89 lines
3.6 KiB
Python
89 lines
3.6 KiB
Python
import numpy as np
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from PytorchBoot.dataset import BaseDataset
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import PytorchBoot.stereotype as stereotype
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from utils.data_load import DataLoadUtil
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@stereotype.dataset("nbv_reconstruction_dataset")
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class NBVReconstructionDataset(BaseDataset):
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def __init__(self, config):
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super(NBVReconstructionDataset, self).__init__(config)
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self.config = config
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self.label_dir = config["label_dir"]
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self.root_dir = config["root_dir"]
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self.datalist = self.get_datalist()
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def get_datalist(self):
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datalist = []
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scene_idx_list = DataLoadUtil.get_scene_idx_list(self.root_dir)
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for scene_idx in scene_idx_list:
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label_path = DataLoadUtil.get_label_path(self.label_dir, scene_idx)
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label_data = DataLoadUtil.load_label(label_path)
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for data_pair in label_data["data_pairs"]:
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scanned_views = data_pair[0]
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next_best_view = data_pair[1]
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max_coverage_rate = label_data["max_coverage_rate"]
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datalist.append(
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{
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"scanned_views": scanned_views,
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"next_best_view": next_best_view,
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"max_coverage_rate": max_coverage_rate,
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"scene_idx": scene_idx,
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}
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)
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return datalist
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def __getitem__(self, index):
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data_item_info = self.datalist[index]
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scanned_views = data_item_info["scanned_views"]
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nbv = data_item_info["next_best_view"]
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max_coverage_rate = data_item_info["max_coverage_rate"]
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scene_idx = data_item_info["scene_idx"]
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scanned_views_pts, scanned_coverages_rate, scanned_cam_pose = [], [], []
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for view in scanned_views:
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frame_idx = view[0]
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coverage_rate = view[1]
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view_path = DataLoadUtil.get_path(self.root_dir, scene_idx, frame_idx)
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pts = DataLoadUtil.load_depth(view_path)
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scanned_views_pts.append(pts)
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scanned_coverages_rate.append(coverage_rate)
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cam_pose = DataLoadUtil.load_cam_info(view_path)["cam_to_world"]
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scanned_cam_pose.append(cam_pose)
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nbv_idx, nbv_coverage_rate = nbv[0], nbv[1]
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nbv_path = DataLoadUtil.get_path(self.root_dir, scene_idx, nbv_idx)
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nbv_pts = DataLoadUtil.load_depth(nbv_path)
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cam_info = DataLoadUtil.load_cam_info(nbv_path)
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nbv_cam_pose = cam_info["cam_to_world"]
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data_item = {
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"scanned_views_pts": np.asarray(scanned_views_pts,dtype=np.float32),
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"scanned_coverages_rate": np.asarray(scanned_coverages_rate,dtype=np.float32),
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"scanned_cam_pose": np.asarray(scanned_cam_pose,dtype=np.float32),
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"nbv_pts": np.asarray(nbv_pts,dtype=np.float32),
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"nbv_coverage_rate": nbv_coverage_rate,
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"nbv_cam_pose": nbv_cam_pose,
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"max_coverage_rate": max_coverage_rate,
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}
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return data_item
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def __len__(self):
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return len(self.datalist)
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if __name__ == "__main__":
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import torch
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config = {
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"root_dir": "C:\\Document\\Local Project\\nbv_rec\\sample_dataset",
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"label_dir": "C:\\Document\\Local Project\\nbv_rec\\sample_output",
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"ratio": 0.1,
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"batch_size": 1,
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"num_workers": 0,
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}
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ds = NBVReconstructionDataset(config)
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dl = ds.get_loader(shuffle=True)
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for idx, data in enumerate(dl):
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for key, value in data.items():
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if isinstance(value, torch.Tensor):
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print(key, ":" ,value.shape)
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print() |