refactor: unify visualization method naming in ImageVisualizer

This commit refactors the ImageVisualizer class by renaming the `visualize_image` method to `visualize`, streamlining the interface for users. The updated method signature and examples in the docstring reflect this change, ensuring consistency in how predictions are visualized. Additionally, the copyright year has been updated to 2024-2025.

Signed-off-by: Samet Akcay <samet.akcay@intel.com>

Author: samet-akcay

src/anomalib/visualization/image/visualizer.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2024 Intel Corporation
+# Copyright (C) 2024-2025 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
 """Image visualization module for anomaly detection.
@@ -16,7 +16,7 @@
     >>> # Create visualizer with default settings
     >>> visualizer = ImageVisualizer()
     >>> # Generate visualization
-    >>> vis_result = visualizer.visualize(image=img, pred_mask=mask)
+    >>> vis_result = visualizer.visualize(predictions)
 
 The module ensures consistent visualization by:
     - Providing standardized field configurations
@@ -186,7 +186,7 @@ def __init__(
         self.text_config = {**DEFAULT_TEXT_CONFIG, **(text_config or {})}
         self.output_dir = output_dir
 
-    def visualize_image(
+    def visualize(
         self,
         predictions: "ImageItem | NumpyImageItem | ImageBatch | NumpyImageBatch",
     ) -> Image.Image | list[Image.Image | None] | None:
@@ -218,7 +218,7 @@ def visualize_image(
             ...     pred_mask=torch.rand(224, 224) > 0.5
             ... )
             >>> visualizer = ImageVisualizer()
-            >>> result = visualizer.visualize_image(item)
+            >>> result = visualizer.visualize(item)
             >>> isinstance(result, Image.Image) or result is None
             True
 
@@ -231,7 +231,7 @@ def visualize_image(
             ...     anomaly_map=np.random.rand(224, 224),
             ...     pred_mask=np.random.rand(224, 224) > 0.5
             ... )
-            >>> result = visualizer.visualize_image(item)
+            >>> result = visualizer.visualize(item)
             >>> isinstance(result, Image.Image) or result is None
             True
 
@@ -242,7 +242,7 @@ def visualize_image(
             ...     image=torch.rand(1, 3, 224, 224),
             ...     anomaly_map=torch.rand(1, 224, 224)
             ... )
-            >>> result = visualizer.visualize_image(single_batch)
+            >>> result = visualizer.visualize(single_batch)
             >>> isinstance(result, Image.Image) or result is None
             True
 
@@ -252,7 +252,7 @@ def visualize_image(
             ...     image=torch.rand(3, 3, 224, 224),
             ...     anomaly_map=torch.rand(3, 224, 224)
             ... )
-            >>> results = visualizer.visualize_image(multi_batch)
+            >>> results = visualizer.visualize(multi_batch)
             >>> isinstance(results, list) and len(results) == 3
             True
 
@@ -323,19 +323,19 @@ def __call__(
         """Make the visualizer callable.
 
         This method allows the visualizer to be used as a callable object.
-        It behaves identically to the ``visualize_image`` method.
+        It behaves identically to the ``visualize`` method.
 
         Args:
-            predictions: The predictions to visualize. Same as ``visualize_image``.
+            predictions: The predictions to visualize. Same as ``visualize``.
 
         Returns:
-            Same as ``visualize_image`` method.
+            Same as ``visualize`` method.
 
         Examples:
             >>> visualizer = ImageVisualizer()
-            >>> result = visualizer(predictions)  # Equivalent to visualizer.visualize_image(predictions)
+            >>> result = visualizer(predictions)  # Equivalent to visualizer.visualize(predictions)
         """
-        return self.visualize_image(predictions)
+        return self.visualize(predictions)
 
     def on_test_batch_end(
         self,