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NUWA-HP60C Depth Camera📖
1.ROS1-Depth camera usage course
  • 1. Environment Construction
  • 2. Camera recording video
2.ROS1-ROS+OpenCV application
  • 1.ROS+Opencv Basics
  • 2.Face recognition
  • 3.Harris corner detection
  • 4. Target Tracking Algorithm
  • 5. Contour moment
  • 6. Polygonal Outlines
  • 7. Discrete Fourier Transform Algorithm
  • 8. Edge Detection Algorithm
  • 9. Face Detection Algorithm
  • 10. Optical flow detection algorithm
  • 11. Contour Detection
  • 12. General contour detection
  • 13. Feature point tracking
  • 14.HLS Color Filtering
  • 15. Hough circle detection
  • 16. Hough Line Detection
  • 17.HSV color filtering
  • 18.LK optical flow algorithm
  • 19. Human Detection Algorithm
  • 20. Phase-correlated displacement detection
  • 21. Image pyramid sampling algorithm
  • 22.RGB color filtering
  • 23. Clear background detection algorithm
  • 24. Streamlining the optical flow algorithm
  • 25. Simple Filter
  • 26. Threshold Image Processing
  • 27. Watershed segmentation algorithm
3.ROS1-Mediapipe course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Face Mesh
  • 5.Face recognition
  • 6.Face effects
  • 7.Face Detection
  • 8.3D Object Recognition
  • 9.Paintbrush
  • 10.Finger control
  • 11.Gesture Recognition
4.ROS1-Depth camera application course
  • 1. Camera calibration
  • 2. QR code
  • 3. Human Pose Estimation
  • 4. Target detection
  • 5. Point cloud data viewing
  • 6. AR Vision
  • 7.AR QR code tracking
  • 8. Color Tracking
  • 9. Object Tracking
5.ROS1-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
6.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
7.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
8.ROS2-Depth Camera Application Course
  • 1. Use of Depth Camera
  • 2. Camera internal parameter calibration
  • 3. Color tracking
  • 4. KCF Object Tracking
  • 5. ORBSLAM2 Basics
  • 6. ORB_SLAM2 PCL Mapping
  • 7. ORB_SLAM2 Octomap Mapping
9.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 Mess
  • 5.Face recognition
11.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
5.Bracket installation video
  • Bracket installation video
Download
  • ROS1_VM
  • ROS2_VM
  • ROS1_Code
  • ROS2_Code
  • DataSheet
  • 3D_Model_File
  • Windows Software
  • ROS2_Basic_VM
Download ZIP

Welcome to NUWA-HP60C Depth Camera repository

1.ROS+Opencv Basics

1. ROS+OpenCV Basics

Function package location: ~/ascam_ws/src/ascam_visual

1.1 Overview

ROS has integrated Opencv3.0 or higher versions during the installation process, so there is almost no need to consider the installation configuration. ROS transmits images in its own sensor_msgs/Image message format and cannot directly process images, but the provided [CvBridge] can perfectly convert and be converted image data formats. [CvBridge] is a ROS library, which is equivalent to a bridge between ROS and Opencv.

Opencv and ROS image data conversion is shown in the figure below:

image-20231017174919295

Although the installation configuration does not require too much consideration, the use environment still needs to be configured, mainly the two files [package.xml] and [CMakeLists.txt]. This function package not only uses [CvBridge], but also requires [Opencv] and [PCL], so they are configured together.

  • package.xml

[cv_bridge]: image conversion dependency package.

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<build_depend>sensor_msgs</build_depend>
<build_export_depend>sensor_msgs</build_export_depend>
<exec_depend>sensor_msgs</exec_depend>
<build_depend>std_msgs</build_depend>
<build_export_depend>std_msgs</build_export_depend>
<exec_depend>std_msgs</exec_depend>
<build_depend>cv_bridge</build_depend>
<build_export_depend>cv_bridge</build_export_depend>
<exec_depend>cv_bridge</exec_depend>
<exec_depend>image_transport</exec_depend>
  • CMakeLists.txt

This file has a lot of configuration content. For specific content, please check the source file. The file location is: ~/ascam_ws/src/ascam_visual/CMakeLists.txt

1.2. Start the camera

1.2.1. Start the camera

Terminal input,

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roslaunch ascamera hp60c.launch

View the topic,

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rostopic list

image-20241114185611900

The following are commonly used,

/ascamera_hp60c/rgb0/image: RGB color image topic

/ascamera_hp60c/depth0/image_raw: Depth depth image topic

/ascamera_hp60c/depth0/points: Depth point cloud data topic

View the encoding format of the topic: rostopic echo + [topic] + encoding, for example,

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rostopic echo /ascamera_hp60c/rgb0/image/encoding
rostopic echo /ascamera_hp60c/depth0/image_raw/encoding

image-20241114185847796

image-20241114185934402

1.2.2, Start the color image subscription node

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rosrun ascam_visual ascam_rgb_image.py # py
rosrun ascam_visual ascam_rgb_image # C++

image-20241114190034278

View the node graph,

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rqt_graph

image-20241114190150400

  • py code analysis

Create a subscriber: the subscribed topic is ["/ascamera_hp60c/rgb0/image"], data type [Image], callback function

[topic()]

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sub = rospy.Subscriber("/ascamera_hp60c/rgb0/image", Image, topic)

Use [CvBridge] to convert data. Here you need to pay attention to the encoding format. If the encoding format is incorrect, the converted image will have problems.

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bridge = CvBridge()
frame = bridge.imgmsg_to_cv2(msg, "bgr8")
  • c++ code analysis

Similar to py code

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//Create a receiver
ros::Subscriber subscriber = n.subscribe<sensor_msgs::Image("/ascamera_hp60c/rgb0/image", 10, RGB_Callback);
//Create cv_bridge example
cv_bridge::CvImagePtr cv_ptr;
//Data conversion
cv_ptr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8);

1.2.3, Start the depth map subscription node

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#Start by selecting 1 from the following commands
rosrun ascam_visual ascam_depth_image.py # py
rosrun ascam_visual ascam_depth_image # C++

image-20241114190448800

View the node graph,

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rqt_graph

image-20241114190518426

  • py code analysis

Create a subscriber: the subscribed topic is ["/ascamera_hp60c/depth0/image_raw"], data type [Image], callback function [topic()]

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sub = rospy.Subscriber("/ascamera_hp60c/depth0/image_raw", Image, topic)

Use [CvBridge] to convert data. Here you need to pay attention to the encoding format. If the encoding format is incorrect, the converted image will have problems.

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# Encoding format
encoding = ['16UC1', '32FC1']
# You can switch different encoding formats to test the effect
frame = bridge.imgmsg_to_cv2(msg, encoding[1])
  • c++ code analysis

Similar to py code,

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//Create a receiver.
ros::Subscriber subscriber = n.subscribe<sensor_msgs::Image>
("/ascamera_hp60c/depth0/image_raw", 10, depth_Callback);
// Create cv_bridge example
cv_bridge::CvImagePtr cv_ptr;
// Data conversion
cv_ptr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::TYPE_16UC1)

1.2.4, start color image inversion

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rosrun ascam_visual ascam_image_flip.py # py

Image view (open two), select different topics to display

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rqt_image_view

image-20241114190901015

  • Code analysis

1), create subscribers

The subscribed topic is ["/ascamera_hp60c/rgb0/image"], data type [Image], callback function [topic()].

2), create publishers

The published topic is ["/ascamera_hp60c/rgb0/image_flip"], data type [Image], queue size [10].

3) Callback function

# Normal image transmission processing
def topic(msg):
if not isinstance(msg, Image):
return
bridge = CvBridge()
frame = bridge.imgmsg_to_cv2(msg, "bgr8")
# Opencv image processing
frame = cv.resize(frame, (640, 480))
frame = cv.flip(frame, 1)
# opencv mat -> ros msg
msg = bridge.cv2_to_imgmsg(frame, "bgr8")
# Image processing is complete, publish directly
pub_img.publish(msg)
Camera recording video
Face recognition

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