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AI View Depth camera📖
01.Linux basic course
  • 1.Introduction to Linux system
  • 2.Ubuntu file system
  • 3.Ubuntu common commands
  • 4.Ubuntu common editors
  • 5.Ubuntu software operation commands
  • 6.Virtual machine installation
  • 7.SSH remote control
  • 8.VNC remote control
  • 9.Transfer files remotely
  • 10.Driver library and communication
  • 11.Static IP and hotspot mode
  • 12.Bind device ID
  • 13.Capacity expansion and resource allocation
  • 14.Update system software sources
  • 15.Set root user password
  • 16.sudo free password
  • 17.Connect to WiFi network
  • 18.View system version
  • 19.Customized service management
  • 20.Back up system image
02.ROS1-camera usage course
  • 1. Camera usage instructions
  • 2. Environment Construction
  • 3. Camera recording video
03.ROS1-OpenCV course
  • 1.Opencv Source CV Introduction
  • 2.Image reading and display
  • 3.Picture writing
  • 4.Picture Quality
  • 5.Pixel Operation
  • 6.Picture scaling
  • 7.Picture cutting
  • 8.Picture translation
  • 9.Picture mirroring
  • 10.Affine transformation
  • 11.Picture rotation
  • 12.Perspective transformation
  • 13.Grayscale processing
  • 14.Image binarization
  • 15.Edge detection
  • 16.Line segment drawing
  • 17.Rectangle and circle drawing
  • 18.Text and picture drawing
4.ROS1-basic course
  • 1.ROS introduction
  • 2.ROS installation
  • 3.ROS common command tools
  • 4.ROS workspace
  • 5.ROS function package
  • 6.ROS node
  • 7.ROS topic publisher
  • 8.ROS topic subscribers
  • 9.ROS service client
  • 10.ROS service server
  • 11.ROS action client
  • 12.ROS action server
  • 13.ROS custom message reception
  • 14.ROS-launch file
  • 15.ROS-TF transformation
  • 16.ROS parameter service
  • 17.ROS-rviz use
  • 18.ROS-rqt tool usage
  • 19.Topic message recording and playback
  • 20.urdf model introduction
  • 21.gazebo introduction
  • 22.ROS distributed communication
5.ROS1-Mediapipe course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Face detection
  • 5.Face recognition
  • 6.Face effects
  • 7.3D object recognition
  • 8.Brush
  • 9.Finger control
  • 10.Gesture recognition
6.ROS+Opencv application
  • 1.Camera calibration
  • 2.QR code
  • 3.Human body pose estimation
  • 4.Target detection
  • 5.ROS+Opencv basics
  • 6.Face recognition
  • 7.Harris corner detection
  • 8.Target tracking algorithm
  • 9.Contour moment
  • 10.Polygon outline
  • 11.Discrete Fourier transform algorithm
  • 12.Edge detection algorithm
  • 13.Face detection algorithm
  • 14.Optical flow detection algorithm
  • 15.Contour detection
  • 16.General contour detection
  • 17.Feature point tracking
  • 18.HLS color filtering
  • 19.Hough circle detection
  • 20.Hough line detection
  • 21.HSV color filtering
  • 22.LK optical flow algorithm
  • 23.Human body detection algorithm
  • 24.Phase-related displacement detection
  • 25.Image pyramid sampling algorithm
  • 26.RGB color filtering
  • 27.Clear background detection algorithm
  • 28.Simplified optical flow algorithm
  • 29.Simple filter
  • 30.Threshold image processing
  • 31.Watershed segmentation algorithm
  • 32.View point cloud data
  • 33.AR vision
  • 34.AR QR code tracking
  • 35.Color tracking
  • 36.Object tracking
07.ROS2-camera usage course
  • Instructions for using camera
08.ROS2-basic course
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
09.ROS2-OpenCV course
  • 1. ROS+opencv application
  • 2. QR code creation and recognition
  • 3. AR vision
10.ROS2-Mediapipe course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Face detection
  • 5.Personal insurance identification
11.ROS2-Depth camera course
  • 1. Use of depth camera
  • 2. Camera internal parameter calibration
  • 3. Color tracking
  • 4. KCF object tracking
  • 5. ORB_SLAM2 basics
  • 6. ORB_SLAM2 PCL mapping
  • 7. ORB_SLAM2 Octomap mapping
12.Bracket installation video
  • Bracket installation video
Download
  • VM_ROS1
  • VM_ROS2
  • Code-ROS1
  • Code-ROS2
  • 3D_File
  • ROS_Basic_VM
Download ZIP

Welcome to AI View Depth camera repository

2. Camera internal parameter calibration

2. Camera internal parameter calibration

1. Preparation before calibration

  • A large chessboard of known size. This tutorial uses a 9x6 chessboard with 20 mm squares, which needs to be flattened during calibration. Calibration uses the internal vertices of the chessboard, so a "10x7" chessboard uses the internal vertex parameters "9x6", as shown in the example below. Any size of calibration board can be used, just change the parameters. A more open area without obstacles and calibration board patterns

  • Monocular camera that publishes images through ROS

2. Start calibration

Install the calibration function package camera_calibration. Take foxy as an example. Enter in the terminal,

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sudo apt install ros-foxy-camera-calibration*

Start the camera before calibration, and turn it off after all calibrations are completed. Start the camera and enter in the terminal,

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ros2 launch orbbec_camera gemini2.launch.py

Use the following command to view the topic terminal input,

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ros2 topic list

The topic we need to use to calibrate RGB color images is /camera/color/image_raw.

Run the calibration program and enter in the terminal,

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ros2 run camera_calibration cameracalibrator --size 9x6 --square 0.02 --ros-args --remap /image:=/camera/color/image_raw

size: The number of internal corner points of the calibrated chessboard, for example, 9X6, the corner points have six rows and nine columns.

square: The side length of the chessboard, in meters.

Topic name: /camera/color/image_raw. If you are starting usb_cam, change it to /image_raw

image-20230426161833099

X: Left and right movement of the chessboard in the camera's field of view

Y: Up and down movement of the chessboard in the camera's field of view

Size: Front and back movement of the chessboard in the camera's field of view

Skew: Tilt and rotation of the chessboard in the camera's field of view

As shown in the figure above, you need to capture the image by flipping up and down, front and back, left and right, so that the X, Y, Size, and Skew on the right turn green, as shown in the figure below, and then click CALIBRATE to start calibration.

image-20230426162103880

After the calibration is completed, click SAVE, as shown in the figure below.

image-20230426162241771

The calibration results are saved to [/tmp/calibrationdata.tar.gz], and the saved path is the terminal directory where the calibration program is started. After the calibration is completed, you can move out the [/tmp/calibrationdata.tar.gz] file to see the content

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sudo mv /tmp/calibrationdata.tar.gz ~

Terminal input,

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cd ~
tar -xvf calibrationdata.tar.gz

You will get the calibrated png file, ost.yaml and ost.txt files in the terminal directory

Since the gemini2 driver loads the built-in parameters calibrated in the code when it is started, it is not necessary to load the calibrated parameters, but when the USB camera is started, the parameters need to be loaded. Therefore, after calibration, the parameters need to replace the original built-in parameters, rename the calibrated ost.yaml to camera_info.yaml, and then replace the original camera_info.yaml, and enter the docker terminal,

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#First copy the file to /opt/ros/foxy/share/usb_cam/config
sudo cp ost.yaml /opt/ros/foxy/share/usb_cam/config
#Switch to the /opt/ros/foxy/share/usb_cam/config directory
cd /opt/ros/foxy/share/usb_cam/config
#Backup the original camera_info.yaml
sudo mv camera_info.yaml camera_info_BK.yaml
#Rename ost.yaml to camera_info.yaml
sudo mv ost.yaml camera_info.yaml
Use of depth camera
Color tracking

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