Overview
Performance Comparison: Average Speedup
Performance was benchmarked across 78 widely used imgproc, core, and I/O functions against the default OpenCV 4.12.0 build, with the average speedup shown in the attached image.
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
Details
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Dimension | Cost/hour |
|---|---|
c6g.2xlarge Recommended | $0.02 |
c6gn.medium | $0.01 |
m6gd.xlarge | $0.01 |
m6g.8xlarge | $0.08 |
m6g.large | $0.01 |
m6gd.12xlarge | $0.15 |
m6gd.2xlarge | $0.02 |
m6gd.4xlarge | $0.04 |
c6g.medium | $0.01 |
c6g.8xlarge | $0.06 |
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.
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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
Version 1.2
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 v1.2:
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/coolCOOL 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/activateVerify 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:
- export LD_LIBRARY_PATH="/opt/cool/cpp_sdk/lib:$LD_LIBRARY_PATH" - export PYTHONPATH="/opt/cool/python_3.12/site-packages/cv2/python-3.12/:$PYTHONPATH"Then run your script normally:
- python3.12 your_script.pyOPTIONAL: 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 11)
set(OpenCV_DIR "/opt/cool/cpp_sdk/lib/cmake/opencv4")
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.
Support
Vendor support
Email: support@opencv.org or 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.
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