() # np.savetxt(f"pts_{cnt}.txt", v) # cnt+=1 # print()