def _read_3d(self):
"""Reads in a 3D SDF file and returns a Sdf object.
Returns
-------
:obj:`Sdf3D`
A 3DSdf created from the data in the file.
"""
if not os.path.exists(self.filepath_):
return None
my_file = open(self.filepath_, 'r')
nx, ny, nz = [int(i) for i in my_file.readline().split()
] #dimension of each axis should all be equal for LSH
ox, oy, oz = [float(i)
for i in my_file.readline().split()] #shape origin
dims = np.array([nx, ny, nz])
origin = np.array([ox, oy, oz])
resolution = float(my_file.readline(
)) # resolution of the grid cells in original mesh coords
sdf_data = np.zeros(dims)
# loop through file, getting each value
count = 0
for k in range(nz):
for j in range(ny):
for i in range(nx):
sdf_data[i][j][k] = float(my_file.readline())
count += 1
my_file.close()
return sdf.Sdf3D(sdf_data, origin, resolution)
def _read_3d(self):
'''
Reads a 3d SDF
'''
my_file = open(self.file_name_, 'r')
nx, ny, nz = [int(i) for i in my_file.readline().split()
] #dimension of each axis should all be equal for LSH
ox, oy, oz = [float(i)
for i in my_file.readline().split()] #shape origin
dims = np.array([nx, ny, nz])
origin = np.array([ox, oy, oz])
resolution = float(my_file.readline(
)) # resolution of the grid cells in original mesh coords
sdf_data = np.zeros(dims)
# loop through file, getting each value
count = 0
for k in range(nz):
for j in range(ny):
for i in range(nx):
sdf_data[i][j][k] = float(my_file.readline())
count += 1
my_file.close()
return sdf.Sdf3D(sdf_data, origin, resolution)
def sample_sdfs(self, num_samples=1, full_cov=True):
"""
Samples sdfs from the GPIS
Params:
num_samples: (int) number of samples to generate
full_cov: (bool) whether or not to use the diagonal or entire covariance matrix
Returns:
list of sdf objects
"""
sdf_samples = self.gp_.posterior_samples(self.grid_pts_, num_samples)
sdfs = []
for i in range(num_samples):
sdf_data = sdf_samples[:,i].reshape(self.dims_)
sdfs.append(sdf.Sdf3D(sdf_data, pose = self.pose_))
return sdfs
def sdf_3d(data):
""" Converts HDF5 data provided in dictionary |data| to an SDF object """
sdf_data = np.array(data[SDF_DATA_KEY])
origin = np.array(data.attrs[SDF_ORIGIN_KEY])
resolution = data.attrs[SDF_RES_KEY]
# should never be used, really
pose = tfx.identity_tf()
scale = 1.0
if SDF_POSE_KEY in data.attrs.keys():
pose = data.attrs[SDF_POSE_KEY]
if SDF_SCALE_KEY in data.attrs.keys():
scale = data.attrs[SDF_SCALE_KEY]
tf = stf.SimilarityTransform3D(pose, scale)
frame = None
if SDF_FRAME_KEY in data.attrs.keys():
frame = data.attrs[SDF_FRAME_KEY]
return sdf.Sdf3D(sdf_data, origin, resolution, tf, frame)