请帮我写一个基于ros平台的sbgm双目识别代码

好的，以下是一个基于ROS平台的SBGM双目视觉配准的代码示例：

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import rospy
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
import cv2
import numpy as np
import os

# SBGM算法参数设置
saliencyThres = 100
saliencyThresMax = 255
saliencyType = cv2.SALIENCY_GMR
sbgm = cv2.createStereoSGBM(minDisparity=0, numDisparities=64, blockSize=11)

# ROS节点初始化
rospy.init_node('sbmg_stereo_matching', anonymous=True)

# ROS图像转OpenCV图像的转换器
bridge = CvBridge()

# 订阅左右两个相机的图像话题
left_image_sub = rospy.Subscriber('/left/image_raw', Image, left_image_callback)
right_image_sub = rospy.Subscriber('/right/image_raw', Image, right_image_callback)

# 左相机图像回调函数
def left_image_callback(msg):
    # 将ROS图像转换为OpenCV图像
    try:
        left_image = bridge.imgmsg_to_cv2(msg, "bgr8")
    except CvBridgeError as e:
        print(e)

    # 进行图像处理和视觉配准
    register_images(left_image, right_image)

# 右相机图像回调函数
def right_image_callback(msg):
    # 将ROS图像转换为OpenCV图像
    try:
        right_image = bridge.imgmsg_to_cv2(msg, "bgr8")
    except CvBridgeError as e:
        print(e)

    # 进行图像处理和视觉配准
    register_images(left_image, right_image)

# 图像处理和视觉配准函数
def register_images(left_image, right_image):
    # 对左右两幅图像进行灰度化和显著性检测
    saliency = cv2.saliency.StaticSaliencySpectralResidual_create()
    gray_left = cv2.cvtColor(left_image, cv2.COLOR_BGR2GRAY)
    gray_right = cv2.cvtColor(right_image, cv2.COLOR_BGR2GRAY)
    (success, saliencyMap_left) = saliency.computeSaliency(gray_left)
    (success, saliencyMap_right) = saliency.computeSaliency(gray_right)
    saliencyMap_left = (saliencyMap_left * 255).astype("uint8")
    saliencyMap_right = (saliencyMap_right * 255).astype("uint8")

    # 通过阈值处理得到显著性二值图像
    _, saliencyMap_left = cv2.threshold(saliencyMap_left, saliencyThres, saliencyThresMax, cv2.THRESH_BINARY)
    _, saliencyMap_right = cv2.threshold(saliencyMap_right, saliencyThres, saliencyThresMax, cv2.THRESH_BINARY)

    # 提取显著性区域的轮廓并进行筛选
    contours_left, _ = cv2.findContours(saliencyMap_left, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contours_right, _ = cv2.findContours(saliencyMap_right, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contours_left = filter_contours(contours_left)
    contours_right = filter_contours(contours_right)

    # 提取显著性区域的特征点
    keypoints_left = extract_keypoints(gray_left, contours_left)
    keypoints_right = extract_keypoints(gray_right, contours_right)

    # 进行关键点匹配和视觉配准
    matches = match_keypoints(keypoints_left, keypoints_right)
    if len(matches) > 10:
        points_left = np.float32([keypoints_left[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
        points_right = np.float32([keypoints_right[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
        disparity = sbgm.compute(gray_left, gray_right).astype(np.float32) / 16.0
        disparity = cv2.normalize(disparity, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
        points_left = np.int32(points_left)
        points_right = np.int32(points_right)
        result = cv2.drawContours(left_image, contours_left, -1, (0, 255, 0), 3)
        result = cv2.drawContours(right_image, contours_right, -1, (0, 255, 0), 3)
        result = cv2.drawMatches(left_image, keypoints_left, right_image, keypoints_right, matches, None, flags=2)
        cv2.imshow("result", result)
        cv2.waitKey(1)

# 过滤掉面积小于一定值的轮廓
def filter_contours(contours, area_threshold=500):
    filtered_contours = []
    for contour in contours:
        if cv2.contourArea(contour) > area_threshold:
            filtered_contours.append(contour)
    return filtered_contours

# 提取特征点
def extract_keypoints(image, contours):
    keypoints = []
    for contour in contours:
        (x, y, w, h) = cv2.boundingRect(contour)
        roi = image[y:y+h, x:x+w]
        detector = cv2.xfeatures2d.SIFT_create()
        kp = detector.detect(roi)
        for k in kp:
            k.pt = (k.pt[0] + x, k.pt[1] + y)
        keypoints.extend(kp)
    return keypoints

# 匹配特征点
def match_keypoints(keypoints_left, keypoints_right):
    matcher = cv2.DescriptorMatcher_create("BruteForce")
    descriptor_left = cv2.xfeatures2d.SIFT_create()
    descriptor_right = cv2.xfeatures2d.SIFT_create()
    (kps_left, features_left) = descriptor_left.compute(gray_left, keypoints_left)
    (kps_right, features_right) = descriptor_right.compute(gray_right, keypoints_right)
    matches = matcher.match(features_left, features_right, None)
    matches = sorted(matches, key=lambda x: x.distance)
    return matches

rospy.spin()
cv2.destroyAllWindows()

需要注意的是，此代码只是一个示例，可能需要根据具体应用场景进行调整和优化。