用python写一个代码，要求先定义一个按行扫描边缘二值图像，将左右的边缘点记录下来并计算两者之间的中点位置并将中点存储下来的函数，然后定义一个利用中点拟合曲线，然后将曲线加粗并在原图上用绿色显示的函数

好的，以下是Python代码实现：

import cv2
import numpy as np

def find_midpoint(image):
    # edge detection
    edges = cv2.Canny(image, 100, 200)

    # find left and right edges
    left_edge = None
    right_edge = None
    for i in range(edges.shape[0]):
        for j in range(edges.shape[1]):
            if edges[i,j] != 0:
                if left_edge is None or j < left_edge[1]:
                    left_edge = (i,j)
                if right_edge is None or j > right_edge[1]:
                    right_edge = (i,j)

    # calculate midpoint
    midpoint = ((left_edge[0] + right_edge[0]) // 2, (left_edge[1] + right_edge[1]) // 2)

    return midpoint

def fit_curve(image, midpoint):
    # create mask
    mask = np.zeros_like(image)
    cv2.circle(mask, midpoint, 20, 255, -1)

    # fit curve
    points = np.column_stack(np.where(mask > 0))
    curve = cv2.fitLine(points, cv2.DIST_L2, 0, 0.01, 0.01)

    # draw curve on image
    color = (0, 255, 0)
    thickness = 3
    cv2.line(image, (int(curve[2] - curve[0]*1000), int(curve[3] - curve[1]*1000)), (int(curve[2] + curve[0]*1000), int(curve[3] + curve[1]*1000)), color, thickness)

    return image

# test the functions
image = cv2.imread('test_image.png', cv2.IMREAD_GRAYSCALE)
midpoint = find_midpoint(image)
result = fit_curve(cv2.cvtColor(image, cv2.COLOR_GRAY2BGR), midpoint)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

其中，`find_midpoint`函数用于找到左右边缘点并计算中点位置，`fit_curve`函数用于拟合曲线并将结果画在原图上。请注意，此代码假定边缘是由黑色像素和白色像素组成的，黑色像素表示背景，白色像素表示边缘。如果您的图像有其他颜色或者是反过来的，请相应地修改代码。