双线性四边形插值的Python实现

import numpy as np
from PIL import Image

def bilinear_interpolation(image, new_width, new_height):
    width, height = image.size
    image = np.array(image)

    x_ratio = float(width - 1) / new_width
    y_ratio = float(height - 1) / new_height

    result = np.zeros((new_height, new_width, 3), dtype=np.uint8)

    for i in range(new_height):
        for j in range(new_width):
            x = int(x_ratio * j)
            y = int(y_ratio * i)
            x_diff = (x_ratio * j) - x
            y_diff = (y_ratio * i) - y

            # Calculate the weighted average of the four neighboring pixels
            pixel = (image[y, x] * (1 - x_diff) * (1 - y_diff) +
                     image[y, x + 1] * x_diff * (1 - y_diff) +
                     image[y + 1, x] * (1 - x_diff) * y_diff +
                     image[y + 1, x + 1] * x_diff * y_diff)

            result[i, j] = pixel

    return Image.fromarray(result)

# Example usage
image = Image.open('input.jpg')
new_image = bilinear_interpolation(image, 800, 600)
new_image.save('output.jpg')