252 lines
9.7 KiB
Python
252 lines
9.7 KiB
Python
import numpy as np
|
|
|
|
def normalize_data(batch_data):
|
|
""" Normalize the batch data, use coordinates of the block centered at origin,
|
|
Input:
|
|
BxNxC array
|
|
Output:
|
|
BxNxC array
|
|
"""
|
|
B, N, C = batch_data.shape
|
|
normal_data = np.zeros((B, N, C))
|
|
for b in range(B):
|
|
pc = batch_data[b]
|
|
centroid = np.mean(pc, axis=0)
|
|
pc = pc - centroid
|
|
m = np.max(np.sqrt(np.sum(pc ** 2, axis=1)))
|
|
pc = pc / m
|
|
normal_data[b] = pc
|
|
return normal_data
|
|
|
|
|
|
def shuffle_data(data, labels):
|
|
""" Shuffle data and labels.
|
|
Input:
|
|
data: B,N,... numpy array
|
|
label: B,... numpy array
|
|
Return:
|
|
shuffled data, label and shuffle indices
|
|
"""
|
|
idx = np.arange(len(labels))
|
|
np.random.shuffle(idx)
|
|
return data[idx, ...], labels[idx], idx
|
|
|
|
def shuffle_points(batch_data):
|
|
""" Shuffle orders of points in each point cloud -- changes FPS behavior.
|
|
Use the same shuffling idx for the entire batch.
|
|
Input:
|
|
BxNxC array
|
|
Output:
|
|
BxNxC array
|
|
"""
|
|
idx = np.arange(batch_data.shape[1])
|
|
np.random.shuffle(idx)
|
|
return batch_data[:,idx,:]
|
|
|
|
def rotate_point_cloud(batch_data):
|
|
""" Randomly rotate the point clouds to augument the dataset
|
|
rotation is per shape based along up direction
|
|
Input:
|
|
BxNx3 array, original batch of point clouds
|
|
Return:
|
|
BxNx3 array, rotated batch of point clouds
|
|
"""
|
|
rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
|
|
for k in range(batch_data.shape[0]):
|
|
rotation_angle = np.random.uniform() * 2 * np.pi
|
|
cosval = np.cos(rotation_angle)
|
|
sinval = np.sin(rotation_angle)
|
|
rotation_matrix = np.array([[cosval, 0, sinval],
|
|
[0, 1, 0],
|
|
[-sinval, 0, cosval]])
|
|
shape_pc = batch_data[k, ...]
|
|
rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
|
|
return rotated_data
|
|
|
|
def rotate_point_cloud_z(batch_data):
|
|
""" Randomly rotate the point clouds to augument the dataset
|
|
rotation is per shape based along up direction
|
|
Input:
|
|
BxNx3 array, original batch of point clouds
|
|
Return:
|
|
BxNx3 array, rotated batch of point clouds
|
|
"""
|
|
rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
|
|
for k in range(batch_data.shape[0]):
|
|
rotation_angle = np.random.uniform() * 2 * np.pi
|
|
cosval = np.cos(rotation_angle)
|
|
sinval = np.sin(rotation_angle)
|
|
rotation_matrix = np.array([[cosval, sinval, 0],
|
|
[-sinval, cosval, 0],
|
|
[0, 0, 1]])
|
|
shape_pc = batch_data[k, ...]
|
|
rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
|
|
return rotated_data
|
|
|
|
def rotate_point_cloud_with_normal(batch_xyz_normal):
|
|
''' Randomly rotate XYZ, normal point cloud.
|
|
Input:
|
|
batch_xyz_normal: B,N,6, first three channels are XYZ, last 3 all normal
|
|
Output:
|
|
B,N,6, rotated XYZ, normal point cloud
|
|
'''
|
|
for k in range(batch_xyz_normal.shape[0]):
|
|
rotation_angle = np.random.uniform() * 2 * np.pi
|
|
cosval = np.cos(rotation_angle)
|
|
sinval = np.sin(rotation_angle)
|
|
rotation_matrix = np.array([[cosval, 0, sinval],
|
|
[0, 1, 0],
|
|
[-sinval, 0, cosval]])
|
|
shape_pc = batch_xyz_normal[k,:,0:3]
|
|
shape_normal = batch_xyz_normal[k,:,3:6]
|
|
batch_xyz_normal[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
|
|
batch_xyz_normal[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), rotation_matrix)
|
|
return batch_xyz_normal
|
|
|
|
def rotate_perturbation_point_cloud_with_normal(batch_data, angle_sigma=0.06, angle_clip=0.18):
|
|
""" Randomly perturb the point clouds by small rotations
|
|
Input:
|
|
BxNx6 array, original batch of point clouds and point normals
|
|
Return:
|
|
BxNx3 array, rotated batch of point clouds
|
|
"""
|
|
rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
|
|
for k in range(batch_data.shape[0]):
|
|
angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip)
|
|
Rx = np.array([[1,0,0],
|
|
[0,np.cos(angles[0]),-np.sin(angles[0])],
|
|
[0,np.sin(angles[0]),np.cos(angles[0])]])
|
|
Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])],
|
|
[0,1,0],
|
|
[-np.sin(angles[1]),0,np.cos(angles[1])]])
|
|
Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0],
|
|
[np.sin(angles[2]),np.cos(angles[2]),0],
|
|
[0,0,1]])
|
|
R = np.dot(Rz, np.dot(Ry,Rx))
|
|
shape_pc = batch_data[k,:,0:3]
|
|
shape_normal = batch_data[k,:,3:6]
|
|
rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), R)
|
|
rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), R)
|
|
return rotated_data
|
|
|
|
|
|
def rotate_point_cloud_by_angle(batch_data, rotation_angle):
|
|
""" Rotate the point cloud along up direction with certain angle.
|
|
Input:
|
|
BxNx3 array, original batch of point clouds
|
|
Return:
|
|
BxNx3 array, rotated batch of point clouds
|
|
"""
|
|
rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
|
|
for k in range(batch_data.shape[0]):
|
|
#rotation_angle = np.random.uniform() * 2 * np.pi
|
|
cosval = np.cos(rotation_angle)
|
|
sinval = np.sin(rotation_angle)
|
|
rotation_matrix = np.array([[cosval, 0, sinval],
|
|
[0, 1, 0],
|
|
[-sinval, 0, cosval]])
|
|
shape_pc = batch_data[k,:,0:3]
|
|
rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
|
|
return rotated_data
|
|
|
|
def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle):
|
|
""" Rotate the point cloud along up direction with certain angle.
|
|
Input:
|
|
BxNx6 array, original batch of point clouds with normal
|
|
scalar, angle of rotation
|
|
Return:
|
|
BxNx6 array, rotated batch of point clouds iwth normal
|
|
"""
|
|
rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
|
|
for k in range(batch_data.shape[0]):
|
|
#rotation_angle = np.random.uniform() * 2 * np.pi
|
|
cosval = np.cos(rotation_angle)
|
|
sinval = np.sin(rotation_angle)
|
|
rotation_matrix = np.array([[cosval, 0, sinval],
|
|
[0, 1, 0],
|
|
[-sinval, 0, cosval]])
|
|
shape_pc = batch_data[k,:,0:3]
|
|
shape_normal = batch_data[k,:,3:6]
|
|
rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
|
|
rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1,3)), rotation_matrix)
|
|
return rotated_data
|
|
|
|
|
|
|
|
def rotate_perturbation_point_cloud(batch_data, angle_sigma=0.06, angle_clip=0.18):
|
|
""" Randomly perturb the point clouds by small rotations
|
|
Input:
|
|
BxNx3 array, original batch of point clouds
|
|
Return:
|
|
BxNx3 array, rotated batch of point clouds
|
|
"""
|
|
rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
|
|
for k in range(batch_data.shape[0]):
|
|
angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip)
|
|
Rx = np.array([[1,0,0],
|
|
[0,np.cos(angles[0]),-np.sin(angles[0])],
|
|
[0,np.sin(angles[0]),np.cos(angles[0])]])
|
|
Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])],
|
|
[0,1,0],
|
|
[-np.sin(angles[1]),0,np.cos(angles[1])]])
|
|
Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0],
|
|
[np.sin(angles[2]),np.cos(angles[2]),0],
|
|
[0,0,1]])
|
|
R = np.dot(Rz, np.dot(Ry,Rx))
|
|
shape_pc = batch_data[k, ...]
|
|
rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), R)
|
|
return rotated_data
|
|
|
|
|
|
def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05):
|
|
""" Randomly jitter points. jittering is per point.
|
|
Input:
|
|
BxNx3 array, original batch of point clouds
|
|
Return:
|
|
BxNx3 array, jittered batch of point clouds
|
|
"""
|
|
B, N, C = batch_data.shape
|
|
assert(clip > 0)
|
|
jittered_data = np.clip(sigma * np.random.randn(B, N, C), -1*clip, clip)
|
|
jittered_data += batch_data
|
|
return jittered_data
|
|
|
|
def shift_point_cloud(batch_data, shift_range=0.1):
|
|
""" Randomly shift point cloud. Shift is per point cloud.
|
|
Input:
|
|
BxNx3 array, original batch of point clouds
|
|
Return:
|
|
BxNx3 array, shifted batch of point clouds
|
|
"""
|
|
B, N, C = batch_data.shape
|
|
shifts = np.random.uniform(-shift_range, shift_range, (B,3))
|
|
for batch_index in range(B):
|
|
batch_data[batch_index,:,:] += shifts[batch_index,:]
|
|
return batch_data
|
|
|
|
|
|
def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25):
|
|
""" Randomly scale the point cloud. Scale is per point cloud.
|
|
Input:
|
|
BxNx3 array, original batch of point clouds
|
|
Return:
|
|
BxNx3 array, scaled batch of point clouds
|
|
"""
|
|
B, N, C = batch_data.shape
|
|
scales = np.random.uniform(scale_low, scale_high, B)
|
|
for batch_index in range(B):
|
|
batch_data[batch_index,:,:] *= scales[batch_index]
|
|
return batch_data
|
|
|
|
def random_point_dropout(batch_pc, max_dropout_ratio=0.875):
|
|
''' batch_pc: BxNx3 '''
|
|
for b in range(batch_pc.shape[0]):
|
|
dropout_ratio = np.random.random()*max_dropout_ratio # 0~0.875
|
|
drop_idx = np.where(np.random.random((batch_pc.shape[1]))<=dropout_ratio)[0]
|
|
if len(drop_idx)>0:
|
|
batch_pc[b,drop_idx,:] = batch_pc[b,0,:] # set to the first point
|
|
return batch_pc
|
|
|
|
|
|
|