numpy和python中的图像的简单，高效的双线性插值 [英] Simple, efficient bilinear interpolation of images in numpy and python

问题描述

如何对在python中表示为numpy数组的图像数据实现双线性插值?

How do I implement bilinear interpolation for image data represented as a numpy array in python?

推荐答案

我发现了有关此主题的许多问题和许多答案，尽管对于数据由网格上的样本组成的常见情况(即矩形图像)，这些方法都没有效果)，并表示为numpy数组.此函数可以将列表作为x和y坐标，并且无需循环即可执行查找和求和.

I found many questions on this topic and many answers, though none were efficient for the common case that the data consists of samples on a grid (i.e. a rectangular image) and represented as a numpy array. This function can take lists as both x and y coordinates and will perform the lookups and summations without need for loops.

def bilinear_interpolate(im, x, y):
    x = np.asarray(x)
    y = np.asarray(y)

    x0 = np.floor(x).astype(int)
    x1 = x0 + 1
    y0 = np.floor(y).astype(int)
    y1 = y0 + 1

    x0 = np.clip(x0, 0, im.shape[1]-1);
    x1 = np.clip(x1, 0, im.shape[1]-1);
    y0 = np.clip(y0, 0, im.shape[0]-1);
    y1 = np.clip(y1, 0, im.shape[0]-1);

    Ia = im[ y0, x0 ]
    Ib = im[ y1, x0 ]
    Ic = im[ y0, x1 ]
    Id = im[ y1, x1 ]

    wa = (x1-x) * (y1-y)
    wb = (x1-x) * (y-y0)
    wc = (x-x0) * (y1-y)
    wd = (x-x0) * (y-y0)

    return wa*Ia + wb*Ib + wc*Ic + wd*Id

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