Visualize images from the batch

[mzweilin]

📝 Description

• This PR visualizes images that are already loaded in a batch, instead of loading them again from the file system. This change allows visualizing adversarial images that are perturbed by a Lightning Callback.

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• 🐞 Bug fix (non-breaking change which fixes an issue)
• 🔨 Refactor (non-breaking change which refactors the code base)
• 🚀 New feature (non-breaking change which adds functionality)
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• 📚 Documentation update
• 🔒 Security update

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[ashwinvaidya17]

Visualizing images from the batch is a useful feature, but we might have to consider the original normalization transforms that are specific to the model or passed to the model.

[ashwinvaidya17]

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.