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import pickle
import numpy as np
with open("./data_sequence.pkl", 'rb') as f:
# with open("./data_sequence_linear.pkl", 'rb') as f:
data_csi = pickle.load(f)
with open("./gt_data.pkl", 'rb') as f:
data_cv = pickle.load(f)
data = []
pad = -1000
for k in range(len(data_csi)):
csi = data_csi[k]
cv = data_cv[k]
x = cv['x']
y = cv['y']
img_path = np.array(cv['img_path'])
time_cv = cv['timestamp']
print(cv['people_name'])
indices = np.argsort(time_cv)
x = x[indices]
y = y[indices]
img_path = img_path[indices]
time_cv = time_cv[indices]
print(x)
print(y)
csi_time = csi['global_time']
local_time = csi['time']
magnitude = csi['magnitude']
phase = csi['phase']
people = csi['people']
indices = np.argsort(csi_time)
local_time = local_time[indices]
magnitude = magnitude[indices]
csi_time = csi_time[indices]
phase = phase[indices]
x_list = []
y_list = []
path_list = []
i = 0
j = 0
print(csi_time)
print(time_cv)
while csi_time[i] < time_cv[j]:
i += 1
x_list.append(pad)
y_list.append(pad)
path_list.append(pad)
# print(len(csi_time))
# print(len(time_cv))
while i < len(csi_time):
while csi_time[i] > time_cv[j]:
j += 1
if j >= len(time_cv):
break
if j >= len(time_cv):
break
x_list.append(x[j])
y_list.append(y[j])
path_list.append(img_path[j])
i += 1
print(len(x_list))
if len(x_list) < len(csi_time):
num = len(csi_time) - len(x_list)
x_list = x_list + [pad] * num
y_list = y_list + [pad] * num
path_list = path_list + [pad] * num
data.append({
'magnitude': magnitude,
'phase': phase,
'x': x_list,
'y': y_list,
'img_path': path_list,
'time': local_time,
'people': people
})
print(people)
print(len(magnitude),len(phase),len(x_list),len(y_list),len(path_list),len(local_time))
output_file = './wiloc.pkl'
# output_file = './wiloc_linear.pkl'
with open(output_file, 'wb') as f:
pickle.dump(data, f)
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