The Intel® Distribution of OpenVINO™ toolkit is a set of tools and libraries for computer vision applications, that uses computer vision and imaging algorithms developed at Intel. It also includes a complete build of OpenCV 4.0.
GoCV supports using the Intel OpenVINO Inference Engine as a backend for the OpenCV deep neural network (DNN) module. For details, please see:
https://github.com/hybridgroup/gocv/blob/master/openvino/ie/README.md
GoCV also supports the Intel OpenVINO Photography Vision Library (PVL) which can be found in the "gocv.io/x/gocv/openvino/pvl" package. For details, please see:
https://github.com/hybridgroup/gocv/blob/master/openvino/pvl/README.md
The most recent version of the Intel OpenVINO toolkit is currently R2. You can obtain it from here:
https://software.intel.com/en-us/openvino-toolkit
One you have downloaded the compressed file, unzip the contents, and then run the install.sh
program within the extracted directory.
How to build/run code
Setup the environment for the Intel OpenVINO toolkit, by running the setupvars.sh
program included with OpenVINO:
source /opt/intel/computer_vision_sdk/bin/setupvars.sh
Then set the needed other exports for building/running GoCV code by running the env.sh
that is in the GoCV openvino
directory:
source openvino/env.sh
You only need to do these two steps one time per session. Once you have run them, you do not need to run them again until you close your terminal window.
Now you can run the version command example to make sure you are compiling/linking against Intel OpenVINO:
$ go run -tags openvino ./cmd/version/main.go
gocv version: 0.18.0
opencv lib version: 4.0.0-pre
Note the use of -tags openvino
is needed when using go run
, go build
, and go test
with OpenVINO, so the CGo compiler can pickup the correct settings for the environment, and ignore the usual defaults.
Examples that use the Intel OpenVINO toolkit can be found in the cmd/openvino
directory of this repository.