H, W, C = img.shape

aH = int(ay * H)

aW = int(ax * W)

# get positions of resized image

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

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

y = (y / ay)

x = (x / ax)

# get positions of original image

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

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

ix = np.minimum(ix, W-1)

iy = np.minimum(iy, H-1)

# get distance of each position of original image

dx2 = x - ix

dy2 = y - iy

dx1 = dx2 + 1

dy1 = dy2 + 1

dx3 = 1 - dx2

dy3 = 1 - dy2

dx4 = 1 + dx3

dy4 = 1 + dy3

dxs = [dx1, dx2, dx3, dx4]

dys = [dy1, dy2, dy3, dy4]

# bi-cubic weight

def weight(t):

a = -1.

at = np.abs(t)

w = np.zeros_like(t)

ind = np.where(at <= 1)

w[ind] = ((a+2) * np.power(at, 3) - (a+3) * np.power(at, 2) + 1)[ind]

ind = np.where((at > 1) & (at <= 2))

w[ind] = (a*np.power(at, 3) - 5*a*np.power(at, 2) + 8*a*at - 4*a)[ind]

return w

w_sum = np.zeros((aH, aW, C), dtype=np.float32)

out = np.zeros((aH, aW, C), dtype=np.float32)

# interpolate

for j in range(-1, 3):

for i in range(-1, 3):

ind_x = np.minimum(np.maximum(ix + i, 0), W-1)

ind_y = np.minimum(np.maximum(iy + j, 0), H-1)

wx = weight(dxs[i+1])

wy = weight(dys[j+1])

wx = np.repeat(np.expand_dims(wx, axis=-1), 3, axis=-1)

wy = np.repeat(np.expand_dims(wy, axis=-1), 3, axis=-1)

w_sum += wx * wy

out += wx * wy * img[ind_y, ind_x]

out /= w_sum

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

out = out.astype(np.uint8)