added full pipeline

utils/pointcloud.py

@@ -1,7 +1,7 @@
 import numpy as np
 from sklearn.cluster import DBSCAN
 
-import open3d as o3d
+#import open3d as o3d
 
 from pyod.models.lof import LOF
 
@@ -52,6 +52,8 @@ def label2color(labels):
     num = labels.shape[0]
     colors = np.zeros((num, 3))
 
+    print(labels)
+
     minl, maxl = np.min(labels), np.max(labels)
     for l in range(minl, maxl + 1):
         colors[labels == l, :] = mini_color_table(l)
@@ -189,19 +191,6 @@ def cluster_dbscan(data, selected_indices, eps, min_samples=5, metric='euclidean
 
     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")
@@ -216,7 +205,7 @@ def write_clusters(path, clusters, type_column=6):
                 return 2
             elif t == 1:
                 return 1
-            elif t == 3:
+            elif t == 2:
                 return 4
             return t
 
@@ -228,15 +217,6 @@ def write_clusters(path, clusters, type_column=6):
         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)
@@ -284,7 +264,7 @@ def split_outliers(pc, columns):
 
 def append_onehotencoded_type(data, factor=1.0):
     types = data[:, 6].astype(int)
-    res = np.zeros((len(types), 8))
+    res = np.zeros((len(types), 3))
     res[np.arange(len(types)), types] = factor
 
     return np.column_stack((data, res))