-
Notifications
You must be signed in to change notification settings - Fork 75
/
GSI.py
67 lines (64 loc) · 2.36 KB
/
GSI.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
"""
@Author: Du Yunhao
@Filename: GSI.py
@Contact: dyh_bupt@163.com
@Time: 2022/3/1 9:18
@Discription: Gaussian-smoothed interpolation
"""
import os
import numpy as np
from os.path import join
from collections import defaultdict
from sklearn.gaussian_process.kernels import RBF
from sklearn.gaussian_process import GaussianProcessRegressor as GPR
# 线性插值
def LinearInterpolation(input_, interval):
input_ = input_[np.lexsort([input_[:, 0], input_[:, 1]])] # 按ID和帧排序
output_ = input_.copy()
'''线性插值'''
id_pre, f_pre, row_pre = -1, -1, np.zeros((10,))
for row in input_:
f_curr, id_curr = row[:2].astype(int)
if id_curr == id_pre: # 同ID
if f_pre + 1 < f_curr < f_pre + interval:
for i, f in enumerate(range(f_pre + 1, f_curr), start=1): # 逐框插值
step = (row - row_pre) / (f_curr - f_pre) * i
row_new = row_pre + step
output_ = np.append(output_, row_new[np.newaxis, :], axis=0)
else: # 不同ID
id_pre = id_curr
row_pre = row
f_pre = f_curr
output_ = output_[np.lexsort([output_[:, 0], output_[:, 1]])]
return output_
# 高斯平滑
def GaussianSmooth(input_, tau):
output_ = list()
ids = set(input_[:, 1])
for id_ in ids:
tracks = input_[input_[:, 1] == id_]
len_scale = np.clip(tau * np.log(tau ** 3 / len(tracks)), tau ** -1, tau ** 2)
gpr = GPR(RBF(len_scale, 'fixed'))
t = tracks[:, 0].reshape(-1, 1)
x = tracks[:, 2].reshape(-1, 1)
y = tracks[:, 3].reshape(-1, 1)
w = tracks[:, 4].reshape(-1, 1)
h = tracks[:, 5].reshape(-1, 1)
gpr.fit(t, x)
xx = gpr.predict(t)[:, 0]
gpr.fit(t, y)
yy = gpr.predict(t)[:, 0]
gpr.fit(t, w)
ww = gpr.predict(t)[:, 0]
gpr.fit(t, h)
hh = gpr.predict(t)[:, 0]
output_.extend([
[t[i, 0], id_, xx[i], yy[i], ww[i], hh[i], 1, -1, -1 , -1] for i in range(len(t))
])
return output_
# GSI
def GSInterpolation(path_in, path_out, interval, tau):
input_ = np.loadtxt(path_in, delimiter=',')
li = LinearInterpolation(input_, interval)
gsi = GaussianSmooth(li, tau)
np.savetxt(path_out, gsi, fmt='%d,%d,%.2f,%.2f,%.2f,%.2f,%.2f,%d,%d,%d')