From bcabc3c5a8180d33dd36ee88be932197601834bd Mon Sep 17 00:00:00 2001
From: Dick Ameln <dick.ameln@intel.com>
Date: Fri, 29 Apr 2022 11:56:43 +0200
Subject: [PATCH 1/8] add methods for adding labels to image

---
 anomalib/post_processing/visualizer.py | 60 ++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)

diff --git a/anomalib/post_processing/visualizer.py b/anomalib/post_processing/visualizer.py
index 4052defe74..95ee04727e 100644
--- a/anomalib/post_processing/visualizer.py
+++ b/anomalib/post_processing/visualizer.py
@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+import math
 from pathlib import Path
 from typing import Optional, Tuple
 
@@ -86,6 +87,65 @@ def add_text(self, image: np.ndarray, text: str, font: int = cv2.FONT_HERSHEY_PL
             cv2.putText(image, line.strip(), (0, (baseline // 2 + text_h) + offset), font, font_size, (0, 0, 255))
         return image
 
+    @staticmethod
+    def add_label(
+        image: np.ndarray,
+        label_name: str,
+        color: Tuple[int, int, int],
+        confidence: Optional[float] = None,
+        font_scale: float = 5e-3,
+        thickness_scale=1e-3,
+    ):
+        """Adds a label to an image.
+
+        Args:
+            image (np.ndarray): Input image.
+            label_name (str): Name of the label that will be displayed on the image.
+            color (Tuple[int, int, int]): RGB values for background color of label.
+            confidence (Optional[float]): confidence score of the label.
+            font_scale (float): scale of the font size relative to image size. Increase for bigger font.
+            thickness_scale (float): scale of the font thickness. Increase for thicker font.
+
+        Returns:
+            np.ndarray: Image with label.
+        """
+        image = image.copy()
+        img_height, img_width, _ = image.shape
+
+        font = cv2.FONT_HERSHEY_PLAIN
+        text = label_name if confidence is None else f"{label_name}: {confidence:.2}"
+
+        # get font sizing
+        font_scale = min(img_width, img_height) * font_scale
+        thickness = math.ceil(min(img_width, img_height) * thickness_scale)
+        (width, height), baseline = cv2.getTextSize(text, font, fontScale=font_scale, thickness=thickness)
+
+        # create label
+        label_patch = np.zeros((height + baseline, width + baseline, 3), dtype=np.uint8)
+        label_patch[:, :] = color
+        cv2.putText(
+            label_patch,
+            text,
+            (0, baseline // 2 + height),
+            font,
+            fontScale=font_scale,
+            thickness=thickness,
+            color=0,
+            lineType=cv2.LINE_AA,
+        )
+
+        # add label to image
+        image[: baseline + height, : baseline + width] = label_patch
+        return image
+
+    def add_normal_label(self, image: np.ndarray, confidence: Optional[float] = None):
+        """Adds the normal label to the image."""
+        return self.add_label(image, "normal", (225, 252, 134), confidence)
+
+    def add_anomalous_label(self, image: np.ndarray, confidence):
+        """Adds the anomalous label to the image."""
+        return self.add_label(image, "anomalous", (255, 100, 100), confidence)
+
     def show(self):
         """Show image on a matplotlib figure."""
         self.figure.show()

From 97fad838f433dd9261fc77ecccd674009b3cf3c2 Mon Sep 17 00:00:00 2001
From: Dick Ameln <dick.ameln@intel.com>
Date: Tue, 3 May 2022 11:46:40 +0200
Subject: [PATCH 2/8] refactor visualizer

---
 anomalib/post_processing/visualizer.py        | 47 ++++++++++---------
 .../utils/callbacks/visualizer_callback.py    | 36 +++++++-------
 tools/inference.py                            |  2 +-
 3 files changed, 44 insertions(+), 41 deletions(-)

diff --git a/anomalib/post_processing/visualizer.py b/anomalib/post_processing/visualizer.py
index 95ee04727e..737d049dc9 100644
--- a/anomalib/post_processing/visualizer.py
+++ b/anomalib/post_processing/visualizer.py
@@ -19,6 +19,7 @@
 from typing import Optional, Tuple
 
 import cv2
+import matplotlib.figure
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -30,41 +31,28 @@ class Visualizer:
     either be logged by accessing the `figure` attribute or can be saved directly by calling `save()` method.
 
     Example:
-        >>> visualizer = Visualizer(num_rows=1, num_cols=5, figure_size=(12, 3))
+        >>> visualizer = Visualizer()
         >>> visualizer.add_image(image=image, title="Image")
         >>> visualizer.close()
-
-    Args:
-        num_rows (int): Number of rows of images in the figure.
-        num_cols (int): Number of columns/images in each row.
-        figure_size (Tuple[int, int]): Size of output figure
     """
 
-    def __init__(self, num_rows: int, num_cols: int, figure_size: Tuple[int, int]):
-        self.figure_index: int = 0
+    def __init__(self):
 
-        self.figure, self.axis = plt.subplots(num_rows, num_cols, figsize=figure_size)
-        self.figure.subplots_adjust(right=0.9)
+        self.images = []
 
-        for axis in self.axis:
-            axis.axes.xaxis.set_visible(False)
-            axis.axes.yaxis.set_visible(False)
+        self.figure: matplotlib.figure.Figure
+        self.axis: np.ndarray
 
-    def add_image(self, image: np.ndarray, title: str, color_map: Optional[str] = None, index: Optional[int] = None):
+    def add_image(self, image: np.ndarray, title: str, color_map: Optional[str] = None):
         """Add image to figure.
 
         Args:
           image (np.ndarray): Image which should be added to the figure.
           title (str): Image title shown on the plot.
           color_map (Optional[str]): Name of matplotlib color map used to map scalar data to colours. Defaults to None.
-          index (Optional[int]): Figure index. Defaults to None.
         """
-        if index is None:
-            index = self.figure_index
-            self.figure_index += 1
-
-        self.axis[index].imshow(image, color_map, vmin=0, vmax=255)
-        self.axis[index].title.set_text(title)
+        image_data = dict(image=image, title=title, color_map=color_map)
+        self.images.append(image_data)
 
     def add_text(self, image: np.ndarray, text: str, font: int = cv2.FONT_HERSHEY_PLAIN):
         """Puts text on an image.
@@ -142,12 +130,26 @@ def add_normal_label(self, image: np.ndarray, confidence: Optional[float] = None
         """Adds the normal label to the image."""
         return self.add_label(image, "normal", (225, 252, 134), confidence)
 
-    def add_anomalous_label(self, image: np.ndarray, confidence):
+    def add_anomalous_label(self, image: np.ndarray, confidence: Optional[float] = None):
         """Adds the anomalous label to the image."""
         return self.add_label(image, "anomalous", (255, 100, 100), confidence)
 
+    def generate(self):
+        """Generate the image."""
+        num_cols = len(self.images)
+        figure_size = (num_cols * 3, 3)
+        self.figure, self.axis = plt.subplots(1, num_cols, figsize=figure_size)
+        self.figure.subplots_adjust(right=0.9)
+
+        for axis, image_dict in zip(self.axis, self.images):
+            axis.axes.xaxis.set_visible(False)
+            axis.axes.yaxis.set_visible(False)
+            axis.imshow(image_dict["image"], image_dict["color_map"], vmin=0, vmax=255)
+            axis.title.set_text(image_dict["title"])
+
     def show(self):
         """Show image on a matplotlib figure."""
+        self.generate()
         self.figure.show()
 
     def save(self, filename: Path):
@@ -156,6 +158,7 @@ def save(self, filename: Path):
         Args:
           filename (Path): Filename to save image
         """
+        self.generate()
         filename.parent.mkdir(parents=True, exist_ok=True)
         self.figure.savefig(filename, dpi=100)
 
diff --git a/anomalib/utils/callbacks/visualizer_callback.py b/anomalib/utils/callbacks/visualizer_callback.py
index 1d2b674b92..e717815e58 100644
--- a/anomalib/utils/callbacks/visualizer_callback.py
+++ b/anomalib/utils/callbacks/visualizer_callback.py
@@ -133,24 +133,24 @@ def on_test_batch_end(
             pred_mask = compute_mask(anomaly_map, threshold)
             vis_img = mark_boundaries(image, pred_mask, color=(1, 0, 0), mode="thick")
 
-            num_cols = 6 if self.task == "segmentation" else 5
-            visualizer = Visualizer(num_rows=1, num_cols=num_cols, figure_size=(12, 3))
-            visualizer.add_image(image=image, title="Image")
-
-            if "mask" in outputs:
-                true_mask = outputs["mask"][i].cpu().numpy() * 255
-                visualizer.add_image(image=true_mask, color_map="gray", title="Ground Truth")
-
-            visualizer.add_image(image=heat_map, title="Predicted Heat Map")
-            visualizer.add_image(image=pred_mask, color_map="gray", title="Predicted Mask")
-            visualizer.add_image(image=vis_img, title="Segmentation Result")
-
-            image_classified = visualizer.add_text(
-                image=image,
-                text=f"""Pred: { "anomalous" if pred_score > threshold else "normal"}({pred_score:.3f}) \n
-                GT: {"anomalous" if bool(gt_label) else "normal"}""",
-            )
-            visualizer.add_image(image=image_classified, title="Classified Image")
+            visualizer = Visualizer()
+
+            if self.task == "segmentation":
+                visualizer.add_image(image=image, title="Image")
+                if "mask" in outputs:
+                    true_mask = outputs["mask"][i].cpu().numpy() * 255
+                    visualizer.add_image(image=true_mask, color_map="gray", title="Ground Truth")
+                visualizer.add_image(image=heat_map, title="Predicted Heat Map")
+                visualizer.add_image(image=pred_mask, color_map="gray", title="Predicted Mask")
+                visualizer.add_image(image=vis_img, title="Segmentation Result")
+            elif self.task == "classification":
+                gt_im = visualizer.add_anomalous_label(image) if gt_label else visualizer.add_normal_label(image)
+                visualizer.add_image(gt_im, title="Image/True label")
+                if pred_score >= threshold:
+                    image_classified = visualizer.add_anomalous_label(heat_map, pred_score)
+                else:
+                    image_classified = visualizer.add_normal_label(heat_map, 1 - pred_score)
+                visualizer.add_image(image=image_classified, title="Prediction")
 
             self._add_images(visualizer, pl_module, trainer, Path(filename))
             visualizer.close()
diff --git a/tools/inference.py b/tools/inference.py
index ec7b9dac06..784f34fa8e 100644
--- a/tools/inference.py
+++ b/tools/inference.py
@@ -80,7 +80,7 @@ def add_label(prediction: np.ndarray, scores: float, font: int = cv2.FONT_HERSHE
     (width, height), baseline = cv2.getTextSize(text, font, font_size, thickness=font_size // 2)
     label_patch = np.zeros((height + baseline, width + baseline, 3), dtype=np.uint8)
     label_patch[:, :] = (225, 252, 134)
-    cv2.putText(label_patch, text, (0, baseline // 2 + height), font, font_size, 0)
+    cv2.putText(label_patch, text, (0, baseline // 2 + height), font, font_size, 0, lineType=cv2.LINE_AA)
     prediction[: baseline + height, : baseline + width] = label_patch
     return prediction
 

From bfbf6d1d1075cda513d83f136e6888a8841a88f8 Mon Sep 17 00:00:00 2001
From: Dick Ameln <dick.ameln@intel.com>
Date: Wed, 4 May 2022 15:05:52 +0200
Subject: [PATCH 3/8] move add_label to post processing helpers

---
 anomalib/models/padim/config.yaml             |  2 +-
 anomalib/post_processing/__init__.py          | 11 ++-
 anomalib/post_processing/post_process.py      | 64 ++++++++++++++
 anomalib/post_processing/visualizer.py        | 84 +------------------
 .../utils/callbacks/visualizer_callback.py    | 14 +++-
 5 files changed, 86 insertions(+), 89 deletions(-)

diff --git a/anomalib/models/padim/config.yaml b/anomalib/models/padim/config.yaml
index 155661fd56..2e7230a9bf 100644
--- a/anomalib/models/padim/config.yaml
+++ b/anomalib/models/padim/config.yaml
@@ -3,7 +3,7 @@ dataset:
   format: mvtec
   path: ./datasets/MVTec
   category: bottle
-  task: segmentation
+  task: classification
   image_size: 256
   train_batch_size: 32
   test_batch_size: 32
diff --git a/anomalib/post_processing/__init__.py b/anomalib/post_processing/__init__.py
index 11b0f0092c..1fd34aecd2 100644
--- a/anomalib/post_processing/__init__.py
+++ b/anomalib/post_processing/__init__.py
@@ -15,10 +15,19 @@
 # and limitations under the License.
 
 from .post_process import (
+    add_anomalous_label,
+    add_normal_label,
     anomaly_map_to_color_map,
     compute_mask,
     superimpose_anomaly_map,
 )
 from .visualizer import Visualizer
 
-__all__ = ["anomaly_map_to_color_map", "superimpose_anomaly_map", "compute_mask", "Visualizer"]
+__all__ = [
+    "add_anomalous_label",
+    "add_normal_label",
+    "anomaly_map_to_color_map",
+    "superimpose_anomaly_map",
+    "compute_mask",
+    "Visualizer",
+]
diff --git a/anomalib/post_processing/post_process.py b/anomalib/post_processing/post_process.py
index 03482ee2f3..85bb8b779d 100644
--- a/anomalib/post_processing/post_process.py
+++ b/anomalib/post_processing/post_process.py
@@ -15,11 +15,75 @@
 # and limitations under the License.
 
 
+import math
+from typing import Optional, Tuple
+
 import cv2
 import numpy as np
 from skimage import morphology
 
 
+def add_label(
+    image: np.ndarray,
+    label_name: str,
+    color: Tuple[int, int, int],
+    confidence: Optional[float] = None,
+    font_scale: float = 5e-3,
+    thickness_scale=1e-3,
+):
+    """Adds a label to an image.
+
+    Args:
+        image (np.ndarray): Input image.
+        label_name (str): Name of the label that will be displayed on the image.
+        color (Tuple[int, int, int]): RGB values for background color of label.
+        confidence (Optional[float]): confidence score of the label.
+        font_scale (float): scale of the font size relative to image size. Increase for bigger font.
+        thickness_scale (float): scale of the font thickness. Increase for thicker font.
+
+    Returns:
+        np.ndarray: Image with label.
+    """
+    image = image.copy()
+    img_height, img_width, _ = image.shape
+
+    font = cv2.FONT_HERSHEY_PLAIN
+    text = label_name if confidence is None else f"{label_name} ({confidence*100:.0f}%)"
+
+    # get font sizing
+    font_scale = min(img_width, img_height) * font_scale
+    thickness = math.ceil(min(img_width, img_height) * thickness_scale)
+    (width, height), baseline = cv2.getTextSize(text, font, fontScale=font_scale, thickness=thickness)
+
+    # create label
+    label_patch = np.zeros((height + baseline, width + baseline, 3), dtype=np.uint8)
+    label_patch[:, :] = color
+    cv2.putText(
+        label_patch,
+        text,
+        (0, baseline // 2 + height),
+        font,
+        fontScale=font_scale,
+        thickness=thickness,
+        color=0,
+        lineType=cv2.LINE_AA,
+    )
+
+    # add label to image
+    image[: baseline + height, : baseline + width] = label_patch
+    return image
+
+
+def add_normal_label(image: np.ndarray, confidence: Optional[float] = None):
+    """Adds the normal label to the image."""
+    return add_label(image, "normal", (225, 252, 134), confidence)
+
+
+def add_anomalous_label(image: np.ndarray, confidence: Optional[float] = None):
+    """Adds the anomalous label to the image."""
+    return add_label(image, "anomalous", (255, 100, 100), confidence)
+
+
 def anomaly_map_to_color_map(anomaly_map: np.ndarray, normalize: bool = True) -> np.ndarray:
     """Compute anomaly color heatmap.
 
diff --git a/anomalib/post_processing/visualizer.py b/anomalib/post_processing/visualizer.py
index 737d049dc9..e53d0c9772 100644
--- a/anomalib/post_processing/visualizer.py
+++ b/anomalib/post_processing/visualizer.py
@@ -14,11 +14,9 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
-import math
 from pathlib import Path
-from typing import Optional, Tuple
+from typing import Optional
 
-import cv2
 import matplotlib.figure
 import matplotlib.pyplot as plt
 import numpy as np
@@ -54,86 +52,6 @@ def add_image(self, image: np.ndarray, title: str, color_map: Optional[str] = No
         image_data = dict(image=image, title=title, color_map=color_map)
         self.images.append(image_data)
 
-    def add_text(self, image: np.ndarray, text: str, font: int = cv2.FONT_HERSHEY_PLAIN):
-        """Puts text on an image.
-
-        Args:
-            image (np.ndarray): Input image.
-            text (str): Text to add.
-            font (Optional[int]): cv2 font type. Defaults to 0.
-
-        Returns:
-            np.ndarray: Image with text.
-        """
-        image = image.copy()
-        font_size = image.shape[1] // 256 + 1  # Text scale is calculated based on the reference size of 256
-
-        for i, line in enumerate(text.split("\n")):
-            (text_w, text_h), baseline = cv2.getTextSize(line.strip(), font, font_size, thickness=1)
-            offset = i * text_h
-            cv2.rectangle(image, (0, offset + baseline // 2), (0 + text_w, 0 + text_h + offset), (255, 255, 255), -1)
-            cv2.putText(image, line.strip(), (0, (baseline // 2 + text_h) + offset), font, font_size, (0, 0, 255))
-        return image
-
-    @staticmethod
-    def add_label(
-        image: np.ndarray,
-        label_name: str,
-        color: Tuple[int, int, int],
-        confidence: Optional[float] = None,
-        font_scale: float = 5e-3,
-        thickness_scale=1e-3,
-    ):
-        """Adds a label to an image.
-
-        Args:
-            image (np.ndarray): Input image.
-            label_name (str): Name of the label that will be displayed on the image.
-            color (Tuple[int, int, int]): RGB values for background color of label.
-            confidence (Optional[float]): confidence score of the label.
-            font_scale (float): scale of the font size relative to image size. Increase for bigger font.
-            thickness_scale (float): scale of the font thickness. Increase for thicker font.
-
-        Returns:
-            np.ndarray: Image with label.
-        """
-        image = image.copy()
-        img_height, img_width, _ = image.shape
-
-        font = cv2.FONT_HERSHEY_PLAIN
-        text = label_name if confidence is None else f"{label_name}: {confidence:.2}"
-
-        # get font sizing
-        font_scale = min(img_width, img_height) * font_scale
-        thickness = math.ceil(min(img_width, img_height) * thickness_scale)
-        (width, height), baseline = cv2.getTextSize(text, font, fontScale=font_scale, thickness=thickness)
-
-        # create label
-        label_patch = np.zeros((height + baseline, width + baseline, 3), dtype=np.uint8)
-        label_patch[:, :] = color
-        cv2.putText(
-            label_patch,
-            text,
-            (0, baseline // 2 + height),
-            font,
-            fontScale=font_scale,
-            thickness=thickness,
-            color=0,
-            lineType=cv2.LINE_AA,
-        )
-
-        # add label to image
-        image[: baseline + height, : baseline + width] = label_patch
-        return image
-
-    def add_normal_label(self, image: np.ndarray, confidence: Optional[float] = None):
-        """Adds the normal label to the image."""
-        return self.add_label(image, "normal", (225, 252, 134), confidence)
-
-    def add_anomalous_label(self, image: np.ndarray, confidence: Optional[float] = None):
-        """Adds the anomalous label to the image."""
-        return self.add_label(image, "anomalous", (255, 100, 100), confidence)
-
     def generate(self):
         """Generate the image."""
         num_cols = len(self.images)
diff --git a/anomalib/utils/callbacks/visualizer_callback.py b/anomalib/utils/callbacks/visualizer_callback.py
index e717815e58..73b53639c4 100644
--- a/anomalib/utils/callbacks/visualizer_callback.py
+++ b/anomalib/utils/callbacks/visualizer_callback.py
@@ -24,7 +24,13 @@
 from skimage.segmentation import mark_boundaries
 
 from anomalib.models.components import AnomalyModule
-from anomalib.post_processing import Visualizer, compute_mask, superimpose_anomaly_map
+from anomalib.post_processing import (
+    Visualizer,
+    add_anomalous_label,
+    add_normal_label,
+    compute_mask,
+    superimpose_anomaly_map,
+)
 from anomalib.pre_processing.transforms import Denormalize
 from anomalib.utils import loggers
 from anomalib.utils.loggers import AnomalibWandbLogger
@@ -144,12 +150,12 @@ def on_test_batch_end(
                 visualizer.add_image(image=pred_mask, color_map="gray", title="Predicted Mask")
                 visualizer.add_image(image=vis_img, title="Segmentation Result")
             elif self.task == "classification":
-                gt_im = visualizer.add_anomalous_label(image) if gt_label else visualizer.add_normal_label(image)
+                gt_im = add_anomalous_label(image) if gt_label else add_normal_label(image)
                 visualizer.add_image(gt_im, title="Image/True label")
                 if pred_score >= threshold:
-                    image_classified = visualizer.add_anomalous_label(heat_map, pred_score)
+                    image_classified = add_anomalous_label(heat_map, pred_score)
                 else:
-                    image_classified = visualizer.add_normal_label(heat_map, 1 - pred_score)
+                    image_classified = add_normal_label(heat_map, 1 - pred_score)
                 visualizer.add_image(image=image_classified, title="Prediction")
 
             self._add_images(visualizer, pl_module, trainer, Path(filename))

From 19812384fa4ad3e9850812aa8d8ed58f92dee4ec Mon Sep 17 00:00:00 2001
From: Dick Ameln <dick.ameln@intel.com>
Date: Wed, 4 May 2022 15:12:30 +0200
Subject: [PATCH 4/8] fix tests

---
 tests/pre_merge/post_processing/test_visualizer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/pre_merge/post_processing/test_visualizer.py b/tests/pre_merge/post_processing/test_visualizer.py
index 660f49bfba..e01348ae88 100644
--- a/tests/pre_merge/post_processing/test_visualizer.py
+++ b/tests/pre_merge/post_processing/test_visualizer.py
@@ -24,7 +24,7 @@ def test_visualize_fully_defected_masks():
     """Test if a fully defected anomaly mask results in a completely white image."""
 
     # create visualizer and add fully defected mask
-    visualizer = Visualizer(num_rows=1, num_cols=2, figure_size=(3, 3))
+    visualizer = Visualizer()
     mask = np.ones((256, 256)) * 255
     visualizer.add_image(image=mask, color_map="gray", title="fully defected mask")
 

From 2b654a5122dbe6f7e315692671caec7c6b3be994 Mon Sep 17 00:00:00 2001
From: Dick Ameln <dick.ameln@intel.com>
Date: Wed, 4 May 2022 16:36:05 +0200
Subject: [PATCH 5/8] support single image in visualizer

---
 anomalib/post_processing/visualizer.py             | 3 ++-
 tests/pre_merge/post_processing/test_visualizer.py | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/anomalib/post_processing/visualizer.py b/anomalib/post_processing/visualizer.py
index e53d0c9772..3a82f94fdf 100644
--- a/anomalib/post_processing/visualizer.py
+++ b/anomalib/post_processing/visualizer.py
@@ -59,7 +59,8 @@ def generate(self):
         self.figure, self.axis = plt.subplots(1, num_cols, figsize=figure_size)
         self.figure.subplots_adjust(right=0.9)
 
-        for axis, image_dict in zip(self.axis, self.images):
+        axes = self.axis if len(self.images) > 1 else [self.axis]
+        for axis, image_dict in zip(axes, self.images):
             axis.axes.xaxis.set_visible(False)
             axis.axes.yaxis.set_visible(False)
             axis.imshow(image_dict["image"], image_dict["color_map"], vmin=0, vmax=255)
diff --git a/tests/pre_merge/post_processing/test_visualizer.py b/tests/pre_merge/post_processing/test_visualizer.py
index e01348ae88..4e8fc3df04 100644
--- a/tests/pre_merge/post_processing/test_visualizer.py
+++ b/tests/pre_merge/post_processing/test_visualizer.py
@@ -27,11 +27,12 @@ def test_visualize_fully_defected_masks():
     visualizer = Visualizer()
     mask = np.ones((256, 256)) * 255
     visualizer.add_image(image=mask, color_map="gray", title="fully defected mask")
+    visualizer.generate()
 
     # retrieve plotted image
     canvas = FigureCanvas(visualizer.figure)
     canvas.draw()
-    plotted_img = visualizer.axis[0].images[0].make_image(canvas.renderer)
+    plotted_img = visualizer.axis.images[0].make_image(canvas.renderer)
 
     # assert that the plotted image is completely white
     assert np.all(plotted_img[0][..., 0] == 255)

From f51be3b22428f06fe8334678b8dfb87e222bf759 Mon Sep 17 00:00:00 2001
From: Dick Ameln <dick.ameln@intel.com>
Date: Wed, 4 May 2022 17:22:22 +0200
Subject: [PATCH 6/8] call generate

---
 anomalib/utils/callbacks/visualizer_callback.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/anomalib/utils/callbacks/visualizer_callback.py b/anomalib/utils/callbacks/visualizer_callback.py
index 73b53639c4..4db481507b 100644
--- a/anomalib/utils/callbacks/visualizer_callback.py
+++ b/anomalib/utils/callbacks/visualizer_callback.py
@@ -158,6 +158,7 @@ def on_test_batch_end(
                     image_classified = add_normal_label(heat_map, 1 - pred_score)
                 visualizer.add_image(image=image_classified, title="Prediction")
 
+            visualizer.generate()
             self._add_images(visualizer, pl_module, trainer, Path(filename))
             visualizer.close()
 

From 6e19e8ed88b84517cc82eb0325df482fb8732f60 Mon Sep 17 00:00:00 2001
From: Dick Ameln <dick.ameln@intel.com>
Date: Thu, 5 May 2022 14:55:48 +0200
Subject: [PATCH 7/8] revert default task type

---
 anomalib/models/padim/config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/anomalib/models/padim/config.yaml b/anomalib/models/padim/config.yaml
index 2e7230a9bf..155661fd56 100644
--- a/anomalib/models/padim/config.yaml
+++ b/anomalib/models/padim/config.yaml
@@ -3,7 +3,7 @@ dataset:
   format: mvtec
   path: ./datasets/MVTec
   category: bottle
-  task: classification
+  task: segmentation
   image_size: 256
   train_batch_size: 32
   test_batch_size: 32

From 6d6054d15c474e1a7714167f357f8a8005eaf4cf Mon Sep 17 00:00:00 2001
From: Dick Ameln <dick.ameln@intel.com>
Date: Thu, 5 May 2022 14:56:42 +0200
Subject: [PATCH 8/8] typing

---
 anomalib/post_processing/visualizer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/anomalib/post_processing/visualizer.py b/anomalib/post_processing/visualizer.py
index 3a82f94fdf..d4a980af51 100644
--- a/anomalib/post_processing/visualizer.py
+++ b/anomalib/post_processing/visualizer.py
@@ -15,7 +15,7 @@
 # and limitations under the License.
 
 from pathlib import Path
-from typing import Optional
+from typing import Dict, List, Optional
 
 import matplotlib.figure
 import matplotlib.pyplot as plt
@@ -36,7 +36,7 @@ class Visualizer:
 
     def __init__(self):
 
-        self.images = []
+        self.images: List[Dict] = []
 
         self.figure: matplotlib.figure.Figure
         self.axis: np.ndarray