empty conn; clean_pcd using OPTICS, excl noise with a bit optimization; 10 cores

handcrafted_solution.py

@@ -486,6 +486,7 @@ def get_vertices_and_edges_from_two_segmentations(ade_seg_np, gest_seg_np, edge_
 
     connections, line_img = Ridge_connects_two_apex_points(gest_seg_np, color_th, apex_pts, edge_th)
     '''
+    '''
     def classifyPairs(apex_pts, apex_pts_idxs, gest_seg_np, apex_mask, eave_end_mask):
       conn = []
       # Plot all possible connection pixels in one mask
@@ -538,36 +539,10 @@ def get_vertices_and_edges_from_two_segmentations(ade_seg_np, gest_seg_np, edge_
       filename_edges_map = f'edges_map_{rid}.jpg'
       if 'line_img' in locals():
         cv2.imwrite(filename_edges_map, line_img)
-      
-      
+    '''  
+    connections = []  
     return vertices, connections
 
-def get_uv_dept_category(vertices, depth, ade_seg):
-    '''Get the depth of the vertices from the depth image'''
-    uv = []
-    for v in vertices:
-        uv.append(v['xy'])
-    uv = np.array(uv)
-    uv_int = uv.astype(np.int32)
-    H, W = depth.shape[:2]
-    uv_int[:, 0] = np.clip( uv_int[:, 0], 0, W-1)
-    uv_int[:, 1] = np.clip( uv_int[:, 1], 0, H-1)
-    vertex_depth = depth[(uv_int[:, 1] , uv_int[:, 0])]
-    vertex_category = ade_seg[(uv_int[:, 1] , uv_int[:, 0])]
-    target_color = set([(120,120,120), (180, 120, 120), (255,9,224)])
-    #filter_ind = [i for i, ele in enumerate(vertex_category) if tuple(ele) in target_color]
-    filter_ind = []
-    for i, ele in enumerate(vertex_category):
-      if tuple(ele) in target_color:
-        filter_ind.append(i)
-
-    print(f'retain {len(filter_ind)} idx')
-    print(vertex_category[filter_ind])
-    #print(vertices)
-    #print(filter_ind)
-    vertices = [vertices[i] for i in filter_ind]
-    return uv[filter_ind], vertex_depth[filter_ind], vertex_category[filter_ind], vertices
-
 def merge_vertices_3d(vert_edge_per_image, th=0.1):
     '''Merge vertices that are close to each other in 3D space and are of same types'''
     all_3d_vertices = []
@@ -800,7 +775,7 @@ def clean_PCD(XYZ, rgb):
       largest_blob = k
   
   for k in unique_labels:
-    class_member_mask = labels == k
+    
     '''
     if k == -1:
       retain_class_mask = retain_class_mask | class_member_mask
@@ -814,9 +789,10 @@ def clean_PCD(XYZ, rgb):
       retain_class_mask = retain_class_mask | class_member_mask
     '''
     if k == largest_blob:
+      class_member_mask = labels == k
       retain_class_mask = retain_class_mask | class_member_mask
-      pt_k = XYZ[class_member_mask]
-      lowest_z = min(pt_k[:,2])
+      #pt_k = XYZ[class_member_mask]
+      #lowest_z = min(pt_k[:,2])
       break      
   
   XYZ = XYZ[retain_class_mask]
@@ -895,9 +871,10 @@ def predict(entry, visualize=False, prune_dist_thr=600, depth_scale=2.5, ) -> Tu
     #all_3d_vertices_clean, connections_3d_clean  = prune_not_connected(all_3d_vertices, connections_3d)    
     #all_3d_vertices, connections_3d  = prune_tall_short(all_3d_vertices, connections_3d, lowest_z, 1000, 0)
     
-  
+    '''
     if len(all_3d_vertices)>35:
       all_3d_vertices, connections_3d  = prune_not_connected(all_3d_vertices, connections_3d)    
+    '''
     if len(all_3d_vertices)>10:
       all_3d_vertices_clean, connections_3d_clean = prune_far(all_3d_vertices, connections_3d, prune_dist_thr=prune_dist_thr)
     else:

script.py

@@ -146,7 +146,7 @@ if __name__ == "__main__":
     print('------------ Now you can do your solution ---------------')
     solution = []
     from concurrent.futures import ProcessPoolExecutor
-    with ProcessPoolExecutor(max_workers=16) as pool:
+    with ProcessPoolExecutor(max_workers=10) as pool:
         results = []
         for i, sample in enumerate(tqdm(dataset)):
             results.append(pool.submit(predict, sample, visualize=False))