avoid clean_pcd process large number of points; roll back to conservative version (retain noise cluster)

handcrafted_solution.py

@@ -775,16 +775,20 @@ def clean_gest(gest_seg_np):
   return gest_seg_np
 
 def clean_PCD(XYZ, rgb):
-  db = DBSCAN(eps=150, min_samples=20).fit(XYZ)
-  labels = db.labels_
-  unique_labels = set(labels)
-  core_samples_mask = np.zeros_like(labels, dtype=bool)
+  lowest_z = 0
   center_thr = 500
-  retain_class_mask = labels == -2 
   largest_blob_size = 0
   largest_blob = 0
-  lowest_z = 0
+  # avoid memory issue
+  if len(XYZ) > 130000 or len(XYZ) < 20: 
+    return XYZ, rgb, lowest_z
+  # clustering
+  db = DBSCAN(eps=150, min_samples=20).fit(XYZ)
+  labels = db.labels_
+  unique_labels = set(labels)
   #print('uni label:', len(unique_labels))
+  #core_samples_mask = np.zeros_like(labels, dtype=bool)  
+  retain_class_mask = labels == -2       
   if len(unique_labels) > 40 or len(unique_labels) == 1:
     return XYZ, rgb, lowest_z
 
@@ -797,11 +801,11 @@ def clean_PCD(XYZ, rgb):
   
   for k in unique_labels:
     class_member_mask = labels == k
-    '''
+    
     if k == -1:
       retain_class_mask = retain_class_mask | class_member_mask
       continue    
-    '''  
+    
     ''' center prior is not valid    
     pt_k = XYZ[class_member_mask]
     Xmean = np.mean(pt_k[:,0])
@@ -813,7 +817,7 @@ def clean_PCD(XYZ, rgb):
       retain_class_mask = retain_class_mask | class_member_mask
       pt_k = XYZ[class_member_mask]
       lowest_z = min(pt_k[:,2])
-      break      
+      #break      
   
   XYZ = XYZ[retain_class_mask]
   rgb = rgb[retain_class_mask]