fostner算法实现的代码

Fostner算法是一种用于计算图像中的点的亚像素精度的方法。下面是一个使用Python实现Fostner算法的示例代码：

import numpy as np
from scipy import ndimage

def fostner(image):
    # Calculate the derivatives of the image
    dx = ndimage.sobel(image, 0)
    dy = ndimage.sobel(image, 1)
    dxy = ndimage.sobel(dx, 1)

    # Calculate the sums of squares and products of derivatives
    dx2 = dx * dx
    dy2 = dy * dy
    dxy2 = dxy * dxy

    # Apply a Gaussian filter to the sums of squares and products
    size = 3
    sigma = 1
    w = np.zeros((size, size))
    for i in range(size):
        for j in range(size):
            x = i - size // 2
            y = j - size // 2
            w[i, j] = np.exp(-(x ** 2 + y ** 2) / (2 * sigma ** 2))
    w /= np.sum(w)
    dx2 = ndimage.convolve(dx2, w)
    dy2 = ndimage.convolve(dy2, w)
    dxy2 = ndimage.convolve(dxy2, w)

    # Calculate the corner response function
    k = 0.04
    det = dx2 * dy2 - dxy2 ** 2
    trace = dx2 + dy2
    response = det - k * trace ** 2

    # Find the local maxima of the corner response function
    size = 5
    threshold = 0.1 * np.max(response)
    mask = np.zeros(image.shape, dtype=bool)
    for i in range(size // 2, image.shape[0] - size // 2):
        for j in range(size // 2, image.shape[1] - size // 2):
            if response[i, j] >= threshold:
                mask[i, j] = True
                mask[i - size // 2:i + size // 2 + 1, j - size // 2:j + size // 2 + 1] = False
                mask[i, j] = True

    # Calculate the subpixel position of the corners
    corners = np.transpose(np.nonzero(mask))
    for i in range(corners.shape[0]):
        x, y = corners[i]
        A = np.array([[dx2[x, y], dxy2[x, y]], [dxy2[x, y], dy2[x, y]]])
        b = np.array([-dx[x, y], -dy[x, y]])
        u, v = np.linalg.solve(A, b)
        corners[i] += np.array([u, v])

    return corners

这个函数将输入的图像作为参数，并返回图像中检测到的角点的子像素级位置。