Python 如何使用numpy实现三维双线性插值？

我已经了解了这段双线性插值代码（添加在这里），但我想将这段代码改进为3D，这意味着将其更新为使用RGB图像（3D，而不仅仅是2D）

如果你有什么建议，我可以这样做，我想知道

这是一维线性插值：

import math

def linear1D_resize(in_array, size):
    """
    `in_array` is the input array.
    `size` is the desired size.
    """
    ratio = (len(in_array) - 1) / (size - 1)
    out_array = []

    for i in range(size):
        low = math.floor(ratio * i)
        high = math.ceil(ratio * i)
        weight = ratio * i - low

        a = in_array[low]
        b = in_array[high]

        out_array.append(a * (1 - weight) + b * weight)

    return out_array

对于2D：

import math
def bilinear_resize(image, height, width):
    """
    `image` is a 2-D numpy array
    `height` and `width` are the desired spatial dimension of the new 2-D array.
    """
    img_height, img_width = image.shape[:2]

    resized = np.empty([height, width])

    x_ratio = float(img_width - 1) / (width - 1) if width > 1 else 0
    y_ratio = float(img_height - 1) / (height - 1) if height > 1 else 0

    for i in range(height):
        for j in range(width):
            x_l, y_l = math.floor(x_ratio * j), math.floor(y_ratio * i)
            x_h, y_h = math.ceil(x_ratio * j), math.ceil(y_ratio * i)

            x_weight = (x_ratio * j) - x_l
            y_weight = (y_ratio * i) - y_l

            a = image[y_l, x_l]
            b = image[y_l, x_h]
            c = image[y_h, x_l]
            d = image[y_h, x_h]

            pixel = a * (1 - x_weight) * (1 - y_weight) + b * x_weight * (1 - y_weight) + c * y_weight * (1 - x_weight) + d * x_weight * y_weight
            resized[i][j] = pixel      # pixel is the scalar with the value comptued by the interpolation

    return resized

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查看一些scipy ndimage插值函数。他们会做你想做的事情，并且“使用numpy”

它们也非常实用、快速，并且经过多次测试

理查德