我目前正在通过python使用realsense相机有以下的需求：（1）显示彩色图像和深度图像（2）追踪图像里的三角形角点并显示到其的距离

好的，针对你的需求，你可以使用Intel RealSense SDK提供的Python API来实现。以下是一些基本的代码示例来帮助你实现你的需求：

1. 显示彩色图像和深度图像

import pyrealsense2 as rs
import numpy as np
import cv2

# 创建RealSense深度相机对象
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
pipeline.start(config)

try:
    while True:
        # 获取彩色图像和深度图像
        frames = pipeline.wait_for_frames()
        color_frame = frames.get_color_frame()
        depth_frame = frames.get_depth_frame()

        # 将深度图像转化为numpy数组
        depth_image = np.asanyarray(depth_frame.get_data())

        # 可视化深度图像
        depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)

        # 显示彩色图像和深度图像
        color_image = np.asanyarray(color_frame.get_data())
        images = np.hstack((color_image, depth_colormap))
        cv2.imshow('RealSense', images)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
finally:
    # 停止相机采集
    pipeline.stop()
    cv2.destroyAllWindows()

2. 追踪图像里的三角形角点并显示到其的距离

import pyrealsense2 as rs
import numpy as np
import cv2

# 创建RealSense深度相机对象
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
pipeline.start(config)

# 创建角点追踪器
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
objp = np.zeros((6*9, 3), np.float32)
objp[:, :2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2)

objpoints = []  # 存储物体点的世界坐标系下的坐标值
imgpoints = []  # 存储图像点的像素坐标值

while True:
    # 获取彩色图像和深度图像
    frames = pipeline.wait_for_frames()
    color_frame = frames.get_color_frame()
    depth_frame = frames.get_depth_frame()

    # 将深度图像转化为numpy数组
    depth_image = np.asanyarray(depth_frame.get_data())

    # 可视化深度图像
    depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)

    # 显示彩色图像和深度图像
    color_image = np.asanyarray(color_frame.get_data())
    images = np.hstack((color_image, depth_colormap))
    cv2.imshow('RealSense', images)

    # 在彩色图像中寻找角点
    gray = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
    ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)

    # 如果找到了角点
    if ret == True:
        # 将角点坐标添加到objpoints和imgpoints中
        objpoints.append(objp)
        corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
        imgpoints.append(corners2)

        # 在彩色图像中绘制角点
        color_image = cv2.drawChessboardCorners(color_image, (9, 6), corners2, ret)

        # 计算角点的距离
        x, y, z = corners2[0][0]
        d = depth_image[int(y), int(x)]
        text = "distance: " + str(d) + "mm"
        cv2.putText(color_image, text, (int(x), int(y) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

    # 显示彩色图像和深度图像
    images = np.hstack((color_image, depth_colormap))
    cv2.imshow('RealSense', images)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 停止相机采集
pipeline.stop()
cv2.destroyAllWindows()

希望以上代码示例能够帮助你实现你的需求！