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Raspberry Pi 4B📖
1.Preparation before use
  • 1.1 Download image
  • 1.2 Write image into SD card
  • 1.3 Start up Raspberry Pi
  • 1.4 Linux common commands
  • 1.5 Precautions
2.Basic course
  • 2.1 Log into Raspberry Pi system
  • 2.2 Change password and root account
  • 2.3 Remote transfer file
  • 2.4 Backup image file
  • 2.5 Fixing Raspberry Pi IP
  • 2.6 Set screen keep on
  • 2.7 Get Pi temperature
  • 2.8 Connect WIFI
  • 2.9 Modify Pi time
  • 2.10 Set the boot self-starting program
3.Expand course
  • 3.1 Serial port communicates with external devices
  • 3.2 2.Serial port login Pi
  • 3.3 Configured as a router
  • 3.4 Install Jupyter Lab
  • 3.5 Install Jupyter Widgets
  • 3.6 More Detail for Widgets
  • 3.7 Install OpenCV
  • 3.8 Install Tensorflow
4.Programming course
C language
  • 1.Development environment
  • 2.Print Hello World
  • 3.Pin output level
  • 4.Read pin level
  • 5.PWM output
Python language
  • 1.Development environment
  • 2.Print basic usage
  • 3.Pin output level
  • 4.Read pin level
  • 5.PWM output
5.AI vision course
A.Getting started with OpenCV
  • 6.Picture Rotation
  • 1.Introduction of OpenCV
  • 2.OpenCV image read and display
  • 3.OpenCV image write
  • 4.OpenCV image quality
  • 5.OpenCV pixel operation
B.OpenCV Geometric transformat
  • 1.Picture Zoom
  • 2.Picture Cut
  • 3.Picture Shift
  • 4.Picture Mirroring
  • 5.Affine Map
  • 7.Perspective Transformation
C.OpenCV picture processing
  • 1.Grayscale processing
  • 2.Image Binarization
  • 3.Edge detection
  • 4.Drawing line segment
  • 5.Drawing rectangle circle
  • 6.Drawing text picture
D.OpenCV image beautification
  • 1.Histogram of color pictures
  • 2.Histogram equalization
  • 3.Image restoration
  • 4.Brightness enhancement
  • 5.Skin whitening
  • 6.Gaussian mean filtering
  • 7.Median filtering
F.AI Advanced Course (Ubuntu system)
  • 1. QR code
  • 2. Human posture estimation
  • 3. Target detection
  • 4. Mediapipe development
  • 5. yolov4-tiny
E.Install TensorFlow
  • Install TensorFlow
6.ROS1 coursenew
Basic Course
  • 1.ROS introduction
  • 2.Project file structure
  • 3.Common commands and tools
  • 4.Publisher
  • 5.Subscribers
  • 6.Customize topic messages and use
  • 7.Client
  • 8.Server
  • 9.Custom service messages and usage
  • 10.TF release and monitoring
Visual Image Processing
  • 1. AR vision
  • 2. AR QR code
  • 3. ROS+Opencv foundation
  • 4. ROS+Opencv application
  • 5. MediaPipe development
7.ROS2 coursenew
Docker Course
  • 1.Overview and installation
  • 2.Common commands
  • 3.Understand and publish images
  • 4.Hardware interaction data processing
  • 5.Enter docker container
  • 6.Update docker images
Basic Course
  • 1.Introduction to ROS2
  • 2.Commands and tools
  • 3.ROS2 Topic communications
  • 4.ROS2 service communications
  • 5.ROS2launch file starts
OpenCV Course
  • 1.OpenCV Basic Course
  • 2.ROS+opencv application
  • 3.QR code recognition
  • 4.AR Vision
  • 5.Mediapipe
8.Accessories installation
  • 1. Installation of heat sink
  • 2. Wiring of cooling fan
  • 3. Double fan armor case Installation
  • 4. Argon NEO aluminum case installation
  • 5. Argon ONE aluminum alloy case installation
  • 6. Metal shell with built-in fan installation
  • 7. Acrylic case with cooling fan installation
  • 8. White-red ABS protective case installation
Download
  • Tools
  • C_code
  • Python code
  • ROS System
Download ZIP

Welcome to Raspberry Pi 4B repository

2.ROS+opencv application

2、ROS+opencv application

This lesson uses a usb no-drive camera as an example

ROS with its own “sensor_ The msgs/Image” message format transmits images, which cannot be directly processed, but the provided 【CvBridge】can perfectly convert and convert image data formats. 【CvBridge】 is an ROS library that serves as a bridge between ROS and Opencv.

The conversion of Opencv and ROS image data is shown in the following figure:

image-20230425150159148

 

 

 

2.1、Subscribe to image data and publish the converted image data

Run Command
​x
#Run and publish image topic data nodes
ros2 run yahboomcar_visual pub_image

# Choose one of the following nodes
# Run USB camera topic node (with image display)
ros2 launch usb_cam demo_launch.py

# Run USB camera topic node (without image display)
ros2 launch usb_cam Demo_launch.py

 

Related points to note:

1、Before entering the docker container, make sure whether the usb no-drive camera device is connected, and add the camera device in the startup script. For the specific tutorial, please see [docker Tutorial ——5、Entering docker Container]

2、Due to the performance of some motherboards is not very good, running the usb camera topic node GUI display will be very card (such as jetson nano), can run the node without image display, and then use the rgt_image_view tool in the following tutorial to view the image

 

View the node communication diagram

Docker terminal input

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ros2 run rqt_graph rqt_graph

image-20230425173134021

When you run the node directive without image display, the /examine_Image node is not displayed in rgt_graph

 

View topic list

Check which image topics are published, enter in docker terminal

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ros2 topic list

image-20230425175046734

The/image is the topic data we have published. Use the following command to print and view the data content of this topic

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ros2 topic echo /image

image-20230425175257775

You can use rqt_ Image_ View tool to view images

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ros2 run rqt_image_view rqt_image_view

image-20230425175651680

After opening, select the topic name/image in the upper left corner to view the image.

notes: Sometimes the first loading after selecting the topic is not out, you can end the process and re-enter the command to enter the interface to display the image

 

2.2、Core code parsing

code path

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/root/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_visual/yahboomcar_visual/pub_image.py

The implementation steps are roughly the same as the previous two, and the program first subscribed to/image_ Raw's topic data is then converted into image data, but here we also perform lattice transformation to convert image data into topic data and publish it, which is image topic data ->image data ->image topic data.

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#Import opecv library and cv_ Bridge Library
import cv2 as cv
from cv_bridge import CvBridge
#Creating CvBridge Objects
self.bridge = CvBridge()
#Define a subscriber to subscribe to USB image topic data
self.sub_img = self.create_subscription(Image,'/image_raw',self.handleTopic,500)
#Defined image topic data publisher
self.pub_img = self.create_publisher(Image,'/image',500)
#Convert msg to image data imgmsg_ To_ CV2, where bgr8 is the image encoding format
frame = self.bridge.imgmsg_to_cv2(msg, "bgr8")
#The image topic data (cv2uto_imgmsg) converted from image data is then published
msg = self.bridge.cv2_to_imgmsg(frame, "bgr8")
self.pub_img.publish(msg)

 

OpenCV Basic Course
QR code recognition

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