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 software running on AWS Graviton 2 Arm-based EC2 instances. Each dimension maps to a specific instance type, so your rate depends on the instance you choose. The options span four families: general-purpose m6g and m6gd, and compute-focused c6g, c6gd, and c6gn. The 'd' variants add local storage, and c6gn adds higher network bandwidth. Within each family, sizes run from medium up to 16xlarge. Larger sizes carry more vCPUs and memory, so the hourly rate rises with size. You can start or stop usage at any time.
Top-of-mind questions for buyers
What does one hour of billing cover, and am I charged when the instance is stopped?
Each hourly rate covers the software running on one EC2 instance of the chosen type. Charges meter while the instance runs. A fully stopped instance stops accruing software charges. You may still pay underlying AWS fees for storage or stopped resources, which bill separately from the software.
How do I choose between the c6g, c6gd, c6gn, m6g, and m6gd instance families?
The c-families are compute-focused and suit image and inference work needing more CPU per unit of memory. The m-families are general-purpose, balancing CPU and memory. The 'd' variants add local instance storage. The c6gn variant adds higher network bandwidth. Your hourly rate follows the family and size you pick.
Does running more than one instance change how I am billed?
Each running instance bills independently at the hourly rate for its instance type. Running several instances adds their hourly charges together on the same invoice. Mixing instance types means each accrues its own rate. There is no single combined meter; the bill is the sum of all active instance-hours.
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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Cloud Optimized OpenCV Library (COOL) based on OpenCV 5 delivers a high-performance build of OpenCV, enabling faster computation of core computer vision operations such as image resizing, adaptive Gaussian thresholding, and contour detection. Designed for AWS Graviton and ARM-based environments, it helps developers achieve improved efficiency for AI, ML, and image processing applications.
Cloud Optimized OpenCV Library (COOL) 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.
High-performance OpenCV 5 build optimized for AWS Graviton5 (Arm Neoverse-V3) with KleidiCV 0.7.0 acceleration. Includes C++ SDK and Python 3.12-3.14 bindings for fast computer vision and AI pre/post-processing.
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