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DataTransformer.py
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DataTransformer.py
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import torch
import torchvision.transforms as transforms
import configparser
from cfgParser import *
import numpy as np
import random
from scipy.spatial.transform import Rotation as R
# this data is taken from the file "calib_velo_to_cam.txt"
def load_velo_to_cam_transform():
R_velo_to_cam = np.array([[7.533745e-03, -9.999714e-01, -6.166020e-04], [1.480249e-02, 7.280733e-04, -9.998902e-01], [9.998621e-01, 7.523790e-03, 1.480755e-02]])
trans_velo_to_cam = np.array([-4.069766e-03, -7.631618e-02, -2.717806e-01])
T_velo_to_cam = np.identity(4)
T_velo_to_cam[:3,:3] = R_velo_to_cam
T_velo_to_cam[:3,3] = trans_velo_to_cam
return T_velo_to_cam
# angle in degrees, axis is either x,y or z
def rotation_matrix(angle, axis):
r=None
if axis == "x":
r = R.from_euler('X', angle, degrees=True).as_matrix()
elif axis == "y":
r = R.from_euler('Y', angle, degrees=True).as_matrix()
elif axis == "z":
r = R.from_euler('Z', angle, degrees=True).as_matrix()
else:
print("Axis has to be either x,y or z")
T = np.identity(4)
T[:3,:3] = r
return T
# subsamples the point cloud a certain nr of times by randomly dropping points. If percentage_removal is 1 then we remove all the points, if it's 0 then we keep all points
def random_subsample(cloud, reflectance = None, label = None, percentage_removal = 0.0):
prob_of_death=1.0-percentage_removal
vertices_marked_for_removal=0
is_vertex_to_be_removed = np.zeros((cloud.shape[0]), dtype = np.int) #(V.rows(), false);
for i in range(0, cloud.shape[0]): #)for(int i = 0; i < V.rows(); i++){
rand = random.uniform(0, 1)
if(rand < prob_of_death):
is_vertex_to_be_removed[i] = 1
vertices_marked_for_removal += 1
if reflectance is not None and label is not None:
return cloud[is_vertex_to_be_removed == 1], reflectance[is_vertex_to_be_removed == 1], label[is_vertex_to_be_removed == 1]
elif reflectance is not None:
return cloud[is_vertex_to_be_removed == 1], reflectance[is_vertex_to_be_removed == 1]
elif label is not None:
return cloud[is_vertex_to_be_removed == 1], label[is_vertex_to_be_removed == 1]
else:
return cloud[is_vertex_to_be_removed == 1]
# This class is used for data augmentation
class DataTransformer():
def __init__(self, config_parser, split = "train"):
transformer_config = config_parser.get_transformer_vars()
# train_config = config_parser.get_train_vars()
self.m_random_translation_xyz_magnitude=transformer_config["random_translation_xyz_magnitude"]
self.m_random_translation_xz_magnitude=transformer_config["random_translation_xz_magnitude"]
self.m_rotation_y_max_angle=transformer_config["rotation_y_max_angle"]
self.m_random_stretch_xyz_magnitude=transformer_config["random_stretch_xyz_magnitude"]
self.m_adaptive_subsampling_falloff_start=transformer_config["adaptive_subsampling_falloff_start"]
self.m_adaptive_subsampling_falloff_end=transformer_config["adaptive_subsampling_falloff_end"]
self.m_random_subsample_percentage=transformer_config["random_subsample_percentage"]
self.m_random_mirror_x=transformer_config["random_mirror_x"]
self.m_random_mirror_z=transformer_config["random_mirror_z"]
self.m_random_rotation_90_degrees_y=transformer_config["random_rotation_90_degrees_y"]
self.m_hsv_jitter=transformer_config["hsv_jitter"]
self.m_chance_of_xyz_noise = transformer_config["chance_of_xyz_noise"]
self.m_xyz_noise_stddev=transformer_config["xyz_noise_stddev"]
self.split = split
# transforms the points in the cloud randomly (data augmentation)
# Input: scan_seq
# each seperate array has shape [i,3]
# Outputs: Augmented clouds as pytorch tensors
def transform(self, clouds):
# only do transformation for the training examples
if self.split != "train":
for i in range(0,len(clouds)):
clouds[i] = torch.tensor(clouds[i], dtype = torch.float)
return clouds
if(self.m_adaptive_subsampling_falloff_end!=0.0):
assert self.m_adaptive_subsampling_falloff_start<self.m_adaptive_subsampling_falloff_end , str(" The falloff for the adaptive subsampling start should be lower than the end. For example we start at 0 meters and we end at 60m. The start is " + self.m_adaptive_subsampling_falloff_start + " and the end is " + self.m_adaptive_subsampling_falloff_end)
pass
if(self.m_random_subsample_percentage!=0.0):
for i in range(0, len(clouds)):
subsample_mask = np.random.choice(a = [False, True], size = (clouds[i].shape[0]), p = [self.m_random_subsample_percentage, 1-self.m_random_subsample_percentage])
clouds[i] = clouds[i][subsample_mask]
if(self.m_random_translation_xyz_magnitude!=0.0):
translation = np.random.rand(3)* self.m_random_translation_xyz_magnitude
for i in range(0, len(clouds)):
clouds[i][:] = clouds[i][:] + translation
if(self.m_random_translation_xz_magnitude!=0.0):
translation = np.random.rand(3)* self.m_random_translation_xz_magnitude
translation[1] = 0
for i in range(0, len(clouds)):
clouds[i][:] = clouds[i][:] + translation
if(self.m_random_stretch_xyz_magnitude!=0.0):
s = stretch_factor_x = 1.0 + random.uniform(-self.m_random_stretch_xyz_magnitude, self.m_random_stretch_xyz_magnitude)
stretch_factor_x = 1.0 + random.uniform(-s, s)
stretch_factor_y = 1.0 + random.uniform(-s, s)
stretch_factor_z = 1.0 + random.uniform(-s, s)
for i in range(0, len(clouds)):
clouds[i][:,0] *= stretch_factor_x
clouds[i][:,1] *= stretch_factor_y
clouds[i][:,2] *= stretch_factor_z
if(self.m_rotation_y_max_angle!=0):
rand_angle_degrees = random.uniform(-self.m_rotation_y_max_angle/2.0, self.m_rotation_y_max_angle/2.0)
r = R.from_euler('Y', rand_angle_degrees, degrees=True).as_matrix()
for i in range(0, len(clouds)):
clouds[i] = (r @ clouds[i].transpose()).transpose()
if(self.m_random_mirror_x):
do_flip = random.random() < 0.5
if do_flip:
for i in range(0, len(clouds)):
clouds[i][:,0] = - clouds[i][:,0]
if(self.m_random_mirror_z):
do_flip = random.random() < 0.5
if do_flip:
for i in range(0, len(clouds)):
clouds[i][:,2] = - clouds[i][:,2]
if(self.m_random_rotation_90_degrees_y):
nr_times = random.randint(0,3)
r = R.from_euler('Y', 90*nr_times, degrees=True).as_matrix()
for i in range(0, len(clouds)):
clouds[i] = (r @ clouds[i].transpose()).transpose()
if (self.m_hsv_jitter==0):
pass
do_xyz_noise = random.random() < self.m_chance_of_xyz_noise
if(do_xyz_noise):
if (self.m_xyz_noise_stddev != 0):
pass
for i in range(0,len(clouds)):
clouds[i] = torch.tensor(clouds[i], dtype = torch.float)
return clouds
if __name__ == "__main__":
config_file="/workspace/schuett_temporal_lattice/config/lnn_train_semantic_kitti.cfg"
config_parser = cfgParser(config_file)
dt = DataTransformer(config_parser)
clouds = []
cloud1 = np.array([[1,2,3], [1,2,3]])
cloud2 = np.array([[3,2,3], [3,2,3]])
clouds.append(cloud1)
clouds.append(cloud2)
print(cloud1)
clouds = dt.transform(clouds)