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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

1. Audio Recording

Audio Recording

Audio RecordingIntroduction to the results of routine experimentsCode ExplanationComplete codeCode structure

 

Introduction to the results of routine experiments

In this section, we use the microphone on the K230 to record the sound.

image-20250403105702644

The example code for this section is located in: [Source Code/10.Media/01.record_audio.py]

We use CanMV IDE to open the sample code and connect K230 to the computer via USB.

Click the Run button in the lower left corner of CanMV IDE, K230 will start recording 10 seconds of audio, and the recording results will be saved in

【/data/audio/audio.wav】 (You can also modify it yourself)

Code Explanation

In order to facilitate everyone to quickly call the audio recording function, we encapsulate the recording function into the AudioRecorder module

Complete code

x
import os
from media.media import *
from media.pyaudio import *
import media.wave as wave
​
class AudioRecorder:
    """
    音频录制类
    Audio recorder class
    """
    def __init__(self):
        """
        初始化音频参数
        Initialize audio parameters
        """
#        self.CHUNK = 44100 // 25    # 每个缓冲区的帧数 / Frames per buffer
        self.FORMAT = paInt16       # 采样格式为16位整型 / 16-bit integer sampling format
        self.CHANNELS = 1           # 单声道 / Mono channel
        self.RATE = 44100          # 采样率44.1kHz / 44.1kHz sampling rate
#        self.RATE = 16000          
        self.CHUNK = self.RATE // 25    # 每个缓冲区的帧数 / Frames per buffer
        self.frames = []            # 存储录音帧 / Store recorded frames
        self.is_recording = False   # 录音状态标志 / Recording status flag
​
        # 初始化PyAudio / Initialize PyAudio
        self.p = PyAudio()
        self.p.initialize(self.CHUNK)
        MediaManager.init()
​
    def exit_check(self):
        """
        检查是否有退出信号
        Check if there is an exit signal
        """
        try:
            os.exitpoint()
        except KeyboardInterrupt as e:
            print("user stop: ", e)
            return True
        return False
​
    def record(self, filename, duration, left_volume=85, right_volume=85, ans=False):
        """
        录制音频
        Record audio
​
        参数 / Parameters:
            filename: 音频保存路径 / Path to save audio file
            duration: 录制时长(秒) / Recording duration in seconds
            left_volume: 左声道音量 / Left channel volume
            right_volume: 右声道音量 / Right channel volume
            ans: 是否启用音频3A功能:自动噪声抑制(ANS) / Open Ans or not
        """
        try:
            # 打开音频输入流 / Open audio input stream
            self.input_stream = self.p.open(
                format=self.FORMAT,
                channels=self.CHANNELS,
                rate=self.RATE,
                input=True,
                frames_per_buffer=self.CHUNK
            )
​
            # 设置音量 / Set volume
            self.input_stream.volume(LEFT, left_volume)
            self.input_stream.volume(RIGHT, right_volume)
​
            if(ans):
                self.input_stream.enable_audio3a(AUDIO_3A_ENABLE_ANS)
​
            print("start record...")
            self.frames = []
            self.is_recording = True
​
            # 开始录制 / Start recording
            for i in range(0, int(self.RATE / self.CHUNK * duration)):
                if not self.is_recording or self.exit_check():
                    break
                data = self.input_stream.read()
                self.frames.append(data)
​
            print("stop record...")
​
            # 保存为WAV文件 / Save as WAV file
            self._save_to_wav(filename)
​
        except BaseException as e:
            print(f"Exception {e}")
        finally:
            self.stop()
​
    def stop(self):
        """
        停止录音并清理资源
        Stop recording and cleanup resources
        """
        self.is_recording = False
        if hasattr(self, 'input_stream'):
            self.input_stream.stop_stream()
            self.input_stream.close()
        self.p.terminate()
        MediaManager.deinit()
​
    def _save_to_wav(self, filename):
        """
        保存录音为WAV文件
        Save recording as WAV file
​
        参数 / Parameters:
            filename: 保存路径 / Save path
        """
        wf = wave.open(filename, 'wb')
        wf.set_channels(self.CHANNELS)
        wf.set_sampwidth(self.p.get_sample_size(self.FORMAT))
        wf.set_framerate(self.RATE)
        wf.write_frames(b''.join(self.frames))
        wf.close()
​
​
# save image raw data, use 7yuv to preview
​
def ensure_dir(directory):
    """
    递归创建目录,适用于MicroPython环境
    """
    # 如果目录为空字符串或根目录,直接返回
    if not directory or directory == '/':
        return
    
    # 处理路径分隔符,确保使用标准格式
    directory = directory.rstrip('/')
    
    try:
        # 尝试获取目录状态,如果目录存在就直接返回
        print(os.stat(directory))
        print(f'目录已存在 The Folder already exists: {directory}')
        return
    except OSError:
        # 目录不存在,需要创建
        # 分割路径以获取父目录
        if '/' in directory:
            parent = directory[:directory.rindex('/')]
            if parent and parent != directory:  # 避免无限递归
                ensure_dir(parent)
        
        try:
            os.mkdir(directory)
            print(f'已创建目录 Created Folder: {directory}')
        except OSError as e:
            # 可能是并发创建导致的冲突,再次检查目录是否存在
            try:
                os.stat(directory)
                print(f'目录已被其他进程创建 The Directory has been created by another process: {directory}')
            except:
                # 如果仍然不存在,则确实出错了
                print(f'创建目录时出错 An error occurred while creating the directory: {e}')
    except Exception as e:
        print(f'处理目录时出错 An error occurred while processing the directory: {e}')
​
# 使用示例 / Usage example
if __name__ == "__main__":
    os.exitpoint(os.EXITPOINT_ENABLE)
    save_path = "/data/audio/"
    # 目录不存在时创建 / create if not exist
    ensure_dir(save_path)
​
    recorder = AudioRecorder()
    print("音频录制开始 Recording start...")
    recorder.record(save_path + 'audio.wav', 10)
    print("音频录制完成 Recording complete...")
​

Code structure

Initialization section of the class:

  • Set the audio parameters: 44.1kHz sampling rate, 16-bit sampling format, mono
  • The buffer size is set to about 25 frames per second (44100/25)
  • Initialize PyAudio and the media manager

Main methods:

  • exit_check(): Detect exit signal and support user to interrupt recording

  • record(): Core recording method

    • Open the audio input stream
    • Set the volume of left and right channels
    • Loop recording of audio data according to the set duration
    • Save recorded data as WAV file
  • stop(): Stop recording and clean up resources

  • _save_to_wav(): Save the recorded audio data as a WAV format file

Example usage:

  • Create a recorder instance
  • Record 10 seconds of audio
  • Save to the specified path

This is an object-oriented audio recording implementation that provides complete recording process control, including:

  • Recording parameter configuration
  • Volume Control
  • Exception handling
  • Resource Management
  • Save in WAV format

Road sign recognition
Play Audio

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