| import os |
| import shutil |
| import numpy as np |
| import scipy.io as sio |
| import torch |
|
|
|
|
| def load_S3DIS_sample(text_path, sample=False): |
| data = np.loadtxt(text_path) |
| point, color = data[:, :3], data[:, 3:] |
|
|
| point = point - point.min(axis=0) |
| point = point / point.max(axis=0) |
| color = color / 255. |
|
|
| return point, color |
|
|
| def load_ScanNet_sample(data_path): |
| |
| all_data = torch.load(data_path) |
| |
| point = np.array(all_data['coord']) |
| color = np.array(all_data['color']) |
| |
| point = point - point.min(axis=0) |
| point = point / point.max(axis=0) |
| color = color / 255. |
| return point, color |
|
|
| def load_KITTI_sample(data_path, close=False): |
| all_data = np.load(data_path) |
| |
| point = all_data[:, :3] |
| color = all_data[:, 3:6] |
| |
| pmin = point.min(axis=0) |
| point = point - pmin |
| pmax = point.max(axis=0) |
| point = point / pmax |
|
|
| return point, color |
|
|
| def load_Objaverse_sample(data_path): |
| all_data = np.load(data_path) |
| |
| point = all_data[:, :3] |
| color = all_data[:, 3:6] |
| |
| pmin = point.min(axis=0) |
| point = point - pmin |
| pmax = point.max(axis=0) |
| point = point / pmax |
| |
| return point, color |
|
|
| def load_Semantic3D_sample(data_path, id, sample=False): |
| all_data = np.load(data_path) |
| |
| point = all_data[:, :3] |
| color = all_data[:, 3:6] |
| |
| pmin = point.min(axis=0) |
| point = point - pmin |
| pmax = point.max(axis=0) |
| point = point / pmax |
|
|
| if id > 1: return point, color |
| if id == 0: |
| filter_mask = (point[:, 0] > 0.4) & (point[:, 1] > 0.4) & (point[:, 2] < 0.4) |
| else: |
| filter_mask = (point[:, 0] > 0.4) & (point[:, 1] < 0.5) |
| point = point[filter_mask] |
| color = color[filter_mask] |
|
|
| pmin = point.min(axis=0) |
| point = point - pmin |
| pmax = point.max(axis=0) |
| point = point / pmax |
|
|
| return point, color |
