def standartize_landmarks(lms, original_shape):
    oh, ow = original_shape
    h, w = (256, 256)
    lms = lms.copy()
    lms[:, 0] = lms[:, 0] * h / oh
    lms[:, 1] = lms[:, 1] * ow / w    
    return lms

# def normalize(X):
#     """Normalize data: substract mean and divide by range"""
#     X = X.astype(np.float32)
#     # X = X - X.mean()
#     X = X / (X.max() - X.min())
#     return X


# def most_active_points(im, flt, n=10):
#     """
#     Finds coordinates of n points that are actiavated the most
#     by specified filter

#     Params
#     ------
#     im : ndarray
#         image to be checked
#     flt : 2D-array
#         filter to be applied
#     n : number of most active
#     """
#     # dummy implementation
#     new_im = conv2(im, flt, mode='same')
#     points = []
#     for i in range(new_im.shape[0]):
#         for j in range(new_im.shape[1]):
#             points.append((i, j, new_im[i, j]))
#     top = heapq.nlargest(n, points, lambda t: t[2])
#     return [(i, j) for i, j, val in top]


# def interp_list(lst, new_length):
#     """
#     'Shrinks' or 'stratches' lst by removing or replicating elements
#     """
#     k = float(len(lst)) / new_length
#     new_lst = [None] * new_length
#     for i in range(new_length):
#         j = int(i * k)
#         new_lst[i] = lst[j]
#     return new_lst
dataset/ETHPy150Open dfdx/masque/masque/utils.py/standartize_landmarks/swapped operands of `/`