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Visualize images from the batch #2063

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Visualize images from the batch #2063

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21 changes: 17 additions & 4 deletions src/anomalib/utils/visualization/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,6 @@
from skimage.segmentation import mark_boundaries

from anomalib import TaskType
from anomalib.data.utils import read_image
from anomalib.utils.post_processing import add_anomalous_label, add_normal_label, draw_boxes, superimpose_anomaly_map

from .base import BaseVisualizer, GeneratorResult, VisualizationStep
Expand Down Expand Up @@ -127,6 +126,21 @@ def generate(self, **kwargs) -> Iterator[GeneratorResult]:
raise ValueError(msg)
return self._visualize_batch(outputs)

def denormalize_imagenet_to_uint8(self, image_normalized: np.ndarray) -> np.ndarray:
"""Convert the NumPy array image from the ImageNet-normalized scale to uint8 [0, 255].

Args:
image_normalized (np.ndarray): A NumPy array of image(s) that are normalized with ImageNet statistics.

Returns:
np.ndarray: Image(s) in the uint8 format.
"""
std = np.array([0.229, 0.224, 0.225]) * 255
mean = np.array([0.485, 0.456, 0.406]) * 255

# We do not clip pixel values here, in case of hiding the problematic input.
return (image_normalized * std + mean).astype(np.uint8)

def _visualize_batch(self, batch: dict) -> Iterator[GeneratorResult]:
"""Yield a visualization result for each item in the batch.

Expand All @@ -139,9 +153,8 @@ def _visualize_batch(self, batch: dict) -> Iterator[GeneratorResult]:
batch_size = batch["image"].shape[0]
for i in range(batch_size):
if "image_path" in batch:
height, width = batch["image"].shape[-2:]
image = (read_image(path=batch["image_path"][i]) * 255).astype(np.uint8)
image = cv2.resize(image, dsize=(width, height), interpolation=cv2.INTER_AREA)
image = batch["image"][i].cpu().numpy().transpose(1, 2, 0) # HWC, RGB
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I am not sure if this is the best approach. I would recommend creating a new visualizer sub-classed from

anomalib/src/anomalib/utils/visualization/base.py

Line 34 in 56843d2

class BaseVisualizer(ABC):
. You can access the batch in a similar manner as

anomalib/src/anomalib/utils/visualization/image.py

Line 124 in 56843d2

outputs = kwargs.get("outputs", None)
. This new class can then safely make the strong assumption that the images are always normalized to imagenet scales. Additionally, it will not break the functionality to load images from the file-path.
We are planning on refactoring the visualization callback so that we can pass the visualizers to the engine rather than hard-coding it but for now you can add the new visualizer to the list here

anomalib/src/anomalib/engine/engine.py

Line 437 in 56843d2

visualizers=ImageVisualizer(task=self.task, normalize=self.normalization == NormalizationMethod.NONE),

Another option is to invert the normalization transforms attached to the model

anomalib/src/anomalib/models/image/padim/lightning_model.py

Line 131 in 56843d2

Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
or from the dataloader. But it might require a more complex solution.

image = self.denormalize_imagenet_to_uint8(image)
elif "video_path" in batch:
height, width = batch["image"].shape[-2:]
image = batch["original_image"][i].squeeze().cpu().numpy()
Expand Down