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K210 Module📖
0.Camera changes
  • Precautions for camera changes
1.K210-AI-Camera introduction
  • 1.K210 main chip introduction
  • 2.K210 visual recognition module introduction
  • 3.Module pin assignments
  • 4.MicroPython Introduction
  • 5. FAQ analysis
  • 6.Bracket installation
2.Development Environment Setup
  • 1.To install the serial port assistant
  • 2.Install CanMV IDE development software
  • 3.Flashcard factory firmware
  • 4.IDE import routine
  • 5.Call custom py library
  • 6.MicroPython API documentation
3.Basic experimental routines
  • 1.Lighting the RGB light experiment
  • 2.Key features of the experiment
  • 3.Timer experiment
  • 4.PWM breathing light experiment
  • 5.LCD display experiment
  • 6.The touchpad read the coordinates of the experiment
  • 7.The camera shows the experimental
  • 8.Memory card read and write files of the experiment
  • 9.External interface experimental
4.Graphical manipulation routines
  • 1.Draw graphics
  • 2.Button and the click event
  • 3.Slider
  • 4.Chart animation
  • 5.Multi-label layout
5.AI visual experiments routines
  • How to import the model file into memory cardnew
  • 1.Color recognition
  • 2.Barcode recognition
  • 3.QR code recognition
  • 4.Apriltags recognition
  • 5.Face detection
  • 6.Face feature detection
  • 7.Mask detection
  • 8.Face recognition
  • 9.Object detection
  • 10.Self-learning classification
  • 11.Handwritten digit recognition
  • 12. Multiple color recognition
  • 13. Human body detectionnew
  • 14. Yolo hand detectionnew
  • 15. License Plate Recognitionnew
  • 16. Autonomous training modelnew
6.Extended routines (sold separately)
6.1.For STM32 Car
  • 10. stm_ K210 Autonomous Learning Classification
  • 1.automatic driving
  • 2.color follow
  • 3.Handwriting digital operation
  • 4.Color reaction
  • 5.Road sign indicating the operation
  • 6.Following the apriltag
  • 7.QR code instruction
  • 8.PTZ tracking color
  • 9.PTZ tracking face
6.2.1 arduino_k210
  • 1.ard_k210-Color recognition
  • 2.ard_k210-Bar code identification
  • 3.ard_k210-QR code identification
  • 4.ard_ K210-mechanical code identification
  • 5.ard_ K210-Face Detection
  • 6. ard_ K210-facial feature detection
  • 7. ard_ K210-mask detection
  • 8. ard_ K210-facial recognition
  • 9. ard_ K210-object detection
  • 10. ard_ K210 Autonomous Learning Classification
  • 11. ard_ K210 Handwritten Digit Recognition
6.2 K210 as a coprocessor
  • ReadMe
6.2.2 stm32_k210
  • 1.stm_ K210 color recognition
  • 2. stm_ K210 barcode recognition
  • 3. stm_ K210 QR code recognition
  • 4. stm_ K210 mechanical code identification
  • 5. stm_ K210 Face Detection
  • 6.stm_ K210 facial feature detection
  • 7. stm_ K210 mask detection
  • 8. stm_ K210 facial recognition
  • 9. stm_ K210 object detection
  • 11. stm_ K210 Handwritten Digit Recognition
6.2.3 microbit_k210
  • 1.microbit_ K210 color recognition
  • 2.microbit_ K210 barcode recognition
  • 3.microbit_ K210 QR code recognition
  • 4.microbit_ K210 mechanical code identification
  • 5. microbit_ K210 Face Detection
  • 6. microbit_ K210 facial feature detection
  • 7. microbit_ K210 mask detection
  • 8. microbit_ K210 facial recognition
  • 9. microbit_ K210 object detection
  • 10. microbit_ K210 autonomous classification learning
  • 11. microbit_ K210 Handwritten Digit Recognition
6.2.4 pico_k210
  • 1. pico_ K210 color recognition
  • 2. Pico_ K210 barcode recognition
  • 3. Pico_ K210 QR code recognition
  • 4. pico_ K210 machine code recognition
  • 5. pico_ K210 Face Detection
  • 6. pico_ K210 facial feature detection
  • 7. pico_ K210 mask recognition
  • 8. pico_ K210 facial recognition
  • 9. pico_ K210 object detection
  • 10. pico_ K210 Autonomous Learning Classification
  • 11. pico_ K210 Handwritten Digit Recognition
6.2.5 MSPM0G3507_k210
  • 1.MSPM0_K210 color recognition
  • 2.MSPM0_K210 barcode recognition
  • 3.MSPM0_K210 QR code recognition
  • 4.MSPM0_K210 mechanical code recognition
  • 5.MSPM0_K210 face detection
  • 6.MSPM0_K210 facial feature detection
  • 7.MSPM0_K210 mask detection
  • 8.MSPM0_K210 face recognition
  • 9.MSPM0_K210 Object Detection
  • 10.MSPM0_K210 Self-study classification
  • 11.MSPM0_K210 handwritten digit recognition
6.2.6 MSPM0G3519_k210
  • 1. MSPM0_K210 Color Recognitionnew
  • 2. MSPM0_K210 Barcode Recognitionnew
  • 3. MSPM0_K210 QR Code Recognitionnew
  • 4. MSPM0_K210 Mechanical Code Recognitionnew
  • 5. MSPM0_K210 Face Detectionnew
  • 6. MSPM0_K210 Facial Feature Detectionnew
  • 7. MSPM0_K210 Mask Detectionnew
  • 8. MSPM0_K210 Face Recognitionnew
  • 9. MSPM0_K210 Object Detectionnew
  • 10. MSPM0_K210 Self-study Classificationnew
  • 11. MSPM0_K210 Handwritten Digit Recognitionnew
6.3 For 24 channel servo drive board
  • 1. 2DOF PTZ Color Tracking
  • 2. 2DOF Face Tracking
  • 3. 2DOF Fruit sorting
6.4 For autopilot map
  • 1.k210 model training
  • 2.Assembly and wiring of stm32 car
  • 3.Recognize left and right sign
  • 4.Identify and remove speed limit
  • 5.Identify parking spaces and park
  • 6.STM32 car autopilot
6.5.1 For MSPM0G3507_Wheeled Chassis Kit
  • 1.MSPM0-Automatic Driving
  • 2.MSPM0-Color follow
  • 3.MSPM0-Number recognition
  • 4.MSPM0-Color reaction
  • 5.MSPM0-Road signs
  • 6.MSPM0-Machine code following
  • 7.MSPM0-QR code recognition
6.5.2 For MSPM0G3519_Wheeled Chassis Kit
  • 1.MSPM0-Automatic Drivingnew
  • 2.MSPM0-Color follownew
  • 3.MSPM0-Number recognitionnew
  • 4.MSPM0-Color reactionnew
  • 5.MSPM0-Road signsnew
  • 6.MSPM0-Machine code followingnew
  • 7.MSPM0-QR code recognitionnew
6.5.3 For STM32_Wheeled Chassis Kit
  • 1.STM32-Automatic Driving
  • 2.STM32-Color follow
  • 3.STM32-Number recognition
  • 4.STM32-K210-Color Reaction
  • 5.STM32-Road signs
  • 6.STM32-Machine code following
  • 7.STM32-QR code recognition
Download
  • Driver development software
  • AI model
  • Source code
  • Instruction manual
  • Hardware_3DModelFile
  • K210_Chip_Data
Download ZIP

Welcome to K210 Module repository

How to import the model file into memory card

Transfer model files to TF card

Transfer model files to TF card1. Model file description2. Import model files3. Run the program

1. Model file description

The model file usually contains the parameters and weight information of the neural network architecture and is the result of the neural network training process. For K210, the model can be used for various machine learning tasks such as face detection, mask recognition, face recognition, and object recognition. Due to the limited resources of the K210 chip, the trained model needs to be transferred to the TF card for storage. The suffix of the model file is .kmodel.

2. Import model files

There are two ways to transfer the model file to the TF card. The first way is to use a card reader. Insert the TF card into the card reader. The card reader is inserted into the USB port of the computer. Download the compressed model file package in the data and decompress it. Copy the folder to the root directory of the H: drive.

4

Method 1 can import all model files at once, eliminating the need to import model files each time, and load model files using different paths. All model files are placed in the KPU folder. Please do not modify KPU and the file names under the KPU directory, otherwise the default routine program will not be able to find the model files.

 

The second method is to import the model file through CanMV IDE without using a card reader. Here we take importing landmark68.kmodel as an example and connect the K210 module to the computer USB port through a microUSB data cable. Then click the connect button in the lower left corner of CanMV IDE. If the connection is successful, the icon will change to the connected state, and the gray play button will turn green.

1

Open Tools->Save file to CanMV Cam.

001

Click Open, then select the model file to be imported and transfer it to the memory card.

Note: The files written here are written to the memory card first. Please delete the file path when saving. Saving with a path is likely to result in an error.

image-20240118164143194

004

3. Run the program

When running the code, the specific saved model path shall prevail. If you follow the first method of this tutorial, the default code will be used without modification.

5

If you follow the second method of this tutorial, you need to delete the redundant paths in the program, that is, only keep the prefix /sd/.

2

Multi-label layout
Color recognition

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