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dataset.py
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dataset.py
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
'''Dataset for DIF-Net.
'''
import os
import numpy as np
import torch
from torch.utils.data import Dataset
from scipy.io import loadmat
class PointCloud_with_FreePoints(Dataset):
def __init__(self, pointcloud_path, on_surface_points, instance_idx=None, expand=-1, max_points=-1):
super().__init__()
self.instance_idx = instance_idx
self.expand = expand
print("Loading point cloud of subject%04d"%self.instance_idx)
# surface points
point_cloud = loadmat(pointcloud_path)
point_cloud = point_cloud['p']
# free space points
free_points = loadmat(pointcloud_path.replace('surface_pts_n_normal','free_space_pts'))
free_points = free_points['p_sdf']
print("Finished loading point cloud")
free_points_coords = free_points[:,:3]
free_points_sdf = free_points[:,3:]
# surface points with normals
self.coords = point_cloud[:, :3]
self.normals = point_cloud[:, 3:]
self.free_points_coords = free_points_coords
self.free_points_sdf = free_points_sdf
self.on_surface_points = on_surface_points
self.max_points = max_points
def __len__(self):
if self.max_points != -1:
return self.max_points // self.on_surface_points
return self.coords.shape[0] // self.on_surface_points
def __getitem__(self, idx):
point_cloud_size = self.coords.shape[0]
free_point_size = self.free_points_coords.shape[0]
off_surface_samples = self.on_surface_points
total_samples = self.on_surface_points + off_surface_samples
# Random coords
rand_idcs = np.random.choice(point_cloud_size, size=self.on_surface_points)
on_surface_coords = self.coords[rand_idcs, :]
on_surface_normals = self.normals[rand_idcs, :]
if self.expand != -1:
on_surface_coords += on_surface_normals*self.expand # expand the shape surface if its structure is too thin
off_surface_coords = np.random.uniform(-1, 1, size=(off_surface_samples//2, 3))
free_rand_idcs = np.random.choice(free_point_size, size=off_surface_samples//2)
free_points_coords = self.free_points_coords[free_rand_idcs,:]
off_surface_normals = np.ones((off_surface_samples, 3)) * -1
sdf = np.zeros((total_samples, 1)) # on-surface = 0
sdf[self.on_surface_points:, :] = -1 # off-surface = -1
# if a free space point has gt SDF value, replace -1 with it.
if self.expand != -1:
sdf[self.on_surface_points+off_surface_samples//2:,:] = (self.free_points_sdf[free_rand_idcs] - self.expand)
else:
sdf[self.on_surface_points+off_surface_samples//2:,:] = self.free_points_sdf[free_rand_idcs]
coords = np.concatenate((on_surface_coords, off_surface_coords,free_points_coords), axis=0)
normals = np.concatenate((on_surface_normals, off_surface_normals), axis=0)
return {'coords': torch.from_numpy(coords).float(),
'sdf': torch.from_numpy(sdf).float(),
'normals': torch.from_numpy(normals).float(),
'instance_idx':torch.Tensor([self.instance_idx]).squeeze().long()}
class PointCloudMulti(Dataset):
def __init__(self,root_dir,on_surface_points,max_num_instances=-1,expand=-1,max_points=-1,**kwargs):
#This class adapted from SIREN https://vsitzmann.github.io/siren/
super().__init__()
self.root_dir = root_dir
print(root_dir)
if isinstance(root_dir,list):
self.instance_dirs = root_dir
else:
self.instance_dirs = []
for file in sorted(os.listdir(root_dir)):
if file.endswith('mat'):
if os.path.isfile(os.path.join(root_dir,file).replace('surface_pts_n_normal','free_space_pts')):
self.instance_dirs.append(os.path.join(root_dir,file))
assert (len(self.instance_dirs) != 0), "No objects in the data directory"
if max_num_instances != -1:
self.instance_dirs = self.instance_dirs[:max_num_instances]
self.all_instances = [PointCloud_with_FreePoints(instance_idx=idx,
pointcloud_path=dir,
on_surface_points=on_surface_points,expand=expand,max_points=max_points) for idx, dir in enumerate(self.instance_dirs)]
self.num_instances = len(self.all_instances)
self.num_per_instance_observations = [len(obj) for obj in self.all_instances]
def __len__(self):
return np.sum(self.num_per_instance_observations)
def get_instance_idx(self, idx):
"""Maps an index into all tuples of all objects to the idx of the tuple relative to the other tuples of that
object
"""
obj_idx = 0
while idx >= 0:
idx -= self.num_per_instance_observations[obj_idx]
obj_idx += 1
return obj_idx - 1, int(idx + self.num_per_instance_observations[obj_idx - 1])
def collate_fn(self, batch_list):
batch_list = zip(*batch_list)
all_parsed = []
for entry in batch_list:
# make them all into a new dict
ret = {}
for k in entry[0][0].keys():
ret[k] = []
# flatten the list of list
for b in entry:
for k in entry[0][0].keys():
ret[k].extend( [bi[k] for bi in b])
for k in ret.keys():
if type(ret[k][0]) == torch.Tensor:
ret[k] = torch.stack(ret[k])
all_parsed.append(ret)
return tuple(all_parsed)
def __getitem__(self, idx):
"""Each __getitem__ call yields a list of self.samples_per_instance observations of a single scene (each a dict),
as well as a list of ground-truths for each observation (also a dict)."""
obj_idx, rel_idx = self.get_instance_idx(idx)
observations = []
observations.append(self.all_instances[obj_idx][rel_idx])
ground_truth = [{'sdf':obj['sdf'] ,
'normals': obj['normals']} for obj in observations]
return observations, ground_truth