27_Bi-cubic_Interpolation.py

import cv2
import numpy as np

# Read image
img = cv2.imread("imori.jpg").astype(np.float32)
H, W, C = img.shape

# Bi-cubic
a = 1.5
aH = int(a * H)
aW = int(a * W)

y = np.arange(aH).repeat(aW).reshape(aW, -1)
x = np.tile(np.arange(aW), (aH, 1))
y = (y / a)
x = (x / a)

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)

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]


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)

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[out > 255] = 255
out = out.astype(np.uint8)

# Save result
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.imwrite("out.jpg", out)