Python GridSampling 예제들

예제 #1

파일: surreal.py 프로젝트: xiangruhuang/HumanCorresViaLearn2Sync

from torch.utils.data import Dataset
import numpy as np
from human_corres.utils import helper
from human_corres.data import Data
import torch_geometric
import torch_geometric.transforms as T
import human_corres as hc
import human_corres.transforms as H

num_test = 5000
TrainTransform = T.Compose([
  T.Center(),
  T.RandomRotate(30, axis=0),
  T.RandomRotate(30, axis=1),
  T.RandomRotate(30, axis=2),
  H.GridSampling(0.01)
])
TestTransform = T.Compose([
  T.Center(),
  H.GridSampling(0.01)
])

class SurrealFEPts(Dataset):
  """Surreal 3D points for Feature Extraction (FE).

  Output: dictionary with keys {points3d, correspondence}
  Data Format:
    points3d: [num_points, 3] real numbers.
    correspondence: [num_points] integers in range [6890].
  """
  def __init__(self, descriptor_dim, split='train', desc='Laplacian_n',

예제 #2

import torch
from torch.utils.data import Dataset
import numpy as np
from human_corres.utils import helper
from human_corres.data import Data
import human_corres as hc
import scipy.io as sio
import torch_geometric.transforms as T
import human_corres.transforms as H

TrainTransform = T.Compose([
    T.Center(),
    T.RandomRotate(30, axis=0),
    T.RandomRotate(30, axis=1),
    T.RandomRotate(30, axis=2),
    H.GridSampling(0.01),
])

TestTransform = T.Compose([
    T.Center(),
    H.GridSampling(0.01),
])


class SMALFEPts(Dataset):
    """SMAL 3D points for Feature Extraction (FE).

  Output: dictionary with keys {points3d, correspondence}
  Data Format:
    points3d: [num_points, 3] real numbers.
    correspondence: [num_points] integers in range [6890].

예제 #3

파일: dgfsurreal.py 프로젝트: xiangruhuang/HumanCorresViaLearn2Sync

from human_corres.utils import helper
from human_corres.data import Data
import torch_geometric
import torch_geometric.transforms as T
import human_corres as hc
import human_corres.transforms as H

num_views = 20
IDlist = np.arange(5000 * num_views)
num_test = 50
TrainTransform = T.Compose([
    T.Center(),
    T.RandomRotate(30, axis=0),
    T.RandomRotate(30, axis=1),
    T.RandomRotate(30, axis=2),
    H.GridSampling(0.01)
])
TestTransform = T.Compose([T.Center(), H.GridSampling(0.01)])


class DGFSurrealFEPts(Dataset):
    """Surreal 3D points for Feature Extraction (FE).

  Output: dictionary with keys {points3d, correspondence}
  Data Format:
    points3d: [num_points, 3] real numbers.
    correspondence: [num_points] integers in range [6890].
  """
    def __init__(self,
                 descriptor_dim,
                 split='train',