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helper_tool.py
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helper_tool.py
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from os.path import join
import numpy as np
import os, sys
import pandas as pd
import utils.cpp_wrappers.cpp_subsampling.grid_subsampling as cpp_subsampling
import utils.nearest_neighbors.lib.python.nearest_neighbors as nearest_neighbors
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, 'utils'))
class DataProcessing:
@staticmethod
def load_pc_semantic3d(filename):
pc_pd = pd.read_csv(filename, header=None, delim_whitespace=True, dtype=np.float16)
pc = pc_pd.values
return pc
@staticmethod
def load_label_semantic3d(filename):
label_pd = pd.read_csv(filename, header=None, delim_whitespace=True, dtype=np.uint8)
cloud_labels = label_pd.values
return cloud_labels
@staticmethod
def load_pc_kitti(pc_path):
scan = np.fromfile(pc_path, dtype=np.float32)
scan = scan.reshape((-1, 4))
points = scan[:, 0:3] # get xyz
return points
@staticmethod
def load_label_kitti(label_path, remap_lut):
label = np.fromfile(label_path, dtype=np.uint32)
label = label.reshape((-1))
sem_label = label & 0xFFFF # semantic label in lower half
inst_label = label >> 16 # instance id in upper half
assert ((sem_label + (inst_label << 16) == label).all())
sem_label = remap_lut[sem_label]
return sem_label.astype(np.int32)
@staticmethod
def get_file_list(dataset_path, test_scan_num):
seq_list = np.sort(os.listdir(dataset_path))
train_file_list = []
test_file_list = []
val_file_list = []
for seq_id in seq_list:
seq_path = join(dataset_path, seq_id)
pc_path = join(seq_path, 'velodyne')
if seq_id == '08':
val_file_list.append([join(pc_path, f) for f in np.sort(os.listdir(pc_path))])
if seq_id == test_scan_num:
test_file_list.append([join(pc_path, f) for f in np.sort(os.listdir(pc_path))])
elif int(seq_id) >= 11 and seq_id == test_scan_num:
test_file_list.append([join(pc_path, f) for f in np.sort(os.listdir(pc_path))])
elif seq_id in ['00', '01', '02', '03', '04', '05', '06', '07', '09', '10']:
train_file_list.append([join(pc_path, f) for f in np.sort(os.listdir(pc_path))])
train_file_list = np.concatenate(train_file_list, axis=0)
val_file_list = np.concatenate(val_file_list, axis=0)
test_file_list = np.concatenate(test_file_list, axis=0)
return train_file_list, val_file_list, test_file_list
@staticmethod
def knn_search(support_pts, query_pts, k):
"""
:param support_pts: points you have, B*N1*3
:param query_pts: points you want to know the neighbour index, B*N2*3
:param k: Number of neighbours in knn search
:return: neighbor_idx: neighboring points indexes, B*N2*k
"""
neighbor_idx = nearest_neighbors.knn_batch(support_pts, query_pts, k, omp=True)
return neighbor_idx.astype(np.int32)
@staticmethod
def data_aug(xyz, color, labels, idx, num_out):
num_in = len(xyz)
dup = np.random.choice(num_in, num_out - num_in) #yuan 在0-num_in范围随机选取num_0ut-num_in个元素
xyz_dup = xyz[dup, ...]
xyz_aug = np.concatenate([xyz, xyz_dup], 0)
color_dup = color[dup, ...]
color_aug = np.concatenate([color, color_dup], 0)
idx_dup = list(range(num_in)) + list(dup)
idx_aug = idx[idx_dup]
label_aug = labels[idx_dup]
return xyz_aug, color_aug, idx_aug, label_aug
@staticmethod
def shuffle_idx(x):
# random shuffle the index
idx = np.arange(len(x))
np.random.shuffle(idx)
return x[idx]
@staticmethod
def shuffle_list(data_list):
indices = np.arange(np.shape(data_list)[0])
np.random.shuffle(indices)
data_list = data_list[indices]
return data_list
@staticmethod
def grid_sub_sampling(points, features=None, labels=None, grid_size=0.1, verbose=0):
"""
CPP wrapper for a grid sub_sampling (method = barycenter for points and features
:param points: (N, 3) matrix of input points
:param features: optional (N, d) matrix of features (floating number)
:param labels: optional (N,) matrix of integer labels
:param grid_size: parameter defining the size of grid voxels
:param verbose: 1 to display
:return: sub_sampled points, with features and/or labels depending of the input
"""
if (features is None) and (labels is None):
return cpp_subsampling.compute(points, sampleDl=grid_size, verbose=verbose)
elif labels is None:
return cpp_subsampling.compute(points, features=features, sampleDl=grid_size, verbose=verbose)
elif features is None:
return cpp_subsampling.compute(points, classes=labels, sampleDl=grid_size, verbose=verbose)
else:
return cpp_subsampling.compute(points, features=features, classes=labels, sampleDl=grid_size,
verbose=verbose)
@staticmethod
def IoU_from_confusions(confusions):
"""
Computes IoU from confusion matrices.
:param confusions: ([..., n_c, n_c] np.int32). Can be any dimension, the confusion matrices should be described by
the last axes. n_c = number of classes
:return: ([..., n_c] np.float32) IoU score
"""
# Compute TP, FP, FN. This assume that the second to last axis counts the truths (like the first axis of a
# confusion matrix), and that the last axis counts the predictions (like the second axis of a confusion matrix)
TP = np.diagonal(confusions, axis1=-2, axis2=-1)
TP_plus_FN = np.sum(confusions, axis=-1)
TP_plus_FP = np.sum(confusions, axis=-2)
# Compute IoU
IoU = TP / (TP_plus_FP + TP_plus_FN - TP + 1e-6)
# Compute mIoU with only the actual classes
mask = TP_plus_FN < 1e-3
counts = np.sum(1 - mask, axis=-1, keepdims=True)
mIoU = np.sum(IoU, axis=-1, keepdims=True) / (counts + 1e-6)
# If class is absent, place mIoU in place of 0 IoU to get the actual mean later
IoU += mask * mIoU
return IoU
@staticmethod
def get_class_weights(dataset_name):
# pre-calculate the number of points in each category
num_per_class = []
if dataset_name is 'S3DIS':
num_per_class = np.array([3370714, 2856755, 4919229, 318158, 375640, 478001, 974733,
650464, 791496, 88727, 1284130, 229758, 2272837], dtype=np.int32)
elif dataset_name is 'Semantic3D':
num_per_class = np.array([5181602, 5012952, 6830086, 1311528, 10476365, 946982, 334860, 269353],
dtype=np.int32)
elif dataset_name is 'SemanticKITTI':
num_per_class = np.array([55437630, 320797, 541736, 2578735, 3274484, 552662, 184064, 78858,
240942562, 17294618, 170599734, 6369672, 230413074, 101130274, 476491114,
9833174, 129609852, 4506626, 1168181])
weight = num_per_class / float(sum(num_per_class))
ce_label_weight = 1 / (weight + 0.02)
return np.expand_dims(ce_label_weight, axis=0)