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K230 Vision Module📖
1. Quick Start
  • 0.Get start
  • 1. K230 firmware download and burning
  • 2. Install the programming environment
  • 3. Debug and run routines
  • 4. Offline running routine
  • 5. Micropython Quick Start
  • 6. GUI Program Instructions
  • 7.K230 module bracket installation tutorial
  • 8.K230 module 2DOF PTZ installation tutorial
2. Basic Courses
  • 1.FPIOA pin assignment
  • 2.RGB lights
  • 3.Buzzer
  • 4.Buttons
  • 5.Serial communication
  • 6.I2C communication
  • 7.PWM
  • 8.WDT watchdog
  • 9.Timer
  • 10.Fourier Transform
  • 11.SHA256 encryption
  • 12.AES encryption
  • 13.Multithreading
  • 14.File reading and writing
  • 15.Image Display
  • 16.Touch Display
  • 17.Camera display
  • 18.Image Flip
  • 19.Camera
3. Draw the image
  • 1. Draw the lines
  • 2. Draw the circle
  • 3. Draw the rectangle
  • 4. Draw the ellipse
  • 5. Draw the arrow
  • 6. Draw the crosshairs
  • 7. Draw text
  • 8. Draw key points
4. Graphics detection
  • 1. Line segment detection
  • 2. Rectangle Detection
  • 3. Circle detection
  • 4. Object edge detection
  • 5. Line inspection basics:fast linear regression
5. Color recognition
  • 1. Color recognition
  • 2. Object Counting
  • 3. Multi-color recognition
  • 4. K230 Color Line Patrol
6.1 CV_lite Image Processing Special Topic
  • 1.Introduction to cv_litenew
  • 2.cv_lite API Reference Manualnew
  • 3.Color Block Recognition - Grayscale Imagenew
  • 4.Color Block Recognition - Color Imagenew
  • 5.Circle Recognition - Grayscale Imagenew
  • 6.Circle Recognition - Color Imagenew
  • 7.Corner Recognition - Grayscale Imagenew
  • 8.Corner Recognition - Color Imagenew
  • 9.Edge Recognition - Grayscale Imagenew
  • 10. Edge Recognition - Color Imagenew
  • 11. Rectangle Recognition - Grayscale Imagenew
  • 12.Rectangle Recognition - Color Imagenew
  • 13.Rectangle recognition combined with corner recognition - grayscale imagenew
  • 14.Rectangle recognition combined with corner recognition - color imagenew
  • 15.Image Binarization - Grayscalenew
  • 16.Image Binarization - Color Imagenew
  • 17.Exposure Adjustmentnew
  • 18.Image Black Hat Operationsnew
  • 19.Image Color Histogramnew
  • 20.Image Closingnew
  • 21.Image Expansionnew
  • 22.Image Corrosionnew
  • 23.Gaussian Blurnew
  • 24.Gradient Transformationnew
  • 25.Mean Fuzzynew
  • 26.Image Openingnew
  • 27.Image Top Hat Operationnew
  • 28.PnP rangingnew
  • 29.Find rectangles and measure distancesnew
  • 30.Image grayscale world white balancenew
6.2 OpenCV Image Processing Special Topicnew
  • 1. OpenCV Introduction
  • 2. OpenCV API Reference
  • 3. Image Basic Transforms
  • 4. Morphological Operations
  • 5. Color Segmentation and Shape Classification
  • 6. Corner and Centroid Detection
  • 7. Hough Circle Detection
  • 8. Hough Line Detection
  • 9. Contour Analysis
  • 10. Drawing Functions
  • 11. Template Matching
  • 12. Image Blur Filtering
  • 13. Edge Detection
  • 14. Thresholding
  • 15. Color Segmentation
  • 16. Histogram and Pixel Transformation
  • 17. Arithmetic and Bitwise Operations
  • 18. Channel Operation and Statistics
  • 19. Image File Read and Write
  • 20. Pixel Level Transformation
7. Code type recognition
  • 1.Barcode recognition
  • 2.QR code recognition
  • 3.AprilTag tag recognition
  • 4.DM code recognition
8. Face Recognition
  • 1.[Must-read] General code structure and prerequisite knowledge of AI vision routines
  • 2.Face Detection
  • 3.Facial key point recognition
  • 4.Face orientation detection
  • 5.Face 3D Network
  • 6.Gaze direction detection
  • 7.Register for face recognition
9. Human body characteristics
  • 1. Human body detection
  • 2. Human key point detection
  • 3. Human posture recognition
  • 4. Fall detection
  • 5. Palm detection
  • 6. Palm key point detection
  • 7. Palm key point classification
  • 8. Gesture Recognition
  • 9. Dynamic gesture recognition
  • 10. Rock, Paper, Scissors
10. Scenario Application
  • 1.OCR character detection
  • 2.OCR character recognition
  • 3.yolov8n target detection
  • 4.yolov8n segmentation
  • 5. Self-learning object recognition
  • 6. License plate detection
  • 7. License Plate Recognition
  • 8. Target Tracking
  • 9. Fruit classification
  • 10. Garbage classification
  • 11. Road sign recognition
11. Audio and video processing
  • 1. Audio Recording
  • 2. Play Audio
  • 3. Video Recording
  • 4. Play Video
  • 5. Keyword wake-up
12. Network Basics
  • 1. Wireless network connection
  • 2. AP hotspot mode
  • 3. TCP-Server Example
  • 4. TCP-Client Example
  • 5. UDP-Server Example
  • 6. UDP-Client Example
  • 7. HTTP-Server Example
  • 8. HTTP-Client Example
  • 9. MQTT Quick Start
  • 10. MQTT-Publisher
  • 11. MQTT-Subscriber
  • 12. RTSP real-time image transmission
  • 13. RTSP real-time image transmission combined with AIDemo
  • 14. Call OpenRouter large model aggregation service
  • 15. Open the Resource Monitoring Panel
13. LVGL Graphical Interface
  • 1. Introduction to LVGL
  • 2. Create a button
  • 3. Create a slider
  • 4. Create an Image
  • 5. Create a text box and keyboard
14. GUI Code Tutorial & Secondary Development
  • 01.GUI System Architecture
  • 02.Core Engine Initialization
  • 03.Application Discovery Mechanism in GUI
  • 04.Desktop Layout Algorithm
  • 05.Advanced UI Styling
  • 06.BaseApp Base Class Analysis
  • 07.Hello_World Practical
  • 08.GUI Application Lifecycle and Resource Management
15. Train your own model
  • 1. Local deployment of yolo training environment
  • 2. Use online platforms for training
16.Touch Course
  • 1.Touch point detectionnew
  • 2.Touch Paintingnew
  • 3.Touch to take a photonew
17. Multi-master communication (purchased separately)
ESP32-K230
  • 1.k230 color recognition
  • 2.k230 barcode recognition
  • 3.k230 QR code recognition
  • 4.k230Apriltag code recognition
  • 5.k230DM code recognition
  • 6.k230 face detection
  • 7.k230 gaze direction
  • 8.k230 face recognition
  • 9.k230 human body detection
  • 10.k230 fall detection
  • 11.k230 palm detection
  • 12.k230 gesture recognition
  • 13.k230OCR character recognition
  • 14.k230 object detection
  • 15.k230 target tracking
  • 16.k230 self-learning object recognition
  • 17.k230 license plate recognition
Jetson-K230
  • 1.k230 color recognition
  • 2.k230 barcode recognition
  • 3.k230 QR code recognition
  • 4.k230Apriltag code recognition
  • 5.k230DM code recognition
  • 6.k230 face detection
  • 7.k230 gaze direction
  • 8.k230 face recognition
  • 9.k230 human body detection
  • 10.k230 fall detection
  • 11.k230 palm detection
  • 12.k230 gesture recognition
  • 13.k230OCR character recognition
  • 14.k230 object detection
  • 15.k230 target tracking
  • 16.k230 self-learning object recognition
  • 17.k230 license plate recognition
MSPM0G3507-K230
  • 1.k230 color recognition
  • 2.k230 barcode recognition
  • 3.k230 QR code recognition
  • 4.k230Apriltag code recognition
  • 5.k230DM code recognition
  • 6.k230 face detection
  • 7.k230 gaze direction
  • 8.k230 face recognition
  • 9.k230 human body detection
  • 10.k230 fall detection
  • 11.k230 palm detection
  • 12.k230 gesture recognition
  • 13.k230OCR character recognition
  • 14.k230 object detection
  • 15.k230 target tracking
  • 16.k230 self-learning object recognition
  • 17.k230 license plate recognition
Pico-K230
  • 1.k230 color recognition
  • 2.k230 barcode recognition
  • 3.k230 QR code recognition
  • 4.k230Apriltag code recognition
  • 5.k230DM code recognition
  • 6.k230 face detection
  • 7.k230 gaze direction
  • 8.k230 face recognition
  • 9.k230 human body detection
  • 10.k230 fall detection
  • 11.k230 palm detection
  • 12.k230 gesture recognition
  • 13.k230OCR character recognition
  • 14.k230 object detection
  • 15.k230 target tracking
  • 16.k230 self-learning object recognition
  • 17.k230 license plate recognition
raspberrypi-K230
  • 1.k230 color recognition
  • 2.k230 barcode recognition
  • 3.k230 QR code recognition
  • 4.k230Apriltag code recognition
  • 5.k230DM code recognition
  • 6.k230 face detection
  • 7.k230 gaze direction
  • 8.k230 face recognition
  • 9.k230 human body detection
  • 10.k230 fall detection
  • 11.k230 palm detection
  • 12.k230 gesture recognition
  • 13.k230OCR character recognition
  • 14.k230 object detection
  • 15.k230 target tracking
  • 16.k230 self-learning object recognition
  • 17.k230 license plate recognition
RDK-K230
  • 1.k230 color recognition
  • 2.k230 barcode recognition
  • 3.k230 QR code recognition
  • 4.k230Apriltag code recognition
  • 5.k230DM code recognition
  • 6.k230 face detection
  • 7.k230 gaze direction
  • 8.k230 face recognition
  • 9.k230 human body detection
  • 10.k230 fall detection
  • 11.k230 palm detection
  • 12.k230 gesture recognition
  • 13.k230OCR character recognition
  • 14.k230 object detection
  • 15.k230 target tracking
  • 16.k230 self-learning object recognition
  • 17.k230 license plate recognition
STM32-K230
  • 1. stm32_k230 color recognition
  • 2. stm32_k230 barcode recognition
  • 3. stm32_k230 QR code recognition
  • 4. stm32_k230Apriltag code recognition
  • 5. stm32_k230DM code recognition
  • 6. stm32_k230 face detection
  • 7.stm32_k230 looking direction
  • 8. stm32_k230 face recognition
  • 9. stm32_k230 human body detection
  • 10. stm32_k230 fall detection
  • 11. stm32_k230 palm detection
  • 12. stm32_k230 gesture recognition
  • 13. stm32_k230OCR character recognition
  • 14. stm32_k230 object detection
  • 15. stm32_k230 target tracking
  • 16. stm32_k230 self-learning object recognition
  • 17.stm32_k230 license plate recognition
Arduino-K230
  • 1.arduino_k230 color recognition
  • 2.arduino_k230 barcode recognition
  • 3.arduino_k230 QR code recognition
  • 4.arduino_k230 Apriltag code recognition
  • 5.arduino_k230 DM code recognition
  • 6.arduino_k230 face detection
  • 7.arduino_k230 looking direction
  • 8.arduino_k230 face recognition
  • 9.arduino_k230 human body detection
  • 10.arduino_k230 fall detection
  • 11.arduino_k230 palm detection
  • 12.arduino_k230 gesture recognition
  • 13.arduino_k230 OCR character recognition
  • 14.arduino_k230 object detection
  • 15.arduino_k230 target tracking
  • 16.arduino_k230 self-learning object recognition
  • 17.arduino_k230 license plate recognition
Microbit-K230
  • 1.microbit_k230 color recognition
  • 2.microbit_k230 barcode recognition
  • 3.microbit_k230 QR code recognition
  • 4.microbit_k230 Apriltag code recognition
  • 5.microbit_k230 DM code recognition
  • 6.microbit_k230 face detection
  • 7.microbit_k230 looking direction
  • 8.microbit_k230 face recognition
  • 9.microbit_k230 human body detection
  • 10.microbit_k230 fall detection
  • 11.microbit_k230 palm detection
  • 12.microbit_k230 gesture recognition
  • 13.microbit_k230 OCR character recognition
  • 14.microbit_k230 object detection
  • 15.microbit_k230 target tracking
  • 16.microbit_k230 self-learning object recognition
  • 17.microbit_k230 license plate recognition
MSPM0G3519-K230
  • 1. K230 Color Recognitionnew
  • 2. K230 Barcode Recognitionnew
  • 3. K230 QR Code Recognitionnew
  • 4. K230 Apriltag Code Recognitionnew
  • 5. K230 DM Code Recognitionnew
  • 6. K230 Face Detectionnew
  • 7. K230 Gaze Directionnew
  • 8. K230 Face Recognitionnew
  • 9. K230 Human Body Detectionnew
  • 10. K230 Fall Detectionnew
  • 11. K230 Palm Detectionnew
  • 12. K230 Gesture Recognitionnew
  • 13. K230 OCR Character Recognitionnew
  • 14. K230 Object Detectionnew
  • 15. K230 Target Trackingnew
  • 16. K230 Self-learning Object Recognitionnew
  • 17. K230 License Plate Recognitionnew
18.Extension case (purchase separately)
For 2DOF Electric Platform
  • 1.Moving target control and automatic tracking system
  • 2.Human body recognition alarm
  • 3.Gesture Recognition
  • 4.Rock Paper Scissors
  • 5.Fruit sorting
  • 6.Color Tracking
  • 7.Face Tracking
MSPM0G3507 wheeled chassis V1/V2 combined with K230 application case
  • 1.Visual tracking
  • 2.Color tracking
  • 3.Color response
  • 4.Road sign recognition
  • 5.Machine code tracking
  • 6.QR code recognition action
  • 7.Autonomous avoid
  • 8.License plate tracking
  • 9.Face tracking
  • 10.Gaze direction tracking
  • 11.Palm tracking
  • 12.Target tracking
  • 13.Human body tracking
  • 14.OCR character recognition action
STM32 wheeled chassis V1/V2 combined with K230 application case
  • 1.Visual tracking
  • 2.Color tracking
  • 3.Color response
  • 4.Road sign recognition
  • 5.Machine code tracking
  • 6.QR code recognition action
  • 7.Autonomous avoid
  • 8.License plate tracking
  • 9.Face tracking
  • 10.Gaze direction tracking
  • 11.Palm tracking
  • 12.Target tracking
  • 13.Human body tracking
  • 14.OCR character recognition action
MSPM0G3519 wheeled chassis V1/V2 combined with K230 application case
  • 1.Visual trackingnew
  • 2.Color trackingnew
  • 3.Color responsenew
  • 4.Road sign recognitionnew
  • 5.Machine code trackingnew
  • 6.QR code recognition actionnew
  • 7.Autonomous avoidnew
  • 8.License plate trackingnew
  • 9.Face trackingnew
  • 10.Gaze direction trackingnew
  • 11.Palm trackingnew
  • 12.Target trackingnew
  • 13.Human body trackingnew
  • 14.OCR character recognition actionnew
19. Expansion case(purchase separately)new
  • 1.Motion target control and Auto track system
  • 2.Vehicle-mounted balance ball motion control system
Download
  • Manual
  • CanMV IDE
  • Software Tools
  • Firmware
  • Source Code
  • Appendix
Download ZIP

Welcome to K230 Vision Module repository

12. RTSP real-time image transmission

RTSP real-time image transmission

RTSP real-time image transmissionRoutine explanationIntroductionPrepareStartup routineOpen the playerRoutine codeIntroduction to RTSP (Real Time Streaming Protocol)What is RTSP?Main FeaturesCommon application scenariosRTSP vs other streaming protocolsProtocol ComparisonRTSP WorkflowRTSP ArchitectureRTSP transmission modeAdvantagesDisadvantages

 

Routine explanation

Introduction

In this section, we will demonstrate how to push the images captured by K230 in the form of RTSP stream. We can use a video player that supports playing RTSP streams to play the real-time transmitted images.

Note: The WIFI chip on the K230 has limited performance. Please use the image transmission near a WIFI signal hotspot.

*This example is experimental and may not run with high quality in different network environments.

If you want to improve the video fluency, you can modify the bit_rate parameter in this line of code. The recommended value range of bit_rate is 10 ~ 300

The smaller the bit_rate value, the better the fluency, but the lower the clarity.

xxxxxxxxxx
# 创建编码器 / Create encoder
     chnAttr = ChnAttrStr(self.encoder.PAYLOAD_TYPE_H264, self.encoder.H264_PROFILE_MAIN, width, height, bit_rate=10, dst_frame_rate=5, src_frame_rate=5)

Prepare

We can download a player that supports playing RTSP streams. The options for Windows systems are

  1. VLC player (vlc-3.0.21-win64.exe in the supporting information, double-click to install)

    Advantages: good compatibility, powerful functions

    Disadvantages: The configuration is numerous and complicated. If you don’t know how to configure it, the display effect will be poor.

  2. YAHBOOM Rtsp Player (built into the companion gadget program)

    Advantages: No configuration required, one-click use, low latency

    Disadvantages: Large size

It is recommended to download both. In general, use YAHBOOM Rtsp Player. If YAHBOOM Rtsp Player fails, use Vlc player to debug.

I haven't tried it on other systems yet. You can search for RTSP players supported by xxx systems. I have tested that Vlc for Android can play RTSP videos on Android phones, but the delay is very high. I haven't found a configuration method yet.

Startup routine

RTSP image transmission has high requirements on network quality. Please try to choose a WIFI with few devices mounted on the router and strong signal.

For the image transmission function, it is recommended to use the mobile phone to open the hotspot (WIFI and data can be turned off), select the 2.4GHz frequency, and place the K230 next to the mobile phone.

It is normal for the image transmission to freeze, and it can be optimized in the following ways:

  1. Check if the WIFI is using 2.4Ghz. The WIFI chip on the K230 only supports 2.4Ghz wireless signals, not 5Ghz or 2.4/5Ghz mixed signals.
  2. Check if there are too many devices mounted on the router

Please put K230 and computer in the same LAN (connect to the same WIFI), and modify the parameters of this line to the WIFI name and password.

# The WIFI name I connect to is test, and the password is 12345678
isConnected = Connect_WIFI("test","12345678")

Then click the Run button, wait for WIFI to connect and start the RTSP server

FAQ:

  1. No response after clicking the Run button

    Solution: Turn off the power of K230, wait for 5 seconds, and then plug it in and run it. K230 needs to be restarted after each RTSP routine is completed.

  2. Error: run connect failed.

    Solution: Turn off the power of K230, wait for 5 seconds, and then plug it in and run it. Under normal circumstances, repeat this operation up to three times to restore it to normal.

    If the error message "run connect failed" is displayed repeatedly, the WIFI chip is probably damaged.

If it starts normally, the K230 serial terminal will output the following

x
[WIFI] 连接网络中 ...
[WIFI] 连接网络成功
[RTSP] 启动中 ..
find sensor gc2093_csi2, type 24, output 1920x1080@60
buffer pool :  3
sensor(0), mode 0, buffer_num 4, buffer_size 0
[RTSP] 启动成功, 地址: rtsp://192.168.207.22:8554/video

This address is the address of your RTSP streaming.

Open the player

Take YAHBOOM Rtsp Player as an example. Double-click to run the program. The interface is as follows:

image-20250221182608021

Note: The actual interface may be slightly different, but the overall functionality is the same

image-20250221182728090

After clicking the connection, it will take some time to load. The loading speed is related to the network speed.

If there is still no response after waiting for 20 seconds, try playing it with VLC Player. If there is still no response, unplug the USB cable to power off, plug it in again after 10 seconds, and re-run the example code

image-20250221195238573

Routine code

For the complete code, please refer to the file: [Source Code/11.Network/07.rtsp/rtsp_no_ai.py]

  1. Overall structure and function:
  • The code mainly implements video capture, encoding and RTSP streaming functions
  • Contains two main parts: WiFi connection module and RTSP server class
  • Use multithreading to process video stream data
  1. Key classes and methods:

The core members of the RtspServer class are:

xxxxxxxxxx
def __init__(self):
    self.session_name = session_name  # 会话名称 Session name
    self.video_type = video_type      # 视频类型(H.264/H.265) Video type (H.264/H.265)
    self.enable_audio = enable_audio  # 是否启用音频 Whether to enable audio
    self.port = port                  # RTSP 端口号 RTSP port number
    self.rtspserver = mm.rtsp_server() # RTSP服务器实例 RTSP server instance
    self.venc_chn = VENC_CHN_ID_0    # 视频编码通道 Video encoding channel

The main methods include:

  • start(): Start the server
  • stop(): Stop the server
  • _init_stream(): Initialize the video stream
  • _do_rtsp_stream(): The core thread for processing video streams
  1. Video processing flow:

a) Collection phase:

xxxxxxxxxx
# Collect images through sensor
# 通过sensor采集图像
rtsp_show_img = self.sensor.snapshot()
​
# Set frame information
# 设置帧信息
frame_info.v_frame.width = rtsp_show_img.width()
frame_info.v_frame.height = rtsp_show_img.height()
frame_info.v_frame.pixel_format = Sensor.YUV420SP

b) Coding stage:

xxxxxxxxxx
# Send frames to encoder
# 发送帧到编码器
self.encoder.SendFrame(self.venc_chn, frame_info)
# Get the encoded stream
# 获取编码后的流
self.encoder.GetStream(self.venc_chn, streamData)

c) Streaming stage:

xxxxxxxxxx
# Send the encoded data to the RTSP server
# 将编码数据发送到RTSP服务器
self.rtspserver.rtspserver_sendvideodata(self.session_name,stream_data, streamData.data_size[pack_idx],1000)
  1. Optimized design:
  • Use multithreading to process video streams to avoid blocking
  • Aggressive garbage collection
  • Set up the video buffer
  • Support image processing of different resolutions
  1. Error handling:
  • WiFi connection failure handling
  • Video stream exception handling
  • Resource release mechanism
  1. Example usage:
xxxxxxxxxx
if __name__ == "__main__":
    # Connect to WiFi
    # 连接WiFi
    isConnected = Connect_WIFI("test","password")
    
    # Create and start the RTSP server
    # 创建并启动RTSP服务器
    rtspserver = RtspServer()
    rtspserver.start()
    
    # Get and print RTSP address
    # 获取并打印RTSP地址
    rtsp_address = rtspserver.get_rtsp_url()

Introduction to RTSP (Real Time Streaming Protocol)

What is RTSP?

RTSP is a network application layer protocol for controlling streaming media servers, defined by RFC 2326. It establishes and controls one or more time-synchronized continuous media streams.

Main Features

  • Strong real-time performance
  • Support on-demand and live broadcast
  • Support streaming media control (play, pause, fast forward, etc.)
  • Works at the application layer, using port 554 by default

Common application scenarios

  1. Video surveillance system

    • Security Camera
    • Industrial Monitoring
    • Traffic monitoring
  2. Live Streaming

    • videoconference
    • Online Education
    • Telemedicine

RTSP vs other streaming protocols

Protocol Comparison

protocolDelayreliabilityInteractivityApplication Scenario
RTSPLowmiddlehighVideo surveillance, on-demand
HLShighhighLowLive broadcast, on-demand
RTMPLowmiddlemiddleLive streaming
WebRTCVery lowmiddlehighReal-time communication

RTSP Workflow

ClientServerOPTIONS 请求 / OPTIONS request支持的方法列表 / Supported methods listDESCRIBE 请求 / DESCRIBE request媒体描述 (SDP) / Media description (SDP)SETUP 请求 / SETUP request建立传输会话 / Establish transport sessionPLAY 请求 / PLAY request开始传输媒体流 / Start media streamPAUSE 请求(可选) / PAUSE request (optional)暂停传输 / Pause media streamTEARDOWN 请求 / TEARDOWN request关闭会话 / Close sessionClientServer

 

RTSP Architecture

客户端
Client
RTSP协议
RTSP Protocol
流媒体服务器
Streaming Server
视频源
Video Source

RTSP transmission mode

RTSP
TCP传输
TCP Transport
UDP传输
UDP Transport
RTP over TCP
RTP over UDP
UDP单播
UDP Unicast
UDP多播
UDP Multicast

Advantages

  1. Good real-time performance and low latency
  2. Support streaming media control function
  3. The bandwidth usage is relatively small
  4. Suitable for on-demand scenarios

Disadvantages

  1. Requires a dedicated streaming server
  2. Might be blocked by a firewall
  3. Does not support adaptive bitrate
  4. Web browsers do not natively support
MQTT-Subscriber
RTSP real-time image transmission combined with AIDemo

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