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Hardware-optimized OpenCV 5.x build tailored for AWS Graviton 4 processors (Neoverse-v2). Accelerate computer vision pipelines on ECS/EKS with integrated KleidiCV, ARMPL, and Python 3.10, 3.11, and 3.12.
Overview
Cloud Optimized OpenCV Library (COOL) is a high-performance distribution of OpenCV, designed specifically for ARM-based environments such as AWS Graviton4. Built with Arm KleidiCV optimizations, this edition delivers significant speedups for fundamental computer vision operations, including image resizing, adaptive Gaussian thresholding, contour detection, 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, COOL 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 Ubuntu-based AMI combines the flexibility of OpenCV with the efficiency of KleidiCV-powered optimizations.
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 OpenCV core operations and significant speedups for functions mostly used in pre and post image/video processing pipelines
Details
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Supported services
Delivery option
OpenCV 5 for Graviton4 - C++ SDK
OpenCV 5 for Graviton4 - Python 3.10
OpenCV 5 for Graviton4 - Python 3.11
OpenCV 5 for Graviton4 - Python 3.12
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Operating system
Linux
Deployed on AWS
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Free trial
Try this product free for 7 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit/hour |
|---|---|---|
Hours | Container Hours | $0.03 |
Dimensions summary
This listing uses a single usage-based pricing dimension. You pay by the Container Hour, meaning billing tracks how long the container runs. There are no tiers or fixed commitments. Costs scale directly with your runtime: run the container longer and you pay more, run it less and you pay less. This suits workloads that vary over time, since you are charged only for the hours the container is active. The software is a Docker image optimized for AWS Graviton4 processors.
Top-of-mind questions for buyers
One Container Hour is one hour the Docker container runs on your AWS Graviton4 instance. Billing tracks active runtime. Partial hours are metered by the AWS usage system. Each running container instance accrues its own hours, so running several containers at once multiplies the hours billed.
Charges apply only while the container runs. When you stop the container, software hours stop accruing. Note that underlying AWS resources, such as storage attached to a stopped instance, may still incur AWS fees separate from this software charge.
The container packages OpenCV, which is open-source software. Versions 4.5.0 and higher use the Apache 2 License; versions 4.4.0 and lower use the 3-clause BSD license. You agree to the applicable license by using the software. Your Marketplace charge covers Container Hours only.
opencv.org
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Delivery details
OpenCV 5 for Graviton4 - C++ SDK
Supported services: Learn more
- Amazon ECS
- Amazon EKS
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
Initial release of the COOL (Graviton-optimized OpenCV) C++ SDK image for Python 3.12 (cpp_sdk tag). Adds a compiler toolchain for building and linking applications directly against the OpenCV C++ API. Uses AWS Marketplace RegisterUsage-based entitlement and per-task/pod-hour billing . Validated on ECS and EKS, on Graviton4-class ARM64 instances.
Additional details
Usage instructions
COOL - OpenCV 5 C++ SDK for AWS Graviton4
Shared libraries, headers and pkg-config files for the Graviton4-optimised OpenCV 5 build with KleidiCV. No Python bindings - use a Python delivery option if you need cv2. Requires a Graviton4 instance family: m8g, c8g, r8g. Built for Arm Neoverse-V2; earlier Graviton generations and AWS Fargate (Graviton2) are not supported targets. Runs on Amazon ECS and Amazon EKS.
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SUBSCRIBE Choose Continue to Subscribe and accept the terms. Wait until the subscription is active - AWS denies image pulls until it is.
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PULL THE IMAGE After subscribing, the Configuration page shows your registry and the image URI for this delivery option. Substitute them below. aws ecr get-login-password --region <region>
| docker login --username AWS --password-stdin <registry> docker pull <image-uri> linux/arm64. ECS and EKS pull it for you; pull by hand only to inspect it. -
PERMISSIONS AND NETWORK The entitlement check lives inside libopencv_core.so and fires when your binary loads it, calling RegisterUsage to confirm entitlement and begin hourly metering. Grant aws-marketplace:RegisterUsage to:
- ECS: the TASK role (taskRoleArn), not the execution role.
- EKS: an IRSA-annotated ServiceAccount. EKS Pod Identity, node roles and long-term access keys are NOT supported for this call. The container also needs egress to the Marketplace Metering endpoint via a NAT gateway, internet gateway or VPC endpoint. Without it the container fails closed and never starts.
- BUILD AND DEPLOY The SDK is at /opt/cool/cpp_sdk (include/, lib/, lib/pkgconfig). LD_LIBRARY_PATH and PKG_CONFIG_PATH are preset, so pkg-config works as-is: pkg-config --cflags --libs opencv5
Build your application FROM this image so the optimised libraries, and the entitlement check they carry, travel with your binary:
FROM <image-uri>
COPY my_app.cpp /src/my_app.cpp
RUN g++ -O2 -std=c++17 -o /usr/local/bin/my_app /src/my_app.cpp
$(pkg-config --cflags --libs opencv5)
ENTRYPOINT ["/usr/local/bin/my_app"]
Add no metering code of your own, and keep the OpenCV libraries from this image rather than substituting your own build.
No ENTRYPOINT is set; the default command runs a verifier. "docker run <image>" reports the version, "docker run <image> bash" opens a shell, and building FROM it lets you set your own CMD directly.
Deploy on ECS or EKS with AWS_REGION set and HOME pointing at a writable path such as /tmp; the AWS credential cache is written under HOME.
WORKLOAD SHAPE: use an ECS Service or Kubernetes Deployment only for a process that stays running. For batch work use run-task or a Kubernetes Job with restartPolicy: Never. A Deployment restarts any container that exits, including one that exits successfully, producing an endless restart loop.
- VERIFY docker run --rm --entrypoint pkg-config <image-uri> --modversion opencv5 Expected: 5.1.0 A local docker run cannot complete the entitlement check - the metering API only accepts calls from ECS, EKS or Fargate, so it reports a platform error and exits. Verify end to end on ECS (exitCode 0) or EKS (pod Succeeded). An unentitled container exits 133 before running any of your code.
Runs as non-root (runAsUser 1000); readOnlyRootFilesystem is supported with a writable /tmp.
Support: support@opencv.org
Support
Vendor support
Vendor support Email: support@opencv.org or (abhishek@opencv.org / gursimar@opencv.org )
AWS infrastructure support
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Similar products
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.
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.
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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