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Getting Started with the MATLAB Coder Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms

Use the MATLAB® Coder™ Support Package for NVIDIA Jetson™ and NVIDIA DRIVE® Platforms with embedded boards from NVIDIA®. The example uses a simple vector addition algorithm to illustrate:

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.

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 의미론적 분할 신경망을 위한 코드 생성.

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 Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning

Generate and deploy a CUDA® executable that classifies human electrocardiogram (ECG) signals using features extracted by the continuous wavelet transform (CWT) and a pretrained convolutional neural network (CNN).

Ground Plane Segmentation and Obstacle Detection on NVIDIA Jetson Xavier NX Embedded Platforms

Ground plane segmentation of 3-D lidar data on Jetson Xavier to find obstacles near a vehicle.

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.

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.

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.

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 안개 보정.

NVIDIA Jetson 플랫폼에서 직렬 포트를 통해 데이터 읽고 쓰기

이 예제에서는 MATLAB® Coder™ Support Package for NVIDIA® Jetson® and NVIDIA DRIVE®를 사용하여 Jetson 보드의 UART 포트를 통해 직렬 데이터를 읽고 쓰는 방법을 보여줍니다.

Send and Receive Data over UDP on NVIDIA Jetson Platforms

Use MATLAB® Coder™ Support Package for NVIDIA® Jetson® and NVIDIA DRIVE® to send and receive UDP data over the network on a Jetson board.

Send and Receive MAVLink Packets on Jetson Boards

Use MATLAB® Coder™ Support Package for NVIDIA® Jetson® and NVIDIA DRIVE® to send and receive MAVLink packets on a Jetson board via serial from a Pixhawk board.

Onboard Computer Path Planning Interface for PX4 SITL Deployable on NVIDIA Jetson

Demonstrates enabling and interfacing onboard computer path planning with PX4® Software-in-the-Loop (SITL).

Stream Camera, Depth and Semantic Segmentation Data from Unreal Engine to NVIDIA Jetson

Stream simulated camera, depth, and semantic segmentation label data from an Unreal Engine® scene to NVIDIA® Jetson™ hardware using the Video Send block in Simulink®. It then shows how to visualize incoming data streams on a monitor connected to the Jetson platform, by deploying separate models for each incoming data stream. The deployed models contain the Network Video Receive and SDL Video Display blocks from the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE® Platforms.

MODBUS TCP/IP Communication Between Client and Server Devices Using NVIDIA Jetson TX2 Hardware

Use the MATLAB® Coder™ Support Package for NVIDIA® Jetson® and NVIDIA DRIVE® Platforms to implement MODBUS® TCP/IP communication between MODBUS client and server devices. It also shows how to communicate between the two devices in four modes of operation, Client Read, Client Write, Server Read, and Server Write.

Deep Learning Vehicle Detector from IP Camera Stream on Jetson

Develop a CUDA® application from a Simulink® model that performs vehicle detection using convolutional neural networks (CNN). This example takes the IP camera stream as an input, and detects vehicles in the frame. This example uses the pretrained vehicle detection network from the Object Detection Using YOLO v2 Deep Learning example of the Computer Vision toolbox™. For more information, see YOLO v2 딥러닝을 사용한 객체 검출 (Computer Vision Toolbox).

Publish and Subscribe to Messages on ThingSpeak Using MQTT Blocks

Build Generated CUDA Code Using CMake Toolchain

Use CMake toolchain to build generated code.