madtune/opencv-cuda repository overview

⁠OpenCV with CUDA Docker Image

This Docker image is designed to provide a complete environment for running OpenCV with CUDA support. It is based on Ubuntu 22.04 and includes the following key components:

• CUDA Version: 12.2
• cuDNN Version: 8.9.2 (compatible with CUDA 12.2)
• OpenCV Version: 4.10.0 (built with CUDA, cuDNN, and Python bindings)
• Python Version: 3.10 (with a virtual environment set up)
• GCC Version: 10
• System Libraries: Includes all necessary dependencies for OpenCV, such as OpenBLAS, FFmpeg, GTK, and more.

⁠Usage

This image is ideal for projects that require high-performance computer vision tasks accelerated by NVIDIA GPUs. It includes pre-built Python bindings for OpenCV, making it easy to integrate into your Python-based workflows.

⁠How to Use:

1. Pull the image:

   docker pull madtune/opencv-cuda:4.10.0
   

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2. Run the container:

   docker run --gpus all -it madtune/opencv-cuda:4.10.0
   

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   The container will start with a Python virtual environment activated, where you can immediately start using OpenCV with CUDA support.

3. Verify CUDA is enabled in OpenCV:

   python -c "import cv2; print(f'Cuda Devices: {cv2.cuda.getCudaEnabledDeviceCount()}'); print('OpenCV version:', cv2.__version__);"
   

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⁠Example Applications:

• Real-time image and video processing
• Deep learning inference with GPU acceleration
• Computer vision projects requiring high computational power

This Docker image is a powerful tool for developers and researchers working in the field of computer vision, allowing for easy deployment and consistent environments across different systems.

⁠Short Description

OpenCV with CUDA Docker Image - A ready-to-use Docker image for high-performance computer vision tasks, built with OpenCV 4.10.0, CUDA 12.2, cuDNN 8.9.2, and Python 3.10. Ideal for projects requiring GPU acceleration and seamless integration with Python.

⁠Long Description

OpenCV with CUDA Docker Image

This Docker image provides a comprehensive environment for running OpenCV with CUDA support on Ubuntu 22.04. It is specifically designed for projects that demand high-performance GPU acceleration for computer vision tasks. Key components include:

• CUDA Version: 12.2
• cuDNN Version: 8.9.2 (compatible with CUDA 12.2)
• OpenCV Version: 4.10.0 (built with CUDA, cuDNN, and Python bindings)
• Python Version: 3.10 (in a virtual environment)
• GCC Version: 10
• System Libraries: Includes essential dependencies such as OpenBLAS, FFmpeg, and more.

⁠Key Features:

• Seamless GPU Acceleration: Leverage NVIDIA GPUs for real-time image and video processing.
• Ready for Deep Learning: Integrates with deep learning frameworks for GPU-accelerated inference.
• Python Integration: Pre-built Python bindings for easy integration into Python workflows.

⁠How to Use:

1. Pull the image:

   docker pull madtune/opencv-cuda:4.10.0
   

   Copy

2. Run the container:

   docker run --gpus all -it madtune/opencv-cuda:4.10.0
   

   Copy

   The container starts with an activated Python virtual environment, ready for OpenCV with CUDA.

3. Verify CUDA in OpenCV:

   python -c "import cv2; print(f'Cuda Devices: {cv2.cuda.getCudaEnabledDeviceCount()}'); print('OpenCV version:', cv2.__version__);"
   

   Copy

⁠Example Applications:

• Real-time processing of images and videos
• GPU-accelerated deep learning inference
• Computationally intensive computer vision projects

This Docker image is perfect for developers and researchers in computer vision, providing a consistent, portable environment optimized for high-performance tasks.