写一个智能车摄像头十字补线代码

在编写智能车辆（如自动驾驶车辆）中用于摄像头的十字补线（通常用于校准或检测车道线）的代码，我们会用到计算机视觉技术和机器学习库，比如Python的OpenCV和Pillow。这是一个基本的Python示例，假设我们有两幅图像（一幅原始图像和一幅处理后的灰度图像），我们将寻找交叉点：

import cv2
import numpy as np

# 假设images是包含原始图像和预处理图像的字典
def find_cross_lines(image1, image2):
    # 转换为灰度
    gray_image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
    gray_image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)

    # 滤波去除噪声
    blurred1 = cv2.GaussianBlur(gray_image1, (5, 5), 0)
    blurred2 = cv2.GaussianBlur(gray_image2, (5, 5), 0)

    # 寻找边缘
    edges1 = cv2.Canny(blurred1, 50, 150)  # 高阈值设置得较高以便更好地分离线条
    edges2 = cv2.Canny(blurred2, 50, 150)

    # 进行Hough变换找出直线
    lines1 = cv2.HoughLines(edges1, 1, np.pi/180, 200)  # 参数调整依据实际场景
    lines2 = cv2.HoughLines(edges2, 1, np.pi/180, 200)

    # 筛选出疑似交叉的两条线
    potential_crosses = []
    for line in lines1:
        rho, theta = line[0]
        for other_line in lines2:
            other_rho, other_theta = other_line[0]
            if abs(theta - other_theta) < np.pi / 4 and abs(rho - other_rho) < 50:  # 交叉点距离相近
                potential_crosses.append((rho, theta))

    # 取平均坐标作为交叉点位置
    if len(potential_crosses) > 1:
        cross_rho = sum([p[0] for p in potential_crosses]) / len(potential_crosses)
        cross_theta = sum([p[1] for p in potential_crosses]) / len(potential_crosses)
        cross_point = (int(cross_rho * np.cos(cross_theta)), int(cross_rho * np.sin(cross_theta)))
        return cross_point
    else:
        return None

# 使用示例
cross_point = find_cross_lines(images['original'], images['processed'])
if cross_point:
    print(f"十字路口的估计位置: {cross_point}")
else:
    print("未找到十字交叉点")