if len(img.shape) > 2:

H, W, C = img.shape

else:

H, W = img.shape

C = 1

aH = int(ay * H)

aW = int(ax * W)

# get position of resized image

y = np.arange(aH).repeat(aW).reshape(aW, -1)

x = np.tile(np.arange(aW), (aH, 1))

# get position of original position

y = (y / ay)

x = (x / ax)

ix = np.floor(x).astype(np.int)

iy = np.floor(y).astype(np.int)

ix = np.minimum(ix, W-2)

iy = np.minimum(iy, H-2)

# get distance

dx = x - ix

dy = y - iy

if C > 1:

dx = np.repeat(np.expand_dims(dx, axis=-1), C, axis=-1)

dy = np.repeat(np.expand_dims(dy, axis=-1), C, axis=-1)

# interpolation

out = (1-dx) * (1-dy) * img[iy, ix] + dx * (1 - dy) * img[iy, ix+1] + (1 - dx) * dy * img[iy+1, ix] + dx * dy * img[iy+1, ix+1]

out = np.clip(out, 0, 255)

out = out.astype(np.uint8)

# first element

pyramid = [gray]

# each scale

for i in range(1, 6):

# define scale

a = 2. ** i

# down scale

p = bl_interpolate(gray, ax=1./a, ay=1. / a)

# add pyramid list

pyramid.append(p)