accept w/o semantics

hoho/hoho.py

@@ -4,6 +4,14 @@ import shutil
 from pathlib import Path
 from typing import Dict
 import warnings 
+import contextlib
+import tempfile 
+from PIL import Image
+import io
+import webdataset as wds 
+import numpy as np
+import importlib
+import subprocess
 
 from PIL import ImageFile
 
@@ -40,13 +48,7 @@ def setup(local_dir='./data/usm-training-data/data'):
         LOCAL_DATADIR.mkdir(parents=True)
     
     return LOCAL_DATADIR
-    
-    
-    
-    
-import importlib
-from pathlib import Path
-import subprocess
+
 
 def download_package(package_name, path_to_save='packages'):
     """
@@ -139,9 +141,7 @@ def Rt_to_eye_target(im, K, R, t):
 
 
 ########## general utilities ##########
-import contextlib
-import tempfile 
-from pathlib import Path
+
 
 @contextlib.contextmanager
 def working_directory(path):
@@ -184,10 +184,6 @@ def proc(row, split='train'):
     return Sample(out)
 
 
-
-
-
-
 from . import read_write_colmap
 def decode_colmap(s):
     with temp_working_directory():
@@ -209,8 +205,7 @@ def decode_colmap(s):
         )
     return cameras, images, points3D
 
-from PIL import Image
-import io
+
 def decode(row):
     cameras, images, points3D = decode_colmap(row)
     
@@ -288,8 +283,6 @@ def get_params():
 
 
 
-import webdataset as wds 
-import numpy as np
 
 
 SHARD_IDS = {'train': (0, 25), 'val': (25, 26), 'public': (26, 27), 'private': (27, 32)}

hoho/vis.py

@@ -51,7 +51,14 @@ def show_wf(row, radius=10):
                     'valley',
                     'hip',
                     'transition_line']
-    return [line(a,b, radius=radius, c=color_mappings.gestalt_color_mapping[EDGE_CLASSES[cls_id]]) for (a,b), cls_id in zip(np.stack([*row['wf_vertices']])[np.stack(row['wf_edges'])], row['edge_semantics'])]
+    if 'edge_semantics' not in row:
+        print ("Warning: edge semantics is not here, skipping")
+        return [line(a,b, radius=radius, c=(214, 251, 248)) for a,b in np.stack([*row['wf_vertices']])[np.stack(row['wf_edges'])]]
+    elif len(np.stack(row['wf_edges'])) ==  len(row['edge_semantics']):
+        return [line(a,b, radius=radius, c=color_mappings.gestalt_color_mapping[EDGE_CLASSES[cls_id]]) for (a,b), cls_id in zip(np.stack([*row['wf_vertices']])[np.stack(row['wf_edges'])], row['edge_semantics'])]
+    else:
+        print ("Warning: edge semantics has different length compared to edges, skipping semantics")
+        return [line(a,b, radius=radius, c=(214, 251, 248)) for a,b in np.stack([*row['wf_vertices']])[np.stack(row['wf_edges'])]]
     # return [line(a,b, radius=radius, c=color_mappings.edge_colors[cls_id]) for (a,b), cls_id in zip(np.stack([*row['wf_vertices']])[np.stack(row['wf_edges'])], row['edge_semantics'])]