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data_prepare.py
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data_prepare.py
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"""Visualization code for point clouds and 3D bounding boxes with mayavi.
Modified by Charles R. Qi
Date: September 2017
"""
import argparse
import os
import numpy as np
import torch
from torchsparse import SparseTensor
# from torchsparse.utils.quantize import sparse_quantize
from torchsparse.utils.quantize import sparse_quantize
from model_zoo import minkunet, spvcnn, spvnas_specialized
import open3d as o3d
cpu_num = 2 # cpu core
os.environ ['OMP_NUM_THREADS'] = str(cpu_num)
os.environ ['OPENBLAS_NUM_THREADS'] = str(cpu_num)
os.environ ['MKL_NUM_THREADS'] = str(cpu_num)
os.environ ['VECLIB_MAXIMUM_THREADS'] = str(cpu_num)
os.environ ['NUMEXPR_NUM_THREADS'] = str(cpu_num)
torch.set_num_threads(cpu_num)
def process_point_cloud(input_point_cloud, input_labels=None, voxel_size=0.15):
input_point_cloud[:, 3] = input_point_cloud[:, 3]
pc_ = np.round(input_point_cloud[:, :3] / voxel_size)
pc_ -= pc_.min(0, keepdims=1)
label_map = create_label_map()
if input_labels is not None:
labels_ = label_map[input_labels].astype(
np.int64) # semantic labels
else:
labels_ = np.zeros(pc_.shape[0], dtype=np.int64)
feat_ = input_point_cloud
if input_labels is not None:
out_pc = input_point_cloud[labels_ != labels_.max(), :3]
pc_ = pc_[labels_ != labels_.max()]
feat_ = feat_[labels_ != labels_.max()]
labels_ = labels_[labels_ != labels_.max()]
else:
out_pc = input_point_cloud
pc_ = pc_
coords_, inds, inverse_map = sparse_quantize(pc_,
return_index=True,
return_inverse=True)
pc = np.zeros((inds.shape[0], 4))
pc[:, :3] = pc_[inds]
feat = feat_[inds]
labels = labels_[inds]
lidar = SparseTensor(
torch.from_numpy(feat).float(),
torch.from_numpy(pc).int())
return {
'pc': out_pc,
'lidar': lidar,
'targets': labels,
'targets_mapped': labels_,
'inverse_map': inverse_map
}
def create_label_map(num_classes=3):
name_label_mapping = {
# 不需要的类
'unlabeled': 0,
'outlier': 1,
'other-ground': 49,
'other-structure': 52,
# ground
'ground': 10,
# plane
'plane': 20,
# other
'other': 30
}
# for k in name_label_mapping:
# name_label_mapping[k] = name_label_mapping[k.replace('moving-', '')]
train_label_name_mapping = {
0: 'ground',
1: 'plane',
2: 'other',
}
label_map = np.zeros(260) + num_classes
for i in range(num_classes):
cls_name = train_label_name_mapping[i]
# print(cls_name)
label_map[name_label_mapping[cls_name]] = min(num_classes, i)
return label_map.astype(np.int64)
cmap = np.array([
[245, 150, 100, 255],
[245, 230, 100, 255],
[150, 60, 30, 255],
[180, 30, 80, 255],
[255, 0, 0, 255],
[30, 30, 255, 255],
[200, 40, 255, 255],
[90, 30, 150, 255],
[255, 0, 255, 255],
[255, 150, 255, 255],
[75, 0, 75, 255],
[75, 0, 175, 255],
[0, 200, 255, 255],
[50, 120, 255, 255],
[0, 175, 0, 255],
[0, 60, 135, 255],
[80, 240, 150, 255],
[150, 240, 255, 255],
[0, 0, 255, 255],
])
cmap = cmap[:, [2, 1, 0, 3]] # convert bgra to rgba
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# 数据集位置
parser.add_argument('--velodyne-dir', type=str, default='Oxford')
parser.add_argument('--model',
type=str,
default='SemanticKITTI_val_SPVCNN@119GMACs')
args = parser.parse_args()
output_dir = os.path.join(args.velodyne_dir, 'outputs')
os.makedirs(output_dir, exist_ok=True)
if torch.cuda.is_available():
device = 'cuda:0'
else:
device = 'cpu'
if 'MinkUNet' in args.model:
model = minkunet(args.model, pretrained=True)
elif 'SPVCNN' in args.model:
model = spvcnn(args.model, pretrained=True)
elif 'SPVNAS' in args.model:
model = spvnas_specialized(args.model, pretrained=True)
else:
raise NotImplementedError
model = model.to(device)
file_list = ['2019-01-11-14-02-26-radar-oxford-10k', '2019-01-14-12-05-52-radar-oxford-10k',\
'2019-01-14-14-48-55-radar-oxford-10k', '2019-01-18-15-20-12-radar-oxford-10k',\
'2019-01-15-13-06-37-radar-oxford-10k', '2019-01-17-14-03-00-radar-oxford-10k',\
'2019-01-18-14-14-42-radar-oxford-10k', '2019-01-17-13-26-39-radar-oxford-10k']
# file_list = ['2012-02-18', '2012-05-11', '2012-02-12', '2012-02-19']
for file in file_list:
# mkdir
if os.path.exists(os.path.join(args.velodyne_dir, file, 'SPVNAS_velodyne_left_plane_segmented')) == 0:
os.mkdir(os.path.join(args.velodyne_dir, file, 'SPVNAS_velodyne_left_plane_segmented'))
input_path = os.path.join(args.velodyne_dir, file, 'velodyne_left')
input_point_clouds = sorted(os.listdir(input_path))
for point_cloud_name in input_point_clouds:
print(input_path+ '/'+ point_cloud_name)
if not point_cloud_name.endswith('.bin'):
continue
label_file_name = point_cloud_name.replace('.bin', '.label')
vis_file_name = point_cloud_name.replace('.bin', '.png')
gt_file_name = point_cloud_name.replace('.bin', '_GT.png')
# Oxford
pc = np.fromfile(f'{input_path}/{point_cloud_name}',
dtype=np.float32).reshape(4, -1).transpose()
pc[:, 2] = -1 * pc[:, 2]
# nclt
# pc = np.fromfile(f'{input_path}/{point_cloud_name}',
# dtype=np.float32).reshape(-1, 4)
if os.path.exists(label_file_name):
label = np.fromfile(f'{args.velodyne_dir}/{label_file_name}',
dtype=np.int32)
else:
label = None
feed_dict = process_point_cloud(pc, label)
inputs = feed_dict['lidar'].to(device)
outputs = model(inputs)
predictions = outputs.argmax(1).cpu().numpy()
# print(predictions.shape)
predictions = predictions[feed_dict['inverse_map']]
predictions = predictions.astype(np.int32)
results = np.concatenate((feed_dict['pc'][:,:4], predictions.reshape(-1,1)), axis=1)
results[:, 2] = -1 * results[:, 2]
# print(results.shape)
# np.savetxt(point_cloud_name[:-4]+'.txt', results, fmt='%.6f')
plane_list = []
ground_list = []
other_list = []
# 对于提供好参数的模型执行下列操作
for i in range(results.shape[0]):
if results[i, 4] == 12:
plane_list.append(results[i, :4])
elif results[i, 4] == 13:
plane_list.append(results[i, :4])
# ground
elif results[i, 4] == 8:
ground_list.append(results[i, :4])
elif results[i, 4] == 9:
ground_list.append(results[i, :4])
elif results[i, 4] == 10:
ground_list.append(results[i, :4])
elif results[i, 4] == 11:
ground_list.append(results[i, :4])
# other
else:
other_list.append(results[i, :4])
plane_list = np.array(plane_list).reshape(-1,4)
plane_list = np.concatenate((plane_list, np.ones((len(plane_list), 1))), axis=1)
planes = plane_list.astype(np.float32)
ground_list = np.array(ground_list).reshape(-1,4)
ground_list = np.concatenate((ground_list, np.ones((len(ground_list), 1))), axis=1)
grounds = ground_list.astype(np.float32)
other_list = np.array(other_list).reshape(-1, 4)
other_list = np.concatenate((other_list, np.zeros((len(other_list), 1))), axis=1)
results = np.concatenate((ground_list, plane_list, other_list), axis=0)
results = results.astype(np.float32)
results.tofile(os.path.join(input_path[:-13]+'SPVNAS_velodyne_left_plane_segmented', point_cloud_name))