import numpy as np
from collections import defaultdict
import os
from tqdm import tqdm
import glob
import torch
from torch_geometric.data import InMemoryDataset
from torch_geometric.data import Data
from torch.utils.data import Dataset
class CustomShapeNet(InMemoryDataset):
categories = {key: val for val, key in enumerate(['Box', 'Cone', 'Cylinder', 'Sphere'])}
def __init__(self, root, collate_per_segment=True, train=True, transform=None, pre_filter=None, pre_transform=None,
headers=True, **kwargs):
self.has_headers = headers
self.collate_per_element = collate_per_segment
self.train = train
super(CustomShapeNet, self).__init__(root, transform, pre_transform, pre_filter)
path = self.processed_paths[0] if train else self.processed_paths[-1]
self.data, self.slices = torch.load(path)
print("Initialized")
@property
def raw_file_names(self):
# Maybe add more data like validation sets
return ['train', 'test']
@property
def processed_file_names(self):
return [f'{x}.pt' for x in self.raw_file_names]
def download(self):
dir_count = len([name for name in os.listdir(self.raw_dir) if os.path.isdir(os.path.join(self.raw_dir, name))])
print(f'{dir_count} folders have been found....')
if dir_count:
return dir_count
raise IOError("No raw pointclouds have been found.")
@property
def num_classes(self):
return len(self.categories)
def _load_dataset(self):
data, slices = None, None
while True:
try:
filepath = os.path.join(self.root, self.processed_dir, f'{"train" if self.train else "test"}.pt')
data, slices = torch.load(filepath)
print('Dataset Loaded')
break
except FileNotFoundError:
self.process()
continue
return data, slices
def _transform_and_filter(self, data):
# ToDo: ANy filter to apply? Then do it here.
if self.pre_filter is not None and not self.pre_filter(data):
data = self.pre_filter(data)
raise NotImplementedError
# ToDo: ANy transformation to apply? Then do it here.
if self.pre_transform is not None:
data = self.pre_transform(data)
raise NotImplementedError
return data
def process(self, delimiter=' '):
datasets = defaultdict(list)
idx, data_folder = (0, self.raw_file_names[0]) if self.train else (1, self.raw_file_names[1])
path_to_clouds = os.path.join(self.raw_dir, data_folder)
if '.headers' in os.listdir(path_to_clouds):
self.has_headers = True
elif 'no.headers' in os.listdir(path_to_clouds):
self.has_headers = False
else:
pass
for pointcloud in tqdm(os.scandir(path_to_clouds)):
if not os.path.isdir(pointcloud):
continue
data, paths = None, list()
for ext in ['dat', 'xyz']:
paths.extend(glob.glob(os.path.join(pointcloud.path, f'*.{ext}')))
for element in paths:
if all([x not in os.path.split(element)[-1] for x in ['pc.dat', 'pc.xyz']]):
# Assign training data to the data container
# Following the original logic;
# y should be the label;
# pos should be the six dimensional vector describing: !its pos not points!!
# x,y,z,x_rot,y_rot,z_rot
# Get the y - Label
y_raw = next(i for i, v in enumerate(self.categories.keys()) if v.lower() in element.lower())
# y_raw = os.path.splitext(element)[0].split('_')[-2]
with open(element,'r') as f:
if self.has_headers:
headers = f.__next__()
# Check if there are no useable nodes in this file, header says 0.
if not int(headers.rstrip().split(delimiter)[0]):
continue
# Iterate over all rows
src = [[float(x) if x not in ['-nan(ind)', 'nan(ind)'] else 0
for x in line.rstrip().split(delimiter)[None:None]] for line in f if line != '']
points = torch.tensor(src, dtype=None).squeeze()
if not len(points.shape) > 1:
continue
# pos = points[:, :3]
# norm = points[:, 3:]
y_all = [y_raw] * points.shape[0]
y = torch.as_tensor(y_all, dtype=torch.int)
# points = torch.as_tensor(points, dtype=torch.float)
# norm = torch.as_tensor(norm, dtype=torch.float)
if self.collate_per_element:
data = Data(y=y, pos=points[:, :3])
else:
if not data:
data = defaultdict(list)
for key, val in dict(y=y, pos= points[:, :3]).items():
data[key].append(val)
# , points=points, norm=points[:3], )
data = self._transform_and_filter(data)
if self.collate_per_element:
datasets[data_folder].append(data)
if not self.collate_per_element:
datasets[data_folder].append(Data(**{key: torch.cat(data[key]) for key in data.keys()}))
if datasets[data_folder]:
os.makedirs(self.processed_dir, exist_ok=True)
torch.save(self.collate(datasets[data_folder]), self.processed_paths[idx])
def __repr__(self):
return f'{self.__class__.__name__}({len(self)})'
class ShapeNetPartSegDataset(Dataset):
"""
Resample raw point cloud to fixed number of points.
Map raw label from range [1, N] to [0, N-1].
"""
def __init__(self, root_dir, collate_per_segment=True, train=True, transform=None, npoints=1024, headers=True):
super(ShapeNetPartSegDataset, self).__init__()
self.npoints = npoints
self.dataset = CustomShapeNet(root=root_dir, collate_per_segment=collate_per_segment,
train=train, transform=transform, headers=headers)
def __getitem__(self, index):
data = self.dataset[index]
points, labels = data.pos, data.y
# Resample to fixed number of points
try:
choice = np.random.choice(points.shape[0], self.npoints, replace=True)
except ValueError:
choice = []
points, labels = points[choice, :], labels[choice]
labels -= 1 if self.num_classes() in labels else 0 # Map label from [1, C] to [0, C-1]
sample = {
'points': points, # torch.Tensor (n, 3)
'labels': labels # torch.Tensor (n,)
}
return sample
def __len__(self):
return len(self.dataset)
def num_classes(self):
return self.dataset.num_classes
class PredictionShapeNet(InMemoryDataset):
categories = {key: val for val, key in enumerate(['Box', 'Cone', 'Cylinder', 'Sphere'])}
def __init__(self, root, transform=None, pre_filter=None, pre_transform=None,
headers=True, **kwargs):
self.has_headers = headers
super(PredictionShapeNet, self).__init__(root, transform, pre_transform, pre_filter)
path = self.processed_paths[0]
self.data, self.slices = torch.load(path)
print("Initialized")
@property
def raw_file_names(self):
# Maybe add more data like validation sets
return ['predict']
@property
def processed_file_names(self):
return [f'{x}.pt' for x in self.raw_file_names]
def download(self):
dir_count = len([name for name in os.listdir(self.raw_dir) if os.path.isdir(os.path.join(self.raw_dir, name))])
print(f'{dir_count} folders have been found....')
if dir_count:
return dir_count
raise IOError("No raw pointclouds have been found.")
@property
def num_classes(self):
return len(self.categories)
def _load_dataset(self):
data, slices = None, None
while True:
try:
filepath = os.path.join(self.root, self.processed_dir, f'{"train" if self.train else "test"}.pt')
data, slices = torch.load(filepath)
print('Dataset Loaded')
break
except FileNotFoundError:
self.process()
continue
return data, slices
def process(self, delimiter=' '):
datasets = defaultdict(list)
for idx, setting in enumerate(self.raw_file_names):
path_to_clouds = os.path.join(self.raw_dir, setting)
if '.headers' in os.listdir(path_to_clouds):
self.has_headers = True
elif 'no.headers' in os.listdir(path_to_clouds):
self.has_headers = False
else:
pass
for pointcloud in tqdm(os.scandir(path_to_clouds)):
if not os.path.isdir(pointcloud):
continue
for extention in ['dat', 'xyz']:
file = os.path.join(pointcloud.path, f'pc.{extention}')
if not os.path.exists(file):
continue
with open(file, 'r') as f:
if self.has_headers:
headers = f.__next__()
# Check if there are no useable nodes in this file, header says 0.
if not int(headers.rstrip().split(delimiter)[0]):
continue
# Iterate over all rows
src = [[float(x) if x not in ['-nan(ind)', 'nan(ind)'] else 0
for x in line.rstrip().split(delimiter)[None:None]] for line in f if line != '']
points = torch.tensor(src, dtype=None).squeeze()
if not len(points.shape) > 1:
continue
# pos = points[:, :3]
# norm = points[:, 3:]
y_fake_all = [-1] * points.shape[0]
y = torch.as_tensor(y_fake_all, dtype=torch.int)
# points = torch.as_tensor(points, dtype=torch.float)
# norm = torch.as_tensor(norm, dtype=torch.float)
data = Data(y=y, pos=points[:, :3])
# , points=points, norm=points[:3], )
# ToDo: ANy filter to apply? Then do it here.
if self.pre_filter is not None and not self.pre_filter(data):
data = self.pre_filter(data)
raise NotImplementedError
# ToDo: ANy transformation to apply? Then do it here.
if self.pre_transform is not None:
data = self.pre_transform(data)
raise NotImplementedError
datasets[setting].append(data)
os.makedirs(self.processed_dir, exist_ok=True)
torch.save(self.collate(datasets[setting]), self.processed_paths[idx])
def __repr__(self):
return f'{self.__class__.__name__}({len(self)})'
class PredictNetPartSegDataset(Dataset):
"""
Resample raw point cloud to fixed number of points.
Map raw label from range [1, N] to [0, N-1].
"""
def __init__(self, root_dir, train=False, transform=None, npoints=2048, headers=True, collate_per_segment=False):
super(PredictNetPartSegDataset, self).__init__()
self.npoints = npoints
self.dataset = PredictionShapeNet(root=root_dir, train=train, transform=transform,
headers=headers, collate_per_segment=collate_per_segment)
def __getitem__(self, index):
data = self.dataset[index]
points, labels = data.pos, data.y
# Resample to fixed number of points
try:
choice = np.random.choice(points.shape[0], self.npoints, replace=True)
except ValueError:
choice = []
points, labels = points[choice, :], labels[choice]
labels -= 1 if self.num_classes() in labels else 0 # Map label from [1, C] to [0, C-1]
sample = {
'points': points, # torch.Tensor (n, 3)
'labels': labels # torch.Tensor (n,)
}
return sample
def __len__(self):
return len(self.dataset)
def num_classes(self):
return self.dataset.num_classes