部署

将 MATLAB^® 算法作为独立可执行文件部署在硬件上

MATLAB Coder™ Support Package for NVIDIA^® Jetson™ and NVIDIA DRIVE^® Platforms 使您能够在硬件上部署 MATLAB 函数。函数部署为独立可执行文件，即使硬件实时连接与主机断开连接，该文件也将继续运行。

函数

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板连接

`jetson`	Connection to NVIDIA Jetson hardware
`drive`	Connection to NVIDIA DRIVE hardware

GPIO

`configurePin`	Configure GPIO pin as digital input or digital output (自 R2021b 起)
`readDigitalPin`	Read logical value from GPIO input pin (自 R2021b 起)
`showPins`	Show diagram of GPIO pins (自 R2021b 起)
`writeDigitalPin`	Write logical value to GPIO output pin (自 R2021b 起)

外围设备

`audiocapture`	Connection between audio input device and NVIDIA hardware (自 R2021a 起)
`audioplayer`	Connection between audio output device and NVIDIA hardware (自 R2021a 起)
`capture`	Capture data from audio device connected to NVIDIA hardware (自 R2021a 起)
`play`	Play audio from audio device connected to NVIDIA hardware (自 R2021a 起)
`camera`	Connection to USB or CSI camera
`getCameraList`	Get a list of available cameras on the NVIDIA hardware
`listAudioDevices`	Get a list of available audio devices on the NVIDIA hardware (自 R2021a 起)
`image`	Display image
`imageDisplay`	NVIDIA display object
`snapshot`	Capture RGB image from Camera
`updatePeripheralInfo`	Scan for and update the list of peripherals connected to the target hardware
`velodynelidar`	Connection to a Velodyne LiDAR sensor (自 R2022b 起)
`read`	Acquire point clouds from `velodynelidar` object buffer (自 R2022b 起)
`start`	Start streaming point clouds from Velodyne LiDAR sensor (自 R2022b 起)
`stop`	Stop streaming point clouds from Velodyne LiDAR sensor (自 R2020b 起)
`webcam`	Connection to USB web camera

文件操作

`getFile`	Transfer file from NVIDIA hardware to host computer
`putFile`	Transfer file from host computer to target hardware
`deleteFile`	Delete file on target hardware

实用工具

`openShell`	Open terminal on host computer to use a Linux shell on NVIDIA hardware
`system`	Run commands in a Linux shell on the NVIDIA hardware
`getL4TVersion`	Get the L4T version of the NVIDIA Jetson hardware
`getPdkorSdkVersion`	Get the version number of the DriveWorks SDK installed on the NVIDIA DRIVE hardware
`getDisplayEnvironment`	Get the display environment value used for redirecting the display on the target
`setDisplayEnvironment`	Set the display environment value used for redirecting the display on the target
`setupCodegenContext`	Select the target hardware to build code for from multiple live connection objects
`getLinuxVersion`	Get information about the Linux environment on the target
`killApplication`	Kill an application on the NVIDIA target by name
`killProcess`	Kill a process on the NVIDIA target by ID
`runApplication`	Launch an application on the NVIDIA target by name
`runExecutable`	Launch an executable on the NVIDIA target by name
`workspaceDir`	Get the build directory on the NVIDIA hardware

处理器在环

`setPILTimeout`	Set the timeout value that PIL uses for reading data
`setPILPort`	Set the TCP/IP port number used by the PIL execution
`getPILTimeout`	Get the timeout value that PIL uses for reading data
`getPILPort`	Get the TCP/IP port number used by the PIL execution

Modbus 通信

`modbus`	Create a Modbus object for NVIDIA boards (自 R2022a 起)
`read`	Read data from a connected Modbus device (自 R2022a 起)
`write`	Perform a write operation to the connected Modbus device (自 R2022a 起)

主题

MATLAB

Build and Run an Executable on NVIDIA Hardware
Build and run an executable on NVIDIA hardware.
Build and Run an Executable on NVIDIA Hardware Using GPU Coder App
Use GPU Coder™ app to build and run an executable on NVIDIA hardware.
Read Video Files on NVIDIA Hardware
Generate CUDA^® code for reading video files on the NVIDIA target by using the videoReader function.
Stop or Restart an Executable Running on NVIDIA Hardware
Stop or restart an executable running on the hardware.
Processor-In-The-Loop Execution from Command Line
Use PIL execution to verify the numerical behavior of the generated code at the MATLAB command line.
Processor-In-The-Loop Execution with the GPU Coder App
Use the GPU Coder app to verify the numerical behavior of the generated code.
Execution-Time Profiling for PIL
Why measure execution times for code generated from entry-point functions.

Simulink

Targeting NVIDIA Embedded Boards (GPU Coder)
Build and deploy to NVIDIA GPU boards.
Numerical Equivalence Testing (GPU Coder)
Compare results from model and generated code simulations.
Parameter Tuning and Signal Monitoring by Using External Mode (GPU Coder)
Tune parameters and monitor signals through a TCP/IP communication channel between development computer and target hardware.

精选示例

全新

Deploy an Edge Detection Application to Target ARM Cores of NVIDIA Jetson

自 R2024a 起

打开实时脚本

Deploy and Run Sobel Edge Detection with I/O on NVIDIA Jetson Nano

Deploy Sobel edge detection application that uses a Raspberry Pi Camera Module V2 and displays the edge detected output on the NVIDIA® Jetson™ Nano Hardware. The Sobel Edge Detection on NVIDIA Jetson Nano Using Raspberry Pi Camera Module V2 example showed how to capture image frames from the Raspberry Pi Camera Module V2 on an NVIDIA Jetson Nano hardware and process them in the MATLAB® environment. This example shows how to generate code for accessing I/O peripherals (camera and display) and perform processing on the NVIDIA Jetson Nano hardware.

打开实时脚本

Sobel Edge Detection on NVIDIA Jetson Nano Using Raspberry Pi Camera Module V2

Capture and process images from a Raspberry Pi® Camera Module V2 connected to the NVIDIA® Jetson™ Nano. The MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE® Platforms allows you to capture images from the Camera Module V2 and bring them into the MATLAB environment for processing. In this example you learn how to develop a Sobel edge detection algorithm by using this capability.

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Semantic Segmentation on NVIDIA DRIVE

Generate and deploy a CUDA® executable for an image segmentation application that uses deep learning. It uses the MATLAB® Coder™ Support Package for NVIDIA® Jetson™ and NVIDIA DRIVE™ Platforms to deploy the executable on the NVIDIA DRIVE™ platform. This example performs code generation on the host computer and builds the generated code on the target platform by using remote build capability of the support package. For more information, see Code Generation for Semantic Segmentation Network (GPU Coder).

打开实时脚本

Processor-in-the-Loop Execution on NVIDIA Targets Using GPU Coder

How the MATLAB® Coder™ Support Package for NVIDIA® Jetson™ and NVIDIA DRIVE™ Platforms enables the GPU Coder™ product to run PIL execution on NVIDIA DRIVE and Jetson hardware platforms. This example uses the GPU Code generation for fog rectification example from GPU Coder to demonstrate PIL execution. For more information, see Fog Rectification (GPU Coder).

打开实时脚本

Top-Hat Filtering to Remove Uneven Background Illumination on NVIDIA Jetson TX2 Developer Kit

Deploy Image Processing Toolbox™ algorithms to NVIDIA® Jetson TX2 board using the GPU Coder™ Support Package for NVIDIA GPUs. The imtophat (Image Processing Toolbox) function that performs morphological top-hat filtering on a grayscale image is used as an example to demonstrate this concept. Top-hat filtering computes the morphological opening of the image (using imopen (Image Processing Toolbox)) and then subtracts the result from the original image. The generated CUDA® code uses shared memory to speed up the operations on the GPU.

Deployment and Classification of Webcam Images on NVIDIA Jetson TX2 Platform

Generate CUDA® code from a DAGNetwork object and deploy the generated code onto the NVIDIA® Jetson® TX2 board using the GPU Coder™ Support Package for NVIDIA GPUs. This example uses the resnet50 deep learning network to classify images from a USB webcam video stream.

Deploy and Run Fog Rectification for Video on NVIDIA Jetson

Generate and deploy a CUDA® executable for a video-based fog rectification application. The example shows the deployable code generation capabilities that the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms provides for the MATLAB VideoReader function. This example generates a CUDA application that reads the contents of a video file, performs fog rectification operation, and displays the output video on the NVIDIA® hardware.

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Deploy and Classify Webcam Images on NVIDIA Jetson Platform from Simulink

Deploy a Simulink® model on the NVIDIA® Jetson™ board for classifying webcam images. This example classifies images from a webcam in real-time by using the pretrained deep convolutional neural network, ResNet-50. The Simulink model in the example uses the camera and display blocks from the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE™ Platforms to capture the live video stream from a webcam and display the prediction results on a monitor connected to the Jetson platform.

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Keyword Spotting in Audio Using MFCC and LSTM Networks on NVIDIA Embedded Hardware from Simulink

Deploy a Simulink® model on the NVIDIA® Jetson™ board for keyword spotting in audio data. This example identifies the keyword(YES) in the input audio data using Mel Frequency Cepstral Coefficients (MFCC) and a pretrained Bidirectional Long Short-Term Memory (BiLSTM) network.

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CAN Bus Communication on NVIDIA Jetson TX2 in Simulink

Deploy a Simulink® model that uses CAN communication for a deep learning application. The Simulink model in this example uses the CAN Transmit and CAN Receive blocks from the MATLAB® Coder™ Support Package for NVIDIA® Jetson® and NVIDIA DRIVE® Platforms to model a CAN bus system on the Jetson TX2 platform. The model uses the CAN bus to transmit the recognized traffic sign objects in a video frame from one CAN node to another CAN node.

打开实时脚本