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

Perform edge detection using NVIDIA Jetson on video captured by a connected camera.

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

Generate code for accessing I/O peripherals (camera and display) and perform processing on the NVIDIA Jetson Nano hardware.

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 Code Generation for Semantic Segmentation Network (GPU Coder).

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 GPU Coder™ 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).

Verify Generated Code on NVIDIA Targets Using PIL in Simulink

Compare Simulink^® simulations to processor-in-the-loop (PIL) simulations on NVIDIA Jetson.

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 MATLAB® Coder™ Support Package for NVIDIA Jetson™ and NVIDIA DRIVE® Platforms. 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.

(GPU Coder)

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 MATLAB® Coder™ Support Package for NVIDIA Jetson™ and NVIDIA DRIVE® Platforms. 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.

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.

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.

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.

Stream Images from NVIDIA Jetson Xavier NX Using Robot Operating System (ROS)

Stream images captured from a webcam on NVIDIA® Jetson™ Xavier NX board to the host computer using ROS communication interface.

GPU Profiling on NVIDIA Jetson Platforms

Analyze the performance of generated CUDA code on the Jetson platform.