Revision history [back]

I've been reading about using the Graph API, and the syntax seems very nice. There's a section in the git documentation where it mentions the capacity to offload work to the GPU:

The idea behind G-API is to declare image processing task in form of expressions and then submit it for execution – using a number of available backends. At the moment, there’s reference “CPU” (OpenCV-based), "GPU" (also OpenCV T-API-based), and experimental “Fluid’ backends available, with other backends coming up next. ...

G-API GPU backend implements the majority of available functions and allows to run OpenCL kernels on available OpenCL-programmable devices. At the moment, GPU backend is based on OpenCV Transparent API; in future versions it will be extended to support integration of arbitrary OpenCL kernels (and likely be renamed to "OpenCL backend").

Right now I've been using the transparent API's which accept UMat in order to offload work on the GPU. How can I use the G-API while also offloading the work to the GPU?

I've been reading about using the Graph API, and the syntax seems very nice. There's a section in the git documentation where it mentions the capacity to offload work to the GPU:

The idea behind G-API is to declare image processing task in form of expressions and then submit it for execution – using a number of available backends. At the moment, there’s reference “CPU” (OpenCV-based), "GPU" (also OpenCV T-API-based), and experimental “Fluid’ backends available, with other backends coming up next. ...

G-API GPU backend implements the majority of available functions and allows to run OpenCL kernels on available OpenCL-programmable devices. At the moment, GPU backend is based on OpenCV Transparent API; in future versions it will be extended to support integration of arbitrary OpenCL kernels (and likely be renamed to "OpenCL backend").

Right now I've been using the standard transparent API's which accept UMat in order to offload work on the GPU. How can I use the G-API while also offloading the work to the GPU?

I've been reading about using the Graph API, and the syntax seems very nice. There's a section in the git documentation where it mentions the capacity to offload work to the GPU:

The idea behind G-API is to declare image processing task in form of expressions and then submit it for execution – using a number of available backends. At the moment, there’s reference “CPU” (OpenCV-based), "GPU" (also OpenCV T-API-based), and experimental “Fluid’ backends available, with other backends coming up next. ...

G-API GPU backend implements the majority of available functions and allows to run OpenCL kernels on available OpenCL-programmable devices. At the moment, GPU backend is based on OpenCV Transparent API; in future versions it will be extended to support integration of arbitrary OpenCL kernels (and likely be renamed to "OpenCL backend").

Right now I've been using the standard transparent API's which accept UMat in order to offload work on the GPU. How can I use the G-API while also offloading the work to the GPU?

I've been reading about using the Graph API, and the syntax seems very nice. There's a section in the git documentation where it mentions the capacity to offload work to the GPU:

The idea behind G-API is to declare image processing task in form of expressions and then submit it for execution – using a number of available backends. At the moment, there’s reference “CPU” (OpenCV-based), "GPU" (also OpenCV T-API-based), and experimental “Fluid’ backends available, with other backends coming up next. ...

G-API GPU backend implements the majority of available functions and allows to run OpenCL kernels on available OpenCL-programmable devices. At the moment, GPU backend is based on OpenCV Transparent API; in future versions it will be extended to support integration of arbitrary OpenCL kernels (and likely be renamed to "OpenCL backend").

Right now I've been using the standard transparent API's which accept UMat in order to offload work on the GPU. How can I use the G-API while also offloading the work to the GPU?

I've been reading about using the Graph API, and the syntax seems very nice. There's a section in the git documentation where it mentions the capacity to offload work to the GPU:

The idea behind G-API is to declare image processing task in form of expressions and then submit it for execution – using a number of available backends. At the moment, there’s reference “CPU” (OpenCV-based), "GPU" (also OpenCV T-API-based), and experimental “Fluid’ backends available, with other backends coming up next. ...

G-API GPU backend implements the majority of available functions and allows to run OpenCL kernels on available OpenCL-programmable devices. At the moment, GPU backend is based on OpenCV Transparent API; in future versions it will be extended to support integration of arbitrary OpenCL kernels (and likely be renamed to "OpenCL backend").

Right now I've been using the standard transparent API's which accept UMat in order to offload work on the GPU. How can I use the G-API while also offloading the work to the GPU?GPU? I see some examples available which describe applying different backends, but I do not see how I can apply them such that I can get the same speeds as using the transparent GPU API's.