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Exécuter du code sur le hardware cible

Déployer une fonction MATLAB^® sous la forme d’un exécutable autonome sur le hardware

MATLAB Support Package for Raspberry Pi^® Hardware vous permet également de déployer une fonction MATLAB sur le hardware. MATLAB Coder™ vous permet de générer du code C pour votre algorithme MATLAB et de le déployer pour qu’il s’exécute de manière autonome sur le hardware Raspberry Pi. L’exécutable autonome continue de s’exécuter même si le hardware est déconnecté de l’ordinateur. Pour en savoir plus, consultez Additional Capabilities with MATLAB Coder.

Remarque

Vous pouvez déployer une fonction MATLAB sur le hardware depuis MATLAB Online™. Pour plus de précisions, consultez Connect to Raspberry Pi Hardware Board in MATLAB Online.

Objets

raspi Connection to Raspberry Pi board

Fonctions

`targetHardware`	Create configuration object for hardware
`deploy`	Deploy MATLAB function on hardware

Applications

Raspberry Pi Resource Monitor App

Monitor and manage Raspberry Pi resources (depuis R2020b)

Rubriques

Additional Capabilities with MATLAB Coder
Include additional capabilities for code generation.
Automatically Run MATLAB Function on Raspberry Pi After Restart
Automatically start a MATLAB function deployed on Raspberry Pi hardware every time you restart the hardware.
Workflow to Deploy MATLAB Function on Raspberry Pi

Workflow to explain the steps involved in deploying a MATLAB function on the hardware.
- ÉTAPE 1: Create or Open a MATLAB Function
- ÉTAPE 2: Run the MATLAB Function Using Live IO from Hardware
- ÉTAPE 3: Add Compilation Directive to MATLAB Function
- ÉTAPE 4: Check MATLAB Function Using Code Analyzer
- ÉTAPE 5: Check MATLAB Function for Unsupported Calls
- ÉTAPE 6: Create Hardware Configuration Object
- ÉTAPE 7: Deploy MATLAB Function on Hardware

Informations connexes

Exemples présentés

Getting Started with Deploying a MATLAB Function on the Raspberry Pi Hardware

Deploy a MATLAB® function as a standalone executable on the Raspberry Pi® Hardware using MATLAB Support Package for Raspberry Pi Hardware.

Deploy an Edge Detection Algorithm on the Raspberry Pi Hardware

Deploy an Edge detection function on the Raspberry Pi® hardware as a standalone executable using MATLAB® Support Package for Raspberry Pi Hardware. In this example, you will learn how to acquire live image from a webcam connected to the Raspberry Pi hardware, run the edge detection function on the acquired image, and display the result on the monitor that is connected to the same Raspberry Pi hardware.

Identify Objects Within Live Video Using ResNet-50 on Raspberry Pi Hardware

Predict the objects in a live video stream on Raspberry Pi® by deploying an image classification algorithm using MATLAB® Support Package for Raspberry Pi Hardware. The algorithm uses ResNet-50 neural network to identify the objects captured by the webcam that is connected to the Raspberry Pi hardware.

Ouvrir le live script

Send Inputs to MATLAB Function from Command Line of Raspberry Pi

Deploy a MATLAB® function that accepts inputs from command line of Raspberry Pi® using MATLAB Support Package for Raspberry Pi Hardware. In this example, a MATLAB function for adding two numbers is used for deployment.

Ouvrir le live script

Send Inputs to MATLAB Function from Command Line of Raspberry Pi in MATLAB Online

Deploy a MATLAB® function that accepts inputs from command line of Raspberry Pi® using MATLAB Support Package for Raspberry Pi Hardware in MATLAB Online. In this example, a MATLAB function for adding two numbers is used for deployment.

Classify Static Image Using Deep Learning on Raspberry Pi

Generate and deploy code for an image classification algorithm using MATLAB® Support Package for Raspberry Pi® Hardware. The algorithm uses the ResNet-50 neural network to identify the image that is passed as an input using the command line of Raspberry Pi.

Ouvrir le live script

Speech Command Recognition Code Generation on Raspberry Pi

Generate code and deploy feature extraction and speech command recognition network on Raspberry Pi hardware.

(Audio Toolbox)

Keyword Spotting in Noise Code Generation on Raspberry Pi

Demonstrates code generation for keyword spotting using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction on Raspberry Pi™. MATLAB® Coder™ with Deep Learning Support enables the generation of a standalone executable (.elf) file on Raspberry Pi. Communication between MATLAB® (.mlx) file and the generated executable file occurs over asynchronous User Datagram Protocol (UDP). The incoming speech signal is displayed using a timescope. A mask is shown as a blue rectangle surrounding spotted instances of the keyword, YES. For more details on MFCC feature extraction and deep learning network training, visit Keyword Spotting in Noise Using MFCC and LSTM Networks (Audio Toolbox).

(Audio Toolbox)

Acoustics-Based Machine Fault Recognition Code Generation on Raspberry Pi

Generate a Raspberry Pi standalone executable for acoustics-based machine fault recognition.

(Audio Toolbox)