ElasticTransform in PyTorch

This code explores the ElasticTransform function from torchvision.transforms.v2, demonstrating its image transformation capabilities with varying parameters. The OxfordIIITPet dataset is used as the source of images. The code systematically varies the alpha (displacement magnitude), sigma (displacement smoothness), and fill (background color) parameters to observe their effects on the resulting images. Multiple sets of images are displayed, each showcasing the transformations resulting from different parameter combinations.

The ElasticTransform function applies random elastic deformations to images, simulating real-world distortions. The alpha parameter controls the intensity of these deformations; higher values lead to more pronounced transformations. sigma adjusts the smoothness of the deformations, with smaller values resulting in sharper, more localized changes. The fill parameter determines the color used to fill in areas of the image that are affected by the transformation.

The code presents two functions, show_images1 and show_images2, both displaying transformed images. show_images1 uses pre-transformed datasets, while show_images2 applies the transformation within the display function. Both functions achieve the same visual outcome, demonstrating the impact of the ElasticTransform parameters.

The accompanying images illustrate the transformations. Each set of images represents a specific parameter configuration, allowing for a visual comparison of the effects of altering alpha, sigma, and fill. For example, increasing alpha results in increasingly distorted images, while adjusting sigma influences the smoothness of the distortion. The fill parameter changes the background color where the transformation creates gaps.

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