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- Add VPI library introduction to README
- Add information about allowlist to README
- Refer to CUPVA with full name in README
- Update minimum Holoscan version to 2.1.0
- Update CMake scripts to download precompiled libraries in build time
Signed-off-by: Mehmet Umut Demircin <mdemircin@nvidia.com>
Copy file name to clipboardExpand all lines: applications/precompiled_pva/README.md
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## About PVA
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PVA is a highly power-efficient VLIW processor integrated into NVIDIA Tegra platforms, specifically designed for advanced image processing and computer vision algorithms. The CUPVA SDK offers a comprehensive and unified programming model for PVA, enabling developers to create and optimize their own algorithms. For access to the SDK and further development opportunities, please contact NVIDIA.
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PVA is a highly power-efficient VLIW processor integrated into NVIDIA Tegra platforms, specifically designed for advanced image processing and computer vision algorithms. The Compute Unified Programmable Vision Accelerator (CUPVA) SDK offers a comprehensive and unified programming model for PVA, enabling developers to create and optimize their own algorithms. For access to the SDK and further development opportunities, please contact NVIDIA.
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## Content
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-`main.cpp`: This file contains a C++ Holoscan application that demonstrates the use of an operator for loading and executing a precompiled PVA library dedicated to performing the unsharp masking algorithm on images. CUPVA SDK and license are not required to run this Holohub application.
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-`pva_unsharp_mask/`: This directory houses the `pva_unsharp_mask.hpp` header file, which declares the `PvaUnsharpMask` class. The `PvaUnsharpMask` class includes an `init` API, invoked for the initial tensor, and a `process` API, used for processing input tensors. Precompiled algorithm library file, `libpva_unsharp_mask.a`, and the corresponding allow list file, `cupva_allowlist_pva_unsharp_mask`, are automatically downloaded by the CMake scripts.
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-`pva_unsharp_mask/`: This directory houses the `pva_unsharp_mask.hpp` header file, which declares the `PvaUnsharpMask` class. The `PvaUnsharpMask` class includes an `init` API, invoked for the initial tensor, and a `process` API, used for processing input tensors. Precompiled algorithm library file, `libpva_unsharp_mask.a`, and the corresponding allowlist file, `cupva_allowlist_pva_unsharp_mask`, are automatically downloaded by the CMake scripts.
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Please note that only PVA executables with signatures included in a secure allowlist database are permitted to execute on the PVA. This ensures that only verified and trusted executables are run, enhancing the security and integrity of the system.
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## Algorithm Overview
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2. Apply a 5x5 unsharp mask filter on the luminance color plane.
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3. Convert the enhanced image back to the RGB format.
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The [VPI library](https://developer.nvidia.com/embedded/vpi) offers numerous algorithm examples that leverage the PVA as the backend.
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Numerous algorithm examples leveraging the PVA can be found in the [Vision Programming Interface (VPI) library](https://developer.nvidia.com/embedded/vpi). VPI enables computer vision software developers to utilize multiple compute engines simultaneously—including CPU, GPU, PVA, VIC, NVENC, and OFA—through a unified interface. For comprehensive details, please refer to the [VPI Documentation](https://docs.nvidia.com/vpi/index.html).
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## Compiling the application
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The application takes an endoscopy video stream as input, applies the unsharp mask filter, and shows it in
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HoloViz window.
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Before running the application, deploy VPU application signature allow-list on target in your host (outside a container):
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Before running the application, deploy VPU application signature allowlist on target in your host (outside a container):
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