denorm image visualizer

src/anomalib/utils/visualization/image.py

@@ -136,11 +136,19 @@ def _visualize_batch(self, batch: dict) -> Iterator[GeneratorResult]:
  Returns:
  Generator that yields a display-ready visualization for each image.
  """
+ from torchvision.transforms.v2 import Normalize
+ import torch
+
+ mean = torch.tensor([0.485, 0.456, 0.406], dtype=torch.float32)
+ std = torch.tensor([0.229, 0.224, 0.225], dtype=torch.float32)
+ denorm = Normalize((-mean / std).tolist(), (1.0 / std).tolist())
+
  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 = (denorm(batch["image"][i])*255).cpu().numpy().astype(np.uint8) #(read_image(path=batch["image_path"][i]) * 255).astype(np.uint8)
+ image = np.transpose(image, (1, 2, 0))
  image = cv2.resize(image, dsize=(width, height), interpolation=cv2.INTER_AREA)
  elif "video_path" in batch:
  height, width = batch["image"].shape[-2:]