def augmentation_transform(self, points, normals=None, verbose=False):
"""Implementation of an augmentation transform for point clouds."""
##########
# Rotation
##########
# Initialize rotation matrix
R = np.eye(points.shape[1])
if points.shape[1] == 3:
if self.config.augment_rotation == 'vertical':
# Create random rotations
theta = np.random.rand() * 2 * np.pi
c, s = np.cos(theta), np.sin(theta)
R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)
elif self.config.augment_rotation == 'all':
# Choose two random angles for the first vector in polar coordinates
theta = np.random.rand() * 2 * np.pi
phi = (np.random.rand() - 0.5) * np.pi
# Create the first vector in carthesian coordinates
u = np.array([np.cos(theta) * np.cos(phi), np.sin(theta) * np.cos(phi), np.sin(phi)])
# Choose a random rotation angle
alpha = np.random.rand() * 2 * np.pi
# Create the rotation matrix with this vector and angle
R = create_3D_rotations(np.reshape(u, (1, -1)), np.reshape(alpha, (1, -1)))[0]
R = R.astype(np.float32)
#######
# Scale
#######
# Choose random scales for each example
min_s = self.config.augment_scale_min
max_s = self.config.augment_scale_max
if self.config.augment_scale_anisotropic:
scale = np.random.rand(points.shape[1]) * (max_s - min_s) + min_s
else:
scale = np.random.rand() * (max_s - min_s) - min_s
# Add random symmetries to the scale factor
symmetries = np.array(self.config.augment_symmetries).astype(np.int32)
symmetries *= np.random.randint(2, size=points.shape[1])
scale = (scale * (1 - symmetries * 2)).astype(np.float32)
#######
# Noise
#######
noise = (np.random.randn(points.shape[0], points.shape[1]) * self.config.augment_noise).astype(np.float32)
##################
# Apply transforms
##################
# Do not use np.dot because it is multi-threaded
#augmented_points = np.dot(points, R) * scale + noise
augmented_points = np.sum(np.expand_dims(points, 2) * R, axis=1) * scale + noise
if normals is None:
return augmented_points, scale, R
else:
# Anisotropic scale of the normals thanks to cross product formula
normal_scale = scale[[1, 2, 0]] * scale[[2, 0, 1]]
augmented_normals = np.dot(normals, R) * normal_scale
# Renormalise
augmented_normals *= 1 / (np.linalg.norm(augmented_normals, axis=1, keepdims=True) + 1e-6)
if verbose:
test_p = [np.vstack([points, augmented_points])]
test_n = [np.vstack([normals, augmented_normals])]
test_l = [np.hstack([points[:, 2]*0, augmented_points[:, 2]*0+1])]
show_ModelNet_examples(test_p, test_n, test_l)
return augmented_points, augmented_normals, scale, R
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
def augmentation_transform(self, points, normals=None, verbose=False):
"""Implementation of an augmentation transform for point clouds."""
##########
# Rotation
##########
# Initialize rotation matrix
R = np.eye(points.shape[1])
if points.shape[1] == 3:
if self.config.augment_rotation == 'vertical':
# Create random rotations
theta = np.random.rand() * 2 * np.pi
c, s = np.cos(theta), np.sin(theta)
R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32)#只沿着z轴转
elif self.config.augment_rotation == 'all':
# Choose two random angles for the first vector in polar coordinates
theta = np.random.rand() * 2 * np.pi
phi = (np.random.rand() - 0.5) * np.pi
# Create the first vector in cartesian coordinates
u = np.array([np.cos(theta) * np.cos(phi), np.sin(theta) * np.cos(phi), np.sin(phi)])
# Choose a random rotation angle
alpha = np.random.rand() * 2 * np.pi
# Create the rotation matrix with this vector and angle
R = create_3D_rotations(np.reshape(u, (1, -1)), np.reshape(alpha, (1, -1)))[0]
R = R.astype(np.float32)
#######
# Scale
#######
# Choose random scales for each example
min_s = self.config.augment_scale_min
max_s = self.config.augment_scale_max
if self.config.augment_scale_anisotropic:
scale = np.random.rand(points.shape[1]) * (max_s - min_s) + min_s
else:
scale = np.random.rand() * (max_s - min_s) - min_s
# Add random symmetries to the scale factor
symmetries = np.array(self.config.augment_symmetries).astype(np.int32)#转化为[ 0 0 0 ]
symmetries *= np.random.randint(2, size=points.shape[1])#生成 points.shape[1] 个2以内的整数，不包括2
scale = (scale * (1 - symmetries * 2)).astype(np.float32)#尺度缩放带上对称处理
#######
# Noise
#######
noise = (np.random.randn(points.shape[0], points.shape[1]) * self.config.augment_noise).astype(np.float32)
##################
# Apply transforms
##################
# Do not use np.dot because it is multi-threaded
#augmented_points = np.dot(points, R) * scale + noise
# point.shape is [N,3,1] after expand_dim R.shape is [3,3] scale.shape is [1,3]
'''
