请给出利用单目相机三角化获得深度图像，并把深度图像转化成laserscan类型数据的代码

以下是利用单目相机三角化获得深度图像，并把深度图像转化成laserscan类型数据的Python代码：

import cv2
import numpy as np
from sensor_msgs.msg import LaserScan

def triangulate_points(K, p1, p2, pts1, pts2):
    E, _ = cv2.findEssentialMat(pts1, pts2, K)
    _, R, t, _ = cv2.recoverPose(E, pts1, pts2, K)
    P1 = np.matmul(K, np.eye(3, 4))
    P2 = np.matmul(K, np.hstack([R, t]))
    pts4d_hom = cv2.triangulatePoints(P1, P2, pts1.T, pts2.T)
    pts4d = cv2.convertPointsFromHomogeneous(pts4d_hom.T)
    return pts4d.squeeze()

def depth_from_triangulation(K, p1, p2, pts1, pts2):
    pts4d = triangulate_points(K, p1, p2, pts1, pts2)
    depths = pts4d[:, 2]
    return depths

def depth_to_laserscan(depths, min_range, max_range, angle_min, angle_max):
    N = len(depths)
    angles = np.linspace(angle_min, angle_max, N)
    ranges = np.clip(depths, min_range, max_range)
    mask = np.logical_and(ranges > min_range, ranges < max_range)
    ranges[~mask] = float('nan')
    scan = LaserScan()
    scan.header.stamp = rospy.Time.now()
    scan.header.frame_id = 'camera_link'
    scan.angle_min = angle_min
    scan.angle_max = angle_max
    scan.angle_increment = (angle_max - angle_min) / N
    scan.range_min = min_range
    scan.range_max = max_range
    scan.ranges = ranges.tolist()
    return scan

# load camera parameters
K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
p1 = np.array([0, 0, 0, 0, 0, 0])
p2 = np.array([0, 0, 0, 0, 0, 0])

# capture image from camera
img = cv2.imread('image.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# detect keypoints and compute descriptors
detector = cv2.ORB_create()
kp1, des1 = detector.detectAndCompute(gray, None)

# match keypoints with previous frame
matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = matcher.match(des1, des2)

# extract matched keypoints
pts1 = np.float32([kp1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
pts2 = np.float32([kp2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)

# compute depths from triangulation
depths = depth_from_triangulation(K, p1, p2, pts1, pts2)

# convert depths to laserscan
scan = depth_to_laserscan(depths, min_range, max_range, angle_min, angle_max)

# publish laserscan
scan_pub = rospy.Publisher('scan', LaserScan, queue_size=10)
scan_pub.publish(scan)

# update previous frame
kp2, des2 = kp1, des1