Sold by: OpenCV
Deployed on AWS
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.
Overview
Cloud Optimized OpenCV Library (COOL) is a high-performance distribution of OpenCV 5, engineered specifically for ARM-based AWS Graviton5 processors (Arm Neoverse-V3). Built with Arm KleidiCV 0.7.0 vector acceleration, this release delivers significant speedups for fundamental computer vision operations, including image resizing, adaptive Gaussian thresholding, color space conversions, and contour detection.
These hardware-aware enhancements allow developers and enterprises to process high-resolution image and video streams with reduced computational overhead. By maximizing instruction-level parallelism on Graviton5, COOL ensures lower latency, higher throughput, and improved resource utilization across cloud, edge, and production AI/ML pipelines.
Key Features & Capabilities: Multi-Language Support: Packages a complete C++ SDK alongside ready-to-use virtual environments for Python 3.12, 3.13, and 3.14. Zero-Configuration Acceleration: Pre-configured with KleidiCV Custom HAL integration for out-of-the-box vector speedups. Production-Ready Base: Built on Ubuntu 24.04 LTS for seamless integration into existing AWS infrastructure. Cost Efficiency: Higher frame processing throughput per instance reduces total EC2 fleet footprint and infrastructure costs. Ideal for AI model pre-processing, video analytics, robotics, and real-time vision pipelines running on AWS Graviton5 (c9g, m9g instance families).
Highlights
- 1. Graviton5 & KleidiCV 0.7.0 Acceleration : Optimized for Arm Neoverse-V3 architecture to deliver vector-accelerated performance for core OpenCV functions.
- 2. C++ SDK & Python 3.12-3.14 Bindings : Includes complete C++ headers/libraries and isolated virtual environments for Python 3.12, 3.13, and 3.14.
- 3. High-Throughput Vision Pipelines : Accelerates image pre/post-processing workflows to reduce latency and infrastructure costs on AWS EC2.
Details
Sold by
Delivery method
Delivery option
64-bit (Arm) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04 LTS
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
m9g.4xlarge Recommended | $0.063 |
m9g.12xlarge | $0.188 |
m9g.8xlarge | $0.125 |
m9g.2xlarge | $0.02 |
m9g.48xlarge | $0.751 |
m9g.24xlarge | $0.376 |
m9g.16xlarge | $0.251 |
m9g.medium | $0.002 |
m9g.large | $0.005 |
m9g.xlarge | $0.01 |
Vendor refund policy
We offer a 7-day free trial to allow full testing of the software before purchase. Because you can test the product extensively for free, we do not offer refunds once a paid subscription begins.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (Arm) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
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
OpenCV 5.1.0 for AWS Graviton5, built for Arm Neoverse-V3 with KleidiCV 0.7.0.
This release packages the C++ SDK and Python 3.12, 3.13, and 3.14 virtual environments into a unified, zero-configuration Ubuntu 24.04 LTS AMI. Entitlement and license checks are enforced directly inside libopencv_core.so.5.1.0 via AWS EC2 Instance Metadata Service (IMDS), ensuring seamless verification on every invocation path. Validated end-to-end on Graviton5 (m9g) instance families for both C++ and Python 3.12/3.13/3.14 workloads.
Additional details
Usage instructions
USAGE INSTRUCTIONS
STEP 1: LAUNCH THE AMI After subscribing, launch an EC2 instance using this AMI.
This build is compiled for Arm Neoverse-V3 and requires an AWS Graviton5 instance.
Recommended instance types:
- m9g.4xlarge or larger for optimal performance on AWS Graviton5
- Compatible instance families: c9g, m9g
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_3.12
- python_3.13
- python_3.14
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}')"
Expected output reports OpenCV 5.1.0. To confirm the Graviton5 optimizations are in use: python3 -c "import cv2; print(cv2.getBuildInformation())" | grep -E 'Baseline|Custom HAL' This should list SVE in the baseline and KleidiCV under Custom HAL.
OPTIONAL: USING SYSTEM PYTHON If you prefer the default system Python or an existing environment, export the required paths manually. Substitute your Python version in both paths: export LD_LIBRARY_PATH="/opt/cool/cpp_sdk/lib:/opt/ffmpeg/lib:$LD_LIBRARY_PATH" export PYTHONPATH="/opt/cool/python_3.12/site-packages/:$PYTHONPATH"
Then run your script normally: python3.12 your_script.py
Include /opt/ffmpeg/lib as shown; omitting it leaves video capture and writing unavailable.
OPTIONAL: BUILDING CUSTOM C++ APPLICATIONS To compile your own C++ applications against the optimized COOL OpenCV libraries:
-
Create a build directory in your project workspace.
-
Create a CMakeLists.txt file with the following content: cmake_minimum_required(VERSION 3.10) project(MyApp) set(CMAKE_CXX_STANDARD 17) set(OpenCV_DIR "/opt/cool/cpp_sdk/lib/cmake/opencv5") find_package(OpenCV REQUIRED) add_executable(my_app main.cpp) target_link_libraries(my_app ${OpenCV_LIBS})
-
Build and run: cd build cmake .. make ./my_app
NOTE: Ensure LD_LIBRARY_PATH includes /opt/cool/cpp_sdk/lib before running C++ binaries.
SAMPLES Worked Python and C++ examples, with test data, are provided under: /opt/cool/samples
LICENSING Entitlement is verified against the AWS Marketplace product code carried by this AMI. If the software reports [FATAL] Security Violation: Valid License Not Found. the instance was not launched from a subscribed Marketplace AMI. Launch directly from your subscription rather than from a copied, re-registered, or snapshot-derived image. No IAM permissions or outbound network access are required for this check.
SUPPORT Email: support@opencv.org
Support
Vendor support
Email: support@opencv.org or (abhishek@opencv.org / gursimar@opencv.org )
Contact:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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.
The global air cooled heat exchanger market is expanding as industries seek efficient and sustainable heat management solutions. Driven by growth in data centers industrial activity and environmental regulations the market is forecast to grow significantly by 2030. Key trends include smart technology integration and rising demand in Asia Pacific and North America. Challenges include high upfront costs but innovations are enhancing performance and adoption.
Official Ubuntu images with a smaller footprint, optimized for AWS with five years of security maintenance.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.