import numpy as np

def transform_to_hu(medical_image):

    intercept = medical_image.RescaleIntercept

    slope = medical_image.RescaleSlope

    image = medical_image.pixel_array

    hu_image = image * slope + intercept

    return hu_image

def apply_windowing(ds, window_center, window_width, normalized=False, zero_centered=True):

    # Get the pixel data and rescale to Hounsfield units (HU)

    pixel_array = ds.pixel_array.astype(np.float32)

    intercept = ds.RescaleIntercept

    slope = ds.RescaleSlope

    hu_array = pixel_array * slope + intercept

    # Apply windowing

    window_min = window_center - (window_width / 2)

    window_max = window_center + (window_width / 2)

    windowed_array = np.clip(hu_array, window_min, window_max)

    if(zero_centered):

        windowed_array = zero_center(windowed_array)

    if(normalized):

    # Normalize the windowed array to [0, 1]

        normalized_array = (windowed_array - window_min) / (window_max - window_min)

        return normalized_array

    else: 

        return windowed_array

def set_window(img, hu=[-800.,1200.]):

    window = np.array(hu)

    newimg = (img-window[0]) / (window[1]-window[0])

    newimg[newimg < 0] = 0

    newimg[newimg > 1] = 1

    newimg = (newimg * 255).astype('uint8')

    return newimg

def zero_center(hu_image):

    hu_image = hu_image - np.mean(hu_image)

    return hu_image