-
Notifications
You must be signed in to change notification settings - Fork 19
/
suppor_lib.py
executable file
·225 lines (181 loc) · 7.79 KB
/
suppor_lib.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
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
import cv2
import numpy as np
from math import radians, cos, sin, asin, sqrt, log
import math
import copy
import scipy
import config
def calc_angle(x_point_s,y_point_s,x_point_e,y_point_e):
'''
Function: compute direction to north from Mercator cordication
Coder: syh
Status: unchecked
'''
angle=0
y_se= y_point_e-y_point_s;
x_se= x_point_e-x_point_s;
if x_se==0 and y_se>0:
angle = 360
if x_se==0 and y_se<0:
angle = 180
if y_se==0 and x_se>0:
angle = 90
if y_se==0 and x_se<0:
angle = 270
if x_se>0 and y_se>0:
angle = math.atan(x_se/y_se)*180/math.pi
elif x_se<0 and y_se>0:
angle = 360 + math.atan(x_se/y_se)*180/math.pi
elif x_se<0 and y_se<0:
angle = 180 + math.atan(x_se/y_se)*180/math.pi
elif x_se>0 and y_se<0:
angle = 180 + math.atan(x_se/y_se)*180/math.pi
return angle
class subject():
'''
Function: data structure
Coder: syh
Status: coding
'''
def __init__(self, num_data_frame):
self.data_frame = list(data_frame() for i in range(num_data_frame))
class data_frame():
'''
Function: data structure
Coder: syh
Status: coding
'''
def __init__(self):
self.p = [0.0, 0.0] #position
self.theta = 0.0 # direction
self.v = 0.0 # speed
self.t = 1.0 # trustworthy for position
self.g = 1.0 # trustworthy for direction
def get_num_subjects(data):
num_subject = np.shape(data)[1] / 2
return num_subject
def get_subjects(data, subject_id=0):
'''get essiancial config'''
num_subject = np.shape(data)[1] / 2
num_data_frame = np.shape(data)[0]
'''subjects'''
subjects = list(subject(num_data_frame=num_data_frame) for i in range(num_subject))
'''set in data from mat to structure'''
for subject_i in range(num_subject):
for data_frame_i in range(num_data_frame):
subjects[subject_i].data_frame[data_frame_i].p[0] = data[data_frame_i, subject_i*2 + 1] #lon
subjects[subject_i].data_frame[data_frame_i].p[1] = data[data_frame_i, subject_i*2] #lat
if subjects[subject_i].data_frame[data_frame_i].p[0] < -180.0 or subjects[subject_i].data_frame[data_frame_i].p[0] > 180.0:
print(j)
for data_frame_i in range(1, num_data_frame - 1):
last_frame = subjects[subject_i].data_frame[data_frame_i - 1]
next_frame = subjects[subject_i].data_frame[data_frame_i + 1]
v = haversine(lon1=last_frame.p[0],
lat1=last_frame.p[1],
lon2=next_frame.p[0],
lat2=next_frame.p[1]) / 2.0
l_x, l_y = lonlat2Mercator(lon=last_frame.p[0],
lat=last_frame.p[1])
n_x, n_y = lonlat2Mercator(lon=next_frame.p[0],
lat=next_frame.p[1])
theta = calc_angle(x_point_s=l_x,
y_point_s=l_y,
x_point_e=n_x,
y_point_e=n_y)
subjects[subject_i].data_frame[data_frame_i].v = v
subjects[subject_i].data_frame[data_frame_i].theta = theta
if(subject_i==1):
subjects[subject_i].data_frame[0].v = v
subjects[subject_i].data_frame[0].theta = theta
if(subject_i==(num_data_frame - 2)):
subjects[subject_i].data_frame[num_data_frame - 1].v = v
subjects[subject_i].data_frame[num_data_frame - 1].theta = theta
return num_subject, num_data_frame, subjects, subjects[subject_id]
def get_transfered_data(lon, lat, theta, data_frame, max_distance_on_position=1.0*math.pi, max_distance_on_degree=180.0, final_discount_to=10**(-2)):
distance_on_position = haversine(lon1=lon,
lat1=lat,
lon2=data_frame.p[0],
lat2=data_frame.p[1])
distance_on_degree = abs(theta - data_frame.theta)
if(distance_on_degree>180):
distance_on_degree = distance_on_degree - 180
thegma_2_on_position = -0.5*(max_distance_on_position**2)/math.log(final_discount_to)
thegma_2_on_degree = -0.5*(max_distance_on_degree**2)/math.log(final_discount_to)
'''guassion trustworthy transfer'''
prob = 1.0 * math.exp(-1.0 / 2.0 * (distance_on_position**2) / (thegma_2_on_position)) * math.exp(-1.0 / 2.0 * (distance_on_degree**2) / (thegma_2_on_degree))
return prob
def get_prob(lon, lat, theta, subjects, subjects_total, cur_data):
prob_dic = []
for i in range(subjects_total):
from config import final_discount_to
x = get_transfered_data(lon=lon,
lat=lat,
theta=theta,
data_frame=subjects[i].data_frame[cur_data],
final_discount_to=final_discount_to)
prob_dic += [x]
'''prob will not be normalized, since we do not encourage move to unmeasured area'''
prob_sum = sum(prob_dic)
prob_dic = np.array(prob_dic)
'''normalize prob dic'''
prob_dic_normalized = prob_dic / prob_sum
distance_per_data = 0.0
for i in range(subjects_total):
if config.if_normalize_v_lable:
distance_per_data += prob_dic_normalized[i] * subjects[i].data_frame[cur_data].v
else:
distance_per_data += prob_dic[i] * subjects[i].data_frame[cur_data].v
if(distance_per_data<=0):
print("!!!! v too small !!!!")
distance_per_data = 0.00001
return (prob_sum/subjects_total), distance_per_data
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
"""
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 1.0
return c * r
def lonlat2Mercator(lon, lat):
'''
Function: convert lon-lat to Mercator cardination
Coder: syh
Status: unchecked
'''
x = lon * 20037508.34 / 180
y = math.log(math.tan((90.0 + lat) * math.pi / 360.0)) / (math.pi / 180.0)
y = y * 20037508.34 / 180.0
return x, y
def fixation2salmap(fixation, mapwidth, mapheight, my_sigma_in_degree = 7, sp = True):
my_sigma_in_degree = 7
fixation_total = np.shape(fixation)[0]
x_degree_per_pixel = 360.0 / mapwidth
y_degree_per_pixel = 180.0 / mapheight
salmap = np.zeros((mapwidth, mapheight))
for x in range(mapwidth):
for y in range(mapheight):
cur_lon = - x * x_degree_per_pixel + 180.0
cur_lat = - y * y_degree_per_pixel + 90.0
for fixation_count in range(fixation_total):
cur_fixation_lon = fixation[fixation_count][0]
cur_fixation_lat = fixation[fixation_count][1]
if sp is True:
distance_to_cur_fixation = haversine(lon1=cur_lon,
lat1=cur_lat,
lon2=cur_fixation_lon,
lat2=cur_fixation_lat)
distance_to_cur_fixation_in_degree = distance_to_cur_fixation / math.pi * 180.0
sal = math.exp(-1.0 / 2.0 * (distance_to_cur_fixation_in_degree**2) / (my_sigma_in_degree**2))
else:
print('currently not supporting none-sp map')
salmap[x, y] += sal
salmap = salmap * (1.0 / np.amax(salmap))
salmap = np.transpose(salmap)
return salmap
def constrain_degree_to_0_360(direction):
return (direction+360.0) if (direction<0) else (direction+0.0)