Visualization bug fixes

src/python/model_api/visualizer/scene/segmentation/instance_segmentation.py

@@ -20,7 +20,8 @@ class InstanceSegmentationScene(Scene):
 
     def __init__(self, image: Image, result: InstanceSegmentationResult, layout: Union[Layout, None] = None) -> None:
         # nosec as random is used for color generation
-        self.color_per_label = {label: f"#{random.randint(0, 0xFFFFFF):06x}" for label in set(result.label_names)}  # noqa: S311 # nosec B311
+        g = random.Random(0)  # noqa: S311 # nosec B311
+        self.color_per_label = {label: f"#{g.randint(0, 0xFFFFFF):06x}" for label in set(result.label_names)}  # nosec B311
         super().__init__(
             base=image,
             label=self._get_labels(result),
@@ -54,7 +55,7 @@ def _get_bounding_boxes(self, result: InstanceSegmentationResult) -> list[Boundi
 
     def _get_overlays(self, result: InstanceSegmentationResult) -> list[Overlay]:
         overlays = []
-        if len(result.saliency_map) > 0:
+        if result.saliency_map is not None and len(result.saliency_map) > 0:
             labels_label_names_mapping = dict(zip(result.labels, result.label_names))
             for label, label_name in labels_label_names_mapping.items():
                 saliency_map = result.saliency_map[label - 1]

src/python/model_api/visualizer/scene/segmentation/segmentation.py

@@ -37,7 +37,7 @@ def _get_overlays(self, result: ImageResultWithSoftPrediction) -> list[Overlay]:
             overlays.append(Overlay(class_map, label=f"Class {i}"))
 
         # Add saliency map
-        if result.saliency_map.size > 0:
+        if result.saliency_map is not None and result.saliency_map.size > 0:
             saliency_map = cv2.cvtColor(result.saliency_map, cv2.COLOR_BGR2RGB)
             overlays.append(Overlay(saliency_map, label="Saliency Map"))
 

src/python/model_api/visualizer/visualizer.py

@@ -1,6 +1,6 @@
 """Visualizer for modelAPI."""
 
-# Copyright (C) 2024 Intel Corporation
+# Copyright (C) 2024-2025 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
 from __future__ import annotations  # TODO: remove when Python3.9 support is dropped
@@ -42,18 +42,32 @@ class Visualizer:
     def __init__(self, layout: Layout | None = None) -> None:
         self.layout = layout
 
-    def show(self, image: Image | np.ndarray, result: Result) -> None:
+    def show(self, image: Image.Image | np.ndarray, result: Result) -> None:
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
         scene = self._scene_from_result(image, result)
         return scene.show()
 
-    def save(self, image: Image | np.ndarray, result: Result, path: Path) -> None:
+    def save(self, image: Image.Image | np.ndarray, result: Result, path: Path) -> None:
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
         scene = self._scene_from_result(image, result)
         scene.save(path)
 
+    def render(self, image: Image.Image | np.ndarray, result: Result) -> Image.Image | np.ndarray:
+        is_numpy = isinstance(image, np.ndarray)
+
+        if is_numpy:
+            image = Image.fromarray(image)
+
+        scene = self._scene_from_result(image, result)
+        result_img: Image = scene.render()
+
+        if is_numpy:
+            return np.array(result_img)
+
+        return result_img
+
     def _scene_from_result(self, image: Image, result: Result) -> Scene:
         scene: Scene
         if isinstance(result, AnomalyResult):

tests/python/unit/visualizer/test_scene.py

@@ -7,6 +7,7 @@
 
 import numpy as np
 from PIL import Image
+import pytest
 
 from model_api.models.result import (
     AnomalyResult,
@@ -71,7 +72,8 @@ def test_detection_scene(mock_image: Image, tmpdir: Path):
     assert Path(tmpdir / "detection_scene.jpg").exists()
 
 
-def test_segmentation_scene(mock_image: Image, tmpdir: Path):
+@pytest.mark.parametrize("with_saliency_map", [True, False])
+def test_segmentation_scene(mock_image: Image, tmpdir: Path, with_saliency_map: bool):
     """Test if the segmentation scene is created."""
     visualizer = Visualizer()
 
@@ -85,7 +87,9 @@ def test_segmentation_scene(mock_image: Image, tmpdir: Path):
         ),
         scores=np.array([0.85, 0.75]),
         label_names=["person", "car"],
-        saliency_map=[np.ones((128, 128), dtype=np.uint8) * 255],
+        saliency_map=[np.ones((128, 128), dtype=np.uint8) * 255]
+        if with_saliency_map
+        else None,
         feature_vector=np.array([1, 2, 3, 4]),
     )
 
@@ -104,7 +108,9 @@ def test_segmentation_scene(mock_image: Image, tmpdir: Path):
         soft_prediction=np.ones(
             (3, 3, 3), dtype=np.float32
         ),  # 3 classes, 3x3 prediction
-        saliency_map=np.ones((3, 3), dtype=np.uint8) * 255,
+        saliency_map=np.ones((3, 3), dtype=np.uint8) * 255
+        if with_saliency_map
+        else None,
         feature_vector=np.array([1, 2, 3, 4]),
     )
 

tests/python/unit/visualizer/test_visualizer.py

@@ -0,0 +1,43 @@
+"""Tests for visualizer."""
+
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+from model_api.models.result import (
+    AnomalyResult,
+)
+from model_api.visualizer import Visualizer
+
+
+def test_render(mock_image: Image, tmpdir: Path):
+    """Test Visualizer.render()."""
+    heatmap = np.ones(mock_image.size, dtype=np.uint8)
+    heatmap *= 255
+
+    mask = np.zeros(mock_image.size, dtype=np.uint8)
+    mask[32:96, 32:96] = 255
+    mask[40:80, 0:128] = 255
+
+    anomaly_result = AnomalyResult(
+        anomaly_map=heatmap,
+        pred_boxes=np.array([[0, 0, 128, 128], [32, 32, 96, 96]]),
+        pred_label="Anomaly",
+        pred_mask=mask,
+        pred_score=0.85,
+    )
+
+    visualizer = Visualizer()
+    rendered_img = visualizer.render(mock_image, anomaly_result)
+
+    assert isinstance(rendered_img, Image.Image)
+    assert np.array(rendered_img).shape == np.array(mock_image).shape
+
+    rendered_img_np = visualizer.render(np.array(mock_image), anomaly_result)
+
+    assert isinstance(rendered_img_np, np.ndarray)
+    assert rendered_img_np.shape == np.array(mock_image).shape