289 lines
8.0 KiB
Python
289 lines
8.0 KiB
Python
import numpy as np
|
|
import open3d as o3d
|
|
from sklearn.cluster import DBSCAN
|
|
|
|
|
|
from pyod.models.knn import KNN
|
|
from pyod.models.sod import SOD
|
|
from pyod.models.abod import ABOD
|
|
from pyod.models.sos import SOS
|
|
from pyod.models.pca import PCA
|
|
from pyod.models.ocsvm import OCSVM
|
|
from pyod.models.mcd import MCD
|
|
from pyod.models.lof import LOF
|
|
from pyod.models.cof import COF
|
|
from pyod.models.cblof import CBLOF
|
|
from pyod.models.loci import LOCI
|
|
from pyod.models.hbos import HBOS
|
|
from pyod.models.lscp import LSCP
|
|
from pyod.models.feature_bagging import FeatureBagging
|
|
|
|
|
|
def mini_color_table(index, norm=True):
|
|
colors = [
|
|
[0.5000, 0.5400, 0.5300], [0.8900, 0.1500, 0.2100], [0.6400, 0.5800, 0.5000],
|
|
[1.0000, 0.3800, 0.0100], [1.0000, 0.6600, 0.1400], [0.4980, 1.0000, 0.0000],
|
|
[0.4980, 1.0000, 0.8314], [0.9412, 0.9725, 1.0000], [0.5412, 0.1686, 0.8863],
|
|
[0.5765, 0.4392, 0.8588], [0.3600, 0.1400, 0.4300], [0.5600, 0.3700, 0.6000],
|
|
]
|
|
|
|
color = colors[index % len(colors)]
|
|
|
|
if not norm:
|
|
color[0] *= 255
|
|
color[1] *= 255
|
|
color[2] *= 255
|
|
|
|
return color
|
|
|
|
|
|
def cluster2Color(cluster, cluster_idx):
|
|
|
|
colors = np.zeros(shape=(len(cluster), 3))
|
|
point_idx = 0
|
|
for point in cluster:
|
|
colors[point_idx, :] = mini_color_table(cluster_idx)
|
|
point_idx += 1
|
|
|
|
return colors
|
|
|
|
|
|
def label2color(labels):
|
|
'''
|
|
labels: np.ndarray with shape (n, )
|
|
colors(return): np.ndarray with shape (n, 3)
|
|
'''
|
|
num = labels.shape[0]
|
|
colors = np.zeros((num, 3))
|
|
|
|
minl, maxl = np.min(labels), np.max(labels)
|
|
for l in range(minl, maxl + 1):
|
|
colors[labels == l, :] = mini_color_table(l)
|
|
|
|
return colors
|
|
|
|
|
|
def read_pointcloud(path, delimiter=' ', hasHeader=True):
|
|
with open(path, 'r') as f:
|
|
if hasHeader:
|
|
# Get rid of the Header
|
|
_ = f.readline()
|
|
# This iterates over all lines, splits them and converts values to floats. Will fail on wrong values.
|
|
pc = [[float(x) for x in line.rstrip().split(delimiter)] for line in f if line != '']
|
|
|
|
return np.asarray(pc)
|
|
|
|
|
|
def write_pointcloud(file, pc, numCols=6):
|
|
np.savetxt(file, pc[:,:numCols], header=str(len(pc)) + ' ' + str(numCols), comments='')
|
|
|
|
|
|
def farthest_point_sampling(pts, K):
|
|
|
|
if pts.shape[0] < K:
|
|
return pts
|
|
|
|
def calc_distances(p0, points):
|
|
return ((p0[:3] - points[:, :3]) ** 2).sum(axis=1)
|
|
|
|
farthest_pts = np.zeros((K, pts.shape[1]))
|
|
farthest_pts[0] = pts[np.random.randint(len(pts))]
|
|
distances = calc_distances(farthest_pts[0], pts)
|
|
for i in range(1, K):
|
|
farthest_pts[i] = pts[np.argmax(distances)]
|
|
distances = np.minimum(distances, calc_distances(farthest_pts[i], pts))
|
|
|
|
return farthest_pts
|
|
|
|
|
|
def cluster_per_column(pc, column):
|
|
|
|
clusters = []
|
|
for i in range(0, int(np.max(pc[:, column]))):
|
|
cluster_pc = pc[pc[:, column] == i, :]
|
|
clusters.append(cluster_pc)
|
|
|
|
return clusters
|
|
|
|
|
|
def cluster_cubes(data, cluster_dims, max_points_per_cluster=-1, min_points_per_cluster=-1):
|
|
|
|
if cluster_dims[0] is 1 and cluster_dims[1] is 1 and cluster_dims[2] is 1:
|
|
print("no need to cluster.")
|
|
return [data]
|
|
|
|
max = data[:,:3].max(axis=0)
|
|
max += max * 0.01
|
|
|
|
min = data[:,:3].min(axis=0)
|
|
min -= min * 0.01
|
|
|
|
size = (max - min)
|
|
|
|
clusters = {}
|
|
|
|
cluster_size = size / np.array(cluster_dims, dtype=np.float32)
|
|
|
|
print('Min: ' + str(min) + ' Max: ' + str(max))
|
|
print('Cluster Size: ' + str(cluster_size))
|
|
|
|
for row in data:
|
|
|
|
# print('Row: ' + str(row))
|
|
|
|
cluster_pos = ((row[:3] - min) / cluster_size).astype(int)
|
|
cluster_idx = cluster_dims[0] * cluster_dims[2] * cluster_pos[1] + cluster_dims[0] * cluster_pos[2] + cluster_pos[0]
|
|
clusters.setdefault(cluster_idx, []).append(row)
|
|
|
|
# Apply farthest point sampling to each cluster
|
|
final_clusters = []
|
|
for key, cluster in clusters.items():
|
|
c = np.vstack(cluster)
|
|
if c.shape[0] < min_points_per_cluster and -1 is not min_points_per_cluster:
|
|
continue
|
|
|
|
if max_points_per_cluster is not -1:
|
|
final_clusters.append(farthest_point_sampling(c, max_points_per_cluster))
|
|
else:
|
|
final_clusters.append(c)
|
|
|
|
return final_clusters
|
|
|
|
|
|
def cluster_dbscan(data, selected_indices, eps, min_samples=5, metric='euclidean', algo='auto'):
|
|
|
|
# print('Clustering. Min Samples: ' + str(min_samples) + ' EPS: ' + str(eps) + "Selected Indices: " + str(selected_indices))
|
|
|
|
# 0,1,2 : pos
|
|
# 3,4,5 : normal
|
|
# 6: type index
|
|
# 7,8,9,10: type index one hot encoded
|
|
# 11,12: normal as angles
|
|
|
|
db_res = DBSCAN(eps=eps, metric=metric, n_jobs=-1, min_samples=min_samples, algorithm=algo).fit(data[:, selected_indices])
|
|
|
|
|
|
labels = db_res.labels_
|
|
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
|
|
n_noise = list(labels).count(-1)
|
|
# print("Noise: " + str(n_noise) + " Clusters: " + str(n_clusters))
|
|
|
|
clusters = {}
|
|
for idx, l in enumerate(labels):
|
|
if l is -1:
|
|
continue
|
|
clusters.setdefault(str(l), []).append(data[idx, :])
|
|
|
|
|
|
npClusters = []
|
|
for cluster in clusters.values():
|
|
npClusters.append(np.array(cluster))
|
|
|
|
return npClusters
|
|
|
|
def draw_clusters(clusters):
|
|
|
|
clouds = []
|
|
|
|
for cluster_idx, cluster in enumerate(clusters):
|
|
|
|
cloud = o3d.PointCloud()
|
|
cloud.points = o3d.Vector3dVector(cluster[:,:3])
|
|
cloud.colors = o3d.Vector3dVector(cluster2Color(cluster, cluster_idx))
|
|
clouds.append(cloud)
|
|
|
|
o3d.draw_geometries(clouds)
|
|
|
|
|
|
def write_clusters(path, clusters, type_column=6):
|
|
|
|
file = open(path, "w")
|
|
file.write(str(len(clusters)) + "\n")
|
|
|
|
for cluster in clusters:
|
|
# print("Types: ", cluster[:, type_column])
|
|
|
|
types = np.unique(cluster[:, type_column], axis=0).astype(int)
|
|
|
|
def type_mapping(t):
|
|
if t == 0:
|
|
return 2
|
|
elif t == 1:
|
|
return 1
|
|
elif t == 3:
|
|
return 4
|
|
return t
|
|
|
|
types = np.array([type_mapping(t) for t in types])
|
|
print("Types: {}, Points: {}".format(types, cluster.shape[0]))
|
|
# draw_sample_data(cluster)
|
|
|
|
np.savetxt(file, types.reshape(1, types.shape[0]),delimiter=';', header='', comments='', fmt='%i')
|
|
np.savetxt(file, cluster[:, :6], header=str(len(cluster)) + ' ' + str(6), comments='')
|
|
|
|
|
|
def draw_sample_data(sample_data, colored_normals = False):
|
|
|
|
cloud = o3d.PointCloud()
|
|
cloud.points = o3d.Vector3dVector(sample_data[:, :3])
|
|
cloud.colors = \
|
|
o3d.Vector3dVector(label2color(sample_data[:, 6].astype(int)) if not colored_normals else sample_data[:, 3:6])
|
|
|
|
o3d.draw_geometries([cloud])
|
|
|
|
|
|
def normalize_pointcloud(pc, factor=1.0):
|
|
|
|
max = pc.max(axis=0)
|
|
min = pc.min(axis=0)
|
|
|
|
f = np.max([abs(max[0] - min[0]), abs(max[1] - min[1]), abs(max[2] - min[2])])
|
|
|
|
pc[:, 0:3] /= (f * factor)
|
|
pc[:, 3:6] /= (np.linalg.norm(pc[:, 3:6], ord=2, axis=1, keepdims=True))
|
|
|
|
return pc
|
|
|
|
|
|
def hierarchical_clustering(data, selected_indices_0, selected_indices_1, eps, min_samples=5, metric='euclidean', algo='auto'):
|
|
|
|
total_clusters = []
|
|
|
|
clusters = cluster_dbscan(data, selected_indices_0, eps, min_samples, metric=metric, algo=algo)
|
|
|
|
for cluster in clusters:
|
|
# cluster = normalize_pointcloud(cluster)
|
|
sub_clusters = cluster_dbscan(cluster, selected_indices_1, eps, min_samples, metric=metric, algo=algo)
|
|
total_clusters.extend(sub_clusters)
|
|
|
|
return total_clusters
|
|
|
|
|
|
def filter_clusters(clusters, filter):
|
|
|
|
filtered_clusters = []
|
|
|
|
for c in clusters:
|
|
if filter(c):
|
|
filtered_clusters.append(c)
|
|
|
|
return filtered_clusters
|
|
|
|
|
|
def split_outliers(pc, columns):
|
|
clf = LOF()# FeatureBagging() # detector_list=[LOF(), KNN()]
|
|
clf.fit(pc[:, columns])
|
|
|
|
# LOF, kNN
|
|
|
|
return pc[clf.labels_ == 0], pc[clf.labels_ == 1]
|
|
|
|
|
|
def append_onehotencoded_type(data, factor = 1.0):
|
|
|
|
types = data[:, 6].astype(int)
|
|
res = np.zeros((len(types), 4))
|
|
res[np.arange(len(types)), types] = factor
|
|
|
|
return np.column_stack((data, res))
|