Fixed small issues and added image reading method

agml/io.py

@@ -15,6 +15,8 @@
 import random
 import inspect
 
+import cv2
+
 from agml.utils.io import (
     get_file_list as _get_file_list,
     get_dir_list as _get_dir_list,
@@ -112,5 +114,15 @@ def random_file(path, **kwargs):
     return random.choice(get_file_list(path, **kwargs))
 
 
+def read_image(path, **kwargs):
+    """Reads an image from a file.
+
+    Args:
+        path (str): The path to the image file.
+        **kwargs: Keyword arguments to pass to `cv2.imread`.
 
+    Returns:
+        numpy.ndarray: The image.
+    """
+    return cv2.imread(path, **kwargs)
 

agml/models/segmentation.py

@@ -32,7 +32,7 @@
 from agml.data.public import source
 from agml.utils.general import resolve_list_value
 from agml.utils.image import resolve_image_size
-from agml.viz.masks import show_image_with_overlaid_mask, show_image_and_mask
+from agml.viz.masks import show_image_and_overlaid_mask, show_image_and_mask
 
 # This is last since `agml.models.base` will check for PyTorch Lightning,
 # and PyTorch Lightning automatically installed torchmetrics with it.
@@ -250,7 +250,7 @@ def show_prediction(self, image, overlay = False, **kwargs):
         image = self._expand_input_images(image)[0]
         mask = self.predict(image, **kwargs)
         if overlay:
-            return show_image_with_overlaid_mask(image, mask, **kwargs)
+            return show_image_and_overlaid_mask(image, mask, **kwargs)
         return show_image_and_mask(image, mask, **kwargs)
 
     def load_benchmark(self, dataset):

agml/viz/boxes.py

@@ -110,6 +110,8 @@ def annotate_object_detection(image,
                     "either `bbox` or `bboxes` for bounding boxes.")
     if bbox_format is not None:
         bboxes = convert_bbox_format(bboxes, bbox_format)
+    if labels is None:
+        labels = [0] * len(bboxes)
 
     # Run a few final checks in order to ensure data is formatted properly.
     image = format_image(image, mask = False)