def calculate_matrix(inliers, m, norm_0, ref_z, direction_z):
a, b, c, d = m
x_max = inliers[np.argmax(inliers[:, 0])]
x_min = inliers[np.argmin(inliers[:, 0])]
y_max = inliers[np.argmax(inliers[:, 1])]
y_min = inliers[np.argmin(inliers[:, 1])]
if utils.l2_distance_2d(x_max[:2], x_min[:2]) < utils.l2_distance_2d(
y_max[:2], y_min[:2]):
b_min, b_max = y_min, y_max
else:
b_min, b_max = x_min, x_max
plane_norm = np.array([a, b, c]) / np.linalg.norm(np.array([a, b, c]))
ref_x = b_min[0]
ref_y = b_min[1]
ref_original = np.array([ref_x, ref_y, ref_z])
x_dir = np.array([b_max[0], b_max[1], ref_z])
dot = np.dot(plane_norm, norm_0)
b1 = (x_dir - ref_original) / np.linalg.norm(x_dir - ref_original)
b2 = plane_norm if dot > 0 else -plane_norm
b3 = np.cross(b2, b1)
dot2 = np.dot(b3, direction_z)
b3 = b3 if dot2 > 0 else -b3
T = np.zeros((4, 4), dtype=np.float32)
T[0:3, 0] = b1
T[0:3, 1] = b2
T[0:3, 2] = b3
T[0:3, 3] = ref_original
T[3, 3] = 1
inv_T = np.linalg.inv(T)
return T, inv_T, b_min, b_max
def generate_geojson(path, save_path, bounding, name=None):
print('generate geojson... ', end='')
data = load_file('head_info', path)
coor = load_file('coordinate', path)
o_x = data["original_x"]
o_y = data["original_y"]
o_z = data["original_z"]
lines = list()
line_id = list()
min_h = list()
max_h = list()
ref_h = list()
dis = list()
depth = list()
for nid, data in bounding.items():
_, _, bounding_3d, deep , _= data
line = LineString([(bounding_3d['y_min'][0]+o_x,bounding_3d['y_min'][1]+o_y),
(bounding_3d['y_max'][0]+o_x,bounding_3d['y_max'][1]+o_y)])
lines.append(line)
line_id.append(nid)
depth.append(deep)
min_h.append(min(bounding_3d['x_min'][2], bounding_3d['x_max'][2])+o_z)
max_h.append(max(bounding_3d['x_min'][2], bounding_3d['x_max'][2])+o_z)
dis.append(utils.l2_distance_2d(bounding_3d['y_min'][0:2],bounding_3d['y_max'][0:2]))
ref_h.append(np.mean(coor[coor.shape[0]//2, bounding[nid][1]['y_min'].y:bounding[nid][1]['y_max'].y,2])+o_z)
df = pd.DataFrame(line_id,columns=['id'])
df['min_h'] = min_h
df['max_h'] = max_h
df['ref_h'] = ref_h
df['dis'] = dis
df['depth'] = depth
gdf = geopandas.GeoDataFrame(df, geometry=lines)
if not gdf.empty:
gdf.to_file(os.path.join(save_path, "segment_line_{}.geojson".format(name)), driver='GeoJSON')
print(" done!")
def nearstneighboor(root_c, remainder):
min_dis = 10000
min_center = None
idx = None
for i, center in remainder.items():
dis = utils.l2_distance_2d(root_c, center)
if dis < min_dis:
min_dis = dis
idx = i
min_center = center
return i, min_center
def merge_df_files(df1, gj2, match):
df2 = geopandas.read_file(open(gj2))
if len(df1) == 0:
file_name2 = gj2.split('/')[-2]
for i in range(df2.shape[0]):
if df2.iloc[i]['id'] not in match.keys():
match[df2.iloc[i]['id']] = [(file_name2, df2.iloc[i]['id'])]
return df2, match
file_name2 = gj2.split('/')[-2]
repeat = list()
table2to1 = dict()
if not if_overlap(df1, df2):
for j in range(df2.shape[0]):
if df2.iloc[j]['id'] + df1.iloc[-1]['id'] + 1 not in match.keys():
match[df2.iloc[j]['id'] + df1.iloc[-1]['id'] + 1] = [
(file_name2, df2.iloc[j]['id'])
]
df2['id'] += df1.iloc[-1]['id'] + 1
df1 = df1.append(df2)
df1 = df1.reset_index(drop=True)
else:
matched_id = list()
base_id = df1.iloc[-1]['id'] + 1
for i in range(df1.shape[0]):
for j in range(df2.shape[0]):
row1 = df1.iloc[i]
line1 = np.array(row1['geometry']).reshape(-1, )
row2 = df2.iloc[j]
line2 = np.array(row2['geometry']).reshape(-1, )
#print("leftright:", leftright)
if abs(
utils.slope(np.array(line1[:2]), np.array(line1[2:])) -
utils.slope(np.array(line2[:2]), np.array(line2[2:]))
) > 0.2:
continue
if utils.l2_distance_2d(np.array(line1[:2]), np.array(
line2[:2])) > 40:
continue
d_line = utils.l2_distance_lines(np.array(line1[:2]),
np.array(line1[2:]),
np.array(line2[:2]),
np.array(line2[2:]))
if d_line > 2.5:
continue
ymin_coor,ymax_coor, cover_rate, baseline = \
utils.line2line_project(np.array(line1[:2]), np.array(line1[2:]),
np.array(line2[:2]), np.array(line2[2:]))
if utils.l2_distance_2d(ymin_coor, ymax_coor) > 30.0:
continue
#if (cover_rate < 1 and cover_rate > 0.7 and d_line < 0.5) or (cover_rate < 0.7 and d_line < 2.5):
if (cover_rate < config.cover_rate * 4
and d_line < 0.5) or (cover_rate < config.cover_rate
and d_line < 2.5):
if row2['id'] in matched_id:
pre_1_id = table2to1[row2['id']]
repeat.append(row1['id'])
index = df1[df1['id'] == pre_1_id].index[0]
row_pre = df1.loc[index]
line_pre = np.array(row_pre['geometry']).reshape(-1, )
ymin_coor,ymax_coor, cover_rate, baseline = \
utils.line2line_project(np.array(line_pre[:2]), np.array(line_pre[2:]),
np.array(line1[:2]), np.array(line1[2:]))
merge_line = LineString([(ymin_coor[0], ymin_coor[1]),
(ymax_coor[0], ymax_coor[1])])
df1.loc[index, 'min_h'] = min(row_pre['min_h'],
row1['min_h'])
df1.loc[index, 'max_h'] = min(row_pre['max_h'],
row1['max_h'])
df1.loc[index, 'ref_h'] = (row1['ref_h'] +
row_pre['ref_h']) / 2
df1.loc[index, 'depth'] = (row1['depth'] +
row_pre['depth']) / 2
df1.loc[index, 'dis'] = utils.l2_distance_2d(
ymin_coor[:2], ymax_coor[:2])
df1.loc[index, 'geometry'] = merge_line
filter_pair = list()
for pair in match[row1['id']]:
if pair not in match[row_pre['id']]:
filter_pair.append(pair)
if baseline == 1:
match[row_pre['id']] = match[
row_pre['id']] + filter_pair
else:
match[row_pre['id']] = filter_pair + match[
row_pre['id']]
else:
table2to1[row2['id']] = row1['id']
matched_id.append(row2['id'])
merge_line = LineString([(ymin_coor[0], ymin_coor[1]),
(ymax_coor[0], ymax_coor[1])])
index = df1[df1['id'] == row1['id']].index[0]
df1.loc[index, 'min_h'] = min(row1['min_h'],
row2['min_h'])
df1.loc[index, 'max_h'] = min(row1['max_h'],
row2['max_h'])
df1.loc[index,
'ref_h'] = (row1['ref_h'] + row2['ref_h']) / 2
df1.loc[index,
'depth'] = (row1['depth'] + row2['depth']) / 2
df1.loc[index, 'dis'] = utils.l2_distance_2d(
ymin_coor[:2], ymax_coor[:2])
df1.loc[index, 'geometry'] = merge_line
if baseline == 1:
match[row1['id']].append((file_name2, row2['id']))
else:
match[row1['id']].insert(0,
(file_name2, row2['id']))
for i in set(repeat):
df1.drop(index=df1[df1['id'] == i].index[0], inplace=True)
match.pop(i)
count = 1
base = df1.iloc[-1]['id']
for i in range(df2.shape[0]):
if i in matched_id:
continue
row2 = df2.iloc[i]
tmp_row2_id = row2['id']
row2['id'] = base + count
if row2['id'] not in match.keys():
match[row2['id']] = [(file_name2, tmp_row2_id)]
df1 = df1.append(row2)
count += 1
df1 = df1.reset_index(drop=True)
return df1, match
points = sorted(points, key=lambda x: x[0][0])
while len(points) > 0:
print('\r...{}'.format(len(points)))
cur_coor, cur_id = points.pop(0)
if cur_id in popped_id:
continue
#print("in cur:", cur_id)
cur_neighboor = [(cur_coor, cur_id)]
#line1 = np.array(row['geometry']).reshape(-1, )
cur_slops = [slop[cur_id]]
cur_dis = [distance[cur_id]]
for coor, nid in points:
if utils.l2_distance_2d(coor, cur_coor) < 10:
cur_neighboor.append((coor, nid))
cur_slops.append(slop[nid])
cur_dis.append(distance[nid])
if len(cur_neighboor) == 1:
continue
best_index = mode(cur_slops)
#print("best_index:", best_index)
try:
index_1 = df[df['id'] == cur_neighboor[best_index][1]].index[0]
except:
print(cur_neighboor[best_index][1])
#print(df[df['id']==cur_neighboor[best_index][1]])
raise InterruptedError()
