import os
import pdb
# import csv
import numpy as np
import cv2
import torch
import pandas as pd
from tqdm import tqdm
import scipy.io
from spconv.pytorch.utils import PointToVoxel
from dv import AedatFile
import numpy as np
import multiprocessing
os.environ[“CUDA_VISIBLE_DEVICES”] = “0”

def transform_points_to_voxels(data_dict={}, voxel_generator=None, device=torch.device(“cuda:0”)):
“”“
将点云转换为voxel,调用spconv的VoxelGeneratorV2
“””
points = data_dict[‘points’]
# 将points打乱
shuffle_idx = np.random.permutation(points.shape[0])
points = points[shuffle_idx]
data_dict[‘points’] = points

# 使用spconv生成voxel输出
points = torch.as_tensor(data_dict[‘points’]).to(device)
voxel_output = voxel_generator(points)

# 假设一份点云数据是N*4，那么经过pillar生成后会得到三份数据
# voxels代表了每个生成的voxel数据，维度是[M, 5, 4]
# coordinates代表了每个生成的voxel所在的zyx轴坐标，维度是[M,3]
# num_points代表了每个生成的voxel中有多少个有效的点维度是[m,]，因为不满5会被0填充
voxels, coordinates, num_points = voxel_output
voxels = voxels.to(device)
coordinates = coordinates.to(device)
num_points = num_points.to(device)
# 选event数量在前5000的voxel 8000 from(4k+,6k+)
# print(torch.where(num_points>=16)[0].shape)
if num_points.shape[0] < save_voxel:
features = voxels[:, :, 3]
coor = coordinates[:, :]
else:
_, voxels_idx = torch.topk(num_points, save_voxel)
# 将每个voxel的1024个p拼接作为voxel初始特征 16
features = voxels[voxels_idx][:, :, 3]
# 前5000个voxel的三维坐标
coor = coordinates[voxels_idx]
# 将y.x.t改为t,x,y
coor[:, [0, 1, 2]] = coor[:, [2, 1, 0]]

return coor, features


if name == ‘main’:
save_voxel = 5000
use_mode = ‘frame_exposure_time’
device = torch.device(“cuda:6”)
# data_path = r"/home/ioe/xxxxx/EventTracking/COESOT/train"
data_path = r"/data2/local_userdata/heyuting/datasets/COESOT/train"
save_path = r"/data2/local_userdata/heyuting/datasets/COESOT/train"
# save_path = r"/home/ioe/xxxxx/COESOT/train"
video_files = os.listdir(data_path)
dvs_img_interval = 1
voxel_generator = PointToVoxel(
# 给定每个voxel的长宽高 [0.05, 0.05, 0.1]
vsize_xyz=[50, 10, 10], # [0.2, 0.25, 0.16] # [50, 10, 10] [50, 35, 26] 因此坐标范围（20,20,20） (20, 34/35, 26)
# 给定点云的范围 [ 0. -40. -3. 70.4 40. 1. ]
coors_range_xyz=[0, 0, 0, 1000, 345, 259],
# 给定每个点云的特征维度，这里是x，y，z，r 其中r是激光雷达反射强度 # 346x260 t,x,y
num_point_features=4,
# 最多选取多少个voxel，训练16000，推理40000
max_num_voxels=16000, # 16000
# 给定每个pillar中有采样多少个点，不够则补0 因此我将neg voxel改为-1;
max_num_points_per_voxel=16, # 1024
device=device
)

for videoID in range(len(video_files)):
foldName = video_files[videoID]
print("==>> foldName: “, foldName)
# if foldName in [‘dvSave-2022_01_26_20_01_46’, ‘dvSave-2022_03_21_11_09_50’,
# ‘dvSave-2022_03_21_16_11_40’, ‘dvSave-2022_03_21_11_12_27’,
# ‘dvSave-2022_03_21_11_19_37’, ‘dvSave-2022_02_25_13_21_41’,
# ‘dvSave-2022_02_25_13_31_18’, ‘dvSave-2022_03_21_09_05_49’]:
# print(”==>> pass it: “, foldName)
# continue
if foldName not in [‘dvSave-2022_03_21_11_22_27’]:
print(”==>> pass it: “, foldName)
continue

# if ‘dvSave-2022_03_21’ not in foldName:
# continue
fileLIST = os.listdir(os.path.join(data_path, foldName))
if not os.path.exists(os.path.join(save_path, foldName, foldName+‘_voxel2’)):
os.mkdir(os.path.join(save_path, foldName, foldName+‘_voxel2’))
else:
continue
mat_save = os.path.join(save_path, foldName, foldName+‘_voxel2/’)

# for FileID in tqdm(range(len(fileLIST))):
# videoName = fileLIST[FileID]
# (filename, extension) = os.path.splitext(videoName)
# if (extension == ‘.aedat4’) : # and (‘dvSave-2022_03_21’ in foldName):
# # if os.path.exists(mat_save + ‘{}.mat’.format(filename)):
# # print(”==>> Skip this video … ")
# # continue
aedat4_file = foldName + ‘.aedat4’
# print(‘filename:’, aedat4_file)
read_path = os.path.join(data_path, foldName, aedat4_file)

# read aeda4;
frame_all = []
frame_exposure_time = []
frame_interval_time = []
with AedatFile(read_path) as f:
# print(f.names)
for frame in f[‘frames’]:
frame_all.append(frame.image)
frame_exposure_time.append([frame.timestamp_start_of_exposure,
frame.timestamp_end_of_exposure]) ## [1607928583397102, 1607928583401102]
frame_interval_time.append([frame.timestamp_start_of_frame,
frame.timestamp_end_of_frame]) ## [1607928583387944, 1607928583410285]
if use_mode == ‘frame_exposure_time’:
frame_timestamp = frame_exposure_time
elif use_mode == ‘frame_interval_time’:
frame_timestamp = frame_interval_time
frame_num = len(frame_timestamp)

events = np.hstack([packet for packet in f[‘events’].numpy()])

t_all = torch.tensor(events[‘timestamp’]).unsqueeze(1).to(device)
x_all = torch.tensor(events[‘x’]).unsqueeze(1).to(device)
y_all = torch.tensor(events[‘y’]).unsqueeze(1).to(device)
p_all = torch.tensor(events[‘polarity’]).unsqueeze(1).to(device)

for frame_no in range(0, int(frame_num / dvs_img_interval) - 1):
start_idx = np.where(events[‘timestamp’] >= frame_timestamp[frame_no][0])[0][0]
end_idx = np.where(events[‘timestamp’] >= frame_timestamp[frame_no][1])[0][0]
sub_event = events[start_idx:end_idx]
idx_length = end_idx - start_idx

t = t_all[start_idx: end_idx]
if start_idx == end_idx:
time_length = 0
t = ((t-t).float() / time_length) * 1000
scipy.io.savemat(mat_save + ‘frame{:0>4d}.mat’.format(frame_no), mdict={‘coor’: np.zeros([100, 3]),
‘features’: np.zeros([100, 16])}) # coor: numpy.nan; features: numpy.nan
print('empty event frame ', frame_no)
continue
else:
time_length = t[-1] - t[0]
# rescale the timestampes to start from 0 up to 1000
t = ((t-t[0]).float() / time_length) * 1000
all_idx = np.where(sub_event[‘polarity’] != -1) # all event
neg_idx = np.where(sub_event[‘polarity’] == 0) # neg event
t = t[all_idx]
x = x_all[all_idx]
y = y_all[all_idx]
p = p_all[all_idx]
p[neg_idx] = -1 # negtive voxel change from 0 to -1. because after append 0 operation.
current_events = torch.cat((t, x, y, p), dim=1)
# if current_events.shape[0] < 10: # remove it
# continue
data_dict = {‘points’: current_events}

coor, features = transform_points_to_voxels(data_dict=data_dict, voxel_generator=voxel_generator,
device=device)
# pdb.set_trace()
coor = coor.cpu().numpy()
features = features.cpu().numpy()
# print(‘coor’, coor)
scipy.io.savemat(mat_save + ‘frame{:0>4d}.mat’.format(frame_no), mdict={‘coor’: coor, ‘features’: features}) # coor: Nx(t,x,y); features:Nx32 or Nx10024中cuda是6号，但是报错：Traceback (most recent call last):
File “event_to_voxel_coesot.py”, line 76, in <module>
device=device
File “/home/heyuting/anaconda3/envs/ceu/lib/python3.7/site-packages/spconv/pytorch/utils.py”, line 49, in init
device=device)
RuntimeError: CUDA error: invalid device ordinal
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.，当我们设置为device = torch.device(“cuda:0”)正常执行，说明原因。 中文回答。