用pythpn写一个hough变换的图像直线检测代码，不使用opencv库

以下是一个基本的Hough变换图像直线检测代码，使用Python和NumPy库：

import numpy as np

def hough_transform(image, theta_res=1, rho_res=1):
    # 计算图像的高度和宽度
    height, width = image.shape[:2]
    # 计算弧度值的范围
    thetas = np.deg2rad(np.arange(-90.0, 90.0, theta_res))
    # 计算极径的范围
    diag_len = np.ceil(np.sqrt(height**2 + width**2))   # 对角线长度
    rhos = np.arange(-diag_len, diag_len + 1, rho_res)
    # 创建一个二维数组，保存投票数
    accumulator = np.zeros((len(rhos), len(thetas)), dtype=np.uint64)
    y_idxs, x_idxs = np.nonzero(image)   # 找到非零像素的坐标
    for i in range(len(x_idxs)):
        x = x_idxs[i]
        y = y_idxs[i]
        for j in range(len(thetas)):
            rho = int((x * np.cos(thetas[j]) + y * np.sin(thetas[j])) + diag_len)
            accumulator[rho, j] += 1
    return accumulator, thetas, rhos

def find_lines(image, num_lines):
    # 进行Hough变换
    accumulator, thetas, rhos = hough_transform(image)
    # 找到前num_lines个直线
    indices = np.argpartition(accumulator.flatten(), -num_lines)[-num_lines:]
    indices = np.unravel_index(indices, accumulator.shape)
    # 计算直线的极坐标参数
    rho = rhos[indices[0]]
    theta = thetas[indices[1]]
    return rho, theta

# 示例代码
import cv2

# 读取图像并转换为灰度图像
image = cv2.imread('test.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 二值化图像
_, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)

# 查找直线
rho, theta = find_lines(thresh, 5)

# 绘制直线
for i in range(len(rho)):
    a = np.cos(theta[i])
    b = np.sin(theta[i])
    x0 = a * rho[i]
    y0 = b * rho[i]
    x1 = int(x0 + 1000 * (-b))
    y1 = int(y0 + 1000 * (a))
    x2 = int(x0 - 1000 * (-b))
    y2 = int(y0 - 1000 * (a))
    cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)

# 显示图像
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

这个代码假设输入图像为灰度图像，使用二值化操作得到边缘图像。它使用Hough变换来查找图像中的直线，并绘制这些直线。