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kitti_dataset.py
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kitti_dataset.py
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'''
Class of dataset
'''
import math
import os
import os.path
import time
import numpy as np
class OdometryDataset():
def __init__(self, root_path = '../', npoints=8192, is_training=True, random_seed = 3):
self.random_seed = random_seed
self.npoints = npoints
self.is_training = is_training
self.datapath = root_path
self.len_list = [0, 4541, 5642, 10303, 11104, 11375, 14136, 15237, 16338, 20409, 22000, 23201]
self.file_map = ['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10']
self.T_trans = np.array([[0, 0, 1, 0],
[-1, 0, 0, 0],
[0, -1, 0, 0],
[0, 0, 0, 1]])
def __getitem__(self, index):#
if not self.is_training:
np.random.seed(self.random_seed)
for seq_idx, seq_num in enumerate(self.len_list):
if index < seq_num:
cur_seq = seq_idx - 1
cur_idx_pc2 = index - self.len_list[seq_idx-1]
if cur_idx_pc2 == 0:
cur_idx_pc1 = 0
else:
cur_idx_pc1 = cur_idx_pc2 - 1 ############### 1 frame gap ###############
break
Tr_path = os.path.join( 'data_odometry_calib/dataset/sequences', str(cur_seq).zfill(2), 'calib.txt')
Tr_data = self.read_calib_file(Tr_path)
Tr_data = Tr_data['Tr']
Tr = Tr_data.reshape(3,4)
Tr = np.vstack((Tr, np.array([0, 0, 0, 1.0])))
cur_lidar_dir = os.path.join(self.datapath, self.file_map[cur_seq])
pc1_bin = os.path.join(cur_lidar_dir, 'velodyne/' + str(cur_idx_pc1).zfill(6) + '.bin')
pc2_bin = os.path.join(cur_lidar_dir, 'velodyne/' + str(cur_idx_pc2).zfill(6) + '.bin')
pose = np.load('ground_truth_pose/kitti_T_diff/' + self.file_map[cur_seq] + '_diff.npy')
point1 = np.fromfile(pc1_bin, dtype=np.float32).reshape(-1, 4)
point2 = np.fromfile(pc2_bin, dtype=np.float32).reshape(-1, 4)
if point1.shape[0] < point2.shape[0]:
n = point1.shape[0]
else:
n = point2.shape[0] # num points of pc1 and pc2
pos1 = point1[:n, :3]
pos2 = point2[:n, :3]
add = np.ones((n, 1))
pos1 = np.concatenate([pos1, add], axis = -1)
pos2 = np.concatenate([pos2, add], axis = -1)
pos1 = np.matmul(Tr, pos1.T)
pos2 = np.matmul(Tr, pos2.T)
pos1 = pos1.T[ :, :3]
pos2 = pos2.T[ :, :3]
T_diff = pose[cur_idx_pc2 : cur_idx_pc2 + 1, :]
T_diff = T_diff.reshape(3, 4)
filler = np.array([0.0, 0.0, 0.0, 1.0])
filler = np.expand_dims(filler, axis = 0) ##1*4
T_diff = np.concatenate([T_diff, filler], axis=0)# 4*4
is_ground = np.logical_or(pos1[:,1] > 1.1, pos1[:,1] > 1.1)
not_ground = np.logical_not(is_ground)
near_mask_x = np.logical_and(pos1[:, 0] < 30, pos1[:, 0] > -30)
near_mask_y = np.logical_and(pos1[:, 2] < 30, pos1[:, 2] > -30)
near_mask = np.logical_and(near_mask_x, near_mask_y)
near_mask = np.logical_and(not_ground, near_mask)
indices_1 = np.where(near_mask)[0]
is_ground = np.logical_or(pos2[:,1] > 1.1, pos2[:,1] > 1.1)
not_ground = np.logical_not(is_ground)
near_mask_x = np.logical_and(pos2[:, 0] < 30, pos2[:, 0] > -30)
near_mask_y = np.logical_and(pos2[:, 2] < 30, pos2[:, 2] > -30)
near_mask = np.logical_and(near_mask_x, near_mask_y)
near_mask = np.logical_and(not_ground, near_mask)
indices_2 = np.where(near_mask)[0]
if len(indices_1) >= self.npoints:
sample_idx1 = np.random.choice(indices_1, self.npoints, replace=False)
else:
sample_idx1 = np.concatenate((indices_1, np.random.choice(indices_1, self.npoints - len(indices_1), replace=True)), axis=-1)
if len(indices_2) >= self.npoints:
sample_idx2 = np.random.choice(indices_2, self.npoints, replace=False)
else:
sample_idx2 = np.concatenate((indices_2, np.random.choice(indices_2, self.npoints - len(indices_2), replace=True)), axis=-1)
pos1 = pos1[sample_idx1, :]
pos2 = pos2[sample_idx2, :]
n = pos1.shape[0]
if self.is_training == True: ### augment
anglex = np.clip(0.01* np.random.randn(),-0.02, 0.02).astype(np.float32)* np.pi / 4.0
angley = np.clip(0.05* np.random.randn(),-0.1, 0.1).astype(np.float32)* np.pi / 4.0
anglez = np.clip(0.01* np.random.randn(),-0.02, 0.02).astype(np.float32)* np.pi / 4.0
cosx = np.cos(anglex)
cosy = np.cos(angley)
cosz = np.cos(anglez)
sinx = np.sin(anglex)
siny = np.sin(angley)
sinz = np.sin(anglez)
Rx = np.array([[1, 0, 0],
[0, cosx, -sinx],
[0, sinx, cosx]])
Ry = np.array([[cosy, 0, siny],
[0, 1, 0],
[-siny, 0, cosy]])
Rz = np.array([[cosz, -sinz, 0],
[sinz, cosz, 0],
[0, 0, 1]])
R_trans = Rx.dot(Ry).dot(Rz)
xx = np.clip(0.1* np.random.randn(),-0.2, 0.2).astype(np.float32)
yy = np.clip(0.05* np.random.randn(),-0.15, 0.15).astype(np.float32)
zz = np.clip(0.5* np.random.randn(),-1, 1).astype(np.float32)
add_3 = np.array([[xx], [yy], [zz]])
T_trans = np.concatenate([R_trans, add_3], axis = -1)
filler = np.array([0.0, 0.0, 0.0, 1.0])
filler = np.expand_dims(filler, axis = 0) ##1*4
T_trans = np.concatenate([T_trans, filler], axis=0)# 4*4
add_T3 = np.ones((n, 1))
pos2_trans = np.concatenate([pos2, add_T3], axis = -1)
pos2_trans = np.matmul(T_trans, pos2_trans.T)
pos2 = pos2_trans.T[ :, :3]
add_T = np.ones((n, 1))
pos2_gt = np.concatenate([pos2, add_T], axis = -1)
pos2_gt = np.matmul(T_diff, pos2_gt.T)
pos2_gt = pos2_gt.T[ :, :3]
T_gt = np.matmul(T_diff, np.linalg.inv(T_trans))
else:
add_T = np.ones((n, 1))
pos2_gt = np.concatenate([pos2, add_T], axis = -1)
pos2_gt = np.matmul(T_diff, pos2_gt.T)
pos2_gt = pos2_gt.T[ :, :3]
T_gt = T_diff
R_gt = T_gt[:3, :3]
t_gt = T_gt[:3, 3:]
z_gt, y_gt, x_gt = self.mat2euler( M = R_gt)
q_gt = self.euler2quat(z = z_gt, y = y_gt, x = x_gt)
return pos2, pos1, q_gt, t_gt
def __len__(self):
return len(self.datapath)
def feed_random(self, random_seed):
self.random_seed = random_seed
return 0
def read_calib_file(self,path): # changed
float_chars = set("0123456789.e+- ")
data = {}
with open(path, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
value = value.strip()
data[key] = value
if float_chars.issuperset(value):
# try to cast to float array
try:
data[key] = np.array(list(map(float, value.split(' '))))
except ValueError:
# casting error: data[key] already eq. value, so pass
pass
return data
def euler2mat(self, anglex, angley, anglez):
cosx = np.cos(anglex)
cosy = np.cos(angley)
cosz = np.cos(anglez)
sinx = np.sin(anglex)
siny = np.sin(angley)
sinz = np.sin(anglez)
Rx = np.array([[1, 0, 0],
[0, cosx, -sinx],
[0, sinx, cosx]])
Ry = np.array([[cosy, 0, siny],
[0, 1, 0],
[-siny, 0, cosy]])
Rz = np.array([[cosz, -sinz, 0],
[sinz, cosz, 0],
[0, 0, 1]])
R_trans = Rx.dot(Ry).dot(Rz)
return R_trans
def mat2euler(self, M, cy_thresh=None, seq='zyx'):
M = np.asarray(M)
if cy_thresh is None:
cy_thresh = np.finfo(M.dtype).eps * 4
r11, r12, r13, r21, r22, r23, r31, r32, r33 = M.flat
# cy: sqrt((cos(y)*cos(z))**2 + (cos(x)*cos(y))**2)
cy = math.sqrt(r33*r33 + r23*r23)
if seq=='zyx':
if cy > cy_thresh: # cos(y) not close to zero, standard form
z = math.atan2(-r12, r11) # atan2(cos(y)*sin(z), cos(y)*cos(z))
y = math.atan2(r13, cy) # atan2(sin(y), cy)
x = math.atan2(-r23, r33) # atan2(cos(y)*sin(x), cos(x)*cos(y))
else: # cos(y) (close to) zero, so x -> 0.0 (see above)
# so r21 -> sin(z), r22 -> cos(z) and
z = math.atan2(r21, r22)
y = math.atan2(r13, cy) # atan2(sin(y), cy)
x = 0.0
elif seq=='xyz':
if cy > cy_thresh:
y = math.atan2(-r31, cy)
x = math.atan2(r32, r33)
z = math.atan2(r21, r11)
else:
z = 0.0
if r31 < 0:
y = np.pi/2
x = math.atan2(r12, r13)
else:
y = -np.pi/2
else:
raise Exception('Sequence not recognized')
return z, y, x
def euler2quat(self, z=0, y=0, x=0, isRadian=True):
''' Return quaternion corresponding to these Euler angles
Uses the z, then y, then x convention above
Parameters
----------
z : scalar
Rotation angle in radians around z-axis (performed first)
y : scalar
Rotation angle in radians around y-axis
x : scalar
Rotation angle in radians around x-axis (performed last)
Returns
-------
quat : array shape (4,)
Quaternion in w, x, y z (real, then vector) format
Notes
-----
We can derive this formula in Sympy using:
1. Formula giving quaternion corresponding to rotation of theta radians
about arbitrary axis:
http://mathworld.wolfram.com/EulerParameters.html
2. Generated formulae from 1.) for quaternions corresponding to
theta radians rotations about ``x, y, z`` axes
3. Apply quaternion multiplication formula -
http://en.wikipedia.org/wiki/Quaternions#Hamilton_product - to
formulae from 2.) to give formula for combined rotations.
'''
if not isRadian:
z = ((np.pi)/180.) * z
y = ((np.pi)/180.) * y
x = ((np.pi)/180.) * x
z = z/2.0
y = y/2.0
x = x/2.0
cz = math.cos(z)
sz = math.sin(z)
cy = math.cos(y)
sy = math.sin(y)
cx = math.cos(x)
sx = math.sin(x)
return np.array([
cx*cy*cz - sx*sy*sz,
cx*sy*sz + cy*cz*sx,
cx*cz*sy - sx*cy*sz,
cx*cy*sz + sx*cz*sy])