def ground_filter_aligned_data(fn, args):
#[pointcloud_path, ground_filtering_out_dir, r, x_offset, y_offset, geoid, sigma_geoid] = args
pts_dir, ref_dir, tmp_dir, out_dir, r, x_offset, y_offset, geoid, sigma_geoid, radius, res_list = args
m,n = fn[:17].split('_')
[mm,nn] = coord(m, n, r, x_offset, y_offset)
if fn not in os.listdir(ref_dir):
return False
# Load mms
data_mms = read_bin_xyzrid_scale(pts_dir + fn, 12)
# Rough filter based on given DTM
data_mms = filter_by_reference(data_mms, ref_dir + fn, geoid, sigma_geoid)
if len(data_mms) < 200:
return False
data_mms = global2local(data_mms, mm, nn)
reduced, _ = ground_filter(data_mms, radius, res_list, r, is_plot = False)
if len(reduced) < 200:
return False
write_points_double(local2global(reduced, mm, nn)[:,:3],
out_dir + fn)
return True
def apply_height_updates_folder(list_pts,
pts_dir,
dict_update_pts,
res_ref,
res_update,
out_dir,
args,
global_shift=0,
is_xyz=False):
r, x_offset, y_offset = args
for fn in tqdm(list_pts):
m, n = fn[:17].split('_')
[mm, nn] = coord(m, n, r, x_offset, y_offset)
# Apply updates
data_updates = dict_update_pts[fn]
# Load mms original points
if is_xyz:
data_mms = load_corr_mms(fn, pts_dir, args)
else:
data_mms = read_bin_xyzrid_scale(pts_dir + fn, 12)
# Trans to local system
data_mms = global2local(data_mms, mm, nn)
data_updated = apply_height_updates(data_mms, data_updates, res_update,
r, global_shift)
# Write updated points
if len(data_updated) > 0:
write_points_double(local2global(data_updated[:, :3], mm, nn),
out_dir + '//' + fn)
pathA = geo_ground_filtering_out_dir
pathB = final_dir
pathC = rest_dir
listA = os.listdir(pathA)
listB = os.listdir(pathB)
listC = os.listdir(pathC)
#fns_dtm = ['ffffff9', 'ffffffa', 'ffffffb', 'ffffffc', 'ffffffd', 'ffffffe', 'fffffff',
# '0000000', '0000001', '0000002', '0000003', '0000004', '0000005', '0000006']
#fns_dtm = ['ffffffe', 'fffffff',
# '0000000', '0000001', '0000002', '0000003', '0000004', '0000005', '0000006']
fns_dtm = ['ffffffc', 'ffffffd', 'ffffffe']
for fn_dtm in fns_dtm:
print(fn_dtm)
data_dtm = []
for flist, fpath in zip([listA, listB, listC], [pathA, pathB, pathC]):
sublist = [fn for fn in flist if fn_dtm == fn[:7]]
for fn in tqdm(sublist):
data_mms = read_bin_double(fpath + fn, 7)
data_dtm.extend(data_mms)
write_points_double(data_dtm, out_dtm_dir + fn_dtm + '.ply')
data_updated = []
for key, value in d_mms.iteritems():
sub_mms = data_mms[value]
update_value = data_update[d_update[key][0], 2]
data_updated.extend(sub_mms -
[0, 0, update_value]) # Must be minus here
data_updated = np.array(data_updated)
_ = calc_difference_mms_ref(data_updated, data_ref, 0.5, r, shift)
if len(data_output) != len(data_mms):
print('updated for ', fn, len(data_output) / float(len(data_mms)))
if len(data_output) > 0:
write_points_double(local2global(data_updated, mm, nn),
out_dir + '//' + fn)
# Trash
# # Generate update grid in 0.1m
# xnew = np.arange(0.05, r, res_update)
# ynew = np.arange(0.05, r, res_update)
# xx, yy = np.meshgrid(xnew, ynew)
# xxf = xx.flatten()
# yyf = yy.flatten()
#
# coords9n = get_neighbour_matrix(9)
# coords = np.array(coords9n)
# xn = coords[:,0]
# yn = coords[:,1]
# ref_out_dir = out_path + 'ref\\'
list_mms = os.listdir(mms_dir)
if len(os.listdir(mms_local_dir)) == 0:
# Move to local coordinate system
from tqdm import tqdm
from environmentSetting import r, x_offset, y_offset
from lib.cell2world import coord
from lib.load import load_mms
from lib.ply import write_points_double
for fn in tqdm(list_mms):
m, n = fn[:17].split('_')
[mm, nn] = coord(m, n, r, x_offset, y_offset)
args = mms_dir, 0, 0, r, x_offset, y_offset
data_mms = load_mms(fn, args)
write_points_double(data_mms[:, :3], mms_local_dir + '//' + fn)
else:
#absolute path of executive software cloudcompare v2.11
CCPath = r"C:/Program Files/CloudCompare/"
os.chdir(CCPath)
# Feature names
features = [
"FEATURE OMNIVARIANCE", "FEATURE SURFACE_VARIATION",
"CURV NORMAL_CHANGE"
]
radius = 0.25
features_cmd = " ".join(
["-" + feat + " " + str(radius) for feat in features])
"""
from lib.apply_transform import apply_height_updates_folder
apply_height_updates_folder(list_mms, mms_dir, dict_update_pts, res_ref, res_update,
gr_utm_dir, (r, x_offset, y_offset), shift, is_xyz = True)
"""
# Save updates values to tile ply
"""
from lib.cell2world import coord
from lib.ply import write_points_double
from lib.util import global2local, local2global
for fn, update_values in dict_update_pts.iteritems():
m,n = fn[:17].split('_')
[mm,nn] = coord(m, n, r, x_offset, y_offset)
write_points_double(local2global(update_values[:,:3], mm, nn),
updates_dir + '//' + fn)
# Calculate difference and report
dict_shift_value, list_shift_img = calc_height_diff_entire(tmp_dir, project_name, gr_utm_dir, ref_dir,
is_after_dtm_alignment = True)
"""
# Proc to final merged dtm
"""
raster_size = r/res_ref #30
radius = 1
from lib.update import update_dtm
from lib.produceDTM import local_to_UTM, local_to_UTM_update_ref, local_to_UTM_rest_ref
# plt.figure()
# plt.imshow(difference)
_ = calc_difference_mms_ref(data_mms, data_ref, 0.5, r)
neigh_list = search_neigh_tiles(fn)
data_update = generate_updates_mms(dict_shift_value, neigh_list)
d_update = rasterize(data_update, res_update, dim=2)
d_mms = rasterize(data_mms, res_update, dim=2)
data_updated = []
for key, value in d_mms.iteritems():
sub_mms = data_mms[value]
update_value = data_update[d_update[key][0], 2]
data_updated.extend(sub_mms -
[0, 0, update_value]) # Must be minus here
data_updated = np.array(data_updated)
_ = calc_difference_mms_ref(data_updated, data_ref, 0.5, r)
if len(data_output) != len(data_mms):
print('updated for ', fn, len(data_output) / float(len(data_mms)))
if len(data_output) > 0:
#write_points(data_output, out_dir + '//_' + fn)
# write_points(data_updated, out_dir + '//' + fn)
write_points_double(data_updated + [mm, nn, 0],
out_dir + '//' + fn)