def apply_height_updates(data_mms,
data_updates,
res_update,
r,
global_shift=0):
# Indexing
d_update = rasterize(data_updates, res_update, dim=2)
d_mms = rasterize(data_mms, res_update, dim=2)
# Points at the borders leads bugs - thus remove the cell-id
d_mms = remove_border_pts(d_mms, res_update, r)
# Initialize container
data_updated = []
for key, value in d_mms.iteritems():
sub_mms = data_mms[value]
update_value = data_updates[d_update[key][0], 2]
sub_mms[:, :3] = sub_mms[:, :3] - [0, 0, update_value] - [
0, 0, global_shift
] # Must be minus here
data_updated.extend(sub_mms)
return np.array(data_updated)
def calc_difference_mms_ref(data_mms,
data_ref,
res_diff,
r,
is_plot=is_plot_flag):
difference = np.zeros((int(r / res_diff), int(r / res_diff)))
d_mms_sub = rasterize(data_mms, res_diff, dim=2)
d_ref_sub = rasterize(data_ref, res_diff, dim=2)
for subkey, subvalue in d_mms_sub.iteritems():
m, n = np.int0(subkey.split('+'))
z_mms = np.median(data_mms[subvalue, 2])
z_ref = data_ref[d_ref_sub[subkey], 2][0] + shift
difference[m, n] = z_mms - z_ref
if is_plot:
plt.figure()
plt.imshow(difference, vmin=-0.3, vmax=0.3)
plt.colorbar()
plt.figure()
plt.hist(difference, bins='auto')
plt.show()
return difference
def update_pointcloud(data_mms, data_update, res_update):
# Create index according to updates resolution
d_update = rasterize(data_update, res_update, dim = 2)
d_mms_runid = rasterize(data_mms, res_update, dim = 2)
# Apply change for each cell regarding updates resolution
data_updated = []
for key, value in d_mms_runid.iteritems():
sub_mms = data_mms[value]
if key in d_update:
update_value = data_update[d_update[key][0],2]
sub_mms[:,:3] = sub_mms[:,:3] - [0,0,update_value]
data_updated.extend(sub_mms) # Must be minus here
return np.array(data_updated)
def grid_difference_filter_based(fn_ref, res_ref, data_mms, nonvalue,
raster_size, threshold_height_diff):
d_mms = rasterize(data_mms, res_ref, dim=2)
# read reference
data_ref, d_ref = read_ascii_xyz(fn_ref,
delimiter=' ',
skiprows=7,
dtype=np.float,
e=res_ref,
dim=2)
# calculate the difference
difference = nonvalue * np.ones((raster_size, raster_size))
reduce_list = []
for key, value in d_mms.iteritems():
m, n = np.int0(key.split('+'))
z_mms = data_mms[value, 2]
mean_mms = np.mean(z_mms)
mean_ref = data_ref[d_ref[key], 2]
delta = mean_mms - mean_ref
if delta > threshold_height_diff:
reduce_list.extend(value)
else:
difference[m, n] = mean_mms - mean_ref
return difference, reduce_list
def down_sampling(merged, res_output, method):
d_merged = rasterize(merged, res_output, dim=3)
reduced_cloud = [
method(merged[value], axis=0) for key, value in d_merged.iteritems()
]
return np.array(reduced_cloud)
def calc_diff_core(fn, args):
mms_dir, ref_dir, res_ref, r, x_offset, y_offset, geoid, sigma_geoid = args
data_mms = load_mms(fn, args)
#data_mms = read_bin(mms_dir + fn, 7)
data_ref = read_bin(ref_dir + fn, 7)
d_ref = rasterize(data_ref, res_ref, dim=2)
shift_value, shift_img = shiftvalue(np.array(data_mms), res_ref,
np.array(data_ref), d_ref, 1., r)
return fn, shift_value, shift_img
def grid_difference(fn_ref, res_ref, data_mms, nonvalue, raster_size):
d_mms = rasterize(data_mms, res_ref, dim=2)
# read reference
data_ref, d_ref = read_ascii_xyz(fn_ref,
delimiter=' ',
skiprows=7,
dtype=np.float,
e=res_ref,
dim=2)
# calculate the difference
difference = nonvalue * np.ones((raster_size, raster_size))
for key, value in d_mms.iteritems():
m, n = np.int0(key.split('+'))
z_mms = data_mms[value, 2]
mean_mms = np.mean(z_mms)
mean_ref = data_ref[d_ref[key], 2]
difference[m, n] = mean_mms - mean_ref
return difference
height_needed = find_time_without_value(dict_shift_value, list_mms)
dict_shift_value, num_changes = insert_values_based_on_neigbours(dict_shift_value, height_needed)
count += 1
#fn = "0000002b_ffffff09.ply"
fn = "0000002a_ffffff08.ply"
#fn = "00000027_ffffff05.ply"
#fn = "00000020_fffffec3.ply"
if 1:
#for fn in list_mms:
data_mms = read_bin(mms_dir + fn, 7)
data_ref = read_bin(ref_dir + fn, 7)
d_ref = rasterize(data_ref, res_ref, dim = 2)
d_mms = rasterize(data_mms, res_ref, dim = 2)
data_mms = np.array(data_mms)
data_ref = np.array(data_ref)
index_list = []
# Correction for old dtm with bad interpolation
for key, value in d_mms.iteritems():
x, y = [int(k) for k in key.split('+')]
z_mms = data_mms[value,2]
z_ref = data_ref[d_ref[key], 2][0] + shift
dz = z_mms -z_ref