使用python的矩形矩阵插值

import numpy as np
from PIL import Image

# 读取图像
image = Image.open('input.jpg')
image_array = np.array(image)

# 定义插值函数
def bilinear_interpolation(image_array, x, y):
    x1 = int(np.floor(x))
    x2 = int(np.ceil(x))
    y1 = int(np.floor(y))
    y2 = int(np.ceil(y))

    if x2 >= image_array.shape[1]:
        x2 = image_array.shape[1] - 1
    if y2 >= image_array.shape[0]:
        y2 = image_array.shape[0] - 1

    # 双线性插值公式
    wa = (x2 - x) * (y2 - y)
    wb = (x2 - x) * (y - y1)
    wc = (x - x1) * (y2 - y)
    wd = (x - x1) * (y - y1)

    interpolated_value = wa * image_array[y1, x1] + wb * image_array[y1, x2] + wc * image_array[y2, x1] + wd * image_array[y2, x2]
    return interpolated_value

# 示例：插值某个点
x = 10.5
y = 15.5
interpolated_value = bilinear_interpolation(image_array, x, y)
print(f"Interpolated value at ({x}, {y}): {interpolated_value}")

# 保存插值后的图像（示例）
new_image_array = np.zeros((image_array.shape[0] * 2, image_array.shape[1] * 2, image_array.shape[2]), dtype=np.uint8)
for i in range(new_image_array.shape[0]):
    for j in range(new_image_array.shape[1]):
        new_image_array[i, j] = bilinear_interpolation(image_array, j / 2, i / 2)

new_image = Image.fromarray(new_image_array)
new_image.save('output.jpg')