NXP_Support_Package​_S32V234

This toolbox represents the MATLAB Installer Add-On for the NXP Vision Toolbox for S32V234

To install the NXP Vision Toolbox for S32V234 Automotive Vision Processor, please follow these steps:

1. Go to MATLAB Add-On Manager and select the NXP Support Package S32V234 toolbox.
2. Select Open Folder option to navigate to the installation path.
3. Run NXP_Support_Package_S32V234.m script and follow the steps shown on the UI

The NXP Vision Toolbox for MATLAB is a complementary integrated development environment for S32V234 processor which is a high-performance automotive processor designed to support safe computation-intensive applications in the area of vision and sensor fusion. The NXP Vision Toolbox for MATLAB enables editing, simulation, compiling and deployment of designs from MATLAB environment.

Based on Mathworks® software including Image Processing, Computer Vision System and Deep Learning Toolboxes the NXP Vision Toolbox offers designers a straightforward development tool that makes vision accelerators programming easy. NXP software, along with the NXP Vision Toolbox provides a comprehensive enablement environment that reduces development time.

Please visit our community - https://community.nxp.com/community/mbdt - for examples and support.

Features:
- Seamless integration with MATLAB environment for easy vision application development, simulations and target running support
- Generate code for Linux™ based applications with direct download to target support
- Built-in support for IO functions to control onboard camera and display
- Integrated NXP Software: Vision SDK, Linux board support package, NXP APU Compiler
- Support A53 and APEX programming directly from MATLAB m-scripting
- Includes collaterals providing convenient access to manuals, tool guides, how-to videos and application notes
- Example projects demonstrating ISP and APEX functionalities based on Mathworks Computer Vision System Toolbox examples
- Example projects demonstrating Machine Learning functionalities based on Mathworks Deep Learning Toolbox examples