/
suppor_lib.py
executable file
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/
suppor_lib.py
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import cv2
import numpy as np
from math import radians, cos, sin, asin, sqrt, log
import math
import copy
import scipy
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):
'''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**(-4)):
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
from config import if_normalize_v_lable
for i in range(subjects_total):
if 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 = (11.75+13.78)/2, sp = True):
from config import heatmap_sigma
if heatmap_sigma is 'my_sigma':
from config import my_sigma
my_sigma_in_degree = my_sigma
elif heatmap_sigma is 'sigma_half_fov':
from config import sigma_half_fov
my_sigma_in_degree = sigma_half_fov
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)