Cloud Optimized OpenCV delivers a high-performance build of OpenCV, enabling faster computation of core computer vision operations such as resize, adaptive gaussian, contour detection functions. This optimized edition is designed for accelerated computer vision workloads on AWS Graviton and ARM-based environments, helping developers achieve improved efficiency for AI, ML, and image processing applications.
Cloud Optimized OpenCV is a high-performance distribution of OpenCV, designed specifically for ARM-based environments such as AWS Graviton. Built with Kleidicv optimizations, this edition delivers significant speedups for fundamental computer vision operations, including resize, resize, adaptive gaussian, contour detection functions and more. These enhancements allow developers and enterprises to process images and video streams more efficiently, making it ideal for AI, machine learning, robotics, and real-time analytics. By leveraging hardware-aware optimizations, OpenCV Graviton Optimized reduces computational overhead and ensures better utilization of ARM-based processors. This results in faster execution, lower latency, and improved scalability across a wide range of vision workloads. Whether you are deploying applications in the cloud, on the edge, or in embedded systems, this build provides a reliable foundation for high-throughput computer vision pipelines. With its lightweight and performance-focused design, Cloud Optimized OpenCV helps teams accelerate development cycles while lowering infrastructure costs. From prototyping AI models to deploying production-grade computer vision systems, this edition combines the flexibility of OpenCV with the efficiency of Kleidicv-powered optimizations, giving you the best of both worlds.
Highlights
Optimized for ARM based AWS Graviton processors and accelerated OpenCV build with Kleidicv enhancements along with further tuning of parallelization for high-performance computer vision workloads.
Faster core operations and significant speedups for functions mostly used in pre and post image/video processing pipelines
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You pay by the hour for the AWS Graviton instance you run. Pricing is not tiered by feature set. Instead, each dimension maps to a specific ARM-based EC2 instance type across four families: c6g and c6gd (compute-focused), c6gn (network-focused), and m6g and m6gd (general purpose). The 'd' variants add local storage. Within each family, sizes range from medium up to 16xlarge. Larger sizes carry more virtual CPUs and memory, so the hourly rate rises with instance size. You choose the family and size that fits your computer vision workload.
Top-of-mind questions for buyers
What do I actually get when I run one of these hourly instances?
You get a running ARM-based Graviton EC2 instance with a build of the Cloud Optimized OpenCV Library preinstalled. The instance type you pick sets the virtual CPU and memory available. The library speeds up core computer vision operations like resize, adaptive gaussian, and contour detection.
Am I charged when an instance is stopped or paused?
Hourly software charges apply only while the instance runs. A stopped instance stops accruing the software charge. Underlying AWS storage or reserved resource fees may still apply through your AWS account. Billing meters running hours, so shutting instances down when idle reduces the software cost.
How do I choose between the c6g, c6gn, and m6g instance families?
Pick based on your workload profile. The c6g and c6gd families focus on compute for vision processing. The c6gn family adds network throughput. The m6g and m6gd families balance compute and memory. The 'd' variants include local storage. Each family bills per hour at rates that rise with size.
opencv.org
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An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Version 1.3
We are pleased to introduce the Cloud Optimized OpenCV Library (COOL) for AWS Graviton 2.
This edition is built specifically for ARM-based environments such as AWS Graviton, with Kleidicv-powered optimizations to deliver faster performance and lower latency for core computer vision workloads.
Key features in v1.3:
Optimized performance for AWS Graviton 2 processors.
Accelerated implementations of core operations
Hardware-aware optimizations using Kleidicv for reduced computational overhead.
Lightweight and scalable design, suitable for cloud, edge, and embedded deployments.
Ideal for AI/ML, robotics, and real-time video or image analytics.
This foundation release provides a reliable and high-performance base for building and deploying advanced computer vision applications while reducing infrastructure costs.
Additional details
Usage instructions
STEP 1: LAUNCH THE AMI
After subscribing, launch an EC2 instance using this AMI.
Recommended instance types:
c6g.2xlarge or larger for optimal performance on AWS Graviton2
Compatible instance families: c6g, m6g, r6g
STEP 2: CONNECT TO THE INSTANCE
Connect using SSH:
ssh -i <your-key.pem> ubuntu@<public-ip>
STEP 3: ACTIVATE THE OPTIMIZED ENVIRONMENT (RECOMMENDED)
The optimized libraries are installed under:
/opt/cool
COOL provides preconfigured Python virtual environments that automatically set all required paths.
Available Python environments:
python_3.10
python_3.11
python_3.12
Example: Activate Python 3.12
- source /opt/cool/venvs/python_3.12/bin/activate
Verify that the optimized OpenCV build is active:
- python3 -c "import cv2; print(f'Active OpenCV: {cv2.version} from {cv2.file}')"
OPTIONAL: USING SYSTEM PYTHON
If you prefer using the default system Python or an existing environment, export the required paths manually:
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This Amazon Machine Image (AMI) provides a Graphical User Interface (GUI) for Ubuntu 24.04, based on the lightweight Xfce desktop environment, specifically designed for cloud deployments. Connect to your instance using either RDP or VNC.
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