code uses a pre-trained segmentation model (U-net) and an input image. It preprocesses the image for the model. • Preprocessing the image Pads the image to ensure its dimensions are divisible by 32, which is necessary for the models that we will use. • Preparing Input for Model Inference After setting up an albumentations transformation pipeline for normalization, we apply the normalization transformation to the padded image, then convert the normalized image to a PyTorch tensor and add a batch dimension. Performs inference using the pre-trained model to obtain a segmentation mask. • Postprocessing the segmentation mask It converts the model's output to a binary mask, removes padding from the mask, and then resizes the masked image.
