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.

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.

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).

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).

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.

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.

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 a Simulink® model on the NVIDIA® Jetson TX2 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 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.