def preProcess(self):
for f in self.files:
print(f'Processing {f}...')
raw_datas = read_data_file(f)
acc = raw_datas.acce
direciton = raw_datas.ahrs
real_pos = raw_datas.waypoint
mag = raw_datas.magn
wifi = raw_datas.wifi
step_points = StepPoint(acc, direciton, real_pos).get_step_points()
self.dataToMap(mag, step_points, "mag")
if wifi.size > 0:
self.dataToMap(wifi, step_points, "wifi")
def task1_visualize_groundTruth(number=0):
if number > len(path_filenames):
number = len(path_filenames)-1
path_data = read_data_file(path_filenames[number])
path_id = path_filenames[number].name.split(".")[0]
plt = visualize_trajectory(
path_data.waypoint[:, 1:3],
floor_plan_filename,
width_meter,
height_meter,
title=path_id,
dir=path_image_save_dir,
show=True,
save=True)
plt.clf()
plt.close()
def calibrate_magnetic_wifi_ibeacon_to_position(path_file_list):
mwi_datas = {}
for path_filename in path_file_list:
print(f'Processing {path_filename}...')
path_datas = read_data_file(path_filename)
acce_datas = path_datas.acce
magn_datas = path_datas.magn
ahrs_datas = path_datas.ahrs
wifi_datas = path_datas.wifi
ibeacon_datas = path_datas.ibeacon
posi_datas = path_datas.waypoint
# 使用加速度数据和角度数据，计算每一步的位置信息
step_positions = compute_step_positions(
acce_datas, ahrs_datas, posi_datas)
# visualize_trajectory(posi_datas[:, 1:3], floor_plan_filename, width_meter, height_meter, title='Ground Truth', show=True)
# visualize_trajectory(step_positions[:, 1:3], floor_plan_filename, width_meter, height_meter, title='Step Position', show=True)
if wifi_datas.size != 0:
# 对wifi数据时间戳进行去重和排序
sep_tss = np.unique(wifi_datas[:, 0].astype(float))
# 得到按时间戳分组后的所有wifi数据
wifi_datas_list = split_ts_seq(wifi_datas, sep_tss)
for wifi_ds in wifi_datas_list:
# 找出所有位置时间戳与当前wifi数据时间戳的差值
diff = np.abs(step_positions[:, 0] - float(wifi_ds[0, 0]))
# 找出差值最小的下标
index = np.argmin(diff)
# 得到最小值的xy坐标计做（x,y）
target_xy_key = tuple(step_positions[index, 1:3])
if target_xy_key in mwi_datas:
# 如果当前位置已有wifi信息，则增加一条记录
mwi_datas[target_xy_key]['wifi'] = np.append(
mwi_datas[target_xy_key]['wifi'], wifi_ds, axis=0)
else:
# 记录当前位置的wifi信息
mwi_datas[target_xy_key] = {
'magnetic': np.zeros((0, 4)),
'wifi': wifi_ds,
'ibeacon': np.zeros((0, 3))
}
# 以同样的逻辑得到不同位置的磁力信息
sep_tss = np.unique(magn_datas[:, 0].astype(float))
magn_datas_list = split_ts_seq(magn_datas, sep_tss)
for magn_ds in magn_datas_list:
diff = np.abs(step_positions[:, 0] - float(magn_ds[0, 0]))
index = np.argmin(diff)
target_xy_key = tuple(step_positions[index, 1:3])
if target_xy_key in mwi_datas:
mwi_datas[target_xy_key]['magnetic'] = np.append(
mwi_datas[target_xy_key]['magnetic'], magn_ds, axis=0)
else:
mwi_datas[target_xy_key] = {
'magnetic': magn_ds,
'wifi': np.zeros((0, 5)),
'ibeacon': np.zeros((0, 3))
}
return mwi_datas
# 读取楼层地图信息（宽高）
with open(floor_info_filename) as f:
floor_info = json.load(f)
width_meter = floor_info["map_info"]["width"]
height_meter = floor_info["map_info"]["height"]
# 读取所有文件名
path_filenames = list(Path(path_data_dir).resolve().glob("*.txt"))
# Task1. Visualize way points (ground-truth locations)
# 这一问直接读数据绘制即可，不需要再处理数据
print('Visualizing ground truth positions...')
for path_filename in path_filenames:
print(f'Processing file: {path_filename}...')
path_data = read_data_file(path_filename)
path_id = path_filename.name.split(".")[0]
fig = visualize_trajectory(
path_data.waypoint[:, 1:3], floor_plan_filename, width_meter, height_meter, title=path_id, show=False)
html_filename = f'{path_image_save_dir}/{path_id}.html'
html_filename = str(Path(html_filename).resolve())
save_figure_to_html(fig, html_filename)
# Task2: Visualize geomagnetic heat map
print('Visualizing more information...')
mwi_datas = calibrate_magnetic_wifi_ibeacon_to_position(path_filenames)
magnetic_strength = extract_magnetic_strength(mwi_datas)
heat_positions = np.array(list(magnetic_strength.keys()))
heat_values = np.array(list(magnetic_strength.values()))
fig = visualize_heatmap(heat_positions, heat_values, floor_plan_filename, width_meter,
def calibrate_magnetic_wifi_ibeacon_to_position(path_file_list):
mwi_datas = {}
for path_filename in path_file_list:
print(f'Processing {path_filename}...')
path_datas = read_data_file(path_filename)
acce_datas = path_datas.acce
magn_datas = path_datas.magn
ahrs_datas = path_datas.ahrs
wifi_datas = path_datas.wifi
ibeacon_datas = path_datas.ibeacon
posi_datas = path_datas.waypoint
step_positions = compute_step_positions(acce_datas, ahrs_datas, posi_datas)
# visualize_trajectory(posi_datas[:, 1:3], floor_plan_filename, width_meter, height_meter, title='Ground Truth', show=True)
# visualize_trajectory(step_positions[:, 1:3], floor_plan_filename, width_meter, height_meter, title='Step Position', show=True)
if wifi_datas.size != 0:
sep_tss = np.unique(wifi_datas[:, 0].astype(float))
wifi_datas_list = split_ts_seq(wifi_datas, sep_tss)
for wifi_ds in wifi_datas_list:
diff = np.abs(step_positions[:, 0] - float(wifi_ds[0, 0]))
index = np.argmin(diff)
target_xy_key = tuple(step_positions[index, 1:3])
if target_xy_key in mwi_datas:
mwi_datas[target_xy_key]['wifi'] = np.append(mwi_datas[target_xy_key]['wifi'], wifi_ds, axis=0)
else:
mwi_datas[target_xy_key] = {
'magnetic': np.zeros((0, 4)),
'wifi': wifi_ds,
'ibeacon': np.zeros((0, 3))
}
if ibeacon_datas.size != 0:
sep_tss = np.unique(ibeacon_datas[:, 0].astype(float))
ibeacon_datas_list = split_ts_seq(ibeacon_datas, sep_tss)
for ibeacon_ds in ibeacon_datas_list:
diff = np.abs(step_positions[:, 0] - float(ibeacon_ds[0, 0]))
index = np.argmin(diff)
target_xy_key = tuple(step_positions[index, 1:3])
if target_xy_key in mwi_datas:
mwi_datas[target_xy_key]['ibeacon'] = np.append(mwi_datas[target_xy_key]['ibeacon'], ibeacon_ds, axis=0)
else:
mwi_datas[target_xy_key] = {
'magnetic': np.zeros((0, 4)),
'wifi': np.zeros((0, 5)),
'ibeacon': ibeacon_ds
}
sep_tss = np.unique(magn_datas[:, 0].astype(float))
magn_datas_list = split_ts_seq(magn_datas, sep_tss)
for magn_ds in magn_datas_list:
diff = np.abs(step_positions[:, 0] - float(magn_ds[0, 0]))
index = np.argmin(diff)
target_xy_key = tuple(step_positions[index, 1:3])
if target_xy_key in mwi_datas:
mwi_datas[target_xy_key]['magnetic'] = np.append(mwi_datas[target_xy_key]['magnetic'], magn_ds, axis=0)
else:
mwi_datas[target_xy_key] = {
'magnetic': magn_ds,
'wifi': np.zeros((0, 5)),
'ibeacon': np.zeros((0, 3))
}
return mwi_datas