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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

9.Contour moment

9. Contour moment

The moment function has a wide range of applications in image analysis, such as pattern recognition, target classification, target recognition and orientation estimation, image encoding and reconstruction, etc. The moment set calculated from an image can not only describe the global characteristics of the image shape, but also provide a lot of information about the different geometric features of the image, such as size, position, direction and shape. The descriptive ability of image moments is widely used in target recognition and orientation estimation in various image processing, computer vision and robotics fields.

First-order moment: related to shape;

Second-order moment: shows the extension of the curve around the average value of the straight line;

Third-order moment: symmetry measurement about the average value; 7 invariant moments can be derived from the second-order moment and the third-order moment. The invariant moment is a statistical property of the image, which satisfies the invariance of translation, scaling and rotation, and is widely used in the field of image recognition.

9.1, Use

Source launch file path: /opt/ros/noetic/share/opencv_apps/launch

Step 1: Start the camera

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roslaunch astra_visual opencv_apps.launch img_flip:=false
  • img_flip parameter: whether the image needs to be flipped horizontally, the default is false.

The [usb_cam-test.launch] file opens the [web_video_server] node by default, and you can directly use the [IP:8080] web page to view the image in real time.

Step 2: Start the corner detection function of Opencv_apps

​x
roslaunch opencv_apps contour_moments.launch # contour moments

Each function case will have a parameter [debug_view], Boolean type, whether to use Opencv to display the image, the default is displayed.

If you don't need to display, set it to [False], for example

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roslaunch opencv_apps contour_moments.launch debug_view:=False

However, after starting in this way, some cases may not be displayed in other ways, because in the source code, some [debug_view] is set to [False], which will turn off image processing.

9.2, Display method

  • rqt_image_view

Enter the following command and select the corresponding topic

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rqt_image_view
  • opencv

The system displays by default, no processing is required.

  • Web viewing

(Under the same LAN) Enter IP+port in the browser, for example:

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192.168.2.116:8080

For specific ip, use your current virtual machine ip.

9.3, Effect display

The contour moment uses parameters. There will be a topic for subscribing to images and publishing images.

ParameterTypeDefaultAnalysis
~use_camera_infobooltrueSubscribe to the topic [camera_info] to obtain the default coordinate system ID, otherwise use the image information directly.
~debug_viewboolfalseWhether to create a window to display the node image
~canny_low_thresholdint0Canny edge detection low threshold
~queue_sizeint3Queue size

Effect diagram:

Target tracking algorithm
Polygon outline

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