Delete preprocessing.py

preprocessing.py

@@ -1,41 +0,0 @@
-import os
-import numpy as np
-import torch
-from transformers import ViTForImageClassification, ViTImageProcessor
-import nibabel as nib  # For loading .nii files
-from PIL import Image  # For loading .jpg and .jpeg files
-
-# Function to preprocess images based on their file format
-def preprocess_image(image_path):
-    ext = os.path.splitext(image_path)[-1].lower()  # Get the file extension
-
-    # Case 1: .nii files (NIfTI format)
-    if ext == '.nii' or ext == '.nii.gz':
-        # Load the .nii image
-        nii_image = nib.load(image_path)
-        image_data = nii_image.get_fdata()
-
-        # Convert to tensor and reshape to [C, H, W] format
-        image_tensor = torch.tensor(image_data).float()
-
-        # Handle cases where the image might have a different shape (e.g., single channel vs multiple channels)
-        if len(image_tensor.shape) == 3:
-            image_tensor = image_tensor.unsqueeze(0)  # Add channel dimension if not present
-
-    # Case 2: .jpg and .jpeg files (JPEG format)
-    elif ext in ['.jpg', '.jpeg']:
-        # Load the image using PIL
-        img = Image.open(image_path).convert('RGB')  # Convert to RGB
-        img = img.resize((224, 224))  # Resize to the input size expected by ViT (224x224)
-
-        # Convert to numpy array and then to tensor
-        img_np = np.array(img)
-        image_tensor = torch.tensor(img_np).permute(2, 0, 1).float()  # Rearrange to [C, H, W]
-
-    else:
-        raise ValueError(f"Unsupported file format: {ext}")
-
-    # Normalize image tensor (if required)
-    image_tensor /= 255.0  # Normalize pixel values to [0, 1]
-
-    return image_tensor