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 using the GPU Coder™ Support Package for NVIDIA® GPUs. 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 using the GPU Coder™ Support Package for NVIDIA GPUs. The GPU Coder Support Package for NVIDIA GPUs 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 GPU Coder™ Support Package for NVIDIA® GPUs 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.

How the GPU Coder™ Support Package for NVIDIA® GPUs 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 demonstarte PIL execution. For more information, see Fog Rectification.

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 GPU Coder™ Support Package for NVIDIA® GPU 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 GPU Coder™ Support Package for NVIDIA GPUs to capture the live video stream from a webcam and display the prediction results on a monitor connected to the Jetson platform.