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Here you can verify if your OpenCV has OpenCL enabled using an implementation of the T-API and also compare the performance of Mat and UMat

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OpenCV's UMat: The basics and performance test

You can verify using this code if your OpenCV has OpenCL enabled using an implementation of the T-API and also compare the performance of both Mat and UMat. This code is part of a post on my blog about UMat.

How to use

Your OpenCV has to be built with the flag WITH_OPENCL set as ON.

This project was developed using Eclipse. Therefore, clone using git clone or download and open it as a Project, configure the Linker and Compiler to match your OpenCV folder.

For compiling with G++ (Considering the OpenCV folders C:\opencv\install\include and C:\opencv\install\x64\mingw\lib):

  • git clone

  • cd UMatTest\src

  • g++ "-IC:\\opencv\\install\\include" -O0 -g3 -Wall -c -fmessage-length=0 -o "UMatTest.o" "UMatTest.cpp"

  • g++ "-LC:\\opencv\\install\\x64\\mingw\\lib" -o UMatTest.exe "UMatTest.o" -lopencv_core340 -lopencv_highgui340 -lopencv_imgcodecs340 -lopencv_imgproc340

  • .\UMatTest

Additional Info

Give a look at my results with an i5 3330/AMD Radeon 7970 on the post on my blog.

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Here you can verify if your OpenCV has OpenCL enabled using an implementation of the T-API and also compare the performance of Mat and UMat

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