Version update

examples.py

@@ -86,11 +86,11 @@ def evaluate_detection_example():
     mask_shape = (480, 640)
 
     # Creating fake data
-    gt_bbox_1 = BBox(10, 10, 10, 10, 0)
-    pred_bbox_1 = BBox(10, 10, 10, 10, 0)
+    gt_bbox_1 = BBox(10, 10, 10, 10, 0, 1)
+    pred_bbox_1 = BBox(10, 10, 10, 10, 0, 1)
 
-    gt_bbox_2 = BBox(110, 110, 320, 280, 1)
-    pred_bbox_2 = BBox(70, 50, 240, 220, 1)
+    gt_bbox_2 = BBox(110, 110, 320, 280, 1, 1)
+    pred_bbox_2 = BBox(70, 50, 240, 220, 1, 1)
 
     # Creating data suplier iterator
     # It is not necessary here, but it's useful if you want to yield data

lapixdl/evaluation/visualize.py

@@ -82,6 +82,8 @@ def show_segmentations(
 
     cmap_colors = __get_colors(cmap)
 
+    assert len(cmap_colors) >= len(class_names), "The color map length must be greater or equal the length of the class names."
+
     rows = len(results)
     fig, axes = plt.subplots(rows, 3)
     if rows == 1:
@@ -140,6 +142,8 @@ def show_detections(results: List[Result[List[BBox]]],
 
     cmap_colors = __get_colors(cmap)
 
+    assert len(cmap_colors) >= len(class_names), "The color map length must be greater or equal the length of the class names."
+
     rows = len(results)
     fig, axes = plt.subplots(rows, 3)
     if rows == 1:

setup.py

@@ -6,7 +6,7 @@
 setup(
     name='lapixdl',
     packages=find_packages(exclude=['tests']),
-    version='0.7.12',
+    version='0.7.13',
     description='Utils for Computer Vision Deep Learning research',
 
     long_description=long_description,