I've started with the Sobel operation, and implemented it in both opencv and opencl. At the same time I've reworked the numpy version as well so that identical results can be obtained. With different implementations at your disposable a great benefit is the way that you can test and benchmark different algorithms.
The working idea for now is to make a decorator that one can add to functions:
@add_backends
def sobel(image):
# numpy implementation
...
This will add an optional backend implementation parameter to the function that the function will try to use:
# use the opencv sobel implementation
sobel(image, backend="opencv")
# use the opencl implementation on an available gpu
sobel(image, backend="opencl")
If the specified implementation is not found, we fall back to the default numpy backend. For global backend selections we are thinking of something in the following line:
use_backend("opencl")
This will try to use opencl wherever possible.
An alternative thought to our current setup is to specify it more explicitly:
opencv.sobel(image)
The following week we will try to finalize this API and implement a few more functions.
