Once the project is built you can install OpenVINO™ Runtime into custom location:
cmake --install <BUILDDIR> --prefix <INSTALLDIR>
For versions prior to 2022.1
- Obtaining Open Model Zoo tools and models
To have the ability to run samples and demos, you need to clone the Open Model Zoo repository and copy the folder under ./deployment_tools to your install directory:
git clone https://github.com/openvinotoolkit/open_model_zoo.git
cmake -E copy_directory ./open_model_zoo/ <INSTALLDIR>/deployment_tools/open_model_zoo/
- Adding OpenCV to your environment
Open Model Zoo samples use OpenCV functionality to load images. To use it for demo builds you need to provide the path to your OpenCV custom build by setting OpenCV_DIR environment variable and add path OpenCV libraries to the LD_LIBRARY_PATH (Linux) or PATH (Windows) variable before running demos.
Linux:
export LD_LIBRARY_PATH=/path/to/opencv_install/lib/:$LD_LIBRARY_PATH
export OpenCV_DIR=/path/to/opencv_install/cmakeWindows:
set PATH=\path\to\opencv_install\bin\;%PATH%
set OpenCV_DIR=\path\to\opencv_install\cmake- Running demo
To check your installation go to the demo directory and run Classification Demo:
Linux and macOS:
cd <INSTALLDIR>/deployment_tools/demo
./demo_squeezenet_download_convert_run.shWindows:
cd <INSTALLDIR>\deployment_tools\demo
demo_squeezenet_download_convert_run.batResult:
Top 10 results:
Image <INSTALLDIR>/deployment_tools/demo/car.png
classid probability label
------- ----------- -----
817 0.6853030 sports car, sport car
479 0.1835197 car wheel
511 0.0917197 convertible
436 0.0200694 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
751 0.0069604 racer, race car, racing car
656 0.0044177 minivan
717 0.0024739 pickup, pickup truck
581 0.0017788 grille, radiator grille
468 0.0013083 cab, hack, taxi, taxicab
661 0.0007443 Model T
[ INFO ] Execution successful
For 2022.1 and after
- Build samples
To build C++ sample applications, run the following commands:
Linux and macOS:
cd <INSTALLDIR>/samples/cpp
./build_samples.shWindows Command Prompt:
cd <INSTALLDIR>\samples\cpp
build_samples_msvc.batWindows PowerShell:
& <path-to-build-samples-folder>/build_samples.ps1- Install OpenVINO Development Tools
NOTE: To build OpenVINO Development Tools (Model Optimizer, Post-Training Optimization Tool, Model Downloader, and Open Model Zoo tools) wheel package locally you are required to use the CMake option:
-DENABLE_WHEEL=ON.
To install OpenVINO Development Tools to work with Caffe models (OpenVINO support for Caffe is currently being deprecated and will be removed entirely in the future), execute the following commands:
Linux and macOS:
#setup virtual environment
python3 -m venv openvino_env
source openvino_env/bin/activate
pip install pip --upgrade
#install local package from install directory
pip install openvino_dev-<version>-py3-none-any.whl[caffe] --find-links=<INSTALLDIR>/toolsWindows:
rem setup virtual environment
python -m venv openvino_env
openvino_env\Scripts\activate.bat
pip install pip --upgrade
rem install local package from install directory
cd <INSTALLDIR>\tools
pip install openvino_dev-<version>-py3-none-any.whl[caffe] --find-links=<INSTALLDIR>\tools- Download the Models
Download the following model to run the Image Classification Sample:
Linux and macOS:
omz_downloader --name googlenet-v1 --output_dir ~/modelsWindows:
omz_downloader --name googlenet-v1 --output_dir %USERPROFILE%\Documents\models- Convert the Model with Model Optimizer
Linux and macOS:
mkdir ~/ir
mo --input_model ~/models/public/googlenet-v1/googlenet-v1.caffemodel --compress_to_fp16 --output_dir ~/irWindows:
mkdir %USERPROFILE%\Documents\ir
mo --input_model %USERPROFILE%\Documents\models\public\googlenet-v1\googlenet-v1.caffemodel --compress_to_fp16 --output_dir %USERPROFILE%\Documents\ir- Run Inference on the Sample
Set up the OpenVINO environment variables:
Linux and macOS:
source <INSTALLDIR>/setupvars.shWindows Command Prompt:
<INSTALLDIR>\setupvars.batWindows PowerShell:
. <path-to-setupvars-folder>/setupvars.ps1The following commands run the Image Classification Code Sample using the dog.bmp file as an input image, the model in IR format from the ir directory, and on different hardware devices:
Linux and macOS:
cd ~/openvino_cpp_samples_build/<architecture>/Release
./classification_sample_async -i ~/Downloads/dog.bmp -m ~/ir/googlenet-v1.xml -d CPUwhere the is the output of uname -m, for example, intel64, armhf, or aarch64.
Windows:
cd %USERPROFILE%\Documents\Intel\OpenVINO\openvino_cpp_samples_build\<architecture>\Release
.\classification_sample_async.exe -i %USERPROFILE%\Downloads\dog.bmp -m %USERPROFILE%\Documents\ir\googlenet-v1.xml -d CPUwhere the is either intel64 or aarch64 depending on the platform architecture.
When the sample application is complete, you see the label and confidence data for the top 10 categories on the display:
Top 10 results:
Image dog.bmp
classid probability
------- -----------
156 0.6875963
215 0.0868125
218 0.0784114
212 0.0597296
217 0.0212105
219 0.0194193
247 0.0086272
157 0.0058511
216 0.0057589
154 0.0052615
For versions prior to 2022.1
For CMake projects, set the InferenceEngine_DIR and when you run CMake tool:
cmake -DInferenceEngine_DIR=/path/to/openvino/build/ .Then you can find Inference Engine by [find_package]:
find_package(InferenceEngine REQUIRED)
target_link_libraries(${PROJECT_NAME} PRIVATE ${InferenceEngine_LIBRARIES})For 2022.1 and after
For CMake projects, set the OpenVINO_DIR and when you run CMake tool:
cmake -DOpenVINO_DIR=<INSTALLDIR>/runtime/cmake .Then you can find OpenVINO Runtime by [find_package]:
find_package(OpenVINO REQUIRED)
add_executable(ov_app main.cpp)
target_link_libraries(ov_app PRIVATE openvino::runtime)
add_executable(ov_c_app main.c)
target_link_libraries(ov_c_app PRIVATE openvino::runtime::c)