def batch_grid_subsampling_kpconv(points,
batches_len,
features=None,
labels=None,
sampleDl=0.1,
max_p=0,
verbose=0,
random_grid_orient=True):
"""
CPP wrapper for a grid subsampling (method = barycenter for points and features)
"""
if (features is None) and (labels is None):
s_points, s_len = cpp_subsampling.subsample_batch(points,
batches_len,
sampleDl=sampleDl,
max_p=max_p,
verbose=verbose)
return torch.from_numpy(s_points), torch.from_numpy(s_len)
elif (labels is None):
s_points, s_len, s_features = cpp_subsampling.subsample_batch(
points,
batches_len,
features=features,
sampleDl=sampleDl,
max_p=max_p,
verbose=verbose)
return torch.from_numpy(s_points), torch.from_numpy(
s_len), torch.from_numpy(s_features)
elif (features is None):
s_points, s_len, s_labels = cpp_subsampling.subsample_batch(
points,
batches_len,
classes=labels,
sampleDl=sampleDl,
max_p=max_p,
verbose=verbose)
return torch.from_numpy(s_points), torch.from_numpy(
s_len), torch.from_numpy(s_labels)
else:
s_points, s_len, s_features, s_labels = cpp_subsampling.subsample_batch(
points,
batches_len,
features=features,
classes=labels,
sampleDl=sampleDl,
max_p=max_p,
verbose=verbose)
return torch.from_numpy(s_points), torch.from_numpy(
s_len), torch.from_numpy(s_features), torch.from_numpy(s_labels)
def batch_grid_subsampling(points, batches_len, features=None, labels=None, ins_labels=None,
sampleDl=0.1, max_p=0, verbose=0, random_grid_orient=True):
"""
CPP wrapper for a grid subsampling (method = barycenter for points and features)
:param points: (N, 3) matrix of input points
:param features: optional (N, d) matrix of features (floating number)
:param labels: optional (N,) matrix of integer labels
:param ins_labels: optional (N,) matrix of integer instance labels
:param sampleDl: parameter defining the size of grid voxels
:param verbose: 1 to display
:return: subsampled points, with features and/or labels depending of the input
"""
R = None
B = len(batches_len)
if random_grid_orient:
########################################################
# Create a random rotation matrix for each batch element
########################################################
# Choose two random angles for the first vector in polar coordinates
theta = np.random.rand(B) * 2 * np.pi
phi = (np.random.rand(B) - 0.5) * np.pi
# Create the first vector in carthesian coordinates
u = np.vstack([np.cos(theta) * np.cos(phi), np.sin(theta) * np.cos(phi), np.sin(phi)])
# Choose a random rotation angle
alpha = np.random.rand(B) * 2 * np.pi
# Create the rotation matrix with this vector and angle
R = create_3D_rotations(u.T, alpha).astype(np.float32)
#################
# Apply rotations
#################
i0 = 0
points = points.copy()
for bi, length in enumerate(batches_len):
# Apply the rotation
points[i0:i0 + length, :] = np.sum(np.expand_dims(points[i0:i0 + length, :], 2) * R[bi], axis=1)
i0 += length
#######################
# Sunsample and realign
#######################
if (features is None) and (labels is None):
s_points, s_len = cpp_subsampling.subsample_batch(points,
batches_len,
sampleDl=sampleDl,
max_p=max_p,
verbose=verbose)
if random_grid_orient:
i0 = 0
for bi, length in enumerate(s_len):
s_points[i0:i0 + length, :] = np.sum(np.expand_dims(s_points[i0:i0 + length, :], 2) * R[bi].T, axis=1)
i0 += length
return s_points, s_len
elif (labels is None):
s_points, s_len, s_features = cpp_subsampling.subsample_batch(points,
batches_len,
features=features,
sampleDl=sampleDl,
max_p=max_p,
verbose=verbose)
if random_grid_orient:
i0 = 0
for bi, length in enumerate(s_len):
# Apply the rotation
s_points[i0:i0 + length, :] = np.sum(np.expand_dims(s_points[i0:i0 + length, :], 2) * R[bi].T, axis=1)
i0 += length
return s_points, s_len, s_features
elif (features is None):
s_points, s_len, s_labels, s_ins_labels = cpp_subsampling.subsample_batch(points,
batches_len,
classes=labels,
ins_classes=ins_labels,
sampleDl=sampleDl,
max_p=max_p,
verbose=verbose)
if random_grid_orient:
i0 = 0
for bi, length in enumerate(s_len):
# Apply the rotation
s_points[i0:i0 + length, :] = np.sum(np.expand_dims(s_points[i0:i0 + length, :], 2) * R[bi].T, axis=1)
i0 += length
return s_points, s_len, s_labels, s_ins_labels
else:
s_points, s_len, s_features, s_labels, s_ins_labels = cpp_subsampling.subsample_batch(points,
batches_len,
features=features,
classes=labels,
ins_classes=ins_labels,
sampleDl=sampleDl,
max_p=max_p,
verbose=verbose)
if random_grid_orient:
i0 = 0
for bi, length in enumerate(s_len):
# Apply the rotation
s_points[i0:i0 + length, :] = np.sum(np.expand_dims(s_points[i0:i0 + length, :], 2) * R[bi].T, axis=1)
i0 += length
return s_points, s_len, s_features, s_labels, s_ins_labels
