--- a
+++ b/Preprocessing Medical Data Pipeline/image_preprocessing.py
@@ -0,0 +1,72 @@
+
+import numpy as np
+from skimage.transform import resize
+
+def image_cropping(images, top, bottom, left, right):
+    x, height, width, _ = images.shape
+
+    if top + bottom >= height or left + right >= width:
+        raise ValueError("Total cropping size cannot exceed the original image size.")
+
+    cropped_images = np.zeros((x, height - top - bottom, width - left - right, 1))
+
+    for i in range(x):
+        cropped_images[i] = images[i, top:height - bottom, left:width - right]
+
+    return cropped_images
+
+def uniform_cropping(images, crop_size):
+    """
+    Applies uniform cropping to an array of images.
+
+    Parameters:
+        images (numpy.ndarray): Input array of images with shape (x, 512, 512, 1).
+        crop_size (int): The desired size of the square crop.
+
+    Returns:
+        numpy.ndarray: Array of cropped images with shape (x, crop_size, crop_size, 1).
+    """
+    x, height, width, _ = images.shape
+
+    if crop_size > height or crop_size > width:
+        raise ValueError("Crop size cannot be larger than the original image size.")
+
+    # Calculate the top-left corner for cropping to keep it uniform
+    top_left_y = (height - crop_size) // 2
+    top_left_x = (width - crop_size) // 2
+
+    cropped_images = np.zeros((x, crop_size, crop_size, 1))
+
+    for i in range(x):
+        cropped_images[i] = images[i, top_left_y:top_left_y + crop_size, top_left_x:top_left_x + crop_size]
+
+    return cropped_images
+
+def uniform_resizing(images, new_size):
+    """
+    Applies uniform resizing to an array of images. ### bicubic_interpolation
+
+    Parameters:
+        images (numpy.ndarray): Input array of images with shape (x, 512, 512, 1).
+        new_size (tuple or int): The desired size of the resized images. If it's an int,
+                                 the new size will be (new_size, new_size).
+
+    Returns:
+        numpy.ndarray: Array of resized images with shape (x, new_height, new_width, 1).
+    """
+    x, height, width, _ = images.shape
+
+    if isinstance(new_size, int):
+        new_height, new_width = new_size, new_size
+    elif isinstance(new_size, tuple) and len(new_size) == 2:
+        new_height, new_width = new_size
+    else:
+        raise ValueError("Invalid new_size argument. It should be an int or a tuple of two ints.")
+
+    resized_images = np.zeros((x, new_height, new_width, 1))
+
+    for i in range(x):
+        resized_images[i, ..., 0] = resize(images[i, ..., 0], (new_height, new_width), mode='reflect', order=1)
+        # resized_images[i, ..., 0] = resize(images[i, ..., 0], (new_height, new_width), mode='constant', preserve_range=True)
+
+    return resized_images
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