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首发时间：2021年6月23日

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作者水平很有限，如果发现错误，请留言轰炸哦！万分感谢！

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目录

获得相机不同传感器之间的外参转换矩阵以及内参矩阵

获取设备的传感器信息

获得深度图单位和米之间的映射

深度图与RGB图配准

获取深度图中像素点的深度值

使用API进行拍照

         Realsense获取像素点在相机坐标系下的三维坐标

其它相关内容的介绍

校准​编辑

获得相机不同传感器之间的外参转换矩阵以及内参矩阵

import pyrealsense2 as rs

pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 1280, 720, rs.format.rgb8, 30)
cfg = pipeline.start(config)
device = cfg.get_device()
name = device.get_info(rs.camera_info.name)
print(name)
profile = cfg.get_stream(rs.stream.depth)
profile1 = cfg.get_stream(rs.stream.color)
intr = profile.as_video_stream_profile().get_intrinsics()
intr1 = profile1.as_video_stream_profile().get_intrinsics()
extrinsics = profile1.get_extrinsics_to(profile)
print(extrinsics)
print("深度传感器内参：", intr)
print("RGB相机内参:", intr1)

Intel RealSense D435
rotation: [0.999641, -0.0260036, 0.00641907, 0.0260053, 0.999662, -0.000173101, -0.0064124, 0.000339969, 0.999979]
translation: [-0.0147544, -0.000367906, -0.000408054]
深度传感器内参： [ 640x480  p[322.424 239.496]  f[386.034 386.034]  Brown Conrady [0 0 0 0 0] ]
RGB相机内参: [ 1280x720  p[643.807 366.839]  f[930.979 930.957]  Inverse Brown Conrady [0 0 0 0 0] ]

注意
   不同分辨率的深度相机和RGB相机对应不同的内参参数，但是外参矩阵是一样的（上述代码的外参指的是RGB相机转换到深度相机的转换矩阵），可以观察到RGB相机有畸变参数，深度相机无畸变参数。

import pyrealsense2 as rs

pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
profile = pipeline.start(config)
frames = pipeline.wait_for_frames()
depth = frames.get_depth_frame()
color = frames.get_color_frame()
# 获取内参
depth_profile = depth.get_profile()
print(depth_profile)
# 
print(type(depth_profile))
# 
print(depth_profile.fps())
# 30
print(depth_profile.stream_index())
# 0
print(depth_profile.stream_name())
# Depth
print(depth_profile.stream_type())
# stream.depth
print('', depth_profile.unique_id)
# >

color_profile = color.get_profile()
print(color_profile)
# 
print(type(color_profile))
# 
print(depth_profile.fps())
# 30
print(depth_profile.stream_index())
# 0

cvsprofile = rs.video_stream_profile(color_profile)
dvsprofile = rs.video_stream_profile(depth_profile)

color_intrin = cvsprofile.get_intrinsics()
print(color_intrin)
# 960x540  p[493.975 265.065]  f[673.775 673.824]  Brown Conrady [0.151657 -0.50863 -0.000700379 -0.000860805 0.471284]
depth_intrin = dvsprofile.get_intrinsics()
print(depth_intrin)
# [ 640x480  p[306.57 254.527]  f[461.453 461.469]  None [0 0 0 0 0] ]
extrin = depth_profile.get_extrinsics_to(color_profile)
print(extrin)
# rotation: [0.999965, 0.00762357, 0.00331248, -0.00754261, 0.999688, -0.0238027, -0.0034929, 0.0237769, 0.999711]
# translation: [0.000304107, 0.0142351, -0.00695471]

 获取设备的传感器信息

import pyrealsense2 as rs
import pyrealsense2 as rs

pipeline = rs.pipeline()
config = rs.config()
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
for s in device.sensors:
    print(s.get_info(rs.camera_info.name))

cfg = pipeline.start(config)
device1 = cfg.get_device()
for s in device1.sensors:
    print(s.get_info(rs.camera_info.name))

aaaa

Stereo Module
RGB Camera
Stereo Module
RGB Camera

两个语句得到的都是pipeline_profile的类，有get_device的方法

pipeline.start(config)

config.resolve(pipeline_wrapper)

获得深度图单位和米之间的映射

import pyrealsense2 as rs
import pyrealsense2 as rs

# Create a pipeline
pipeline = rs.pipeline()
# Start streaming
profile = pipeline.start()

# Getting the depth sensor's depth scale (see rs-align example for explanation)
depth_sensor = profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
print("Depth Scale is: ", depth_scale)

Depth Scale is:  0.0010000000474974513

      get_depth_scale的目标是获得深度图单位与米单位之间的映射关系，所以上述表示一个深度图单位等于0.00025米。第二个例子通过get_data方法获得的数值与米单位之间需要乘以一个比例系数。

深度图与RGB图配准

# First import the library
import pyrealsense2 as rs
# Import Numpy for easy array manipulation
import numpy as np
# Import OpenCV for easy image rendering
import cv2

# Create a pipeline
pipeline = rs.pipeline()

# Create a config and configure the pipeline to stream
#  different resolutions of color and depth streams
config = rs.config()

# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))

found_rgb = False
for s in device.sensors:
    if s.get_info(rs.camera_info.name) == 'RGB Camera':
        found_rgb = True
        break
if not found_rgb:
    print("The demo requires Depth camera with Color sensor")
    exit(0)

config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)

if device_product_line == 'L500':
    config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)
else:
    config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)

# Start streaming
profile = pipeline.start(config)

# Getting the depth sensor's depth scale (see rs-align example for explanation)
depth_sensor = profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
print("Depth Scale is: ", depth_scale)

# We will be removing the background of objects more than
#  clipping_distance_in_meters meters away
clipping_distance_in_meters = 1  # 1 meter
clipping_distance = clipping_distance_in_meters / depth_scale

# Create an align object
# rs.align allows us to perform alignment of depth frames to others frames
# The "align_to" is the stream type to which we plan to align depth frames.
align_to = rs.stream.color
align = rs.align(align_to)

# Streaming loop
try:
    while True:
        # Get frameset of color and depth
        frames = pipeline.wait_for_frames()
        # frames.get_depth_frame() is a 640x360 depth image

        # Align the depth frame to color frame
        aligned_frames = align.process(frames)

        # Get aligned frames
        aligned_depth_frame = aligned_frames.get_depth_frame()  # aligned_depth_frame is a 640x480 depth image
        color_frame = aligned_frames.get_color_frame()

        # Validate that both frames are valid
        if not aligned_depth_frame or not color_frame:
            continue

        depth_image = np.asanyarray(aligned_depth_frame.get_data())
        color_image = np.asanyarray(color_frame.get_data())
        # the size of color_frame is (720,1280,3)
        # Remove background - Set pixels further than clipping_distance to grey
        grey_color = 153
        depth_image_3d = np.dstack((depth_image, depth_image, depth_image))
        # depth image is 1 channel, color is 3 channels
        # depth_image_3d shape is (720,1280,3)
        bg_removed = np.where((depth_image_3d > clipping_distance) | (depth_image_3d <= 0), grey_color, color_image)
        # the size of bg_removed is (720,1280,3)
        # Render images:
        #   depth align to color on left
        #   depth on right
        depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
        images = np.hstack((bg_removed, depth_colormap))

        cv2.namedWindow('Align Example', cv2.WINDOW_NORMAL)
        cv2.imshow('Align Example', images)
        key = cv2.waitKey(1)
        # Press esc or 'q' to close the image window
        if key & 0xFF == ord('q') or key == 27:
            cv2.destroyAllWindows()
            break
finally:
    pipeline.stop()

 无论初始对于彩色流或是深度流是如何设置分辨率的，经过align.process之后的彩色流和深度流都是(720,1280)大小的(这个大小根据彩色流的设置而定），虽然深度流需要重构为三维来让彩色流和深度流拼接。虽然深度流的大小转换为和彩色流一样的大小，但是分辨率还是根据深度流的设置参数而定的，与大小无关

获取深度图中像素点的深度值

get_distance()方法得到的数据是以米为单位的

import cv2
import numpy as np
import pyrealsense2 as rs

pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
# Start streaming
profile = pipeline.start(config)
while True:
    frames = pipeline.wait_for_frames()
    depth_frames = frames.get_depth_frame()
    depth_image = np.asarray(depth_frames.get_data())
    depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
    distance = depth_frames.get_distance(100, 200)
    print(distance)
    cv2.imshow("depth image:", depth_colormap)
    key = cv2.waitKey(1)
    if key == 27:
        break
pipeline.stop()

3.8570001125335693
3.8570001125335693
3.8570001125335693
4.11400032043457
3.809000253677368
3.8330001831054688
3.763000249862671
4.25600004196167
5.27400016784668

 使用API进行拍照

import cv2
import numpy as np
import pyrealsense2 as rs
import os

# 配置
pipe = rs.pipeline()
cfg = rs.config()
cfg.enable_stream(rs.stream.color, 1280, 720, rs.format.rgb8, 30)

i = 0
profile = pipe.start(cfg)

while True:
    # 获取图片帧
    frameset = pipe.wait_for_frames()
    color_frame = frameset.get_color_frame()
    color_img = np.asanyarray(color_frame.get_data())

    # 更改通道的顺序为RGB
    cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
    cv2.imshow('RealSense', color_img)
    k = cv2.waitKey(1)
    # Esc退出，
    if k == 27:
        cv2.destroyAllWindows()
        break
    # 输入空格保存图片
    elif k == ord(' '):
        i = i + 1
        cv2.imwrite(os.path.join("D:\\Realsense\\pic_capture", str(i) + '.jpg'), color_img)
        print("Frames{} Captured".format(i))

pipe.stop()

 只要将数据转换为numpy数组的方式，就可以通过opencv库进行图片的保存

Realsense获取像素点在相机坐标系下的三维坐标

import pyrealsense2 as rs
import numpy as np
import cv2
import json


def get_aligned_images():
    frames = pipeline.wait_for_frames()  # 等待获取图像帧
    aligned_frames = align.process(frames)  # 获取对齐帧
    aligned_depth_frame = aligned_frames.get_depth_frame()  # 获取对齐帧中的depth帧
    color_frame = aligned_frames.get_color_frame()  # 获取对齐帧中的color帧

    ############### 相机参数的获取 #######################
    intr = color_frame.profile.as_video_stream_profile().intrinsics  # 获取相机内参
    depth_intrin = aligned_depth_frame.profile.as_video_stream_profile().intrinsics  # 获取深度参数（像素坐标系转相机坐标系会用到）
    camera_parameters = {'fx': intr.fx, 'fy': intr.fy,
                         'ppx': intr.ppx, 'ppy': intr.ppy,
                         'height': intr.height, 'width': intr.width,
                         'depth_scale': profile.get_device().first_depth_sensor().get_depth_scale()
                         }
    # 保存内参到本地
    with open('D:\\Realsense\\intrinsics.json', 'w') as fp:
        json.dump(camera_parameters, fp)
    #######################################################

    depth_image = np.asanyarray(aligned_depth_frame.get_data())  # 深度图（默认16位）
    depth_image_8bit = cv2.convertScaleAbs(depth_image, alpha=0.03)  # 深度图（8位）
    depth_image_3d = np.dstack((depth_image_8bit, depth_image_8bit, depth_image_8bit))  # 3通道深度图
    color_image = np.asanyarray(color_frame.get_data())  # RGB图

    # 返回相机内参、深度参数、彩色图、深度图、齐帧中的depth帧
    return intr, depth_intrin, color_image, depth_image_3d, aligned_depth_frame


pipeline = rs.pipeline()  # 定义流程pipeline
config = rs.config()  # 定义配置config
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)  # 配置depth流
config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)  # 配置color流
profile = pipeline.start(config)  # 流程开始
align_to = rs.stream.color  # 与color流对齐
align = rs.align(align_to)
while True:
    intr, depth_intrin, rgb, depth, aligned_depth_frame = get_aligned_images()  # 获取对齐的图像与相机内参
    # 定义需要得到真实三维信息的像素点（x, y)，本例程以中心点为例
    x = 320
    y = 240
    dis = aligned_depth_frame.get_distance(x, y)  # （x, y)点的真实深度值
    print("distance:",dis)
    camera_coordinate = rs.rs2_deproject_pixel_to_point(intr, [x, y], dis)
    # （x, y)点在相机坐标系下的真实值，为一个三维向量。
    # 其中camera_coordinate[2]仍为dis，camera_coordinate[0]和camera_coordinate[1]为相机坐标系下的xy真实距离。
    print(camera_coordinate)

    cv2.imshow('RGB image', rgb)  # 显示彩色图像

    key = cv2.waitKey(1)
    # Press esc or 'q' to close the image window
    if key & 0xFF == ord('q') or key == 27:
        pipeline.stop()
        break
cv2.destroyAllWindows()

 

 

    获得像素点在相机坐标系下的三维坐标之后，通过手眼标定就可以转化为在机械臂基底坐标系下的坐标，进而执行下一步操作。所得到的三维坐标应该是以米为单位的。

其它相关内容的介绍

像素坐标：
      通过SDK提供的图像流都关联一个独立的2D以像素为单位的坐标系。[0,0]点位于左上角，[w-1,h-1]点位于右下角。w和h分别代表列和行，从相机的角度来看，x轴指向右边，y轴指向下边。这个坐标系就是所谓的像素坐标系，用来索引特定的像素点。

点坐标：
       通过SDK提供的图像流都关联一个独立的3D以米为单位的坐标系。这个坐标系的原点[0,0,0]指的是物理成像仪的中心。在这个空间中，x轴正向指向右，y轴正向指向下，z轴正向指向前。该空间中的坐标称为“点”，用于描述三维空间中可能在特定图像中可见的位置。

相机内参
流的2D和3D坐标系的转换关系是通过相机内参来描述的，包含在rs2_intrinsics结构体中。不同的RealSense设备的内参是不同的，rs2_intrinsics结构体必须要能够描述由这些设备产生的图像。

相机外参
每种图像流的三维坐标系是不同的，比如说，通常来说深度图像是通过一个或多个红外成像仪生成的，而彩色流是通过一个独立的彩色成像仪形成的。这些不同的流所对应的三维坐标系之间的关系是通过外参进行描述的，包含在rs2_extrinsics的结构体中。

校准

 

这张图片也就是说，在 Viewer 上进行校准，当误差小于一定值时，可以忽略。

如果校准过程中报错：可能是没有足够的有效深度像素，通常可以通过确保投影仪处于打开状态来补救。