Easy Installation of CUDNN and NVIDIA Video Codec SDK for OpenCV

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Easy Installation of CUDNN and NVIDIA Video Codec SDK for OpenCV

Table of Contents

Introduction
Changes in CUDA API Support for OpenCV 2.1 Decoding and Encoding Updates 2.2 Opencv and Nvidia Website
Installation Process 3.1 Installing FFmpeg 3.2 Downloading Video Codec SDK 3.3 Copying Libraries 3.4 Completing SDK Installation
Installing Cudnn (Could NN) 4.1 Downloading Cudnn Toolkit 4.2 Installation Steps
Conclusion

Introduction

In this article, we will discuss the installation process of MVQuit and Codename for OpenCV. We will also explore the changes in the CUDA API support for OpenCV and how it affects the decoding and encoding updates. Additionally, we will provide step-by-step instructions for installing FFmpeg, downloading the Video Codec SDK, and installing Cudnn (Could NN). Let's dive in!

Changes in CUDA API Support for OpenCV

With the recent changes in the CUDA API, OpenCV support for decoding and encoding updates has been impacted. As Alexander Alaiken pointed out, OpenCV is no longer supporting the CUDA API for these functionalities. This means that the updates related to decoding and encoding are not compatible with the current versions of OpenCV.

To stay updated on the changes and alternatives for decoding and encoding, it is recommended to check the Nvidia website. Nvidia provides valuable resources and recommendations for video decoding and encoding, including the suggestion to use the duplicated Nvidia Video Decoder and Record instead of the unsupported OpenCV API.

Installation Process

Before we proceed with the installation process, it is important to note that installing FFmpeg is a crucial step. FFmpeg is a powerful tool that supports decoding and encoding and is widely supported by both OpenCV and Nvidia. By installing FFmpeg, we establish a bridge between OpenCV and Nvidia for future compatibility.

To install FFmpeg, use the following command:

<command>

If you plan on using Python in the future, it is recommended to install Python libraries. However, this step is not mandatory for the installation process we will be covering in this article, which focuses on C++.

Now, let's move on to the Video Codec SDK installation process.

Downloading Video Codec SDK

To begin, visit the Nvidia website and register for an account. Once registered, navigate to the Video Codec SDK section and agree to the terms and conditions. Click on the "Download Now" button to start the download process.

Please note that the download may take some time due to the large size of the folder.

Copying Libraries

After the download is complete, extract all the files from the folder. Locate the Video category in the extracted files and navigate to the Linux Stops folder. In this folder, you will find two libraries that need to be copied.

Copy these two libraries to the user/local directory, where you have installed CUDA. This step ensures that the required libraries are in the correct location for OpenCV to access them.

Completing SDK Installation

If you have followed the previous steps correctly, the installation of the Video Codec SDK is almost complete. The final step involves copying the interface site. Go to the subfolder named "includes/site" within the extracted files.

Copy all the files within this folder and paste them in the same location as before (user/local).

Congratulations! You have successfully installed the Video Codec SDK, and the required set up for OpenCV is complete.

Installing Cudnn (Could NN)

Cudnn, also known as Could NN, is a powerful tool for neural networks. Although we might not need it immediately, it is recommended to install it for potential future use.

To install Cudnn, first, refer to the documentation provided. This will guide you through the installation process, which begins with installing the Cuda Toolkit. If you have already installed the Cuda Toolkit, you can proceed to the next step.

After that, navigate to the Cudnn download page on the Nvidia website. Make sure you are logged in with your registered account. Download the non-library for Linux, which corresponds to your system requirements.

Once the download is complete, extract the files and follow the instructions provided in the documentation. This will involve running specific commands in the terminal to copy the necessary files.

By completing these steps, you will have successfully installed Cudnn and completed the installation process for OpenCV.

Conclusion

In this article, we discussed the installation process for MVQuit and Codename for OpenCV. We explored the changes in CUDA API support and how they affect decoding and encoding updates. By following the step-by-step instructions, you should now have OpenCV, FFmpeg, Video Codec SDK, and Cudnn installed, setting up a powerful environment for your future projects.

Highlights