Getting Memory Error while performing operations on numpy array

I am performing an operation which involves different operations(subtraction, squaring,broadcasting) on numpy array of higher dimensions. My code is giving Memory Error on performing such operations.

My code below-

from skimage.segmentation import find_boundaries

w0 = 10
sigma = 5

def make_weight_map(masks):
    """
    Generate the weight maps as specified in the UNet paper
    for a set of binary masks.

    Parameters
    ----------
    masks: array-like
        A 3D array of shape (n_masks, image_height, image_width),
        where each slice of the matrix along the 0th axis represents one binary mask.

    Returns
    -------
    array-like
        A 2D array of shape (image_height, image_width)

    """
    masks = masks.numpy()
    nrows, ncols = masks.shape[1:]
    masks = (masks > 0).astype(int)
    distMap = np.zeros((nrows * ncols, masks.shape[0]))
    X1, Y1 = np.meshgrid(np.arange(nrows), np.arange(ncols))
    X1, Y1 = np.c_[X1.ravel(), Y1.ravel()].T

    #In the below for loop, I am getting the Memory Error
    for i, mask in enumerate(masks):
        # find the boundary of each mask,
        # compute the distance of each pixel from this boundary
        bounds = find_boundaries(mask, mode='inner')
        X2, Y2 = np.nonzero(bounds)
        xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2
        ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2
        distMap[:, i] = np.sqrt(xSum + ySum).min(axis=0)
    ix = np.arange(distMap.shape[0])
    if distMap.shape[1] == 1:
        d1 = distMap.ravel()
        border_loss_map = w0 * np.exp((-1 * (d1) ** 2) / (2 * (sigma ** 2)))
    else:
        if distMap.shape[1] == 2:
            d1_ix, d2_ix = np.argpartition(distMap, 1, axis=1)[:, :2].T
        else:
            d1_ix, d2_ix = np.argpartition(distMap, 2, axis=1)[:, :2].T
        d1 = distMap[ix, d1_ix]
        d2 = distMap[ix, d2_ix]
        border_loss_map = w0 * np.exp((-1 * (d1 + d2) ** 2) / (2 * (sigma ** 2)))
    xBLoss = np.zeros((nrows, ncols))
    xBLoss[X1, Y1] = border_loss_map
    # class weight map
    loss = np.zeros((nrows, ncols))
    w_1 = 1 - masks.sum() / loss.size
    w_0 = 1 - w_1
    loss[masks.sum(0) == 1] = w_1
    loss[masks.sum(0) == 0] = w_0
    ZZ = xBLoss + loss
    return ZZ

To reproduce a problem, a numpy array of dimensions 4,584, 565 could recreate the issue.

Traceback of the error-

---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
<ipython-input-32-0f30ef7dc24d> in <module>
----> 1 img = make_weight_map(img)

<ipython-input-31-e75a6281476f> in make_weight_map(masks)
     34         xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2
     35         ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2
---> 36         distMap[:, i] = np.sqrt(xSum + ySum).min(axis=0)
     37     ix = np.arange(distMap.shape[0])
     38     if distMap.shape[1] == 1:

MemoryError:

When using a input of 4,584, 565, the shape are-

X1.shape
(329960,)
X2.shape,Y2.shape# for first iteration
(15239,) (15239,)

The main issue is arising in this line (X2.reshape(-1, 1) - X1.reshape(1, -1)) because the shape of X2 is (15239,1) and X1 IS (1,329960) so first it had to perform a humongous broadcasting operation.

distmap, I am not able to calculate because my code is halting before that.
Also, If I try to perform the following subtraction operation with the above dimension, code is halting there too.

X2.reshape(-1, 1) - X1.reshape(1, -1)

I am using a system of 32 Gb RAM, I also tried running on cloud of 64 Gb RAM. I have checked the below questions and either they are not providing the solutions to my problem or I am not able to apply to my use case.

Python/Numpy MemoryError
Working with big data in python and numpy, not enough ram, how to save partial results on disc?
Memory growth with broadcast operations in NumPy